From f445c0e68cf8e1faca0b2aa8dfb9d48231cec301 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 13:01:56 +0200 Subject: [PATCH 001/334] llama : fix llm_build_k_shift to use correct n_rot (#4889) * llama : fix llm_build_k_shift to use correct n_rot ggml-ci * llama : always use hparams.n_rot for ggml_rope_custom ggml-ci * convert : fix persimmon conversion to write correct n_rot --- common/common.cpp | 3 ++ convert-hf-to-gguf.py | 9 ++++- gguf-py/gguf/tensor_mapping.py | 7 ++++ llama.cpp | 65 +++++++++++++++++----------------- 4 files changed, 51 insertions(+), 33 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index b2cb0e257..3aefed01d 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1055,6 +1055,9 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & } static ggml_type kv_cache_type_from_str(const std::string & s) { + if (s == "f32") { + return GGML_TYPE_F32; + } if (s == "f16") { return GGML_TYPE_F16; } diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 203eaf64b..813aeeed6 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -817,10 +817,17 @@ class PersimmonModel(Model): hidden_size = self.hparams["hidden_size"] self.gguf_writer.add_name('persimmon-8b-chat') + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hidden_size) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + + # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller + # than the head size? + # ref: https://github.com/ggerganov/llama.cpp/pull/4889 + #self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) + self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 80c1d5449..24a089037 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -57,6 +57,7 @@ class TensorNameMap: "transformer.norm_f", # mpt "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon + "model.final_layernorm", # persimmon "lm_head.ln", # phi2 ), @@ -98,6 +99,7 @@ class TensorNameMap: "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + "model.layers.{bid}.self_attn.query_key_value", # persimmon "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 ), @@ -141,6 +143,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "model.layers.{bid}.self_attn.dense", # persimmon "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo @@ -184,6 +187,7 @@ class TensorNameMap: "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon + "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 @@ -225,6 +229,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo @@ -237,10 +242,12 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", + "model.layers.{bid}.self_attn.q_layernorm", # persimmon ), MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", + "model.layers.{bid}.self_attn.k_layernorm", # persimmon ), MODEL_TENSOR.ROPE_FREQS: ( diff --git a/llama.cpp b/llama.cpp index d39ff94c7..0bab95563 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4104,7 +4104,6 @@ static void llm_build_k_shift( struct ggml_cgraph * graph, llm_rope_type type, int64_t n_ctx, - int n_rot, float freq_base, float freq_scale, const llm_build_cb & cb) { @@ -4112,14 +4111,13 @@ static void llm_build_k_shift( const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head_k = hparams.n_embd_head_k; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int32_t n_rot = hparams.n_rot; const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx; const float ext_factor = cparams.yarn_ext_factor; const float attn_factor = cparams.yarn_attn_factor; const float beta_fast = cparams.yarn_beta_fast; const float beta_slow = cparams.yarn_beta_slow; - GGML_ASSERT(n_embd_head_k % n_rot == 0); - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx); cb(K_shift, "K_shift", -1); @@ -4523,7 +4521,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4561,14 +4559,14 @@ struct llm_build_context { Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -4691,6 +4689,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4708,7 +4707,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4734,12 +4733,12 @@ struct llm_build_context { case MODEL_7B: Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); Kcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); break; @@ -4812,6 +4811,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4829,7 +4829,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4870,13 +4870,13 @@ struct llm_build_context { // using mode = 2 for neox mode Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -5033,9 +5033,8 @@ struct llm_build_context { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - - const int64_t n_rot = n_embd_head_k / 2; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head/2 == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5052,7 +5051,7 @@ struct llm_build_context { cb(KQ_mask, "KQ_mask", -1); if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5112,7 +5111,7 @@ struct llm_build_context { // RoPE the first n_rot of q/k, pass the other half, and concat. struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, + ctx0, tmpq, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpq) * n_embd_head, ggml_element_size(tmpq) * n_embd_head * n_head, 0 @@ -5120,7 +5119,7 @@ struct llm_build_context { cb(qrot, "qrot", il); struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, + ctx0, tmpk, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, 0 @@ -5129,29 +5128,29 @@ struct llm_build_context { // get the second half of tmpq, e.g tmpq[n_rot:, :, :] struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, + ctx0, tmpq, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpq) * n_embd_head, ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * n_rot + ggml_element_size(tmpq) * hparams.n_rot ); cb(qpass, "qpass", il); struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, + ctx0, tmpk, hparams.n_rot, n_head, n_tokens, ggml_element_size(tmpk) * n_embd_head, ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * n_rot + ggml_element_size(tmpk) * hparams.n_rot ); cb(kpass, "kpass", il); struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx, + ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(qrotated, "qrotated", il); struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx, + ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(krotated, "krotated", il); @@ -5531,6 +5530,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5548,7 +5548,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5661,7 +5661,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5693,13 +5693,13 @@ struct llm_build_context { // using mode = 2 for neox mode Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx, + ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -5778,7 +5778,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5874,6 +5874,7 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -5891,7 +5892,7 @@ struct llm_build_context { // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5917,13 +5918,13 @@ struct llm_build_context { cb(Vcur, "Vcur", il); Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + ctx0, ggml_reshape_3d(ctx0, Qcur, hparams.n_rot, n_head, n_tokens), inp_pos, n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + ctx0, ggml_reshape_3d(ctx0, Kcur, hparams.n_rot, n_head_kv, n_tokens), inp_pos, n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); From 2d00741e12c5db4a33dfccd1125f5de4adec9a5b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 13:03:38 +0200 Subject: [PATCH 002/334] py : fix lint (#4889) --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 813aeeed6..a1c79fd47 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -825,7 +825,7 @@ class PersimmonModel(Model): # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller # than the head size? # ref: https://github.com/ggerganov/llama.cpp/pull/4889 - #self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) + # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) self.gguf_writer.add_head_count(head_count) From 4315a94366708828f949f9db89d2a8d99b634459 Mon Sep 17 00:00:00 2001 From: howlger Date: Fri, 12 Jan 2024 12:05:32 +0100 Subject: [PATCH 003/334] common : streamline the formatting of help (#4890) * common : streamline the formatting of help - Separate alternative parameters by a comma - Do not indent `--version` differently * Update common/common.cpp --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 3aefed01d..062a8b4de 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -818,7 +818,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf("\n"); printf("options:\n"); printf(" -h, --help show this help message and exit\n"); - printf(" --version show version and build info\n"); + printf(" --version show version and build info\n"); printf(" -i, --interactive run in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); @@ -915,7 +915,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" number of layers to store in VRAM\n"); printf(" -ngld N, --n-gpu-layers-draft N\n"); printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" -ts SPLIT, --tensor-split SPLIT\n"); printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); #ifdef GGML_USE_CUBLAS @@ -950,7 +950,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -stc N --print-token-count N\n"); + printf(" -ptc N, --print-token-count N\n"); printf(" print token count every N tokens (default: %d)\n", params.n_print); printf("\n"); #ifndef LOG_DISABLE_LOGS From 3cabe80630c7eeb57713cd02249053a8cf6894fa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 13:10:19 +0200 Subject: [PATCH 004/334] llama : fix typo "imp_embd" -> "inp_embd" --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 0bab95563..29f8873f6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5040,7 +5040,7 @@ struct llm_build_context { struct ggml_tensor * inpL; inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); - cb(inpL, "imp_embd", -1); + cb(inpL, "inp_embd", -1); // inp_pos - contains the positions struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); From 1b280c9fffd682b6924010a4437f0275f2921fa9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 12 Jan 2024 12:30:41 +0100 Subject: [PATCH 005/334] CUDA: fix softmax compile for old CUDA versions (#4862) --- ggml-cuda.cu | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index dd19699f6..a345b0c4a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -116,6 +116,8 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) + #define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 @@ -605,16 +607,16 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { } static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) - (void) a; - bad_arch(); -#else +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); } return a; -#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#else + (void) a; + bad_arch(); +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } static __device__ __forceinline__ float warp_reduce_max(float x) { @@ -626,16 +628,16 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { } static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) - (void) x; - bad_arch(); -#else +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); } return x; -#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#else + (void) x; + bad_arch(); +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX } static __device__ __forceinline__ float op_repeat(const float a, const float b) { @@ -5613,7 +5615,7 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int template static __global__ void soft_max_f16(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX const int ncols_data = ncols_template == 0 ? ncols_par : ncols_template; const int ncols_smem = GGML_PAD(ncols_data, 2*WARP_SIZE)/2; @@ -5738,7 +5740,7 @@ static __global__ void soft_max_f16(const float * x, const float * y, float * ds #else (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale; bad_arch(); -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX } template @@ -8574,15 +8576,15 @@ static void ggml_cuda_op_soft_max( float scale = 1.0f; memcpy(&scale, dst->op_params, sizeof(float)); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - const bool use_f16_soft_max = false; -#else +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION >= CUDART_HMAX #ifdef GGML_CUDA_F16 const bool use_f16_soft_max = true; #else const bool use_f16_soft_max = false; #endif // GGML_CUDA_F16 -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#else + const bool use_f16_soft_max = false; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && CUDART_VERSION >= CUDART_HMAX if (use_f16_soft_max) { soft_max_f16_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); From 5537d9d36bfdb4379555431f574d3d78ce6e7955 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 14:33:21 +0200 Subject: [PATCH 006/334] gitignore : imatrix --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index cf1b692e9..fba207045 100644 --- a/.gitignore +++ b/.gitignore @@ -43,6 +43,7 @@ models-mnt /embedding /gguf /gguf-llama-simple +/imatrix /infill /libllama.so /llama-bench From e790eef21ce659f5c16d59f8a5c8dcf6cde0692a Mon Sep 17 00:00:00 2001 From: Zay <95888118+isaiahbjork@users.noreply.github.com> Date: Fri, 12 Jan 2024 05:48:00 -0700 Subject: [PATCH 007/334] llama.swiftui : update models layout (#4826) * Updated Models Layout - Added a models drawer - Added downloading directly from Hugging Face - Load custom models from local folder - Delete models by swiping left * trimmed trailing white space * Updated Models Layout --- .../llama.swiftui.xcodeproj/project.pbxproj | 8 +- .../llama.swiftui/Models/LlamaState.swift | 89 ++++++++ .../llama.swiftui/UI/ContentView.swift | 213 +++++++++--------- .../llama.swiftui/UI/DownloadButton.swift | 2 + .../llama.swiftui/UI/InputButton.swift | 131 +++++++++++ 5 files changed, 338 insertions(+), 105 deletions(-) create mode 100644 examples/llama.swiftui/llama.swiftui/UI/InputButton.swift diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index a8848a49f..3950b9e9d 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -8,6 +8,7 @@ /* Begin PBXBuildFile section */ 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; }; + 79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; }; 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; }; 8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; }; 8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; }; @@ -22,6 +23,7 @@ /* Begin PBXFileReference section */ 549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; }; + 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = InputButton.swift; sourceTree = ""; }; 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = ""; }; 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; }; 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = ""; }; @@ -119,6 +121,7 @@ 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */, 8A1C83782AC328BD0096AF73 /* ContentView.swift */, F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */, + 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */, ); path = UI; sourceTree = ""; @@ -213,6 +216,7 @@ 8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */, 8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */, 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */, + 79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -345,7 +349,7 @@ CLANG_ENABLE_MODULES = YES; CODE_SIGN_STYLE = Automatic; CURRENT_PROJECT_VERSION = 1; - DEVELOPMENT_TEAM = STLSG3FG8Q; + DEVELOPMENT_TEAM = K5UQJPP73A; ENABLE_PREVIEWS = YES; GENERATE_INFOPLIST_FILE = YES; INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES; @@ -377,7 +381,7 @@ CLANG_ENABLE_MODULES = YES; CODE_SIGN_STYLE = Automatic; CURRENT_PROJECT_VERSION = 1; - DEVELOPMENT_TEAM = STLSG3FG8Q; + DEVELOPMENT_TEAM = K5UQJPP73A; ENABLE_PREVIEWS = YES; GENERATE_INFOPLIST_FILE = YES; INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES; diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift index 17cb5b9dd..5bde18917 100644 --- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift +++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift @@ -1,9 +1,19 @@ import Foundation +struct Model: Identifiable { + var id = UUID() + var name: String + var url: String + var filename: String + var status: String? +} + @MainActor class LlamaState: ObservableObject { @Published var messageLog = "" @Published var cacheCleared = false + @Published var downloadedModels: [Model] = [] + @Published var undownloadedModels: [Model] = [] let NS_PER_S = 1_000_000_000.0 private var llamaContext: LlamaContext? @@ -13,23 +23,102 @@ class LlamaState: ObservableObject { } init() { + loadModelsFromDisk() + loadDefaultModels() + } + + private func loadModelsFromDisk() { + do { + let documentsURL = getDocumentsDirectory() + let modelURLs = try FileManager.default.contentsOfDirectory(at: documentsURL, includingPropertiesForKeys: nil, options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants]) + for modelURL in modelURLs { + let modelName = modelURL.deletingPathExtension().lastPathComponent + downloadedModels.append(Model(name: modelName, url: "", filename: modelURL.lastPathComponent, status: "downloaded")) + } + } catch { + print("Error loading models from disk: \(error)") + } + } + + private func loadDefaultModels() { do { try loadModel(modelUrl: defaultModelUrl) } catch { messageLog += "Error!\n" } + + for model in defaultModels { + let fileURL = getDocumentsDirectory().appendingPathComponent(model.filename) + if FileManager.default.fileExists(atPath: fileURL.path) { + + } else { + var undownloadedModel = model + undownloadedModel.status = "download" + undownloadedModels.append(undownloadedModel) + } + } } + func getDocumentsDirectory() -> URL { + let paths = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask) + return paths[0] + } + private let defaultModels: [Model] = [ + Model(name: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",url: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf", status: "download"), + Model( + name: "TinyLlama-1.1B Chat (Q8_0, 1.1 GiB)", + url: "https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q8_0.gguf?download=true", + filename: "tinyllama-1.1b-chat-v1.0.Q8_0.gguf", status: "download" + ), + + Model( + name: "TinyLlama-1.1B (F16, 2.2 GiB)", + url: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", + filename: "tinyllama-1.1b-f16.gguf", status: "download" + ), + + Model( + name: "Phi-2.7B (Q4_0, 1.6 GiB)", + url: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true", + filename: "phi-2-q4_0.gguf", status: "download" + ), + + Model( + name: "Phi-2.7B (Q8_0, 2.8 GiB)", + url: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true", + filename: "phi-2-q8_0.gguf", status: "download" + ), + + Model( + name: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)", + url: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true", + filename: "mistral-7b-v0.1.Q4_0.gguf", status: "download" + ), + Model( + name: "OpenHermes-2.5-Mistral-7B (Q3_K_M, 3.52 GiB)", + url: "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q3_K_M.gguf?download=true", + filename: "openhermes-2.5-mistral-7b.Q3_K_M.gguf", status: "download" + ) + ] func loadModel(modelUrl: URL?) throws { if let modelUrl { messageLog += "Loading model...\n" llamaContext = try LlamaContext.create_context(path: modelUrl.path()) messageLog += "Loaded model \(modelUrl.lastPathComponent)\n" + + // Assuming that the model is successfully loaded, update the downloaded models + updateDownloadedModels(modelName: modelUrl.lastPathComponent, status: "downloaded") } else { messageLog += "Load a model from the list below\n" } } + + private func updateDownloadedModels(modelName: String, status: String) { + undownloadedModels.removeAll { $0.name == modelName } + } + + func complete(text: String) async { guard let llamaContext else { return diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index 7c81ea256..30c2dc431 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -2,115 +2,57 @@ import SwiftUI struct ContentView: View { @StateObject var llamaState = LlamaState() - @State private var multiLineText = "" - - private static func cleanupModelCaches() { - // Delete all models (*.gguf) - let fileManager = FileManager.default - let documentsUrl = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0] - do { - let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil) - for fileURL in fileURLs { - if fileURL.pathExtension == "gguf" { - try fileManager.removeItem(at: fileURL) - } - } - } catch { - print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)") - } - } + @State private var showingHelp = false // To track if Help Sheet should be shown var body: some View { - VStack { - ScrollView(.vertical, showsIndicators: true) { - Text(llamaState.messageLog) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) + NavigationView { + VStack { + ScrollView(.vertical, showsIndicators: true) { + Text(llamaState.messageLog) + .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) + .padding() + .onTapGesture { + UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil) + } + } + + TextEditor(text: $multiLineText) + .frame(height: 80) + .padding() + .border(Color.gray, width: 0.5) + + HStack { + Button("Send") { + sendText() + } + + Button("Bench") { + bench() + } + + Button("Clear") { + clear() + } + + Button("Copy") { + UIPasteboard.general.string = llamaState.messageLog + } + } + .buttonStyle(.bordered) .padding() - .onTapGesture { - UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil) - } - } - TextEditor(text: $multiLineText) - .frame(height: 80) + NavigationLink(destination: DrawerView(llamaState: llamaState)) { + Text("View Models") + } .padding() - .border(Color.gray, width: 0.5) - HStack { - Button("Send") { - sendText() - } - - Button("Bench") { - bench() - } - - Button("Clear") { - clear() - } - - Button("Copy") { - UIPasteboard.general.string = llamaState.messageLog - } - }.buttonStyle(.bordered) - - VStack(alignment: .leading) { - DownloadButton( - llamaState: llamaState, - modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)", - modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true", - filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "TinyLlama-1.1B (Q8_0, 1.1 GiB)", - modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true", - filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "TinyLlama-1.1B (F16, 2.2 GiB)", - modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", - filename: "tinyllama-1.1b-f16.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "Phi-2.7B (Q4_0, 1.6 GiB)", - modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true", - filename: "phi-2-q4_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "Phi-2.7B (Q8_0, 2.8 GiB)", - modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true", - filename: "phi-2-q8_0.gguf" - ) - - DownloadButton( - llamaState: llamaState, - modelName: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)", - modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true", - filename: "mistral-7b-v0.1.Q4_0.gguf" - ) - - Button("Clear downloaded models") { - ContentView.cleanupModelCaches() - llamaState.cacheCleared = true - } - - LoadCustomButton(llamaState: llamaState) } - .padding(.top, 4) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) + .padding() + .navigationBarTitle("Model Settings", displayMode: .inline) + } - .padding() } func sendText() { @@ -131,8 +73,73 @@ struct ContentView: View { await llamaState.clear() } } + struct DrawerView: View { + + @ObservedObject var llamaState: LlamaState + @State private var showingHelp = false + func delete(at offsets: IndexSet) { + offsets.forEach { offset in + let model = llamaState.downloadedModels[offset] + let fileURL = getDocumentsDirectory().appendingPathComponent(model.filename) + do { + try FileManager.default.removeItem(at: fileURL) + } catch { + print("Error deleting file: \(error)") + } + } + + // Remove models from downloadedModels array + llamaState.downloadedModels.remove(atOffsets: offsets) + } + + func getDocumentsDirectory() -> URL { + let paths = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask) + return paths[0] + } + var body: some View { + List { + Section(header: Text("Download Models From Hugging Face")) { + HStack { + InputButton(llamaState: llamaState) + } + } + Section(header: Text("Downloaded Models")) { + ForEach(llamaState.downloadedModels) { model in + DownloadButton(llamaState: llamaState, modelName: model.name, modelUrl: model.url, filename: model.filename) + } + .onDelete(perform: delete) + } + Section(header: Text("Default Models")) { + ForEach(llamaState.undownloadedModels) { model in + DownloadButton(llamaState: llamaState, modelName: model.name, modelUrl: model.url, filename: model.filename) + } + } + + } + .listStyle(GroupedListStyle()) + .navigationBarTitle("Model Settings", displayMode: .inline).toolbar { + ToolbarItem(placement: .navigationBarTrailing) { + Button("Help") { + showingHelp = true + } + } + }.sheet(isPresented: $showingHelp) { // Sheet for help modal + VStack(alignment: .leading) { + VStack(alignment: .leading) { + Text("1. Make sure the model is in GGUF Format") + .padding() + Text("2. Copy the download link of the quantized model") + .padding() + } + Spacer() + } + } + } + } } -//#Preview { -// ContentView() -//} +struct ContentView_Previews: PreviewProvider { + static var previews: some View { + ContentView() + } +} diff --git a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift index c9f322ca1..4584d6eaa 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift @@ -53,6 +53,8 @@ struct DownloadButton: View { llamaState.cacheCleared = false + let model = Model(name: modelName, url: modelUrl, filename: filename, status: "downloaded") + llamaState.downloadedModels.append(model) status = "downloaded" } } catch let err { diff --git a/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift b/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift new file mode 100644 index 000000000..c5ffbad4e --- /dev/null +++ b/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift @@ -0,0 +1,131 @@ +import SwiftUI + +struct InputButton: View { + @ObservedObject var llamaState: LlamaState + @State private var inputLink: String = "" + @State private var status: String = "download" + @State private var filename: String = "" + + @State private var downloadTask: URLSessionDownloadTask? + @State private var progress = 0.0 + @State private var observation: NSKeyValueObservation? + + private static func extractModelInfo(from link: String) -> (modelName: String, filename: String)? { + guard let url = URL(string: link), + let lastPathComponent = url.lastPathComponent.components(separatedBy: ".").first, + let modelName = lastPathComponent.components(separatedBy: "-").dropLast().joined(separator: "-").removingPercentEncoding, + let filename = lastPathComponent.removingPercentEncoding else { + return nil + } + + return (modelName, filename) + } + + private static func getFileURL(filename: String) -> URL { + FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename) + } + + private func download() { + guard let extractedInfo = InputButton.extractModelInfo(from: inputLink) else { + // Handle invalid link or extraction failure + return + } + + let (modelName, filename) = extractedInfo + self.filename = filename // Set the state variable + + status = "downloading" + print("Downloading model \(modelName) from \(inputLink)") + guard let url = URL(string: inputLink) else { return } + let fileURL = InputButton.getFileURL(filename: filename) + + downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in + if let error = error { + print("Error: \(error.localizedDescription)") + return + } + + guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else { + print("Server error!") + return + } + + do { + if let temporaryURL = temporaryURL { + try FileManager.default.copyItem(at: temporaryURL, to: fileURL) + print("Writing to \(filename) completed") + + llamaState.cacheCleared = false + + let model = Model(name: modelName, url: self.inputLink, filename: filename, status: "downloaded") + llamaState.downloadedModels.append(model) + status = "downloaded" + } + } catch let err { + print("Error: \(err.localizedDescription)") + } + } + + observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in + self.progress = progress.fractionCompleted + } + + downloadTask?.resume() + } + + var body: some View { + VStack { + HStack { + TextField("Paste Quantized Download Link", text: $inputLink) + .textFieldStyle(RoundedBorderTextFieldStyle()) + + Button(action: { + downloadTask?.cancel() + status = "download" + }) { + Text("Cancel") + } + } + + if status == "download" { + Button(action: download) { + Text("Download Custom Model") + } + } else if status == "downloading" { + Button(action: { + downloadTask?.cancel() + status = "download" + }) { + Text("Downloading \(Int(progress * 100))%") + } + } else if status == "downloaded" { + Button(action: { + let fileURL = InputButton.getFileURL(filename: self.filename) + if !FileManager.default.fileExists(atPath: fileURL.path) { + download() + return + } + do { + try llamaState.loadModel(modelUrl: fileURL) + } catch let err { + print("Error: \(err.localizedDescription)") + } + }) { + Text("Load Custom Model") + } + } else { + Text("Unknown status") + } + } + .onDisappear() { + downloadTask?.cancel() + } + .onChange(of: llamaState.cacheCleared) { newValue in + if newValue { + downloadTask?.cancel() + let fileURL = InputButton.getFileURL(filename: self.filename) + status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download" + } + } + } +} From 930f907d3ece1eb5b0a1ec5e209983a66dcbfa68 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 12 Jan 2024 18:54:53 +0100 Subject: [PATCH 008/334] export-lora : use LLAMA_FILE_MAGIC_GGLA (#4894) This commit replaces the magic number used in export-lora.cpp with the one defined in llama.h, which is indirectly included via common.h. Signed-off-by: Daniel Bevenius --- examples/export-lora/export-lora.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 58fbe204d..4cd5d99bb 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -245,9 +245,8 @@ static struct lora_data * load_lora(struct lora_info * info) { params_ggml.no_alloc = true; result->ctx = ggml_init(params_ggml); - uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla' uint32_t magic = file.read_u32(); - if (magic != LLAMA_FILE_MAGIC_LORA) { + if (magic != LLAMA_FILE_MAGIC_GGLA) { die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str()); } uint32_t version = file.read_u32(); From 584d674be622fbf1578694ada6e62eebedbfd377 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 20:54:12 +0200 Subject: [PATCH 009/334] llama : remove redundant assert for StableLM (#4901) --- llama.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 29f8873f6..ce413f605 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5530,7 +5530,6 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; From e7e4df031b9e29d4b55a4e0b0295187f6b213db1 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 12 Jan 2024 20:07:38 +0100 Subject: [PATCH 010/334] llama : ggml-backend integration (#4766) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov Co-authored-by: Johannes Gäßler --- common/common.cpp | 65 +- common/common.h | 1 + examples/batched-bench/batched-bench.cpp | 3 + examples/llama-bench/llama-bench.cpp | 146 +- examples/server/server.cpp | 40 +- ggml-alloc.c | 34 +- ggml-alloc.h | 4 +- ggml-backend-impl.h | 38 +- ggml-backend.c | 693 ++++--- ggml-backend.h | 60 +- ggml-cuda.cu | 907 +++++---- ggml-cuda.h | 26 +- ggml-impl.h | 2 + ggml-metal.m | 55 +- ggml-opencl.cpp | 335 +++- ggml-opencl.h | 16 +- ggml.c | 30 +- ggml.h | 9 +- llama.cpp | 2320 +++++++++------------- llama.h | 18 +- tests/test-backend-ops.cpp | 26 +- 21 files changed, 2533 insertions(+), 2295 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 062a8b4de..322b9f91e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -543,9 +543,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD params.n_gpu_layers = std::stoi(argv[i]); -#else +#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); #endif @@ -554,9 +553,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD params.n_gpu_layers_draft = std::stoi(argv[i]); -#else +#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); #endif @@ -565,25 +563,44 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifdef GGML_USE_CUBLAS params.main_gpu = std::stoi(argv[i]); -#else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n"); -#endif +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the main GPU has no effect.\n"); +#endif // GGML_USE_CUBLAS + } else if (arg == "--split-mode" || arg == "-sm") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string arg_next = argv[i]; + if (arg_next == "none") { + params.split_mode = LLAMA_SPLIT_NONE; + } else if (arg_next == "layer") { + params.split_mode = LLAMA_SPLIT_LAYER; + } else if (arg_next == "row") { + params.split_mode = LLAMA_SPLIT_ROW; + } else { + invalid_param = true; + break; + } +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUBLAS } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; break; } -#ifdef GGML_USE_CUBLAS std::string arg_next = argv[i]; // split string by , and / const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); - + if (split_arg.size() >= LLAMA_MAX_DEVICES) { + invalid_param = true; + break; + } for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { if (i < split_arg.size()) { params.tensor_split[i] = std::stof(split_arg[i]); @@ -591,14 +608,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.tensor_split[i] = 0.0f; } } -#else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n"); -#endif // GGML_USE_CUBLAS - } else if (arg == "--no-mul-mat-q" || arg == "-nommq") { -#ifdef GGML_USE_CUBLAS - params.mul_mat_q = false; -#else - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n"); +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS } else if (arg == "--no-mmap") { params.use_mmap = false; @@ -915,14 +926,15 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" number of layers to store in VRAM\n"); printf(" -ngld N, --n-gpu-layers-draft N\n"); printf(" number of layers to store in VRAM for the draft model\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); -#ifdef GGML_USE_CUBLAS - printf(" -nommq, --no-mul-mat-q\n"); - printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n"); - printf(" Not recommended since this is both slower and uses more VRAM.\n"); -#endif // GGML_USE_CUBLAS + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); #endif printf(" -gan N, --grp-attn-n N\n"); printf(" group-attention factor (default: %d)\n", params.grp_attn_n); @@ -1041,6 +1053,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & mparams.n_gpu_layers = params.n_gpu_layers; } mparams.main_gpu = params.main_gpu; + mparams.split_mode = params.split_mode; mparams.tensor_split = params.tensor_split; mparams.use_mmap = params.use_mmap; mparams.use_mlock = params.use_mlock; diff --git a/common/common.h b/common/common.h index 1359e76ab..f29be5b5a 100644 --- a/common/common.h +++ b/common/common.h @@ -59,6 +59,7 @@ struct gpt_params { float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_beams = 0; // if non-zero then use beam search of given width. diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 57596ed98..7924db267 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -88,7 +88,10 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); + const std::vector t_split (LLAMA_MAX_DEVICES, 0.0f); + model_params.n_gpu_layers = n_gpu_layers; + model_params.tensor_split = t_split.data(); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 7f7186cde..97325b5bd 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -128,6 +128,25 @@ static std::string get_gpu_info() { // command line params enum output_formats {CSV, JSON, MARKDOWN, SQL}; +static const char * output_format_str(output_formats format) { + switch (format) { + case CSV: return "csv"; + case JSON: return "json"; + case MARKDOWN: return "md"; + case SQL: return "sql"; + default: GGML_ASSERT(!"invalid output format"); + } +} + +static const char * split_mode_str(llama_split_mode mode) { + switch (mode) { + case LLAMA_SPLIT_NONE: return "none"; + case LLAMA_SPLIT_LAYER: return "layer"; + case LLAMA_SPLIT_ROW: return "row"; + default: GGML_ASSERT(!"invalid split mode"); + } +} + struct cmd_params { std::vector model; std::vector n_prompt; @@ -137,6 +156,7 @@ struct cmd_params { std::vector type_v; std::vector n_threads; std::vector n_gpu_layers; + std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; std::vector mul_mat_q; @@ -155,6 +175,7 @@ static const cmd_params cmd_params_defaults = { /* type_v */ {GGML_TYPE_F16}, /* n_threads */ {get_num_physical_cores()}, /* n_gpu_layers */ {99}, + /* split_mode */ {LLAMA_SPLIT_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, @@ -169,21 +190,22 @@ static void print_usage(int /* argc */, char ** argv) { printf("\n"); printf("options:\n"); printf(" -h, --help\n"); - printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); - printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ctk , --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv , --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); - printf(" -ts, --tensor_split \n"); - printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); - printf(" -o, --output (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql"); - printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ctk , --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv , --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); + printf(" -ts, --tensor_split (default: 0)\n"); + printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); + printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); + printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); printf("\n"); printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); } @@ -306,6 +328,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (arg == "-sm" || arg == "--split-mode") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + std::vector modes; + for (const auto & m : p) { + llama_split_mode mode; + if (m == "none") { + mode = LLAMA_SPLIT_NONE; + } else if (m == "layer") { + mode = LLAMA_SPLIT_LAYER; + } else if (m == "row") { + mode = LLAMA_SPLIT_ROW; + } else { + invalid_param = true; + break; + } + modes.push_back(mode); + } + params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); } else if (arg == "-mg" || arg == "--main-gpu") { if (++i >= argc) { invalid_param = true; @@ -392,6 +436,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } + if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } @@ -410,6 +455,7 @@ struct cmd_params_instance { ggml_type type_v; int n_threads; int n_gpu_layers; + llama_split_mode split_mode; int main_gpu; bool no_kv_offload; bool mul_mat_q; @@ -419,6 +465,7 @@ struct cmd_params_instance { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; + mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); @@ -428,6 +475,7 @@ struct cmd_params_instance { bool equal_mparams(const cmd_params_instance & other) const { return model == other.model && n_gpu_layers == other.n_gpu_layers && + split_mode == other.split_mode && main_gpu == other.main_gpu && tensor_split == other.tensor_split; } @@ -446,45 +494,13 @@ struct cmd_params_instance { } }; -static std::vector get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) { - std::vector instances; - - for (const auto & m : params.model) - for (const auto & nl : params.n_gpu_layers) - for (const auto & mg : params.main_gpu) - for (const auto & ts : params.tensor_split) - for (const auto & nb : params.n_batch) - for (const auto & tk : params.type_k) - for (const auto & tv : params.type_v) - for (const auto & mmq : params.mul_mat_q) - for (const auto & nkvo : params.no_kv_offload) - for (const auto & nt : params.n_threads) { - cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ n_prompt, - /* .n_gen = */ n_gen, - /* .n_batch = */ nb, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .n_gpu_layers = */ nl, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, - /* .tensor_split = */ ts, - }; - instances.push_back(instance); - } - return instances; -} - static std::vector get_cmd_params_instances(const cmd_params & params) { std::vector instances; -#if 1 // this ordering minimizes the number of times that each model needs to be reloaded for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) + for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) for (const auto & nb : params.n_batch) @@ -506,6 +522,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, @@ -527,6 +544,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, @@ -535,24 +553,6 @@ static std::vector get_cmd_params_instances(const cmd_param instances.push_back(instance); } } -#else - // this ordering separates the prompt and generation tests - for (const auto & n_prompt : params.n_prompt) { - if (n_prompt == 0) { - continue; - } - auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt); - instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end()); - } - - for (const auto & n_gen : params.n_gen) { - if (n_gen == 0) { - continue; - } - auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0); - instances.insert(instances.end(), instances_gen.begin(), instances_gen.end()); - } -#endif return instances; } @@ -576,6 +576,7 @@ struct test { ggml_type type_k; ggml_type type_v; int n_gpu_layers; + llama_split_mode split_mode; int main_gpu; bool no_kv_offload; bool mul_mat_q; @@ -597,6 +598,7 @@ struct test { type_k = inst.type_k; type_v = inst.type_v; n_gpu_layers = inst.n_gpu_layers; + split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; mul_mat_q = inst.mul_mat_q; @@ -660,7 +662,8 @@ struct test { "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", - "n_gpu_layers", "main_gpu", "no_kv_offload", + "n_gpu_layers", "split_mode", + "main_gpu", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", @@ -711,7 +714,8 @@ struct test { cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), - std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload), + std::to_string(n_gpu_layers), split_mode_str(split_mode), + std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(mul_mat_q), tensor_split_str, std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), @@ -867,6 +871,9 @@ struct markdown_printer : public printer { if (field == "n_gpu_layers") { return "ngl"; } + if (field == "split_mode") { + return "sm"; + } if (field == "n_threads") { return "threads"; } @@ -907,6 +914,9 @@ struct markdown_printer : public printer { if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) { fields.push_back("main_gpu"); } + if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { + fields.push_back("split_mode"); + } if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { fields.push_back("mul_mat_q"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1d30a15a6..c1ab8f9dc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2005,12 +2005,15 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); printf(" number of layers to store in VRAM\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); - printf(" -nommq, --no-mul-mat-q\n"); - printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n"); - printf(" Not recommended since this is both slower and uses more VRAM.\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); #endif printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); @@ -2253,6 +2256,33 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); #endif + } + else if (arg == "--split-mode" || arg == "-sm") + { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string arg_next = argv[i]; + if (arg_next == "none") + { + params.split_mode = LLAMA_SPLIT_NONE; + } + else if (arg_next == "layer") + { + params.split_mode = LLAMA_SPLIT_LAYER; + } + else if (arg_next == "row") + { + params.split_mode = LLAMA_SPLIT_ROW; + } + else { + invalid_param = true; + break; + } +#ifndef GGML_USE_CUBLAS + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUBLAS } else if (arg == "--tensor-split" || arg == "-ts") { diff --git a/ggml-alloc.c b/ggml-alloc.c index a27dd54b0..89b85d348 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -102,8 +102,6 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { } } - AT_PRINTF("block %d\n", best_fit_block); - if (best_fit_block == -1) { // the last block is our last resort struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1]; @@ -117,6 +115,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { return; } } + struct free_block * block = &alloc->free_blocks[best_fit_block]; void * addr = block->addr; block->addr = (char*)block->addr + size; @@ -129,6 +128,8 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { } } + AT_PRINTF("block %d, addr %p\n", best_fit_block, addr); + tensor->data = addr; tensor->buffer = alloc->buffer; if (!alloc->measure) { @@ -229,6 +230,7 @@ void ggml_tallocr_reset(ggml_tallocr_t alloc) { alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows } else { alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; + ggml_backend_buffer_reset(alloc->buffer); } } @@ -263,9 +265,9 @@ ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) { return alloc; } -ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { +ggml_tallocr_t ggml_tallocr_new_measure_from_buft(struct ggml_backend_buffer_type * buft) { // create a backend buffer to get the correct tensor allocation sizes - ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, 1); + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, 1); // TODO: move alloc initialization to a common ggml_tallocr_new_impl function ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); @@ -275,13 +277,22 @@ ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backe return alloc; } -ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { - ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, size); +ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { + return ggml_tallocr_new_measure_from_buft(ggml_backend_get_default_buffer_type(backend)); +} + +ggml_tallocr_t ggml_tallocr_new_from_buft(struct ggml_backend_buffer_type * buft, size_t size) { + // create a backend buffer to get the correct tensor allocation sizes + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); alloc->buffer_owned = true; return alloc; } +ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { + return ggml_tallocr_new_from_buft(ggml_backend_get_default_buffer_type(backend), size); +} + ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) { ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); @@ -779,10 +790,21 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte if (nbytes == 0) { // all the tensors in the context are already allocated +#ifndef NDEBUG + fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__); +#endif return NULL; } ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes); + if (buffer == NULL) { + // failed to allocate buffer +#ifndef NDEBUG + fprintf(stderr, "%s: failed to allocate buffer\n", __func__); +#endif + return NULL; + } + ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer); for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { diff --git a/ggml-alloc.h b/ggml-alloc.h index 64a412468..4e5997521 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -52,8 +52,10 @@ typedef struct ggml_tallocr * ggml_tallocr_t; GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment); GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment); -GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_tallocr_t ggml_tallocr_new_from_buft(struct ggml_backend_buffer_type * buft, size_t size); GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer +GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_buft(struct ggml_backend_buffer_type * buft); GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend); GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc); diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index ca21b4743..1db32901f 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -16,9 +16,10 @@ extern "C" { typedef void * ggml_backend_buffer_type_context_t; struct ggml_backend_buffer_type_i { + const char * (*get_name) (ggml_backend_buffer_type_t buft); ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment - size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding + size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend // check if tensor data is in host memory // should be equivalent to supports_backend(buft, ggml_backend_cpu_init()) @@ -34,16 +35,15 @@ extern "C" { typedef void * ggml_backend_buffer_context_t; struct ggml_backend_buffer_i { - void (*free_buffer) (ggml_backend_buffer_t buffer); - //void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras - void * (*get_base) (ggml_backend_buffer_t buffer); - void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - // (optional) copy tensor between different buffer-type, allow for single-copy tranfers - void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); + const char * (*get_name) (ggml_backend_buffer_t buffer); + void (*free_buffer)(ggml_backend_buffer_t buffer); + void * (*get_base) (ggml_backend_buffer_t buffer); + void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer + void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); + void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras }; struct ggml_backend_buffer { @@ -51,6 +51,7 @@ extern "C" { ggml_backend_buffer_type_t buft; ggml_backend_buffer_context_t context; size_t size; + enum ggml_backend_buffer_usage usage; }; ggml_backend_buffer_t ggml_backend_buffer_init( @@ -59,6 +60,8 @@ extern "C" { ggml_backend_buffer_context_t context, size_t size); + // do not use directly, use ggml_backend_tensor_copy instead + bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst); // // Backend @@ -74,22 +77,20 @@ extern "C" { // buffer allocation ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend); - // (optional) asynchroneous tensor data access + // (optional) asynchronous tensor data access void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); - // (optional) asynchroneous tensor copy - void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - + // (optional) complete all pending operations void (*synchronize)(ggml_backend_t backend); // compute graph with a plan - ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); + ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - // compute graph without a plan + // compute graph without a plan (async) bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation @@ -102,7 +103,6 @@ extern "C" { ggml_backend_context_t context; }; - // // Backend registry // diff --git a/ggml-backend.c b/ggml-backend.c index 53e741cb8..4c2d8b0b2 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -15,6 +15,10 @@ // backend buffer type +const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name(buft); +} + ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { return buft->iface.alloc_buffer(buft, size); } @@ -58,11 +62,16 @@ ggml_backend_buffer_t ggml_backend_buffer_init( /* .buft = */ buft, /* .context = */ context, /* .size = */ size, + /* .usage = */ GGML_BACKEND_BUFFER_USAGE_ANY }; return buffer; } +const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name(buffer); +} + void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { if (buffer == NULL) { return; @@ -94,11 +103,11 @@ void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_t } size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) { - return ggml_backend_buft_get_alignment(ggml_backend_buffer_type(buffer)); + return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer)); } size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { - return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type(buffer), tensor); + return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); } void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -106,13 +115,31 @@ void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { } bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { - return ggml_backend_buft_is_host(ggml_backend_buffer_type(buffer)); + return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer)); } -ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer) { +void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { + buffer->usage = usage; +} + +ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) { return buffer->buft; } +void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) { + if (buffer->iface.reset) { + buffer->iface.reset(buffer); + } +} + +bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) { + ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer; + if (dst_buf->iface.cpy_tensor) { + return src->buffer->iface.cpy_tensor(dst_buf, src, dst); + } + return false; +} + // backend const char * ggml_backend_name(ggml_backend_t backend) { @@ -146,30 +173,42 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - backend->iface.set_tensor_async(backend, tensor, data, offset, size); + if (backend->iface.set_tensor_async == NULL) { + ggml_backend_tensor_set(tensor, data, offset, size); + } else { + backend->iface.set_tensor_async(backend, tensor, data, offset, size); + } } void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - backend->iface.get_tensor_async(backend, tensor, data, offset, size); + if (backend->iface.get_tensor_async == NULL) { + ggml_backend_tensor_get(tensor, data, offset, size); + } else { + backend->iface.get_tensor_async(backend, tensor, data, offset, size); + } } void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set"); + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - tensor->buffer->iface.set_tensor(tensor->buffer, tensor, data, offset, size); + tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size); } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - tensor->buffer->iface.get_tensor(tensor->buffer, tensor, data, offset, size); + tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size); } void ggml_backend_synchronize(ggml_backend_t backend) { @@ -190,19 +229,10 @@ void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_pla void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { backend->iface.graph_plan_compute(backend, plan); - - // TODO: optional sync - ggml_backend_synchronize(backend); } bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - if (!backend->iface.graph_compute(backend, cgraph)) { - return false; - } - - // TODO: optional sync - ggml_backend_synchronize(backend); - return true; + return backend->iface.graph_compute(backend, cgraph); } bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -227,28 +257,20 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml } void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) { - //printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]); - //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]); GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); - // fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); - if (src == dst) { return; } - // TODO: allow backends to support copy to/from same backend - - if (dst->buffer->iface.cpy_tensor_from != NULL) { - dst->buffer->iface.cpy_tensor_from(dst->buffer, src, dst); - } else if (src->buffer->iface.cpy_tensor_to != NULL) { - src->buffer->iface.cpy_tensor_to(src->buffer, src, dst); - } else { - // shouldn't be hit when copying from/to CPU - #ifndef NDEBUG - fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to " - "are implemented for %s and %s, falling back to get/set\n", src->name, dst->name); - #endif + if (ggml_backend_buffer_is_host(src->buffer)) { + ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); + } else if (ggml_backend_buffer_is_host(dst->buffer)) { + ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); + } else if (!ggml_backend_buffer_copy_tensor(src, dst)) { +#ifndef NDEBUG + fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer)); +#endif size_t nbytes = ggml_nbytes(src); void * data = malloc(nbytes); ggml_backend_tensor_get(src, data, 0, nbytes); @@ -257,6 +279,31 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst } } +void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { + GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); + + if (src == dst) { + return; + } + + if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) { + if (backend->iface.cpy_tensor_async != NULL) { + if (backend->iface.cpy_tensor_async(backend, src, dst)) { + return; + } + } + } + + size_t nbytes = ggml_nbytes(src); + if (ggml_backend_buffer_is_host(src->buffer)) { + ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes); + } + else { + ggml_backend_tensor_copy(src, dst); + } +} + + // backend registry #define GGML_MAX_BACKENDS_REG 16 @@ -392,6 +439,12 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) { // backend CPU +static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { + return "CPU"; + + GGML_UNUSED(buffer); +} + static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { return (void *)buffer->context; } @@ -412,14 +465,12 @@ static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, con GGML_UNUSED(buffer); } -static void ggml_backend_cpu_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); +static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; GGML_UNUSED(buffer); } @@ -429,30 +480,38 @@ static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static struct ggml_backend_buffer_i cpu_backend_buffer_i = { + /* .get_name = */ ggml_backend_cpu_buffer_name, /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, /* .get_base = */ ggml_backend_cpu_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_cpu_buffer_cpy_tensor_to, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, }; // for buffers from ptr, free is not called static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { + /* .get_name = */ ggml_backend_cpu_buffer_name, /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed /* .get_base = */ ggml_backend_cpu_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_cpu_buffer_cpy_tensor_to, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, }; static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 +static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU"; + + GGML_UNUSED(buft); +} + static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? @@ -483,6 +542,7 @@ static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes @@ -501,6 +561,18 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { #include +static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_HBM"; + + GGML_UNUSED(buft); +} + +static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { + return "CPU_HBM"; + + GGML_UNUSED(buf); +} + static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { hbw_free(buffer->context); } @@ -514,17 +586,18 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_ return NULL; } - // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name; buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; return buffer; } -ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type() { +ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes @@ -568,7 +641,7 @@ struct ggml_backend_plan_cpu { struct ggml_cgraph cgraph; }; -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); @@ -634,8 +707,7 @@ static struct ggml_backend_i cpu_backend_i = { /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, + /* .cpy_tensor_async = */ NULL, /* .synchronize = */ NULL, /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, @@ -661,7 +733,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { } bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_cpu_name; + return backend && backend->iface.get_name == ggml_backend_cpu_name; } void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { @@ -685,7 +757,7 @@ static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user // scheduler -#define GGML_MAX_BACKENDS 4 +#define GGML_MAX_BACKENDS 16 #define GGML_MAX_SPLITS 256 #define GGML_MAX_SPLIT_INPUTS 16 @@ -695,21 +767,29 @@ struct ggml_backend_sched_split { int i_end; struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; int n_inputs; + // graph view of this split struct ggml_cgraph graph; }; struct ggml_backend_sched { + bool is_reset; // true if the scheduler has been reset since the last graph split + int n_backends; ggml_backend_t backends[GGML_MAX_BACKENDS]; + ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS]; ggml_tallocr_t tallocs[GGML_MAX_BACKENDS]; ggml_gallocr_t galloc; + // hash keys of the nodes in the graph struct ggml_hash_set hash_set; - ggml_tallocr_t * node_talloc; // [hash_set.size] - struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS] + // hash values (arrays of [hash_set.size]) + ggml_tallocr_t * node_talloc; // tallocr assigned to each node (indirectly this is the backend) + struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // copies of each node for each destination backend + // copy of the graph with modified inputs struct ggml_cgraph * graph; + struct ggml_backend_sched_split splits[GGML_MAX_SPLITS]; int n_splits; @@ -750,14 +830,22 @@ static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) return INT_MAX; } -static ggml_backend_t get_buffer_backend(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { +static ggml_tallocr_t sched_allocr_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { if (buffer == NULL) { return NULL; } + + // check if this is already allocate in a allocr buffer (from user manual allocations) + for (int i = 0; i < sched->n_backends; i++) { + if (ggml_tallocr_get_buffer(sched->tallocs[i]) == buffer) { + return sched->tallocs[i]; + } + } + // find highest prio backend that supports the buffer type for (int i = 0; i < sched->n_backends; i++) { if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) { - return sched->backends[i]; + return sched->tallocs[i]; } } GGML_ASSERT(false && "tensor buffer type not supported by any backend"); @@ -767,7 +855,6 @@ static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_talloc if (allocr == NULL) { return NULL; } - // find highest prio backend that supports the buffer type for (int i = 0; i < sched->n_backends; i++) { if (sched->tallocs[i] == allocr) { return sched->backends[i]; @@ -777,7 +864,7 @@ static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_talloc } #if 0 -static char causes[GGML_DEFAULT_GRAPH_SIZE*8 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove +static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) #define GET_CAUSE(node) causes[hash_id(node)] #else @@ -786,45 +873,37 @@ static char causes[GGML_DEFAULT_GRAPH_SIZE*8 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_IN #endif // returns the backend that should be used for the node based on the current locations -static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { - // if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there - // ie. kv cache updates - // note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend. +static ggml_tallocr_t sched_allocr_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { + // assign pre-allocated nodes to their backend // dst - ggml_backend_t cur_backend = get_buffer_backend(sched, node->buffer); - if (cur_backend != NULL) { + ggml_tallocr_t cur_allocr = sched_allocr_from_buffer(sched, node->buffer); + if (cur_allocr != NULL) { SET_CAUSE(node, "1.dst"); - return cur_backend; + return cur_allocr; } - // view_src - if (node->view_src != NULL && get_buffer_backend(sched, node->view_src->buffer) != NULL) { - SET_CAUSE(node, "1.vsrc"); - return get_buffer_backend(sched, node->view_src->buffer); + if (node->view_src != NULL) { + cur_allocr = sched_allocr_from_buffer(sched, node->view_src->buffer); + if (cur_allocr != NULL) { + SET_CAUSE(node, "1.vsrc"); + return cur_allocr; + } } - - // src - int cur_prio = INT_MAX; - size_t cur_size = 0; - + // assign nodes that use weights to the backend of the weights for (int i = 0; i < GGML_MAX_SRC; i++) { const struct ggml_tensor * src = node->src[i]; if (src == NULL) { break; } - ggml_backend_t src_backend = get_buffer_backend(sched, src->buffer); - if (src_backend != NULL) { - int src_prio = sched_backend_prio(sched, src_backend); - size_t src_size = ggml_nbytes(src); - if (src_prio < cur_prio && src_size >= cur_size) { - cur_prio = src_prio; - cur_size = src_size; - cur_backend = src_backend; - SET_CAUSE(node, "1.src%d", i); - } + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + ggml_tallocr_t src_allocr = sched_allocr_from_buffer(sched, src->buffer); + // operations with weights are always run on the same backend as the weights + SET_CAUSE(node, "1.wgt%d", i); + return src_allocr; } } - return cur_backend; + + return NULL; } static char * fmt_size(size_t size) { @@ -857,7 +936,7 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra } ggml_tallocr_t node_allocr = node_allocr(node); ggml_backend_t node_backend = node_allocr ? get_allocr_backend(sched, node_allocr) : NULL; // FIXME: - fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name, + fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", GET_CAUSE(node)); for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; @@ -866,7 +945,7 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra } ggml_tallocr_t src_allocr = node_allocr(src); ggml_backend_t src_backend = src_allocr ? get_allocr_backend(sched, src_allocr) : NULL; - fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name, + fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } fprintf(stderr, "\n"); @@ -882,15 +961,17 @@ static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, co return dup; } + +//#define DEBUG_PASS1 +//#define DEBUG_PASS2 +//#define DEBUG_PASS3 +//#define DEBUG_PASS4 + // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend -// TODO: merge passes static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - // reset state - size_t hash_size = sched->hash_set.size; - memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); - memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); - memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); + // reset splits sched->n_splits = 0; + sched->is_reset = false; struct ggml_init_params params = { /* .mem_size = */ sizeof(sched->context_buffer), @@ -898,26 +979,22 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g /* .no_alloc = */ true }; - if (sched->ctx != NULL) { - ggml_free(sched->ctx); - } + ggml_free(sched->ctx); sched->ctx = ggml_init(params); + if (sched->ctx == NULL) { + fprintf(stderr, "%s: failed to initialize context\n", __func__); + GGML_ASSERT(false); + } - // pass 1: assign backends to ops with allocated inputs + // pass 1: assign backends to ops with pre-allocated inputs for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; if (node_allocr(leaf) != NULL) { // do not overwrite user assignments continue; } - ggml_backend_t leaf_backend = get_buffer_backend(sched, leaf->buffer); - if (leaf_backend == NULL && leaf->view_src != NULL) { - leaf_backend = get_buffer_backend(sched, leaf->view_src->buffer); - } - if (leaf_backend != NULL) { - node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend); - } + node_allocr(leaf) = sched_allocr_from_cur(sched, leaf); } for (int i = 0; i < graph->n_nodes; i++) { @@ -926,50 +1003,102 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // do not overwrite user assignments continue; } - ggml_backend_t node_backend = sched_backend_from_cur(sched, node); - if (node_backend != NULL) { - node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend); + node_allocr(node) = sched_allocr_from_cur(sched, node); + // src + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + if (node_allocr(src) == NULL) { + node_allocr(src) = sched_allocr_from_cur(sched, src); + } } } - //printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#ifdef DEBUG_PASS1 + fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif - // pass 2: assign backends to ops from current assignments - // TODO: - // - reuse sched_backend_from_cur - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr == NULL) { - int cur_prio = INT_MAX; - size_t cur_size = 0; - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - break; - } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != NULL) { - int src_prio = sched_allocr_prio(sched, src_allocr); - size_t src_size = ggml_nbytes(src); - if (src_prio < cur_prio && src_size >= cur_size) { - cur_prio = src_prio; - cur_size = src_size; - node_allocr = src_allocr; - SET_CAUSE(node, "2.src%d", j); - } - } + // pass 2: expand current backend assignments + // assign the same backend to adjacent nodes + // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend) + // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops + + // pass 2.1 expand gpu up + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = graph->n_nodes - 1; i >= 0; i--) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; } + ggml_tallocr_t node_allocr = node_allocr(node); if (node_allocr != NULL) { - node_allocr(node) = node_allocr; + if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) { + // skip cpu (lowest prio backend) + cur_allocr = NULL; + } else { + cur_allocr = node_allocr; + } + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.1"); } } } - //printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); - // pass 3: assign backends to remaining src from dst (should only be leafs) + // pass 2.2 expand gpu down + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr != NULL) { + if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) { + // skip cpu (lowest prio backend) + cur_allocr = NULL; + } else { + cur_allocr = node_allocr; + } + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.2"); + } + } + } + + // pass 2.3 expand rest up + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = graph->n_nodes - 1; i >= 0; i--) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr != NULL) { + cur_allocr = node_allocr; + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.3"); + } + } + } +#ifdef DEBUG_PASS2 + fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif + + // pass 3: assign backends to remaining src from dst and view_src for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); + ggml_tallocr_t cur_allocr = node_allocr(node); + if (node->view_src != NULL && cur_allocr == NULL) { + cur_allocr = node_allocr(node) = node_allocr(node->view_src); + SET_CAUSE(node, "3.vsrc"); + } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { @@ -977,81 +1106,105 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g } ggml_tallocr_t src_allocr = node_allocr(src); if (src_allocr == NULL) { - node_allocr(src) = node_allocr; + if (src->view_src != NULL) { + // views are always on the same backend as the source + node_allocr(src) = node_allocr(src->view_src); + SET_CAUSE(src, "3.vsrc"); + } else { + node_allocr(src) = cur_allocr; + SET_CAUSE(src, "3.cur"); + } } } } - //printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#ifdef DEBUG_PASS3 + fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif // pass 4: split graph, find tensors that need to be copied - // TODO: - // - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost - // find first backend - int cur_split = 0; - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - if (node->view_src == NULL) { - sched->splits[0].tallocr = node_allocr(node); - break; - } - } - sched->splits[0].i_start = 0; - sched->splits[0].n_inputs = 0; - memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK - ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; - size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - - if (ggml_is_view_op(node->op)) { - continue; - } - - ggml_tallocr_t node_allocr = node_allocr(node); - - if (node_allocr != cur_allocr) { - sched->splits[cur_split].i_end = i; - cur_split++; - GGML_ASSERT(cur_split < GGML_MAX_SPLITS); - sched->splits[cur_split].tallocr = node_allocr; - sched->splits[cur_split].i_start = i; - sched->splits[cur_split].n_inputs = 0; - memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK - cur_allocr = node_allocr; - cur_backend_id = sched_allocr_prio(sched, cur_allocr); - } - - // find inputs that are not on the same backend - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { + { + int cur_split = 0; + // find the backend of the first split, skipping view ops + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (!ggml_is_view_op(node->op)) { + sched->splits[0].tallocr = node_allocr(node); break; } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != node_allocr) { - int n_inputs = sched->splits[cur_split].n_inputs++; - GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); - sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src; + } + sched->splits[0].i_start = 0; + sched->splits[0].n_inputs = 0; + memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK + ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; + size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; - // create copies - size_t id = hash_id(src); - if (sched->node_copies[id][cur_backend_id] == NULL) { - struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); - sched->node_copies[id][cur_backend_id] = tensor_copy; - node_allocr(tensor_copy) = cur_allocr; - ggml_backend_t backend = get_allocr_backend(sched, cur_allocr); - ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + if (ggml_is_view_op(node->op)) { + continue; + } + + ggml_tallocr_t node_allocr = node_allocr(node); + + if (node_allocr != cur_allocr) { + sched->splits[cur_split].i_end = i; + cur_split++; + GGML_ASSERT(cur_split < GGML_MAX_SPLITS); + sched->splits[cur_split].tallocr = node_allocr; + sched->splits[cur_split].i_start = i; + sched->splits[cur_split].n_inputs = 0; + cur_allocr = node_allocr; + cur_backend_id = sched_allocr_prio(sched, cur_allocr); + } + + // find inputs that are not on the same backend + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + GGML_ASSERT(src_allocr != NULL); // all inputs should be assigned by now + if (src_allocr != node_allocr) { + // check if the input is already in the split + bool found = false; + for (int k = 0; k < sched->splits[cur_split].n_inputs; k++) { + if (sched->splits[cur_split].inputs[k] == src) { + found = true; + break; + } + } + + if (!found) { + int n_inputs = sched->splits[cur_split].n_inputs++; + //printf("split %d input %d: %s (%s)\n", cur_split, n_inputs, src->name, ggml_backend_name(get_allocr_backend(sched, src_allocr))); + GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); + sched->splits[cur_split].inputs[n_inputs] = src; + } + + // create a copy of the input in the split's backend + size_t id = hash_id(src); + if (sched->node_copies[id][cur_backend_id] == NULL) { + ggml_backend_t backend = get_allocr_backend(sched, cur_allocr); + struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + + sched->node_copies[id][cur_backend_id] = tensor_copy; + node_allocr(tensor_copy) = cur_allocr; + SET_CAUSE(tensor_copy, "4.cpy"); + } + node->src[j] = sched->node_copies[id][cur_backend_id]; } - node->src[j] = sched->node_copies[id][cur_backend_id]; } } + sched->splits[cur_split].i_end = graph->n_nodes; + sched->n_splits = cur_split + 1; } - sched->splits[cur_split].i_end = graph->n_nodes; - sched->n_splits = cur_split + 1; +#ifdef DEBUG_PASS4 + fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#endif - //fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout); - -#if 1 +#ifndef NDEBUG // sanity check: all sources should have the same backend as the node for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -1059,6 +1212,11 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g if (node_allocr == NULL) { fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); } + if (node->view_src != NULL && node_allocr != node_allocr(node->view_src)) { + fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n", + node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL", + node->view_src->name, node_allocr(node->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(node->view_src))) : "NULL"); + } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { @@ -1070,8 +1228,14 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL", j, src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL"); } + if (src->view_src != NULL && src_allocr != node_allocr(src->view_src)) { + fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n", + src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL", + src->view_src->name, node_allocr(src->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(src->view_src))) : "NULL"); + } } } + fflush(stderr); #endif // create copies of the graph for each split @@ -1085,6 +1249,8 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)]; + // add a dependency to the input source so that it is not freed before the copy is done + GGML_ASSERT(input_cpy->src[0] == NULL || input_cpy->src[0] == input); input_cpy->src[0] = input; graph_copy->nodes[graph_copy->n_nodes++] = input_cpy; } @@ -1119,24 +1285,16 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { uint64_t copy_start_us = ggml_time_us(); for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_backend_prio(sched, split_backend)]; - if (input->buffer == NULL) { - if (input->view_src == NULL) { - fprintf(stderr, "input %s has no buffer and no view_src\n", input->name); - exit(1); - } - // FIXME: may need to use the sched buffer instead - ggml_backend_view_init(input->view_src->buffer, input); - } - if (input_cpy->buffer == NULL) { - fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name); - exit(1); - } - //GGML_ASSERT(input->buffer->backend != input_cpy->buffer->backend); - //GGML_ASSERT(input_cpy->buffer->backend == split_backend); - ggml_backend_tensor_copy(input, input_cpy); + struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][split_backend_id]; + + GGML_ASSERT(input->buffer != NULL); + GGML_ASSERT(input_cpy->buffer != NULL); + + // TODO: avoid this copy if it was already copied in a previous split, and the input didn't change + // this is important to avoid copying constants such as KQ_mask and inp_pos multiple times + ggml_backend_tensor_copy_async(split_backend, input, input_cpy); } - // ggml_backend_synchronize(split_backend); + //ggml_backend_synchronize(split_backend); // necessary to measure copy time int64_t copy_end_us = ggml_time_us(); copy_us[split_backend_id] += copy_end_us - copy_start_us; @@ -1148,7 +1306,7 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { uint64_t compute_start_us = ggml_time_us(); ggml_backend_graph_compute(split_backend, &split->graph); - // ggml_backend_synchronize(split_backend); + //ggml_backend_synchronize(split_backend); // necessary to measure compute time uint64_t compute_end_us = ggml_time_us(); compute_us[split_backend_id] += compute_end_us - compute_start_us; } @@ -1168,26 +1326,41 @@ static void sched_reset(ggml_backend_sched_t sched) { for (int i = 0; i < sched->n_backends; i++) { ggml_tallocr_reset(sched->tallocs[i]); } + // reset state for the next run + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); + memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); + memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); + + sched->is_reset = true; } -ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) { +ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { + GGML_ASSERT(n_backends > 0); GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS); - struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched)); - memset(sched, 0, sizeof(struct ggml_backend_sched)); + struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); + + // initialize hash table + sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + sched->node_talloc = calloc(sizeof(sched->node_talloc[0]) * sched->hash_set.size, 1); + sched->node_copies = calloc(sizeof(sched->node_copies[0]) * sched->hash_set.size, 1); sched->n_backends = n_backends; for (int i = 0; i < n_backends; i++) { sched->backends[i] = backends[i]; + sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]); } sched->galloc = ggml_gallocr_new(); // init measure allocs for each backend for (int i = 0; i < n_backends; i++) { - sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]); + sched->tallocs[i] = ggml_tallocr_new_measure_from_buft(sched->bufts[i]); } + sched_reset(sched); + return sched; } @@ -1199,6 +1372,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { ggml_tallocr_free(sched->tallocs[i]); } ggml_gallocr_free(sched->galloc); + ggml_free(sched->ctx); free(sched->hash_set.keys); free(sched->node_talloc); free(sched->node_copies); @@ -1206,12 +1380,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { } void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { - // initialize hash tables - size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS; - sched->hash_set.size = hash_size; - sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size); - sched->node_talloc = malloc(sizeof(sched->node_talloc[0]) * hash_size); - sched->node_copies = malloc(sizeof(sched->node_copies[0]) * hash_size); + GGML_ASSERT(ggml_tallocr_is_measure(sched->tallocs[0])); // can only be initialized once sched_split_graph(sched, measure_graph); sched_alloc_splits(sched); @@ -1220,28 +1389,41 @@ void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgr for (int i = 0; i < sched->n_backends; i++) { size_t size = ggml_tallocr_max_size(sched->tallocs[i]); ggml_tallocr_free(sched->tallocs[i]); - sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size); + sched->tallocs[i] = ggml_tallocr_new_from_buft(sched->bufts[i], size); } sched_reset(sched); } void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + + if (!sched->is_reset) { + sched_reset(sched); + } sched_split_graph(sched, graph); sched_alloc_splits(sched); sched_compute_splits(sched); +} + +void ggml_backend_sched_reset(ggml_backend_sched_t sched) { sched_reset(sched); } +int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) { + return sched->n_splits; +} + ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) { int backend_index = sched_backend_prio(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); return sched->tallocs[backend_index]; } ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) { int backend_index = sched_backend_prio(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); return ggml_tallocr_get_buffer(sched->tallocs[backend_index]); } @@ -1251,10 +1433,19 @@ void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml node_allocr(node) = sched->tallocs[backend_index]; } +ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { + ggml_tallocr_t allocr = node_allocr(node); + if (allocr == NULL) { + return NULL; + } + return get_allocr_backend(sched, allocr); +} + // utils + void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { GGML_ASSERT(tensor->buffer == NULL); - //GGML_ASSERT(tensor->data == NULL); // views of pre-allocted tensors may have the data set, but still need to be initialized + //GGML_ASSERT(tensor->data == NULL); // views of pre-allocated tensors may have the data set in ggml_new_tensor, but still need to be initialized by the backend GGML_ASSERT(tensor->view_src != NULL); GGML_ASSERT(tensor->view_src->buffer != NULL); GGML_ASSERT(tensor->view_src->data != NULL); @@ -1320,6 +1511,7 @@ static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor struct ggml_tensor * dst = node_copies[id]; if (dst->view_src != NULL) { + graph_init_tensor(hash_set, node_copies, node_init, src->view_src); ggml_backend_view_init(dst->view_src->buffer, dst); } else { @@ -1353,6 +1545,21 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s struct ggml_context * ctx_allocated = ggml_init(params); struct ggml_context * ctx_unallocated = ggml_init(params); + if (ctx_allocated == NULL || ctx_unallocated == NULL) { + fprintf(stderr, "failed to allocate context for graph copy\n"); + free(hash_set.keys); + free(node_copies); + free(node_init); + ggml_free(ctx_allocated); + ggml_free(ctx_unallocated); + return (struct ggml_backend_graph_copy) { + /* .buffer = */ NULL, + /* .ctx_allocated = */ NULL, + /* .ctx_unallocated = */ NULL, + /* .graph = */ NULL, + }; + } + // dup nodes for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -1361,6 +1568,20 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s // allocate nodes ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend); + if (buffer == NULL) { + fprintf(stderr, "failed to allocate buffer for graph copy\n"); + free(hash_set.keys); + free(node_copies); + free(node_init); + ggml_free(ctx_allocated); + ggml_free(ctx_unallocated); + return (struct ggml_backend_graph_copy) { + /* .buffer = */ NULL, + /* .ctx_allocated = */ NULL, + /* .ctx_unallocated = */ NULL, + /* .graph = */ NULL, + }; + } //printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024); @@ -1397,8 +1618,12 @@ void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) { ggml_free(copy.ctx_unallocated); } -void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) { +bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) { struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph); + if (copy.buffer == NULL) { + return false; + } + struct ggml_cgraph * g1 = graph; struct ggml_cgraph * g2 = copy.graph; @@ -1428,4 +1653,6 @@ void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t } ggml_backend_graph_copy_free(copy); + + return true; } diff --git a/ggml-backend.h b/ggml-backend.h index 85ff67b0e..4eb244af1 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -17,22 +17,31 @@ extern "C" { // // buffer type - GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size); - GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); - GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); - GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); - GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); + GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); + GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); + GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); + GGML_API size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); + GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); + GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); // buffer - GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); - GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); - GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); - GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer); + enum ggml_backend_buffer_usage { + GGML_BACKEND_BUFFER_USAGE_ANY = 0, + GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1, + }; + + GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); + GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); + GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer); // // Backend @@ -140,23 +149,24 @@ extern "C" { typedef struct ggml_backend_sched * ggml_backend_sched_t; // Initialize a backend scheduler - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends); - - GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); - + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph - GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + // Get the number of splits of the last graph + GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend); GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); - // Allocate a graph on the backend scheduler - GGML_API void ggml_backend_sched_graph_compute( - ggml_backend_sched_t sched, - struct ggml_cgraph * graph); + // Allocate and compute graph on the backend scheduler + GGML_API void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + // Reset all assignments and allocators - must be called before using the sched allocators to allocate inputs + GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // // Utils @@ -176,7 +186,7 @@ extern "C" { typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data); // Compare the output of two backends - GGML_API void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data); + GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data); // Tensor initialization GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index a345b0c4a..2db50437c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8,8 +8,13 @@ #include #include #include +#include #include - +#include +#include +#include "ggml-cuda.h" +#include "ggml.h" +#include "ggml-backend-impl.h" #if defined(GGML_USE_HIPBLAS) #include @@ -77,6 +82,7 @@ #define cudaMemcpyKind hipMemcpyKind #define cudaMemset hipMemset #define cudaMemsetAsync hipMemsetAsync +#define cudaMemGetInfo hipMemGetInfo #define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize #define cudaSetDevice hipSetDevice #define cudaStreamCreateWithFlags hipStreamCreateWithFlags @@ -112,10 +118,6 @@ #endif // defined(GGML_USE_HIPBLAS) -#include "ggml-cuda.h" -#include "ggml.h" -#include "ggml-backend-impl.h" - #define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) #define CC_PASCAL 600 @@ -564,7 +566,7 @@ static void ggml_cuda_set_device(const int device) { static int g_device_count = -1; static int g_main_device = 0; -static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0}; +static std::array g_default_tensor_split = {}; struct cuda_device_capabilities { int cc; // compute capability @@ -575,10 +577,6 @@ struct cuda_device_capabilities { static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, false, 0} }; -static void * g_scratch_buffer = nullptr; -static size_t g_scratch_size = 0; // disabled by default -static size_t g_scratch_offset = 0; - static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; [[noreturn]] @@ -7548,8 +7546,9 @@ void ggml_init_cublas() { CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); - g_tensor_split[id] = total_vram; + g_default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; + #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) g_device_caps[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; #else @@ -7558,7 +7557,7 @@ void ggml_init_cublas() { g_device_caps[id].smpb = prop.sharedMemPerBlock; } for (int id = 0; id < g_device_count; ++id) { - g_tensor_split[id] /= total_vram; + g_default_tensor_split[id] /= total_vram; } for (int id = 0; id < g_device_count; ++id) { @@ -7582,30 +7581,6 @@ void ggml_init_cublas() { } } -void ggml_cuda_set_tensor_split(const float * tensor_split) { - if (tensor_split == nullptr) { - return; - } - bool all_zero = true; - for (int i = 0; i < g_device_count; ++i) { - if (tensor_split[i] != 0.0f) { - all_zero = false; - break; - } - } - if (all_zero) { - return; - } - float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] /= split_sum; - } -} - void * ggml_cuda_host_malloc(size_t size) { if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { return nullptr; @@ -8057,11 +8032,11 @@ static void ggml_cuda_op_mul_mat_q( (void) src1_ddf_i; } -static int64_t get_row_rounding(ggml_type type) { +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; for (int id = 0; id < g_device_count; ++id) { - if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { + if (tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { if (min_compute_capability > g_device_caps[id].cc) { min_compute_capability = g_device_caps[id].cc; } @@ -8122,6 +8097,21 @@ static int64_t get_row_rounding(ggml_type type) { #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) } +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + + if (id == g_device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + static void ggml_cuda_op_mul_mat_vec_q( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, @@ -8739,6 +8729,11 @@ static void ggml_cuda_set_peer_access(const int n_tokens) { peer_access_enabled = enable_peer_access; } +// FIXME: move this somewhere else +struct ggml_backend_cuda_split_buffer_type_context { + std::array tensor_split; +}; + static void ggml_cuda_op_mul_mat( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op, const bool convert_src1_to_q8_1) { @@ -8790,6 +8785,14 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); + std::array tensor_split; + if (split) { + // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_GPU_SPLIT check + // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; + tensor_split = buft_ctx->tensor_split; + } + struct dev_data { cuda_pool_alloc src0_dd_alloc; cuda_pool_alloc src1_ddf_alloc; @@ -8817,17 +8820,17 @@ static void ggml_cuda_op_mul_mat( // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes if (split) { - const int64_t rounding = get_row_rounding(src0->type); + const int64_t rounding = get_row_rounding(src0->type, tensor_split); if (id != 0) { - dev[id].row_low = ne01*g_tensor_split[id]; + dev[id].row_low = ne01*tensor_split[id]; if (dev[id].row_low < ne01) { dev[id].row_low -= dev[id].row_low % rounding; } } if (id != g_device_count - 1) { - dev[id].row_high = ne01*g_tensor_split[id + 1]; + dev[id].row_high = ne01*tensor_split[id + 1]; if (dev[id].row_high < ne01) { dev[id].row_high -= dev[id].row_high % rounding; } @@ -9373,10 +9376,17 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; - for (int id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - min_compute_capability = g_device_caps[id].cc; + + if (split) { + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; + auto & tensor_split = buft_ctx->tensor_split; + for (int id = 0; id < g_device_count; ++id) { + if (min_compute_capability > g_device_caps[id].cc && tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { + min_compute_capability = g_device_caps[id].cc; + } } + } else { + min_compute_capability = g_device_caps[g_main_device].cc; } #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) @@ -9415,7 +9425,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { @@ -9877,247 +9887,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { - const int64_t nrows = ggml_nrows(tensor); - - const int64_t ne0 = tensor->ne[0]; - - const size_t nb1 = tensor->nb[1]; - - ggml_backend_type backend = tensor->backend; - ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - - for (int id = 0; id < g_device_count; ++id) { - if (backend == GGML_BACKEND_GPU && id != g_main_device) { - continue; - } - - ggml_cuda_set_device(id); - - int64_t row_low, row_high; - if (backend == GGML_BACKEND_GPU) { - row_low = 0; - row_high = nrows; - } else if (backend == GGML_BACKEND_GPU_SPLIT) { - const int64_t rounding = get_row_rounding(tensor->type); - - row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; - row_low -= row_low % rounding; - - if (id == g_device_count - 1) { - row_high = nrows; - } else { - row_high = nrows*g_tensor_split[id + 1]; - row_high -= row_high % rounding; - } - } else { - GGML_ASSERT(false); - } - if (row_low == row_high) { - continue; - } - - int64_t nrows_split = row_high - row_low; - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf; - CUDA_CHECK(cudaMalloc(&buf, size)); - char * buf_host = (char *)data + offset_split; - - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); - } - - CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice)); - - extra->data_device[id] = buf; - - if (backend == GGML_BACKEND_GPU_SPLIT) { - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); - } - } - } - - tensor->extra = extra; -} - -void ggml_cuda_free_data(struct ggml_tensor * tensor) { - if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { - return; - } - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - - for (int id = 0; id < g_device_count; ++id) { - ggml_cuda_set_device(id); - if (extra->data_device[id] != nullptr) { - CUDA_CHECK(cudaFree(extra->data_device[id])); - } - - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); - } - } - } - - delete extra; -} - -static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr; -static size_t g_temp_tensor_extra_index = 0; - -static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { - if (g_temp_tensor_extras == nullptr) { - g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; - } - - size_t alloc_index = g_temp_tensor_extra_index; - g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES; - ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; - memset(extra, 0, sizeof(*extra)); - - return extra; -} - -static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) { - if (scratch && g_scratch_size == 0) { - return; - } - - tensor->backend = GGML_BACKEND_GPU; - - // recursively assign CUDA buffers until a compute tensor is found - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) { - const ggml_op src0_op = tensor->src[0]->op; - if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) { - ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc); - } - } - if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) { - ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc); - } - - if (scratch && no_alloc) { - return; - } - - ggml_tensor_extra_gpu * extra; - - const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) || - tensor->op == GGML_OP_VIEW || - force_inplace; - const size_t size = ggml_nbytes(tensor); - - ggml_cuda_set_device(g_main_device); - if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - size_t offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&offset, tensor->op_params, sizeof(size_t)); - } - extra = ggml_cuda_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = src0_ddc + offset; - } else if (tensor->op == GGML_OP_CPY) { - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; - void * src1_ddv = src1_extra->data_device[g_main_device]; - extra = ggml_cuda_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = src1_ddv; - } else if (scratch) { - GGML_ASSERT(size <= g_scratch_size); - if (g_scratch_offset + size > g_scratch_size) { - g_scratch_offset = 0; - } - - char * data = (char *) g_scratch_buffer; - if (data == nullptr) { - CUDA_CHECK(cudaMalloc(&data, g_scratch_size)); - g_scratch_buffer = data; - } - extra = ggml_cuda_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = data + g_scratch_offset; - - g_scratch_offset += size; - - GGML_ASSERT(g_scratch_offset <= g_scratch_size); - } else { // allocate new buffers outside of scratch - void * data; - CUDA_CHECK(cudaMalloc(&data, size)); - CUDA_CHECK(cudaMemset(data, 0, size)); - extra = new ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - extra->data_device[g_main_device] = data; - } - - tensor->extra = extra; -} - -void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) { - if (g_scratch_size == 0) { - return; - } - if (g_scratch_buffer == nullptr) { - ggml_cuda_set_device(g_main_device); - CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size)); - } - - ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra(); - - const bool inplace = tensor->view_src != nullptr; - - if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - size_t view_offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&view_offset, tensor->op_params, sizeof(size_t)); - } - extra->data_device[g_main_device] = src0_ddc + view_offset; - } else { - extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset; - } - - tensor->extra = extra; -} - -void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) { - GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - GGML_ASSERT(ggml_is_contiguous(tensor)); - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_cuda_set_device(g_main_device); - CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice)); -} - -void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, true, false, false); -} - -void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, true, false, true); -} - -void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, false, false, false); -} - -void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, false, true, false); -} - -void ggml_cuda_set_main_device(const int main_device) { +static void ggml_cuda_set_main_device(const int main_device) { if (main_device >= g_device_count) { fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", main_device, g_device_count, g_main_device); @@ -10126,30 +9896,12 @@ void ggml_cuda_set_main_device(const int main_device) { if (g_main_device != main_device && g_device_count > 1) { g_main_device = main_device; - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device)); - fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name); + //cudaDeviceProp prop; + //CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device)); + //fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name); } } -void ggml_cuda_set_scratch_size(const size_t scratch_size) { - // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously - // it still won't always work as expected, but it's better than nothing - if (scratch_size > g_scratch_size) { - ggml_cuda_free_scratch(); - } - g_scratch_size = std::max(g_scratch_size, scratch_size); -} - -void ggml_cuda_free_scratch() { - if (g_scratch_buffer == nullptr) { - return; - } - - CUDA_CHECK(cudaFree(g_scratch_buffer)); - g_scratch_buffer = nullptr; -} - bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { if (!g_cublas_loaded) return false; @@ -10328,21 +10080,31 @@ void ggml_cuda_get_device_description(int device, char * description, size_t des #define UNUSED GGML_UNUSED +struct ggml_backend_cuda_context { + int device; + std::string name; +}; + // cuda buffer -struct ggml_backend_buffer_context_cuda { +struct ggml_backend_cuda_buffer_context { int device; void * dev_ptr = nullptr; ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; size_t temp_tensor_extra_index = 0; + std::string name; - ggml_backend_buffer_context_cuda(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} + ggml_backend_cuda_buffer_context(int device, void * dev_ptr) : + device(device), dev_ptr(dev_ptr), + name(GGML_CUDA_NAME + std::to_string(device)) { + } - ~ggml_backend_buffer_context_cuda() { + ~ggml_backend_cuda_buffer_context() { delete[] temp_tensor_extras; } ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { + // TODO: remove GGML_CUDA_MAX_NODES, allocate dynamically and reuse in backend_buffer_reset if (temp_tensor_extras == nullptr) { temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; } @@ -10356,19 +10118,28 @@ struct ggml_backend_buffer_context_cuda { } }; +static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; +} + static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; CUDA_CHECK(cudaFree(ctx->dev_ptr)); delete ctx; } static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->dev_ptr; } static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { assert(tensor->view_src->buffer->buft == buffer->buft); @@ -10397,14 +10168,12 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[ctx->device][0])); } } - - UNUSED(buffer); } static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); @@ -10415,49 +10184,82 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost)); + CUDA_CHECK(cudaDeviceSynchronize()); +} + +static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_backend_buffer_is_cuda(src->buffer)) { + ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; + ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context; + + ggml_cuda_set_device(src_ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + ggml_cuda_set_device(dst_ctx->device); + CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaMemcpy((char *)dst->data, (const char *)src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice)); + CUDA_CHECK(cudaDeviceSynchronize()); + + return true; + } + return false; } static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context; + ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size)); + CUDA_CHECK(cudaDeviceSynchronize()); } -static struct ggml_backend_buffer_i cuda_backend_buffer_interface = { +static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { + /* .get_name = */ ggml_backend_cuda_buffer_get_name, /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, /* .get_base = */ ggml_backend_cuda_buffer_get_base, /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, /* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor, /* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor, - /* .cpy_tensor_from = */ NULL, - /* .cpy_tensor_to = */ NULL, + /* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor, /* .clear = */ ggml_backend_cuda_buffer_clear, + /* .reset = */ NULL, }; // cuda buffer type -static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - int device = (int) (intptr_t) buft->context; +struct ggml_backend_cuda_buffer_type_context { + int device; + std::string name; +}; - ggml_cuda_set_device(device); +static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + + ggml_cuda_set_device(buft_ctx->device); size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 void * dev_ptr; - CUDA_CHECK(cudaMalloc(&dev_ptr, size)); + cudaError_t err = cudaMalloc(&dev_ptr, size); + if (err != cudaSuccess) { + fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); + return nullptr; + } - ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda(device, dev_ptr); + ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr); - return ggml_backend_buffer_init(buft, cuda_backend_buffer_interface, ctx, size); + return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); } static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { @@ -10466,7 +10268,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_ty UNUSED(buft); } -static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, ggml_tensor * tensor) { +static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); int64_t nrows_split = row_high - row_low; @@ -10487,21 +10289,32 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t } static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_cuda(backend); + if (!ggml_backend_is_cuda(backend)) { + return false; + } - UNUSED(buft); + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + + return buft_ctx->device == cuda_ctx->device; } static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { + /* .get_name = */ ggml_backend_cuda_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, - /* .is_host = */ nullptr, + /* .is_host = */ NULL, }; ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { - static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; + // FIXME: this is not thread safe + if (device >= ggml_backend_cuda_get_device_count()) { + return nullptr; + } + + static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; static bool ggml_backend_cuda_buffer_type_initialized = false; @@ -10509,7 +10322,7 @@ ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) { ggml_backend_cuda_buffer_types[i] = { /* .iface = */ ggml_backend_cuda_buffer_type_interface, - /* .context = */ (ggml_backend_buffer_type_context_t) (intptr_t) i, + /* .context = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)}, }; } ggml_backend_cuda_buffer_type_initialized = true; @@ -10518,8 +10331,306 @@ ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { return &ggml_backend_cuda_buffer_types[device]; } +// cuda split buffer + +struct ggml_backend_cuda_split_buffer_context { + ~ggml_backend_cuda_split_buffer_context() { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int id = 0; id < g_device_count; ++id) { + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + if (extra->events[id][is] != nullptr) { + CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); + } + } + if (extra->data_device[id] != nullptr) { + CUDA_CHECK(cudaFree(extra->data_device[id])); + } + } + delete extra; + } + } + + std::vector tensor_extras; +}; + +static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_CUDA_NAME "_Split"; + + UNUSED(buffer); +} + +// unused at the moment +//static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { +// return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; +//} + +static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; + delete ctx; +} + +static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + UNUSED(buffer); +} + +static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_cuda_set_device(id); + char * buf; + CUDA_CHECK(cudaMalloc(&buf, size)); + + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); + } + + extra->data_device[id] = buf; + + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); + } + } + tensor->backend = GGML_BACKEND_GPU_SPLIT; + tensor->extra = extra; +} + +static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + CUDA_CHECK(cudaMemcpy(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice)); + } +} + +static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + CUDA_CHECK(cudaMemcpy(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost)); + } +} + +static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + UNUSED(buffer); + UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { + /* .get_name = */ ggml_backend_cuda_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_cuda_split_buffer_clear, + /* .reset = */ NULL, +}; + +// cuda split buffer type + +static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_CUDA_NAME "_Split"; + + UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); +} + +static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + + UNUSED(buft); +} + +static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int id = 0; id < g_device_count; ++id) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_cuda(backend); + + UNUSED(buft); +} + +static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_cuda_split_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cuda_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, + /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, +}; + +ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = g_default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < g_device_count; ++i) { + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < g_device_count; ++i) { + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, + /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + // host buffer type +static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_CUDA_NAME "_Host"; + + UNUSED(buft); +} + +static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { + return GGML_CUDA_NAME "_Host"; + + UNUSED(buffer); +} + static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_cuda_host_free(buffer->context); } @@ -10532,9 +10643,9 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); } - // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_cuda_host_buffer_name; buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; return buffer; @@ -10543,6 +10654,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { /* .iface = */ { + /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, @@ -10557,31 +10669,27 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { // backend -struct ggml_backend_context_cuda { - int device; -}; - static const char * ggml_backend_cuda_name(ggml_backend_t backend) { - return GGML_CUDA_NAME; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - UNUSED(backend); + return cuda_ctx->name.c_str(); } static void ggml_backend_cuda_free(ggml_backend_t backend) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; delete cuda_ctx; delete backend; } static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; return ggml_backend_cuda_buffer_type(cuda_ctx->device); } static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -10590,7 +10698,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens } static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -10598,39 +10706,27 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); } +static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + + if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx->device][0])); + return true; + } + + return false; +} + static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0])); UNUSED(backend); } -static ggml_backend_graph_plan_t ggml_backend_cuda_graph_plan_create(ggml_backend_t backend, ggml_cgraph * cgraph) { - GGML_ASSERT(!"not implemented"); - - return nullptr; - - UNUSED(backend); - UNUSED(cgraph); -} - -static void ggml_backend_cuda_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); -} - -static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); -} - static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context; + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -10640,53 +10736,31 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; + } - assert(node->backend == GGML_BACKEND_GPU); +#ifndef NDEBUG + assert(node->backend == GGML_BACKEND_GPU || node->backend == GGML_BACKEND_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_GPU); + assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); assert(node->src[j]->extra != nullptr); } } +#endif bool ok = ggml_cuda_compute_forward(¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); - -#if 0 - if (node->type == GGML_TYPE_F32) { - cudaDeviceSynchronize(); - std::vector tmp(ggml_nelements(node), 0.0f); - cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost); - printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op), - ggml_type_name(node->src[0]->type), - node->src[1] ? ggml_type_name(node->src[1]->type) : "none", - node->src[0]->name, - node->src[1] ? node->src[1]->name : "none"); - double sum = 0.0; - double sq_sum = 0.0; - for (int i = 0; i < ggml_nelements(node); i++) { - printf("%f ", tmp[i]); - sum += tmp[i]; - sq_sum += tmp[i]*tmp[i]; - } - printf("\n"); - printf("sum: %f, ", sum); - printf("sq_sum: %f\n", sq_sum); - } -#endif } - UNUSED(backend); - return true; } @@ -10801,18 +10875,17 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten UNUSED(backend); } -static ggml_backend_i cuda_backend_i = { +static ggml_backend_i ggml_backend_cuda_interface = { /* .get_name = */ ggml_backend_cuda_name, /* .free = */ ggml_backend_cuda_free, /* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async, /* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, + /* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async, /* .synchronize = */ ggml_backend_cuda_synchronize, - /* .graph_plan_create = */ ggml_backend_cuda_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_cuda_graph_plan_free, - /* .graph_plan_compute = */ ggml_backend_cuda_graph_plan_compute, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, /* .supports_op = */ ggml_backend_cuda_supports_op, }; @@ -10828,12 +10901,13 @@ ggml_backend_t ggml_backend_cuda_init(int device) { // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_cuda_set_main_device(device); - ggml_backend_context_cuda * ctx = new ggml_backend_context_cuda { - /* .device = */ device + ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context { + /* .device = */ device, + /* .name = */ GGML_CUDA_NAME + std::to_string(device), }; ggml_backend_t cuda_backend = new ggml_backend { - /* .interface = */ cuda_backend_i, + /* .interface = */ ggml_backend_cuda_interface, /* .context = */ ctx }; @@ -10841,9 +10915,24 @@ ggml_backend_t ggml_backend_cuda_init(int device) { } bool ggml_backend_is_cuda(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_cuda_name; + return backend && backend->iface.get_name == ggml_backend_cuda_name; } +int ggml_backend_cuda_get_device_count() { + return ggml_cuda_get_device_count(); +} + +void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { + ggml_cuda_get_device_description(device, description, description_size); +} + +void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { + ggml_cuda_set_device(device); + + CUDA_CHECK(cudaMemGetInfo(free, total)); +} + +// backend registry static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data); return cuda_backend; diff --git a/ggml-cuda.h b/ggml-cuda.h index cdb0c0c41..d19cbf3fd 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -27,22 +27,6 @@ GGML_API void * ggml_cuda_host_malloc(size_t size); GGML_API void ggml_cuda_host_free(void * ptr); GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split); -GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor); -GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor); - -GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor); -GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor); -GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor); - -GGML_API void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor); -GGML_API void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset); -GGML_API void ggml_cuda_copy_to_device(struct ggml_tensor * tensor); - -GGML_API void ggml_cuda_set_main_device(int main_device); -GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q); -GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size); -GGML_API void ggml_cuda_free_scratch(void); GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); GGML_API int ggml_cuda_get_device_count(void); @@ -52,13 +36,17 @@ GGML_API void ggml_cuda_get_device_description(int device, char * description, GGML_API ggml_backend_t ggml_backend_cuda_init(int device); GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); -GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend); GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); - -// pinned host buffer for use with CPU backend for faster copies between CPU and GPU +// split tensor buffer that splits matrices by rows across multiple devices +GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); +// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); +GGML_API int ggml_backend_cuda_get_device_count(void); +GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); + #ifdef __cplusplus } #endif diff --git a/ggml-impl.h b/ggml-impl.h index 2faced080..2c58075ac 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -228,6 +228,8 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { #define GGML_HASHTABLE_FULL ((size_t)-1) #define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2) +struct ggml_hash_set ggml_hash_set_new(size_t size); + bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key); // returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted diff --git a/ggml-metal.m b/ggml-metal.m index 6e5594432..c03624073 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2520,10 +2520,10 @@ static void ggml_backend_metal_free_device(void) { } } -static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { - struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; +static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { + return "Metal"; - return ctx->all_data; + UNUSED(buffer); } static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { @@ -2541,6 +2541,12 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) free(ctx); } +static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { + struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; + + return ctx->all_data; +} + static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); @@ -2553,14 +2559,12 @@ static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, c UNUSED(buffer); } -static void ggml_backend_metal_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); - - UNUSED(buffer); -} - -static void ggml_backend_metal_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); +static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; UNUSED(buffer); } @@ -2572,18 +2576,25 @@ static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_ } static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { + /* .get_name = */ ggml_backend_metal_buffer_get_name, /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_tensor = */ NULL, /* .set_tensor = */ ggml_backend_metal_buffer_set_tensor, /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, - /* .cpy_tensor_from = */ ggml_backend_metal_buffer_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_metal_buffer_cpy_tensor_to, + /* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor, /* .clear = */ ggml_backend_metal_buffer_clear, + /* .reset = */ NULL, }; // default buffer type +static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "Metal"; + + UNUSED(buft); +} + static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); @@ -2656,6 +2667,7 @@ static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t bu ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = { /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes @@ -2679,6 +2691,14 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz ctx->n_buffers = 0; const size_t size_page = sysconf(_SC_PAGESIZE); + + // page-align the data ptr + { + const uintptr_t offs = (uintptr_t) data % size_page; + data = (void *) ((char *) data - offs); + size += offs; + } + size_t size_aligned = size; if ((size_aligned % size_page) != 0) { size_aligned += (size_page - (size_aligned % size_page)); @@ -2779,14 +2799,13 @@ static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct UNUSED(backend); } -static struct ggml_backend_i metal_backend_i = { +static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, /* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, + /* .cpy_tensor_async = */ NULL, /* .synchronize = */ NULL, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, @@ -2805,7 +2824,7 @@ ggml_backend_t ggml_backend_metal_init(void) { ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend)); *metal_backend = (struct ggml_backend) { - /* .interface = */ metal_backend_i, + /* .interface = */ ggml_backend_metal_i, /* .context = */ ctx, }; @@ -2813,7 +2832,7 @@ ggml_backend_t ggml_backend_metal_init(void) { } bool ggml_backend_is_metal(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_metal_name; + return backend && backend->iface.get_name == ggml_backend_metal_name; } void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 496f9cdca..2bb93638f 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1,5 +1,6 @@ #include "ggml.h" #include "ggml-opencl.h" +#include "ggml-backend-impl.h" #include #include @@ -10,7 +11,7 @@ #include #include -#define CL_TARGET_OPENCL_VERSION 110 +#define CL_TARGET_OPENCL_VERSION 120 #include #if defined(_MSC_VER) @@ -929,6 +930,12 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co } void ggml_cl_init(void) { + static bool initialized = false; + if (initialized) { + return; + } + initialized = true; + cl_int err; struct cl_device; @@ -1483,8 +1490,8 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr } else { d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); } - cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); + cl_mem d_Y = src1->backend == GGML_BACKEND_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); + cl_mem d_D = dst->backend == GGML_BACKEND_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); size_t x_offset = 0; @@ -1501,7 +1508,9 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { // copy src1 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); + if (src1->backend == GGML_BACKEND_CPU) { + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); + } CL_CHECK(clFinish(queue)); @@ -1522,8 +1531,10 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr } // copy dst to host - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); + if (dst->backend == GGML_BACKEND_CPU) { + float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); + CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); + } } } } @@ -1532,8 +1543,12 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr if (src0->backend != GGML_BACKEND_GPU) { ggml_cl_pool_free(d_X, x_size); } - ggml_cl_pool_free(d_Y, y_size); - ggml_cl_pool_free(d_D, d_size); + if (src1->backend != GGML_BACKEND_GPU) { + ggml_cl_pool_free(d_Y, y_size); + } + if (dst->backend != GGML_BACKEND_GPU) { + ggml_cl_pool_free(d_D, d_size); + } } static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) { @@ -1598,6 +1613,8 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); } + // FIXME: convert on device + for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { // convert src1 to fp16 // TODO: use multiple threads @@ -1643,11 +1660,13 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr } // copy dst to host, then convert to float - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); - - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - - ggml_fp16_to_fp32_row(tmp, d, d_ne); + if (dst->backend == GGML_BACKEND_CPU) { + CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); + float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); + ggml_fp16_to_fp32_row(tmp, d, d_ne); + } else { + // FIXME: convert dst to fp32 on device + } } } } @@ -1801,7 +1820,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * } -bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) { const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; @@ -1895,3 +1914,291 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) { tensor->extra = dst; GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); } + +// ggml-backend + +// buffer + +struct ggml_backend_opencl_buffer_context { + ~ggml_backend_opencl_buffer_context() { + if (buffer) { + clReleaseMemObject(buffer); + } + for (auto * sub_buffer : sub_buffers) { + clReleaseMemObject(sub_buffer); + } + } + + cl_mem buffer; + std::vector sub_buffers; +}; + +static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000; + +static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) { + return "OpenCL"; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + delete ctx; +} + +static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) { + return cl_ptr_base; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + if (tensor->view_src != NULL && tensor->view_offs == 0) { + tensor->extra = tensor->view_src->extra; + } else { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)}; + cl_int err; + cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); + CL_CHECK(err); + ctx->sub_buffers.push_back(sub_buffer); + tensor->extra = sub_buffer; + } + tensor->backend = GGML_BACKEND_GPU; +} + +static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + cl_mem tensor_buffer = (cl_mem) tensor->extra; + CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); + CL_CHECK(clFinish(queue)); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + cl_mem tensor_buffer = (cl_mem) tensor->extra; + CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); + CL_CHECK(clFinish(queue)); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL)); + CL_CHECK(clFinish(queue)); +} + +static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) { + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + for (auto * sub_buffer : ctx->sub_buffers) { + clReleaseMemObject(sub_buffer); + } + ctx->sub_buffers.clear(); +} + +static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = { + /* .get_name = */ ggml_backend_opencl_buffer_get_name, + /* .free_buffer = */ ggml_backend_opencl_buffer_free_buffer, + /* .get_base = */ ggml_backend_opencl_buffer_get_base, + /* .init_tensor = */ ggml_backend_opencl_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_opencl_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_opencl_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_opencl_buffer_clear, + /* .reset = */ ggml_backend_opencl_buffer_reset, +}; + +// buffer type + +static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) { + return "OpenCL"; + + GGML_UNUSED(buffer_type); +} + +static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) { + ggml_cl_init(); + + cl_int err; + cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err); + if (err != CL_SUCCESS) { + fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0); + return nullptr; + } + + ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}}; + + return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size); +} + +static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) { + // FIXME: not thread safe, device may not be initialized yet + static cl_uint alignment = -1; + if (alignment == (cl_uint)-1) { + ggml_cl_init(); + clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL); + } + return alignment; + + GGML_UNUSED(buffer_type); +} + +static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) { + //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend + return ggml_backend_is_cpu(backend); + + GGML_UNUSED(buffer_type); +} + +static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = { + /* .get_name = */ ggml_backend_opencl_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment, + /* .get_alloc_size = */ NULL, + /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; + + +ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() { + static ggml_backend_buffer_type buffer_type = { + /* .iface = */ ggml_backend_opencl_buffer_type_interface, + /* .context = */ nullptr, + }; + + return &buffer_type; +} + +#if 0 +// host buffer type + +static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return "CL_Host"; + + GGML_UNUSED(buft); +} + +static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) { + return "CL_Host"; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_cl_host_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_cl_host_malloc(size); + + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.get_name = ggml_backend_opencl_host_buffer_name; + buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_opencl_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_opencl_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .context = */ nullptr, + }; + + return &ggml_backend_opencl_buffer_type_host; +} + +// backend + +static const char * ggml_backend_opencl_name(ggml_backend_t backend) { + return "OpenCL"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_opencl_free(ggml_backend_t backend) { + GGML_UNUSED(backend); +} + +static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) { + return ggml_backend_opencl_buffer_type(); + + GGML_UNUSED(backend); +} + +static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { + for (int i = 0; i < graph->n_nodes; ++i) { + ggml_tensor * node = graph->nodes[i]; + switch (node->op) { + case GGML_OP_MUL_MAT: + ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0); + break; + case GGML_OP_MUL: + ggml_cl_mul(node->src[0], node->src[1], node); + break; + default: + GGML_ASSERT(false); + } + } + + return true; + + GGML_UNUSED(backend); +} + +static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_MUL_MAT: + return ggml_cl_can_mul_mat(op->src[0], op->src[1], op); + case GGML_OP_MUL: + // return ggml_can_repeat_rows(op->src[1], op->src[0]); + return true; + default: + return false; + } + + GGML_UNUSED(backend); +} + +static ggml_backend_i opencl_backend_i = { + /* .get_name = */ ggml_backend_opencl_name, + /* .free = */ ggml_backend_opencl_free, + /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_from_async = */ NULL, + /* .cpy_tensor_to_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_opencl_graph_compute, + /* .supports_op = */ ggml_backend_opencl_supports_op, +}; + +ggml_backend_t ggml_backend_opencl_init() { + ggml_backend_t backend = new ggml_backend { + /* .interface = */ opencl_backend_i, + /* .context = */ nullptr + }; + + return backend; +} + +bool ggml_backend_is_opencl(ggml_backend_t backend) { + return backend && backend->iface.get_name == ggml_backend_opencl_name; +} +#endif diff --git a/ggml-opencl.h b/ggml-opencl.h index 44d05bd64..919b00d63 100644 --- a/ggml-opencl.h +++ b/ggml-opencl.h @@ -1,6 +1,7 @@ #pragma once #include "ggml.h" +#include "ggml-backend.h" #ifdef __cplusplus extern "C" { @@ -9,17 +10,26 @@ extern "C" { GGML_API void ggml_cl_init(void); GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst); GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); -GGML_API void * ggml_cl_host_malloc(size_t size); -GGML_API void ggml_cl_host_free(void * ptr); +// GGML_API void * ggml_cl_host_malloc(size_t size); +// GGML_API void ggml_cl_host_free(void * ptr); GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor); GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); +// backend API + +// GGML_API ggml_backend_t ggml_backend_opencl_init(void); + +// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend); + +GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void); +// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void); + #ifdef __cplusplus } #endif diff --git a/ggml.c b/ggml.c index f5caeba08..6dbd7626c 100644 --- a/ggml.c +++ b/ggml.c @@ -2354,6 +2354,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { } void ggml_free(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + // make this function thread safe ggml_critical_section_start(); @@ -4362,6 +4366,23 @@ struct ggml_tensor * ggml_cpy( return ggml_cpy_impl(ctx, a, b); } +struct ggml_tensor * ggml_cast( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_type type) { + bool is_node = false; + + struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); + ggml_format_name(result, "%s (copy)", a->name); + + result->op = GGML_OP_CPY; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = a; + result->src[1] = result; + + return result; +} + // ggml_cont static struct ggml_tensor * ggml_cont_impl( @@ -14871,7 +14892,7 @@ size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tenso return i; } -static struct ggml_hash_set ggml_hash_set_new(size_t size) { +struct ggml_hash_set ggml_hash_set_new(size_t size) { size = ggml_hash_size(size); struct ggml_hash_set result; result.size = size; @@ -16620,7 +16641,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { return GGML_EXIT_SUCCESS; } -struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { +struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) { if (n_threads <= 0) { n_threads = GGML_DEFAULT_N_THREADS; } @@ -16682,14 +16703,15 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } break; case GGML_OP_MUL_MAT_ID: { + cur = 0; const struct ggml_tensor * src0 = node->src[2]; const struct ggml_tensor * src1 = node->src[1]; const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type; if (src1->type != vec_dot_type) { - cur = ggml_row_size(vec_dot_type, ggml_nelements(src1)); + cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); } const int n_as = ggml_get_op_params_i32(node, 1); - cur = GGML_PAD(cur, sizeof(int64_t)); // align + cur += GGML_PAD(cur, sizeof(int64_t)); // align cur += n_as * sizeof(int64_t); // matrix_row_counts cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows } break; diff --git a/ggml.h b/ggml.h index 4c2ff6c66..b18ba7812 100644 --- a/ggml.h +++ b/ggml.h @@ -1165,6 +1165,11 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_cast( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_type type); + // make contiguous GGML_API struct ggml_tensor * ggml_cont( struct ggml_context * ctx, @@ -1842,8 +1847,8 @@ extern "C" { // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); + GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); + GGML_API int ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data diff --git a/llama.cpp b/llama.cpp index ce413f605..fe1d8947c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1,5 +1,4 @@ #define LLAMA_API_INTERNAL -//#define LLAMA_GGML_BACKEND_CUDA_TEST // for testing only - enables ggml-cuda through ggml-backend, disables partial offloading #include "llama.h" #include "unicode.h" @@ -152,10 +151,6 @@ static bool is_float_close(float a, float b, float abs_tol) { return std::fabs(b - a) <= abs_tol; } -#ifdef GGML_USE_CPU_HBM -#include -#endif - static void zeros(std::ofstream & file, size_t n) { char zero = 0; for (size_t i = 0; i < n; ++i) { @@ -1190,12 +1185,6 @@ struct llama_mlock { #endif }; -typedef void (*offload_func_t)(struct ggml_tensor * tensor); - -static void ggml_offload_nop(struct ggml_tensor * tensor) { - (void) tensor; -} - static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { std::vector result(8, 0); const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); @@ -1211,19 +1200,14 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_ return std::string(result.data(), result.size()); } -static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) { +static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) { ggml_backend_buffer_type_t buft = nullptr; -#ifdef GGML_USE_METAL - if (n_gpu_layers > 0) { - buft = ggml_backend_metal_buffer_type(); +#if defined(GGML_USE_CUBLAS) + // host buffers should only be used when data is expected to be copied to/from the GPU + if (host_buffer) { + buft = ggml_backend_cuda_host_buffer_type(); } -#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (n_gpu_layers > 0) { - buft = ggml_backend_cuda_buffer_type(0); - } -#elif defined(GGML_USE_CUBLAS) - buft = ggml_backend_cuda_host_buffer_type(); #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); #endif @@ -1231,10 +1215,45 @@ static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) { if (buft == nullptr) { buft = ggml_backend_cpu_buffer_type(); } - return buft; - GGML_UNUSED(n_gpu_layers); + GGML_UNUSED(host_buffer); +} + +static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_METAL + buft = ggml_backend_metal_buffer_type(); +#elif defined(GGML_USE_CUBLAS) + buft = ggml_backend_cuda_buffer_type(gpu); +#elif defined(GGML_USE_CLBLAST) + buft = ggml_backend_opencl_buffer_type(); +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_cpu(true); + } + return buft; + + GGML_UNUSED(gpu); +} + +static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_CUBLAS + if (ggml_backend_cuda_get_device_count() > 1) { + buft = ggml_backend_cuda_split_buffer_type(tensor_split); + } +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_offload(fallback_gpu); + } + return buft; + + GGML_UNUSED(tensor_split); } // @@ -1445,24 +1464,24 @@ struct llama_kv_cache { std::vector k_l; // per layer std::vector v_l; - struct ggml_context * ctx = NULL; + std::vector ctxs; + std::vector bufs; - ggml_backend_buffer_t buf = NULL; + size_t total_size() const { + size_t size = 0; + for (ggml_backend_buffer_t buf : bufs) { + size += ggml_backend_buffer_get_size(buf); + } + return size; + } ~llama_kv_cache() { -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (ggml_cublas_loaded()) { - for (size_t i = 0; i < k_l.size(); ++i) { - ggml_cuda_free_data(k_l[i]); - ggml_cuda_free_data(v_l[i]); - } - } -#endif - if (ctx) { + for (struct ggml_context * ctx : ctxs) { ggml_free(ctx); } - - ggml_backend_buffer_free(buf); + for (ggml_backend_buffer_t buf : bufs) { + ggml_backend_buffer_free(buf); + } } }; @@ -1539,16 +1558,32 @@ struct llama_model { std::vector layers; + llama_split_mode split_mode; + int main_gpu; int n_gpu_layers; // gguf metadata std::unordered_map gguf_kv; - // context - struct ggml_context * ctx = NULL; + // layer -> buffer type mapping + struct layer_buft { + layer_buft() : buft_matrix(nullptr), buft(nullptr) {} + layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {} + layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {} - // the model memory buffer - ggml_backend_buffer_t buf = NULL; + ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication + ggml_backend_buffer_type_t buft; // everything else + }; + + layer_buft buft_input; + layer_buft buft_output; + std::vector buft_layer; + + // contexts where the model tensors metadata is stored + std::vector ctxs; + + // the model memory buffers for the tensor data + std::vector bufs; // model memory mapped file std::unique_ptr mapping; @@ -1564,39 +1599,32 @@ struct llama_model { int64_t t_start_us = 0; ~llama_model() { -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (ggml_cublas_loaded()) { - for (size_t i = 0; i < tensors_by_name.size(); ++i) { - ggml_cuda_free_data(tensors_by_name[i].second); - } - ggml_cuda_free_scratch(); - } -#endif - -#if defined(GGML_USE_CLBLAST) - for (size_t i = 0; i < tensors_by_name.size(); ++i) { - ggml_cl_free_data(tensors_by_name[i].second); - } -#endif - if (ctx) { + for (struct ggml_context * ctx : ctxs) { ggml_free(ctx); } - - ggml_backend_buffer_free(buf); + for (ggml_backend_buffer_t buf : bufs) { + ggml_backend_buffer_free(buf); + } } }; struct llama_context { llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {} ~llama_context() { - ggml_allocr_free(alloc); - ggml_backend_buffer_free(buf_alloc); - ggml_backend_free(backend); + ggml_backend_sched_free(sched); + + for (ggml_backend_t backend : backends) { + ggml_backend_free(backend); + } } llama_cparams cparams; - ggml_backend_t backend = nullptr; + std::vector backends; +#ifdef GGML_USE_METAL + ggml_backend_t backend_metal = nullptr; +#endif + ggml_backend_t backend_cpu = nullptr; const llama_model & model; @@ -1630,8 +1658,9 @@ struct llama_context { // memory buffers used to evaluate the model std::vector buf_compute_meta; - ggml_backend_buffer_t buf_alloc = NULL; - ggml_allocr * alloc = NULL; + ggml_backend_sched_t sched = nullptr; + // allocator for the input tensors + ggml_tallocr * alloc = nullptr; // temporary buffer for copying data to/from the backend std::vector> buf_copy; @@ -1646,16 +1675,17 @@ struct llama_context { // static bool llama_kv_cache_init( - const struct llama_hparams & hparams, struct llama_kv_cache & cache, + const llama_model & model, ggml_type ktype, ggml_type vtype, uint32_t n_ctx, - int n_gpu_layers, bool offload) { + const struct llama_hparams & hparams = model.hparams; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const uint32_t n_layer = hparams.n_layer; + const int64_t n_layer = hparams.n_layer; cache.has_shift = false; @@ -1666,62 +1696,65 @@ static bool llama_kv_cache_init( cache.cells.clear(); cache.cells.resize(n_ctx); - struct ggml_init_params params; - params.mem_size = 2u*n_layer*ggml_tensor_overhead(); - params.mem_buffer = NULL; - params.no_alloc = true; +#ifdef GGML_USE_CLBLAST + offload = false; +#endif - cache.ctx = ggml_init(params); + // count used buffer types + std::map buft_layer_count; + if (offload) { + for (int64_t i = 0; i < n_layer; ++i) { + buft_layer_count[model.buft_layer[i].buft]++; + } + } else { + buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer; + } - size_t vram_kv_cache = 0; - - if (!cache.ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__); - return false; + // create a context for each buffer type + std::map ctx_map; + for (auto & it : buft_layer_count) { + int n_layers = it.second; + struct ggml_init_params params = { + /*.mem_size =*/ 2u*n_layers*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__); + return false; + } + ctx_map[it.first] = ctx; + cache.ctxs.push_back(ctx); } cache.k_l.reserve(n_layer); cache.v_l.reserve(n_layer); - const int i_gpu_start = (int) n_layer - n_gpu_layers; - for (int i = 0; i < (int) n_layer; i++) { - ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx); - ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx); + struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); + ggml_tensor * k = ggml_new_tensor_1d(ctx, ktype, n_embd_k_gqa*n_ctx); + ggml_tensor * v = ggml_new_tensor_1d(ctx, vtype, n_embd_v_gqa*n_ctx); ggml_format_name(k, "cache_k_l%d", i); ggml_format_name(v, "cache_v_l%d", i); cache.k_l.push_back(k); cache.v_l.push_back(v); -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (i >= i_gpu_start) { - if (offload) { - ggml_cuda_assign_buffers_no_scratch(k); - ggml_cuda_assign_buffers_no_scratch(v); - vram_kv_cache += ggml_nbytes(k); - vram_kv_cache += ggml_nbytes(v); - // HACK: mark tensor as allocated - k->data = v->data = (void *)(uintptr_t)1; - } + } + + // allocate tensors and initialize the buffers to avoid NaNs in the padding + for (auto it : ctx_map) { + ggml_backend_buffer_type_t buft = it.first; + ggml_context * ctx = it.second; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (!buf) { + LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__); + return false; } -#endif // GGML_USE_CUBLAS + ggml_backend_buffer_clear(buf, 0); + LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); + cache.bufs.push_back(buf); } - // allocate tensors - cache.buf = ggml_backend_alloc_ctx_tensors_from_buft(cache.ctx, llama_default_buffer_type(n_gpu_layers)); - - // buf may be NULL with full offload - if (cache.buf) { - // initialize the buffer to avoid NaNs in the padding - ggml_backend_buffer_clear(cache.buf, 0); - } - - if (vram_kv_cache > 0) { - LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); - } - - GGML_UNUSED(i_gpu_start); - GGML_UNUSED(offload); - return true; } @@ -2354,9 +2387,8 @@ struct llama_model_loader { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) { + struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta) { struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta); - tensor->backend = backend; // TODO: ggml_set_backend ggml_set_name(tensor, ggml_get_name(meta)); n_created++; @@ -2364,7 +2396,7 @@ struct llama_model_loader { return tensor; } - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, ggml_backend_type backend, bool required = true) { + struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, bool required = true) { struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str()); if (cur == NULL) { @@ -2374,12 +2406,6 @@ struct llama_model_loader { throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } - if (backend == GGML_BACKEND_GPU_SPLIT) { - if (ne.size() == 1) { - throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str())); - } - } - { bool is_ok = true; for (size_t i = 0; i < ne.size(); ++i) { @@ -2397,7 +2423,7 @@ struct llama_model_loader { } } - return create_tensor_for(ctx, cur, backend); + return create_tensor_for(ctx, cur); } void done_getting_tensors() const { @@ -2416,26 +2442,36 @@ struct llama_model_loader { return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx); } - void init_mapping(bool prefetch = true) { - /* - // prefetch only CPU tensors - if (use_mmap) { - size_t size_pref = 0; // prefetch - - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - if (cur->backend == GGML_BACKEND_CPU) { - size_t tensor_end = gguf_get_tensor_offset(ctx_gguf, i) + ggml_nbytes(cur); - size_pref = std::max(size_pref, tensor_end); - } - } - mapping.reset(new llama_mmap(&file, gguf_get_data_offset(ctx_gguf) + size_pref, ggml_is_numa())); - } - */ + void init_mapping(bool prefetch = true, llama_mlock * lmlock = nullptr) { // prefetch the whole file - all the data is needed anyway if (use_mmap) { mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa())); } + + // compute the total size of all tensors for progress reporting + for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { + struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + size_data += ggml_nbytes(cur); + } + + if (use_mmap && mapping) { + if (lmlock) { + lmlock->init(mapping->addr); + } + mmap_used_first = mapping->size; + } + } + + void get_mapping_range(size_t * first, size_t * last, ggml_context * ctx) const { + GGML_ASSERT(mapping); + + *first = mapping->size; + *last = 0; + for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) { + const size_t offs = file_offset(ggml_get_name(tensor)); + *first = std::min(*first, offs); + *last = std::max(*last, offs + ggml_nbytes(tensor)); + } } // for backwards compatibility, does not support ggml-backend @@ -2443,8 +2479,11 @@ struct llama_model_loader { const size_t offs = file_offset(ggml_get_name(cur)); if (use_mmap && mapping) { - GGML_ASSERT(cur->data == nullptr); - cur->data = (uint8_t *)mapping->addr + offs; + if (cur->data == nullptr) { + cur->data = (uint8_t *)mapping->addr + offs; + } else { + memcpy(cur->data, (uint8_t *)mapping->addr + offs, ggml_nbytes(cur)); + } } else { GGML_ASSERT(cur->data != nullptr); file.seek(offs, SEEK_SET); @@ -2452,37 +2491,23 @@ struct llama_model_loader { } } + size_t size_done = 0; + size_t size_data = 0; + size_t mmap_used_first = -1; + size_t mmap_used_last = 0; + // Returns false if cancelled by progress_callback - bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const { - size_t size_data = 0; - - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - size_data += ggml_nbytes(cur); - } - - if (use_mmap && buf_mmap) { - if (lmlock) { - lmlock->init(mapping->addr); - } - } - -#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST) - const bool legacy_offload = true; -#else - const bool legacy_offload = false; -#endif + bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) { + GGML_ASSERT(size_data != 0 && "call init_mapping() first"); std::vector> read_buf; - size_t size_done = 0; - - size_t mmap_first = -1; - size_t mmap_last = 0; - for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) { struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i)); - GGML_ASSERT(cur); // unused tensors should have been caught by load_data already + if (!cur) { + // some tensors may be allocated in a different context + continue; + } if (progress_callback) { if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) { @@ -2492,67 +2517,48 @@ struct llama_model_loader { const size_t offs = file_offset(ggml_get_name(cur)); - if (!legacy_offload || cur->backend == GGML_BACKEND_CPU) { - if (use_mmap && mapping) { - if (buf_mmap) { - ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs); - if (lmlock) { - lmlock->grow_to(offs + ggml_nbytes(cur)); - } - mmap_first = std::min(mmap_first, offs); - mmap_last = std::max(mmap_last, offs + ggml_nbytes(cur)); - } else { - ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur)); + if (use_mmap && mapping) { + if (buf_mmap && cur->data == nullptr) { + ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs); + if (lmlock) { + lmlock->grow_to(offs + ggml_nbytes(cur)); } + mmap_used_first = std::min(mmap_used_first, offs); + mmap_used_last = std::max(mmap_used_last, offs + ggml_nbytes(cur)); } else { - if (ggml_backend_buffer_is_host(cur->buffer)) { - file.seek(offs, SEEK_SET); - file.read_raw(cur->data, ggml_nbytes(cur)); - } else { - read_buf.resize(ggml_nbytes(cur)); - file.seek(offs, SEEK_SET); - file.read_raw(read_buf.data(), ggml_nbytes(cur)); - ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur)); - } + ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur)); } } else { - // HACK: mark tensor as allocated - cur->data = (void *)(uintptr_t)1; - void * data; - if (use_mmap && mapping) { - data = (uint8_t *) mapping->addr + offs; + if (ggml_backend_buffer_is_host(cur->buffer)) { + file.seek(offs, SEEK_SET); + file.read_raw(cur->data, ggml_nbytes(cur)); } else { read_buf.resize(ggml_nbytes(cur)); file.seek(offs, SEEK_SET); file.read_raw(read_buf.data(), ggml_nbytes(cur)); - data = read_buf.data(); + ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur)); } - -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - ggml_cuda_transform_tensor(data, cur); -#elif defined(GGML_USE_CLBLAST) - GGML_ASSERT(cur->backend == GGML_BACKEND_GPU); - ggml_cl_transform_tensor(data, cur); -#else - GGML_ASSERT(!"GPU tensor without a GPU backend"); - GGML_UNUSED(data); -#endif } size_done += ggml_nbytes(cur); } - // unmap offloaded tensors and metadata - if (use_mmap && mapping) { - mapping->unmap_fragment(0, mmap_first); - mapping->unmap_fragment(mmap_last, mapping->size); + // check if this is the last call and do final cleanup + if (size_done >= size_data) { + // unmap offloaded tensors and metadata + if (use_mmap && mapping) { + mapping->unmap_fragment(0, mmap_used_first); + if (mmap_used_last != 0) { + mapping->unmap_fragment(mmap_used_last, mapping->size); + } + } + if (progress_callback) { + // Even though the model is done loading, we still honor + // cancellation since we need to free allocations. + return progress_callback(1.0f, progress_callback_user_data); + } } - if (progress_callback) { - // Even though the model is done loading, we still honor - // cancellation since we need to free allocations. - return progress_callback(1.0f, progress_callback_user_data); - } return true; } }; @@ -3181,6 +3187,7 @@ static bool llm_load_tensors( llama_model_loader & ml, llama_model & model, int n_gpu_layers, + enum llama_split_mode split_mode, int main_gpu, const float * tensor_split, bool use_mlock, @@ -3188,702 +3195,563 @@ static bool llm_load_tensors( void * progress_callback_user_data) { model.t_start_us = ggml_time_us(); - auto & ctx = model.ctx; auto & hparams = model.hparams; + model.split_mode = split_mode; + model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; - size_t ctx_size = ggml_tensor_overhead() * ml.n_tensors; + const int64_t n_layer = hparams.n_layer; + const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0); - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0); + // there is very little benefit to offloading the input layer, so always keep it on the CPU + model.buft_input = llama_default_buffer_type_cpu(true); - // create the ggml context + model.buft_layer.resize(n_layer); + + // assign cpu layers + for (int64_t i = 0; i < i_gpu_start; ++i) { + model.buft_layer[i] = llama_default_buffer_type_cpu(true); + } + +#ifdef GGML_USE_CUBLAS + if (split_mode == LLAMA_SPLIT_LAYER) { + // calculate the split points + int device_count = ggml_backend_cuda_get_device_count(); + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); + float splits[GGML_CUDA_MAX_DEVICES]; + if (all_zero) { + // default split, by free memory + for (int i = 0; i < device_count; ++i) { + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(i, &total, &free); + splits[i] = free; + } + } else { + std::copy(tensor_split, tensor_split + device_count, splits); + } + + // sum and normalize the splits to get the split points + float split_sum = 0.0f; + for (int i = 0; i < device_count; ++i) { + split_sum += splits[i]; + splits[i] = split_sum; + } + for (int i = 0; i < device_count; ++i) { + splits[i] /= split_sum; + } + + // assign the repeating layers to the devices according to the splits + int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); + for (int64_t i = i_gpu_start; i < n_layer; ++i) { + int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits; + model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); + } + // assign the output layer + if (n_gpu_layers > n_layer) { + int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits; + model.buft_output = llama_default_buffer_type_offload(layer_gpu); + } else { + model.buft_output = llama_default_buffer_type_cpu(true); + } + } else +#endif { + ggml_backend_buffer_type_t split_buft; + if (split_mode == LLAMA_SPLIT_ROW) { + split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); + } else { + // LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported + split_buft = llama_default_buffer_type_offload(main_gpu); + } + // assign the repeating layers + for (int64_t i = i_gpu_start; i < n_layer; ++i) { + model.buft_layer[i] = { + split_buft, + llama_default_buffer_type_offload(main_gpu) + }; + } + // assign the output layer + if (n_gpu_layers > n_layer) { + model.buft_output = { + split_buft, + llama_default_buffer_type_offload(main_gpu) + }; + } else { + model.buft_output = llama_default_buffer_type_cpu(true); + } + } + + // count used buffer types + std::map buft_layer_count; + buft_layer_count[model.buft_input.buft]++; + buft_layer_count[model.buft_input.buft_matrix]++; + buft_layer_count[model.buft_output.buft]++; + buft_layer_count[model.buft_output.buft_matrix]++; + for (int64_t i = 0; i < n_layer; ++i) { + buft_layer_count[model.buft_layer[i].buft]++; + buft_layer_count[model.buft_layer[i].buft_matrix]++; + } + + // create one context per buffer type + size_t ctx_size = ggml_tensor_overhead()*ml.n_tensors; + std::map ctx_map; + for (auto & it : buft_layer_count) { struct ggml_init_params params = { /*.mem_size =*/ ctx_size, /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; - - model.ctx = ggml_init(params); - if (!model.ctx) { - throw std::runtime_error(format("ggml_init() failed")); + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create context")); } + ctx_map[it.first] = ctx; + model.ctxs.push_back(ctx); } - (void) main_gpu; - - enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; - enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; - -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (ggml_cublas_loaded()) { - LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__); - ggml_cuda_set_main_device(main_gpu); - - llama_backend_offload = GGML_BACKEND_GPU; - llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT; - } -#elif defined(GGML_USE_CLBLAST) - LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); - llama_backend_offload = GGML_BACKEND_GPU; - llama_backend_offload_split = GGML_BACKEND_GPU; -#endif + LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0); // create tensors for the weights { const int64_t n_embd = hparams.n_embd; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const int64_t n_layer = hparams.n_layer; + const int64_t n_embd_gqa = n_embd_v_gqa; const int64_t n_vocab = hparams.n_vocab; + const int64_t n_ff = hparams.n_ff; + + GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); + + ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); + ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); + ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); + auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); }; + auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); }; + + model.layers.resize(n_layer); const auto tn = LLM_TN(model.arch); switch (model.arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors - layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend, false); - layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend, false); - layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend, false); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_inp = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, backend, false); + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, false); if (layer.ffn_gate_inp == nullptr) { GGML_ASSERT(hparams.n_expert == 0); GGML_ASSERT(hparams.n_expert_used == 0); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } else { GGML_ASSERT(hparams.n_expert > 0); GGML_ASSERT(hparams.n_expert_used > 0); // MoE branch for (uint32_t x = 0; x < hparams.n_expert; ++x) { - layer.ffn_gate_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}, backend_split); - layer.ffn_down_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}, backend_split); - layer.ffn_up_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}, backend_split); + layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}); + layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}); + layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}); } } } } break; case LLM_ARCH_BAICHUAN: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_FALCON: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) { - layer.attn_norm_2 = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend); - layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, backend); + layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}); } - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_STARCODER: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_PERSIMMON: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); - const int i_gpu_start = n_layer - n_gpu_layers; - model.layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); - layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend); - layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}, backend); - layer.attn_k_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend); - layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend); + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}); + + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}); } } break; case LLM_ARCH_BLOOM: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.tok_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU); - model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_norm = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); + model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_MPT: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false); + layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); } } break; case LLM_ARCH_STABLELM: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - /* - llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ] - */ - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_QWEN: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - } - - const uint32_t n_ff = hparams.n_ff / 2; - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd * 3}, backend); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}); } } break; case LLM_ARCH_PHI2: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); - model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; case LLM_ARCH_PLAMO: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); - layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; case LLM_ARCH_GPT2: { - model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); // output { - ggml_backend_type backend_norm; - ggml_backend_type backend_output; - - if (n_gpu_layers > int(n_layer)) { - backend_norm = llama_backend_offload; - backend_output = llama_backend_offload_split; - } else { - backend_norm = GGML_BACKEND_CPU; - backend_output = GGML_BACKEND_CPU; - } - - model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); - model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); - model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); } - const uint32_t n_ff = hparams.n_ff; - const int64_t n_embd_gqa = n_embd_v_gqa; - GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa()); - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - - const int i_gpu_start = n_layer - n_gpu_layers; - - model.layers.resize(n_layer); - - for (uint32_t i = 0; i < n_layer; ++i) { - const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT - const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); - layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); - layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); - layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); - layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); - layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; default: @@ -3893,78 +3761,51 @@ static bool llm_load_tensors( ml.done_getting_tensors(); - ml.init_mapping(); + ml.init_mapping(true, use_mlock ? &model.mlock_mmap : nullptr); - // allocate tensors - size_t vram_weights = 0; - size_t buf_size = 0; + // create the backend buffers + std::vector> ctx_bufs; - ggml_backend_buffer_type_t buft = llama_default_buffer_type(n_gpu_layers); + for (auto & it : ctx_map) { + ggml_backend_buffer_type_t buft = it.first; + ggml_context * ctx = it.second; + ggml_backend_buffer_t buf = nullptr; - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { - // GGML_BACKEND_GPU tensors are for CUDA and OpenCL only, which are handled separately without ggml-backend - if (t->backend == GGML_BACKEND_CPU) { - buf_size += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), ggml_backend_buft_get_alignment(buft)); - } else { - vram_weights += ggml_nbytes(t); + // only the mmap region containing the tensors in the model is mapped to the backend buffer + // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers + // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size + if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) { + size_t first, last; + ml.get_mapping_range(&first, &last, ctx); + buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first); } - } - - // create backend buffer - ggml_backend_buffer_t buf_mmap = nullptr; - #ifdef GGML_USE_METAL - if (n_gpu_layers > 0) { - if (ml.use_mmap) { + else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) { const size_t max_size = ggml_get_max_tensor_size(ctx); - model.buf = ggml_backend_metal_buffer_from_ptr(ml.mapping->addr, ml.mapping->size, max_size); - buf_mmap = model.buf; - } else { - model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_metal_buffer_type()); + size_t first, last; + ml.get_mapping_range(&first, &last, ctx); + buf = ggml_backend_metal_buffer_from_ptr((char *) ml.mapping->addr + first, last - first, max_size); } - } -#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) - // for testing only - if (n_gpu_layers > 0) { - model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cuda_buffer_type(0)); - } #endif - - if (model.buf == nullptr) { - // CPU backend, and indirectly CUDA and OpenCL - if (ml.use_mmap) { - model.buf = ggml_backend_cpu_buffer_from_ptr(ml.mapping->addr, ml.mapping->size); - buf_mmap = model.buf; - } else { - // allocate only CPU tensors - model.buf = ggml_backend_buft_alloc_buffer(buft, buf_size); - ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(model.buf); - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { - if (t->backend == GGML_BACKEND_CPU) { - ggml_tallocr_alloc(alloc, t); - } + else { + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (buf != nullptr && use_mlock && ggml_backend_buffer_is_host(buf)) { + model.mlock_buf.init (ggml_backend_buffer_get_base(buf)); + model.mlock_buf.grow_to(ggml_backend_buffer_get_size(buf)); } - ggml_tallocr_free(alloc); } - } - - if (use_mlock && ggml_backend_buffer_is_host(model.buf)) { - model.mlock_buf.init (ggml_backend_buffer_get_base(model.buf)); - model.mlock_buf.grow_to(ggml_backend_buffer_get_size(model.buf)); + if (buf == nullptr) { + throw std::runtime_error("failed to allocate buffer"); + } + // indicate that this buffer contains weights + // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight + ggml_backend_buffer_set_usage(buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); + model.bufs.push_back(buf); + ctx_bufs.emplace_back(ctx, buf); } // print memory requirements { - size_t sys_mem_required = ctx_size + buf_size; - - if (sys_mem_required > 0) { - LLAMA_LOG_INFO("%s: system memory used = %7.2f MiB\n", __func__, sys_mem_required / 1024.0 / 1024.0); - } - if (vram_weights > 0) { - LLAMA_LOG_INFO("%s: VRAM used = %7.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0); - } - -#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); @@ -3976,23 +3817,26 @@ static bool llm_load_tensors( const int max_offloadable_layers = hparams.n_layer + 1; LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); -#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) - } -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - ggml_cuda_set_tensor_split(tensor_split); -#else - GGML_UNUSED(tensor_split); -#endif // GGML_USE_CUBLAS + for (ggml_backend_buffer_t buf : model.bufs) { + LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); + } + } // populate tensors_by_name - for (int i = 0; i < ml.n_tensors; ++i) { - struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i)); - model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); + for (ggml_context * ctx : model.ctxs) { + for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { + model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); + } } - if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL)) { - return false; + // load tensor data + for (auto & it : ctx_bufs) { + ggml_context * ctx = it.first; + ggml_backend_buffer_t buf = it.second; + if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf, use_mlock ? &model.mlock_mmap : NULL)) { + return false; + } } model.mapping = std::move(ml.mapping); @@ -4026,13 +3870,13 @@ static int llama_model_load(const std::string & fname, llama_model & model, cons } if (!llm_load_tensors( - ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock, + ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data )) { return -2; } } catch (const std::exception & err) { - LLAMA_LOG_ERROR("error loading model: %s\n", err.what()); + LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what()); return -1; } @@ -4476,8 +4320,6 @@ struct llm_build_context { do_rope_shift (worst_case || kv_self.has_shift), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { - GGML_ASSERT(!!kv_self.ctx); - // all initializations should be done in init() } @@ -4557,6 +4399,12 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } + // these nodes are added to the graph together so that they are not reordered + // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(gf, Qcur); + ggml_build_forward_expand(gf, Kcur); + ggml_build_forward_expand(gf, Vcur); + Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, @@ -6077,199 +5925,13 @@ struct llm_build_context { } }; -// -// tensor offloading helpers -// -// TODO: will be removed with backend v2 - -enum llm_offload_func_e { - OFFLOAD_FUNC_NOP, - OFFLOAD_FUNC, - OFFLOAD_FUNC_FRC, // force offload - OFFLOAD_FUNC_KQV, - OFFLOAD_FUNC_NR, - OFFLOAD_FUNC_EMB, // embeddings - OFFLOAD_FUNC_OUT, -}; - -// TODO: will be removed with backend v2 -struct llm_offload_trie { - struct node { - ~node() { - for (int i = 0; i < 256; ++i) { - if (children[i]) { - delete children[i]; - } - } - } - - node * children[256] = { nullptr }; - llm_offload_func_e func = OFFLOAD_FUNC_NOP; - }; - - llm_offload_trie() { - root = new node; - } - - llm_offload_trie(const std::unordered_map & map) { - root = new node; - - for (const auto & kv : map) { - add(kv.first, kv.second); - } - } - - ~llm_offload_trie() { - delete root; - } - - void add(const char * name, llm_offload_func_e func) { - node * cur = root; - - for (int i = 0; ; ++i) { - const uint8_t c = name[i]; - - if (!c) { - break; - } - - if (!cur->children[c]) { - cur->children[c] = new node; - } - - cur = cur->children[c]; - } - - cur->func = func; - } - - llm_offload_func_e find(const char * name) const { - const node * cur = root; - - for (int i = 0; ; ++i) { - const uint8_t c = name[i]; - - if (!c) { - break; - } - - if (!cur->children[c]) { - return OFFLOAD_FUNC_NOP; - } - - cur = cur->children[c]; - } - - return cur->func; - } - - node * root = nullptr; -}; - -// TODO: will be removed with backend v2 -static const std::unordered_map k_offload_map = { - //{ "inp_tokens", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel - //{ "inp_embd", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel - { "pos_embd", OFFLOAD_FUNC_NR }, - - { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope) - { "KQ_mask", OFFLOAD_FUNC_FRC }, - { "K_shift", OFFLOAD_FUNC_FRC }, - - { "K_shifted", OFFLOAD_FUNC }, - - { "inp_norm", OFFLOAD_FUNC_NR }, - { "inp_norm_w", OFFLOAD_FUNC_NR }, - { "inp_norm_wb", OFFLOAD_FUNC_NR }, - - { "norm", OFFLOAD_FUNC }, - { "norm_w", OFFLOAD_FUNC }, - { "norm_wb", OFFLOAD_FUNC }, - - { "attn_norm", OFFLOAD_FUNC }, - { "attn_norm_2", OFFLOAD_FUNC }, - - { "wqkv", OFFLOAD_FUNC_KQV }, - { "bqkv", OFFLOAD_FUNC_KQV }, - { "wqkv_clamped", OFFLOAD_FUNC_KQV }, - - { "tmpk", OFFLOAD_FUNC_KQV }, - { "tmpq", OFFLOAD_FUNC_KQV }, - { "tmpv", OFFLOAD_FUNC_KQV }, - { "Kcur", OFFLOAD_FUNC_KQV }, - { "Qcur", OFFLOAD_FUNC_KQV }, - { "Vcur", OFFLOAD_FUNC_KQV }, - - { "krot", OFFLOAD_FUNC_KQV }, - { "qrot", OFFLOAD_FUNC_KQV }, - { "kpass", OFFLOAD_FUNC_KQV }, - { "qpass", OFFLOAD_FUNC_KQV }, - { "krotated", OFFLOAD_FUNC_KQV }, - { "qrotated", OFFLOAD_FUNC_KQV }, - - { "q", OFFLOAD_FUNC_KQV }, - { "k", OFFLOAD_FUNC_KQV }, - { "kq", OFFLOAD_FUNC_KQV }, - { "kq_scaled", OFFLOAD_FUNC_KQV }, - { "kq_scaled_alibi", OFFLOAD_FUNC_KQV }, - { "kq_masked", OFFLOAD_FUNC_KQV }, - { "kq_soft_max", OFFLOAD_FUNC_KQV }, - { "kq_soft_max_ext", OFFLOAD_FUNC_KQV }, - { "v", OFFLOAD_FUNC_KQV }, - { "kqv", OFFLOAD_FUNC_KQV }, - { "kqv_merged", OFFLOAD_FUNC_KQV }, - { "kqv_merged_cont", OFFLOAD_FUNC_KQV }, - { "kqv_wo", OFFLOAD_FUNC_KQV }, - { "kqv_out", OFFLOAD_FUNC_KQV }, - - { "ffn_inp", OFFLOAD_FUNC }, - { "ffn_norm", OFFLOAD_FUNC }, - - { "ffn_up", OFFLOAD_FUNC }, - { "ffn_up_b", OFFLOAD_FUNC }, - { "ffn_gate", OFFLOAD_FUNC }, - { "ffn_gate_b", OFFLOAD_FUNC }, - { "ffn_gate_par", OFFLOAD_FUNC }, - { "ffn_act", OFFLOAD_FUNC }, - { "ffn_down", OFFLOAD_FUNC }, - { "ffn_down_b", OFFLOAD_FUNC }, - { "ffn_out", OFFLOAD_FUNC }, - - { "ffn_silu", OFFLOAD_FUNC }, - { "ffn_gelu", OFFLOAD_FUNC }, - { "ffn_relu", OFFLOAD_FUNC }, - { "ffn_sqr(relu)", OFFLOAD_FUNC }, - - { "ffn_moe_logits", OFFLOAD_FUNC }, - { "ffn_moe_probs", OFFLOAD_FUNC }, - { "ffn_moe_argsort", OFFLOAD_FUNC }, - { "ffn_moe_weights", OFFLOAD_FUNC }, - { "ffn_moe_weights_sum", OFFLOAD_FUNC }, - { "ffn_moe_weights_norm", OFFLOAD_FUNC }, - { "ffn_moe_weighted", OFFLOAD_FUNC }, - { "ffn_moe_up", OFFLOAD_FUNC }, - { "ffn_moe_gate", OFFLOAD_FUNC }, - { "ffn_moe_silu", OFFLOAD_FUNC }, - { "ffn_moe_gate_par", OFFLOAD_FUNC }, - { "ffn_moe_down", OFFLOAD_FUNC }, - { "ffn_moe_out", OFFLOAD_FUNC }, - - { "l_out", OFFLOAD_FUNC }, - - { "result_norm", OFFLOAD_FUNC_EMB }, - { "result_output_no_bias", OFFLOAD_FUNC_EMB }, - { "result_output", OFFLOAD_FUNC_OUT }, -}; - -static llm_offload_trie k_offload_func_trie(k_offload_map); - static struct ggml_cgraph * llama_build_graph( llama_context & lctx, const llama_batch & batch) { const auto & model = lctx.model; // check if we should build the worst-case graph (for memory measurement) - const bool worst_case = ggml_allocr_is_measure(lctx.alloc); + const bool worst_case = ggml_tallocr_is_measure(lctx.alloc); // keep track of the input that has already been allocated bool alloc_inp_tokens = false; @@ -6278,16 +5940,8 @@ static struct ggml_cgraph * llama_build_graph( bool alloc_inp_KQ_mask = false; bool alloc_inp_K_shift = false; -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - const bool do_offload = true; -#else - const bool do_offload = true; // TODO: set to false after finishing refactoring -#endif - - int n_non_view = 0; // number of non-view tensors that have been processed by the callback - // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) - // TODO: will be removed with backend v2 + // TODO: improve handling of input and output tensors, then replace this with ggml_set_name llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) { if (il >= 0) { ggml_format_name(cur, "%s-%d", name, il); @@ -6298,12 +5952,11 @@ static struct ggml_cgraph * llama_build_graph( // // allocate input tensors and set input data // - // TODO: will be removed with backend v2 if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) { + if (!ggml_tallocr_is_measure(lctx.alloc) && batch.token) { const int64_t n_tokens = cur->ne[0]; ggml_backend_tensor_set(cur, batch.token, 0, n_tokens*ggml_element_size(cur)); @@ -6312,10 +5965,10 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_tokens = true; } - if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0 && batch.embd) { + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) { + if (!ggml_tallocr_is_measure(lctx.alloc) && batch.embd) { const int64_t n_embd = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; @@ -6326,9 +5979,9 @@ static struct ggml_cgraph * llama_build_graph( } if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) { + if (!ggml_tallocr_is_measure(lctx.alloc) && batch.pos) { const int64_t n_tokens = cur->ne[0]; static_assert(std::is_same::value, "llama_pos must be int32_t"); @@ -6339,9 +5992,9 @@ static struct ggml_cgraph * llama_build_graph( } if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc)) { + if (!ggml_tallocr_is_measure(lctx.alloc)) { const int64_t n_kv = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; @@ -6379,9 +6032,9 @@ static struct ggml_cgraph * llama_build_graph( } if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) { - ggml_allocr_alloc(lctx.alloc, cur); + ggml_tallocr_alloc(lctx.alloc, cur); - if (!ggml_allocr_is_measure(lctx.alloc)) { + if (!ggml_tallocr_is_measure(lctx.alloc)) { const int64_t n_ctx = cur->ne[0]; int32_t * data; @@ -6403,136 +6056,6 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_K_shift = true; } - - // view tensors are not processed further - if (cur->view_src != nullptr) { - return; - } - - if (cur->op != GGML_OP_NONE) { - n_non_view++; - } - - // - // offload layers - // - // TODO: will be removed with backend v2 - -//#define LLAMA_OFFLOAD_DEBUG - - if (!do_offload) { - return; - } - - const int n_layer = model.hparams.n_layer; - - const int n_gpu_layers = model.n_gpu_layers; - const int i_gpu_start = n_layer - n_gpu_layers; - - // should we offload the final norm? yes if we are not computing embeddings - const bool offload_emb = lctx.embedding.empty(); - - static const std::unordered_map> k_offload_func_name = { - { OFFLOAD_FUNC_NOP, "CPU" }, - { OFFLOAD_FUNC_OUT, "CPU" }, -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - { OFFLOAD_FUNC, "GPU (CUDA)" }, - { OFFLOAD_FUNC_FRC, "GPU (CUDA) FRC" }, - { OFFLOAD_FUNC_KQV, "GPU (CUDA) KQV" }, - { OFFLOAD_FUNC_NR, "GPU (CUDA) NR" }, - { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" }, -#else - { OFFLOAD_FUNC, "CPU" }, - { OFFLOAD_FUNC_FRC, "CPU" }, - { OFFLOAD_FUNC_KQV, "CPU" }, - { OFFLOAD_FUNC_NR, "CPU" }, - { OFFLOAD_FUNC_EMB, "CPU" }, -#endif // GGML_USE_CUBLAS - }; - - // check the global map for what offload function to use for this tensor - llm_offload_func_e func_e = k_offload_func_trie.find(name); - - if (func_e == OFFLOAD_FUNC_NOP) { -#ifdef LLAMA_OFFLOAD_DEBUG - // if a tensor hasn't been offloaded, we warn the user - if (worst_case) { - LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__, - cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837"); - } -#endif - - return; - } - - // count the number of layers and respect the provided n_gpu_layers - switch (func_e) { - case OFFLOAD_FUNC_NOP: - case OFFLOAD_FUNC_OUT: - break; - case OFFLOAD_FUNC: - if (n_gpu_layers < n_layer) { - if (il < i_gpu_start) { - func_e = OFFLOAD_FUNC_NOP; - } - } - break; - case OFFLOAD_FUNC_FRC: - if (!lctx.cparams.offload_kqv) { - func_e = OFFLOAD_FUNC_NOP; - } break; - case OFFLOAD_FUNC_KQV: - if (!lctx.cparams.offload_kqv) { - func_e = OFFLOAD_FUNC_NOP; - } else { - if (n_gpu_layers < n_layer) { - if (il < i_gpu_start) { - func_e = OFFLOAD_FUNC_NOP; - } - } - } - break; - case OFFLOAD_FUNC_NR: - if (n_gpu_layers <= n_layer + 0) { - func_e = OFFLOAD_FUNC_NOP; - } - break; - case OFFLOAD_FUNC_EMB: - if (!offload_emb || n_gpu_layers < n_layer) { - func_e = OFFLOAD_FUNC_NOP; - } - break; - default: GGML_ASSERT(false); - } - - offload_func_t func = ggml_offload_nop; - - // this is needed for compatibility with Metal for example -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc; -#else - static offload_func_t ggml_offload_gpu = ggml_offload_nop; -#endif - - switch (func_e) { - case OFFLOAD_FUNC_NOP: - case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break; - case OFFLOAD_FUNC: - case OFFLOAD_FUNC_KQV: - case OFFLOAD_FUNC_FRC: - case OFFLOAD_FUNC_NR: - case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break; - default: GGML_ASSERT(false); - } - - // apply offload function to the tensor - func(cur); - -#ifdef LLAMA_OFFLOAD_DEBUG - if (worst_case) { - LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str()); - } -#endif }; struct ggml_cgraph * result = NULL; @@ -6600,27 +6123,6 @@ static struct ggml_cgraph * llama_build_graph( llm.free(); - if (worst_case) { - int n_non_view_total = 0; - - for (int i = 0; i < result->n_nodes; ++i) { - if (result->nodes[i]->view_src == nullptr) { - n_non_view_total++; - } - } - - LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total); - - if (n_non_view != n_non_view_total) { - LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__); - LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n", __func__); - LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n", __func__); - LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n", __func__); - LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n", __func__); - LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__); - } - } - return result; } @@ -6666,8 +6168,6 @@ static int llama_decode_internal( auto & kv_self = lctx.kv_self; - GGML_ASSERT(!!kv_self.ctx); - const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; @@ -6721,12 +6221,10 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - ggml_allocr_reset(lctx.alloc); + ggml_backend_sched_reset(lctx.sched); ggml_cgraph * gf = llama_build_graph(lctx, batch); - ggml_allocr_alloc_graph(lctx.alloc, gf); - // the output is always the last tensor in the graph struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; GGML_ASSERT(strcmp(res->name, "result_output") == 0); @@ -6738,30 +6236,6 @@ static int llama_decode_internal( GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); } -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - char * buf_alloc_base = (char *)ggml_backend_buffer_get_base(lctx.buf_alloc); - for (int i = 0; i < gf->n_leafs; i++) { - ggml_tensor * node = gf->leafs[i]; - if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) { - ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base); - ggml_cuda_copy_to_device(node); - } - } - - for (int i = 0; i < gf->n_nodes; i++) { - ggml_tensor * node = gf->nodes[i]; - if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) { - ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base); - } - } - - // HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed - if (!lctx.embedding.empty()) { - embeddings->backend = GGML_BACKEND_CPU; - } - res->backend = GGML_BACKEND_CPU; -#endif - // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); // for big prompts, if BLAS is enabled, it is better to use only one thread @@ -6784,15 +6258,17 @@ static int llama_decode_internal( #endif #ifdef GGML_USE_METAL - if (ggml_backend_is_metal(lctx.backend)) { - ggml_backend_metal_set_n_cb(lctx.backend, n_threads); + if (ggml_backend_is_metal(lctx.backend_metal)) { + ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); } #endif - if (ggml_backend_is_cpu(lctx.backend)) { - ggml_backend_cpu_set_n_threads(lctx.backend, n_threads); + if (lctx.backend_cpu != nullptr) { + ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); } - ggml_backend_graph_compute(lctx.backend, gf); + ggml_backend_sched_graph_compute(lctx.sched, gf); + + // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); #ifdef GGML_USE_MPI ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); @@ -6840,30 +6316,33 @@ static int llama_decode_internal( logits_out.clear(); #endif + ggml_backend_t res_backend = ggml_backend_sched_get_node_backend(lctx.sched, res); + GGML_ASSERT(res_backend != nullptr); if (batch.logits) { logits_out.resize(n_vocab * n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { if (batch.logits[i] == 0) { continue; } - ggml_backend_tensor_get(res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(res_backend, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[i] = true; #endif } } else if (lctx.logits_all) { logits_out.resize(n_vocab * n_tokens); - ggml_backend_tensor_get(res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); + ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float)); #ifndef NDEBUG std::fill(logits_valid.begin(), logits_valid.end(), true); #endif } else { logits_out.resize(n_vocab); - ggml_backend_tensor_get(res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); + ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float)); #ifndef NDEBUG logits_valid[0] = true; #endif } + ggml_backend_synchronize(res_backend); } // extract embeddings @@ -6871,7 +6350,9 @@ static int llama_decode_internal( auto & embedding_out = lctx.embedding; embedding_out.resize(n_embd); - ggml_backend_tensor_get(embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float)); + ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings); + ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float)); + ggml_backend_synchronize(embeddings_backend); } // measure the performance only for the single-token evals @@ -9347,48 +8828,23 @@ static int llama_apply_lora_from_file_internal( LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling); - // create a name -> tensor map of the model to accelerate lookups - // find the max tensor size to estimate the required temporary buffer size - size_t max_tensor_size = 0; - std::unordered_map model_tensors; - for (const auto & kv : model.tensors_by_name) { - model_tensors.insert(kv); - size_t f32_size = ggml_nelements(kv.second) * sizeof(float); - max_tensor_size = std::max(max_tensor_size, f32_size); - } - - // create a temporary ggml context to store the lora tensors - // TODO: use ggml-alloc - size_t lora_ctx_size = max_tensor_size * 3; - LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0); - std::vector lora_buf(lora_ctx_size); - - struct ggml_init_params params; - params.mem_size = lora_buf.size(); - params.mem_buffer = lora_buf.data(); - params.no_alloc = false; - - using unique_context = std::unique_ptr; - - unique_context lora_ctx(nullptr, ggml_free); - lora_ctx.reset(ggml_init(params)); - std::unordered_map lora_tensors; - // load base model std::unique_ptr ml; - - if (path_base_model) { + if (path_base_model) { LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr)); - ml->init_mapping(false); // no prefetching + ml->init_mapping(/*prefetch*/ false); // no prefetching } - // read tensors and apply - bool warned = false; - int n_tensors = 0; - - std::vector work_buffer; + struct tensor_meta { + std::string name; + ggml_type type; + int32_t ne[2]; + size_t offset; + }; + std::map tensor_meta_map; + // load all tensor meta while (true) { if (fin.tell() == fin.size) { // eof @@ -9401,7 +8857,7 @@ static int llama_apply_lora_from_file_internal( fin.read_raw(&n_dims, sizeof(n_dims)); fin.read_raw(&name_len, sizeof(name_len)); - fin.read_raw(&ftype, sizeof(ftype)); + fin.read_raw(&ftype, sizeof(ftype)); if (n_dims != 1 && n_dims != 2) { LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims); @@ -9415,31 +8871,23 @@ static int llama_apply_lora_from_file_internal( std::string name; { - GGML_ASSERT(name_len <= 1024); - char buf[1024]; + GGML_ASSERT(name_len < GGML_MAX_NAME); + char buf[GGML_MAX_NAME]; fin.read_raw(buf, name_len); name = std::string(buf, name_len); } - // check for lora suffix and get the type of tensor - const std::string lora_suffix = ".lora"; - size_t pos = name.rfind(lora_suffix); - if (pos == std::string::npos) { + // check for lora suffix + std::string lora_suffix; + if (name.length() > 6) { + lora_suffix = name.substr(name.length() - 6); + } + if (lora_suffix != ".loraA" && lora_suffix != ".loraB") { LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str()); return 1; } - std::string lora_type = name.substr(pos + lora_suffix.length()); - std::string base_name = name; - base_name.erase(pos); - // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str()); - - if (model_tensors.find(base_name) == model_tensors.end()) { - LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data()); - return 1; - } - - // create ggml tensor + // tensor type ggml_type wtype; switch (ftype) { case 0: wtype = GGML_TYPE_F32; break; @@ -9451,122 +8899,177 @@ static int llama_apply_lora_from_file_internal( return false; } } - ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]); - ggml_set_name(lora_tensor, name.c_str()); - // load tensor data + // data offset size_t offset = fin.tell(); - size_t tensor_data_size = ggml_nbytes(lora_tensor); offset = (offset + 31) & -32; - fin.seek(offset, SEEK_SET); - fin.read_raw(lora_tensor->data, tensor_data_size); - lora_tensors[name] = lora_tensor; + // skip tensor data + fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET); - // check if we have both A and B tensors and apply - if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() && - lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) { + tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset }); + } - ggml_tensor * dest_t = model_tensors[base_name]; + bool warned = false; + int n_tensors = 0; - offload_func_t offload_func = ggml_offload_nop; - offload_func_t offload_func_force_inplace = ggml_offload_nop; + // apply + ggml_backend_t backend_cpu = ggml_backend_cpu_init(); + if (backend_cpu == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__); + return 1; + } + ggml_backend_cpu_set_n_threads(backend_cpu, n_threads); -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) { - if (dest_t->type != GGML_TYPE_F16) { - throw std::runtime_error(format( - "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type)); - } - offload_func = ggml_cuda_assign_buffers; - offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace; - } -#endif // GGML_USE_CUBLAS + std::vector> read_buf; + for (const auto & it : model.tensors_by_name) { + const std::string & base_name = it.first; + ggml_tensor * model_t = it.second; - ggml_tensor * base_t; - if (ml) { - struct gguf_context * ctx_gguf = ml->ctx_gguf; + if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() || + tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) { + continue; + } - // load from base model - if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) { - LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); - return 1; - } + tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA"); + tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB"); - base_t = ml->get_tensor_meta(base_name.c_str()); - ml->load_data_for(base_t); - } else { - base_t = dest_t; - } + ggml_init_params lora_init_params = { + /* .mem_size */ ggml_tensor_overhead()*128 + ggml_graph_overhead(), + /* .mem_buffer */ nullptr, + /* .no_alloc */ true, + }; + ggml_context * lora_ctx = ggml_init(lora_init_params); + if (lora_ctx == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__); + ggml_backend_free(backend_cpu); + return 1; + } - if (ggml_is_quantized(base_t->type)) { - if (!warned) { - LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, " - "use a f16 or f32 base model with --lora-base\n", __func__); - warned = true; - } - } + // create tensors + ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]); + ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]); + ggml_set_name(loraA, metaA.name.c_str()); + ggml_set_name(loraB, metaB.name.c_str()); - ggml_tensor * loraA = lora_tensors[base_name + ".loraA"]; - GGML_ASSERT(loraA->type == GGML_TYPE_F32); - ggml_set_name(loraA, "loraA"); - - ggml_tensor * loraB = lora_tensors[base_name + ".loraB"]; - GGML_ASSERT(loraB->type == GGML_TYPE_F32); - ggml_set_name(loraB, "loraB"); - - if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { - LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" - " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]); + ggml_tensor * base_t; + if (ml) { + if (gguf_find_tensor(ml->ctx_gguf, base_name.c_str()) < 0) { + LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); return 1; } + base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str())); + } else { + base_t = ggml_dup_tensor(lora_ctx, model_t); + } + ggml_set_name(base_t, base_name.c_str()); + // allocate in backend buffer + ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type()); + if (lora_buf == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__); + return 1; + } + + // load tensor data + auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) { + read_buf.resize(ggml_nbytes(tensor)); + fin.seek(tensor_meta.offset, SEEK_SET); + fin.read_raw(read_buf.data(), ggml_nbytes(tensor)); + ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size()); + }; + load_tensor(metaA, loraA); + load_tensor(metaB, loraB); + + // load base model tensor data + if (ml) { + ml->load_data_for(base_t); + } else { + ggml_backend_tensor_copy(model_t, base_t); + } + + if (ggml_is_quantized(base_t->type) && !warned) { + LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, " + "use a f16 or f32 base model with --lora-base\n", __func__); + warned = true; + } + + if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { + LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" + " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]); + ggml_free(lora_ctx); + ggml_backend_buffer_free(lora_buf); + ggml_backend_free(backend_cpu); + return 1; + } + + auto build_lora_graph = [&]() { // w = w + BA*s - ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB); - offload_func(BA); + ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB); ggml_set_name(BA, "BA"); if (scaling != 1.0f) { - BA = ggml_scale_inplace(lora_ctx.get(), BA, scaling); - offload_func(BA); + BA = ggml_scale(lora_ctx, BA, scaling); ggml_set_name(BA, "BA_scaled"); } ggml_tensor * r; - if (base_t == dest_t) { - r = ggml_add_inplace(lora_ctx.get(), dest_t, BA); - offload_func_force_inplace(r); - ggml_set_name(r, "r_add_inplace"); - } - else { - r = ggml_add(lora_ctx.get(), base_t, BA); - offload_func(r); - ggml_set_name(r, "r_add"); + r = ggml_add_inplace(lora_ctx, base_t, BA); + ggml_set_name(r, "r_add"); - r = ggml_cpy(lora_ctx.get(), r, dest_t); - offload_func(r); - ggml_set_name(r, "r_cpy"); + if (base_t->type != model_t->type) { + // convert the result to the model type + r = ggml_cast(lora_ctx, r, model_t->type); + ggml_set_name(r, "r_cast"); } - struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get()); - ggml_build_forward_expand(gf, r); + return r; + }; - ggml_graph_compute_helper(work_buffer, gf, n_threads); + ggml_cgraph * gf = ggml_new_graph(lora_ctx); + ggml_tensor * r = build_lora_graph(); + ggml_build_forward_expand(gf, r); - // the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other - GGML_ASSERT(lora_tensors.size() == 2); + ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type()); + if (graph_buf == nullptr) { + LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__); + ggml_free(lora_ctx); + ggml_backend_buffer_free(lora_buf); + ggml_backend_free(backend_cpu); + return 1; + } - // we won't need these tensors again, reset the context to save memory - lora_ctx.reset(ggml_init(params)); - lora_tensors.clear(); + ggml_backend_graph_compute(backend_cpu, gf); - n_tensors++; - if (n_tensors % 4 == 0) { - LLAMA_LOG_INFO("."); - } + ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r)); + +#if 0 + // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU + //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE); + + // sched compute + ggml_build_forward_expand(gf, build_graph()); + ggml_backend_sched_init_measure(sched, gf); + + // create the graph again, since the previous one was destroyed by the measure + ggml_graph_clear(gf); + ggml_build_forward_expand(gf, build_graph()); + ggml_backend_sched_graph_compute(sched, gf); + ggml_backend_sched_free(sched); +#endif + + ggml_backend_buffer_free(lora_buf); + ggml_backend_buffer_free(graph_buf); + ggml_free(lora_ctx); + + n_tensors++; + if (n_tensors % 4 == 0) { + LLAMA_LOG_INFO("."); } } + ggml_backend_free(backend_cpu); + const int64_t t_lora_us = ggml_time_us() - t_start_lora_us; LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0); @@ -9579,6 +9082,7 @@ static int llama_apply_lora_from_file_internal( struct llama_model_params llama_model_default_params() { struct llama_model_params result = { /*.n_gpu_layers =*/ 0, + /*.split_mode =*/ LLAMA_SPLIT_LAYER, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, /*.progress_callback =*/ nullptr, @@ -9590,7 +9094,8 @@ struct llama_model_params llama_model_default_params() { }; #ifdef GGML_USE_METAL - result.n_gpu_layers = 1; + // note: we usually have plenty of VRAM, so by default offload all layers to the GPU + result.n_gpu_layers = 999; #endif return result; @@ -9780,41 +9285,53 @@ struct llama_context * llama_new_context_with_model( GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); - // reserve memory for context buffers if (!hparams.vocab_only) { - // initialize backend + // initialize backends #ifdef GGML_USE_METAL if (model->n_gpu_layers > 0) { - ctx->backend = ggml_backend_metal_init(); - if (ctx->backend == nullptr) { + ctx->backend_metal = ggml_backend_metal_init(); + if (ctx->backend_metal == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__); + llama_free(ctx); + return nullptr; } + ctx->backends.push_back(ctx->backend_metal); } -#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST) - // for testing only +#elif defined(GGML_USE_CUBLAS) if (model->n_gpu_layers > 0) { - ctx->backend = ggml_backend_cuda_init(0); - if (ctx->backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CUDA backend\n", __func__); + // with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used + if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) { + ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } else { + // LLAMA_SPLIT_LAYER requires a backend for each GPU + for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) { + ggml_backend_t backend = ggml_backend_cuda_init(device); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } } } #endif - - if (ctx->backend == nullptr && ggml_backend_buffer_is_host(model->buf)) { - ctx->backend = ggml_backend_cpu_init(); - if (ctx->backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); - } - } - - if (ctx->backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize a backend\n", __func__); - delete ctx; + ctx->backend_cpu = ggml_backend_cpu_init(); + if (ctx->backend_cpu == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); + llama_free(ctx); return nullptr; } + ctx->backends.push_back(ctx->backend_cpu); - if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, type_k, type_v, - cparams.n_ctx, model->n_gpu_layers, cparams.offload_kqv)) { + if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, + cparams.n_ctx, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -9850,11 +9367,22 @@ struct llama_context * llama_new_context_with_model( } { - // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data + // buffer types used for the compute buffer of each backend + std::vector backend_buft; + for (auto * backend : ctx->backends) { + if (ggml_backend_is_cpu(backend)) { + // use host buffers for the CPU backend compute buffer + backend_buft.push_back(llama_default_buffer_type_cpu(true)); + } else { + backend_buft.push_back(ggml_backend_get_default_buffer_type(backend)); + } + } + + // buffer used to store the computation graph and the tensor meta data ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); - // create measure allocator - ctx->alloc = ggml_allocr_new_measure_from_backend(ctx->backend); + ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES); + ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); // build worst-case graph int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch); @@ -9862,50 +9390,19 @@ struct llama_context * llama_new_context_with_model( llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0)); - // measure memory requirements for the graph - size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf); + // initialize scheduler with the worst-case graph + ggml_backend_sched_init_measure(ctx->sched, gf); + // note: the number of splits during measure is higher than during inference due to the kv shift + int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); + LLAMA_LOG_INFO("%s: graph splits (measure): %d\n", __func__, n_splits); + ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); - LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute_meta.size() + alloc_size) / 1024.0 / 1024.0); - - // create allocator again with exact memory requirements - ggml_allocr_free(ctx->alloc); - - ctx->buf_alloc = ggml_backend_alloc_buffer(ctx->backend, alloc_size); - ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc); -#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST) - if (model->n_gpu_layers > 0) { - // the CPU buffer adds this padding in case the malloc buffer is not aligned, so we need to do the same for the GPU buffer, since we use the same offsets - ggml_cuda_set_scratch_size(alloc_size + 64); - LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0); - - // calculate total VRAM usage - auto add_tensor = [](const ggml_tensor * t, size_t & size) { - if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) { - size += ggml_nbytes(t); - } - }; - size_t model_vram_size = 0; - for (const auto & kv : model->tensors_by_name) { - add_tensor(kv.second, model_vram_size); - } - - size_t kv_vram_size = 0; - for (auto & k : ctx->kv_self.k_l) { - add_tensor(k, kv_vram_size); - } - for (auto & v : ctx->kv_self.v_l) { - add_tensor(v, kv_vram_size); - } - - size_t ctx_vram_size = alloc_size + kv_vram_size; - size_t total_vram_size = model_vram_size + ctx_vram_size; - - LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__, - total_vram_size / 1024.0 / 1024.0, - model_vram_size / 1024.0 / 1024.0, - ctx_vram_size / 1024.0 / 1024.0); + for (ggml_backend_t backend : ctx->backends) { + ggml_backend_buffer_t buf = ggml_backend_sched_get_buffer(ctx->sched, backend); + LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, + ggml_backend_buffer_name(buf), + ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); } -#endif } } @@ -10002,9 +9499,8 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3 } int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { - return snprintf(buf, buf_size, "%s %s%s %s", + return snprintf(buf, buf_size, "%s %s %s", llama_model_arch_name(model->arch).c_str(), - model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } @@ -10026,7 +9522,14 @@ uint64_t llama_model_n_params(const struct llama_model * model) { } struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) { - return ggml_get_tensor(model->ctx, name); + auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(), + [name](const std::pair & it) { + return it.first == name; + }); + if (it == model->tensors_by_name.end()) { + return nullptr; + } + return it->second; } uint32_t llama_model_quantize( @@ -10211,7 +9714,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_embedding = ctx->embedding.size() * sizeof(float); const size_t s_kv_size = sizeof(size_t); const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = ggml_backend_buffer_get_size(ctx->kv_self.buf); + const size_t s_kv = ctx->kv_self.total_size(); const size_t s_total = ( + s_rng_size @@ -10340,7 +9843,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const auto n_embd_v_gqa = hparams.n_embd_v_gqa(); const auto n_ctx = cparams.n_ctx; - const size_t kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf); + const size_t kv_buf_size = kv_self.total_size(); const uint32_t kv_head = kv_self.head; const uint32_t kv_size = kv_self.size; const uint32_t kv_used = kv_self.used; @@ -10353,46 +9856,19 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat if (kv_buf_size) { const size_t elt_size = ggml_element_size(kv_self.k_l[0]); - ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); - ggml_cgraph * gf = ggml_new_graph(cpy_ctx); - - std::vector kout2d(n_layer); - std::vector vout2d(n_layer); - - for (int il = 0; il < (int) n_layer; ++il) { - kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head); - vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa); - - ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], - n_embd_k_gqa, kv_head, - elt_size*n_embd_k_gqa, 0); - - ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il], - kv_head, n_embd_v_gqa, - elt_size*n_ctx, 0); - - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il])); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v2d, vout2d[il])); - } - - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend); - - ggml_backend_graph_compute(ctx->backend, gf); - std::vector tmp_buf; for (int il = 0; il < (int) n_layer; ++il) { - tmp_buf.resize(ggml_nbytes(kout2d[il])); - ggml_backend_tensor_get(kout2d[il], tmp_buf.data(), 0, tmp_buf.size()); + tmp_buf.resize(elt_size*n_embd_k_gqa*kv_head); + ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); - tmp_buf.resize(ggml_nbytes(vout2d[il])); - ggml_backend_tensor_get(vout2d[il], tmp_buf.data(), 0, tmp_buf.size()); - data_ctx->write(tmp_buf.data(), tmp_buf.size()); + // v is not contiguous, copy row by row + tmp_buf.resize(elt_size*kv_head); + for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*elt_size*n_ctx, tmp_buf.size()); + data_ctx->write(tmp_buf.data(), tmp_buf.size()); + } } - - ggml_free(cpy_ctx); - - ggml_backend_buffer_free(buf); } for (uint32_t i = 0; i < kv_size; ++i) { @@ -10491,48 +9967,22 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used); if (kv_buf_size) { - GGML_ASSERT(ggml_backend_buffer_get_size(kv_self.buf) == kv_buf_size); + GGML_ASSERT(kv_self.total_size() == kv_buf_size); const size_t elt_size = ggml_element_size(kv_self.k_l[0]); - ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); - ggml_cgraph * gf = ggml_new_graph(cpy_ctx); + for (int il = 0; il < (int) n_layer; ++il) { + size_t k_size = elt_size*n_embd_k_gqa*kv_head; + ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); + inp += k_size; - std::vector kin2d(n_layer); - std::vector vin2d(n_layer); - - for (int il = 0; il < n_layer; ++il) { - kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head); - vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa); - - ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il], - n_embd_k_gqa, kv_head, - elt_size*n_embd_k_gqa, 0); - - ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il], - kv_head, n_embd_v_gqa, - elt_size*n_ctx, 0); - - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d)); - ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin2d[il], v2d)); + // v is not contiguous, copy row by row + size_t v_row_size = elt_size*kv_head; + for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) { + ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*elt_size*n_ctx, v_row_size); + inp += v_row_size; + } } - - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend); - - // load data into the tensors - for (int il = 0; il < n_layer; ++il) { - ggml_backend_tensor_set(kin2d[il], inp, 0, ggml_nbytes(kin2d[il])); - inp += ggml_nbytes(kin2d[il]); - - ggml_backend_tensor_set(vin2d[il], inp, 0, ggml_nbytes(vin2d[il])); - inp += ggml_nbytes(vin2d[il]); - } - - ggml_backend_graph_compute(ctx->backend, gf); - - ggml_free(cpy_ctx); - - ggml_backend_buffer_free(buf); } ctx->kv_self.head = kv_head; diff --git a/llama.h b/llama.h index 43d41b8f6..689e12d7c 100644 --- a/llama.h +++ b/llama.h @@ -118,6 +118,12 @@ extern "C" { LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, }; + enum llama_split_mode { + LLAMA_SPLIT_NONE = 0, // single GPU + LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs + LLAMA_SPLIT_ROW = 2, // split rows across GPUs + }; + typedef struct llama_token_data { llama_token id; // token id float logit; // log-odds of the token @@ -180,8 +186,16 @@ extern "C" { struct llama_model_params { int32_t n_gpu_layers; // number of layers to store in VRAM - int32_t main_gpu; // the GPU that is used for scratch and small tensors - const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES) + enum llama_split_mode split_mode; // how to split the model across multiple GPUs + + // main_gpu interpretation depends on split_mode: + // LLAMA_SPLIT_NONE: the GPU that is used for the entire model + // LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results + // LLAMA_SPLIT_LAYER: ignored + int32_t main_gpu; + + // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES + const float * tensor_split; // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. // If the provided progress_callback returns true, model loading continues. diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 7a60d7743..d9b8b106a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -376,6 +376,11 @@ struct test_case { // allocate ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1); + if (buf == NULL) { + printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1)); + ggml_free(ctx); + return false; + } // build graph ggml_build_forward_expand(gf, out); @@ -463,19 +468,23 @@ struct test_case { GGML_UNUSED(index); }; - ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud); + const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud); - if (ud.ok) { - printf("\033[1;32mOK\033[0m\n"); - } else { - printf("\033[1;31mFAIL\033[0m\n"); + if (!cmp_ok) { + printf("compare failed "); } ggml_backend_buffer_free(buf); ggml_free(ctx); - return ud.ok; + if (ud.ok && cmp_ok) { + printf("\033[1;32mOK\033[0m\n"); + return true; + } + + printf("\033[1;31mFAIL\033[0m\n"); + return false; } bool eval_perf(ggml_backend_t backend, const char * op_name) { @@ -519,6 +528,11 @@ struct test_case { // allocate ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend); + if (buf == NULL) { + printf("failed to allocate tensors\n"); + ggml_free(ctx); + return false; + } // randomize tensors initialize_tensors(ctx); From 3fe81781e3bf98b8e44946240a19f3a6ad08a11a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 12 Jan 2024 20:38:54 +0100 Subject: [PATCH 011/334] CUDA: faster q8_0 -> f16 dequantization (#4895) --- ggml-cuda.cu | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2db50437c..bd3814c72 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -523,6 +523,8 @@ static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 +#define CUDA_Q8_0_NE_ALIGN 2048 + // dmmv = dequantize_mul_mat_vec #ifndef GGML_CUDA_DMMV_X #define GGML_CUDA_DMMV_X 32 @@ -2327,6 +2329,45 @@ static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __res y[i] = x[i]; } +template +static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) { +#if __CUDA_ARCH__ >= CC_PASCAL + constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE; + + const int i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x; + const int * x0 = ((int *) vx) + blockIdx.x * nint; + half2 * y2 = (half2 *) (y + i0); + + __shared__ int vals[nint]; + +#pragma unroll + for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) { + if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) { + break; + } + + const int ix = ix0 + threadIdx.x; + vals[ix] = x0[ix]; + } + +#pragma unroll + for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) { + if (need_check && i0 + iy + 2*threadIdx.x >= k) { + return; + } + + const half * b0 = ((const half *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0); + const half d = *b0; + const char2 qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)]; + + y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d)); + } +#else + (void) vx; (void) y; (void) k; + bad_arch(); +#endif // __CUDA_ARCH__ >= CC_PASCAL +} + // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q @@ -6181,6 +6222,17 @@ static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restri dequantize_block<<>>(vx, y, k); } +static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN; + if (k % CUDA_Q8_0_NE_ALIGN == 0) { + const bool need_check = false; + dequantize_block_q8_0_f16<<>>(vx, y, k); + } else { + const bool need_check = true; + dequantize_block_q8_0_f16<<>>(vx, y, k); + } +} + template static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -6246,6 +6298,7 @@ static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict_ } static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { + int id; switch (type) { case GGML_TYPE_Q4_0: return dequantize_block_cuda; @@ -6256,6 +6309,10 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { case GGML_TYPE_Q5_1: return dequantize_block_cuda; case GGML_TYPE_Q8_0: + CUDA_CHECK(cudaGetDevice(&id)); + if (g_device_caps[id].cc >= CC_PASCAL) { + return dequantize_block_q8_0_f16_cuda; + } return dequantize_block_cuda; case GGML_TYPE_Q2_K: return dequantize_row_q2_K_cuda; From 52ee4540c0f2e11d52c839db6eb51d014ce060e1 Mon Sep 17 00:00:00 2001 From: Maximilian Winter Date: Fri, 12 Jan 2024 20:46:45 +0100 Subject: [PATCH 012/334] examples : add pydantic models to GBNF grammar generator (#4883) * Create pydantic-models-to-grammar.py * Added some comments for usage * Refactored Grammar Generator Added example and usage instruction. * Update pydantic_models_to_grammar.py * Update pydantic-models-to-grammar-examples.py * Renamed module and imported it. * Update pydantic-models-to-grammar.py * Renamed file and fixed grammar generator issue. --- .../pydantic-models-to-grammar-examples.py | 136 ++ examples/pydantic_models_to_grammar.py | 1151 +++++++++++++++++ 2 files changed, 1287 insertions(+) create mode 100644 examples/pydantic-models-to-grammar-examples.py create mode 100644 examples/pydantic_models_to_grammar.py diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic-models-to-grammar-examples.py new file mode 100644 index 000000000..a8a4919cf --- /dev/null +++ b/examples/pydantic-models-to-grammar-examples.py @@ -0,0 +1,136 @@ +# Function calling example using pydantic models. + +import json +from enum import Enum +from typing import Union, Optional + +import requests +from pydantic import BaseModel, Field + +import importlib +from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation + +# Function to get completion on the llama.cpp server with grammar. +def create_completion(prompt, grammar): + headers = {"Content-Type": "application/json"} + data = {"prompt": prompt, "grammar": grammar} + + response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data) + data = response.json() + + print(data["content"]) + return data["content"] + + +# A function for the agent to send a message to the user. +class SendMessageToUser(BaseModel): + """ + Send a message to the User. + """ + chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.") + message: str = Field(..., description="Message you want to send to the user.") + + def run(self): + print(self.message) + + +# Enum for the calculator function. +class MathOperation(Enum): + ADD = "add" + SUBTRACT = "subtract" + MULTIPLY = "multiply" + DIVIDE = "divide" + + +# Very simple calculator tool for the agent. +class Calculator(BaseModel): + """ + Perform a math operation on two numbers. + """ + number_one: Union[int, float] = Field(..., description="First number.") + operation: MathOperation = Field(..., description="Math operation to perform.") + number_two: Union[int, float] = Field(..., description="Second number.") + + def run(self): + if self.operation == MathOperation.ADD: + return self.number_one + self.number_two + elif self.operation == MathOperation.SUBTRACT: + return self.number_one - self.number_two + elif self.operation == MathOperation.MULTIPLY: + return self.number_one * self.number_two + elif self.operation == MathOperation.DIVIDE: + return self.number_one / self.number_two + else: + raise ValueError("Unknown operation.") + + +# Here the grammar gets generated by passing the available function models to generate_gbnf_grammar_and_documentation function. This also generates a documentation usable by the LLM. +# pydantic_model_list is the list of pydanitc models +# outer_object_name is an optional name for an outer object around the actual model object. Like a "function" object with "function_parameters" which contains the actual model object. If None, no outer object will be generated +# outer_object_content is the name of outer object content. +# model_prefix is the optional prefix for models in the documentation. (Default="Output Model") +# fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields") +gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( + pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function", + outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters") + +print(gbnf_grammar) +print(documentation) + +system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation + +user_message = "What is 42 * 42?" +prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" + +text = create_completion(prompt=prompt, grammar=gbnf_grammar) +# This should output something like this: +# { +# "function": "calculator", +# "function_parameters": { +# "number_one": 42, +# "operation": "multiply", +# "number_two": 42 +# } +# } +function_dictionary = json.loads(text) +if function_dictionary["function"] == "calculator": + function_parameters = {**function_dictionary["function_parameters"]} + + print(Calculator(**function_parameters).run()) + # This should output: 1764 + + +# A example structured output based on pydantic models. The LLM will create an entry for a Book database out of an unstructured text. +class Category(Enum): + """ + The category of the book. + """ + Fiction = "Fiction" + NonFiction = "Non-Fiction" + + +class Book(BaseModel): + """ + Represents an entry about a book. + """ + title: str = Field(..., description="Title of the book.") + author: str = Field(..., description="Author of the book.") + published_year: Optional[int] = Field(..., description="Publishing year of the book.") + keywords: list[str] = Field(..., description="A list of keywords.") + category: Category = Field(..., description="Category of the book.") + summary: str = Field(..., description="Summary of the book.") + + +# We need no additional parameters other than our list of pydantic models. +gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book]) + +system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation + +text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands.""" +prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant" + +text = create_completion(prompt=prompt, grammar=gbnf_grammar) + +json_data = json.loads(text) + +print(Book(**json_data)) diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py new file mode 100644 index 000000000..41b98fdc1 --- /dev/null +++ b/examples/pydantic_models_to_grammar.py @@ -0,0 +1,1151 @@ +import inspect +import json +from copy import copy +from inspect import isclass, getdoc +from types import NoneType + +from pydantic import BaseModel, create_model, Field +from typing import Any, Type, List, get_args, get_origin, Tuple, Union, Optional, _GenericAlias +from enum import Enum +from typing import get_type_hints, Callable +import re + + +class PydanticDataType(Enum): + """ + Defines the data types supported by the grammar_generator. + + Attributes: + STRING (str): Represents a string data type. + BOOLEAN (str): Represents a boolean data type. + INTEGER (str): Represents an integer data type. + FLOAT (str): Represents a float data type. + OBJECT (str): Represents an object data type. + ARRAY (str): Represents an array data type. + ENUM (str): Represents an enum data type. + CUSTOM_CLASS (str): Represents a custom class data type. + """ + STRING = "string" + TRIPLE_QUOTED_STRING = "triple_quoted_string" + MARKDOWN_STRING = "markdown_string" + BOOLEAN = "boolean" + INTEGER = "integer" + FLOAT = "float" + OBJECT = "object" + ARRAY = "array" + ENUM = "enum" + ANY = "any" + NULL = "null" + CUSTOM_CLASS = "custom-class" + CUSTOM_DICT = "custom-dict" + SET = "set" + + +def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str: + if isclass(pydantic_type) and issubclass(pydantic_type, str): + return PydanticDataType.STRING.value + elif isclass(pydantic_type) and issubclass(pydantic_type, bool): + return PydanticDataType.BOOLEAN.value + elif isclass(pydantic_type) and issubclass(pydantic_type, int): + return PydanticDataType.INTEGER.value + elif isclass(pydantic_type) and issubclass(pydantic_type, float): + return PydanticDataType.FLOAT.value + elif isclass(pydantic_type) and issubclass(pydantic_type, Enum): + return PydanticDataType.ENUM.value + + elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel): + return format_model_and_field_name(pydantic_type.__name__) + elif get_origin(pydantic_type) == list: + element_type = get_args(pydantic_type)[0] + return f"{map_pydantic_type_to_gbnf(element_type)}-list" + elif get_origin(pydantic_type) == set: + element_type = get_args(pydantic_type)[0] + return f"{map_pydantic_type_to_gbnf(element_type)}-set" + elif get_origin(pydantic_type) == Union: + union_types = get_args(pydantic_type) + union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types] + return f"union-{'-or-'.join(union_rules)}" + elif get_origin(pydantic_type) == Optional: + element_type = get_args(pydantic_type)[0] + return f"optional-{map_pydantic_type_to_gbnf(element_type)}" + elif isclass(pydantic_type): + return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}" + elif get_origin(pydantic_type) == dict: + key_type, value_type = get_args(pydantic_type) + return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}" + else: + return "unknown" + + +def format_model_and_field_name(model_name: str) -> str: + parts = re.findall('[A-Z][^A-Z]*', model_name) + if not parts: # Check if the list is empty + return model_name.lower().replace("_", "-") + return '-'.join(part.lower().replace("_", "-") for part in parts) + + +def generate_list_rule(element_type): + """ + Generate a GBNF rule for a list of a given element type. + + :param element_type: The type of the elements in the list (e.g., 'string'). + :return: A string representing the GBNF rule for a list of the given type. + """ + rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list" + element_rule = map_pydantic_type_to_gbnf(element_type) + list_rule = fr'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"' + return list_rule + + +def get_members_structure(cls, rule_name): + if issubclass(cls, Enum): + # Handle Enum types + members = [f'\"\\\"{member.value}\\\"\"' for name, member in cls.__members__.items()] + return f"{cls.__name__.lower()} ::= " + " | ".join(members) + if cls.__annotations__ and cls.__annotations__ != {}: + result = f'{rule_name} ::= "{{"' + type_list_rules = [] + # Modify this comprehension + members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param_type)}' + for name, param_type in cls.__annotations__.items() + if name != 'self'] + + result += '"," '.join(members) + result += ' "}"' + return result, type_list_rules + elif rule_name == "custom-class-any": + result = f'{rule_name} ::= ' + result += 'value' + type_list_rules = [] + return result, type_list_rules + else: + init_signature = inspect.signature(cls.__init__) + parameters = init_signature.parameters + result = f'{rule_name} ::= "{{"' + type_list_rules = [] + # Modify this comprehension too + members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param.annotation)}' + for name, param in parameters.items() + if name != 'self' and param.annotation != inspect.Parameter.empty] + + result += '", "'.join(members) + result += ' "}"' + return result, type_list_rules + + +def regex_to_gbnf(regex_pattern: str) -> str: + """ + Translate a basic regex pattern to a GBNF rule. + Note: This function handles only a subset of simple regex patterns. + """ + gbnf_rule = regex_pattern + + # Translate common regex components to GBNF + gbnf_rule = gbnf_rule.replace('\\d', '[0-9]') + gbnf_rule = gbnf_rule.replace('\\s', '[ \t\n]') + + # Handle quantifiers and other regex syntax that is similar in GBNF + # (e.g., '*', '+', '?', character classes) + + return gbnf_rule + + +def generate_gbnf_integer_rules(max_digit=None, min_digit=None): + """ + + Generate GBNF Integer Rules + + Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits. + + Parameters: + max_digit (int): The maximum number of digits for the integer. Default is None. + min_digit (int): The minimum number of digits for the integer. Default is None. + + Returns: + integer_rule (str): The identifier for the integer rule generated. + additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits. + + """ + additional_rules = [] + + # Define the rule identifier based on max_digit and min_digit + integer_rule = "integer-part" + if max_digit is not None: + integer_rule += f"-max{max_digit}" + if min_digit is not None: + integer_rule += f"-min{min_digit}" + + # Handling Integer Rules + if max_digit is not None or min_digit is not None: + # Start with an empty rule part + integer_rule_part = '' + + # Add mandatory digits as per min_digit + if min_digit is not None: + integer_rule_part += '[0-9] ' * min_digit + + # Add optional digits up to max_digit + if max_digit is not None: + optional_digits = max_digit - (min_digit if min_digit is not None else 0) + integer_rule_part += ''.join(['[0-9]? ' for _ in range(optional_digits)]) + + # Trim the rule part and append it to additional rules + integer_rule_part = integer_rule_part.strip() + if integer_rule_part: + additional_rules.append(f'{integer_rule} ::= {integer_rule_part}') + + return integer_rule, additional_rules + + +def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None, min_precision=None): + """ + Generate GBNF float rules based on the given constraints. + + :param max_digit: Maximum number of digits in the integer part (default: None) + :param min_digit: Minimum number of digits in the integer part (default: None) + :param max_precision: Maximum number of digits in the fractional part (default: None) + :param min_precision: Minimum number of digits in the fractional part (default: None) + :return: A tuple containing the float rule and additional rules as a list + + Example Usage: + max_digit = 3 + min_digit = 1 + max_precision = 2 + min_precision = 1 + generate_gbnf_float_rules(max_digit, min_digit, max_precision, min_precision) + + Output: + ('float-3-1-2-1', ['integer-part-max3-min1 ::= [0-9] [0-9] [0-9]?', 'fractional-part-max2-min1 ::= [0-9] [0-9]?', 'float-3-1-2-1 ::= integer-part-max3-min1 "." fractional-part-max2-min + *1']) + + Note: + GBNF stands for Generalized Backus-Naur Form, which is a notation technique to specify the syntax of programming languages or other formal grammars. + """ + additional_rules = [] + + # Define the integer part rule + integer_part_rule = "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( + f"-min{min_digit}" if min_digit is not None else "") + + # Define the fractional part rule based on precision constraints + fractional_part_rule = "fractional-part" + fractional_rule_part = '' + if max_precision is not None or min_precision is not None: + fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + ( + f"-min{min_precision}" if min_precision is not None else "") + # Minimum number of digits + fractional_rule_part = '[0-9]' * (min_precision if min_precision is not None else 1) + # Optional additional digits + fractional_rule_part += ''.join([' [0-9]?'] * ( + (max_precision - (min_precision if min_precision is not None else 1)) if max_precision is not None else 0)) + additional_rules.append(f'{fractional_part_rule} ::= {fractional_rule_part}') + + # Define the float rule + float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}" + additional_rules.append(f'{float_rule} ::= {integer_part_rule} "." {fractional_part_rule}') + + # Generating the integer part rule definition, if necessary + if max_digit is not None or min_digit is not None: + integer_rule_part = '[0-9]' + if min_digit is not None and min_digit > 1: + integer_rule_part += ' [0-9]' * (min_digit - 1) + if max_digit is not None: + integer_rule_part += ''.join([' [0-9]?'] * (max_digit - (min_digit if min_digit is not None else 1))) + additional_rules.append(f'{integer_part_rule} ::= {integer_rule_part.strip()}') + + return float_rule, additional_rules + + +def generate_gbnf_rule_for_type(model_name, field_name, + field_type, is_optional, processed_models, created_rules, + field_info=None) -> \ + Tuple[str, list]: + """ + Generate GBNF rule for a given field type. + + :param model_name: Name of the model. + + :param field_name: Name of the field. + :param field_type: Type of the field. + :param is_optional: Whether the field is optional. + :param processed_models: List of processed models. + :param created_rules: List of created rules. + :param field_info: Additional information about the field (optional). + + :return: Tuple containing the GBNF type and a list of additional rules. + :rtype: Tuple[str, list] + """ + rules = [] + + field_name = format_model_and_field_name(field_name) + gbnf_type = map_pydantic_type_to_gbnf(field_type) + + if isclass(field_type) and issubclass(field_type, BaseModel): + nested_model_name = format_model_and_field_name(field_type.__name__) + nested_model_rules = generate_gbnf_grammar(field_type, processed_models, created_rules) + rules.extend(nested_model_rules) + gbnf_type, rules = nested_model_name, rules + elif isclass(field_type) and issubclass(field_type, Enum): + enum_values = [f'\"\\\"{e.value}\\\"\"' for e in field_type] # Adding escaped quotes + enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}" + rules.append(enum_rule) + gbnf_type, rules = model_name + "-" + field_name, rules + elif get_origin(field_type) == list or field_type == list: # Array + element_type = get_args(field_type)[0] + element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-element", + element_type, is_optional, processed_models, + created_rules) + rules.extend(additional_rules) + array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ + rules.append(array_rule) + gbnf_type, rules = model_name + "-" + field_name, rules + + elif get_origin(field_type) == set or field_type == set: # Array + element_type = get_args(field_type)[0] + element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-element", + element_type, is_optional, processed_models, + created_rules) + rules.extend(additional_rules) + array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ + rules.append(array_rule) + gbnf_type, rules = model_name + "-" + field_name, rules + + elif gbnf_type.startswith("custom-class-"): + nested_model_rules, field_types = get_members_structure(field_type, gbnf_type) + rules.append(nested_model_rules) + elif gbnf_type.startswith("custom-dict-"): + key_type, value_type = get_args(field_type) + + additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-key-type", + key_type, is_optional, processed_models, + created_rules) + additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(model_name, + f"{field_name}-value-type", + value_type, is_optional, + processed_models, created_rules) + gbnf_type = fr'{gbnf_type} ::= "{{" ( {additional_key_type} ":" {additional_value_type} ("," {additional_key_type} ":" {additional_value_type})* )? "}}" ' + + rules.extend(additional_key_rules) + rules.extend(additional_value_rules) + elif gbnf_type.startswith("union-"): + union_types = get_args(field_type) + union_rules = [] + + for union_type in union_types: + if isinstance(union_type, _GenericAlias): + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, + field_name, union_type, + False, + processed_models, created_rules) + union_rules.append(union_gbnf_type) + rules.extend(union_rules_list) + + + elif not issubclass(union_type, NoneType): + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, + field_name, union_type, + False, + processed_models, created_rules) + union_rules.append(union_gbnf_type) + rules.extend(union_rules_list) + + # Defining the union grammar rule separately + if len(union_rules) == 1: + union_grammar_rule = f"{model_name}-{field_name}-optional ::= {' | '.join(union_rules)} | null" + else: + union_grammar_rule = f"{model_name}-{field_name}-union ::= {' | '.join(union_rules)}" + rules.append(union_grammar_rule) + if len(union_rules) == 1: + gbnf_type = f"{model_name}-{field_name}-optional" + else: + gbnf_type = f"{model_name}-{field_name}-union" + elif isclass(field_type) and issubclass(field_type, str): + if field_info and hasattr(field_info, 'json_schema_extra') and field_info.json_schema_extra is not None: + + triple_quoted_string = field_info.json_schema_extra.get('triple_quoted_string', False) + markdown_string = field_info.json_schema_extra.get('markdown_string', False) + + gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value + gbnf_type = PydanticDataType.MARKDOWN_STRING.value if markdown_string else gbnf_type + + elif field_info and hasattr(field_info, 'pattern'): + # Convert regex pattern to grammar rule + regex_pattern = field_info.regex.pattern + gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}" + else: + gbnf_type = PydanticDataType.STRING.value + + elif isclass(field_type) and issubclass(field_type, float) and field_info and hasattr(field_info, + 'json_schema_extra') and field_info.json_schema_extra is not None: + # Retrieve precision attributes for floats + max_precision = field_info.json_schema_extra.get('max_precision') if field_info and hasattr(field_info, + 'json_schema_extra') else None + min_precision = field_info.json_schema_extra.get('min_precision') if field_info and hasattr(field_info, + 'json_schema_extra') else None + max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + + # Generate GBNF rule for float with given attributes + gbnf_type, rules = generate_gbnf_float_rules(max_digit=max_digits, min_digit=min_digits, + max_precision=max_precision, + min_precision=min_precision) + + elif isclass(field_type) and issubclass(field_type, int) and field_info and hasattr(field_info, + 'json_schema_extra') and field_info.json_schema_extra is not None: + # Retrieve digit attributes for integers + max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, + 'json_schema_extra') else None + + # Generate GBNF rule for integer with given attributes + gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits) + else: + gbnf_type, rules = gbnf_type, [] + + if gbnf_type not in created_rules: + return gbnf_type, rules + else: + if gbnf_type in created_rules: + return gbnf_type, rules + + +def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created_rules: dict) -> (list, bool, bool): + """ + + Generate GBnF Grammar + + Generates a GBnF grammar for a given model. + + :param model: A Pydantic model class to generate the grammar for. Must be a subclass of BaseModel. + :param processed_models: A set of already processed models to prevent infinite recursion. + :param created_rules: A dict containing already created rules to prevent duplicates. + :return: A list of GBnF grammar rules in string format. And two booleans indicating if an extra markdown or triple quoted string is in the grammar. + Example Usage: + ``` + model = MyModel + processed_models = set() + created_rules = dict() + + gbnf_grammar = generate_gbnf_grammar(model, processed_models, created_rules) + ``` + """ + if model in processed_models: + return [] + + processed_models.add(model) + model_name = format_model_and_field_name(model.__name__) + + if not issubclass(model, BaseModel): + # For non-Pydantic classes, generate model_fields from __annotations__ or __init__ + if hasattr(model, '__annotations__') and model.__annotations__: + model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} + else: + init_signature = inspect.signature(model.__init__) + parameters = init_signature.parameters + model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() + if name != 'self'} + else: + # For Pydantic models, use model_fields and check for ellipsis (required fields) + model_fields = model.__annotations__ + + model_rule_parts = [] + nested_rules = [] + has_markdown_code_block = False + has_triple_quoted_string = False + look_for_markdown_code_block = False + look_for_triple_quoted_string = False + for field_name, field_info in model_fields.items(): + if not issubclass(model, BaseModel): + field_type, default_value = field_info + # Check if the field is optional (not required) + is_optional = (default_value is not inspect.Parameter.empty) and (default_value is not Ellipsis) + else: + field_type = field_info + field_info = model.model_fields[field_name] + is_optional = field_info.is_required is False and get_origin(field_type) is Optional + rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, + format_model_and_field_name(field_name), + field_type, is_optional, + processed_models, created_rules, field_info) + look_for_markdown_code_block = True if rule_name == "markdown_string" else False + look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False + if not look_for_markdown_code_block and not look_for_triple_quoted_string: + if rule_name not in created_rules: + created_rules[rule_name] = additional_rules + model_rule_parts.append(f' ws \"\\\"{field_name}\\\"\" ": " {rule_name}') # Adding escaped quotes + nested_rules.extend(additional_rules) + else: + has_triple_quoted_string = look_for_markdown_code_block + has_markdown_code_block = look_for_triple_quoted_string + + fields_joined = r' "," "\n" '.join(model_rule_parts) + model_rule = fr'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"' + + if look_for_markdown_code_block or look_for_triple_quoted_string: + model_rule += ' ws "}"' + + if has_triple_quoted_string: + model_rule += '"\\n" triple-quoted-string' + if has_markdown_code_block: + model_rule += '"\\n" markdown-code-block' + all_rules = [model_rule] + nested_rules + + return all_rules, has_markdown_code_block, has_triple_quoted_string + + +def generate_gbnf_grammar_from_pydantic_models(models: List[Type[BaseModel]], outer_object_name: str = None, + outer_object_content: str = None, list_of_outputs: bool = False) -> str: + """ + Generate GBNF Grammar from Pydantic Models. + + This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated + * grammar. + + Parameters: + models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + list_of_outputs (str, optional): Allows a list of output objects + Returns: + str: The generated GBNF grammar string. + + Examples: + models = [UserModel, PostModel] + grammar = generate_gbnf_grammar_from_pydantic(models) + print(grammar) + # Output: + # root ::= UserModel | PostModel + # ... + """ + processed_models = set() + all_rules = [] + created_rules = {} + if outer_object_name is None: + + for model in models: + model_rules, _, _ = generate_gbnf_grammar(model, + processed_models, created_rules) + all_rules.extend(model_rules) + + if list_of_outputs: + root_rule = r'root ::= ws "[" grammar-models ("," grammar-models)* "]"' + "\n" + else: + root_rule = r'root ::= ws grammar-models' + "\n" + root_rule += "grammar-models ::= " + " | ".join( + [format_model_and_field_name(model.__name__) for model in models]) + all_rules.insert(0, root_rule) + return "\n".join(all_rules) + elif outer_object_name is not None: + if list_of_outputs: + root_rule = fr'root ::= ws "[" {format_model_and_field_name(outer_object_name)} ("," {format_model_and_field_name(outer_object_name)})* "]"' + "\n" + else: + root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n" + + model_rule = fr'{format_model_and_field_name(outer_object_name)} ::= ws "{{" ws "\"{outer_object_name}\"" ": " grammar-models' + + fields_joined = " | ".join( + [fr'{format_model_and_field_name(model.__name__)}-grammar-model' for model in models]) + + grammar_model_rules = f'\ngrammar-models ::= {fields_joined}' + mod_rules = [] + for model in models: + mod_rule = fr'{format_model_and_field_name(model.__name__)}-grammar-model ::= ws' + mod_rule += fr'"\"{format_model_and_field_name(model.__name__)}\"" "," ws "\"{outer_object_content}\"" ws ":" ws {format_model_and_field_name(model.__name__)}' + '\n' + mod_rules.append(mod_rule) + grammar_model_rules += "\n" + "\n".join(mod_rules) + look_for_markdown_code_block = False + look_for_triple_quoted_string = False + for model in models: + model_rules, markdown_block, triple_quoted_string = generate_gbnf_grammar(model, + processed_models, created_rules) + all_rules.extend(model_rules) + if markdown_block: + look_for_markdown_code_block = True + + if triple_quoted_string: + look_for_triple_quoted_string = True + + if not look_for_markdown_code_block and not look_for_triple_quoted_string: + model_rule += ' ws "}"' + all_rules.insert(0, root_rule + model_rule + grammar_model_rules) + return "\n".join(all_rules) + + +def get_primitive_grammar(grammar): + """ + Returns the needed GBNF primitive grammar for a given GBNF grammar string. + + Args: + grammar (str): The string containing the GBNF grammar. + + Returns: + str: GBNF primitive grammar string. + """ + type_list = [] + if "string-list" in grammar: + type_list.append(str) + if "boolean-list" in grammar: + type_list.append(bool) + if "integer-list" in grammar: + type_list.append(int) + if "float-list" in grammar: + type_list.append(float) + additional_grammar = [generate_list_rule(t) for t in type_list] + primitive_grammar = r""" +boolean ::= "true" | "false" +null ::= "null" +string ::= "\"" ( + [^"\\] | + "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\"" ws +ws ::= ([ \t\n] ws)? +float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws + +integer ::= [0-9]+""" + + any_block = "" + if "custom-class-any" in grammar: + any_block = ''' +value ::= object | array | string | number | boolean | null + +object ::= + "{" ws ( + string ":" ws value + ("," ws string ":" ws value)* + )? "}" ws + +array ::= + "[" ws ( + value + ("," ws value)* + )? "]" ws + +number ::= integer | float''' + + markdown_code_block_grammar = "" + if "markdown-code-block" in grammar: + markdown_code_block_grammar = r''' +markdown-code-block ::= opening-triple-ticks markdown-code-block-content closing-triple-ticks +markdown-code-block-content ::= ( [^`] | "`" [^`] | "`" "`" [^`] )* +opening-triple-ticks ::= "```" "python" "\n" | "```" "c" "\n" | "```" "cpp" "\n" | "```" "txt" "\n" | "```" "text" "\n" | "```" "json" "\n" | "```" "javascript" "\n" | "```" "css" "\n" | "```" "html" "\n" | "```" "markdown" "\n" +closing-triple-ticks ::= "```" "\n"''' + + if "triple-quoted-string" in grammar: + markdown_code_block_grammar = r""" +triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes +triple-quoted-string-content ::= ( [^'] | "'" [^'] | "'" "'" [^'] )* +triple-quotes ::= "'''" """ + return "\n" + '\n'.join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar + + +def generate_field_markdown(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1) -> str: + indent = ' ' * depth + field_markdown = f"{indent}- **{field_name}** (`{field_type.__name__}`): " + + # Extracting field description from Pydantic Field using __model_fields__ + field_info = model.model_fields.get(field_name) + field_description = field_info.description if field_info and field_info.description else "No description available." + + field_markdown += field_description + '\n' + + # Handling nested BaseModel fields + if isclass(field_type) and issubclass(field_type, BaseModel): + field_markdown += f"{indent} - Details:\n" + for name, type_ in field_type.__annotations__.items(): + field_markdown += generate_field_markdown(name, type_, field_type, depth + 2) + + return field_markdown + + +def generate_markdown_report(pydantic_models: List[Type[BaseModel]]) -> str: + markdown = "" + for model in pydantic_models: + markdown += f"### {format_model_and_field_name(model.__name__)}\n" + + # Check if the model's docstring is different from BaseModel's docstring + class_doc = getdoc(model) + base_class_doc = getdoc(BaseModel) + class_description = class_doc if class_doc and class_doc != base_class_doc else "No specific description available." + + markdown += f"{class_description}\n\n" + markdown += "#### Fields\n" + + if isclass(model) and issubclass(model, BaseModel): + for name, field_type in model.__annotations__.items(): + markdown += generate_field_markdown(format_model_and_field_name(name), field_type, model) + markdown += "\n" + + return markdown + + +def format_json_example(example: dict, depth: int) -> str: + """ + Format a JSON example into a readable string with indentation. + + Args: + example (dict): JSON example to be formatted. + depth (int): Indentation depth. + + Returns: + str: Formatted JSON example string. + """ + indent = ' ' * depth + formatted_example = '{\n' + for key, value in example.items(): + value_text = f"'{value}'" if isinstance(value, str) else value + formatted_example += f"{indent}{key}: {value_text},\n" + formatted_example = formatted_example.rstrip(',\n') + '\n' + indent + '}' + return formatted_example + + +def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_prefix="Model", + fields_prefix="Fields", documentation_with_field_description=True) -> str: + """ + Generate text documentation for a list of Pydantic models. + + Args: + pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + model_prefix (str): Prefix for the model section. + fields_prefix (str): Prefix for the fields section. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation. + """ + documentation = "" + pyd_models = [(model, True) for model in pydantic_models] + for model, add_prefix in pyd_models: + if add_prefix: + documentation += f"{model_prefix}: {format_model_and_field_name(model.__name__)}\n" + else: + documentation += f"Model: {format_model_and_field_name(model.__name__)}\n" + + # Handling multi-line model description with proper indentation + + class_doc = getdoc(model) + base_class_doc = getdoc(BaseModel) + class_description = class_doc if class_doc and class_doc != base_class_doc else "" + if class_description != "": + documentation += " Description: " + documentation += "\n" + format_multiline_description(class_description, 2) + "\n" + + if add_prefix: + # Indenting the fields section + documentation += f" {fields_prefix}:\n" + else: + documentation += f" Fields:\n" + if isclass(model) and issubclass(model, BaseModel): + for name, field_type in model.__annotations__.items(): + # if name == "markdown_code_block": + # continue + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + if get_origin(field_type) == Union: + element_types = get_args(field_type) + for element_type in element_types: + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + documentation += generate_field_text(name, field_type, model, + documentation_with_field_description=documentation_with_field_description) + documentation += "\n" + + if hasattr(model, 'Config') and hasattr(model.Config, + 'json_schema_extra') and 'example' in model.Config.json_schema_extra: + documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n" + json_example = json.dumps(model.Config.json_schema_extra['example']) + documentation += format_multiline_description(json_example, 2) + "\n" + + return documentation + + +def generate_field_text(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + documentation_with_field_description=True) -> str: + """ + Generate text documentation for a Pydantic model field. + + Args: + field_name (str): Name of the field. + field_type (Type[Any]): Type of the field. + model (Type[BaseModel]): Pydantic model class. + depth (int): Indentation depth in the documentation. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation for the field. + """ + indent = ' ' * depth + + field_info = model.model_fields.get(field_name) + field_description = field_info.description if field_info and field_info.description else "" + + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + elif get_origin(field_type) == Union: + element_types = get_args(field_type) + types = [] + for element_type in element_types: + types.append(format_model_and_field_name(element_type.__name__)) + field_text = f"{indent}{field_name} ({' or '.join(types)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + else: + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + + if not documentation_with_field_description: + return field_text + + if field_description != "": + field_text += f"{indent} Description: " + field_description + "\n" + + # Check for and include field-specific examples if available + if hasattr(model, 'Config') and hasattr(model.Config, + 'json_schema_extra') and 'example' in model.Config.json_schema_extra: + field_example = model.Config.json_schema_extra['example'].get(field_name) + if field_example is not None: + example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example + field_text += f"{indent} Example: {example_text}\n" + + if isclass(field_type) and issubclass(field_type, BaseModel): + field_text += f"{indent} Details:\n" + for name, type_ in field_type.__annotations__.items(): + field_text += generate_field_text(name, type_, field_type, depth + 2) + + return field_text + + +def format_multiline_description(description: str, indent_level: int) -> str: + """ + Format a multiline description with proper indentation. + + Args: + description (str): Multiline description. + indent_level (int): Indentation level. + + Returns: + str: Formatted multiline description. + """ + indent = ' ' * indent_level + return indent + description.replace('\n', '\n' + indent) + + +def save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path="./grammar.gbnf", + documentation_file_path="./grammar_documentation.md"): + """ + Save GBNF grammar and documentation to specified files. + + Args: + grammar (str): GBNF grammar string. + documentation (str): Documentation string. + grammar_file_path (str): File path to save the GBNF grammar. + documentation_file_path (str): File path to save the documentation. + + Returns: + None + """ + try: + with open(grammar_file_path, 'w') as file: + file.write(grammar + get_primitive_grammar(grammar)) + print(f"Grammar successfully saved to {grammar_file_path}") + except IOError as e: + print(f"An error occurred while saving the grammar file: {e}") + + try: + with open(documentation_file_path, 'w') as file: + file.write(documentation) + print(f"Documentation successfully saved to {documentation_file_path}") + except IOError as e: + print(f"An error occurred while saving the documentation file: {e}") + + +def remove_empty_lines(string): + """ + Remove empty lines from a string. + + Args: + string (str): Input string. + + Returns: + str: String with empty lines removed. + """ + lines = string.splitlines() + non_empty_lines = [line for line in lines if line.strip() != ""] + string_no_empty_lines = "\n".join(non_empty_lines) + return string_no_empty_lines + + +def generate_and_save_gbnf_grammar_and_documentation(pydantic_model_list, + grammar_file_path="./generated_grammar.gbnf", + documentation_file_path="./generated_grammar_documentation.md", + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True): + """ + Generate GBNF grammar and documentation, and save them to specified files. + + Args: + pydantic_model_list: List of Pydantic model classes. + grammar_file_path (str): File path to save the generated GBNF grammar. + documentation_file_path (str): File path to save the generated documentation. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + None + """ + documentation = generate_text_documentation(pydantic_model_list, model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, + outer_object_content, list_of_outputs) + grammar = remove_empty_lines(grammar) + save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path) + + +def generate_gbnf_grammar_and_documentation(pydantic_model_list, outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", list_of_outputs: bool = False, + documentation_with_field_description=True): + """ + Generate GBNF grammar and documentation for a list of Pydantic models. + + Args: + pydantic_model_list: List of Pydantic model classes. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + tuple: GBNF grammar string, documentation string. + """ + documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, + outer_object_content, list_of_outputs) + grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) + return grammar, documentation + + +def generate_gbnf_grammar_and_documentation_from_dictionaries(dictionaries: List[dict], + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True): + """ + Generate GBNF grammar and documentation from a list of dictionaries. + + Args: + dictionaries (List[dict]): List of dictionaries representing Pydantic models. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + tuple: GBNF grammar string, documentation string. + """ + pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries) + documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, + outer_object_content, list_of_outputs) + grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) + return grammar, documentation + + +def create_dynamic_model_from_function(func: Callable): + """ + Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method. + + Args: + func (Callable): A function with type hints from which to create the model. + + Returns: + A dynamic Pydantic model class with the provided function as a 'run' method. + """ + # Extracting type hints from the provided function + type_hints = get_type_hints(func) + type_hints.pop('return', None) + + # Handling default values and annotations + dynamic_fields = {} + defaults = getattr(func, '__defaults__', ()) or () + defaults_index = len(type_hints) - len(defaults) + + for index, (name, typ) in enumerate(type_hints.items()): + if index >= defaults_index: + default_value = defaults[index - defaults_index] + dynamic_fields[name] = (typ, default_value) + else: + dynamic_fields[name] = (typ, ...) + + # Creating the dynamic model + dynamicModel = create_model(f'{func.__name__}', **dynamic_fields) + + dynamicModel.__doc__ = getdoc(func) + + # Wrapping the original function to handle instance 'self' + def run_method_wrapper(self): + func_args = {name: getattr(self, name) for name in type_hints} + return func(**func_args) + + # Adding the wrapped function as a 'run' method + setattr(dynamicModel, 'run', run_method_wrapper) + + return dynamicModel + + +def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): + """ + Add a 'run' method to a dynamic Pydantic model, using the provided function. + + Args: + - model (Type[BaseModel]): Dynamic Pydantic model class. + - func (Callable): Function to be added as a 'run' method to the model. + + Returns: + - Type[BaseModel]: Pydantic model class with the added 'run' method. + """ + + def run_method_wrapper(self): + func_args = {name: getattr(self, name) for name in model.model_fields} + return func(**func_args) + + # Adding the wrapped function as a 'run' method + setattr(model, 'run', run_method_wrapper) + + return model + + +def create_dynamic_models_from_dictionaries(dictionaries: List[dict]): + """ + Create a list of dynamic Pydantic model classes from a list of dictionaries. + + Args: + - dictionaries (List[dict]): List of dictionaries representing model structures. + + Returns: + - List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. + """ + dynamic_models = [] + for func in dictionaries: + model_name = format_model_and_field_name(func.get("name", "")) + dyn_model = convert_dictionary_to_to_pydantic_model(func, model_name) + dynamic_models.append(dyn_model) + return dynamic_models + + +def map_grammar_names_to_pydantic_model_class(pydantic_model_list): + output = {} + for model in pydantic_model_list: + output[format_model_and_field_name(model.__name__)] = model + + return output + + +from enum import Enum + + +def json_schema_to_python_types(schema): + type_map = { + 'any': Any, + 'string': str, + 'number': float, + 'integer': int, + 'boolean': bool, + 'array': list, + } + return type_map[schema] + + +def list_to_enum(enum_name, values): + return Enum(enum_name, {value: value for value in values}) + + +def convert_dictionary_to_to_pydantic_model(dictionary: dict, model_name: str = 'CustomModel') -> Type[BaseModel]: + """ + Convert a dictionary to a Pydantic model class. + + Args: + - dictionary (dict): Dictionary representing the model structure. + - model_name (str): Name of the generated Pydantic model. + + Returns: + - Type[BaseModel]: Generated Pydantic model class. + """ + fields = {} + + if "properties" in dictionary: + for field_name, field_data in dictionary.get("properties", {}).items(): + if field_data == 'object': + submodel = convert_dictionary_to_to_pydantic_model(dictionary, f'{model_name}_{field_name}') + fields[field_name] = (submodel, ...) + else: + field_type = field_data.get('type', 'str') + + if field_data.get("enum", []): + fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...) + if field_type == "array": + items = field_data.get("items", {}) + if items != {}: + array = {"properties": items} + array_type = convert_dictionary_to_to_pydantic_model(array, f'{model_name}_{field_name}_items') + fields[field_name] = (List[array_type], ...) + else: + fields[field_name] = (list, ...) + elif field_type == 'object': + submodel = convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}_{field_name}') + fields[field_name] = (submodel, ...) + else: + field_type = json_schema_to_python_types(field_type) + fields[field_name] = (field_type, ...) + if "function" in dictionary: + + for field_name, field_data in dictionary.get("function", {}).items(): + if field_name == "name": + model_name = field_data + elif field_name == "description": + fields["__doc__"] = field_data + elif field_name == "parameters": + return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') + if "parameters" in dictionary: + field_data = {"function": dictionary} + return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') + + custom_model = create_model(model_name, **fields) + return custom_model + + + From fa5c1fb44a2724292da545d6b7cf2a1ac0e0b989 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 12 Jan 2024 20:38:34 +0100 Subject: [PATCH 013/334] backend_sched : fix assignments ggml-ci --- ggml-backend.c | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/ggml-backend.c b/ggml-backend.c index 4c2d8b0b2..505dbba47 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1087,6 +1087,24 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g } } } + + // pass 2.4 expand rest down + { + ggml_tallocr_t cur_allocr = NULL; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr != NULL) { + cur_allocr = node_allocr; + } else { + node_allocr(node) = cur_allocr; + SET_CAUSE(node, "2.4"); + } + } + } #ifdef DEBUG_PASS2 fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); #endif @@ -1146,6 +1164,8 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g ggml_tallocr_t node_allocr = node_allocr(node); + GGML_ASSERT(node_allocr != NULL); // all nodes should be assigned by now + if (node_allocr != cur_allocr) { sched->splits[cur_split].i_end = i; cur_split++; From f238461236f4e0e18cac1a554af23c7deadc9b01 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 14:02:30 +0200 Subject: [PATCH 014/334] ggml : fix 32-bit ARM compat for IQ2_XS (whisper/1758) * ggml : fix 32-bit ARM compat * ggml : fix fix * ggml : fix fix fix --- ggml-quants.c | 39 +++++++++++++++++++++++++++++++++++---- 1 file changed, 35 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index a24b4b244..601d155d7 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -272,10 +272,13 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 // vaddvq_s16 // vpaddq_s16 +// vpaddq_s32 // vaddvq_s32 // vaddvq_f32 // vmaxvq_f32 // vcvtnq_s32_f32 +// vzip1_u8 +// vzip2_u8 inline static int32_t vaddvq_s16(int16x8_t v) { return @@ -291,6 +294,12 @@ inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { return vcombine_s16(a0, b0); } +inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) { + int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a)); + int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b)); + return vcombine_s32(a0, b0); +} + inline static int32_t vaddvq_s32(int32x4_t v) { return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); } @@ -316,6 +325,28 @@ inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { return res; } +inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) { + uint8x8_t res; + + res[0] = a[0]; res[1] = b[0]; + res[2] = a[1]; res[3] = b[1]; + res[4] = a[2]; res[5] = b[2]; + res[6] = a[3]; res[7] = b[3]; + + return res; +} + +inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { + uint8x8_t res; + + res[0] = a[4]; res[1] = b[4]; + res[2] = a[5]; res[3] = b[5]; + res[4] = a[6]; res[5] = b[6]; + res[6] = a[7]; res[7] = b[7]; + + return res; +} + // vld1q_s16_x2 // vld1q_u8_x2 // vld1q_u8_x4 @@ -7554,9 +7585,9 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - int8x16x4_t q2u; - int8x16x4_t q2s; - int8x16x4_t q8b; + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; int32x4x4_t scales32; @@ -7578,7 +7609,7 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); int32x4_t sumi = vdupq_n_s32(0); for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { - q8b = vld1q_s8_x4(q8); q8 += 64; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); From de473f5f8e19ba5e659cdf5af65fb9251dce16c5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 12 Jan 2024 22:02:43 +0200 Subject: [PATCH 015/334] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 3e2c579d5..edcdb530a 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -979cc23b345006504cfc1f67c0fdf627805e3319 +400c07f00508e6f60fb25405444b5669c365b0a9 From 15ebe59210e7fd9817ff67f51fa1a5ee2d004294 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 13:44:37 +0200 Subject: [PATCH 016/334] convert : update phi-2 to latest HF repo (#4903) * convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff --- convert-hf-to-gguf.py | 39 +++++++++++++++++++++---------- gguf-py/gguf/constants.py | 3 +++ gguf-py/gguf/tensor_mapping.py | 2 ++ llama.cpp | 42 ++++++++++++++++++++++++++-------- 4 files changed, 65 insertions(+), 21 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a1c79fd47..b133f3b49 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -23,6 +23,15 @@ if 'NO_LOCAL_GGUF' not in os.environ: import gguf +# check for any of the given keys in the dictionary and return the value of the first key found +def get_key_opts(d, keys): + for k in keys: + if k in d: + return d[k] + print(f"Could not find any of {keys}") + sys.exit() + + ###### MODEL DEFINITIONS ###### class SentencePieceTokenTypes(IntEnum): @@ -257,10 +266,11 @@ class Model: toktypes.append(gguf.TokenType.USER_DEFINED) elif reverse_vocab[i] in added_vocab: tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) + if hasattr(tokenizer, "added_tokens_decoder"): + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) else: tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.NORMAL) @@ -1068,17 +1078,22 @@ class GPT2Model(Model): class Phi2Model(Model): def set_gguf_parameters(self): - block_count = self.hparams["n_layer"] + block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"]) + + rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"]) + n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"]) + n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"]) self.gguf_writer.add_name("Phi2") - self.gguf_writer.add_context_length(self.hparams["n_positions"]) - self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"])) + + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(4 * n_embd) self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(self.hparams["n_head"]) - self.gguf_writer.add_head_count_kv(self.hparams["n_head"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"])) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_add_bos_token(False) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index f0a1c51f8..972b4e9a7 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -389,6 +389,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.OUTPUT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 24a089037..e5b146106 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -191,6 +191,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.w1", # qwen "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 + "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo ), @@ -232,6 +233,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 + "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo ), diff --git a/llama.cpp b/llama.cpp index fe1d8947c..1d2eb569f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -574,6 +574,9 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, @@ -3676,8 +3679,19 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + + if (layer.wqkv == nullptr) { + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + } layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); @@ -5637,15 +5651,25 @@ struct llm_build_context { // self-attention { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); - cb(cur, "wqkv", il); + struct ggml_tensor * Qcur = nullptr; + struct ggml_tensor * Kcur = nullptr; + struct ggml_tensor * Vcur = nullptr; - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); + if (model.layers[il].wqkv) { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); + cb(cur, "wqkv", il); - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + } else { + Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq); + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk); + Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv); + } cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); From ee8243adaa9a9f51ff449213383874e49efe368f Mon Sep 17 00:00:00 2001 From: makomk Date: Sat, 13 Jan 2024 14:16:11 +0000 Subject: [PATCH 017/334] server : fix crash with multimodal models without BOS token (#4904) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c1ab8f9dc..7b33aea1f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1835,7 +1835,7 @@ struct llama_server_context slot.cache_tokens = prompt_tokens; - if (slot.n_past == slot.num_prompt_tokens) + if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); From 356327feb3f66980ab687040495d722696d98970 Mon Sep 17 00:00:00 2001 From: Ziad Ben Hadj-Alouane Date: Sat, 13 Jan 2024 09:20:46 -0500 Subject: [PATCH 018/334] server : fix deadlock that occurs in multi-prompt scenarios (#4905) * * fix deadlock * * dont ruint all whitespace --- examples/server/server.cpp | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7b33aea1f..79eacf828 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1350,14 +1350,17 @@ struct llama_server_context res.result_json["model"] = slot.oaicompat_model; } + queue_results.push_back(res); + condition_results.notify_all(); + + // done with results, unlock + lock.unlock(); + // parent multitask, if any, needs to be updated if (slot.multitask_id != -1) { update_multi_task(slot.multitask_id, slot.task_id, res); } - - queue_results.push_back(res); - condition_results.notify_all(); } void send_embedding(llama_client_slot &slot) @@ -1603,6 +1606,7 @@ struct llama_server_context } // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue + std::vector agg_results; auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { @@ -1623,8 +1627,9 @@ struct llama_server_context } aggregate_result.result_json = json{ "results", result_jsons }; - std::lock_guard lock(mutex_results); - queue_results.push_back(aggregate_result); + + agg_results.push_back(aggregate_result); + condition_results.notify_all(); queue_iterator = queue_multitasks.erase(queue_iterator); @@ -1634,6 +1639,13 @@ struct llama_server_context ++queue_iterator; } } + + // done with tasks, unlock + lock.unlock(); + + // copy aggregate results of complete multi-tasks to the results queue + std::lock_guard lock_results(mutex_results); + queue_results.insert(queue_results.end(), agg_results.begin(), agg_results.end()); } bool update_slots() { From 7dc78764e2ff86512e6e31cb0fcb8087df4b4708 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 13 Jan 2024 15:52:53 +0100 Subject: [PATCH 019/334] compare-llama-bench: tweak output format (#4910) --- scripts/compare-llama-bench.py | 34 ++++++++++++++++++++++++++-------- 1 file changed, 26 insertions(+), 8 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index bc1714487..70737f976 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -10,15 +10,15 @@ import sqlite3 try: import git from tabulate import tabulate -except ImportError: +except ImportError as e: print("ERROR: the following Python libraries are required: GitPython, tabulate.") - sys.exit(1) + raise e # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ - "cuda", "opencl", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", - "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", - "n_gpu_layers", "main_gpu", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas", + "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", + "type_k", "type_v", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: @@ -37,6 +37,7 @@ PRETTY_NAMES = { DEFAULT_SHOW = ["model_type"] # Always show these properties by default. DEFAULT_HIDE = ["model_filename"] # Always hide these properties by default. GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "] # Strip prefixes for smaller tables. +MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"} DESCRIPTION = """Creates tables from llama-bench data written to an SQLite database. Example usage (Linux): @@ -308,8 +309,13 @@ else: if gpu_blas and "gpu_info" not in properties_different: show.append("gpu_info") - show += DEFAULT_SHOW show += properties_different + + index_default = 0 + for prop in ["cpu_info", "gpu_info", "n_gpu_layers", "main_gpu"]: + if prop in show: + index_default += 1 + show = show[:index_default] + DEFAULT_SHOW + show[index_default:] for prop in DEFAULT_HIDE: try: show.remove(prop) @@ -334,6 +340,12 @@ for bool_property in BOOL_PROPERTIES: for row_table in table: row_table[ip] = "Yes" if int(row_table[ip]) == 1 else "No" +if "model_type" in show: + ip = show.index("model_type") + for (old, new) in MODEL_SUFFIX_REPLACE.items(): + for row_table in table: + row_table[ip] = row_table[ip].replace(old, new) + if "model_size" in show: ip = show.index("model_size") for row_table in table: @@ -341,10 +353,16 @@ if "model_size" in show: if "gpu_info" in show: ip = show.index("gpu_info") - for gns in GPU_NAME_STRIP: - for row_table in table: + for row_table in table: + for gns in GPU_NAME_STRIP: row_table[ip] = row_table[ip].replace(gns, "") + gpu_names = row_table[ip].split("/") + num_gpus = len(gpu_names) + all_names_the_same = len(set(gpu_names)) == 1 + if len(gpu_names) >= 2 and all_names_the_same: + row_table[ip] = f"{num_gpus}x {gpu_names[0]}" + headers = [PRETTY_NAMES[p] for p in show] headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"] From b38b5e93ae31019e87f692b69d27124eae6aac02 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 18:03:45 +0200 Subject: [PATCH 020/334] metal : refactor kernel loading code (#4794) * metal : detect more GPU families * metal : refactor kernel loading * metal : set kernel family requirements * metal : fix kernel init + fix compile options * metal : take into account simdgroup reduction support * metal : print only skipped kernels * metal : fix check for simdgroup reduction support * metal : check for Metal 3 * metal : free allocations * metal : normalize encoder:setComputePipelineStatus calls ggml-ci * metal : fix Metal3 family check ggml-ci * metal : check for simdgroup matrix mul. feature ggml-ci --- ggml-metal.m | 1048 +++++++++++++++++++++++++------------------------- 1 file changed, 530 insertions(+), 518 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index c03624073..6c28a7ee3 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -26,6 +26,8 @@ #define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) +#define GGML_METAL_MAX_KERNELS 256 + struct ggml_metal_buffer { const char * name; @@ -35,6 +37,134 @@ struct ggml_metal_buffer { id metal; }; +struct ggml_metal_kernel { + id function; + id pipeline; +}; + +enum ggml_metal_kernel_type { + GGML_METAL_KERNEL_TYPE_ADD, + GGML_METAL_KERNEL_TYPE_ADD_ROW, + GGML_METAL_KERNEL_TYPE_MUL, + GGML_METAL_KERNEL_TYPE_MUL_ROW, + GGML_METAL_KERNEL_TYPE_DIV, + GGML_METAL_KERNEL_TYPE_DIV_ROW, + GGML_METAL_KERNEL_TYPE_SCALE, + GGML_METAL_KERNEL_TYPE_SCALE_4, + GGML_METAL_KERNEL_TYPE_TANH, + GGML_METAL_KERNEL_TYPE_RELU, + GGML_METAL_KERNEL_TYPE_GELU, + GGML_METAL_KERNEL_TYPE_GELU_QUICK, + GGML_METAL_KERNEL_TYPE_SILU, + GGML_METAL_KERNEL_TYPE_SOFT_MAX, + GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, + GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, + GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, + GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, + GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, + GGML_METAL_KERNEL_TYPE_RMS_NORM, + GGML_METAL_KERNEL_TYPE_GROUP_NORM, + GGML_METAL_KERNEL_TYPE_NORM, + GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_ROPE_F32, + GGML_METAL_KERNEL_TYPE_ROPE_F16, + GGML_METAL_KERNEL_TYPE_ALIBI_F32, + GGML_METAL_KERNEL_TYPE_IM2COL_F16, + GGML_METAL_KERNEL_TYPE_UPSCALE_F32, + GGML_METAL_KERNEL_TYPE_PAD_F32, + GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, + GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, + GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, + GGML_METAL_KERNEL_TYPE_CPY_F32_F16, + GGML_METAL_KERNEL_TYPE_CPY_F32_F32, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, + GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, + //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, + //GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, + GGML_METAL_KERNEL_TYPE_CPY_F16_F16, + GGML_METAL_KERNEL_TYPE_CPY_F16_F32, + GGML_METAL_KERNEL_TYPE_CONCAT, + GGML_METAL_KERNEL_TYPE_SQR, + GGML_METAL_KERNEL_TYPE_SUM_ROWS, + + GGML_METAL_KERNEL_TYPE_COUNT +}; + struct ggml_metal_context { int n_cb; @@ -50,134 +180,13 @@ struct ggml_metal_context { int n_buffers; struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; + struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; + int concur_list[GGML_MAX_CONCUR]; int concur_list_len; - // custom kernels -#define GGML_METAL_DECL_KERNEL(name) \ - id function_##name; \ - id pipeline_##name - - GGML_METAL_DECL_KERNEL(add); - GGML_METAL_DECL_KERNEL(add_row); // TODO: avoid this extra kernel, instead extend the "add" kernel to support broadcast - GGML_METAL_DECL_KERNEL(mul); - GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast - GGML_METAL_DECL_KERNEL(div); - GGML_METAL_DECL_KERNEL(div_row); - GGML_METAL_DECL_KERNEL(scale); - GGML_METAL_DECL_KERNEL(scale_4); - GGML_METAL_DECL_KERNEL(tanh); - GGML_METAL_DECL_KERNEL(relu); - GGML_METAL_DECL_KERNEL(gelu); - GGML_METAL_DECL_KERNEL(gelu_quick); - GGML_METAL_DECL_KERNEL(silu); - GGML_METAL_DECL_KERNEL(soft_max); - GGML_METAL_DECL_KERNEL(soft_max_4); - GGML_METAL_DECL_KERNEL(diag_mask_inf); - GGML_METAL_DECL_KERNEL(diag_mask_inf_8); - GGML_METAL_DECL_KERNEL(get_rows_f32); - GGML_METAL_DECL_KERNEL(get_rows_f16); - GGML_METAL_DECL_KERNEL(get_rows_q4_0); - GGML_METAL_DECL_KERNEL(get_rows_q4_1); - GGML_METAL_DECL_KERNEL(get_rows_q5_0); - GGML_METAL_DECL_KERNEL(get_rows_q5_1); - GGML_METAL_DECL_KERNEL(get_rows_q8_0); - GGML_METAL_DECL_KERNEL(get_rows_q2_K); - GGML_METAL_DECL_KERNEL(get_rows_q3_K); - GGML_METAL_DECL_KERNEL(get_rows_q4_K); - GGML_METAL_DECL_KERNEL(get_rows_q5_K); - GGML_METAL_DECL_KERNEL(get_rows_q6_K); - GGML_METAL_DECL_KERNEL(get_rows_i32); - GGML_METAL_DECL_KERNEL(get_rows_iq2_xxs); - GGML_METAL_DECL_KERNEL(get_rows_iq2_xs); - GGML_METAL_DECL_KERNEL(rms_norm); - GGML_METAL_DECL_KERNEL(group_norm); - GGML_METAL_DECL_KERNEL(norm); - GGML_METAL_DECL_KERNEL(mul_mv_f32_f32); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f16); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f32); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row); - GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4); - GGML_METAL_DECL_KERNEL(mul_mv_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mv_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_f32_f32); - //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f16); - GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32); - //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_1row); - //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_l4); - GGML_METAL_DECL_KERNEL(mul_mv_id_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_f32_f32); - GGML_METAL_DECL_KERNEL(mul_mm_f16_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_f32_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_f16_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q4_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q5_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q5_1_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q8_0_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q2_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q3_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q4_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q5_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_q6_K_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xxs_f32); - GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xs_f32); - GGML_METAL_DECL_KERNEL(rope_f32); - GGML_METAL_DECL_KERNEL(rope_f16); - GGML_METAL_DECL_KERNEL(alibi_f32); - GGML_METAL_DECL_KERNEL(im2col_f16); - GGML_METAL_DECL_KERNEL(upscale_f32); - GGML_METAL_DECL_KERNEL(pad_f32); - GGML_METAL_DECL_KERNEL(argsort_f32_i32_asc); - GGML_METAL_DECL_KERNEL(argsort_f32_i32_desc); - GGML_METAL_DECL_KERNEL(leaky_relu_f32); - GGML_METAL_DECL_KERNEL(cpy_f32_f16); - GGML_METAL_DECL_KERNEL(cpy_f32_f32); - GGML_METAL_DECL_KERNEL(cpy_f32_q8_0); - GGML_METAL_DECL_KERNEL(cpy_f32_q4_0); - GGML_METAL_DECL_KERNEL(cpy_f32_q4_1); - //GGML_METAL_DECL_KERNEL(cpy_f32_q5_0); - //GGML_METAL_DECL_KERNEL(cpy_f32_q5_1); - GGML_METAL_DECL_KERNEL(cpy_f16_f16); - GGML_METAL_DECL_KERNEL(cpy_f16_f32); - GGML_METAL_DECL_KERNEL(concat); - GGML_METAL_DECL_KERNEL(sqr); - GGML_METAL_DECL_KERNEL(sum_rows); - -#undef GGML_METAL_DECL_KERNEL + bool support_simdgroup_reduction; + bool support_simdgroup_mm; }; // MSL code @@ -298,19 +307,22 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { return NULL; } - MTLCompileOptions* options = nil; + // dictionary of preprocessor macros + NSMutableDictionary * prep = [NSMutableDictionary dictionary]; + #ifdef GGML_QKK_64 - options = [MTLCompileOptions new]; - options.preprocessorMacros = @{ @"QK_K" : @(64) }; + prep[@"QK_K"] = @(64); #endif - // try to disable fast-math - // NOTE: this seems to have no effect whatsoever - // instead, in order to disable fast-math, we have to build default.metallib from the command line - // using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air - // and go through the "pre-compiled library found" path above + + MTLCompileOptions* options = [MTLCompileOptions new]; + options.preprocessorMacros = prep; + //[options setFastMathEnabled:false]; ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; + + [options release]; + [prep release]; } if (error) { @@ -323,16 +335,41 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { // print MTL GPU family: GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]); + const NSInteger MTLGPUFamilyMetal3 = 5001; + // determine max supported GPU family // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf - for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { - if ([ctx->device supportsFamily:i]) { - GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); - break; + { + for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { + if ([ctx->device supportsFamily:i]) { + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); + break; + } + } + + for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) { + if ([ctx->device supportsFamily:i]) { + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i); + break; + } + } + + for (int i = MTLGPUFamilyMetal3 + 5; i >= MTLGPUFamilyMetal3; --i) { + if ([ctx->device supportsFamily:i]) { + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3 + 3, i); + break; + } } } + ctx->support_simdgroup_reduction = [ctx->device supportsFamily:MTLGPUFamilyApple7]; + ctx->support_simdgroup_reduction |= [ctx->device supportsFamily:MTLGPUFamilyMetal3]; + + ctx->support_simdgroup_mm = [ctx->device supportsFamily:MTLGPUFamilyApple7]; + + GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); + GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); if (ctx->device.maxTransferRate != 0) { @@ -346,141 +383,152 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { { NSError * error = nil; + for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { + ctx->kernels[i].function = nil; + ctx->kernels[i].pipeline = nil; + } + /* - GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \ - (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \ - (int) ctx->pipeline_##name.threadExecutionWidth); \ + GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ + (int) kernel->pipeline.threadExecutionWidth); \ */ -#define GGML_METAL_ADD_KERNEL(name) \ - ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ - ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \ - if (error) { \ - GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ - return NULL; \ +#define GGML_METAL_ADD_KERNEL(e, name, supported) \ + if (supported) { \ + struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ + kernel->function = [ctx->library newFunctionWithName:@"kernel_"#name]; \ + kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:kernel->function error:&error]; \ + GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ + (int) kernel->pipeline.threadExecutionWidth); \ + if (error) { \ + GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ + return NULL; \ + } \ + } else { \ + GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \ } - GGML_METAL_ADD_KERNEL(add); - GGML_METAL_ADD_KERNEL(add_row); - GGML_METAL_ADD_KERNEL(mul); - GGML_METAL_ADD_KERNEL(mul_row); - GGML_METAL_ADD_KERNEL(div); - GGML_METAL_ADD_KERNEL(div_row); - GGML_METAL_ADD_KERNEL(scale); - GGML_METAL_ADD_KERNEL(scale_4); - GGML_METAL_ADD_KERNEL(tanh); - GGML_METAL_ADD_KERNEL(relu); - GGML_METAL_ADD_KERNEL(gelu); - GGML_METAL_ADD_KERNEL(gelu_quick); - GGML_METAL_ADD_KERNEL(silu); - GGML_METAL_ADD_KERNEL(soft_max); - GGML_METAL_ADD_KERNEL(soft_max_4); - GGML_METAL_ADD_KERNEL(diag_mask_inf); - GGML_METAL_ADD_KERNEL(diag_mask_inf_8); - GGML_METAL_ADD_KERNEL(get_rows_f32); - GGML_METAL_ADD_KERNEL(get_rows_f16); - GGML_METAL_ADD_KERNEL(get_rows_q4_0); - GGML_METAL_ADD_KERNEL(get_rows_q4_1); - GGML_METAL_ADD_KERNEL(get_rows_q5_0); - GGML_METAL_ADD_KERNEL(get_rows_q5_1); - GGML_METAL_ADD_KERNEL(get_rows_q8_0); - GGML_METAL_ADD_KERNEL(get_rows_q2_K); - GGML_METAL_ADD_KERNEL(get_rows_q3_K); - GGML_METAL_ADD_KERNEL(get_rows_q4_K); - GGML_METAL_ADD_KERNEL(get_rows_q5_K); - GGML_METAL_ADD_KERNEL(get_rows_q6_K); - GGML_METAL_ADD_KERNEL(get_rows_i32); - GGML_METAL_ADD_KERNEL(get_rows_iq2_xxs); - GGML_METAL_ADD_KERNEL(get_rows_iq2_xs); - GGML_METAL_ADD_KERNEL(rms_norm); - GGML_METAL_ADD_KERNEL(group_norm); - GGML_METAL_ADD_KERNEL(norm); - GGML_METAL_ADD_KERNEL(mul_mv_f32_f32); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f16); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f32); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row); - GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4); - GGML_METAL_ADD_KERNEL(mul_mv_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mv_iq2_xs_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_f32_f32); - //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f16); - GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32); - //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_1row); - //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_l4); - GGML_METAL_ADD_KERNEL(mul_mv_id_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xs_f32); - if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { - GGML_METAL_ADD_KERNEL(mul_mm_f32_f32); - GGML_METAL_ADD_KERNEL(mul_mm_f16_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mm_iq2_xs_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_f32_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_f16_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q4_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q5_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q5_1_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q8_0_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q2_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q3_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q4_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q5_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_q6_K_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xxs_f32); - GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xs_f32); - } - GGML_METAL_ADD_KERNEL(rope_f32); - GGML_METAL_ADD_KERNEL(rope_f16); - GGML_METAL_ADD_KERNEL(alibi_f32); - GGML_METAL_ADD_KERNEL(im2col_f16); - GGML_METAL_ADD_KERNEL(upscale_f32); - GGML_METAL_ADD_KERNEL(pad_f32); - GGML_METAL_ADD_KERNEL(argsort_f32_i32_asc); - GGML_METAL_ADD_KERNEL(argsort_f32_i32_desc); - GGML_METAL_ADD_KERNEL(leaky_relu_f32); - GGML_METAL_ADD_KERNEL(cpy_f32_f16); - GGML_METAL_ADD_KERNEL(cpy_f32_f32); - GGML_METAL_ADD_KERNEL(cpy_f32_q8_0); - GGML_METAL_ADD_KERNEL(cpy_f32_q4_0); - GGML_METAL_ADD_KERNEL(cpy_f32_q4_1); - //GGML_METAL_ADD_KERNEL(cpy_f32_q5_0); - //GGML_METAL_ADD_KERNEL(cpy_f32_q5_1); - GGML_METAL_ADD_KERNEL(cpy_f16_f16); - GGML_METAL_ADD_KERNEL(cpy_f16_f32); - GGML_METAL_ADD_KERNEL(concat); - GGML_METAL_ADD_KERNEL(sqr); - GGML_METAL_ADD_KERNEL(sum_rows); + // simd_sum and simd_max requires MTLGPUFamilyApple7 -#undef GGML_METAL_ADD_KERNEL + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); } return ctx; @@ -488,137 +536,21 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); -#define GGML_METAL_DEL_KERNEL(name) \ - [ctx->function_##name release]; \ - [ctx->pipeline_##name release]; - - GGML_METAL_DEL_KERNEL(add); - GGML_METAL_DEL_KERNEL(add_row); - GGML_METAL_DEL_KERNEL(mul); - GGML_METAL_DEL_KERNEL(mul_row); - GGML_METAL_DEL_KERNEL(div); - GGML_METAL_DEL_KERNEL(div_row); - GGML_METAL_DEL_KERNEL(scale); - GGML_METAL_DEL_KERNEL(scale_4); - GGML_METAL_DEL_KERNEL(tanh); - GGML_METAL_DEL_KERNEL(relu); - GGML_METAL_DEL_KERNEL(gelu); - GGML_METAL_DEL_KERNEL(gelu_quick); - GGML_METAL_DEL_KERNEL(silu); - GGML_METAL_DEL_KERNEL(soft_max); - GGML_METAL_DEL_KERNEL(soft_max_4); - GGML_METAL_DEL_KERNEL(diag_mask_inf); - GGML_METAL_DEL_KERNEL(diag_mask_inf_8); - GGML_METAL_DEL_KERNEL(get_rows_f32); - GGML_METAL_DEL_KERNEL(get_rows_f16); - GGML_METAL_DEL_KERNEL(get_rows_q4_0); - GGML_METAL_DEL_KERNEL(get_rows_q4_1); - GGML_METAL_DEL_KERNEL(get_rows_q5_0); - GGML_METAL_DEL_KERNEL(get_rows_q5_1); - GGML_METAL_DEL_KERNEL(get_rows_q8_0); - GGML_METAL_DEL_KERNEL(get_rows_q2_K); - GGML_METAL_DEL_KERNEL(get_rows_q3_K); - GGML_METAL_DEL_KERNEL(get_rows_q4_K); - GGML_METAL_DEL_KERNEL(get_rows_q5_K); - GGML_METAL_DEL_KERNEL(get_rows_q6_K); - GGML_METAL_DEL_KERNEL(get_rows_i32); - GGML_METAL_DEL_KERNEL(get_rows_iq2_xxs); - GGML_METAL_DEL_KERNEL(get_rows_iq2_xs); - GGML_METAL_DEL_KERNEL(rms_norm); - GGML_METAL_DEL_KERNEL(group_norm); - GGML_METAL_DEL_KERNEL(norm); - GGML_METAL_DEL_KERNEL(mul_mv_f32_f32); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f16); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f32); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row); - GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4); - GGML_METAL_DEL_KERNEL(mul_mv_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mv_iq2_xs_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_f32_f32); - //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f16); - GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32); - //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_1row); - //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_l4); - GGML_METAL_DEL_KERNEL(mul_mv_id_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xs_f32); - if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) { - GGML_METAL_DEL_KERNEL(mul_mm_f32_f32); - GGML_METAL_DEL_KERNEL(mul_mm_f16_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mm_iq2_xs_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_f32_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_f16_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q4_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q4_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q5_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q5_1_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q8_0_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q2_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q3_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q4_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q5_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_q6_K_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xxs_f32); - GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xs_f32); - } - GGML_METAL_DEL_KERNEL(rope_f32); - GGML_METAL_DEL_KERNEL(rope_f16); - GGML_METAL_DEL_KERNEL(alibi_f32); - GGML_METAL_DEL_KERNEL(im2col_f16); - GGML_METAL_DEL_KERNEL(upscale_f32); - GGML_METAL_DEL_KERNEL(pad_f32); - GGML_METAL_DEL_KERNEL(argsort_f32_i32_asc); - GGML_METAL_DEL_KERNEL(argsort_f32_i32_desc); - GGML_METAL_DEL_KERNEL(leaky_relu_f32); - GGML_METAL_DEL_KERNEL(cpy_f32_f16); - GGML_METAL_DEL_KERNEL(cpy_f32_f32); - GGML_METAL_DEL_KERNEL(cpy_f32_q8_0); - GGML_METAL_DEL_KERNEL(cpy_f32_q4_0); - GGML_METAL_DEL_KERNEL(cpy_f32_q4_1); - //GGML_METAL_DEL_KERNEL(cpy_f32_q5_0); - //GGML_METAL_DEL_KERNEL(cpy_f32_q5_1); - GGML_METAL_DEL_KERNEL(cpy_f16_f16); - GGML_METAL_DEL_KERNEL(cpy_f16_f32); - GGML_METAL_DEL_KERNEL(concat); - GGML_METAL_DEL_KERNEL(sqr); - GGML_METAL_DEL_KERNEL(sum_rows); - -#undef GGML_METAL_DEL_KERNEL for (int i = 0; i < ctx->n_buffers; ++i) { [ctx->buffers[i].metal release]; } + for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { + if (ctx->kernels[i].pipeline) { + [ctx->kernels[i].pipeline release]; + } + + if (ctx->kernels[i].function) { + [ctx->kernels[i].function release]; + } + } + [ctx->library release]; [ctx->queue release]; [ctx->device release]; @@ -930,7 +862,7 @@ void ggml_metal_graph_find_concurrency( } } -static bool ggml_metal_supports_op(const struct ggml_tensor * op) { +static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -956,9 +888,11 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SUM_ROWS: + return true; case GGML_OP_SOFT_MAX: case GGML_OP_RMS_NORM: case GGML_OP_GROUP_NORM: + return ctx->support_simdgroup_reduction; case GGML_OP_NORM: case GGML_OP_ALIBI: case GGML_OP_ROPE: @@ -967,9 +901,10 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { case GGML_OP_PAD: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: + return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - return true; + return ctx->support_simdgroup_reduction; case GGML_OP_CPY: case GGML_OP_DUP: case GGML_OP_CONT: @@ -1007,6 +942,7 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) { return false; } } + bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @@ -1077,7 +1013,7 @@ bool ggml_metal_graph_compute( } break; } - if (!ggml_metal_supports_op(dst)) { + if (!ggml_metal_supports_op(ctx, dst)) { GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); GGML_ASSERT(!"unsupported op"); } @@ -1143,7 +1079,9 @@ bool ggml_metal_graph_compute( { const int64_t nb = ne00; - [encoder setComputePipelineState:ctx->pipeline_concat]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1197,18 +1135,18 @@ bool ggml_metal_graph_compute( nb = ne00 / 4; switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->pipeline_add_row; break; - case GGML_OP_MUL: pipeline = ctx->pipeline_mul_row; break; - case GGML_OP_DIV: pipeline = ctx->pipeline_div_row; break; + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; default: GGML_ASSERT(false); } bcast_row = true; } else { switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->pipeline_add; break; - case GGML_OP_MUL: pipeline = ctx->pipeline_mul; break; - case GGML_OP_DIV: pipeline = ctx->pipeline_div; break; + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; default: GGML_ASSERT(false); } } @@ -1275,9 +1213,9 @@ bool ggml_metal_graph_compute( // not sure how to avoid this // TODO: make a simpler cpy_bytes kernel - const int nth = MIN((int) ctx->pipeline_cpy_f32_f32.maxTotalThreadsPerThreadgroup, ne00); + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; - [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -1297,10 +1235,14 @@ bool ggml_metal_graph_compute( [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } - [encoder setComputePipelineState:ctx->pipeline_add]; + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1330,7 +1272,7 @@ bool ggml_metal_graph_compute( [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - const int nth = MIN((int) ctx->pipeline_add.maxTotalThreadsPerThreadgroup, ne00); + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1342,13 +1284,16 @@ bool ggml_metal_graph_compute( int64_t n = ggml_nelements(dst); + id pipeline = nil; + if (n % 4 == 0) { n /= 4; - [encoder setComputePipelineState:ctx->pipeline_scale_4]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; } else { - [encoder setComputePipelineState:ctx->pipeline_scale]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; @@ -1359,7 +1304,9 @@ bool ggml_metal_graph_compute( switch (ggml_get_unary_op(gf->nodes[i])) { case GGML_UNARY_OP_TANH: { - [encoder setComputePipelineState:ctx->pipeline_tanh]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1369,7 +1316,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_RELU: { - [encoder setComputePipelineState:ctx->pipeline_relu]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1379,7 +1328,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_GELU: { - [encoder setComputePipelineState:ctx->pipeline_gelu]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1390,7 +1341,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_GELU_QUICK: { - [encoder setComputePipelineState:ctx->pipeline_gelu_quick]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1401,7 +1354,9 @@ bool ggml_metal_graph_compute( } break; case GGML_UNARY_OP_SILU: { - [encoder setComputePipelineState:ctx->pipeline_silu]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1420,18 +1375,23 @@ bool ggml_metal_graph_compute( { GGML_ASSERT(ggml_is_contiguous(src0)); - [encoder setComputePipelineState:ctx->pipeline_sqr]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; const int64_t n = ggml_nelements(dst); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_SUM_ROWS: { GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - [encoder setComputePipelineState:ctx->pipeline_sum_rows]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -1465,20 +1425,23 @@ bool ggml_metal_graph_compute( { int nth = 32; // SIMD width + id pipeline = nil; + if (ne00%4 == 0) { while (nth < ne00/4 && nth < 256) { nth *= 2; } - [encoder setComputePipelineState:ctx->pipeline_soft_max_4]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; } else { while (nth < ne00 && nth < 1024) { nth *= 2; } - [encoder setComputePipelineState:ctx->pipeline_soft_max]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } const float scale = ((float *) dst->op_params)[0]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; if (id_src1) { [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1498,11 +1461,15 @@ bool ggml_metal_graph_compute( { const int n_past = ((int32_t *)(dst->op_params))[0]; + id pipeline = nil; + if (ne00%8 == 0) { - [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf_8]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; } else { - [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; } + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -1562,23 +1529,28 @@ bool ggml_metal_graph_compute( ne00 % 32 == 0 && ne00 >= 64 && (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + switch (src0->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break; - case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_0_f32]; break; - case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_1_f32]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break; - case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xxs_f32]; break; - case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xs_f32]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1602,12 +1574,14 @@ bool ggml_metal_graph_compute( int nrows = 1; //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + id pipeline = nil; + // use custom matrix x vector kernel switch (src0t) { case GGML_TYPE_F32: { GGML_ASSERT(src1t == GGML_TYPE_F32); - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; nrows = 4; } break; case GGML_TYPE_F16: @@ -1616,16 +1590,16 @@ bool ggml_metal_graph_compute( nth1 = 1; if (src1t == GGML_TYPE_F32) { if (ne11 * ne12 < 4) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; nrows = ne11; } else { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; nrows = 4; } } else { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f16]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; nrows = 4; } } break; @@ -1633,73 +1607,73 @@ bool ggml_metal_graph_compute( { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; } break; case GGML_TYPE_Q4_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; } break; case GGML_TYPE_Q5_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; } break; case GGML_TYPE_Q5_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; } break; case GGML_TYPE_Q8_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q8_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; } break; case GGML_TYPE_Q2_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q2_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; } break; case GGML_TYPE_Q3_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q3_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; } break; case GGML_TYPE_Q4_K: { nth0 = 4; //1; nth1 = 8; //32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; } break; case GGML_TYPE_Q5_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; } break; case GGML_TYPE_Q6_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_q6_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; } break; case GGML_TYPE_IQ2_XXS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xxs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; } break; case GGML_TYPE_IQ2_XS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; } break; default: { @@ -1712,6 +1686,7 @@ bool ggml_metal_graph_compute( GGML_ASSERT(ne00 >= nth0*nth1); } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1818,23 +1793,28 @@ bool ggml_metal_graph_compute( if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && ne20 % 32 == 0 && ne20 >= 64 && ne11 > ne11_mm_min) { + + id pipeline = nil; + switch (src2->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_0_f32]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_1_f32]; break; - case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_0_f32]; break; - case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_1_f32]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q8_0_f32]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q2_K_f32]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q3_K_f32]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_K_f32]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_K_f32]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q6_K_f32]; break; - case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xxs_f32]; break; - case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xs_f32]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -1874,91 +1854,93 @@ bool ggml_metal_graph_compute( int nrows = 1; //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + id pipeline = nil; + // use custom matrix x vector kernel switch (src2t) { case GGML_TYPE_F32: { GGML_ASSERT(src1t == GGML_TYPE_F32); - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f32_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; } break; case GGML_TYPE_F16: { GGML_ASSERT(src1t == GGML_TYPE_F32); nth0 = 32; nth1 = 1; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f16_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; } break; case GGML_TYPE_Q4_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; } break; case GGML_TYPE_Q4_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; } break; case GGML_TYPE_Q5_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; } break; case GGML_TYPE_Q5_1: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_1_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; } break; case GGML_TYPE_Q8_0: { nth0 = 8; nth1 = 8; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q8_0_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; } break; case GGML_TYPE_Q2_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q2_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; } break; case GGML_TYPE_Q3_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q3_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; } break; case GGML_TYPE_Q4_K: { nth0 = 4; //1; nth1 = 8; //32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; } break; case GGML_TYPE_Q5_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; } break; case GGML_TYPE_Q6_K: { nth0 = 2; nth1 = 32; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q6_K_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; } break; case GGML_TYPE_IQ2_XXS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xxs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; } break; case GGML_TYPE_IQ2_XS: { nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xs_f32]; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; } break; default: { @@ -1973,6 +1955,7 @@ bool ggml_metal_graph_compute( const int64_t _ne1 = 1; // kernels needs a reference in constant memory + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -2040,25 +2023,28 @@ bool ggml_metal_graph_compute( } break; case GGML_OP_GET_ROWS: { + id pipeline = nil; + switch (src0->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_get_rows_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break; - case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_0]; break; - case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_1]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break; - case GGML_TYPE_I32: [encoder setComputePipelineState:ctx->pipeline_get_rows_i32]; break; - case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xxs]; break; - case GGML_TYPE_IQ2_XS : [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xs]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -2086,7 +2072,9 @@ bool ggml_metal_graph_compute( nth *= 2; } - [encoder setComputePipelineState:ctx->pipeline_rms_norm]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2115,7 +2103,9 @@ bool ggml_metal_graph_compute( // nth *= 2; //} - [encoder setComputePipelineState:ctx->pipeline_group_norm]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2137,7 +2127,9 @@ bool ggml_metal_graph_compute( const int nth = MIN(256, ne00); - [encoder setComputePipelineState:ctx->pipeline_norm]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2164,7 +2156,9 @@ bool ggml_metal_graph_compute( const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - [encoder setComputePipelineState:ctx->pipeline_alibi_f32]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2209,12 +2203,15 @@ bool ggml_metal_graph_compute( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + id pipeline = nil; + switch (src0->type) { - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_rope_f16]; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; default: GGML_ASSERT(false); }; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; @@ -2277,12 +2274,15 @@ bool ggml_metal_graph_compute( const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; + id pipeline = nil; + switch (src0->type) { case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_im2col_f16]; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; default: GGML_ASSERT(false); }; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; @@ -2305,7 +2305,9 @@ bool ggml_metal_graph_compute( const int sf = dst->op_params[0]; - [encoder setComputePipelineState:ctx->pipeline_upscale_f32]; + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -2326,7 +2328,7 @@ bool ggml_metal_graph_compute( [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; - const int nth = MIN((int) ctx->pipeline_upscale_f32.maxTotalThreadsPerThreadgroup, ne0); + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2334,7 +2336,9 @@ bool ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F32); - [encoder setComputePipelineState:ctx->pipeline_pad_f32]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; @@ -2367,12 +2371,15 @@ bool ggml_metal_graph_compute( enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + id pipeline = nil; + switch (order) { - case GGML_SORT_ASC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_asc]; break; - case GGML_SORT_DESC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_desc]; break; + case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; + case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; default: GGML_ASSERT(false); }; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2386,7 +2393,9 @@ bool ggml_metal_graph_compute( float slope; memcpy(&slope, dst->op_params, sizeof(float)); - [encoder setComputePipelineState:ctx->pipeline_leaky_relu_f32]; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; @@ -2403,33 +2412,36 @@ bool ggml_metal_graph_compute( int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); + id pipeline = nil; + switch (src0t) { case GGML_TYPE_F32: { GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); switch (dstt) { - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16]; break; - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; break; - case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q8_0]; break; - case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q4_0]; break; - case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q4_1]; break; - //case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q5_0]; break; - //case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q5_1]; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; + //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; + //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; default: GGML_ASSERT(false && "not implemented"); }; } break; case GGML_TYPE_F16: { switch (dstt) { - case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break; - case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f32]; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; default: GGML_ASSERT(false && "not implemented"); }; } break; default: GGML_ASSERT(false && "not implemented"); } + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; @@ -2794,9 +2806,9 @@ static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml } static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - return ggml_metal_supports_op(op); + struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; - UNUSED(backend); + return ggml_metal_supports_op(metal_ctx, op); } static struct ggml_backend_i ggml_backend_metal_i = { From c30b1ef39aeba497a943416d2897d69fee055b96 Mon Sep 17 00:00:00 2001 From: texmex76 <40733439+texmex76@users.noreply.github.com> Date: Sat, 13 Jan 2024 17:06:20 +0100 Subject: [PATCH 021/334] gguf : fix potential infinite for-loop (#4600) Co-authored-by: Bernhard Gstrein --- ggml.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml.c b/ggml.c index 6dbd7626c..de6ef34bd 100644 --- a/ggml.c +++ b/ggml.c @@ -19184,7 +19184,7 @@ void gguf_free(struct gguf_context * ctx) { if (ctx->kv) { // free string memory - not great.. - for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { + for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; if (kv->key.data) { @@ -19200,7 +19200,7 @@ void gguf_free(struct gguf_context * ctx) { if (kv->type == GGUF_TYPE_ARRAY) { if (kv->value.arr.data) { if (kv->value.arr.type == GGUF_TYPE_STRING) { - for (uint32_t j = 0; j < kv->value.arr.n; ++j) { + for (uint64_t j = 0; j < kv->value.arr.n; ++j) { struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; if (str->data) { free(str->data); @@ -19216,7 +19216,7 @@ void gguf_free(struct gguf_context * ctx) { } if (ctx->infos) { - for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { + for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; if (info->name.data) { From 722d33f34ec74c6f7046109f936d0928ffe171bc Mon Sep 17 00:00:00 2001 From: Yann Follet <131855179+YannFollet@users.noreply.github.com> Date: Sun, 14 Jan 2024 00:09:08 +0800 Subject: [PATCH 022/334] main : add parameter --no-display-prompt (#4541) * add the parameter : --no-display-prompt , combine with --log-disable it will display only the generated tokens * remove empty line --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 6 +++++- common/common.h | 1 + examples/main/main.cpp | 7 ++++++- 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 322b9f91e..c11006bcb 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -617,6 +617,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.numa = true; } else if (arg == "--verbose-prompt") { params.verbose_prompt = true; + } else if (arg == "--no-display-prompt") { + params.display_prompt = false; } else if (arg == "-r" || arg == "--reverse-prompt") { if (++i >= argc) { invalid_param = true; @@ -936,11 +938,12 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); #endif + printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); + printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); printf(" group-attention factor (default: %d)\n", params.grp_attn_n); printf(" -gaw N, --grp-attn-w N\n"); printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); - printf(" --verbose-prompt print prompt before generation\n"); printf(" -dkvc, --dump-kv-cache\n"); printf(" verbose print of the KV cache\n"); printf(" -nkvo, --no-kv-offload\n"); @@ -1582,6 +1585,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); + fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); } // diff --git a/common/common.h b/common/common.h index f29be5b5a..096468243 100644 --- a/common/common.h +++ b/common/common.h @@ -126,6 +126,7 @@ struct gpt_params { bool use_mlock = false; // use mlock to keep model in memory bool numa = false; // attempt optimizations that help on some NUMA systems bool verbose_prompt = false; // print prompt tokens before generation + bool display_prompt = true; // print prompt before generation bool infill = false; // use infill mode bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading diff --git a/examples/main/main.cpp b/examples/main/main.cpp index c53b29978..58b7f807a 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -477,6 +477,7 @@ int main(int argc, char ** argv) { bool is_antiprompt = false; bool input_echo = true; + bool display = true; bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size(); int n_past = 0; @@ -491,6 +492,7 @@ int main(int argc, char ** argv) { // the first thing we will do is to output the prompt, so set color accordingly console::set_display(console::prompt); + display = params.display_prompt; std::vector embd; std::vector embd_guidance; @@ -707,7 +709,7 @@ int main(int argc, char ** argv) { } // display text - if (input_echo) { + if (input_echo && display) { for (auto id : embd) { const std::string token_str = llama_token_to_piece(ctx, id); printf("%s", token_str.c_str()); @@ -724,6 +726,7 @@ int main(int argc, char ** argv) { // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); + display = true; } // if not currently processing queued inputs; @@ -796,6 +799,7 @@ int main(int argc, char ** argv) { // color user input only console::set_display(console::user_input); + display = params.display_prompt; std::string line; bool another_line = true; @@ -806,6 +810,7 @@ int main(int argc, char ** argv) { // done taking input, reset color console::set_display(console::reset); + display = true; // Add tokens to embd only if the input buffer is non-empty // Entering a empty line lets the user pass control back From 6b48ed089377330cdb362970a51c1c89b6d857a8 Mon Sep 17 00:00:00 2001 From: Someone Date: Sat, 13 Jan 2024 16:29:16 +0000 Subject: [PATCH 023/334] workflows: unbreak nix-build-aarch64, and split it out (#4915) The fix should be just the `sudo apt-get update` --- .github/workflows/nix-ci-aarch64.yml | 55 ++++++++++++++++++++++++++++ .github/workflows/nix-ci.yml | 41 --------------------- 2 files changed, 55 insertions(+), 41 deletions(-) create mode 100644 .github/workflows/nix-ci-aarch64.yml diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml new file mode 100644 index 000000000..be7c26d40 --- /dev/null +++ b/.github/workflows/nix-ci-aarch64.yml @@ -0,0 +1,55 @@ +name: Nix aarch64 builds + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + +jobs: + nix-build-aarch64: + if: ${{ vars.CACHIX_NAME != '' }} + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install QEMU + # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 + run: | + sudo apt-get update + sudo apt-get install -y qemu-user-static qemu-system-aarch64 + sudo usermod -a -G kvm $USER + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-platforms = aarch64-linux + extra-system-features = nixos-test kvm + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: Set-up cachix to push the results to + uses: cachix/cachix-action@v13 + with: + authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' + name: ${{ vars.CACHIX_NAME }} + - name: Show all output paths + run: > + nix run github:nix-community/nix-eval-jobs + -- --gc-roots-dir gcroot + --flake + ".#packages.aarch64-linux" + - name: Build + run: > + nix run github:Mic92/nix-fast-build + -- --skip-cached --no-nom + --systems aarch64-linux + --flake + ".#checks.aarch64-linux" diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index a38c6ead4..845b93bfb 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -69,44 +69,3 @@ jobs: -- --skip-cached --no-nom --flake ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" - nix-build-aarch64: - if: ${{ vars.CACHIX_NAME != '' }} - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install QEMU - # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 - run: | - sudo apt-get install -y qemu-user-static qemu-system-aarch64 - sudo usermod -a -G kvm $USER - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-platforms = aarch64-linux - extra-system-features = nixos-test kvm - extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: Set-up cachix to push the results to - uses: cachix/cachix-action@v13 - with: - authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: ${{ vars.CACHIX_NAME }} - - name: Show all output paths - run: > - nix run github:nix-community/nix-eval-jobs - -- --gc-roots-dir gcroot - --flake - ".#packages.aarch64-linux" - - name: Build - run: > - nix run github:Mic92/nix-fast-build - -- --skip-cached --no-nom - --systems aarch64-linux - --flake - ".#checks.aarch64-linux" From df845cc982e7e2ea7b9900e29d55b15338faa78d Mon Sep 17 00:00:00 2001 From: David Friehs Date: Sat, 13 Jan 2024 17:29:43 +0100 Subject: [PATCH 024/334] llama : minimize size used for state save/load (#4820) * examples : save-load-state: save only required state * llama : only reserve n_vocab * n_batch at most for logits llama_decode asserts that only n_batch tokens are passed each call, and n_ctx is expected to be bigger than n_batch. * llama : always reserve n_vocab * n_batch for logits llama_context de-serialization breaks if the contexts have differing capacity for logits and llama_decode will at maximum resize to n_vocab * n_batch. * llama : only save and restore used logits for batch sizes of 512 this reduces save state in the best case by around 62 MB, which can be a lot if planning to save on each message to allow regenerating messages. * llama : use ostringstream and istringstream for save and load * llama : serialize rng into minimum amount of space required * llama : break session version due to serialization changes --- examples/save-load-state/save-load-state.cpp | 21 ++++---- llama.cpp | 53 +++++++------------- llama.h | 2 +- 3 files changed, 29 insertions(+), 47 deletions(-) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 48d801110..ef952e2bd 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -45,13 +45,13 @@ int main(int argc, char ** argv) { // save state (rng, logits, embedding and kv_cache) to file { std::vector state_mem(llama_get_state_size(ctx)); + const size_t written = llama_copy_state_data(ctx, state_mem.data()); - { - FILE *fp_write = fopen("dump_state.bin", "wb"); - llama_copy_state_data(ctx, state_mem.data()); // could also copy directly to memory mapped file - fwrite(state_mem.data(), 1, state_mem.size(), fp_write); - fclose(fp_write); - } + FILE *fp_write = fopen("dump_state.bin", "wb"); + fwrite(state_mem.data(), 1, written, fp_write); + fclose(fp_write); + + fprintf(stderr, "%s : serialized state into %zd out of a maximum of %zd bytes\n", __func__, written, state_mem.size()); } // save state (last tokens) @@ -100,18 +100,17 @@ int main(int argc, char ** argv) { std::vector state_mem(llama_get_state_size(ctx2)); FILE * fp_read = fopen("dump_state.bin", "rb"); + const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read); + fclose(fp_read); - const size_t ret = fread(state_mem.data(), 1, state_mem.size(), fp_read); - if (ret != state_mem.size()) { + if (read != llama_set_state_data(ctx2, state_mem.data())) { fprintf(stderr, "\n%s : failed to read state\n", __func__); llama_free(ctx2); llama_free_model(model); return 1; } - llama_set_state_data(ctx2, state_mem.data()); - - fclose(fp_read); + fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size()); } // restore state (last tokens) diff --git a/llama.cpp b/llama.cpp index 1d2eb569f..275456088 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9379,12 +9379,8 @@ struct llama_context * llama_new_context_with_model( ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); } - // resized during inference - if (params.logits_all) { - ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab); - } else { - ctx->logits.reserve(hparams.n_vocab); - } + // resized during inference, reserve maximum + ctx->logits.reserve(hparams.n_vocab*cparams.n_batch); if (params.embedding){ ctx->embedding.resize(hparams.n_embd); @@ -9731,8 +9727,8 @@ size_t llama_get_state_size(const struct llama_context * ctx) { // for reference, std::mt19937(1337) serializes to 6701 bytes. const size_t s_rng_size = sizeof(size_t); const size_t s_rng = LLAMA_MAX_RNG_STATE; - const size_t s_logits_capacity = sizeof(size_t); const size_t s_logits_size = sizeof(size_t); + // assume worst case for logits although only currently set ones are serialized const size_t s_logits = ctx->logits.capacity() * sizeof(float); const size_t s_embedding_size = sizeof(size_t); const size_t s_embedding = ctx->embedding.size() * sizeof(float); @@ -9743,7 +9739,6 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_total = ( + s_rng_size + s_rng - + s_logits_capacity + s_logits_size + s_logits + s_embedding_size @@ -9812,37 +9807,27 @@ struct llama_data_file_context : llama_data_context { static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { // copy rng { - std::stringstream rng_ss; + std::ostringstream rng_ss; rng_ss << ctx->rng; - const size_t rng_size = rng_ss.str().size(); - char rng_buf[LLAMA_MAX_RNG_STATE]; + const std::string & rng_str = rng_ss.str(); + const size_t rng_size = rng_str.size(); - memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE); - memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size()); + GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE); - data_ctx->write(&rng_size, sizeof(rng_size)); - data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE); + data_ctx->write(&rng_size, sizeof(rng_size)); + data_ctx->write(rng_str.data(), rng_size); } // copy logits { - const size_t logits_cap = ctx->logits.capacity(); const size_t logits_size = ctx->logits.size(); - data_ctx->write(&logits_cap, sizeof(logits_cap)); data_ctx->write(&logits_size, sizeof(logits_size)); if (logits_size) { data_ctx->write(ctx->logits.data(), logits_size * sizeof(float)); } - - // If there is a gap between the size and the capacity, write padding - size_t padding_size = (logits_cap - logits_size) * sizeof(float); - if (padding_size > 0) { - std::vector padding(padding_size, 0); // Create a buffer filled with zeros - data_ctx->write(padding.data(), padding_size); - } } // copy embeddings @@ -9925,13 +9910,13 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { // set rng { size_t rng_size; - char rng_buf[LLAMA_MAX_RNG_STATE]; + memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size); - memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size); - memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE; + GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE); - std::stringstream rng_ss; - rng_ss.str(std::string(&rng_buf[0], rng_size)); + std::string rng_str((char *)inp, rng_size); inp += rng_size; + + std::istringstream rng_ss(rng_str); rng_ss >> ctx->rng; GGML_ASSERT(!rng_ss.fail()); @@ -9939,20 +9924,18 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { // set logits { - size_t logits_cap; size_t logits_size; - memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap); memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size); - GGML_ASSERT(ctx->logits.capacity() == logits_cap); + GGML_ASSERT(ctx->logits.capacity() >= logits_size); if (logits_size) { ctx->logits.resize(logits_size); - memcpy(ctx->logits.data(), inp, logits_size * sizeof(float)); - } - inp += logits_cap * sizeof(float); + memcpy(ctx->logits.data(), inp, logits_size * sizeof(float)); + inp += logits_size * sizeof(float); + } } // set embeddings diff --git a/llama.h b/llama.h index 689e12d7c..01d6fafaa 100644 --- a/llama.h +++ b/llama.h @@ -43,7 +43,7 @@ #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN -#define LLAMA_SESSION_VERSION 3 +#define LLAMA_SESSION_VERSION 4 #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. From 2d57de525541247132e354f561ff48775fba5d85 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 18:46:37 +0200 Subject: [PATCH 025/334] metal : disable log for loaded kernels (#4794) --- ggml-metal.m | 3 --- 1 file changed, 3 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 6c28a7ee3..57e444827 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -398,9 +398,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ kernel->function = [ctx->library newFunctionWithName:@"kernel_"#name]; \ kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:kernel->function error:&error]; \ - GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ - (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ - (int) kernel->pipeline.threadExecutionWidth); \ if (error) { \ GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ return NULL; \ From f172de03f11465dc6c5a0fc3a22f8ec254c6832c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 18:47:38 +0200 Subject: [PATCH 026/334] llama : fix detokenization of non-special added-tokens (#4916) Co-authored-by: goerch --- llama.cpp | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index 275456088..2190ea7aa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10305,6 +10305,8 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { case LLAMA_VOCAB_TYPE_SPM: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. if (llama_is_normal_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; llama_unescape_whitespace(result); @@ -10313,6 +10315,13 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, result.c_str(), result.length()); return result.length(); + } else if (llama_is_user_defined_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT if (length < 3) { return -3; @@ -10327,14 +10336,12 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } buf[0] = llama_token_to_byte(model->vocab, token); return 1; - } else { - // TODO: for now we accept all unsupported token types, - // suppressing them like CONTROL tokens. - // GGML_ASSERT(false); } break; } case LLAMA_VOCAB_TYPE_BPE: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. if (llama_is_normal_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; result = llama_decode_text(result); @@ -10343,12 +10350,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, result.c_str(), result.length()); return result.length(); + } else if (llama_is_user_defined_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); } else if (llama_is_control_token(model->vocab, token)) { ; - } else { - // TODO: for now we accept all unsupported token types, - // suppressing them like CONTROL tokens. - // GGML_ASSERT(false); } break; } From 0ea069b87bd296c556824e57455433b6c0357340 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 19:31:26 +0200 Subject: [PATCH 027/334] server : fix prompt caching with system prompt (#4914) --- examples/server/server.cpp | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 79eacf828..93f999298 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1180,8 +1180,9 @@ struct llama_server_context return slot.images.size() > 0; } - void send_error(task_server& task, std::string error) + void send_error(task_server& task, const std::string &error) { + LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); std::unique_lock lock(mutex_results); task_result res; res.id = task.id; @@ -1570,12 +1571,22 @@ struct llama_server_context LOG_TEE("slot unavailable\n"); // send error result send_error(task, "slot unavailable"); - return; + break; } if (task.data.contains("system_prompt")) { + if (!all_slots_are_idle) { + send_error(task, "system prompt can only be updated when all slots are idle"); + break; + } process_system_prompt_data(task.data["system_prompt"]); + + // reset cache_tokens for all slots + for (llama_client_slot &slot : slots) + { + slot.cache_tokens.clear(); + } } slot->reset(); @@ -1652,8 +1663,7 @@ struct llama_server_context // attend tasks process_tasks(); - // update the system prompt wait until all slots are idle state - if (system_need_update && all_slots_are_idle) + if (system_need_update) { LOG_TEE("updating system prompt\n"); update_system_prompt(); From 4be5ef556de830c5c4f6e45c05ef4427823fe607 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Jan 2024 20:45:45 +0200 Subject: [PATCH 028/334] metal : remove old API (#4919) ggml-ci --- Makefile | 9 -- examples/CMakeLists.txt | 3 - examples/metal/CMakeLists.txt | 4 - examples/metal/metal.cpp | 103 ------------- ggml-metal.h | 55 +------ ggml-metal.m | 276 +++------------------------------- llama.cpp | 4 +- 7 files changed, 27 insertions(+), 427 deletions(-) delete mode 100644 examples/metal/CMakeLists.txt delete mode 100644 examples/metal/metal.cpp diff --git a/Makefile b/Makefile index 05fe9a0f6..995b89f7a 100644 --- a/Makefile +++ b/Makefile @@ -43,10 +43,6 @@ ifeq ($(UNAME_S),Darwin) endif endif -ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' -BUILD_TARGETS += metal -endif - default: $(BUILD_TARGETS) test: $(TEST_TARGETS) @@ -671,11 +667,6 @@ lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -ifdef LLAMA_METAL -metal: examples/metal/metal.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) -endif - ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift (cd examples/batched.swift; make build) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index fa127a3aa..f67d74c55 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -37,9 +37,6 @@ else() add_subdirectory(lookup) add_subdirectory(train-text-from-scratch) add_subdirectory(imatrix) - if (LLAMA_METAL) - add_subdirectory(metal) - endif() if (LLAMA_BUILD_SERVER) add_subdirectory(server) endif() diff --git a/examples/metal/CMakeLists.txt b/examples/metal/CMakeLists.txt deleted file mode 100644 index f16d49165..000000000 --- a/examples/metal/CMakeLists.txt +++ /dev/null @@ -1,4 +0,0 @@ -set(TEST_TARGET metal) -add_executable(${TEST_TARGET} metal.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TEST_TARGET} PRIVATE ggml) diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp deleted file mode 100644 index 16c1146f9..000000000 --- a/examples/metal/metal.cpp +++ /dev/null @@ -1,103 +0,0 @@ -// Evaluate a statically exported ggml computation graph with Metal -// -// - First, export a LLaMA graph: -// -// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export -// -// - Run this tool to evaluate the exported graph: -// -// $ ./bin/metal llama.ggml -// -// The purpose of this tool is mostly for debugging and demonstration purposes. -// The main limitation of exporting computation graphs is that their sizes are static which often -// can be a problem for real-world applications. -// - -#include "ggml.h" -#include "ggml-metal.h" - -#include -#include -#include - -int main(int argc, char ** argv) { - ggml_time_init(); - - if (argc != 2) { - fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]); - return -1; - } - - const char * fname_cgraph = argv[1]; - - // load the compute graph - struct ggml_context * ctx_data = NULL; - struct ggml_context * ctx_eval = NULL; - - struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); - - // this allocates all Metal resources and memory buffers - auto * ctx_metal = ggml_metal_init(1); - - const size_t max_size_data = ggml_get_max_tensor_size(ctx_data); - const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval); - ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data); - ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval); - - // main - { - struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd"); - *(int32_t *) input->data = 1; // BOS - - ggml_metal_set_tensor(ctx_metal, input); - - // warmup - ggml_metal_graph_compute(ctx_metal, gf); - - const int n_iter = 16; - - const int64_t t0 = ggml_time_us(); - - // the actual inference happens here - for (int i = 0; i < n_iter; ++i) { - ggml_metal_graph_compute(ctx_metal, gf); - } - - const int64_t t1 = ggml_time_us(); - - printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter); - } - - // debug output - { - struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1]; - ggml_metal_get_tensor(ctx_metal, logits); - - float * ptr = (float *) ggml_get_data(logits); - - printf("logits: "); - for (int i = 0; i < 10; i++) { - printf("%8.4f ", ptr[i]); - } - printf("\n"); - int imax = 0; - double sum = 0.0; - double vmax = -1e9; - for (int i = 0; i < 32000; i++) { - sum += (double) ptr[i]; - if (ptr[i] > vmax) { - vmax = ptr[i]; - imax = i; - } - } - printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax); - } - - ggml_metal_free(ctx_metal); - - ggml_free(ctx_data); - ggml_free(ctx_eval); - - return 0; -} - diff --git a/ggml-metal.h b/ggml-metal.h index c4b7325da..cd5e2995f 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -36,64 +36,13 @@ struct ggml_cgraph; extern "C" { #endif -// -// internal API -// temporary exposed to user-code -// - -struct ggml_metal_context; - -void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data); - -// number of command buffers to use -struct ggml_metal_context * ggml_metal_init(int n_cb); -void ggml_metal_free(struct ggml_metal_context * ctx); - -void * ggml_metal_host_malloc(size_t n); -void ggml_metal_host_free (void * data); - -// set the number of command buffers to use -void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb); - -// creates a mapping between a host memory buffer and a device memory buffer -// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute -// - the mapping is used during computation to determine the arguments of the compute kernels -// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal -// - max_size specifies the maximum size of a tensor and is used to create shared views such -// that it is guaranteed that the tensor will fit in at least one of the views -// -bool ggml_metal_add_buffer( - struct ggml_metal_context * ctx, - const char * name, - void * data, - size_t size, - size_t max_size); - -// set data from host memory into the device -void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); - -// get data from the device into host memory -void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); - -// try to find operations that can be run concurrently in the graph -// you should run it again if the topology of your graph changes -void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem); - -// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized -int ggml_metal_if_optimized(struct ggml_metal_context * ctx); - -// output the concur_list for ggml_alloc -int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx); - -// same as ggml_graph_compute but uses Metal -// creates gf->n_threads command buffers in parallel -bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf); - // // backend API // user-code should use only these functions // +GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data); + GGML_API ggml_backend_t ggml_backend_metal_init(void); GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); diff --git a/ggml-metal.m b/ggml-metal.m index 57e444827..cae52c983 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -24,8 +24,6 @@ #define UNUSED(x) (void)(x) -#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) - #define GGML_METAL_MAX_KERNELS 256 struct ggml_metal_buffer { @@ -182,9 +180,6 @@ struct ggml_metal_context { struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; - int concur_list[GGML_MAX_CONCUR]; - int concur_list_len; - bool support_simdgroup_reduction; bool support_simdgroup_mm; }; @@ -200,7 +195,6 @@ struct ggml_metal_context { @implementation GGMLMetalClass @end - static void ggml_metal_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { fprintf(stderr, "%s", msg); @@ -211,11 +205,6 @@ static void ggml_metal_default_log_callback(enum ggml_log_level level, const cha ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback; void * ggml_metal_log_user_data = NULL; -void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { - ggml_metal_log_callback = log_callback; - ggml_metal_log_user_data = user_data; -} - GGML_ATTRIBUTE_FORMAT(2, 3) static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ if (ggml_metal_log_callback != NULL) { @@ -238,7 +227,18 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ } } -struct ggml_metal_context * ggml_metal_init(int n_cb) { +static void * ggml_metal_host_malloc(size_t n) { + void * data = NULL; + const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); + if (result != 0) { + GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); + return NULL; + } + + return data; +} + +static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: allocating\n", __func__); id device; @@ -264,7 +264,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); ctx->queue = [ctx->device newCommandQueue]; ctx->n_buffers = 0; - ctx->concur_list_len = 0; ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); @@ -531,7 +530,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { return ctx; } -void ggml_metal_free(struct ggml_metal_context * ctx) { +static void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); for (int i = 0; i < ctx->n_buffers; ++i) { @@ -557,33 +556,6 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { free(ctx); } -void * ggml_metal_host_malloc(size_t n) { - void * data = NULL; - const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); - if (result != 0) { - GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); - return NULL; - } - - return data; -} - -void ggml_metal_host_free(void * data) { - free(data); -} - -void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) { - ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); -} - -int ggml_metal_if_optimized(struct ggml_metal_context * ctx) { - return ctx->concur_list_len; -} - -int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) { - return ctx->concur_list; -} - // temporarily defined here for compatibility between ggml-backend and the old API struct ggml_backend_metal_buffer { @@ -656,209 +628,6 @@ static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru return nil; } -bool ggml_metal_add_buffer( - struct ggml_metal_context * ctx, - const char * name, - void * data, - size_t size, - size_t max_size) { - if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { - GGML_METAL_LOG_ERROR("%s: error: too many buffers\n", __func__); - return false; - } - - if (data) { - // verify that the buffer does not overlap with any of the existing buffers - for (int i = 0; i < ctx->n_buffers; ++i) { - const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data; - - if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { - GGML_METAL_LOG_ERROR("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); - return false; - } - } - - const size_t size_page = sysconf(_SC_PAGESIZE); - - size_t size_aligned = size; - if ((size_aligned % size_page) != 0) { - size_aligned += (size_page - (size_aligned % size_page)); - } - - // the buffer fits into the max buffer size allowed by the device - if (size_aligned <= ctx->device.maxBufferLength) { - ctx->buffers[ctx->n_buffers].name = name; - ctx->buffers[ctx->n_buffers].data = data; - ctx->buffers[ctx->n_buffers].size = size; - - ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0); - return false; - } - - GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0); - - ++ctx->n_buffers; - } else { - // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into - // one of the views - const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case - const size_t size_step = ctx->device.maxBufferLength - size_ovlp; - const size_t size_view = ctx->device.maxBufferLength; - - for (size_t i = 0; i < size; i += size_step) { - const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); - - ctx->buffers[ctx->n_buffers].name = name; - ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i); - ctx->buffers[ctx->n_buffers].size = size_step_aligned; - - ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); - return false; - } - - GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); - if (i + size_step < size) { - GGML_METAL_LOG_INFO("\n"); - } - - ++ctx->n_buffers; - } - } - -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", - ctx->device.currentAllocatedSize / 1024.0 / 1024.0, - ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } else { - GGML_METAL_LOG_INFO("\n"); - } -#else - GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0); -#endif - } - - return true; -} - -void ggml_metal_set_tensor( - struct ggml_metal_context * ctx, - struct ggml_tensor * t) { - size_t offs; - id id_dst = ggml_metal_get_buffer(ctx, t, &offs); - - memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t)); -} - -void ggml_metal_get_tensor( - struct ggml_metal_context * ctx, - struct ggml_tensor * t) { - size_t offs; - id id_src = ggml_metal_get_buffer(ctx, t, &offs); - - memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t)); -} - -void ggml_metal_graph_find_concurrency( - struct ggml_metal_context * ctx, - struct ggml_cgraph * gf, bool check_mem) { - int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time - int nodes_unused[GGML_MAX_CONCUR]; - - for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; } - for (int i = 0; i < gf->n_nodes; i++) { nodes_unused[i] = 1; } - ctx->concur_list_len = 0; - - int n_left = gf->n_nodes; - int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list - int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos - - while (n_left > 0) { - // number of nodes at a layer (that can be issued concurrently) - int concurrency = 0; - for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) { - if (nodes_unused[i]) { - // if the requirements for gf->nodes[i] are satisfied - int exe_flag = 1; - - // scan all srcs - for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) { - struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind]; - if (src_cur) { - // if is leaf nodes it's satisfied. - // TODO: ggml_is_leaf() - if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) { - continue; - } - - // otherwise this src should be the output from previous nodes. - int is_found = 0; - - // scan 2*search_depth back because we inserted barrier. - //for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) { - for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) { - if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) { - is_found = 1; - break; - } - } - if (is_found == 0) { - exe_flag = 0; - break; - } - } - } - if (exe_flag && check_mem) { - // check if nodes[i]'s data will be overwritten by a node before nodes[i]. - // if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3] - int64_t data_start = (int64_t) gf->nodes[i]->data; - int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]); - for (int j = n_start; j < i; j++) { - if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \ - && gf->nodes[j]->op != GGML_OP_VIEW \ - && gf->nodes[j]->op != GGML_OP_TRANSPOSE \ - && gf->nodes[j]->op != GGML_OP_PERMUTE) { - if (((int64_t)gf->nodes[j]->data) >= data_start + length || \ - ((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) { - continue; - } - - exe_flag = 0; - } - } - } - if (exe_flag) { - ctx->concur_list[level_pos + concurrency] = i; - nodes_unused[i] = 0; - concurrency++; - ctx->concur_list_len++; - } - } - } - n_left -= concurrency; - // adding a barrier different layer - ctx->concur_list[level_pos + concurrency] = -1; - ctx->concur_list_len++; - // jump all sorted nodes at nodes_bak - while (!nodes_unused[n_start]) { - n_start++; - } - level_pos += concurrency + 1; - } - - if (ctx->concur_list_len > GGML_MAX_CONCUR) { - GGML_METAL_LOG_WARN("%s: too many elements for metal ctx->concur_list!\n", __func__); - } -} - static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: @@ -940,19 +709,15 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const } } -bool ggml_metal_graph_compute( +static bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @autoreleasepool { - // if there is ctx->concur_list, dispatch concurrently - // else fallback to serial dispatch MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor; - const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR; - - const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes; - edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial; + const int n_nodes = gf->n_nodes; + edesc.dispatchType = MTLDispatchTypeSerial; // create multiple command buffers and enqueue them // then, we encode the graph into the command buffers in parallel @@ -983,7 +748,7 @@ bool ggml_metal_graph_compute( const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); for (int ind = node_start; ind < node_end; ++ind) { - const int i = has_concur ? ctx->concur_list[ind] : ind; + const int i = ind; if (i == -1) { [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; @@ -2823,6 +2588,11 @@ static struct ggml_backend_i ggml_backend_metal_i = { /* .supports_op = */ ggml_backend_metal_supports_op, }; +void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { + ggml_metal_log_callback = log_callback; + ggml_metal_log_user_data = user_data; +} + ggml_backend_t ggml_backend_metal_init(void) { struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); @@ -2849,7 +2619,7 @@ void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; - ggml_metal_set_n_cb(ctx, n_cb); + ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); } bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { diff --git a/llama.cpp b/llama.cpp index 2190ea7aa..66494974a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1266,7 +1266,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g struct llama_state { llama_state() { #ifdef GGML_USE_METAL - ggml_metal_log_set_callback(log_callback, log_callback_user_data); + ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data); #endif } @@ -10470,7 +10470,7 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) { g_state.log_callback = log_callback ? log_callback : llama_log_callback_default; g_state.log_callback_user_data = user_data; #ifdef GGML_USE_METAL - ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); + ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); #endif } From c71d608ce7a1584bf5072f197919dd24f3a6163f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 13 Jan 2024 21:41:37 +0100 Subject: [PATCH 029/334] ggml: cache sin/cos for RoPE (#4908) --- ggml.c | 46 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/ggml.c b/ggml.c index de6ef34bd..bcfb6652c 100644 --- a/ggml.c +++ b/ggml.c @@ -11638,6 +11638,21 @@ static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, fl return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); } +static void ggml_rope_cache_init( + float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale +) { + float theta = theta_base; + for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + rope_yarn( + theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + ); + cache[i0 + 1] *= sin_sign; + + theta *= theta_scale; + } +} + void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { @@ -11720,6 +11735,12 @@ static void ggml_compute_forward_rope_f32( for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = 0; i2 < ne2; i2++) { const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox + ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -11753,18 +11774,13 @@ static void ggml_compute_forward_rope_f32( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn( - theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta - ); - sin_theta *= sin_sign; + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; - theta_base *= theta_scale; - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -11888,6 +11904,12 @@ static void ggml_compute_forward_rope_f16( for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = 0; i2 < ne2; i2++) { const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox + ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -11921,13 +11943,8 @@ static void ggml_compute_forward_rope_f16( } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn( - theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta - ); - sin_theta *= sin_sign; - - theta_base *= theta_scale; + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -16722,6 +16739,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa } } break; case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } break; From 76484fbfd355df388f71d6edaa98e1692a74de7e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 00:14:46 +0200 Subject: [PATCH 030/334] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index edcdb530a..753d227a7 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -400c07f00508e6f60fb25405444b5669c365b0a9 +1890780da4ea10db88736fcde85f285abf6c64b0 From 807179ec583dcb882f97d9704577c06beb2c5ec9 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 09:44:30 +0200 Subject: [PATCH 031/334] Make Q3_K_S be the same as olf Q3_K_L for Mixtral-8x7B (#4906) Co-authored-by: Iwan Kawrakow --- llama.cpp | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 66494974a..8e20e72a2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8489,9 +8489,16 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty ++qs.i_feed_forward_w2; } else if (name.find("attn_output.weight") != std::string::npos) { if (arch != LLM_ARCH_FALCON) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + if (qs.model.hparams.n_expert == 8) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || + ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { + new_type = GGML_TYPE_Q5_K; + } + } else { + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + } } else { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; } From 147b17ac94a24d524e367cda26a9ff6245689f34 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 09:45:56 +0200 Subject: [PATCH 032/334] 2-bit quantizations (#4897) * imatrix: load * imatrix: WIP * imatrix: Add Q2_K quantization * imatrix: also guard against Q2_K_S quantization without importance matrix * imatrix: guard even more against low-bit quantization misuse --------- Co-authored-by: Iwan Kawrakow --- examples/benchmark/benchmark-matmult.cpp | 4 +- examples/quantize/quantize.cpp | 133 +++- ggml-quants.c | 950 +++++++++++++++++++++-- ggml-quants.h | 12 +- ggml.c | 36 +- ggml.h | 9 +- llama.cpp | 84 +- llama.h | 1 + tests/test-backend-ops.cpp | 2 +- 9 files changed, 1149 insertions(+), 82 deletions(-) diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index 434e1d6bd..e89f3de2f 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -194,7 +194,7 @@ int main(int argc, char ** argv) { // Set up a the benchmark matrices // printf("Creating new tensor q11 & Running quantize\n"); struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements, hist_cur.data()); + ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr); // Set up a the compute graph // printf("Creating new tensor q31\n"); @@ -207,7 +207,7 @@ int main(int argc, char ** argv) { // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements, hist_cur.data()); + ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr); // printf("Creating new tensor q32\n"); struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index f878f6911..f4e2175f1 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -5,6 +5,10 @@ #include #include #include +#include +#include +#include +#include struct quant_option { std::string name; @@ -17,6 +21,8 @@ static const std::vector QUANT_OPTIONS = { { "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", }, { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", }, { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, + { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", }, + { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, @@ -72,10 +78,14 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp // [[noreturn]] static void usage(const char * executable) { - printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); + printf(" --imatrixfile_name: use data in file_name as importance matrix for quant optimizations\n"); + printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf("Note: --include-weights and --exclude-weights cannot be used together\n"); printf("\nAllowed quantization types:\n"); for (auto & it : QUANT_OPTIONS) { if (it.name != "COPY") { @@ -83,11 +93,93 @@ static void usage(const char * executable) { } else { printf(" "); } - printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str()); + printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str()); } exit(1); } +static void load_imatrix(const std::string& imatrix_file, std::unordered_map>& imatrix_data) { + std::ifstream in(imatrix_file.c_str(), std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__,imatrix_file.c_str()); + return; + } + int n_entries; + in.read((char*)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file.c_str()); + return; + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str()); + return; + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto& e = imatrix_data[std::move(name)]; + int ncall; + in.read((char*)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n",__func__,i); + imatrix_data = {}; + return; + } + e.resize(nval); + in.read((char*)e.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n",__func__,i); + imatrix_data = {}; + return; + } + if (ncall > 0) { + for (auto& v : e) v /= ncall; + } + } + printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str()); +} + +static void prepare_imatrix(const std::string& imatrix_file, + const std::vector& included_weights, + const std::vector& excluded_weights, + std::unordered_map>& imatrix_data) { + if (!imatrix_file.empty()) { + load_imatrix(imatrix_file, imatrix_data); + } + if (imatrix_data.empty()) { + return; + } + if (!excluded_weights.empty()) { + for (auto& name : excluded_weights) { + for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) { + auto pos = it->first.find(name); + if (pos != std::string::npos) it = imatrix_data.erase(it); + else ++it; + } + } + } + if (!included_weights.empty()) { + std::unordered_map> tmp; + for (auto& name : included_weights) { + for (auto& e : imatrix_data) { + auto pos = e.first.find(name); + if (pos != std::string::npos) { + tmp.emplace(std::move(e)); + } + } + } + imatrix_data = std::move(tmp); + } + if (!imatrix_data.empty()) { + printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size())); + } +} + int main(int argc, char ** argv) { if (argc < 3) { usage(argv[0]); @@ -96,6 +188,8 @@ int main(int argc, char ** argv) { llama_model_quantize_params params = llama_model_quantize_default_params(); int arg_idx = 1; + std::string imatrix_file; + std::vector included_weights, excluded_weights; for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) { @@ -104,14 +198,42 @@ int main(int argc, char ** argv) { params.allow_requantize = true; } else if (strcmp(argv[arg_idx], "--pure") == 0) { params.pure = true; + } else if (strcmp(argv[arg_idx], "--imatrix") == 0) { + if (arg_idx < argc-1) { + imatrix_file = argv[++arg_idx]; + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--include-weights") == 0) { + if (arg_idx < argc-1) { + included_weights.push_back(argv[++arg_idx]); + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) { + if (arg_idx < argc-1) { + excluded_weights.push_back(argv[++arg_idx]); + } else { + usage(argv[0]); + } } else { usage(argv[0]); } } if (argc - arg_idx < 2) { + printf("%s: bad arguments\n", argv[0]); usage(argv[0]); } + if (!included_weights.empty() && !excluded_weights.empty()) { + usage(argv[0]); + } + + std::unordered_map> imatrix_data; + prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data); + if (!imatrix_data.empty()) { + params.imatrix = &imatrix_data; + } llama_backend_init(false); @@ -163,6 +285,13 @@ int main(int argc, char ** argv) { } } + if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) && imatrix_data.empty()) { + fprintf(stderr, "\n===============================================================================================\n"); + fprintf(stderr, "Please do not use IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); + fprintf(stderr, "===============================================================================================\n\n\n"); + return 1; + } + print_build_info(); fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str()); diff --git a/ggml-quants.c b/ggml-quants.c index 601d155d7..9290d54cf 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -5,6 +5,8 @@ #include #include #include +#include // for qsort +#include // for GGML_ASSERT #ifdef __ARM_NEON @@ -1639,6 +1641,241 @@ size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q2_K)); } +static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights, + uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, + float rmin, float rdelta, int nstep, bool use_mad) { + float min = x[0]; + float max = x[0]; + float sum_w = weights ? weights[0] : x[0]*x[0]; + float sum_x = sum_w * x[0]; + for (int i = 1; i < n; ++i) { + if (x[i] < min) min = x[i]; + if (x[i] > max) max = x[i]; + float w = weights ? weights[i] : x[i]*x[i]; + sum_w += w; + sum_x += w * x[i]; + } + if (min > 0) { + min = 0; + } + if (max <= min) { + for (int i = 0; i < n; ++i) L[i] = 0; + *the_min = -min; + return 0.f; + } + float iscale = nmax/(max - min); + float scale = 1/iscale; + float best_mad = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + L[i] = MAX(0, MIN(nmax, l)); + float diff = scale * L[i] + min - x[i]; + diff = use_mad ? fabsf(diff) : diff*diff; + float w = weights ? weights[i] : x[i]*x[i]; + best_mad += w * diff; + } + if (nstep < 1) { + *the_min = -min; + return scale; + } + for (int is = 0; is <= nstep; ++is) { + iscale = (rmin + rdelta*is + nmax)/(max - min); + float sum_l = 0, sum_l2 = 0, sum_xl = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + l = MAX(0, MIN(nmax, l)); + Laux[i] = l; + float w = weights ? weights[i] : x[i]*x[i]; + sum_l += w*l; + sum_l2 += w*l*l; + sum_xl += w*l*x[i]; + } + float D = sum_w * sum_l2 - sum_l * sum_l; + if (D > 0) { + float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D; + float this_min = (sum_l2 * sum_x - sum_l * sum_xl)/D; + if (this_min > 0) { + this_min = 0; + this_scale = sum_xl / sum_l2; + } + float mad = 0; + for (int i = 0; i < n; ++i) { + float diff = this_scale * Laux[i] + this_min - x[i]; + diff = use_mad ? fabsf(diff) : diff*diff; + float w = weights ? weights[i] : x[i]*x[i]; + mad += w * diff; + } + if (mad < best_mad) { + for (int i = 0; i < n; ++i) { + L[i] = Laux[i]; + } + best_mad = mad; + scale = this_scale; + min = this_min; + } + } + } + *the_min = -min; + return scale; +} + +static float make_qp_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, const float * quant_weights) { + float max = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + } + if (!max) { // all zero + for (int i = 0; i < n; ++i) { L[i] = 0; } + return 0.f; + } + float iscale = nmax / max; + for (int i = 0; i < n; ++i) { + L[i] = nearest_int(iscale * x[i]); + } + float scale = 1/iscale; + float best_mse = 0; + for (int i = 0; i < n; ++i) { + float diff = x[i] - scale*L[i]; + float w = quant_weights[i]; + best_mse += w*diff*diff; + } + for (int is = -4; is <= 4; ++is) { + if (is == 0) continue; + float iscale_is = (0.1f*is + nmax)/max; + float scale_is = 1/iscale_is; + float mse = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale_is*x[i]); + l = MIN(nmax, l); + float diff = x[i] - scale_is*l; + float w = quant_weights[i]; + mse += w*diff*diff; + } + if (mse < best_mse) { + best_mse = mse; + iscale = iscale_is; + } + } + float sumlx = 0; + float suml2 = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MIN(nmax, l); + L[i] = l; + float w = quant_weights[i]; + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + for (int itry = 0; itry < 5; ++itry) { + int n_changed = 0; + for (int i = 0; i < n; ++i) { + float w = quant_weights[i]; + float slx = sumlx - w*x[i]*L[i]; + float sl2 = suml2 - w*L[i]*L[i]; + if (slx > 0 && sl2 > 0) { + int new_l = nearest_int(x[i] * sl2 / slx); + new_l = MIN(nmax, new_l); + if (new_l != L[i]) { + slx += w*x[i]*new_l; + sl2 += w*new_l*new_l; + if (slx*slx*suml2 > sumlx*sumlx*sl2) { + L[i] = new_l; sumlx = slx; suml2 = sl2; + ++n_changed; + } + } + } + } + if (!n_changed) { + break; + } + } + return sumlx / suml2; +} + +static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restrict y, int k, const float * restrict quant_weights) { + GGML_ASSERT(quant_weights); + assert(k % QK_K == 0); + const int nb = k / QK_K; + const bool requantize = true; + + uint8_t L[QK_K]; + uint8_t Laux[16]; + float mins[QK_K/16]; + float scales[QK_K/16]; + float sw[QK_K/16]; + float weight[QK_K/16]; + uint8_t Ls[QK_K/16], Lm[QK_K/16]; + + for (int i = 0; i < nb; i++) { + memset(sw, 0, QK_K/16*sizeof(float)); + float sumx2 = 0; + for (int j = 0; j < QK_K; ++j) sumx2 += x[j]*x[j]; + float sigma2 = sumx2/QK_K; + for (int j = 0; j < QK_K/16; ++j) { + const float * restrict qw = quant_weights + QK_K * i + 16*j; + for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); + for (int l = 0; l < 16; ++l) sw[j] += weight[l]; + scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + } + + float dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); + float mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); + y[i].d = GGML_FP32_TO_FP16(dm); + y[i].dmin = GGML_FP32_TO_FP16(mm); + dm = GGML_FP16_TO_FP32(y[i].d); + mm = GGML_FP16_TO_FP32(y[i].dmin); + + for (int j = 0; j < QK_K/16; ++j) { + y[i].scales[j] = Ls[j] | (Lm[j] << 4); + } + + if (requantize) { + for (int j = 0; j < QK_K/16; ++j) { + const float d = dm * (y[i].scales[j] & 0xF); + if (!d) continue; + const float m = mm * (y[i].scales[j] >> 4); + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int((x[16*j + ii] + m)/d); + l = MAX(0, MIN(3, l)); + L[16*j + ii] = l; + } + } + } + +#if QK_K == 256 + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); + } + } +#else + for (int l = 0; l < 16; ++l) { + y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); + } +#endif + + x += QK_K; + + } +} + +size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row); + if (!quant_weights) { + quantize_row_q2_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q2_K_impl(src, (block_q2_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + //========================= 3-bit (de)-quantization void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) { @@ -2584,14 +2821,6 @@ static const uint8_t ksigns_iq2xs[128] = { static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; -void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k) { - (void)x; - (void)y; - (void)k; - assert(k % QK_K == 0); - //fprintf(stderr, "=========================== %s: not implemented\n", __func__); -} - void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2618,33 +2847,8 @@ void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y } } -void quantize_row_iq2_xxs(const float * restrict x, void * restrict vy, int k) { - assert(k % QK_K == 0); - block_iq2_xxs * restrict y = vy; - quantize_row_iq2_xxs_reference(x, y, k); -} - -size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_iq2_xxs * restrict y = (block_iq2_xxs *)dst + j/QK_K; - quantize_row_iq2_xxs_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_iq2_xxs)); -} - // ====================== 2.3125 bpw (de)-quantization -void quantize_row_iq2_xs_reference(const float * restrict x, block_iq2_xs * restrict y, int k) { - (void)x; - (void)y; - (void)k; - assert(k % QK_K == 0); - //fprintf(stderr, "=========================== %s: not implemented\n", __func__); -} - void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2670,23 +2874,6 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, } } -void quantize_row_iq2_xs(const float * restrict x, void * restrict vy, int k) { - assert(k % QK_K == 0); - block_iq2_xs * restrict y = vy; - quantize_row_iq2_xs_reference(x, y, k); -} - -size_t ggml_quantize_iq2_xs(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK_K == 0); - (void)hist; // TODO: collect histograms - - for (int j = 0; j < n; j += k) { - block_iq2_xs * restrict y = (block_iq2_xs *)dst + j/QK_K; - quantize_row_iq2_xs_reference(src + j, y, k); - } - return (n/QK_K*sizeof(block_iq2_xs)); -} - //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -7730,3 +7917,666 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest *s = 0.125f * sumf; #endif } + +// ================================ IQ2 quantization ============================================= + +typedef struct { + uint64_t * grid; + int * map; + uint16_t * neighbours; +} iq2_entry_t; + +static iq2_entry_t iq2_data[2] = { + {NULL, NULL, NULL}, + {NULL, NULL, NULL}, +}; + +static inline int iq2_data_index(int grid_size) { + GGML_ASSERT(grid_size == 256 || grid_size == 512); + return grid_size == 256 ? 0 : 1; +} + +static int iq2_compare_func(const void * left, const void * right) { + const int * l = (const int *)left; + const int * r = (const int *)right; + return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0; +} + +static void q2xs_init_impl(int grid_size) { + const int gindex = iq2_data_index(grid_size); + if (iq2_data[gindex].grid) { + return; + } + static const uint16_t kgrid_256[256] = { + 0, 2, 5, 8, 10, 17, 20, 32, 34, 40, 42, 65, 68, 80, 88, 97, + 100, 128, 130, 138, 162, 257, 260, 272, 277, 320, 388, 408, 512, 514, 546, 642, + 1025, 1028, 1040, 1057, 1060, 1088, 1090, 1096, 1120, 1153, 1156, 1168, 1188, 1280, 1282, 1288, + 1312, 1350, 1385, 1408, 1425, 1545, 1552, 1600, 1668, 1700, 2048, 2053, 2056, 2068, 2088, 2113, + 2116, 2128, 2130, 2184, 2308, 2368, 2562, 2580, 4097, 4100, 4112, 4129, 4160, 4192, 4228, 4240, + 4245, 4352, 4360, 4384, 4432, 4442, 4480, 4644, 4677, 5120, 5128, 5152, 5157, 5193, 5248, 5400, + 5474, 5632, 5654, 6145, 6148, 6160, 6208, 6273, 6400, 6405, 6560, 6737, 8192, 8194, 8202, 8260, + 8289, 8320, 8322, 8489, 8520, 8704, 8706, 9217, 9220, 9232, 9280, 9302, 9472, 9537, 9572, 9872, + 10248, 10272, 10388, 10820, 16385, 16388, 16400, 16408, 16417, 16420, 16448, 16456, 16470, 16480, 16513, 16516, + 16528, 16640, 16672, 16737, 16768, 16773, 16897, 16912, 16968, 16982, 17000, 17408, 17416, 17440, 17536, 17561, + 17682, 17700, 17920, 18433, 18436, 18448, 18496, 18501, 18688, 18776, 18785, 18818, 19013, 19088, 20480, 20488, + 20497, 20505, 20512, 20608, 20616, 20740, 20802, 20900, 21137, 21648, 21650, 21770, 22017, 22100, 22528, 22545, + 22553, 22628, 22848, 23048, 24580, 24592, 24640, 24680, 24832, 24917, 25112, 25184, 25600, 25605, 25872, 25874, + 25988, 26690, 32768, 32770, 32778, 32833, 32898, 33028, 33048, 33088, 33297, 33793, 33796, 33808, 33813, 33856, + 33888, 34048, 34118, 34196, 34313, 34368, 34400, 34818, 35076, 35345, 36868, 36880, 36900, 36928, 37025, 37142, + 37248, 37445, 37888, 37922, 37956, 38225, 39041, 39200, 40962, 41040, 41093, 41225, 41472, 42008, 43088, 43268, + }; + static const uint16_t kgrid_512[512] = { + 0, 2, 5, 8, 10, 17, 20, 22, 25, 32, 34, 37, 40, 65, 68, 70, + 73, 80, 82, 85, 88, 97, 100, 128, 130, 133, 136, 145, 148, 153, 160, 257, + 260, 262, 265, 272, 274, 277, 280, 282, 289, 292, 320, 322, 325, 328, 337, 340, + 352, 360, 385, 388, 400, 512, 514, 517, 520, 529, 532, 544, 577, 580, 592, 597, + 640, 650, 1025, 1028, 1030, 1033, 1040, 1042, 1045, 1048, 1057, 1060, 1088, 1090, 1093, 1096, + 1105, 1108, 1110, 1120, 1153, 1156, 1168, 1280, 1282, 1285, 1288, 1297, 1300, 1312, 1345, 1348, + 1360, 1377, 1408, 1537, 1540, 1552, 1574, 1600, 1602, 1668, 2048, 2050, 2053, 2056, 2058, 2065, + 2068, 2080, 2085, 2113, 2116, 2128, 2136, 2176, 2208, 2218, 2305, 2308, 2320, 2368, 2433, 2441, + 2560, 2592, 2600, 2710, 2720, 4097, 4100, 4102, 4105, 4112, 4114, 4117, 4120, 4129, 4132, 4160, + 4162, 4165, 4168, 4177, 4180, 4192, 4202, 4225, 4228, 4240, 4352, 4354, 4357, 4360, 4369, 4372, + 4384, 4417, 4420, 4432, 4480, 4500, 4502, 4609, 4612, 4614, 4624, 4672, 4704, 5120, 5122, 5125, + 5128, 5137, 5140, 5152, 5185, 5188, 5193, 5200, 5220, 5248, 5377, 5380, 5392, 5440, 5632, 5652, + 5705, 6145, 6148, 6160, 6162, 6208, 6228, 6278, 6400, 6405, 6502, 6737, 6825, 8192, 8194, 8197, + 8200, 8202, 8209, 8212, 8224, 8257, 8260, 8272, 8320, 8352, 8449, 8452, 8464, 8512, 8520, 8549, + 8704, 8738, 8832, 8872, 9217, 9220, 9232, 9257, 9280, 9472, 9537, 9554, 9625, 9729, 9754, 9894, + 10240, 10248, 10250, 10272, 10325, 10376, 10402, 10600, 10640, 10760, 10784, 10882, 10888, 10890, 16385, 16388, + 16390, 16393, 16400, 16402, 16405, 16408, 16417, 16420, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16480, + 16485, 16513, 16516, 16528, 16640, 16642, 16645, 16648, 16657, 16660, 16672, 16705, 16708, 16720, 16768, 16773, + 16802, 16897, 16900, 16912, 16914, 16937, 16960, 17408, 17410, 17413, 17416, 17425, 17428, 17433, 17440, 17473, + 17476, 17488, 17536, 17556, 17665, 17668, 17680, 17700, 17728, 17818, 17920, 17930, 17988, 18000, 18433, 18436, + 18448, 18496, 18501, 18516, 18530, 18688, 18705, 18756, 18768, 18793, 18948, 20480, 20482, 20485, 20488, 20497, + 20500, 20512, 20520, 20545, 20548, 20560, 20608, 20737, 20740, 20752, 20757, 20800, 20802, 20992, 21060, 21162, + 21505, 21508, 21520, 21537, 21568, 21600, 21633, 21665, 21760, 21768, 21888, 21896, 22049, 22120, 22177, 22528, + 22548, 22593, 22608, 22681, 22810, 22848, 22850, 23173, 24577, 24580, 24592, 24640, 24660, 24674, 24710, 24745, + 24832, 25124, 25162, 25234, 25600, 25622, 25872, 25920, 25925, 26020, 26625, 26730, 26917, 27142, 27220, 27234, + 32768, 32770, 32773, 32776, 32785, 32788, 32800, 32810, 32833, 32836, 32848, 32896, 32898, 32936, 32938, 33025, + 33028, 33030, 33040, 33088, 33105, 33113, 33280, 33312, 33408, 33410, 33440, 33448, 33793, 33796, 33808, 33810, + 33813, 33856, 33888, 33929, 34048, 34116, 34213, 34328, 34410, 34816, 34824, 34853, 34906, 34944, 34946, 34984, + 35078, 35362, 35456, 35464, 35478, 35496, 36865, 36868, 36880, 36928, 36950, 36996, 37120, 37154, 37220, 37462, + 37513, 37888, 37893, 37956, 37968, 37976, 38185, 38288, 38290, 38465, 38993, 39078, 39241, 39445, 39520, 40960, + 40962, 40968, 40970, 40992, 41002, 41120, 41297, 41305, 41382, 41472, 41474, 41480, 41514, 41600, 41632, 42048, + 42133, 42597, 42648, 43018, 43040, 43042, 43048, 43168, 43176, 43268, 43396, 43398, 43560, 43562, 43665, 43690, + }; + const int kmap_size = 43692; + const int nwant = 2; + const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512; + uint64_t * kgrid_q2xs; + int * kmap_q2xs; + uint16_t * kneighbors_q2xs; + + printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + uint64_t * the_grid = (uint64_t *)malloc(grid_size*sizeof(uint64_t)); + for (int k = 0; k < grid_size; ++k) { + int8_t * pos = (int8_t *)(the_grid + k); + for (int i = 0; i < 8; ++i) { + int l = (kgrid[k] >> 2*i) & 0x3; + pos[i] = 2*l + 1; + } + } + kgrid_q2xs = the_grid; + iq2_data[gindex].grid = the_grid; + kmap_q2xs = (int *)malloc(kmap_size*sizeof(int)); + iq2_data[gindex].map = kmap_q2xs; + for (int i = 0; i < kmap_size; ++i) kmap_q2xs[i] = -1; + uint64_t aux64; + uint8_t * aux8 = (uint8_t *)&aux64; + for (int i = 0; i < grid_size; ++i) { + aux64 = kgrid_q2xs[i]; + uint16_t index = 0; + for (int k=0; k<8; ++k) { + uint16_t q = (aux8[k] - 1)/2; + index |= (q << 2*k); + } + kmap_q2xs[index] = i; + } + int8_t pos[8]; + int * dist2 = (int *)malloc(2*grid_size*sizeof(int)); + int num_neighbors = 0, num_not_in_map = 0; + for (int i = 0; i < kmap_size; ++i) { + if (kmap_q2xs[i] >= 0) continue; + ++num_not_in_map; + for (int k = 0; k < 8; ++k) { + int l = (i >> 2*k) & 0x3; + pos[k] = 2*l + 1; + } + for (int j = 0; j < grid_size; ++j) { + const int8_t * pg = (const int8_t *)(kgrid_q2xs + j); + int d2 = 0; + for (int k = 0; k < 8; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]); + dist2[2*j+0] = d2; + dist2[2*j+1] = j; + } + qsort(dist2, grid_size, 2*sizeof(int), iq2_compare_func); + int n = 0; int d2 = dist2[0]; + int nhave = 1; + for (int j = 0; j < grid_size; ++j) { + if (dist2[2*j] > d2) { + if (nhave == nwant) break; + d2 = dist2[2*j]; + ++nhave; + } + ++n; + } + num_neighbors += n; + } + printf("%s: %d neighbours in total\n", __func__, num_neighbors); + kneighbors_q2xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); + iq2_data[gindex].neighbours = kneighbors_q2xs; + int counter = 0; + for (int i = 0; i < kmap_size; ++i) { + if (kmap_q2xs[i] >= 0) continue; + for (int k = 0; k < 8; ++k) { + int l = (i >> 2*k) & 0x3; + pos[k] = 2*l + 1; + } + for (int j = 0; j < grid_size; ++j) { + const int8_t * pg = (const int8_t *)(kgrid_q2xs + j); + int d2 = 0; + for (int k = 0; k < 8; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]); + dist2[2*j+0] = d2; + dist2[2*j+1] = j; + } + qsort(dist2, grid_size, 2*sizeof(int), iq2_compare_func); + kmap_q2xs[i] = -(counter + 1); + int d2 = dist2[0]; + uint16_t * start = &kneighbors_q2xs[counter++]; + int n = 0, nhave = 1; + for (int j = 0; j < grid_size; ++j) { + if (dist2[2*j] > d2) { + if (nhave == nwant) break; + d2 = dist2[2*j]; + ++nhave; + } + kneighbors_q2xs[counter++] = dist2[2*j+1]; + ++n; + } + *start = n; + } + free(dist2); +} + +void ggml_init_iq2_quantization(enum ggml_type type) { + if (type == GGML_TYPE_IQ2_XXS) { + q2xs_init_impl(256); + } + else if (type == GGML_TYPE_IQ2_XS) { + q2xs_init_impl(512); + } + else { + fprintf(stderr, "======================== Why are you calling %s with type %d?\n", __func__, (int)type); + } +} + +static void q2xs_deinit_impl(int grid_size) { + GGML_ASSERT(grid_size == 256 || grid_size == 512 || grid_size == 1024); + const int gindex = iq2_data_index(grid_size); + if (iq2_data[gindex].grid) { + free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL; + free(iq2_data[gindex].map); iq2_data[gindex].map = NULL; + free(iq2_data[gindex].neighbours); iq2_data[gindex].neighbours = NULL; + } +} + +void ggml_deinit_iq2_quantization(enum ggml_type type) { + if (type == GGML_TYPE_IQ2_XXS) { + q2xs_deinit_impl(256); + } + else if (type == GGML_TYPE_IQ2_XS) { + q2xs_deinit_impl(512); + } + else { + fprintf(stderr, "======================== Why are you calling %s with type %d?\n", __func__, (int)type); + } +} + +static int iq2_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid, + const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L) { + int num_neighbors = neighbours[0]; + GGML_ASSERT(num_neighbors > 0); + float best_d2 = FLT_MAX; + int grid_index = -1; + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float d2 = 0; + for (int i = 0; i < 8; ++i) { + float q = pg[i]; + float diff = scale*q - xval[i]; + d2 += weight[i]*diff*diff; + } + if (d2 < best_d2) { + best_d2 = d2; grid_index = neighbours[j]; + } + } + GGML_ASSERT(grid_index >= 0); + const int8_t * pg = (const int8_t *)(grid + grid_index); + for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2; + return grid_index; +} + +static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(256); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(quant_weights); + GGML_ASSERT(kgrid_q2xs); + GGML_ASSERT(kmap_q2xs); + GGML_ASSERT(kneighbors_q2xs); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 3; + + const int nbl = n/256; + + block_iq2_xxs * y = vy; + + float scales[QK_K/32]; + float weight[32]; + float xval[32]; + int8_t L[32]; + int8_t Laux[32]; + float waux[32]; + bool is_on_grid[4]; + bool is_on_grid_aux[4]; + uint8_t block_signs[4]; + uint32_t q2[2*(QK_K/32)]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(q2, 0, QK_K/4); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = sumx2/QK_K; + + for (int ib = 0; ib < QK_K/32; ++ib) { + const float * xb = xbl + 32*ib; + const float * qw = quant_weights + QK_K*ibl + 32*ib; + for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + for (int i = 0; i < 32; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 4; ++k) { + int nflip = 0; + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i); + } + } + if (nflip%2) { + int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin]; + for (int i = 1; i < 8; ++i) { + float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i]; + if (ax < min) { + min = ax; imin = i; + } + } + xval[8*k+imin] = -xval[8*k+imin]; + s ^= (1 << imin); + } + block_signs[k] = s & 127; + } + float max = xval[0]; + for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + memset(L, 0, 32); + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + for (int is = -9; is <= 9; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/max; + float this_scale = 1/id; + for (int k = 0; k < 4; ++k) { + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 32; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 32; ++i) L[i] = Laux[i]; + for (int k = 0; k < 4; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 4; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 2*i); + } + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k); + } + const int8_t * pg = (const int8_t *)(kgrid_q2xs + grid_index); + for (int i = 0; i < 8; ++i) L[8*k+i] = (pg[i] - 1)/2; + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 32; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale) + // and correspondingly flip quant signs. + scale = -scale; + for (int k = 0; k < 4; ++k) block_signs[k] = (~block_signs[k]) & 127; + } + for (int k = 0; k < 4; ++k) { + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + q2[2*ib+0] |= (grid_index << 8*k); + q2[2*ib+1] |= (block_signs[k] << 7*k); + } + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + memset(y[ibl].qs, 0, QK_K/4); + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d); + float id = 1/d; + float sumqx = 0, sumq2 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + q2[2*ib+1] |= ((uint32_t)l << 28); + const float * xb = xbl + 32*ib; + const float * qw = quant_weights + QK_K*ibl + 32*ib; + for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib); + const float db = d * (1 + 2*l); + uint32_t u = 0; + for (int k = 0; k < 4; ++k) { + const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127); + const float * xk = xb + 8*k; + const float * wk = weight + 8*k; + const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); + float best_mse = 0; int best_index = aux8[k]; + for (int j = 0; j < 8; ++j) { + float diff = db * grid[j] * signs[j] - xk[j]; + best_mse += wk[j] * diff * diff; + } + for (int idx = 0; idx < 256; ++idx) { + grid = (const uint8_t *)(kgrid_q2xs + idx); + float mse = 0; + for (int j = 0; j < 8; ++j) { + float diff = db * grid[j] * signs[j] - xk[j]; + mse += wk[j] * diff * diff; + } + if (mse < best_mse) { + best_mse = mse; best_index = idx; + } + } + u |= (best_index << 8*k); + grid = (const uint8_t *)(kgrid_q2xs + best_index); + //grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); + for (int j = 0; j < 8; ++j) { + float q = db * grid[j] * signs[j]; + sumqx += wk[j] * q * xk[j]; + sumq2 += wk[j] * q * q; + } + } + q2[2*ib] = u; + if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); + } + memcpy(y[ibl].qs, q2, QK_K/4); + } +} + +static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(512); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(quant_weights); + GGML_ASSERT(kmap_q2xs); + GGML_ASSERT(kgrid_q2xs); + GGML_ASSERT(kneighbors_q2xs); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 3; + + const int nbl = n/256; + + block_iq2_xs * y = vy; + + float scales[QK_K/16]; + float weight[16]; + float xval[16]; + int8_t L[16]; + int8_t Laux[16]; + float waux[16]; + bool is_on_grid[2]; + bool is_on_grid_aux[2]; + uint8_t block_signs[2]; + uint16_t q2[2*(QK_K/16)]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(q2, 0, QK_K/4); + memset(y[ibl].scales, 0, QK_K/32); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = sumx2/QK_K; + + for (int ib = 0; ib < QK_K/16; ++ib) { + const float * xb = xbl + 16*ib; + const float * qw = quant_weights + QK_K*ibl + 16*ib; + for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 2; ++k) { + int nflip = 0; + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i); + } + } + if (nflip%2) { + int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin]; + for (int i = 1; i < 8; ++i) { + float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i]; + if (ax < min) { + min = ax; imin = i; + } + } + xval[8*k+imin] = -xval[8*k+imin]; + s ^= (1 << imin); + } + block_signs[k] = s & 127; + } + float max = xval[0]; + for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + memset(L, 0, 16); + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + is_on_grid[0] = is_on_grid[1] = true; + for (int is = -9; is <= 9; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/max; + float this_scale = 1/id; + for (int k = 0; k < 2; ++k) { + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 16; ++i) L[i] = Laux[i]; + for (int k = 0; k < 2; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 2; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 2*i); + L[8*k + i] = l; + } + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + scale = -scale; + for (int k = 0; k < 2; ++k) block_signs[k] = (~block_signs[k]) & 127; + } + for (int k = 0; k < 2; ++k) { + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + q2[2*ib+k] = grid_index | (block_signs[k] << 9); + } + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + memset(y[ibl].qs, 0, QK_K/4); + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d); + float id = 1/d; + for (int ib = 0; ib < QK_K/16; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + if (ib%2 == 0) y[ibl].scales[ib/2] = l; + else y[ibl].scales[ib/2] |= (l << 4); + } + memcpy(y[ibl].qs, q2, QK_K/4); + + } +} + +size_t quantize_iq2_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq2_xxs_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq2_xxs); + } + return nrow * nblock * sizeof(block_iq2_xxs); +} + +size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq2_xs_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq2_xs); + } + return nrow * nblock * sizeof(block_iq2_xs); +} + diff --git a/ggml-quants.h b/ggml-quants.h index df5e7ae80..e5d110230 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -196,8 +196,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); -void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k); -void quantize_row_iq2_xs_reference (const float * restrict x, block_iq2_xs * restrict y, int k); void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); @@ -212,8 +210,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); -void quantize_row_iq2_xxs(const float * restrict x, void * restrict y, int k); -void quantize_row_iq2_xs (const float * restrict x, void * restrict y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); @@ -246,3 +242,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); + +// +// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") +// +size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); + diff --git a/ggml.c b/ggml.c index bcfb6652c..52467475a 100644 --- a/ggml.c +++ b/ggml.c @@ -585,8 +585,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, - .from_float = quantize_row_iq2_xxs, - .from_float_reference = (ggml_from_float_t) quantize_row_iq2_xxs_reference, + .from_float = NULL, + .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, @@ -596,8 +596,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, - .from_float = quantize_row_iq2_xs, - .from_float_reference = (ggml_from_float_t) quantize_row_iq2_xs_reference, + .from_float = NULL, + .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, @@ -18665,8 +18665,11 @@ size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * return (n/QK8_0*sizeof(block_q8_0)); } -size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) { +size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, + int nrows, int n_per_row, int64_t * hist, const float * imatrix) { + (void)imatrix; size_t result = 0; + int n = nrows * n_per_row; switch (type) { case GGML_TYPE_Q4_0: { @@ -18701,8 +18704,11 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i case GGML_TYPE_Q2_K: { GGML_ASSERT(start % QK_K == 0); - block_q2_K * block = (block_q2_K*)dst + start / QK_K; - result = ggml_quantize_q2_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q2_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q3_K: { @@ -18731,14 +18737,22 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i case GGML_TYPE_IQ2_XXS: { GGML_ASSERT(start % QK_K == 0); - block_iq2_xxs * block = (block_iq2_xxs*)dst + start / QK_K; - result = ggml_quantize_iq2_xxs(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + GGML_ASSERT(imatrix); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq2_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_IQ2_XS: { GGML_ASSERT(start % QK_K == 0); - block_iq2_xs * block = (block_iq2_xs*)dst + start / QK_K; - result = ggml_quantize_iq2_xs(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + GGML_ASSERT(imatrix); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_F16: { diff --git a/ggml.h b/ggml.h index b18ba7812..1187074f7 100644 --- a/ggml.h +++ b/ggml.h @@ -2067,10 +2067,13 @@ extern "C" { GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist); GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_iq2_xs (const float * src, void * dst, int n, int k, int64_t * hist); - GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist); + GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, + int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix); + + // These are needed for IQ2_XS and IQ2_XXS quantizations + GGML_API void ggml_init_iq2_quantization(enum ggml_type type); + GGML_API void ggml_deinit_iq2_quantization(enum ggml_type type); // // Importance matrix diff --git a/llama.cpp b/llama.cpp index 8e20e72a2..107b05114 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8429,9 +8429,23 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) { new_type = GGML_TYPE_Q8_0; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + new_type = GGML_TYPE_Q5_K; + } else if (new_type != GGML_TYPE_Q8_0) { new_type = GGML_TYPE_Q6_K; } + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + if (name.find("attn_v.weight") != std::string::npos) { + if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; + else new_type = GGML_TYPE_Q2_K; + ++qs.i_attention_wv; + } + else if (name.find("ffn_down") != std::string::npos) { + if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q2_K; + ++qs.i_feed_forward_w2; + } + else if (name == "token_embd.weight") new_type = GGML_TYPE_Q2_K; } else if (name.find("attn_v.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { @@ -8601,6 +8615,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (params->only_copy) { ftype = model.ftype; } + const std::unordered_map> * imatrix_data = nullptr; + if (params->imatrix) { + imatrix_data = static_cast>*>(params->imatrix); + if (imatrix_data) { + printf("================================ Have weights data with %d entries\n",int(imatrix_data->size())); + } + } const size_t align = GGUF_DEFAULT_ALIGNMENT; struct gguf_context * ctx_out = gguf_init_empty(); @@ -8658,6 +8679,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // placeholder for the meta data ::zeros(fout, meta_size); + std::set used_iq2; + for (int i = 0; i < ml.n_tensors; ++i) { struct ggml_tensor * tensor = ml.get_tensor_meta(i); @@ -8710,6 +8733,35 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else { const size_t nelements = ggml_nelements(tensor); + if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS) && used_iq2.find(new_type) == used_iq2.end()) { + ggml_init_iq2_quantization(new_type); + used_iq2.insert(new_type); + } + + const float * imatrix = nullptr; + if (imatrix_data) { + auto it = imatrix_data->find(tensor->name); + if (it == imatrix_data->end()) { + printf("\n====== %s: did not find weights for %s\n", __func__, tensor->name); + } else { + if (it->second.size() == (size_t)tensor->ne[0]) { + imatrix = it->second.data(); + } else { + printf("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__, + int(it->second.size()), int(tensor->ne[0]), tensor->name); + } + } + } + if ((new_type == GGML_TYPE_IQ2_XXS || + new_type == GGML_TYPE_IQ2_XS || + (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { + fprintf(stderr, "\n\n============================================================\n"); + fprintf(stderr, "Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); + fprintf(stderr, "The result will be garbage, so bailing out\n"); + fprintf(stderr, "============================================================\n\n"); + throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name)); + } + float * f32_data; if (tensor->type == GGML_TYPE_F32) { @@ -8730,21 +8782,28 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s new_data = work.data(); std::array hist_cur = {}; - static const int chunk_size = 32 * 512; + const int n_per_row = tensor->ne[0]; + const int nrows = nelements / n_per_row; + + static const int min_chunk_size = 32 * 512; + const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row); + const int nchunk = (nelements + chunk_size - 1)/chunk_size; const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1; if (nthread_use < 2) { - new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data()); + new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur.data(), imatrix); } else { - size_t counter = 0; + int counter = 0; new_size = 0; - auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() { + auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size, + nrows, n_per_row, imatrix]() { std::array local_hist = {}; + const int nrows_per_chunk = chunk_size / n_per_row; size_t local_size = 0; while (true) { std::unique_lock lock(mutex); - size_t first = counter; counter += chunk_size; - if (first >= nelements) { + int first_row = counter; counter += nrows_per_chunk; + if (first_row >= nrows) { if (local_size > 0) { for (int j=0; j %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); + LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); int64_t tot_count = 0; for (size_t i = 0; i < hist_cur.size(); i++) { hist_all[i] += hist_cur[i]; @@ -8774,6 +8834,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } if (tot_count > 0) { + LLAMA_LOG_INFO(" | hist: "); for (size_t i = 0; i < hist_cur.size(); i++) { LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements)); } @@ -8802,6 +8863,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s fout.close(); + for (auto type : used_iq2) { + ggml_deinit_iq2_quantization(type); + } + gguf_free(ctx_out); LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); @@ -9166,6 +9231,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { /*.quantize_output_tensor =*/ true, /*.only_copy =*/ false, /*.pure =*/ false, + /*.imatrix =*/ nullptr, }; return result; diff --git a/llama.h b/llama.h index 01d6fafaa..79c8335b6 100644 --- a/llama.h +++ b/llama.h @@ -249,6 +249,7 @@ extern "C" { bool quantize_output_tensor; // quantize output.weight bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored bool pure; // disable k-quant mixtures and quantize all tensors to the same type + void * imatrix; // pointer to importance matrix data } llama_model_quantize_params; // grammar types diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d9b8b106a..22a7856d4 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -56,7 +56,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0); std::vector dataq(ggml_row_size(tensor->type, size)); int64_t hist[16]; - ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist); + ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, nullptr); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { // This is going to create some weird integers though. From ac32902a87147f78d63c931aa8a23dee762660e7 Mon Sep 17 00:00:00 2001 From: Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com> Date: Sun, 14 Jan 2024 00:41:44 -0800 Subject: [PATCH 033/334] llama : support WinXP build with MinGW 8.1.0 (#3419) --- CMakeLists.txt | 8 ++++++-- llama.cpp | 4 ++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 668669c6d..2741568ed 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,4 @@ -cmake_minimum_required(VERSION 3.13) # for add_link_options +cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. project("llama.cpp" C CXX) set(CMAKE_EXPORT_COMPILE_COMMANDS ON) @@ -76,6 +76,10 @@ if (NOT MSVC) option(LLAMA_F16C "llama: enable F16C" ${INS_ENB}) endif() +if (WIN32) + option(LLAMA_WIN_VER "llama: Windows Version" 0x602) +endif() + # 3rd party libs option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) option(LLAMA_BLAS "llama: use BLAS" OFF) @@ -686,7 +690,7 @@ endif() if (MINGW) # Target Windows 8 for PrefetchVirtualMemory - add_compile_definitions(_WIN32_WINNT=0x602) + add_compile_definitions(_WIN32_WINNT=${LLAMA_WIN_VER}) endif() # diff --git a/llama.cpp b/llama.cpp index 107b05114..51e9bdaed 100644 --- a/llama.cpp +++ b/llama.cpp @@ -987,6 +987,7 @@ struct llama_mmap { } if (prefetch > 0) { +#if _WIN32_WINNT >= 0x602 // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG); HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll"); @@ -1004,6 +1005,9 @@ struct llama_mmap { llama_format_win_err(GetLastError()).c_str()); } } +#else + throw std::runtime_error("PrefetchVirtualMemory unavailable"); +#endif } } From 5f5fe1bd608fa2ed42af97b5f2ea31be6625fc48 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Sun, 14 Jan 2024 09:44:39 +0100 Subject: [PATCH 034/334] metal : correctly set SIMD support flags on iOS (#4923) * Correctly set support_simdgroup_reduction and support_simdgroup_mm on iPhone/iPad * log a little bit more info on iOS --- ggml-metal.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-metal.m b/ggml-metal.m index cae52c983..2ca726055 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -330,7 +330,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { } } -#if TARGET_OS_OSX // print MTL GPU family: GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]); @@ -370,6 +369,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); +#if TARGET_OS_OSX GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); if (ctx->device.maxTransferRate != 0) { GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6); From a128c38de862431f1aae9ccc40b792fbc1b8b682 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 10:53:39 +0200 Subject: [PATCH 035/334] Fix ffn_down quantization mix for MoE models (#4927) * Fix ffn_down quantization mix for MoE models In #4872 I did not consider the part where every third tensor is quantized with more bits. Fir MoE this leads to tensors of the same layer being quantized with different number of bits, which is not considered as a possibility in the inference implementation (it is assumed all experts use the same quantization). * Fix the fix * Review suggestion --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 34 ++++++++++++++++++++++++++-------- 1 file changed, 26 insertions(+), 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index 51e9bdaed..b1d6015e2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8480,13 +8480,31 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = GGML_TYPE_Q8_0; } } else if (name.find("ffn_down") != std::string::npos) { + const int n_expert = std::max(1, (int)qs.model.hparams.n_expert); + int i_layer, n_layer; + if (n_expert == 1) { + i_layer = qs.i_feed_forward_w2; + n_layer = qs.n_feed_forward_w2; + } else { + // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly + // sprinkled in the model. Hence, simply dividing i_feed_forward_w2 by n_expert does not work + // for getting the current layer as I initially thought, and we need to resort to parsing the + // tensor name. + n_layer = qs.n_feed_forward_w2 / n_expert; + if (sscanf(name.c_str(), "blk.%d.ffn_down", &i_layer) != 1) { + throw std::runtime_error(format("Failed to determine layer for tensor %s", name.c_str())); + } + if (i_layer < 0 || i_layer >= n_layer) { + throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name.c_str(), n_layer)); + } + } if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) { - if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q4_K; + if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { - new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q5_K - : arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K + new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K + : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { @@ -8494,14 +8512,14 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { if (arch == LLM_ARCH_FALCON) { - new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q6_K : - use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; + new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K : + use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } else { - if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; + if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; } } - else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) { + else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) { new_type = GGML_TYPE_Q5_K; } ++qs.i_feed_forward_w2; From 03c526749041c863b0cd842b26b8907e1ea0e0b1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 11:03:19 +0200 Subject: [PATCH 036/334] llama : use LLAMA_LOG_ macros for logging --- llama.cpp | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/llama.cpp b/llama.cpp index b1d6015e2..51821965e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1114,7 +1114,7 @@ struct llama_mlock { suggest = false; } - fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s", + LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s", size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : ""); return false; } @@ -1123,7 +1123,7 @@ struct llama_mlock { static void raw_unlock(void * addr, size_t size) { if (munlock(addr, size)) { - fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno)); + LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno)); } } #elif defined(_WIN32) @@ -1141,7 +1141,7 @@ struct llama_mlock { return true; } if (tries == 2) { - fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n", + LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n", len, size, llama_format_win_err(GetLastError()).c_str()); return false; } @@ -1150,7 +1150,7 @@ struct llama_mlock { // set size and try again. SIZE_T min_ws_size, max_ws_size; if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) { - fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n", + LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n", llama_format_win_err(GetLastError()).c_str()); return false; } @@ -1163,7 +1163,7 @@ struct llama_mlock { min_ws_size += increment; max_ws_size += increment; if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) { - fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n", + LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n", llama_format_win_err(GetLastError()).c_str()); return false; } @@ -1172,7 +1172,7 @@ struct llama_mlock { static void raw_unlock(void * ptr, size_t len) { if (!VirtualUnlock(ptr, len)) { - fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n", + LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n", llama_format_win_err(GetLastError()).c_str()); } } @@ -1184,7 +1184,7 @@ struct llama_mlock { } bool raw_lock(const void * addr, size_t len) const { - fprintf(stderr, "warning: mlock not supported on this system\n"); + LLAMA_LOG_WARN("warning: mlock not supported on this system\n"); return false; } @@ -2085,13 +2085,13 @@ namespace GGUFMeta { __func__, override_type_to_str(override->tag), override->key); switch (override->tag) { case LLAMA_KV_OVERRIDE_BOOL: { - printf("%s\n", override->bool_value ? "true" : "false"); + LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false"); } break; case LLAMA_KV_OVERRIDE_INT: { - printf("%" PRId64 "\n", override->int_value); + LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value); } break; case LLAMA_KV_OVERRIDE_FLOAT: { - printf("%.6f\n", override->float_value); + LLAMA_LOG_INFO("%.6f\n", override->float_value); } break; default: // Shouldn't be possible to end up here, but just in case... @@ -6993,7 +6993,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break; #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); + LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); #endif auto source = std::distance(buffer.begin(), it); @@ -7006,7 +7006,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length); #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str()); + LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str()); #endif it++; } @@ -7022,7 +7022,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length); #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str()); + LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str()); #endif it++; @@ -7038,7 +7038,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< raw_text_base_length = right_reminder_length; #ifdef PRETOKENIZERDEBUG - fprintf(stderr, "RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); + LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str()); #endif } else { if (source == 0) { @@ -7095,7 +7095,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } #ifdef PRETOKENIZERDEBUG - fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); @@ -7116,7 +7116,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); #ifdef PRETOKENIZERDEBUG - fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_bpe tokenizer(vocab); tokenizer.tokenize(raw_text, output); @@ -8641,7 +8641,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (params->imatrix) { imatrix_data = static_cast>*>(params->imatrix); if (imatrix_data) { - printf("================================ Have weights data with %d entries\n",int(imatrix_data->size())); + LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size())); } } @@ -8764,12 +8764,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (imatrix_data) { auto it = imatrix_data->find(tensor->name); if (it == imatrix_data->end()) { - printf("\n====== %s: did not find weights for %s\n", __func__, tensor->name); + LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name); } else { if (it->second.size() == (size_t)tensor->ne[0]) { imatrix = it->second.data(); } else { - printf("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__, + LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__, int(it->second.size()), int(tensor->ne[0]), tensor->name); } } @@ -8777,10 +8777,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS || (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { - fprintf(stderr, "\n\n============================================================\n"); - fprintf(stderr, "Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); - fprintf(stderr, "The result will be garbage, so bailing out\n"); - fprintf(stderr, "============================================================\n\n"); + LLAMA_LOG_ERROR("\n\n============================================================\n"); + LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); + LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n"); + LLAMA_LOG_ERROR("============================================================\n\n"); throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name)); } From 9408cfdad6b1c090a7e1419d4434edc260b7e47e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 11:08:09 +0200 Subject: [PATCH 037/334] scripts : sync-ggml-am.sh option to skip commits --- scripts/sync-ggml-am.sh | 14 +++++++++++++- scripts/sync-ggml.last | 2 +- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 248cf1023..6b2514a11 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -5,7 +5,7 @@ # Usage: # # $ cd /path/to/llama.cpp -# $ ./scripts/sync-ggml-am.sh +# $ ./scripts/sync-ggml-am.sh -skip hash0,hash1,hash2... # set -e @@ -24,6 +24,11 @@ fi lc=$(cat $SRC_LLAMA/scripts/sync-ggml.last) echo "Syncing ggml changes since commit $lc" +to_skip="" +if [ "$1" == "-skip" ]; then + to_skip=$2 +fi + cd $SRC_GGML git log --oneline $lc..HEAD @@ -40,6 +45,13 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then fi while read c; do + if [ -n "$to_skip" ]; then + if [[ $to_skip == *"$c"* ]]; then + echo "Skipping $c" + continue + fi + fi + git format-patch -k $c~1..$c --stdout -- \ include/ggml/ggml*.h \ src/ggml*.h \ diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 753d227a7..be9e408fb 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -1890780da4ea10db88736fcde85f285abf6c64b0 +b306d6e996ec0ace77118fa5098822cdc7f9c88f From bb0c1392479398f9aba86d9ec98db0b95ede6e6d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Jan 2024 13:26:53 +0200 Subject: [PATCH 038/334] llama : check LLAMA_TRACE env for extra logging (#4929) * llama : minor fix indent * llama : check LLAMA_TRACE env for extra logging ggml-ci --- llama.cpp | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/llama.cpp b/llama.cpp index 51821965e..63f37ecdb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2190,6 +2190,11 @@ struct llama_model_loader { LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") { + int trace = 0; + if (getenv("LLAMA_TRACE")) { + trace = atoi(getenv("LLAMA_TRACE")); + } + struct gguf_init_params params = { /*.no_alloc = */ true, /*.ctx = */ &ctx_meta, @@ -2242,11 +2247,10 @@ struct llama_model_loader { type_max = type; } - // TODO: make runtime configurable -#if 0 - struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); - LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str()); -#endif + if (trace > 0) { + struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str()); + } } switch (type_max) { @@ -6451,15 +6455,15 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { static const char * hex = "0123456789ABCDEF"; switch (llama_vocab_get_type(vocab)) { - case LLAMA_VOCAB_TYPE_SPM: { - const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 }; - return vocab.token_to_id.at(buf); - } - case LLAMA_VOCAB_TYPE_BPE: { - return vocab.token_to_id.at(bytes_to_unicode_bpe(ch)); - } - default: - GGML_ASSERT(false); + case LLAMA_VOCAB_TYPE_SPM: { + const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 }; + return vocab.token_to_id.at(buf); + } + case LLAMA_VOCAB_TYPE_BPE: { + return vocab.token_to_id.at(bytes_to_unicode_bpe(ch)); + } + default: + GGML_ASSERT(false); } } From 467a882fd2e5b6172897b49aa45aa29bd3f27685 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 14 Jan 2024 16:21:12 +0200 Subject: [PATCH 039/334] Add ability to use importance matrix for all k-quants (#4930) Co-authored-by: Iwan Kawrakow --- examples/quantize/quantize.cpp | 2 +- ggml-quants.c | 443 ++++++++++++++++++++++++++++++++- ggml-quants.h | 5 +- ggml.c | 28 ++- 4 files changed, 462 insertions(+), 16 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index f4e2175f1..2ae046933 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -82,7 +82,7 @@ static void usage(const char * executable) { printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); - printf(" --imatrixfile_name: use data in file_name as importance matrix for quant optimizations\n"); + printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n"); printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n"); printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); printf("Note: --include-weights and --exclude-weights cannot be used together\n"); diff --git a/ggml-quants.c b/ggml-quants.c index 9290d54cf..0750fe1bb 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1244,7 +1244,8 @@ static inline int nearest_int(float fval) { return (i & 0x007fffff) - 0x00400000; } -static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type) { +static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type, + const float * restrict qw) { float max = 0; float amax = 0; for (int i = 0; i < n; ++i) { @@ -1270,14 +1271,13 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * rmse_type = -rmse_type; return_early = true; } - int weight_type = rmse_type%2; float sumlx = 0; float suml2 = 0; for (int i = 0; i < n; ++i) { int l = nearest_int(iscale * x[i]); l = MAX(-nmax, MIN(nmax-1, l)); L[i] = l + nmax; - float w = weight_type == 1 ? x[i] * x[i] : 1; + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); sumlx += w*x[i]*l; suml2 += w*l*l; } @@ -1293,7 +1293,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * for (int i = 0; i < n; ++i) { int l = nearest_int(iscale * x[i]); l = MAX(-nmax, MIN(nmax-1, l)); - float w = weight_type == 1 ? x[i] * x[i] : 1; + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); sumlx += w*x[i]*l; suml2 += w*l*l; } @@ -2089,6 +2089,112 @@ size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q3_K)); } +static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q3_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + int8_t L[QK_K]; + float scales[QK_K / 16]; + float weight[16]; + float sw[QK_K / 16]; + int8_t Ls[QK_K / 16]; + + for (int i = 0; i < nb; i++) { + + float sumx2 = 0; + for (int j = 0; j < QK_K; ++j) sumx2 += x[j]*x[j]; + float sigma2 = 2*sumx2/QK_K; + + for (int j = 0; j < QK_K/16; ++j) { + if (quant_weights) { + const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL; + for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]); + } else { + for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l]; + } + float sumw = 0; + for (int l = 0; l < 16; ++l) sumw += weight[l]; + sw[j] = sumw; + + scales[j] = make_qx_quants(16, 4, x + 16*j, L + 16*j, 1, weight); + + } + + memset(y[i].scales, 0, 12); + + float d_block = make_qx_quants(QK_K/16, 32, scales, Ls, 1, sw); + for (int j = 0; j < QK_K/16; ++j) { + int l = Ls[j]; + if (j < 8) { + y[i].scales[j] = l & 0xF; + } else { + y[i].scales[j-8] |= ((l & 0xF) << 4); + } + l >>= 4; + y[i].scales[j%4 + 8] |= (l << (2*(j/4))); + } + y[i].d = GGML_FP32_TO_FP16(d_block); + + int8_t sc; + for (int j = 0; j < QK_K/16; ++j) { + sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4; + sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32; + float d = GGML_FP16_TO_FP32(y[i].d) * sc; + if (!d) { + continue; + } + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int(x[16*j + ii]/d); + l = MAX(-4, MIN(3, l)); + L[16*j + ii] = l + 4; + } + } + + memset(y[i].hmask, 0, QK_K/8); + // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc. + int m = 0; + uint8_t hm = 1; + for (int j = 0; j < QK_K; ++j) { + if (L[j] > 3) { + y[i].hmask[m] |= hm; + L[j] -= 4; + } + if (++m == QK_K/8) { + m = 0; hm <<= 1; + } + } + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); + } + } + + x += QK_K; + } +#endif +} + +size_t quantize_q3_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row); + if (!quant_weights) { + quantize_row_q3_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q3_K_impl(src, (block_q3_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== 4-bit (de)-quantization void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) { @@ -2254,6 +2360,108 @@ size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q4_K)); } +static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q4_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + uint8_t L[QK_K]; + uint8_t Laux[32]; + float weights[32]; + float mins[QK_K/32]; + float scales[QK_K/32]; + + for (int i = 0; i < nb; i++) { + + float sum_x2 = 0; + for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; + float sigma2 = sum_x2/QK_K; + float av_x = sqrtf(sigma2); + + float max_scale = 0; // as we are deducting the min, scales are always positive + float max_min = 0; + for (int j = 0; j < QK_K/32; ++j) { + if (quant_weights) { + const float * qw = quant_weights + QK_K*i + 32*j; + for (int l = 0; l < 32; ++l) weights[l] = qw[l] * sqrtf(sigma2 + x[32*j + l]*x[32*j + l]); + } else { + for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); + } + scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + //scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false); + float scale = scales[j]; + if (scale > max_scale) { + max_scale = scale; + } + float min = mins[j]; + if (min > max_min) { + max_min = min; + } + } + + float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; + float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + for (int j = 0; j < QK_K/32; ++j) { + uint8_t ls = nearest_int(inv_scale*scales[j]); + uint8_t lm = nearest_int(inv_min*mins[j]); + ls = MIN(63, ls); + lm = MIN(63, lm); + if (j < 4) { + y[i].scales[j] = ls; + y[i].scales[j+4] = lm; + } else { + y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4); + y[i].scales[j-4] |= ((ls >> 4) << 6); + y[i].scales[j-0] |= ((lm >> 4) << 6); + } + } + y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); + y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + + uint8_t sc, m; + for (int j = 0; j < QK_K/32; ++j) { + get_scale_min_k4(j, y[i].scales, &sc, &m); + const float d = GGML_FP16_TO_FP32(y[i].d) * sc; + if (!d) continue; + const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m; + for (int ii = 0; ii < 32; ++ii) { + int l = nearest_int((x[32*j + ii] + dm)/d); + l = MAX(0, MIN(15, l)); + L[32*j + ii] = l; + } + } + uint8_t * q = y[i].qs; + for (int j = 0; j < QK_K; j += 64) { + for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4); + q += 32; + } + + x += QK_K; + + } +#endif +} + +size_t quantize_q4_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row); + if (!quant_weights) { + quantize_row_q4_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q4_K_impl(src, (block_q4_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== 5-bit (de)-quantization void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) { @@ -2349,7 +2557,7 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict #else float max_scale = 0, amax = 0; for (int j = 0; j < QK_K/16; ++j) { - scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1); + scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL); float abs_scale = fabsf(scales[j]); if (abs_scale > amax) { amax = abs_scale; @@ -2460,6 +2668,123 @@ size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n return (n/QK_K*sizeof(block_q5_K)); } +static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q5_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + uint8_t L[QK_K]; + float mins[QK_K/32]; + float scales[QK_K/32]; + float weights[32]; + uint8_t Laux[32]; + + for (int i = 0; i < nb; i++) { + + float sum_x2 = 0; + for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; + float sigma2 = sum_x2/QK_K; + float av_x = sqrtf(sigma2); + + float max_scale = 0; // as we are deducting the min, scales are always positive + float max_min = 0; + for (int j = 0; j < QK_K/32; ++j) { + if (quant_weights) { + const float * qw = quant_weights + QK_K*i + 32*j; + for (int l = 0; l < 32; ++l) weights[l] = qw[l] * sqrtf(sigma2 + x[32*j + l]*x[32*j + l]); + } else { + for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); + } + scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + float scale = scales[j]; + if (scale > max_scale) { + max_scale = scale; + } + float min = mins[j]; + if (min > max_min) { + max_min = min; + } + } + + float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; + float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + for (int j = 0; j < QK_K/32; ++j) { + uint8_t ls = nearest_int(inv_scale*scales[j]); + uint8_t lm = nearest_int(inv_min*mins[j]); + ls = MIN(63, ls); + lm = MIN(63, lm); + if (j < 4) { + y[i].scales[j] = ls; + y[i].scales[j+4] = lm; + } else { + y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4); + y[i].scales[j-4] |= ((ls >> 4) << 6); + y[i].scales[j-0] |= ((lm >> 4) << 6); + } + } + y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); + y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + + uint8_t sc, m; + for (int j = 0; j < QK_K/32; ++j) { + get_scale_min_k4(j, y[i].scales, &sc, &m); + const float d = GGML_FP16_TO_FP32(y[i].d) * sc; + if (!d) continue; + const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m; + for (int ii = 0; ii < 32; ++ii) { + int l = nearest_int((x[32*j + ii] + dm)/d); + l = MAX(0, MIN(31, l)); + L[32*j + ii] = l; + } + } + + uint8_t * restrict qh = y[i].qh; + uint8_t * restrict ql = y[i].qs; + memset(qh, 0, QK_K/8); + + uint8_t m1 = 1, m2 = 2; + for (int n = 0; n < QK_K; n += 64) { + for (int j = 0; j < 32; ++j) { + int l1 = L[n + j]; + if (l1 > 15) { + l1 -= 16; qh[j] |= m1; + } + int l2 = L[n + j + 32]; + if (l2 > 15) { + l2 -= 16; qh[j] |= m2; + } + ql[j] = l1 | (l2 << 4); + } + m1 <<= 2; m2 <<= 2; + ql += 32; + } + + x += QK_K; + + } +#endif +} + +size_t quantize_q5_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row); + if (!quant_weights) { + quantize_row_q5_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q5_K_impl(src, (block_q5_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== 6-bit (de)-quantization void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) { @@ -2476,7 +2801,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict for (int ib = 0; ib < QK_K/16; ++ib) { - const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1); + const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, NULL); scales[ib] = scale; const float abs_scale = fabsf(scale); @@ -2608,6 +2933,112 @@ size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * return (n/QK_K*sizeof(block_q6_K)); } +static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) { +#if QK_K != 256 + (void)quant_weights; + quantize_row_q6_K_reference(x, y, n_per_row); +#else + assert(n_per_row % QK_K == 0); + const int nb = n_per_row / QK_K; + + int8_t L[QK_K]; + float scales[QK_K/16]; + //float weights[16]; + + for (int i = 0; i < nb; i++) { + + //float sum_x2 = 0; + //for (int j = 0; j < QK_K; ++j) sum_x2 += x[j]*x[j]; + //float sigma2 = sum_x2/QK_K; + + float max_scale = 0; + float max_abs_scale = 0; + + for (int ib = 0; ib < QK_K/16; ++ib) { + + float scale; + if (quant_weights) { + const float * qw = quant_weights + QK_K*i + 16*ib; + //for (int j = 0; j < 16; ++j) weights[j] = qw[j] * sqrtf(sigma2 + x[16*ib + j]*x[16*ib + j]); + //scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, weights); + scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, qw); + } else { + scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, NULL); + } + scales[ib] = scale; + + const float abs_scale = fabsf(scale); + if (abs_scale > max_abs_scale) { + max_abs_scale = abs_scale; + max_scale = scale; + } + + } + + if (!max_abs_scale) { + memset(&y[i], 0, sizeof(block_q6_K)); + y[i].d = GGML_FP32_TO_FP16(0.f); + x += QK_K; + continue; + } + + float iscale = -128.f/max_scale; + y[i].d = GGML_FP32_TO_FP16(1/iscale); + for (int ib = 0; ib < QK_K/16; ++ib) { + y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib])); + } + + for (int j = 0; j < QK_K/16; ++j) { + float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j]; + if (!d) { + continue; + } + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int(x[16*j + ii]/d); + l = MAX(-32, MIN(31, l)); + L[16*j + ii] = l + 32; + } + } + + uint8_t * restrict ql = y[i].ql; + uint8_t * restrict qh = y[i].qh; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + const uint8_t q1 = L[j + l + 0] & 0xF; + const uint8_t q2 = L[j + l + 32] & 0xF; + const uint8_t q3 = L[j + l + 64] & 0xF; + const uint8_t q4 = L[j + l + 96] & 0xF; + ql[l+ 0] = q1 | (q3 << 4); + ql[l+32] = q2 | (q4 << 4); + qh[l] = (L[j + l] >> 4) | ((L[j + l + 32] >> 4) << 2) | ((L[j + l + 64] >> 4) << 4) | ((L[j + l + 96] >> 4) << 6); + } + ql += 64; + qh += 32; + } + + x += QK_K; + + } +#endif +} + +size_t quantize_q6_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + int row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row); + if (!quant_weights) { + quantize_row_q6_K_reference(src, dst, nrow*n_per_row); + } + else { + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_q6_K_impl(src, (block_q6_K*)qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += row_size; + } + } + return nrow * row_size; +} + // ====================== "True" 2-bit (de)-quantization static const uint64_t iq2xxs_grid[256] = { diff --git a/ggml-quants.h b/ggml-quants.h index e5d110230..99467936a 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -249,4 +249,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); - +size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q5_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_q6_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml.c b/ggml.c index 52467475a..ef5888ab2 100644 --- a/ggml.c +++ b/ggml.c @@ -18713,26 +18713,38 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i case GGML_TYPE_Q3_K: { GGML_ASSERT(start % QK_K == 0); - block_q3_K * block = (block_q3_K*)dst + start / QK_K; - result = ggml_quantize_q3_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q3_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q4_K: { GGML_ASSERT(start % QK_K == 0); - block_q4_K * block = (block_q4_K*)dst + start / QK_K; - result = ggml_quantize_q4_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q4_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q5_K: { GGML_ASSERT(start % QK_K == 0); - block_q5_K * block = (block_q5_K*)dst + start / QK_K; - result = ggml_quantize_q5_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q5_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_Q6_K: { GGML_ASSERT(start % QK_K == 0); - block_q6_K * block = (block_q6_K*)dst + start / QK_K; - result = ggml_quantize_q6_K(src + start, block, n, n, hist); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_q6_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_IQ2_XXS: { From a836c8f534ab789b02da149fbdaf7735500bff74 Mon Sep 17 00:00:00 2001 From: David Pflug Date: Sun, 14 Jan 2024 10:46:00 -0500 Subject: [PATCH 040/334] llama : fix missing quotes (#4937) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 63f37ecdb..7af38718c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7099,7 +7099,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } #ifdef PRETOKENIZERDEBUG - LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); @@ -7120,7 +7120,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); #ifdef PRETOKENIZERDEBUG - LLAMA_LOG_WARN(TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); + LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif llm_tokenizer_bpe tokenizer(vocab); tokenizer.tokenize(raw_text, output); From 4a3156de2fac9a8ee4279de7804d4e352dcfe121 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 15 Jan 2024 07:48:06 +0200 Subject: [PATCH 041/334] CUDA: faster dequantize kernels for Q4_0 and Q4_1 (#4938) Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 77 +++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 73 insertions(+), 4 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index bd3814c72..a870718a7 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1105,6 +1105,61 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in #endif // GGML_CUDA_F16 } +template +static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { + + const int i = blockIdx.x; + + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_0 * x = (const block_q4_0 *)vx + ib; + const float d = __half2float(x->d); + const float dm = -8*d; + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d * (q[l] & 0xF) + dm; + y[l+16] = d * (q[l] >> 4) + dm; + } +} + +template +static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) { + + const int i = blockIdx.x; + + // assume 32 threads + const int tid = threadIdx.x; + const int il = tid/8; + const int ir = tid%8; + const int ib = 8*i + ir; + if (ib >= nb32) { + return; + } + + dst_t * y = yy + 256*i + 32*ir + 4*il; + + const block_q4_1 * x = (const block_q4_1 *)vx + ib; + const float2 d = __half22float2(x->dm); + + const uint8_t * q = x->qs + 4*il; + + for (int l = 0; l < 4; ++l) { + y[l+ 0] = d.x * (q[l] & 0xF) + d.y; + y[l+16] = d.x * (q[l] >> 4) + d.y; + } +} + //================================== k-quants template @@ -6253,6 +6308,20 @@ static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cu #endif } +template +static void dequantize_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + dequantize_block_q4_0<<>>(vx, y, nb32); +} + +template +static void dequantize_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb32 = k / 32; + const int nb = (k + 255) / 256; + dequantize_block_q4_1<<>>(vx, y, nb32); +} + template static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -6301,9 +6370,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { int id; switch (type) { case GGML_TYPE_Q4_0: - return dequantize_block_cuda; + return dequantize_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_block_cuda; + return dequantize_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: @@ -6338,9 +6407,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: - return dequantize_block_cuda; + return dequantize_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_block_cuda; + return dequantize_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: From 2faaef39799c97a53bec3898141478700da25757 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 15 Jan 2024 10:09:38 +0200 Subject: [PATCH 042/334] llama : check for 256 divisibility for IQ2_XS, IQ2_XXS (#4950) Co-authored-by: Iwan Kawrakow --- llama.cpp | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 7af38718c..f9718060d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8559,7 +8559,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty //} bool convert_incompatible_tensor = false; if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || - new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) { + new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || + new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -8571,6 +8572,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } if (convert_incompatible_tensor) { switch (new_type) { + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break; case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break; case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; From ddb008d845cd50bb090bf051f570130524042936 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 15 Jan 2024 13:27:00 +0200 Subject: [PATCH 043/334] cuda : fix dequantize kernel names (#4938) --- ggml-cuda.cu | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index a870718a7..c3e14bc96 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6309,14 +6309,14 @@ static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cu } template -static void dequantize_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; dequantize_block_q4_0<<>>(vx, y, nb32); } template -static void dequantize_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { +static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; dequantize_block_q4_1<<>>(vx, y, nb32); @@ -6370,9 +6370,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { int id; switch (type) { case GGML_TYPE_Q4_0: - return dequantize_q4_0_cuda; + return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_q4_1_cuda; + return dequantize_row_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: @@ -6407,9 +6407,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q4_0: - return dequantize_q4_0_cuda; + return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: - return dequantize_q4_1_cuda; + return dequantize_row_q4_1_cuda; case GGML_TYPE_Q5_0: return dequantize_block_cuda; case GGML_TYPE_Q5_1: From d9aa4ffa6e0296d42f1f676dd85de97c8491eb73 Mon Sep 17 00:00:00 2001 From: "Victor Z. Peng" Date: Mon, 15 Jan 2024 04:41:46 -0800 Subject: [PATCH 044/334] awq-py : fix typo in awq-py/README.md (#4947) --- awq-py/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/awq-py/README.md b/awq-py/README.md index 59354f4e3..16e68d027 100644 --- a/awq-py/README.md +++ b/awq-py/README.md @@ -43,7 +43,7 @@ Example for llama model # For llama7b and llama2 models python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf # For mistral and mpt models -python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf +python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf ``` ## Quantize From 4483396751c79dea540808b9cb9238245d06da2b Mon Sep 17 00:00:00 2001 From: David Friehs Date: Mon, 15 Jan 2024 14:06:52 +0100 Subject: [PATCH 045/334] llama : apply classifier-free guidance to logits directly (#4951) --- common/sampling.cpp | 9 ++++--- llama.cpp | 60 ++++++++++++++++++++++++++++++--------------- llama.h | 17 +++++++++---- 3 files changed, 57 insertions(+), 29 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 8e45909f1..dd1ffeb1b 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -190,6 +190,11 @@ static llama_token llama_sampling_sample_impl( logits[it->first] += it->second; } + if (ctx_cfg) { + float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); + llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); + } + cur.clear(); for (llama_token token_id = 0; token_id < n_vocab; token_id++) { @@ -198,10 +203,6 @@ static llama_token llama_sampling_sample_impl( llama_token_data_array cur_p = { cur.data(), cur.size(), false }; - if (ctx_cfg) { - llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale); - } - // apply penalties const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); diff --git a/llama.cpp b/llama.cpp index f9718060d..46c4d11c8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7898,39 +7898,59 @@ static void llama_log_softmax(float * array, size_t size) { } } +void llama_sample_apply_guidance( + struct llama_context * ctx, + float * logits, + float * logits_guidance, + float scale) { + GGML_ASSERT(ctx); + + const auto t_start_sample_us = ggml_time_us(); + const auto n_vocab = llama_n_vocab(llama_get_model(ctx)); + + llama_log_softmax(logits, n_vocab); + llama_log_softmax(logits_guidance, n_vocab); + + for (int i = 0; i < n_vocab; ++i) { + auto & l = logits[i]; + const auto & g = logits_guidance[i]; + + l = scale * (l - g) + g; + } + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; +} + void llama_sample_classifier_free_guidance( struct llama_context * ctx, llama_token_data_array * candidates, struct llama_context * guidance_ctx, float scale) { - int64_t t_start_sample_us = ggml_time_us(); - GGML_ASSERT(ctx); + int64_t t_start_sample_us; - auto n_vocab = llama_n_vocab(llama_get_model(ctx)); + t_start_sample_us = ggml_time_us(); + const size_t n_vocab = llama_n_vocab(llama_get_model(ctx)); - GGML_ASSERT(n_vocab == (int)candidates->size); + GGML_ASSERT(n_vocab == candidates->size); GGML_ASSERT(!candidates->sorted); - std::vector logits_base; - logits_base.reserve(candidates->size); - for (size_t i = 0; i < candidates->size; ++i) { - logits_base.push_back(candidates->data[i].logit); - } - llama_log_softmax(logits_base.data(), candidates->size); - - float* logits_guidance = llama_get_logits(guidance_ctx); - llama_log_softmax(logits_guidance, n_vocab); - - for (int i = 0; i < n_vocab; ++i) { - float logit_guidance = logits_guidance[i]; - float logit_base = logits_base[i]; - candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance; + std::vector logits_base(n_vocab); + for (size_t i = 0; i < n_vocab; ++i) { + logits_base[i] = candidates->data[i].logit; } - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + float * logits_guidance = llama_get_logits(guidance_ctx); + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale); + t_start_sample_us = ggml_time_us(); + + for (size_t i = 0; i < n_vocab; ++i) { + candidates->data[i].logit = logits_base[i]; } + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; } llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) { diff --git a/llama.h b/llama.h index 79c8335b6..a570b0d69 100644 --- a/llama.h +++ b/llama.h @@ -714,14 +714,21 @@ extern "C" { float penalty_present); /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806 - /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted. - /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. - /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. - LLAMA_API void llama_sample_classifier_free_guidance( + /// @param logits Logits extracted from the original generation context. + /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. + /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. + LLAMA_API void llama_sample_apply_guidance( + struct llama_context * ctx, + float * logits, + float * logits_guidance, + float scale); + + LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance( struct llama_context * ctx, llama_token_data_array * candidates, struct llama_context * guidance_ctx, - float scale); + float scale), + "use llama_sample_apply_guidance() instead"); /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. LLAMA_API void llama_sample_softmax( From 3e5ca7931c68152e4ec18d126e9c832dd84914c8 Mon Sep 17 00:00:00 2001 From: ngc92 <7938269+ngc92@users.noreply.github.com> Date: Mon, 15 Jan 2024 20:40:48 +0200 Subject: [PATCH 046/334] pass cpu-architecture arguments only to host code (C;C++) (#4943) --- CMakeLists.txt | 34 +++++++++++++++++++--------------- 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 2741568ed..7bd640966 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -594,6 +594,13 @@ if (NOT MSVC) endif() endif() +function(add_compile_option_cpp ARG) + # Adds a compile option to C/C++ only, but not for Cuda. + # Use, e.g., for CPU-architecture flags. + add_compile_options($<$:${ARG}>) + add_compile_options($<$:${ARG}>) +endfunction() + if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) message(STATUS "ARM detected") if (MSVC) @@ -628,8 +635,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE include(cmake/FindSIMD.cmake) endif () if (LLAMA_AVX512) - add_compile_options($<$:/arch:AVX512>) - add_compile_options($<$:/arch:AVX512>) + add_compile_option_cpp(/arch:AVX512) # MSVC has no compile-time flags enabling specific # AVX512 extensions, neither it defines the # macros corresponding to the extensions. @@ -643,37 +649,35 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE add_compile_definitions($<$:__AVX512VNNI__>) endif() elseif (LLAMA_AVX2) - add_compile_options($<$:/arch:AVX2>) - add_compile_options($<$:/arch:AVX2>) + add_compile_option_cpp(/arch:AVX2) elseif (LLAMA_AVX) - add_compile_options($<$:/arch:AVX>) - add_compile_options($<$:/arch:AVX>) + add_compile_option_cpp(/arch:AVX) endif() else() if (LLAMA_NATIVE) - add_compile_options(-march=native) + add_compile_option_cpp(-march=native) endif() if (LLAMA_F16C) - add_compile_options(-mf16c) + add_compile_option_cpp(-mf16c) endif() if (LLAMA_FMA) - add_compile_options(-mfma) + add_compile_option_cpp(-mfma) endif() if (LLAMA_AVX) - add_compile_options(-mavx) + add_compile_option_cpp(-mavx) endif() if (LLAMA_AVX2) - add_compile_options(-mavx2) + add_compile_option_cpp(-mavx2) endif() if (LLAMA_AVX512) - add_compile_options(-mavx512f) - add_compile_options(-mavx512bw) + add_compile_option_cpp(-mavx512f) + add_compile_option_cpp(-mavx512bw) endif() if (LLAMA_AVX512_VBMI) - add_compile_options(-mavx512vbmi) + add_compile_option_cpp(-mavx512vbmi) endif() if (LLAMA_AVX512_VNNI) - add_compile_options(-mavx512vnni) + add_compile_option_cpp(-mavx512vnni) endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") From e0324285a569d0583cf2f4a07a2402221ee25f58 Mon Sep 17 00:00:00 2001 From: stduhpf Date: Tue, 16 Jan 2024 12:04:32 +0100 Subject: [PATCH 047/334] speculative : threading options (#4959) * speculative: expose draft threading * fix usage format * accept -td and -tbd args * speculative: revert default behavior when -td is unspecified * fix trailing whitespace --- common/common.cpp | 22 ++++++++++++++++++++++ common/common.h | 2 ++ examples/speculative/speculative.cpp | 4 ++++ 3 files changed, 28 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index c11006bcb..2b0865fff 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -167,6 +167,24 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (params.n_threads_batch <= 0) { params.n_threads_batch = std::thread::hardware_concurrency(); } + } else if (arg == "-td" || arg == "--threads-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_threads_draft = std::stoi(argv[i]); + if (params.n_threads_draft <= 0) { + params.n_threads_draft = std::thread::hardware_concurrency(); + } + } else if (arg == "-tbd" || arg == "--threads-batch-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_threads_batch_draft = std::stoi(argv[i]); + if (params.n_threads_batch_draft <= 0) { + params.n_threads_batch_draft = std::thread::hardware_concurrency(); + } } else if (arg == "-p" || arg == "--prompt") { if (++i >= argc) { invalid_param = true; @@ -845,6 +863,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N\n"); printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n"); + printf(" -td N, --threads-draft N"); + printf(" number of threads to use during generation (default: same as --threads)"); + printf(" -tbd N, --threads-batch-draft N\n"); + printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n"); printf(" -p PROMPT, --prompt PROMPT\n"); printf(" prompt to start generation with (default: empty)\n"); printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); diff --git a/common/common.h b/common/common.h index 096468243..1f43e6282 100644 --- a/common/common.h +++ b/common/common.h @@ -46,7 +46,9 @@ struct gpt_params { uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); + int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_threads_batch_draft = -1; int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 20f1fb5bf..7b3af01f3 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -65,6 +65,10 @@ int main(int argc, char ** argv) { // load the draft model params.model = params.model_draft; params.n_gpu_layers = params.n_gpu_layers_draft; + if (params.n_threads_draft > 0) { + params.n_threads = params.n_threads_draft; + } + params.n_threads_batch = params.n_threads_batch_draft; std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); { From d75c232e1da56f19ac4d2530dadbe0ab3a11fde5 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 16 Jan 2024 12:14:19 +0100 Subject: [PATCH 048/334] finetune : use LLAMA_FILE_MAGIC_GGLA (#4961) This commit replaces the magic number LLAMA_FILE_MAGIC_LORA used in finetune.cpp with LLAMA_FILE_MAGIC_GGLA defined in llama.h. Signed-off-by: Daniel Bevenius --- examples/finetune/finetune.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index eaca42fc1..a6620fd73 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1138,9 +1138,8 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor return tn_buf.data(); }; - uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla' // write_magic - file.write_u32(LLAMA_FILE_MAGIC_LORA); // magic + file.write_u32(LLAMA_FILE_MAGIC_GGLA); // magic file.write_u32(1); // version // write_hparams file.write_u32(lora->hparams.lora_r); From a0b3ac8c48b66206b9c5921ce57bd5c0ea6557c3 Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Tue, 16 Jan 2024 03:16:33 -0800 Subject: [PATCH 049/334] ggml : introduce GGML_CALL function annotation (#4850) This change makes it possible to build ggml-cuda.cu and ggml-metal.m as independent dynamic shared objects, that may be conditionally linked at runtime in a multiplatform binary. It introduces a GGML_CALL annotation that documents which functions have a cyclic call relationship, between the application code and GPU modules. This change does nothing, unless the build defines -DGGML_MULTIPLATFORM which causes back-references and function pointers to conform to MS ABI which is supported by NVCC, ROCm, XCode, GCC and Clang across platforms --- ggml-backend-impl.h | 60 +++++++++++----------- ggml-backend.c | 80 ++++++++++++++--------------- ggml-backend.h | 50 +++++++++--------- ggml-cuda.cu | 121 ++++++++++++++++++++++---------------------- ggml-cuda.h | 32 ++++++------ ggml-metal.h | 4 +- ggml-metal.m | 42 +++++++-------- ggml.c | 32 ++++++------ ggml.h | 58 ++++++++++++--------- 9 files changed, 244 insertions(+), 235 deletions(-) diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 1db32901f..1397828d9 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -16,14 +16,14 @@ extern "C" { typedef void * ggml_backend_buffer_type_context_t; struct ggml_backend_buffer_type_i { - const char * (*get_name) (ggml_backend_buffer_type_t buft); - ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); - size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment - size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding - bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend + const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft); + ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); + size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment + size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding + bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend // check if tensor data is in host memory // should be equivalent to supports_backend(buft, ggml_backend_cpu_init()) - bool (*is_host) (ggml_backend_buffer_type_t buft); + bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft); }; struct ggml_backend_buffer_type { @@ -35,15 +35,15 @@ extern "C" { typedef void * ggml_backend_buffer_context_t; struct ggml_backend_buffer_i { - const char * (*get_name) (ggml_backend_buffer_t buffer); - void (*free_buffer)(ggml_backend_buffer_t buffer); - void * (*get_base) (ggml_backend_buffer_t buffer); - void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer - void (*clear) (ggml_backend_buffer_t buffer, uint8_t value); - void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras + const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer); + void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer); + void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer); + void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer + void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value); + void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras }; struct ggml_backend_buffer { @@ -54,7 +54,7 @@ extern "C" { enum ggml_backend_buffer_usage usage; }; - ggml_backend_buffer_t ggml_backend_buffer_init( + GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( ggml_backend_buffer_type_t buft, struct ggml_backend_buffer_i iface, ggml_backend_buffer_context_t context, @@ -70,31 +70,31 @@ extern "C" { typedef void * ggml_backend_context_t; struct ggml_backend_i { - const char * (*get_name)(ggml_backend_t backend); + const char * (*GGML_CALL get_name)(ggml_backend_t backend); - void (*free)(ggml_backend_t backend); + void (*GGML_CALL free)(ggml_backend_t backend); // buffer allocation - ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend); + ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend); // (optional) asynchronous tensor data access - void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); + void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst); // (optional) complete all pending operations - void (*synchronize)(ggml_backend_t backend); + void (*GGML_CALL synchronize)(ggml_backend_t backend); // compute graph with a plan - ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); - void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); + void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async) - bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation - bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); }; struct ggml_backend { @@ -107,9 +107,9 @@ extern "C" { // Backend registry // - typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data); + typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data); - void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data); + GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data); #ifdef __cplusplus } diff --git a/ggml-backend.c b/ggml-backend.c index 505dbba47..f5424fb90 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -19,7 +19,7 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { return buft->iface.get_name(buft); } -ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { return buft->iface.alloc_buffer(buft, size); } @@ -27,7 +27,7 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) { return buft->iface.get_alignment(buft); } -size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { +GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { // get_alloc_size is optional, defaults to ggml_nbytes if (buft->iface.get_alloc_size) { return buft->iface.get_alloc_size(buft, tensor); @@ -48,7 +48,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) { // backend buffer -ggml_backend_buffer_t ggml_backend_buffer_init( +GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( ggml_backend_buffer_type_t buft, struct ggml_backend_buffer_i iface, ggml_backend_buffer_context_t context, @@ -95,7 +95,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { return base; } -void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { +GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { // init_tensor is optional if (buffer->iface.init_tensor) { buffer->iface.init_tensor(buffer, tensor); @@ -191,7 +191,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten } } -void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); @@ -201,7 +201,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size); } -void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); @@ -318,9 +318,9 @@ struct ggml_backend_reg { static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG]; static size_t ggml_backend_registry_count = 0; -static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data); +GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data); -static void ggml_backend_registry_init(void) { +GGML_CALL static void ggml_backend_registry_init(void) { static bool initialized = false; if (initialized) { @@ -333,18 +333,18 @@ static void ggml_backend_registry_init(void) { // add forward decls here to avoid including the backend headers #ifdef GGML_USE_CUBLAS - extern void ggml_backend_cuda_reg_devices(void); + extern GGML_CALL void ggml_backend_cuda_reg_devices(void); ggml_backend_cuda_reg_devices(); #endif #ifdef GGML_USE_METAL - extern ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); - extern ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); + extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); + extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL); #endif } -void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { +GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG); size_t id = ggml_backend_registry_count; @@ -439,33 +439,33 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) { // backend CPU -static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { return "CPU"; GGML_UNUSED(buffer); } -static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { return (void *)buffer->context; } -static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { free(buffer->context); } -static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); GGML_UNUSED(buffer); } -static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { memcpy(data, (const char *)tensor->data + offset, size); GGML_UNUSED(buffer); } -static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { if (ggml_backend_buffer_is_host(src->buffer)) { memcpy(dst->data, src->data, ggml_nbytes(src)); return true; @@ -475,7 +475,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con GGML_UNUSED(buffer); } -static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { memset(buffer->context, value, buffer->size); } @@ -506,13 +506,13 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 -static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU"; GGML_UNUSED(buft); } -static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? @@ -521,25 +521,25 @@ static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_back return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size); } -static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return TENSOR_ALIGNMENT; GGML_UNUSED(buft); } -static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_cpu(backend); GGML_UNUSED(buft); } -static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { +GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return true; GGML_UNUSED(buft); } -ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, @@ -561,23 +561,23 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { #include -static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU_HBM"; GGML_UNUSED(buft); } -static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { +GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { return "CPU_HBM"; GGML_UNUSED(buf); } -static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { hbw_free(buffer->context); } -static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { //void * ptr = hbw_malloc(size); void * ptr; int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); @@ -617,20 +617,20 @@ struct ggml_backend_cpu_context { size_t work_size; }; -static const char * ggml_backend_cpu_name(ggml_backend_t backend) { +GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) { return "CPU"; GGML_UNUSED(backend); } -static void ggml_backend_cpu_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; free(cpu_ctx->work_data); free(cpu_ctx); free(backend); } -static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { return ggml_backend_cpu_buffer_type(); GGML_UNUSED(backend); @@ -641,7 +641,7 @@ struct ggml_backend_plan_cpu { struct ggml_cgraph cgraph; }; -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { +GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); @@ -656,7 +656,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend return cpu_plan; } -static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; free(cpu_plan->cplan.work_data); @@ -665,7 +665,7 @@ static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backen GGML_UNUSED(backend); } -static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); @@ -673,7 +673,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac GGML_UNUSED(backend); } -static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); @@ -690,7 +690,7 @@ static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c return true; } -static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { +GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_MUL_MAT: return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; @@ -732,7 +732,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { return cpu_backend; } -bool ggml_backend_is_cpu(ggml_backend_t backend) { +GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_cpu_name; } @@ -743,11 +743,11 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { ctx->n_threads = n_threads; } -ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { +GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size); } -static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) { +GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) { return ggml_backend_cpu_init(); GGML_UNUSED(params); diff --git a/ggml-backend.h b/ggml-backend.h index 4eb244af1..12b4b4ab7 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -17,12 +17,12 @@ extern "C" { // // buffer type - GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); - GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); - GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); - GGML_API size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); - GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); - GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); + GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); + GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); + GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); + GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); + GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); + GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); // buffer enum ggml_backend_buffer_usage { @@ -30,18 +30,18 @@ extern "C" { GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1, }; - GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); - GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); - GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); - GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); - GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); - GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer); - GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer); + GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); + GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); + GGML_API GGML_CALL void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); + GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer); // // Backend @@ -58,8 +58,8 @@ extern "C" { GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); GGML_API void ggml_backend_synchronize(ggml_backend_t backend); @@ -80,13 +80,13 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_cpu_init(void); - GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); + GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend); + GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); // Create a backend buffer from an existing pointer - GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); + GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); - GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); + GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); #ifdef GGML_USE_CPU_HBM GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); @@ -183,7 +183,7 @@ extern "C" { GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph); GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy); - typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data); + typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data); // Compare the output of two backends GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c3e14bc96..568c411af 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7615,11 +7615,11 @@ struct cuda_pool_alloc { static bool g_cublas_loaded = false; -bool ggml_cublas_loaded(void) { +GGML_CALL bool ggml_cublas_loaded(void) { return g_cublas_loaded; } -void ggml_init_cublas() { +GGML_CALL void ggml_init_cublas() { static bool initialized = false; if (!initialized) { @@ -7707,7 +7707,7 @@ void ggml_init_cublas() { } } -void * ggml_cuda_host_malloc(size_t size) { +GGML_CALL void * ggml_cuda_host_malloc(size_t size) { if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { return nullptr; } @@ -7725,7 +7725,7 @@ void * ggml_cuda_host_malloc(size_t size) { return ptr; } -void ggml_cuda_host_free(void * ptr) { +GGML_CALL void ggml_cuda_host_free(void * ptr) { CUDA_CHECK(cudaFreeHost(ptr)); } @@ -9242,7 +9242,7 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); } -bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { if (!g_cublas_loaded) return false; const int64_t ne10 = src1->ne[0]; @@ -10013,7 +10013,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -static void ggml_cuda_set_main_device(const int main_device) { +GGML_CALL static void ggml_cuda_set_main_device(const int main_device) { if (main_device >= g_device_count) { fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", main_device, g_device_count, g_main_device); @@ -10028,7 +10028,7 @@ static void ggml_cuda_set_main_device(const int main_device) { } } -bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { if (!g_cublas_loaded) return false; ggml_cuda_func_t func; @@ -10186,7 +10186,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ return true; } -int ggml_cuda_get_device_count() { +GGML_CALL int ggml_cuda_get_device_count() { int device_count; if (cudaGetDeviceCount(&device_count) != cudaSuccess) { return 0; @@ -10194,7 +10194,7 @@ int ggml_cuda_get_device_count() { return device_count; } -void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { +GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); snprintf(description, description_size, "%s", prop.name); @@ -10244,27 +10244,27 @@ struct ggml_backend_cuda_buffer_context { } }; -static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->name.c_str(); } -static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { +GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; } -static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; CUDA_CHECK(cudaFree(ctx->dev_ptr)); delete ctx; } -static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->dev_ptr; } -static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { +GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { @@ -10296,7 +10296,7 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g } } -static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -10307,7 +10307,7 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg CUDA_CHECK(cudaDeviceSynchronize()); } -static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -10318,7 +10318,7 @@ static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, co CUDA_CHECK(cudaDeviceSynchronize()); } -static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_cuda(src->buffer)) { ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context; @@ -10335,7 +10335,7 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co return false; } -static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; ggml_cuda_set_device(ctx->device); @@ -10357,19 +10357,18 @@ static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { }; // cuda buffer type - struct ggml_backend_cuda_buffer_type_context { int device; std::string name; }; -static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) { ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; return ctx->name.c_str(); } -static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; ggml_cuda_set_device(buft_ctx->device); @@ -10388,13 +10387,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); } -static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; UNUSED(buft); } -static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { +GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); int64_t nrows_split = row_high - row_low; @@ -10414,7 +10413,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t UNUSED(buft); } -static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { if (!ggml_backend_is_cuda(backend)) { return false; } @@ -10434,7 +10433,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { /* .is_host = */ NULL, }; -ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { // FIXME: this is not thread safe if (device >= ggml_backend_cuda_get_device_count()) { return nullptr; @@ -10479,7 +10478,7 @@ struct ggml_backend_cuda_split_buffer_context { std::vector tensor_extras; }; -static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { return GGML_CUDA_NAME "_Split"; UNUSED(buffer); @@ -10490,19 +10489,19 @@ static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_ // return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; //} -static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; delete ctx; } -static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced return (void *)0x1000; UNUSED(buffer); } -static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { +GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; @@ -10552,7 +10551,7 @@ static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buf tensor->extra = extra; } -static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { // split tensors must always be set in their entirety at once GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); @@ -10586,7 +10585,7 @@ static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buff } } -static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { // split tensors must always be set in their entirety at once GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); @@ -10620,7 +10619,7 @@ static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buff } } -static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { UNUSED(buffer); UNUSED(value); } @@ -10639,13 +10638,13 @@ static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { // cuda split buffer type -static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) { return GGML_CUDA_NAME "_Split"; UNUSED(buft); } -static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point // instead, we allocate them for each tensor separately in init_tensor // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, @@ -10655,13 +10654,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(gg return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); } -static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; UNUSED(buft); } -static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { +GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; size_t total_size = 0; @@ -10688,13 +10687,13 @@ static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_bu return total_size; } -static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_cuda(backend); UNUSED(buft); } -static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { +GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return false; UNUSED(buft); @@ -10709,7 +10708,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, }; -ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { // FIXME: this is not thread safe static std::map, struct ggml_backend_buffer_type> buft_map; @@ -10745,23 +10744,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten // host buffer type -static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { return GGML_CUDA_NAME "_Host"; UNUSED(buft); } -static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { return GGML_CUDA_NAME "_Host"; UNUSED(buffer); } -static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_cuda_host_free(buffer->context); } -static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { void * ptr = ggml_cuda_host_malloc(size); if (ptr == nullptr) { @@ -10777,7 +10776,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm return buffer; } -ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { /* .iface = */ { /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, @@ -10795,26 +10794,26 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { // backend -static const char * ggml_backend_cuda_name(ggml_backend_t backend) { +GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; return cuda_ctx->name.c_str(); } -static void ggml_backend_cuda_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_cuda_free(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; delete cuda_ctx; delete backend; } -static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; return ggml_backend_cuda_buffer_type(cuda_ctx->device); } -static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); @@ -10823,7 +10822,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0])); } -static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); @@ -10832,7 +10831,7 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])); } -static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) { @@ -10843,7 +10842,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggm return false; } -static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0])); @@ -10851,7 +10850,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { UNUSED(backend); } -static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -10890,7 +10889,7 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph return true; } -static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -11016,7 +11015,7 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .supports_op = */ ggml_backend_cuda_supports_op, }; -ggml_backend_t ggml_backend_cuda_init(int device) { +GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { ggml_init_cublas(); // TODO: remove from ggml.c if (device < 0 || device >= ggml_cuda_get_device_count()) { @@ -11040,35 +11039,35 @@ ggml_backend_t ggml_backend_cuda_init(int device) { return cuda_backend; } -bool ggml_backend_is_cuda(ggml_backend_t backend) { +GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_cuda_name; } -int ggml_backend_cuda_get_device_count() { +GGML_CALL int ggml_backend_cuda_get_device_count() { return ggml_cuda_get_device_count(); } -void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { +GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { ggml_cuda_get_device_description(device, description, description_size); } -void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { +GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { ggml_cuda_set_device(device); CUDA_CHECK(cudaMemGetInfo(free, total)); } // backend registry -static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { +GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) { ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data); return cuda_backend; UNUSED(params); } -extern "C" int ggml_backend_cuda_reg_devices(); +extern "C" GGML_CALL int ggml_backend_cuda_reg_devices(); -int ggml_backend_cuda_reg_devices() { +GGML_CALL int ggml_backend_cuda_reg_devices() { int device_count = ggml_cuda_get_device_count(); //int device_count = 1; // DEBUG: some tools require delaying CUDA initialization for (int i = 0; i < device_count; i++) { diff --git a/ggml-cuda.h b/ggml-cuda.h index d19cbf3fd..b1ebd61d7 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -18,34 +18,34 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 // Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`. -GGML_API void ggml_init_cublas(void); +GGML_API GGML_CALL void ggml_init_cublas(void); // Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`. -GGML_API bool ggml_cublas_loaded(void); +GGML_API GGML_CALL bool ggml_cublas_loaded(void); -GGML_API void * ggml_cuda_host_malloc(size_t size); -GGML_API void ggml_cuda_host_free(void * ptr); +GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size); +GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr); -GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); -GGML_API int ggml_cuda_get_device_count(void); -GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL int ggml_cuda_get_device_count(void); +GGML_API GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size); // backend API -GGML_API ggml_backend_t ggml_backend_cuda_init(int device); +GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device); -GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); +GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend); -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); -GGML_API int ggml_backend_cuda_get_device_count(void); -GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); -GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); +GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void); +GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); #ifdef __cplusplus } diff --git a/ggml-metal.h b/ggml-metal.h index cd5e2995f..8b0bfc5f1 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -47,11 +47,11 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void); GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); -GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size); +GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size); GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb); -GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); // helper to check if the device supports a specific family // ideally, the user code should be doing these checks diff --git a/ggml-metal.m b/ggml-metal.m index 2ca726055..867f2fd48 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2294,13 +2294,13 @@ static void ggml_backend_metal_free_device(void) { } } -static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { +GGML_CALL static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { return "Metal"; UNUSED(buffer); } -static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { +GGML_CALL static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; for (int i = 0; i < ctx->n_buffers; i++) { @@ -2315,25 +2315,25 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) free(ctx); } -static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { +GGML_CALL static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; return ctx->all_data; } -static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); UNUSED(buffer); } -static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { +GGML_CALL static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { memcpy(data, (const char *)tensor->data + offset, size); UNUSED(buffer); } -static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { +GGML_CALL static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { if (ggml_backend_buffer_is_host(src->buffer)) { memcpy(dst->data, src->data, ggml_nbytes(src)); return true; @@ -2343,7 +2343,7 @@ static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, c UNUSED(buffer); } -static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { +GGML_CALL static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; memset(ctx->all_data, value, ctx->all_size); @@ -2363,13 +2363,13 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { // default buffer type -static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { +GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "Metal"; UNUSED(buft); } -static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); const size_t size_page = sysconf(_SC_PAGESIZE); @@ -2421,24 +2421,24 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); } -static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 32; UNUSED(buft); } -static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { +GGML_CALL static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend); UNUSED(buft); } -static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { +GGML_CALL static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return true; UNUSED(buft); } -ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { +GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = { /* .iface = */ { /* .get_name = */ ggml_backend_metal_buffer_type_get_name, @@ -2456,7 +2456,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { // buffer from ptr -ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { +GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); ctx->all_data = data; @@ -2543,31 +2543,31 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz // backend -static const char * ggml_backend_metal_name(ggml_backend_t backend) { +GGML_CALL static const char * ggml_backend_metal_name(ggml_backend_t backend) { return "Metal"; UNUSED(backend); } -static void ggml_backend_metal_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_metal_free(ggml_backend_t backend) { struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; ggml_metal_free(ctx); free(backend); } -static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { return ggml_backend_metal_buffer_type(); UNUSED(backend); } -static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_graph_compute(metal_ctx, cgraph); } -static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { +GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_supports_op(metal_ctx, op); @@ -2630,9 +2630,9 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } -ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning +GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning -ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) { +GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) { return ggml_backend_metal_init(); GGML_UNUSED(params); diff --git a/ggml.c b/ggml.c index ef5888ab2..5779f32d2 100644 --- a/ggml.c +++ b/ggml.c @@ -1990,19 +1990,19 @@ void ggml_print_objects(const struct ggml_context * ctx) { GGML_PRINT("%s: --- end ---\n", __func__); } -int64_t ggml_nelements(const struct ggml_tensor * tensor) { +GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } -int64_t ggml_nrows(const struct ggml_tensor * tensor) { +GGML_CALL int64_t ggml_nrows(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } -size_t ggml_nbytes(const struct ggml_tensor * tensor) { +GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) { size_t nbytes; size_t blck_size = ggml_blck_size(tensor->type); if (blck_size == 1) { @@ -2025,15 +2025,15 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN); } -int ggml_blck_size(enum ggml_type type) { +GGML_CALL int ggml_blck_size(enum ggml_type type) { return type_traits[type].blck_size; } -size_t ggml_type_size(enum ggml_type type) { +GGML_CALL size_t ggml_type_size(enum ggml_type type) { return type_traits[type].type_size; } -size_t ggml_row_size(enum ggml_type type, int64_t ne) { +GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) { assert(ne % ggml_blck_size(type) == 0); return ggml_type_size(type)*ne/ggml_blck_size(type); } @@ -2042,15 +2042,15 @@ double ggml_type_sizef(enum ggml_type type) { return ((double)(type_traits[type].type_size))/type_traits[type].blck_size; } -const char * ggml_type_name(enum ggml_type type) { +GGML_CALL const char * ggml_type_name(enum ggml_type type) { return type_traits[type].type_name; } -bool ggml_is_quantized(enum ggml_type type) { +GGML_CALL bool ggml_is_quantized(enum ggml_type type) { return type_traits[type].is_quantized; } -const char * ggml_op_name(enum ggml_op op) { +GGML_CALL const char * ggml_op_name(enum ggml_op op) { return GGML_OP_NAME[op]; } @@ -2062,7 +2062,7 @@ const char * ggml_unary_op_name(enum ggml_unary_op op) { return GGML_UNARY_OP_NAME[op]; } -const char * ggml_op_desc(const struct ggml_tensor * t) { +GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) { if (t->op == GGML_OP_UNARY) { enum ggml_unary_op uop = ggml_get_unary_op(t); return ggml_unary_op_name(uop); @@ -2072,7 +2072,7 @@ const char * ggml_op_desc(const struct ggml_tensor * t) { } } -size_t ggml_element_size(const struct ggml_tensor * tensor) { +GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) { return ggml_type_size(tensor->type); } @@ -2154,11 +2154,11 @@ size_t ggml_tensor_overhead(void) { return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE; } -bool ggml_is_transposed(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } -bool ggml_is_contiguous(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return @@ -2177,7 +2177,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -bool ggml_is_permuted(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3]; @@ -3079,7 +3079,7 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) { return (float *)(tensor->data); } -enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { +GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { GGML_ASSERT(tensor->op == GGML_OP_UNARY); return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0); } @@ -11653,7 +11653,7 @@ static void ggml_rope_cache_init( } } -void ggml_rope_yarn_corr_dims( +GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims diff --git a/ggml.h b/ggml.h index 1187074f7..837c52e68 100644 --- a/ggml.h +++ b/ggml.h @@ -187,6 +187,16 @@ # define GGML_API #endif +#ifdef GGML_MULTIPLATFORM +# if defined(_WIN32) +# define GGML_CALL +# else +# define GGML_CALL __attribute__((__ms_abi__)) +# endif +#else +# define GGML_CALL +#endif + // TODO: support for clang #ifdef __GNUC__ # define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint))) @@ -649,41 +659,41 @@ extern "C" { GGML_API void ggml_print_object (const struct ggml_object * obj); GGML_API void ggml_print_objects(const struct ggml_context * ctx); - GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor); - GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor); - GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor); - GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN + GGML_API GGML_CALL int64_t ggml_nelements (const struct ggml_tensor * tensor); + GGML_API GGML_CALL int64_t ggml_nrows (const struct ggml_tensor * tensor); + GGML_API GGML_CALL size_t ggml_nbytes (const struct ggml_tensor * tensor); + GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN - GGML_API int ggml_blck_size(enum ggml_type type); - GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block - GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row + GGML_API GGML_CALL int ggml_blck_size(enum ggml_type type); + GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block + GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row GGML_DEPRECATED( GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float "use ggml_row_size() instead"); - GGML_API const char * ggml_type_name(enum ggml_type type); - GGML_API const char * ggml_op_name (enum ggml_op op); - GGML_API const char * ggml_op_symbol(enum ggml_op op); + GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type); + GGML_API GGML_CALL const char * ggml_op_name (enum ggml_op op); + GGML_API const char * ggml_op_symbol(enum ggml_op op); - GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op); - GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name + GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op); + GGML_API GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name - GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor); + GGML_API GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor); - GGML_API bool ggml_is_quantized(enum ggml_type type); + GGML_API GGML_CALL bool ggml_is_quantized(enum ggml_type type); // TODO: temporary until model loading of ggml examples is refactored GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype); - GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor); - GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor); - GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor); - GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); - GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars + GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor); + GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); + GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1); @@ -770,7 +780,7 @@ extern "C" { GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); - GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); + GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name); @@ -1413,7 +1423,7 @@ extern "C" { float beta_slow); // compute correction dims for YaRN RoPE scaling - void ggml_rope_yarn_corr_dims( + GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); // xPos RoPE, in-place, returns view(a) From 122ed4840cc6d209df6043e027f9f8a03aee01da Mon Sep 17 00:00:00 2001 From: Maximilian Winter Date: Tue, 16 Jan 2024 13:10:48 +0100 Subject: [PATCH 050/334] examples : fix and improv docs for the grammar generator (#4909) * Create pydantic-models-to-grammar.py * Added some comments for usage * Refactored Grammar Generator Added example and usage instruction. * Update pydantic_models_to_grammar.py * Update pydantic-models-to-grammar-examples.py * Renamed module and imported it. * Update pydantic-models-to-grammar.py * Renamed file and fixed grammar generator issue. * Fixed some issues and bugs of the grammar generator. Imporved Documentation * Update pydantic_models_to_grammar.py --- examples/pydantic_models_to_grammar.py | 877 +++++++++++++++---------- 1 file changed, 519 insertions(+), 358 deletions(-) diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py index 41b98fdc1..848c1c367 100644 --- a/examples/pydantic_models_to_grammar.py +++ b/examples/pydantic_models_to_grammar.py @@ -4,6 +4,7 @@ from copy import copy from inspect import isclass, getdoc from types import NoneType +from docstring_parser import parse from pydantic import BaseModel, create_model, Field from typing import Any, Type, List, get_args, get_origin, Tuple, Union, Optional, _GenericAlias from enum import Enum @@ -25,9 +26,10 @@ class PydanticDataType(Enum): ENUM (str): Represents an enum data type. CUSTOM_CLASS (str): Represents a custom class data type. """ + STRING = "string" TRIPLE_QUOTED_STRING = "triple_quoted_string" - MARKDOWN_STRING = "markdown_string" + MARKDOWN_CODE_BLOCK = "markdown_code_block" BOOLEAN = "boolean" INTEGER = "integer" FLOAT = "float" @@ -78,10 +80,10 @@ def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str: def format_model_and_field_name(model_name: str) -> str: - parts = re.findall('[A-Z][^A-Z]*', model_name) + parts = re.findall("[A-Z][^A-Z]*", model_name) if not parts: # Check if the list is empty return model_name.lower().replace("_", "-") - return '-'.join(part.lower().replace("_", "-") for part in parts) + return "-".join(part.lower().replace("_", "-") for part in parts) def generate_list_rule(element_type): @@ -93,29 +95,31 @@ def generate_list_rule(element_type): """ rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list" element_rule = map_pydantic_type_to_gbnf(element_type) - list_rule = fr'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"' + list_rule = rf'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"' return list_rule def get_members_structure(cls, rule_name): if issubclass(cls, Enum): # Handle Enum types - members = [f'\"\\\"{member.value}\\\"\"' for name, member in cls.__members__.items()] + members = [f'"\\"{member.value}\\""' for name, member in cls.__members__.items()] return f"{cls.__name__.lower()} ::= " + " | ".join(members) if cls.__annotations__ and cls.__annotations__ != {}: result = f'{rule_name} ::= "{{"' type_list_rules = [] # Modify this comprehension - members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param_type)}' - for name, param_type in cls.__annotations__.items() - if name != 'self'] + members = [ + f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}' + for name, param_type in cls.__annotations__.items() + if name != "self" + ] result += '"," '.join(members) result += ' "}"' return result, type_list_rules elif rule_name == "custom-class-any": - result = f'{rule_name} ::= ' - result += 'value' + result = f"{rule_name} ::= " + result += "value" type_list_rules = [] return result, type_list_rules else: @@ -124,9 +128,11 @@ def get_members_structure(cls, rule_name): result = f'{rule_name} ::= "{{"' type_list_rules = [] # Modify this comprehension too - members = [f' \"\\\"{name}\\\"\" ":" {map_pydantic_type_to_gbnf(param.annotation)}' - for name, param in parameters.items() - if name != 'self' and param.annotation != inspect.Parameter.empty] + members = [ + f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}' + for name, param in parameters.items() + if name != "self" and param.annotation != inspect.Parameter.empty + ] result += '", "'.join(members) result += ' "}"' @@ -141,8 +147,8 @@ def regex_to_gbnf(regex_pattern: str) -> str: gbnf_rule = regex_pattern # Translate common regex components to GBNF - gbnf_rule = gbnf_rule.replace('\\d', '[0-9]') - gbnf_rule = gbnf_rule.replace('\\s', '[ \t\n]') + gbnf_rule = gbnf_rule.replace("\\d", "[0-9]") + gbnf_rule = gbnf_rule.replace("\\s", "[ \t\n]") # Handle quantifiers and other regex syntax that is similar in GBNF # (e.g., '*', '+', '?', character classes) @@ -158,12 +164,12 @@ def generate_gbnf_integer_rules(max_digit=None, min_digit=None): Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits. Parameters: - max_digit (int): The maximum number of digits for the integer. Default is None. - min_digit (int): The minimum number of digits for the integer. Default is None. + max_digit (int): The maximum number of digits for the integer. Default is None. + min_digit (int): The minimum number of digits for the integer. Default is None. Returns: - integer_rule (str): The identifier for the integer rule generated. - additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits. + integer_rule (str): The identifier for the integer rule generated. + additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits. """ additional_rules = [] @@ -178,21 +184,21 @@ def generate_gbnf_integer_rules(max_digit=None, min_digit=None): # Handling Integer Rules if max_digit is not None or min_digit is not None: # Start with an empty rule part - integer_rule_part = '' + integer_rule_part = "" # Add mandatory digits as per min_digit if min_digit is not None: - integer_rule_part += '[0-9] ' * min_digit + integer_rule_part += "[0-9] " * min_digit # Add optional digits up to max_digit if max_digit is not None: optional_digits = max_digit - (min_digit if min_digit is not None else 0) - integer_rule_part += ''.join(['[0-9]? ' for _ in range(optional_digits)]) + integer_rule_part += "".join(["[0-9]? " for _ in range(optional_digits)]) # Trim the rule part and append it to additional rules integer_rule_part = integer_rule_part.strip() if integer_rule_part: - additional_rules.append(f'{integer_rule} ::= {integer_rule_part}') + additional_rules.append(f"{integer_rule} ::= {integer_rule_part}") return integer_rule, additional_rules @@ -224,21 +230,26 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None additional_rules = [] # Define the integer part rule - integer_part_rule = "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( + integer_part_rule = ( + "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( f"-min{min_digit}" if min_digit is not None else "") + ) # Define the fractional part rule based on precision constraints fractional_part_rule = "fractional-part" - fractional_rule_part = '' + fractional_rule_part = "" if max_precision is not None or min_precision is not None: fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + ( - f"-min{min_precision}" if min_precision is not None else "") + f"-min{min_precision}" if min_precision is not None else "" + ) # Minimum number of digits - fractional_rule_part = '[0-9]' * (min_precision if min_precision is not None else 1) + fractional_rule_part = "[0-9]" * (min_precision if min_precision is not None else 1) # Optional additional digits - fractional_rule_part += ''.join([' [0-9]?'] * ( - (max_precision - (min_precision if min_precision is not None else 1)) if max_precision is not None else 0)) - additional_rules.append(f'{fractional_part_rule} ::= {fractional_rule_part}') + fractional_rule_part += "".join( + [" [0-9]?"] * ((max_precision - ( + min_precision if min_precision is not None else 1)) if max_precision is not None else 0) + ) + additional_rules.append(f"{fractional_part_rule} ::= {fractional_rule_part}") # Define the float rule float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}" @@ -246,20 +257,19 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None # Generating the integer part rule definition, if necessary if max_digit is not None or min_digit is not None: - integer_rule_part = '[0-9]' + integer_rule_part = "[0-9]" if min_digit is not None and min_digit > 1: - integer_rule_part += ' [0-9]' * (min_digit - 1) + integer_rule_part += " [0-9]" * (min_digit - 1) if max_digit is not None: - integer_rule_part += ''.join([' [0-9]?'] * (max_digit - (min_digit if min_digit is not None else 1))) - additional_rules.append(f'{integer_part_rule} ::= {integer_rule_part.strip()}') + integer_rule_part += "".join([" [0-9]?"] * (max_digit - (min_digit if min_digit is not None else 1))) + additional_rules.append(f"{integer_part_rule} ::= {integer_rule_part.strip()}") return float_rule, additional_rules -def generate_gbnf_rule_for_type(model_name, field_name, - field_type, is_optional, processed_models, created_rules, - field_info=None) -> \ - Tuple[str, list]: +def generate_gbnf_rule_for_type( + model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None +) -> Tuple[str, list]: """ Generate GBNF rule for a given field type. @@ -282,20 +292,19 @@ def generate_gbnf_rule_for_type(model_name, field_name, if isclass(field_type) and issubclass(field_type, BaseModel): nested_model_name = format_model_and_field_name(field_type.__name__) - nested_model_rules = generate_gbnf_grammar(field_type, processed_models, created_rules) + nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules) rules.extend(nested_model_rules) gbnf_type, rules = nested_model_name, rules elif isclass(field_type) and issubclass(field_type, Enum): - enum_values = [f'\"\\\"{e.value}\\\"\"' for e in field_type] # Adding escaped quotes + enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}" rules.append(enum_rule) gbnf_type, rules = model_name + "-" + field_name, rules - elif get_origin(field_type) == list or field_type == list: # Array + elif get_origin(field_type) == list: # Array element_type = get_args(field_type)[0] - element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-element", - element_type, is_optional, processed_models, - created_rules) + element_rule_name, additional_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules + ) rules.extend(additional_rules) array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ rules.append(array_rule) @@ -303,10 +312,9 @@ def generate_gbnf_rule_for_type(model_name, field_name, elif get_origin(field_type) == set or field_type == set: # Array element_type = get_args(field_type)[0] - element_rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-element", - element_type, is_optional, processed_models, - created_rules) + element_rule_name, additional_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules + ) rules.extend(additional_rules) array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """ rules.append(array_rule) @@ -318,15 +326,13 @@ def generate_gbnf_rule_for_type(model_name, field_name, elif gbnf_type.startswith("custom-dict-"): key_type, value_type = get_args(field_type) - additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-key-type", - key_type, is_optional, processed_models, - created_rules) - additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(model_name, - f"{field_name}-value-type", - value_type, is_optional, - processed_models, created_rules) - gbnf_type = fr'{gbnf_type} ::= "{{" ( {additional_key_type} ":" {additional_value_type} ("," {additional_key_type} ":" {additional_value_type})* )? "}}" ' + additional_key_type, additional_key_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-key-type", key_type, is_optional, processed_models, created_rules + ) + additional_value_type, additional_value_rules = generate_gbnf_rule_for_type( + model_name, f"{field_name}-value-type", value_type, is_optional, processed_models, created_rules + ) + gbnf_type = rf'{gbnf_type} ::= "{{" ( {additional_key_type} ": " {additional_value_type} ("," "\n" ws {additional_key_type} ":" {additional_value_type})* )? "}}" ' rules.extend(additional_key_rules) rules.extend(additional_value_rules) @@ -336,19 +342,16 @@ def generate_gbnf_rule_for_type(model_name, field_name, for union_type in union_types: if isinstance(union_type, _GenericAlias): - union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, - field_name, union_type, - False, - processed_models, created_rules) + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type( + model_name, field_name, union_type, False, processed_models, created_rules + ) union_rules.append(union_gbnf_type) rules.extend(union_rules_list) - elif not issubclass(union_type, NoneType): - union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(model_name, - field_name, union_type, - False, - processed_models, created_rules) + union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type( + model_name, field_name, union_type, False, processed_models, created_rules + ) union_rules.append(union_gbnf_type) rules.extend(union_rules_list) @@ -363,45 +366,58 @@ def generate_gbnf_rule_for_type(model_name, field_name, else: gbnf_type = f"{model_name}-{field_name}-union" elif isclass(field_type) and issubclass(field_type, str): - if field_info and hasattr(field_info, 'json_schema_extra') and field_info.json_schema_extra is not None: - - triple_quoted_string = field_info.json_schema_extra.get('triple_quoted_string', False) - markdown_string = field_info.json_schema_extra.get('markdown_string', False) + if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None: + triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False) + markdown_string = field_info.json_schema_extra.get("markdown_code_block", False) gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value - gbnf_type = PydanticDataType.MARKDOWN_STRING.value if markdown_string else gbnf_type + gbnf_type = PydanticDataType.MARKDOWN_CODE_BLOCK.value if markdown_string else gbnf_type - elif field_info and hasattr(field_info, 'pattern'): + elif field_info and hasattr(field_info, "pattern"): # Convert regex pattern to grammar rule regex_pattern = field_info.regex.pattern gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}" else: gbnf_type = PydanticDataType.STRING.value - elif isclass(field_type) and issubclass(field_type, float) and field_info and hasattr(field_info, - 'json_schema_extra') and field_info.json_schema_extra is not None: + elif ( + isclass(field_type) + and issubclass(field_type, float) + and field_info + and hasattr(field_info, "json_schema_extra") + and field_info.json_schema_extra is not None + ): # Retrieve precision attributes for floats - max_precision = field_info.json_schema_extra.get('max_precision') if field_info and hasattr(field_info, - 'json_schema_extra') else None - min_precision = field_info.json_schema_extra.get('min_precision') if field_info and hasattr(field_info, - 'json_schema_extra') else None - max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None - min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None + max_precision = ( + field_info.json_schema_extra.get("max_precision") if field_info and hasattr(field_info, + "json_schema_extra") else None + ) + min_precision = ( + field_info.json_schema_extra.get("min_precision") if field_info and hasattr(field_info, + "json_schema_extra") else None + ) + max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None + min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None # Generate GBNF rule for float with given attributes - gbnf_type, rules = generate_gbnf_float_rules(max_digit=max_digits, min_digit=min_digits, - max_precision=max_precision, - min_precision=min_precision) + gbnf_type, rules = generate_gbnf_float_rules( + max_digit=max_digits, min_digit=min_digits, max_precision=max_precision, min_precision=min_precision + ) - elif isclass(field_type) and issubclass(field_type, int) and field_info and hasattr(field_info, - 'json_schema_extra') and field_info.json_schema_extra is not None: + elif ( + isclass(field_type) + and issubclass(field_type, int) + and field_info + and hasattr(field_info, "json_schema_extra") + and field_info.json_schema_extra is not None + ): # Retrieve digit attributes for integers - max_digits = field_info.json_schema_extra.get('max_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None - min_digits = field_info.json_schema_extra.get('min_digit') if field_info and hasattr(field_info, - 'json_schema_extra') else None + max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None + min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info, + "json_schema_extra") else None # Generate GBNF rule for integer with given attributes gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits) @@ -443,13 +459,13 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created if not issubclass(model, BaseModel): # For non-Pydantic classes, generate model_fields from __annotations__ or __init__ - if hasattr(model, '__annotations__') and model.__annotations__: + if hasattr(model, "__annotations__") and model.__annotations__: model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} else: init_signature = inspect.signature(model.__init__) parameters = init_signature.parameters - model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() - if name != 'self'} + model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() if + name != "self"} else: # For Pydantic models, use model_fields and check for ellipsis (required fields) model_fields = model.__annotations__ @@ -469,51 +485,55 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created field_type = field_info field_info = model.model_fields[field_name] is_optional = field_info.is_required is False and get_origin(field_type) is Optional - rule_name, additional_rules = generate_gbnf_rule_for_type(model_name, - format_model_and_field_name(field_name), - field_type, is_optional, - processed_models, created_rules, field_info) - look_for_markdown_code_block = True if rule_name == "markdown_string" else False + rule_name, additional_rules = generate_gbnf_rule_for_type( + model_name, format_model_and_field_name(field_name), field_type, is_optional, processed_models, + created_rules, field_info + ) + look_for_markdown_code_block = True if rule_name == "markdown_code_block" else False look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False if not look_for_markdown_code_block and not look_for_triple_quoted_string: if rule_name not in created_rules: created_rules[rule_name] = additional_rules - model_rule_parts.append(f' ws \"\\\"{field_name}\\\"\" ": " {rule_name}') # Adding escaped quotes + model_rule_parts.append(f' ws "\\"{field_name}\\"" ":" ws {rule_name}') # Adding escaped quotes nested_rules.extend(additional_rules) else: - has_triple_quoted_string = look_for_markdown_code_block - has_markdown_code_block = look_for_triple_quoted_string + has_triple_quoted_string = look_for_triple_quoted_string + has_markdown_code_block = look_for_markdown_code_block fields_joined = r' "," "\n" '.join(model_rule_parts) - model_rule = fr'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"' - - if look_for_markdown_code_block or look_for_triple_quoted_string: - model_rule += ' ws "}"' + model_rule = rf'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"' + has_special_string = False if has_triple_quoted_string: + model_rule += '"\\n" ws "}"' model_rule += '"\\n" triple-quoted-string' + has_special_string = True if has_markdown_code_block: + model_rule += '"\\n" ws "}"' model_rule += '"\\n" markdown-code-block' + has_special_string = True all_rules = [model_rule] + nested_rules - return all_rules, has_markdown_code_block, has_triple_quoted_string + return all_rules, has_special_string -def generate_gbnf_grammar_from_pydantic_models(models: List[Type[BaseModel]], outer_object_name: str = None, - outer_object_content: str = None, list_of_outputs: bool = False) -> str: +def generate_gbnf_grammar_from_pydantic_models( + models: List[Type[BaseModel]], outer_object_name: str = None, outer_object_content: str = None, + list_of_outputs: bool = False +) -> str: """ Generate GBNF Grammar from Pydantic Models. This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated * grammar. - Parameters: - models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - list_of_outputs (str, optional): Allows a list of output objects + Args: + models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + list_of_outputs (str, optional): Allows a list of output objects Returns: - str: The generated GBNF grammar string. + str: The generated GBNF grammar string. Examples: models = [UserModel, PostModel] @@ -527,52 +547,53 @@ def generate_gbnf_grammar_from_pydantic_models(models: List[Type[BaseModel]], ou all_rules = [] created_rules = {} if outer_object_name is None: - for model in models: - model_rules, _, _ = generate_gbnf_grammar(model, - processed_models, created_rules) + model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules) all_rules.extend(model_rules) if list_of_outputs: - root_rule = r'root ::= ws "[" grammar-models ("," grammar-models)* "]"' + "\n" + root_rule = r'root ::= (" "| "\n") "[" ws grammar-models ("," ws grammar-models)* ws "]"' + "\n" else: - root_rule = r'root ::= ws grammar-models' + "\n" + root_rule = r'root ::= (" "| "\n") grammar-models' + "\n" root_rule += "grammar-models ::= " + " | ".join( [format_model_and_field_name(model.__name__) for model in models]) all_rules.insert(0, root_rule) return "\n".join(all_rules) elif outer_object_name is not None: if list_of_outputs: - root_rule = fr'root ::= ws "[" {format_model_and_field_name(outer_object_name)} ("," {format_model_and_field_name(outer_object_name)})* "]"' + "\n" + root_rule = ( + rf'root ::= (" "| "\n") "[" ws {format_model_and_field_name(outer_object_name)} ("," ws {format_model_and_field_name(outer_object_name)})* ws "]"' + + "\n" + ) else: root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n" - model_rule = fr'{format_model_and_field_name(outer_object_name)} ::= ws "{{" ws "\"{outer_object_name}\"" ": " grammar-models' + model_rule = ( + rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"{outer_object_name}\"" ":" ws grammar-models' + ) fields_joined = " | ".join( - [fr'{format_model_and_field_name(model.__name__)}-grammar-model' for model in models]) + [rf"{format_model_and_field_name(model.__name__)}-grammar-model" for model in models]) - grammar_model_rules = f'\ngrammar-models ::= {fields_joined}' + grammar_model_rules = f"\ngrammar-models ::= {fields_joined}" mod_rules = [] for model in models: - mod_rule = fr'{format_model_and_field_name(model.__name__)}-grammar-model ::= ws' - mod_rule += fr'"\"{format_model_and_field_name(model.__name__)}\"" "," ws "\"{outer_object_content}\"" ws ":" ws {format_model_and_field_name(model.__name__)}' + '\n' + mod_rule = rf"{format_model_and_field_name(model.__name__)}-grammar-model ::= " + mod_rule += ( + rf'"\"{model.__name__}\"" "," ws "\"{outer_object_content}\"" ":" ws {format_model_and_field_name(model.__name__)}' + "\n" + ) mod_rules.append(mod_rule) grammar_model_rules += "\n" + "\n".join(mod_rules) - look_for_markdown_code_block = False - look_for_triple_quoted_string = False + for model in models: - model_rules, markdown_block, triple_quoted_string = generate_gbnf_grammar(model, - processed_models, created_rules) + model_rules, has_special_string = generate_gbnf_grammar(model, processed_models, + created_rules) + + if not has_special_string: + model_rules[0] += r'"\n" ws "}"' + all_rules.extend(model_rules) - if markdown_block: - look_for_markdown_code_block = True - if triple_quoted_string: - look_for_triple_quoted_string = True - - if not look_for_markdown_code_block and not look_for_triple_quoted_string: - model_rule += ' ws "}"' all_rules.insert(0, root_rule + model_rule + grammar_model_rules) return "\n".join(all_rules) @@ -582,10 +603,10 @@ def get_primitive_grammar(grammar): Returns the needed GBNF primitive grammar for a given GBNF grammar string. Args: - grammar (str): The string containing the GBNF grammar. + grammar (str): The string containing the GBNF grammar. Returns: - str: GBNF primitive grammar string. + str: GBNF primitive grammar string. """ type_list = [] if "string-list" in grammar: @@ -611,7 +632,7 @@ integer ::= [0-9]+""" any_block = "" if "custom-class-any" in grammar: - any_block = ''' + any_block = """ value ::= object | array | string | number | boolean | null object ::= @@ -626,7 +647,7 @@ array ::= ("," ws value)* )? "]" ws -number ::= integer | float''' +number ::= integer | float""" markdown_code_block_grammar = "" if "markdown-code-block" in grammar: @@ -641,90 +662,32 @@ closing-triple-ticks ::= "```" "\n"''' triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes triple-quoted-string-content ::= ( [^'] | "'" [^'] | "'" "'" [^'] )* triple-quotes ::= "'''" """ - return "\n" + '\n'.join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar + return "\n" + "\n".join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar -def generate_field_markdown(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1) -> str: - indent = ' ' * depth - field_markdown = f"{indent}- **{field_name}** (`{field_type.__name__}`): " - - # Extracting field description from Pydantic Field using __model_fields__ - field_info = model.model_fields.get(field_name) - field_description = field_info.description if field_info and field_info.description else "No description available." - - field_markdown += field_description + '\n' - - # Handling nested BaseModel fields - if isclass(field_type) and issubclass(field_type, BaseModel): - field_markdown += f"{indent} - Details:\n" - for name, type_ in field_type.__annotations__.items(): - field_markdown += generate_field_markdown(name, type_, field_type, depth + 2) - - return field_markdown - - -def generate_markdown_report(pydantic_models: List[Type[BaseModel]]) -> str: - markdown = "" - for model in pydantic_models: - markdown += f"### {format_model_and_field_name(model.__name__)}\n" - - # Check if the model's docstring is different from BaseModel's docstring - class_doc = getdoc(model) - base_class_doc = getdoc(BaseModel) - class_description = class_doc if class_doc and class_doc != base_class_doc else "No specific description available." - - markdown += f"{class_description}\n\n" - markdown += "#### Fields\n" - - if isclass(model) and issubclass(model, BaseModel): - for name, field_type in model.__annotations__.items(): - markdown += generate_field_markdown(format_model_and_field_name(name), field_type, model) - markdown += "\n" - - return markdown - - -def format_json_example(example: dict, depth: int) -> str: +def generate_markdown_documentation( + pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", + documentation_with_field_description=True +) -> str: """ - Format a JSON example into a readable string with indentation. + Generate markdown documentation for a list of Pydantic models. Args: - example (dict): JSON example to be formatted. - depth (int): Indentation depth. + pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + model_prefix (str): Prefix for the model section. + fields_prefix (str): Prefix for the fields section. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - str: Formatted JSON example string. - """ - indent = ' ' * depth - formatted_example = '{\n' - for key, value in example.items(): - value_text = f"'{value}'" if isinstance(value, str) else value - formatted_example += f"{indent}{key}: {value_text},\n" - formatted_example = formatted_example.rstrip(',\n') + '\n' + indent + '}' - return formatted_example - - -def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_prefix="Model", - fields_prefix="Fields", documentation_with_field_description=True) -> str: - """ - Generate text documentation for a list of Pydantic models. - - Args: - pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. - model_prefix (str): Prefix for the model section. - fields_prefix (str): Prefix for the fields section. - documentation_with_field_description (bool): Include field descriptions in the documentation. - - Returns: - str: Generated text documentation. + str: Generated text documentation. """ documentation = "" pyd_models = [(model, True) for model in pydantic_models] for model, add_prefix in pyd_models: if add_prefix: - documentation += f"{model_prefix}: {format_model_and_field_name(model.__name__)}\n" + documentation += f"{model_prefix}: {model.__name__}\n" else: - documentation += f"Model: {format_model_and_field_name(model.__name__)}\n" + documentation += f"Model: {model.__name__}\n" # Handling multi-line model description with proper indentation @@ -733,7 +696,7 @@ def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_pr class_description = class_doc if class_doc and class_doc != base_class_doc else "" if class_description != "": documentation += " Description: " - documentation += "\n" + format_multiline_description(class_description, 2) + "\n" + documentation += format_multiline_description(class_description, 0) + "\n" if add_prefix: # Indenting the fields section @@ -753,35 +716,192 @@ def generate_text_documentation(pydantic_models: List[Type[BaseModel]], model_pr for element_type in element_types: if isclass(element_type) and issubclass(element_type, BaseModel): pyd_models.append((element_type, False)) - documentation += generate_field_text(name, field_type, model, - documentation_with_field_description=documentation_with_field_description) + documentation += generate_field_markdown( + name, field_type, model, documentation_with_field_description=documentation_with_field_description + ) documentation += "\n" - if hasattr(model, 'Config') and hasattr(model.Config, - 'json_schema_extra') and 'example' in model.Config.json_schema_extra: + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n" - json_example = json.dumps(model.Config.json_schema_extra['example']) + json_example = json.dumps(model.Config.json_schema_extra["example"]) documentation += format_multiline_description(json_example, 2) + "\n" return documentation -def generate_field_text(field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, - documentation_with_field_description=True) -> str: +def generate_field_markdown( + field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + documentation_with_field_description=True +) -> str: + """ + Generate markdown documentation for a Pydantic model field. + + Args: + field_name (str): Name of the field. + field_type (Type[Any]): Type of the field. + model (Type[BaseModel]): Pydantic model class. + depth (int): Indentation depth in the documentation. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation for the field. + """ + indent = " " * depth + + field_info = model.model_fields.get(field_name) + field_description = field_info.description if field_info and field_info.description else "" + + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + elif get_origin(field_type) == Union: + element_types = get_args(field_type) + types = [] + for element_type in element_types: + types.append(format_model_and_field_name(element_type.__name__)) + field_text = f"{indent}{field_name} ({' or '.join(types)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + else: + field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})" + if field_description != "": + field_text += ":\n" + else: + field_text += "\n" + + if not documentation_with_field_description: + return field_text + + if field_description != "": + field_text += f" Description: " + field_description + "\n" + + # Check for and include field-specific examples if available + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: + field_example = model.Config.json_schema_extra["example"].get(field_name) + if field_example is not None: + example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example + field_text += f"{indent} Example: {example_text}\n" + + if isclass(field_type) and issubclass(field_type, BaseModel): + field_text += f"{indent} Details:\n" + for name, type_ in field_type.__annotations__.items(): + field_text += generate_field_markdown(name, type_, field_type, depth + 2) + + return field_text + + +def format_json_example(example: dict, depth: int) -> str: + """ + Format a JSON example into a readable string with indentation. + + Args: + example (dict): JSON example to be formatted. + depth (int): Indentation depth. + + Returns: + str: Formatted JSON example string. + """ + indent = " " * depth + formatted_example = "{\n" + for key, value in example.items(): + value_text = f"'{value}'" if isinstance(value, str) else value + formatted_example += f"{indent}{key}: {value_text},\n" + formatted_example = formatted_example.rstrip(",\n") + "\n" + indent + "}" + return formatted_example + + +def generate_text_documentation( + pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", + documentation_with_field_description=True +) -> str: + """ + Generate text documentation for a list of Pydantic models. + + Args: + pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + model_prefix (str): Prefix for the model section. + fields_prefix (str): Prefix for the fields section. + documentation_with_field_description (bool): Include field descriptions in the documentation. + + Returns: + str: Generated text documentation. + """ + documentation = "" + pyd_models = [(model, True) for model in pydantic_models] + for model, add_prefix in pyd_models: + if add_prefix: + documentation += f"{model_prefix}: {model.__name__}\n" + else: + documentation += f"Model: {model.__name__}\n" + + # Handling multi-line model description with proper indentation + + class_doc = getdoc(model) + base_class_doc = getdoc(BaseModel) + class_description = class_doc if class_doc and class_doc != base_class_doc else "" + if class_description != "": + documentation += " Description: " + documentation += "\n" + format_multiline_description(class_description, 2) + "\n" + + if isclass(model) and issubclass(model, BaseModel): + documentation_fields = "" + for name, field_type in model.__annotations__.items(): + # if name == "markdown_code_block": + # continue + if get_origin(field_type) == list: + element_type = get_args(field_type)[0] + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + if get_origin(field_type) == Union: + element_types = get_args(field_type) + for element_type in element_types: + if isclass(element_type) and issubclass(element_type, BaseModel): + pyd_models.append((element_type, False)) + documentation_fields += generate_field_text( + name, field_type, model, documentation_with_field_description=documentation_with_field_description + ) + if documentation_fields != "": + if add_prefix: + documentation += f" {fields_prefix}:\n{documentation_fields}" + else: + documentation += f" Fields:\n{documentation_fields}" + documentation += "\n" + + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: + documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n" + json_example = json.dumps(model.Config.json_schema_extra["example"]) + documentation += format_multiline_description(json_example, 2) + "\n" + + return documentation + + +def generate_field_text( + field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + documentation_with_field_description=True +) -> str: """ Generate text documentation for a Pydantic model field. Args: - field_name (str): Name of the field. - field_type (Type[Any]): Type of the field. - model (Type[BaseModel]): Pydantic model class. - depth (int): Indentation depth in the documentation. - documentation_with_field_description (bool): Include field descriptions in the documentation. + field_name (str): Name of the field. + field_type (Type[Any]): Type of the field. + model (Type[BaseModel]): Pydantic model class. + depth (int): Indentation depth in the documentation. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - str: Generated text documentation for the field. + str: Generated text documentation for the field. """ - indent = ' ' * depth + indent = " " * depth field_info = model.model_fields.get(field_name) field_description = field_info.description if field_info and field_info.description else "" @@ -817,9 +937,9 @@ def generate_field_text(field_name: str, field_type: Type[Any], model: Type[Base field_text += f"{indent} Description: " + field_description + "\n" # Check for and include field-specific examples if available - if hasattr(model, 'Config') and hasattr(model.Config, - 'json_schema_extra') and 'example' in model.Config.json_schema_extra: - field_example = model.Config.json_schema_extra['example'].get(field_name) + if hasattr(model, "Config") and hasattr(model.Config, + "json_schema_extra") and "example" in model.Config.json_schema_extra: + field_example = model.Config.json_schema_extra["example"].get(field_name) if field_example is not None: example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example field_text += f"{indent} Example: {example_text}\n" @@ -837,39 +957,40 @@ def format_multiline_description(description: str, indent_level: int) -> str: Format a multiline description with proper indentation. Args: - description (str): Multiline description. - indent_level (int): Indentation level. + description (str): Multiline description. + indent_level (int): Indentation level. Returns: - str: Formatted multiline description. + str: Formatted multiline description. """ - indent = ' ' * indent_level - return indent + description.replace('\n', '\n' + indent) + indent = " " * indent_level + return indent + description.replace("\n", "\n" + indent) -def save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path="./grammar.gbnf", - documentation_file_path="./grammar_documentation.md"): +def save_gbnf_grammar_and_documentation( + grammar, documentation, grammar_file_path="./grammar.gbnf", documentation_file_path="./grammar_documentation.md" +): """ Save GBNF grammar and documentation to specified files. Args: - grammar (str): GBNF grammar string. - documentation (str): Documentation string. - grammar_file_path (str): File path to save the GBNF grammar. - documentation_file_path (str): File path to save the documentation. + grammar (str): GBNF grammar string. + documentation (str): Documentation string. + grammar_file_path (str): File path to save the GBNF grammar. + documentation_file_path (str): File path to save the documentation. Returns: - None + None """ try: - with open(grammar_file_path, 'w') as file: + with open(grammar_file_path, "w") as file: file.write(grammar + get_primitive_grammar(grammar)) print(f"Grammar successfully saved to {grammar_file_path}") except IOError as e: print(f"An error occurred while saving the grammar file: {e}") try: - with open(documentation_file_path, 'w') as file: + with open(documentation_file_path, "w") as file: file.write(documentation) print(f"Documentation successfully saved to {documentation_file_path}") except IOError as e: @@ -881,10 +1002,10 @@ def remove_empty_lines(string): Remove empty lines from a string. Args: - string (str): Input string. + string (str): Input string. Returns: - str: String with empty lines removed. + str: String with empty lines removed. """ lines = string.splitlines() non_empty_lines = [line for line in lines if line.strip() != ""] @@ -892,95 +1013,109 @@ def remove_empty_lines(string): return string_no_empty_lines -def generate_and_save_gbnf_grammar_and_documentation(pydantic_model_list, - grammar_file_path="./generated_grammar.gbnf", - documentation_file_path="./generated_grammar_documentation.md", - outer_object_name: str = None, - outer_object_content: str = None, - model_prefix: str = "Output Model", - fields_prefix: str = "Output Fields", - list_of_outputs: bool = False, - documentation_with_field_description=True): +def generate_and_save_gbnf_grammar_and_documentation( + pydantic_model_list, + grammar_file_path="./generated_grammar.gbnf", + documentation_file_path="./generated_grammar_documentation.md", + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True, +): """ Generate GBNF grammar and documentation, and save them to specified files. Args: - pydantic_model_list: List of Pydantic model classes. - grammar_file_path (str): File path to save the generated GBNF grammar. - documentation_file_path (str): File path to save the generated documentation. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - model_prefix (str): Prefix for the model section in the documentation. - fields_prefix (str): Prefix for the fields section in the documentation. - list_of_outputs (bool): Whether the output is a list of items. - documentation_with_field_description (bool): Include field descriptions in the documentation. + pydantic_model_list: List of Pydantic model classes. + grammar_file_path (str): File path to save the generated GBNF grammar. + documentation_file_path (str): File path to save the generated documentation. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - None + None """ - documentation = generate_text_documentation(pydantic_model_list, model_prefix, fields_prefix, - documentation_with_field_description=documentation_with_field_description) - grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, - outer_object_content, list_of_outputs) + documentation = generate_markdown_documentation( + pydantic_model_list, model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description + ) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, + list_of_outputs) grammar = remove_empty_lines(grammar) save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path) -def generate_gbnf_grammar_and_documentation(pydantic_model_list, outer_object_name: str = None, - outer_object_content: str = None, - model_prefix: str = "Output Model", - fields_prefix: str = "Output Fields", list_of_outputs: bool = False, - documentation_with_field_description=True): +def generate_gbnf_grammar_and_documentation( + pydantic_model_list, + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True, +): """ Generate GBNF grammar and documentation for a list of Pydantic models. Args: - pydantic_model_list: List of Pydantic model classes. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - model_prefix (str): Prefix for the model section in the documentation. - fields_prefix (str): Prefix for the fields section in the documentation. - list_of_outputs (bool): Whether the output is a list of items. - documentation_with_field_description (bool): Include field descriptions in the documentation. + pydantic_model_list: List of Pydantic model classes. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - tuple: GBNF grammar string, documentation string. + tuple: GBNF grammar string, documentation string. """ - documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, - documentation_with_field_description=documentation_with_field_description) - grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, - outer_object_content, list_of_outputs) + documentation = generate_markdown_documentation( + copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description + ) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, + list_of_outputs) grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) return grammar, documentation -def generate_gbnf_grammar_and_documentation_from_dictionaries(dictionaries: List[dict], - outer_object_name: str = None, - outer_object_content: str = None, - model_prefix: str = "Output Model", - fields_prefix: str = "Output Fields", - list_of_outputs: bool = False, - documentation_with_field_description=True): +def generate_gbnf_grammar_and_documentation_from_dictionaries( + dictionaries: List[dict], + outer_object_name: str = None, + outer_object_content: str = None, + model_prefix: str = "Output Model", + fields_prefix: str = "Output Fields", + list_of_outputs: bool = False, + documentation_with_field_description=True, +): """ Generate GBNF grammar and documentation from a list of dictionaries. Args: - dictionaries (List[dict]): List of dictionaries representing Pydantic models. - outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. - outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. - model_prefix (str): Prefix for the model section in the documentation. - fields_prefix (str): Prefix for the fields section in the documentation. - list_of_outputs (bool): Whether the output is a list of items. - documentation_with_field_description (bool): Include field descriptions in the documentation. + dictionaries (List[dict]): List of dictionaries representing Pydantic models. + outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. + outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. + model_prefix (str): Prefix for the model section in the documentation. + fields_prefix (str): Prefix for the fields section in the documentation. + list_of_outputs (bool): Whether the output is a list of items. + documentation_with_field_description (bool): Include field descriptions in the documentation. Returns: - tuple: GBNF grammar string, documentation string. + tuple: GBNF grammar string, documentation string. """ pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries) - documentation = generate_text_documentation(copy(pydantic_model_list), model_prefix, fields_prefix, - documentation_with_field_description=documentation_with_field_description) - grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, - outer_object_content, list_of_outputs) + documentation = generate_markdown_documentation( + copy(pydantic_model_list), model_prefix, fields_prefix, + documentation_with_field_description=documentation_with_field_description + ) + grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, + list_of_outputs) grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar)) return grammar, documentation @@ -990,41 +1125,61 @@ def create_dynamic_model_from_function(func: Callable): Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method. Args: - func (Callable): A function with type hints from which to create the model. + func (Callable): A function with type hints from which to create the model. Returns: - A dynamic Pydantic model class with the provided function as a 'run' method. + A dynamic Pydantic model class with the provided function as a 'run' method. """ - # Extracting type hints from the provided function - type_hints = get_type_hints(func) - type_hints.pop('return', None) - # Handling default values and annotations + # Get the signature of the function + sig = inspect.signature(func) + + # Parse the docstring + docstring = parse(func.__doc__) + dynamic_fields = {} - defaults = getattr(func, '__defaults__', ()) or () - defaults_index = len(type_hints) - len(defaults) + param_docs = [] + for param in sig.parameters.values(): + # Exclude 'self' parameter + if param.name == "self": + continue - for index, (name, typ) in enumerate(type_hints.items()): - if index >= defaults_index: - default_value = defaults[index - defaults_index] - dynamic_fields[name] = (typ, default_value) + # Assert that the parameter has a type annotation + if param.annotation == inspect.Parameter.empty: + raise TypeError(f"Parameter '{param.name}' in function '{func.__name__}' lacks a type annotation") + + # Find the parameter's description in the docstring + param_doc = next((d for d in docstring.params if d.arg_name == param.name), None) + + # Assert that the parameter has a description + if not param_doc or not param_doc.description: + raise ValueError( + f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring") + + # Add parameter details to the schema + param_doc = next((d for d in docstring.params if d.arg_name == param.name), None) + param_docs.append((param.name, param_doc)) + if param.default == inspect.Parameter.empty: + default_value = ... else: - dynamic_fields[name] = (typ, ...) - + default_value = param.default + dynamic_fields[param.name] = ( + param.annotation if param.annotation != inspect.Parameter.empty else str, default_value) # Creating the dynamic model - dynamicModel = create_model(f'{func.__name__}', **dynamic_fields) + dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) - dynamicModel.__doc__ = getdoc(func) + for param_doc in param_docs: + dynamic_model.model_fields[param_doc[0]].description = param_doc[1].description + + dynamic_model.__doc__ = docstring.short_description - # Wrapping the original function to handle instance 'self' def run_method_wrapper(self): - func_args = {name: getattr(self, name) for name in type_hints} + func_args = {name: getattr(self, name) for name, _ in dynamic_fields.items()} return func(**func_args) # Adding the wrapped function as a 'run' method - setattr(dynamicModel, 'run', run_method_wrapper) - - return dynamicModel + setattr(dynamic_model, "run", run_method_wrapper) + return dynamic_model def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): @@ -1032,11 +1187,11 @@ def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): Add a 'run' method to a dynamic Pydantic model, using the provided function. Args: - - model (Type[BaseModel]): Dynamic Pydantic model class. - - func (Callable): Function to be added as a 'run' method to the model. + model (Type[BaseModel]): Dynamic Pydantic model class. + func (Callable): Function to be added as a 'run' method to the model. Returns: - - Type[BaseModel]: Pydantic model class with the added 'run' method. + Type[BaseModel]: Pydantic model class with the added 'run' method. """ def run_method_wrapper(self): @@ -1044,7 +1199,7 @@ def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): return func(**func_args) # Adding the wrapped function as a 'run' method - setattr(model, 'run', run_method_wrapper) + setattr(model, "run", run_method_wrapper) return model @@ -1054,15 +1209,15 @@ def create_dynamic_models_from_dictionaries(dictionaries: List[dict]): Create a list of dynamic Pydantic model classes from a list of dictionaries. Args: - - dictionaries (List[dict]): List of dictionaries representing model structures. + dictionaries (List[dict]): List of dictionaries representing model structures. Returns: - - List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. + List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. """ dynamic_models = [] for func in dictionaries: model_name = format_model_and_field_name(func.get("name", "")) - dyn_model = convert_dictionary_to_to_pydantic_model(func, model_name) + dyn_model = convert_dictionary_to_pydantic_model(func, model_name) dynamic_models.append(dyn_model) return dynamic_models @@ -1080,12 +1235,12 @@ from enum import Enum def json_schema_to_python_types(schema): type_map = { - 'any': Any, - 'string': str, - 'number': float, - 'integer': int, - 'boolean': bool, - 'array': list, + "any": Any, + "string": str, + "number": float, + "integer": int, + "boolean": bool, + "array": list, } return type_map[schema] @@ -1094,58 +1249,64 @@ def list_to_enum(enum_name, values): return Enum(enum_name, {value: value for value in values}) -def convert_dictionary_to_to_pydantic_model(dictionary: dict, model_name: str = 'CustomModel') -> Type[BaseModel]: +def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "CustomModel") -> Type[BaseModel]: """ Convert a dictionary to a Pydantic model class. Args: - - dictionary (dict): Dictionary representing the model structure. - - model_name (str): Name of the generated Pydantic model. + dictionary (dict): Dictionary representing the model structure. + model_name (str): Name of the generated Pydantic model. Returns: - - Type[BaseModel]: Generated Pydantic model class. + Type[BaseModel]: Generated Pydantic model class. """ fields = {} if "properties" in dictionary: for field_name, field_data in dictionary.get("properties", {}).items(): - if field_data == 'object': - submodel = convert_dictionary_to_to_pydantic_model(dictionary, f'{model_name}_{field_name}') + if field_data == "object": + submodel = convert_dictionary_to_pydantic_model(dictionary, f"{model_name}_{field_name}") fields[field_name] = (submodel, ...) else: - field_type = field_data.get('type', 'str') + field_type = field_data.get("type", "str") if field_data.get("enum", []): fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...) - if field_type == "array": + elif field_type == "array": items = field_data.get("items", {}) if items != {}: array = {"properties": items} - array_type = convert_dictionary_to_to_pydantic_model(array, f'{model_name}_{field_name}_items') + array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items") fields[field_name] = (List[array_type], ...) else: fields[field_name] = (list, ...) - elif field_type == 'object': - submodel = convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}_{field_name}') + elif field_type == "object": + submodel = convert_dictionary_to_pydantic_model(field_data, f"{model_name}_{field_name}") fields[field_name] = (submodel, ...) + elif field_type == "required": + required = field_data.get("enum", []) + for key, field in fields.items(): + if key not in required: + fields[key] = (Optional[fields[key][0]], ...) else: field_type = json_schema_to_python_types(field_type) fields[field_name] = (field_type, ...) if "function" in dictionary: - for field_name, field_data in dictionary.get("function", {}).items(): if field_name == "name": model_name = field_data elif field_name == "description": fields["__doc__"] = field_data elif field_name == "parameters": - return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') + return convert_dictionary_to_pydantic_model(field_data, f"{model_name}") + if "parameters" in dictionary: field_data = {"function": dictionary} - return convert_dictionary_to_to_pydantic_model(field_data, f'{model_name}') - + return convert_dictionary_to_pydantic_model(field_data, f"{model_name}") + if "required" in dictionary: + required = dictionary.get("required", []) + for key, field in fields.items(): + if key not in required: + fields[key] = (Optional[fields[key][0]], ...) custom_model = create_model(model_name, **fields) return custom_model - - - From 7c8d3abd1a17c28fc56b1a4814bc4b29f91d7454 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Tue, 16 Jan 2024 14:33:02 +0100 Subject: [PATCH 051/334] metal : log `recommendedMaxWorkingSetSize` on iOS 16+ (#4936) * metal: Log `recommendedMaxWorkingSetSize` on iOS 16+ * Only log on iOS and macOS, ignoring tvOS and other platforms * Check for Xcode version before using recommendedMaxWorkingSetSize --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 58 ++++++++++++++++++++++++---------------------------- 1 file changed, 27 insertions(+), 31 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 867f2fd48..44134d1d9 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -369,8 +369,12 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); + +#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) + if (@available(macOS 10.12, iOS 16.0, *)) { + GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); + } +#elif TARGET_OS_OSX if (ctx->device.maxTransferRate != 0) { GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6); } else { @@ -2369,6 +2373,25 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe UNUSED(buft); } +static void ggml_backend_metal_log_allocated_size(id device) { +#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) + if (@available(macOS 10.12, iOS 16.0, *)) { + GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", + device.currentAllocatedSize / 1024.0 / 1024.0, + device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + + if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { + GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); + } else { + GGML_METAL_LOG_INFO("\n"); + } + } else { + GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); + } +#endif + UNUSED(device); +} + GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context)); @@ -2401,22 +2424,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff } GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); - - -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", - device.currentAllocatedSize / 1024.0 / 1024.0, - device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } else { - GGML_METAL_LOG_INFO("\n"); - } -#else - GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); -#endif - + ggml_backend_metal_log_allocated_size(device); return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); } @@ -2524,19 +2532,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, } } -#if TARGET_OS_OSX - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", - device.currentAllocatedSize / 1024.0 / 1024.0, - device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } else { - GGML_METAL_LOG_INFO("\n"); - } -#else - GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); -#endif + ggml_backend_metal_log_allocated_size(device); return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); } From 3a48d558a69c88ac17efcaa5900cd9eb19596ac4 Mon Sep 17 00:00:00 2001 From: Alex Azarov Date: Tue, 16 Jan 2024 14:41:27 +0100 Subject: [PATCH 052/334] metal : replace loop of dispatch_async with dispatch_apply (#4934) * Replace loop of dispatch_async with dispatch_apply * Update ggml-metal.m --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 2882 +++++++++++++++++++++++++------------------------- 1 file changed, 1439 insertions(+), 1443 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 44134d1d9..c21dc465a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -737,1475 +737,249 @@ static bool ggml_metal_graph_compute( ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc]; } - for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { - const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; + const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; + dispatch_apply(n_cb, ctx->d_queue, ^(size_t iter) { + const int cb_idx = iter; - dispatch_async(ctx->d_queue, ^{ - size_t offs_src0 = 0; - size_t offs_src1 = 0; - size_t offs_dst = 0; + size_t offs_src0 = 0; + size_t offs_src1 = 0; + size_t offs_dst = 0; - id command_buffer = ctx->command_buffers[cb_idx]; - id encoder = ctx->command_encoders[cb_idx]; + id command_buffer = ctx->command_buffers[cb_idx]; + id encoder = ctx->command_encoders[cb_idx]; - const int node_start = (cb_idx + 0) * n_nodes_per_cb; - const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); + const int node_start = (cb_idx + 0) * n_nodes_per_cb; + const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); - for (int ind = node_start; ind < node_end; ++ind) { - const int i = ind; + for (int ind = node_start; ind < node_end; ++ind) { + const int i = ind; - if (i == -1) { - [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; - continue; - } + if (i == -1) { + [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; + continue; + } - //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); + //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); - struct ggml_tensor * src0 = gf->nodes[i]->src[0]; - struct ggml_tensor * src1 = gf->nodes[i]->src[1]; - struct ggml_tensor * dst = gf->nodes[i]; + struct ggml_tensor * src0 = gf->nodes[i]->src[0]; + struct ggml_tensor * src1 = gf->nodes[i]->src[1]; + struct ggml_tensor * dst = gf->nodes[i]; - switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop -> next node - } continue; - default: - { - } break; - } + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + { + // noop -> next node + } continue; + default: + { + } break; + } - if (!ggml_metal_supports_op(ctx, dst)) { - GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); - GGML_ASSERT(!"unsupported op"); - } + if (!ggml_metal_supports_op(ctx, dst)) { + GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); + GGML_ASSERT(!"unsupported op"); + } #ifndef GGML_METAL_NDEBUG - [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; + [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; #endif - const int64_t ne00 = src0 ? src0->ne[0] : 0; - const int64_t ne01 = src0 ? src0->ne[1] : 0; - const int64_t ne02 = src0 ? src0->ne[2] : 0; - const int64_t ne03 = src0 ? src0->ne[3] : 0; + const int64_t ne00 = src0 ? src0->ne[0] : 0; + const int64_t ne01 = src0 ? src0->ne[1] : 0; + const int64_t ne02 = src0 ? src0->ne[2] : 0; + const int64_t ne03 = src0 ? src0->ne[3] : 0; - const uint64_t nb00 = src0 ? src0->nb[0] : 0; - const uint64_t nb01 = src0 ? src0->nb[1] : 0; - const uint64_t nb02 = src0 ? src0->nb[2] : 0; - const uint64_t nb03 = src0 ? src0->nb[3] : 0; + const uint64_t nb00 = src0 ? src0->nb[0] : 0; + const uint64_t nb01 = src0 ? src0->nb[1] : 0; + const uint64_t nb02 = src0 ? src0->nb[2] : 0; + const uint64_t nb03 = src0 ? src0->nb[3] : 0; - const int64_t ne10 = src1 ? src1->ne[0] : 0; - const int64_t ne11 = src1 ? src1->ne[1] : 0; - const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); + const int64_t ne10 = src1 ? src1->ne[0] : 0; + const int64_t ne11 = src1 ? src1->ne[1] : 0; + const int64_t ne12 = src1 ? src1->ne[2] : 0; + const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); - const uint64_t nb10 = src1 ? src1->nb[0] : 0; - const uint64_t nb11 = src1 ? src1->nb[1] : 0; - const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); + const uint64_t nb10 = src1 ? src1->nb[0] : 0; + const uint64_t nb11 = src1 ? src1->nb[1] : 0; + const uint64_t nb12 = src1 ? src1->nb[2] : 0; + const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; + const int64_t ne0 = dst ? dst->ne[0] : 0; + const int64_t ne1 = dst ? dst->ne[1] : 0; + const int64_t ne2 = dst ? dst->ne[2] : 0; + const int64_t ne3 = dst ? dst->ne[3] : 0; - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; + const uint64_t nb0 = dst ? dst->nb[0] : 0; + const uint64_t nb1 = dst ? dst->nb[1] : 0; + const uint64_t nb2 = dst ? dst->nb[2] : 0; + const uint64_t nb3 = dst ? dst->nb[3] : 0; - const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; - const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; - const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; + const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; + const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; + const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; - id id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; - id id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; - id id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; + id id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; + id id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; + id id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; - //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); - //if (src0) { - // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, - // ggml_is_contiguous(src0), src0->name); - //} - //if (src1) { - // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, - // ggml_is_contiguous(src1), src1->name); - //} - //if (dst) { - // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, - // dst->name); - //} + //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); + //if (src0) { + // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, + // ggml_is_contiguous(src0), src0->name); + //} + //if (src1) { + // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, + // ggml_is_contiguous(src1), src1->name); + //} + //if (dst) { + // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, + // dst->name); + //} - switch (dst->op) { - case GGML_OP_CONCAT: - { - const int64_t nb = ne00; + switch (dst->op) { + case GGML_OP_CONCAT: + { + const int64_t nb = ne00; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; - const int nth = MIN(1024, ne0); + const int nth = MIN(1024, ne0); - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ADD: - case GGML_OP_MUL: - case GGML_OP_DIV: - { - const size_t offs = 0; + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_DIV: + { + const size_t offs = 0; - bool bcast_row = false; + bool bcast_row = false; - int64_t nb = ne00; + int64_t nb = ne00; - id pipeline = nil; + id pipeline = nil; - if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { - GGML_ASSERT(ggml_is_contiguous(src0)); - - // src1 is a row - GGML_ASSERT(ne11 == 1); - - nb = ne00 / 4; - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; - default: GGML_ASSERT(false); - } - - bcast_row = true; - } else { - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; - default: GGML_ASSERT(false); - } - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; - - if (bcast_row) { - const int64_t n = ggml_nelements(dst)/4; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } else { - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - } break; - case GGML_OP_ACC: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - GGML_ASSERT(dstt == GGML_TYPE_F32); - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - - const size_t pnb1 = ((int32_t *) dst->op_params)[0]; - const size_t pnb2 = ((int32_t *) dst->op_params)[1]; - const size_t pnb3 = ((int32_t *) dst->op_params)[2]; - const size_t offs = ((int32_t *) dst->op_params)[3]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - // run a separete kernel to cpy src->dst - // not sure how to avoid this - // TODO: make a simpler cpy_bytes kernel - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_SCALE: - { + if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { GGML_ASSERT(ggml_is_contiguous(src0)); - const float scale = *(const float *) dst->op_params; + // src1 is a row + GGML_ASSERT(ne11 == 1); - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - n /= 4; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; + nb = ne00 / 4; + switch (dst->op) { + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; + default: GGML_ASSERT(false); } - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; + bcast_row = true; + } else { + switch (dst->op) { + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; + default: GGML_ASSERT(false); + } + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; + + if (bcast_row) { + const int64_t n = ggml_nelements(dst)/4; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(gf->nodes[i])) { - case GGML_UNARY_OP_TANH: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_RELU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_SILU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - default: - { - GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ASSERT(false); - } - } break; - case GGML_OP_SQR: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SUM_ROWS: - { - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SOFT_MAX: - { - int nth = 32; // SIMD width - - id pipeline = nil; - - if (ne00%4 == 0) { - while (nth < ne00/4 && nth < 256) { - nth *= 2; - } - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; - } else { - while (nth < ne00 && nth < 1024) { - nth *= 2; - } - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; - } - - const float scale = ((float *) dst->op_params)[0]; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - if (id_src1) { - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_DIAG_MASK_INF: - { - const int n_past = ((int32_t *)(dst->op_params))[0]; - - id pipeline = nil; - - if (ne00%8 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; - - if (ne00%8 == 0) { - [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - else { - [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - } break; - case GGML_OP_MUL_MAT: - { - GGML_ASSERT(ne00 == ne10); - - // TODO: assert that dim2 and dim3 are contiguous - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - const uint r2 = ne12/ne02; - const uint r3 = ne13/ne03; - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - int ne11_mm_min = 1; - -#if 0 - // the numbers below are measured on M2 Ultra for 7B and 13B models - // these numbers do not translate to other devices or model sizes - // TODO: need to find a better approach - if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) { - switch (src0t) { - case GGML_TYPE_F16: ne11_mm_min = 2; break; - case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; - case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; - case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; - case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; - case GGML_TYPE_Q5_0: // not tested yet - case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet - case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; - default: ne11_mm_min = 1; break; - } - } -#endif - - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && - !ggml_is_transposed(src0) && - !ggml_is_transposed(src1) && - src1t == GGML_TYPE_F32 && - ne00 % 32 == 0 && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { - //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; - default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - // use custom matrix x vector kernel - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; - nrows = 4; - } break; - case GGML_TYPE_F16: - { - nth0 = 32; - nth1 = 1; - if (src1t == GGML_TYPE_F32) { - if (ne11 * ne12 < 4) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; - nrows = ne11; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; - nrows = 4; - } - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; - nrows = 4; - } - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; - } break; - default: - { - GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); - GGML_ASSERT(false && "not implemented"); - } - }; - - if (ggml_is_quantized(src0t)) { - GGML_ASSERT(ne00 >= nth0*nth1); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; - - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || - src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { - const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif - } - else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (ne11 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_MUL_MAT_ID: - { - //GGML_ASSERT(ne00 == ne10); - //GGML_ASSERT(ne03 == ne13); - - GGML_ASSERT(src0t == GGML_TYPE_I32); - - const int n_as = ((int32_t *) dst->op_params)[1]; - - // TODO: make this more general - GGML_ASSERT(n_as <= 8); - - // max size of the src1ids array in the kernel stack - GGML_ASSERT(ne11 <= 512); - - struct ggml_tensor * src2 = gf->nodes[i]->src[2]; - - const int64_t ne20 = src2 ? src2->ne[0] : 0; - const int64_t ne21 = src2 ? src2->ne[1] : 0; - const int64_t ne22 = src2 ? src2->ne[2] : 0; - const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); - const uint64_t nb21 = src2 ? src2->nb[1] : 0; - const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); - - const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); - - GGML_ASSERT(!ggml_is_transposed(src2)); - GGML_ASSERT(!ggml_is_transposed(src1)); - - GGML_ASSERT(src1t == GGML_TYPE_F32); - - const uint r2 = ne12/ne22; - const uint r3 = ne13/ne23; - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - int ne11_mm_min = n_as; - - const int idx = ((int32_t *) dst->op_params)[0]; - - // batch size - GGML_ASSERT(ne01 == ne11); - - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - // !!! - // TODO: for now, always use mat-vec kernels until we figure out how to improve the - // indirect matrix multiplication - // !!! - if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && - ne20 % 32 == 0 && ne20 >= 64 && - ne11 > ne11_mm_min) { - - id pipeline = nil; - - switch (src2->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; - default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:7]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:9]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:16]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; - [encoder setBytes:&idx length:sizeof(idx) atIndex:18]; - // TODO: how to make this an array? read Metal docs - for (int j = 0; j < 8; ++j) { - // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 - struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; - - size_t offs_src_cur = 0; - id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); - - [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; - } - - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - // use custom matrix x vector kernel - switch (src2t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - nth0 = 32; - nth1 = 1; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; - } break; - default: - { - GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); - GGML_ASSERT(false && "not implemented"); - } - }; - - if (ggml_is_quantized(src2t)) { - GGML_ASSERT(ne20 >= nth0*nth1); - } - - const int64_t _ne1 = 1; // kernels needs a reference in constant memory - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6]; - [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8]; - [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:20]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:21]; - [encoder setBytes:&idx length:sizeof(idx) atIndex:22]; - // TODO: how to make this an array? read Metal docs - for (int j = 0; j < 8; ++j) { - // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 - struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; - - size_t offs_src_cur = 0; - id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); - - [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; - } - - if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || - src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || - src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { - const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif - } - else if (src2t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src2t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (_ne1 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_GET_ROWS: - { - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; - case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; - } break; - case GGML_OP_RMS_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - int nth = 32; // SIMD width - - while (nth < ne00/4 && nth < 1024) { - nth *= 2; - } - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_GROUP_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - - //float eps; - //memcpy(&eps, dst->op_params, sizeof(float)); - - const float eps = 1e-6f; // TODO: temporarily hardcoded - - const int32_t n_groups = ((int32_t *) dst->op_params)[0]; - - int nth = 32; // SIMD width - - //while (nth < ne00/4 && nth < 1024) { - // nth *= 2; - //} - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&eps length:sizeof( float) atIndex:9]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_NORM: - { - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - const int nth = MIN(256, ne00); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT((src0t == GGML_TYPE_F32)); - - const int nth = MIN(1024, ne00); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; - [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; - [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ROPE: - { - GGML_ASSERT(ne10 == ne02); - - const int nth = MIN(1024, ne00); - - const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; - default: GGML_ASSERT(false); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_IM2COL: - { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int32_t N = src1->ne[is_2D ? 3 : 2]; - const int32_t IC = src1->ne[is_2D ? 2 : 1]; - const int32_t IH = is_2D ? src1->ne[1] : 1; - const int32_t IW = src1->ne[0]; - - const int32_t KH = is_2D ? src0->ne[1] : 1; - const int32_t KW = src0->ne[0]; - - const int32_t OH = is_2D ? dst->ne[2] : 1; - const int32_t OW = dst->ne[1]; - - const int32_t CHW = IC * KH * KW; - - const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; - const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; - default: GGML_ASSERT(false); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; - [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; - [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; - [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; - [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; - [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; - [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; - [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; - [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; - [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; - - [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; - } break; - case GGML_OP_UPSCALE: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int sf = dst->op_params[0]; - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; - + } else { const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_PAD: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } + } break; + case GGML_OP_ACC: + { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + GGML_ASSERT(dstt == GGML_TYPE_F32); - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + const size_t pnb1 = ((int32_t *) dst->op_params)[0]; + const size_t pnb2 = ((int32_t *) dst->op_params)[1]; + const size_t pnb3 = ((int32_t *) dst->op_params)[2]; + const size_t offs = ((int32_t *) dst->op_params)[3]; - const int nth = MIN(1024, ne0); + const bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ARGSORT: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); + if (!inplace) { + // run a separete kernel to cpy src->dst + // not sure how to avoid this + // TODO: make a simpler cpy_bytes kernel - const int nrows = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - - id pipeline = nil; - - switch (order) { - case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; - case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; - default: GGML_ASSERT(false); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - - [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)]; - } break; - case GGML_OP_LEAKY_RELU: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - float slope; - memcpy(&slope, dst->op_params, sizeof(float)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_DUP: - case GGML_OP_CPY: - case GGML_OP_CONT: - { - GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); - - int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); - - id pipeline = nil; - - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); - - switch (dstt) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; - //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; - //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - }; - } break; - case GGML_TYPE_F16: - { - switch (dstt) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - }; - } break; - default: GGML_ASSERT(false && "not implemented"); - } + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -2227,31 +1001,1253 @@ static bool ggml_metal_graph_compute( [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - default: - { - GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ASSERT(false); } - } + + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; + [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; + [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; + [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; + [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; + [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + + [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_SCALE: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + + const float scale = *(const float *) dst->op_params; + + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + n /= 4; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(gf->nodes[i])) { + case GGML_UNARY_OP_TANH: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_RELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_GELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + + [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_GELU_QUICK: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + + [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_SILU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + + [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + default: + { + GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + GGML_ASSERT(false); + } + } break; + case GGML_OP_SQR: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SUM_ROWS: + { + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SOFT_MAX: + { + int nth = 32; // SIMD width + + id pipeline = nil; + + if (ne00%4 == 0) { + while (nth < ne00/4 && nth < 256) { + nth *= 2; + } + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; + } else { + while (nth < ne00 && nth < 1024) { + nth *= 2; + } + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; + } + + const float scale = ((float *) dst->op_params)[0]; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + if (id_src1) { + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_DIAG_MASK_INF: + { + const int n_past = ((int32_t *)(dst->op_params))[0]; + + id pipeline = nil; + + if (ne00%8 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; + + if (ne00%8 == 0) { + [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } + else { + [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } + } break; + case GGML_OP_MUL_MAT: + { + GGML_ASSERT(ne00 == ne10); + + // TODO: assert that dim2 and dim3 are contiguous + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + const uint r2 = ne12/ne02; + const uint r3 = ne13/ne03; + + // find the break-even point where the matrix-matrix kernel becomes more efficient compared + // to the matrix-vector kernel + int ne11_mm_min = 1; + +#if 0 + // the numbers below are measured on M2 Ultra for 7B and 13B models + // these numbers do not translate to other devices or model sizes + // TODO: need to find a better approach + if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) { + switch (src0t) { + case GGML_TYPE_F16: ne11_mm_min = 2; break; + case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; + case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; + case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; + case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; + case GGML_TYPE_Q5_0: // not tested yet + case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet + case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; + case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; + default: ne11_mm_min = 1; break; + } + } +#endif + + // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs + // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel + if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && + !ggml_is_transposed(src0) && + !ggml_is_transposed(src1) && + src1t == GGML_TYPE_F32 && + ne00 % 32 == 0 && ne00 >= 64 && + (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { + //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; + default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; + [encoder setThreadgroupMemoryLength:8192 atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + } else { + int nth0 = 32; + int nth1 = 1; + int nrows = 1; + //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + // use custom matrix x vector kernel + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; + nrows = 4; + } break; + case GGML_TYPE_F16: + { + nth0 = 32; + nth1 = 1; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; + nrows = ne11; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; + nrows = 4; + } + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; + nrows = 4; + } + } break; + case GGML_TYPE_Q4_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; + } break; + case GGML_TYPE_Q4_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; + } break; + case GGML_TYPE_Q5_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; + } break; + case GGML_TYPE_Q5_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; + } break; + case GGML_TYPE_Q8_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; + } break; + case GGML_TYPE_Q2_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; + } break; + case GGML_TYPE_Q3_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; + } break; + case GGML_TYPE_Q4_K: + { + nth0 = 4; //1; + nth1 = 8; //32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; + } break; + case GGML_TYPE_Q5_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; + } break; + case GGML_TYPE_Q6_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; + } break; + default: + { + GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); + GGML_ASSERT(false && "not implemented"); + } + }; + + if (ggml_is_quantized(src0t)) { + GGML_ASSERT(ne00 >= nth0*nth1); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; + + if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || + src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || + src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { + const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q3_K) { +#ifdef GGML_QKK_64 + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#else + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#endif + } + else if (src0t == GGML_TYPE_Q5_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q6_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else { + const int64_t ny = (ne11 + nrows - 1)/nrows; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + } + } break; + case GGML_OP_MUL_MAT_ID: + { + //GGML_ASSERT(ne00 == ne10); + //GGML_ASSERT(ne03 == ne13); + + GGML_ASSERT(src0t == GGML_TYPE_I32); + + const int n_as = ((int32_t *) dst->op_params)[1]; + + // TODO: make this more general + GGML_ASSERT(n_as <= 8); + + // max size of the src1ids array in the kernel stack + GGML_ASSERT(ne11 <= 512); + + struct ggml_tensor * src2 = gf->nodes[i]->src[2]; + + const int64_t ne20 = src2 ? src2->ne[0] : 0; + const int64_t ne21 = src2 ? src2->ne[1] : 0; + const int64_t ne22 = src2 ? src2->ne[2] : 0; + const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); + + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); + + const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); + + GGML_ASSERT(!ggml_is_transposed(src2)); + GGML_ASSERT(!ggml_is_transposed(src1)); + + GGML_ASSERT(src1t == GGML_TYPE_F32); + + const uint r2 = ne12/ne22; + const uint r3 = ne13/ne23; + + // find the break-even point where the matrix-matrix kernel becomes more efficient compared + // to the matrix-vector kernel + int ne11_mm_min = n_as; + + const int idx = ((int32_t *) dst->op_params)[0]; + + // batch size + GGML_ASSERT(ne01 == ne11); + + // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs + // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel + // !!! + // TODO: for now, always use mat-vec kernels until we figure out how to improve the + // indirect matrix multiplication + // !!! + if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && + ne20 % 32 == 0 && ne20 >= 64 && + ne11 > ne11_mm_min) { + + id pipeline = nil; + + switch (src2->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; + default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:5]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; + [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:7]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:9]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:16]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; + [encoder setBytes:&idx length:sizeof(idx) atIndex:18]; + // TODO: how to make this an array? read Metal docs + for (int j = 0; j < 8; ++j) { + // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 + struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; + + size_t offs_src_cur = 0; + id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); + + [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; + } + + [encoder setThreadgroupMemoryLength:8192 atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + } else { + int nth0 = 32; + int nth1 = 1; + int nrows = 1; + //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + // use custom matrix x vector kernel + switch (src2t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; + } break; + case GGML_TYPE_F16: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + nth0 = 32; + nth1 = 1; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; + } break; + case GGML_TYPE_Q4_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; + } break; + case GGML_TYPE_Q4_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; + } break; + case GGML_TYPE_Q5_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; + } break; + case GGML_TYPE_Q5_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; + } break; + case GGML_TYPE_Q8_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; + } break; + case GGML_TYPE_Q2_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; + } break; + case GGML_TYPE_Q3_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; + } break; + case GGML_TYPE_Q4_K: + { + nth0 = 4; //1; + nth1 = 8; //32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; + } break; + case GGML_TYPE_Q5_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; + } break; + case GGML_TYPE_Q6_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; + } break; + default: + { + GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); + GGML_ASSERT(false && "not implemented"); + } + }; + + if (ggml_is_quantized(src2t)) { + GGML_ASSERT(ne20 >= nth0*nth1); + } + + const int64_t _ne1 = 1; // kernels needs a reference in constant memory + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; + [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6]; + [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8]; + [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; + [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; + [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:20]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:21]; + [encoder setBytes:&idx length:sizeof(idx) atIndex:22]; + // TODO: how to make this an array? read Metal docs + for (int j = 0; j < 8; ++j) { + // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 + struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; + + size_t offs_src_cur = 0; + id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); + + [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; + } + + if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || + src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || + src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { + const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_Q3_K) { +#ifdef GGML_QKK_64 + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#else + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; +#endif + } + else if (src2t == GGML_TYPE_Q5_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src2t == GGML_TYPE_Q6_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else { + const int64_t ny = (_ne1 + nrows - 1)/nrows; + [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + } + } break; + case GGML_OP_GET_ROWS: + { + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; + [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; + } break; + case GGML_OP_RMS_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + int nth = 32; // SIMD width + + while (nth < ne00/4 && nth < 1024) { + nth *= 2; + } + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + const int64_t nrows = ggml_nrows(src0); + + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_GROUP_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + + //float eps; + //memcpy(&eps, dst->op_params, sizeof(float)); + + const float eps = 1e-6f; // TODO: temporarily hardcoded + + const int32_t n_groups = ((int32_t *) dst->op_params)[0]; + + int nth = 32; // SIMD width + + //while (nth < ne00/4 && nth < 1024) { + // nth *= 2; + //} + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&eps length:sizeof( float) atIndex:9]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_NORM: + { + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + const int nth = MIN(256, ne00); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; + + const int64_t nrows = ggml_nrows(src0); + + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ALIBI: + { + GGML_ASSERT((src0t == GGML_TYPE_F32)); + + const int nth = MIN(1024, ne00); + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); + + const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; + [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; + [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ROPE: + { + GGML_ASSERT(ne10 == ne02); + + const int nth = MIN(1024, ne00); + + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; + default: GGML_ASSERT(false); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; + [encoder setBytes:&mode length:sizeof( int) atIndex:21]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_IM2COL: + { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int32_t N = src1->ne[is_2D ? 3 : 2]; + const int32_t IC = src1->ne[is_2D ? 2 : 1]; + const int32_t IH = is_2D ? src1->ne[1] : 1; + const int32_t IW = src1->ne[0]; + + const int32_t KH = is_2D ? src0->ne[1] : 1; + const int32_t KW = src0->ne[0]; + + const int32_t OH = is_2D ? dst->ne[2] : 1; + const int32_t OW = dst->ne[1]; + + const int32_t CHW = IC * KH * KW; + + const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; + const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; + default: GGML_ASSERT(false); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } break; + case GGML_OP_UPSCALE: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int sf = dst->op_params[0]; + + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_PAD: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ARGSORT: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); + + const int nrows = ggml_nrows(src0); + + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + + id pipeline = nil; + + switch (order) { + case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; + case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; + default: GGML_ASSERT(false); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + + [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)]; + } break; + case GGML_OP_LEAKY_RELU: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + float slope; + memcpy(&slope, dst->op_params, sizeof(float)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_DUP: + case GGML_OP_CPY: + case GGML_OP_CONT: + { + GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); + + int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); + + id pipeline = nil; + + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); + + switch (dstt) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; + //case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; + //case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + }; + } break; + case GGML_TYPE_F16: + { + switch (dstt) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + }; + } break; + default: GGML_ASSERT(false && "not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + default: + { + GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + GGML_ASSERT(false); + } + } #ifndef GGML_METAL_NDEBUG - [encoder popDebugGroup]; + [encoder popDebugGroup]; #endif - } + } - if (encoder != nil) { - [encoder endEncoding]; - encoder = nil; - } + if (encoder != nil) { + [encoder endEncoding]; + encoder = nil; + } - [command_buffer commit]; - }); - } - - // wait for all threads to finish - dispatch_barrier_sync(ctx->d_queue, ^{}); + [command_buffer commit]; + }); // check status of command buffers // needed to detect if the device ran out-of-memory for example (#1881) From 862f5e41ab1fdf12d6f59455aad3f5dd8258f805 Mon Sep 17 00:00:00 2001 From: Neuman Vong Date: Wed, 17 Jan 2024 00:47:34 +1100 Subject: [PATCH 053/334] android : introduce starter project example (#4926) * Introduce starter project for Android Based on examples/llama.swiftui. * Add github workflow * Set NDK version * Only build arm64-v8a in CI * Sync bench code * Rename CI prop to skip-armeabi-v7a * Remove unused tests --- .github/workflows/build.yml | 25 ++ examples/llama.android/.gitignore | 33 ++ examples/llama.android/README.md | 0 examples/llama.android/app/.gitignore | 1 + examples/llama.android/app/build.gradle.kts | 91 ++++ examples/llama.android/app/proguard-rules.pro | 21 + .../app/src/main/AndroidManifest.xml | 30 ++ .../app/src/main/cpp/CMakeLists.txt | 50 +++ .../app/src/main/cpp/llama-android.cpp | 394 ++++++++++++++++++ .../java/com/example/llama/Downloadable.kt | 119 ++++++ .../src/main/java/com/example/llama/Llm.kt | 172 ++++++++ .../java/com/example/llama/MainActivity.kt | 154 +++++++ .../java/com/example/llama/MainViewModel.kt | 104 +++++ .../java/com/example/llama/ui/theme/Color.kt | 11 + .../java/com/example/llama/ui/theme/Theme.kt | 70 ++++ .../java/com/example/llama/ui/theme/Type.kt | 34 ++ .../res/drawable/ic_launcher_background.xml | 170 ++++++++ .../res/drawable/ic_launcher_foreground.xml | 30 ++ .../main/res/mipmap-anydpi/ic_launcher.xml | 6 + .../res/mipmap-anydpi/ic_launcher_round.xml | 6 + .../src/main/res/mipmap-hdpi/ic_launcher.webp | Bin 0 -> 1404 bytes .../res/mipmap-hdpi/ic_launcher_round.webp | Bin 0 -> 2898 bytes .../src/main/res/mipmap-mdpi/ic_launcher.webp | Bin 0 -> 982 bytes .../res/mipmap-mdpi/ic_launcher_round.webp | Bin 0 -> 1772 bytes .../main/res/mipmap-xhdpi/ic_launcher.webp | Bin 0 -> 1900 bytes .../res/mipmap-xhdpi/ic_launcher_round.webp | Bin 0 -> 3918 bytes .../main/res/mipmap-xxhdpi/ic_launcher.webp | Bin 0 -> 2884 bytes .../res/mipmap-xxhdpi/ic_launcher_round.webp | Bin 0 -> 5914 bytes .../main/res/mipmap-xxxhdpi/ic_launcher.webp | Bin 0 -> 3844 bytes .../res/mipmap-xxxhdpi/ic_launcher_round.webp | Bin 0 -> 7778 bytes .../app/src/main/res/values/colors.xml | 10 + .../app/src/main/res/values/strings.xml | 3 + .../app/src/main/res/values/themes.xml | 5 + .../app/src/main/res/xml/backup_rules.xml | 13 + .../main/res/xml/data_extraction_rules.xml | 19 + examples/llama.android/build.gradle.kts | 5 + examples/llama.android/gradle.properties | 23 + .../gradle/wrapper/gradle-wrapper.jar | Bin 0 -> 59203 bytes .../gradle/wrapper/gradle-wrapper.properties | 6 + examples/llama.android/gradlew | 185 ++++++++ examples/llama.android/settings.gradle.kts | 17 + 41 files changed, 1807 insertions(+) create mode 100644 examples/llama.android/.gitignore create mode 100644 examples/llama.android/README.md create mode 100644 examples/llama.android/app/.gitignore create mode 100644 examples/llama.android/app/build.gradle.kts create mode 100644 examples/llama.android/app/proguard-rules.pro create mode 100644 examples/llama.android/app/src/main/AndroidManifest.xml create mode 100644 examples/llama.android/app/src/main/cpp/CMakeLists.txt create mode 100644 examples/llama.android/app/src/main/cpp/llama-android.cpp create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/Llm.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt create mode 100644 examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt create mode 100644 examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml create mode 100644 examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml create mode 100644 examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml create mode 100644 examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml create mode 100644 examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp create mode 100644 examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp create mode 100644 examples/llama.android/app/src/main/res/values/colors.xml create mode 100644 examples/llama.android/app/src/main/res/values/strings.xml create mode 100644 examples/llama.android/app/src/main/res/values/themes.xml create mode 100644 examples/llama.android/app/src/main/res/xml/backup_rules.xml create mode 100644 examples/llama.android/app/src/main/res/xml/data_extraction_rules.xml create mode 100644 examples/llama.android/build.gradle.kts create mode 100644 examples/llama.android/gradle.properties create mode 100644 examples/llama.android/gradle/wrapper/gradle-wrapper.jar create mode 100644 examples/llama.android/gradle/wrapper/gradle-wrapper.properties create mode 100755 examples/llama.android/gradlew create mode 100644 examples/llama.android/settings.gradle.kts diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0a28a1111..367df07a7 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -515,6 +515,31 @@ jobs: - name: Build Xcode project run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build + android-build: + runs-on: ubuntu-latest + + steps: + - name: Clone + uses: actions/checkout@v3 + + - name: Set up JDK + uses: actions/setup-java@v3 + with: + java-version: 17 + distribution: zulu + + - name: Setup Android SDK + uses: android-actions/setup-android@v3 + with: + log-accepted-android-sdk-licenses: false + + - name: Build + run: | + cd examples/llama.android + + # Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820). + ./gradlew build --no-daemon -Pskip-armeabi-v7a + # freeBSD-latest: # runs-on: macos-12 # steps: diff --git a/examples/llama.android/.gitignore b/examples/llama.android/.gitignore new file mode 100644 index 000000000..347e252ef --- /dev/null +++ b/examples/llama.android/.gitignore @@ -0,0 +1,33 @@ +# Gradle files +.gradle/ +build/ + +# Local configuration file (sdk path, etc) +local.properties + +# Log/OS Files +*.log + +# Android Studio generated files and folders +captures/ +.externalNativeBuild/ +.cxx/ +*.apk +output.json + +# IntelliJ +*.iml +.idea/ +misc.xml +deploymentTargetDropDown.xml +render.experimental.xml + +# Keystore files +*.jks +*.keystore + +# Google Services (e.g. APIs or Firebase) +google-services.json + +# Android Profiling +*.hprof diff --git a/examples/llama.android/README.md b/examples/llama.android/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/examples/llama.android/app/.gitignore b/examples/llama.android/app/.gitignore new file mode 100644 index 000000000..796b96d1c --- /dev/null +++ b/examples/llama.android/app/.gitignore @@ -0,0 +1 @@ +/build diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts new file mode 100644 index 000000000..7815a8025 --- /dev/null +++ b/examples/llama.android/app/build.gradle.kts @@ -0,0 +1,91 @@ +plugins { + id("com.android.application") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "com.example.llama" + compileSdk = 34 + + ndkVersion = "26.1.10909125" + + defaultConfig { + applicationId = "com.example.llama" + minSdk = 33 + targetSdk = 34 + versionCode = 1 + versionName = "1.0" + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + vectorDrawables { + useSupportLibrary = true + } + ndk { + // Workaround for https://github.com/llvm/llvm-project/issues/65820 + // affecting armeabi-v7a. Skip armeabi-v7a when invoked with + // -Pskip-armeabi-v7a (e.g., ./gradlew build -Pskip-armeabi-v7a). + if (project.hasProperty("skip-armeabi-v7a")) { + abiFilters += listOf("arm64-v8a", "x86_64", "x86") + } + } + externalNativeBuild { + cmake { + cppFlags += listOf() + arguments += listOf() + } + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + buildFeatures { + compose = true + } + composeOptions { + kotlinCompilerExtensionVersion = "1.5.1" + } + packaging { + resources { + excludes += "/META-INF/{AL2.0,LGPL2.1}" + } + } + externalNativeBuild { + cmake { + path = file("src/main/cpp/CMakeLists.txt") + version = "3.22.1" + } + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2") + implementation("androidx.activity:activity-compose:1.8.2") + implementation(platform("androidx.compose:compose-bom:2023.08.00")) + implementation("androidx.compose.ui:ui") + implementation("androidx.compose.ui:ui-graphics") + implementation("androidx.compose.ui:ui-tooling-preview") + implementation("androidx.compose.material3:material3") + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") + androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00")) + androidTestImplementation("androidx.compose.ui:ui-test-junit4") + debugImplementation("androidx.compose.ui:ui-tooling") + debugImplementation("androidx.compose.ui:ui-test-manifest") +} diff --git a/examples/llama.android/app/proguard-rules.pro b/examples/llama.android/app/proguard-rules.pro new file mode 100644 index 000000000..f1b424510 --- /dev/null +++ b/examples/llama.android/app/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/examples/llama.android/app/src/main/AndroidManifest.xml b/examples/llama.android/app/src/main/AndroidManifest.xml new file mode 100644 index 000000000..41a358a29 --- /dev/null +++ b/examples/llama.android/app/src/main/AndroidManifest.xml @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt new file mode 100644 index 000000000..85139329a --- /dev/null +++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt @@ -0,0 +1,50 @@ + +# For more information about using CMake with Android Studio, read the +# documentation: https://d.android.com/studio/projects/add-native-code.html. +# For more examples on how to use CMake, see https://github.com/android/ndk-samples. + +# Sets the minimum CMake version required for this project. +cmake_minimum_required(VERSION 3.22.1) + +# Declares the project name. The project name can be accessed via ${ PROJECT_NAME}, +# Since this is the top level CMakeLists.txt, the project name is also accessible +# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level +# build script scope). +project("llama-android") + +include(FetchContent) +FetchContent_Declare( + llama + GIT_REPOSITORY https://github.com/ggerganov/llama.cpp + GIT_TAG master +) + +# Also provides "common" +FetchContent_MakeAvailable(llama) + +# Creates and names a library, sets it as either STATIC +# or SHARED, and provides the relative paths to its source code. +# You can define multiple libraries, and CMake builds them for you. +# Gradle automatically packages shared libraries with your APK. +# +# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define +# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME} +# is preferred for the same purpose. +# +# In order to load a library into your app from Java/Kotlin, you must call +# System.loadLibrary() and pass the name of the library defined here; +# for GameActivity/NativeActivity derived applications, the same library name must be +# used in the AndroidManifest.xml file. +add_library(${CMAKE_PROJECT_NAME} SHARED + # List C/C++ source files with relative paths to this CMakeLists.txt. + llama-android.cpp) + +# Specifies libraries CMake should link to your target library. You +# can link libraries from various origins, such as libraries defined in this +# build script, prebuilt third-party libraries, or Android system libraries. +target_link_libraries(${CMAKE_PROJECT_NAME} + # List libraries link to the target library + llama + common + android + log) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp new file mode 100644 index 000000000..d5e705dce --- /dev/null +++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp @@ -0,0 +1,394 @@ +#include +#include +#include +#include +#include +#include +#include "llama.h" +#include "common/common.h" + +// Write C++ code here. +// +// Do not forget to dynamically load the C++ library into your application. +// +// For instance, +// +// In MainActivity.java: +// static { +// System.loadLibrary("llama-android"); +// } +// +// Or, in MainActivity.kt: +// companion object { +// init { +// System.loadLibrary("llama-android") +// } +// } + +#define TAG "llama-android.cpp" +#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__) +#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__) + +jclass la_int_var; +jmethodID la_int_var_value; +jmethodID la_int_var_inc; + +static void log_callback(ggml_log_level level, const char * fmt, void * data) { + if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data); + else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data); + else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data); + else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data); +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { + llama_model_params model_params = llama_model_default_params(); + + auto path_to_model = env->GetStringUTFChars(filename, 0); + LOGi("Loading model from %s", path_to_model); + + auto model = llama_load_model_from_file(path_to_model, model_params); + env->ReleaseStringUTFChars(filename, path_to_model); + + if (!model) { + LOGe("load_model() failed"); + env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed"); + return 0; + } + + return reinterpret_cast(model); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) { + llama_free_model(reinterpret_cast(model)); +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { + auto model = reinterpret_cast(jmodel); + + if (!model) { + LOGe("new_context(): model cannot be null"); + env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null"); + return 0; + } + + int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2)); + LOGi("Using %d threads", n_threads); + + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.seed = 1234; + ctx_params.n_ctx = 2048; + ctx_params.n_threads = n_threads; + ctx_params.n_threads_batch = n_threads; + + llama_context * context = llama_new_context_with_model(model, ctx_params); + + if (!context) { + LOGe("llama_new_context_with_model() returned null)"); + env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), + "llama_new_context_with_model() returned null)"); + return 0; + } + + return reinterpret_cast(context); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) { + llama_free(reinterpret_cast(context)); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) { + llama_backend_free(); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) { + llama_log_set(log_callback, NULL); +} + +extern "C" +JNIEXPORT jstring JNICALL +Java_com_example_llama_Llm_bench_1model( + JNIEnv *env, + jobject, + jlong context_pointer, + jlong model_pointer, + jlong batch_pointer, + jint pp, + jint tg, + jint pl, + jint nr + ) { + auto pp_avg = 0.0; + auto tg_avg = 0.0; + auto pp_std = 0.0; + auto tg_std = 0.0; + + const auto context = reinterpret_cast(context_pointer); + const auto model = reinterpret_cast(model_pointer); + const auto batch = reinterpret_cast(batch_pointer); + + const int n_ctx = llama_n_ctx(context); + + LOGi("n_ctx = %d", n_ctx); + + int i, j; + int nri; + for (nri = 0; nri < nr; nri++) { + LOGi("Benchmark prompt processing (pp)"); + + llama_batch_clear(*batch); + + const int n_tokens = pp; + for (i = 0; i < n_tokens; i++) { + llama_batch_add(*batch, 0, i, { 0 }, false); + } + + batch->logits[batch->n_tokens - 1] = true; + llama_kv_cache_clear(context); + + const auto t_pp_start = ggml_time_us(); + if (llama_decode(context, *batch) != 0) { + LOGi("llama_decode() failed during prompt processing"); + } + const auto t_pp_end = ggml_time_us(); + + // bench text generation + + LOGi("Benchmark text generation (tg)"); + + llama_kv_cache_clear(context); + const auto t_tg_start = ggml_time_us(); + for (i = 0; i < tg; i++) { + + llama_batch_clear(*batch); + for (j = 0; j < pl; j++) { + llama_batch_add(*batch, 0, i, { j }, true); + } + + LOGi("llama_decode() text generation: %d", i); + if (llama_decode(context, *batch) != 0) { + LOGi("llama_decode() failed during text generation"); + } + } + + const auto t_tg_end = ggml_time_us(); + + llama_kv_cache_clear(context); + + const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0; + const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0; + + const auto speed_pp = double(pp) / t_pp; + const auto speed_tg = double(pl * tg) / t_tg; + + pp_avg += speed_pp; + tg_avg += speed_tg; + + pp_std += speed_pp * speed_pp; + tg_std += speed_tg * speed_tg; + + LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg); + } + + pp_avg /= double(nr); + tg_avg /= double(nr); + + if (nr > 1) { + pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1)); + tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1)); + } else { + pp_std = 0; + tg_std = 0; + } + + char model_desc[128]; + llama_model_desc(model, model_desc, sizeof(model_desc)); + + const auto model_size = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0; + const auto model_n_params = double(llama_model_n_params(model)) / 1e9; + + const auto backend = "(Android)"; // TODO: What should this be? + + std::stringstream result; + result << std::setprecision(2); + result << "| model | size | params | backend | test | t/s |\n"; + result << "| --- | --- | --- | --- | --- | --- |\n"; + result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n"; + result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n"; + + return env->NewStringUTF(result.str().c_str()); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { + llama_batch_free(*reinterpret_cast(batch_pointer)); +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { + + // Source: Copy of llama.cpp:llama_batch_init but heap-allocated. + + llama_batch *batch = new llama_batch { + 0, + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + 0, + 0, + 0, + }; + + if (embd) { + batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd); + } else { + batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens); + } + + batch->pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens); + batch->n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens); + batch->seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens); + for (int i = 0; i < n_tokens; ++i) { + batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max); + } + batch->logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens); + + return reinterpret_cast(batch); +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) { + llama_backend_init(numa); +} + +extern "C" +JNIEXPORT jstring JNICALL +Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) { + return env->NewStringUTF(llama_print_system_info()); +} + +extern "C" +JNIEXPORT jint JNICALL +Java_com_example_llama_Llm_completion_1init( + JNIEnv *env, + jobject, + jlong context_pointer, + jlong batch_pointer, + jstring jtext, + jint n_len + ) { + + const auto text = env->GetStringUTFChars(jtext, 0); + const auto context = reinterpret_cast(context_pointer); + const auto batch = reinterpret_cast(batch_pointer); + + const auto tokens_list = llama_tokenize(context, text, 1); + + auto n_ctx = llama_n_ctx(context); + auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size()); + + LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req); + + if (n_kv_req > n_ctx) { + LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough"); + } + + for (auto id : tokens_list) { + LOGi("%s", llama_token_to_piece(context, id).c_str()); + } + + llama_batch_clear(*batch); + + // evaluate the initial prompt + for (auto i = 0; i < tokens_list.size(); i++) { + llama_batch_add(*batch, tokens_list[i], i, { 0 }, false); + } + + // llama_decode will output logits only for the last token of the prompt + batch->logits[batch->n_tokens - 1] = true; + + if (llama_decode(context, *batch) != 0) { + LOGe("llama_decode() failed"); + } + + env->ReleaseStringUTFChars(jtext, text); + + return batch->n_tokens; +} + +extern "C" +JNIEXPORT jstring JNICALL +Java_com_example_llama_Llm_completion_1loop( + JNIEnv * env, + jobject, + jlong context_pointer, + jlong batch_pointer, + jint n_len, + jobject intvar_ncur +) { + const auto context = reinterpret_cast(context_pointer); + const auto batch = reinterpret_cast(batch_pointer); + const auto model = llama_get_model(context); + + if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur); + if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I"); + if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V"); + + auto n_vocab = llama_n_vocab(model); + auto logits = llama_get_logits_ith(context, batch->n_tokens - 1); + + std::vector candidates; + candidates.reserve(n_vocab); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + // sample the most likely token + const auto new_token_id = llama_sample_token_greedy(context, &candidates_p); + + const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value); + if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + return env->NewStringUTF(""); + } + + auto new_token_chars = llama_token_to_piece(context, new_token_id); + LOGi("new_token_chars: `%s`", new_token_chars.c_str()); + auto new_token = env->NewStringUTF(new_token_chars.c_str()); + + llama_batch_clear(*batch); + llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true); + + env->CallVoidMethod(intvar_ncur, la_int_var_inc); + + if (llama_decode(context, *batch) != 0) { + LOGe("llama_decode() returned null"); + } + + return new_token; +} + +extern "C" +JNIEXPORT void JNICALL +Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { + llama_kv_cache_clear(reinterpret_cast(context)); +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt b/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt new file mode 100644 index 000000000..78c231ae5 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt @@ -0,0 +1,119 @@ +package com.example.llama + +import android.app.DownloadManager +import android.net.Uri +import android.util.Log +import androidx.compose.material3.Button +import androidx.compose.material3.Text +import androidx.compose.runtime.Composable +import androidx.compose.runtime.getValue +import androidx.compose.runtime.mutableDoubleStateOf +import androidx.compose.runtime.mutableStateOf +import androidx.compose.runtime.remember +import androidx.compose.runtime.rememberCoroutineScope +import androidx.compose.runtime.setValue +import androidx.core.database.getLongOrNull +import androidx.core.net.toUri +import kotlinx.coroutines.delay +import kotlinx.coroutines.launch +import java.io.File + +data class Downloadable(val name: String, val source: Uri, val destination: File) { + companion object { + @JvmStatic + private val tag: String? = this::class.qualifiedName + + sealed interface State + data object Ready: State + data class Downloading(val id: Long): State + data class Downloaded(val downloadable: Downloadable): State + data class Error(val message: String): State + + @JvmStatic + @Composable + fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) { + var status: State by remember { + mutableStateOf( + if (item.destination.exists()) Downloaded(item) + else Ready + ) + } + var progress by remember { mutableDoubleStateOf(0.0) } + + val coroutineScope = rememberCoroutineScope() + + suspend fun waitForDownload(result: Downloading, item: Downloadable): State { + while (true) { + val cursor = dm.query(DownloadManager.Query().setFilterById(result.id)) + + if (cursor == null) { + Log.e(tag, "dm.query() returned null") + return Error("dm.query() returned null") + } + + if (!cursor.moveToFirst() || cursor.count < 1) { + cursor.close() + Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?") + return Ready + } + + val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR) + val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES) + val sofar = cursor.getLongOrNull(pix) ?: 0 + val total = cursor.getLongOrNull(tix) ?: 1 + cursor.close() + + if (sofar == total) { + return Downloaded(item) + } + + progress = (sofar * 1.0) / total + + delay(1000L) + } + } + + fun onClick() { + when (val s = status) { + is Downloaded -> { + viewModel.load(item.destination.path) + } + + is Downloading -> { + coroutineScope.launch { + status = waitForDownload(s, item) + } + } + + else -> { + item.destination.delete() + + val request = DownloadManager.Request(item.source).apply { + setTitle("Downloading model") + setDescription("Downloading model: ${item.name}") + setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI) + setDestinationUri(item.destination.toUri()) + } + + viewModel.log("Saving ${item.name} to ${item.destination.path}") + Log.i(tag, "Saving ${item.name} to ${item.destination.path}") + + val id = dm.enqueue(request) + status = Downloading(id) + onClick() + } + } + } + + Button(onClick = { onClick() }, enabled = status !is Downloading) { + when (status) { + is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%") + is Downloaded -> Text("Load ${item.name}") + is Ready -> Text("Download ${item.name}") + is Error -> Text("Download ${item.name}") + } + } + } + + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt new file mode 100644 index 000000000..5f3270372 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt @@ -0,0 +1,172 @@ +package com.example.llama + +import android.util.Log +import kotlinx.coroutines.CoroutineDispatcher +import kotlinx.coroutines.asCoroutineDispatcher +import kotlinx.coroutines.flow.Flow +import kotlinx.coroutines.flow.flow +import kotlinx.coroutines.flow.flowOn +import kotlinx.coroutines.withContext +import java.util.concurrent.Executors +import kotlin.concurrent.thread + +class Llm { + private val tag: String? = this::class.simpleName + + private val threadLocalState: ThreadLocal = ThreadLocal.withInitial { State.Idle } + + private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor { + thread(start = false, name = "Llm-RunLoop") { + Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}") + + // No-op if called more than once. + System.loadLibrary("llama-android") + + // Set llama log handler to Android + log_to_android() + backend_init(false) + + Log.d(tag, system_info()) + + it.run() + }.apply { + uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable -> + Log.e(tag, "Unhandled exception", exception) + } + } + }.asCoroutineDispatcher() + + private val nlen: Int = 64 + + private external fun log_to_android() + private external fun load_model(filename: String): Long + private external fun free_model(model: Long) + private external fun new_context(model: Long): Long + private external fun free_context(context: Long) + private external fun backend_init(numa: Boolean) + private external fun backend_free() + private external fun free_batch(batch: Long) + private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long + private external fun bench_model( + context: Long, + model: Long, + batch: Long, + pp: Int, + tg: Int, + pl: Int, + nr: Int + ): String + + private external fun system_info(): String + + private external fun completion_init( + context: Long, + batch: Long, + text: String, + nLen: Int + ): Int + + private external fun completion_loop( + context: Long, + batch: Long, + nLen: Int, + ncur: IntVar + ): String + + private external fun kv_cache_clear(context: Long) + + suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String { + return withContext(runLoop) { + when (val state = threadLocalState.get()) { + is State.Loaded -> { + Log.d(tag, "bench(): $state") + bench_model(state.context, state.model, state.batch, pp, tg, pl, nr) + } + + else -> throw IllegalStateException("No model loaded") + } + } + } + + suspend fun load(pathToModel: String) { + withContext(runLoop) { + when (threadLocalState.get()) { + is State.Idle -> { + val model = load_model(pathToModel) + if (model == 0L) throw IllegalStateException("load_model() failed") + + val context = new_context(model) + if (context == 0L) throw IllegalStateException("new_context() failed") + + val batch = new_batch(512, 0, 1) + if (batch == 0L) throw IllegalStateException("new_batch() failed") + + Log.i(tag, "Loaded model $pathToModel") + threadLocalState.set(State.Loaded(model, context, batch)) + } + else -> throw IllegalStateException("Model already loaded") + } + } + } + + fun send(message: String): Flow = flow { + when (val state = threadLocalState.get()) { + is State.Loaded -> { + val ncur = IntVar(completion_init(state.context, state.batch, message, nlen)) + while (ncur.value <= nlen) { + val str = completion_loop(state.context, state.batch, nlen, ncur) + if (str.isEmpty()) { + break + } + emit(str) + } + kv_cache_clear(state.context) + } + else -> {} + } + }.flowOn(runLoop) + + /** + * Unloads the model and frees resources. + * + * This is a no-op if there's no model loaded. + */ + suspend fun unload() { + withContext(runLoop) { + when (val state = threadLocalState.get()) { + is State.Loaded -> { + free_context(state.context) + free_model(state.model) + free_batch(state.batch) + + threadLocalState.set(State.Idle) + } + else -> {} + } + } + } + + companion object { + private class IntVar(value: Int) { + @Volatile + var value: Int = value + private set + + fun inc() { + synchronized(this) { + value += 1 + } + } + } + + private sealed interface State { + data object Idle: State + data class Loaded(val model: Long, val context: Long, val batch: Long): State + } + + // Enforce only one instance of Llm. + private val _instance: Llm = Llm() + + fun instance(): Llm = _instance + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt new file mode 100644 index 000000000..9da04f7d3 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt @@ -0,0 +1,154 @@ +package com.example.llama + +import android.app.ActivityManager +import android.app.DownloadManager +import android.content.ClipData +import android.content.ClipboardManager +import android.net.Uri +import android.os.Bundle +import android.os.StrictMode +import android.os.StrictMode.VmPolicy +import android.text.format.Formatter +import androidx.activity.ComponentActivity +import androidx.activity.compose.setContent +import androidx.activity.viewModels +import androidx.compose.foundation.layout.Box +import androidx.compose.foundation.layout.Column +import androidx.compose.foundation.layout.Row +import androidx.compose.foundation.layout.fillMaxSize +import androidx.compose.foundation.layout.padding +import androidx.compose.foundation.lazy.LazyColumn +import androidx.compose.foundation.lazy.items +import androidx.compose.foundation.lazy.rememberLazyListState +import androidx.compose.material3.Button +import androidx.compose.material3.LocalContentColor +import androidx.compose.material3.MaterialTheme +import androidx.compose.material3.OutlinedTextField +import androidx.compose.material3.Surface +import androidx.compose.material3.Text +import androidx.compose.runtime.Composable +import androidx.compose.ui.Modifier +import androidx.compose.ui.unit.dp +import androidx.core.content.getSystemService +import com.example.llama.ui.theme.LlamaAndroidTheme +import java.io.File + +class MainActivity( + activityManager: ActivityManager? = null, + downloadManager: DownloadManager? = null, + clipboardManager: ClipboardManager? = null, +): ComponentActivity() { + private val tag: String? = this::class.simpleName + + private val activityManager by lazy { activityManager ?: getSystemService()!! } + private val downloadManager by lazy { downloadManager ?: getSystemService()!! } + private val clipboardManager by lazy { clipboardManager ?: getSystemService()!! } + + private val viewModel: MainViewModel by viewModels() + + // Get a MemoryInfo object for the device's current memory status. + private fun availableMemory(): ActivityManager.MemoryInfo { + return ActivityManager.MemoryInfo().also { memoryInfo -> + activityManager.getMemoryInfo(memoryInfo) + } + } + + override fun onCreate(savedInstanceState: Bundle?) { + super.onCreate(savedInstanceState) + + StrictMode.setVmPolicy( + VmPolicy.Builder(StrictMode.getVmPolicy()) + .detectLeakedClosableObjects() + .build() + ) + + val free = Formatter.formatFileSize(this, availableMemory().availMem) + val total = Formatter.formatFileSize(this, availableMemory().totalMem) + + viewModel.log("Current memory: $free / $total") + viewModel.log("Downloads directory: ${getExternalFilesDir(null)}") + + val extFilesDir = getExternalFilesDir(null) + + val models = listOf( + Downloadable( + "Phi-2 7B (Q4_0, 1.6 GiB)", + Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"), + File(extFilesDir, "phi-2-q4_0.gguf"), + ), + Downloadable( + "TinyLlama 1.1B (f16, 2.2 GiB)", + Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"), + File(extFilesDir, "tinyllama-1.1-f16.gguf"), + ), + Downloadable( + "Phi 2 DPO (Q3_K_M, 1.48 GiB)", + Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"), + File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf") + ), + ) + + setContent { + LlamaAndroidTheme { + // A surface container using the 'background' color from the theme + Surface( + modifier = Modifier.fillMaxSize(), + color = MaterialTheme.colorScheme.background + ) { + MainCompose( + viewModel, + clipboardManager, + downloadManager, + models, + ) + } + + } + } + } +} + +@Composable +fun MainCompose( + viewModel: MainViewModel, + clipboard: ClipboardManager, + dm: DownloadManager, + models: List +) { + Column { + val scrollState = rememberLazyListState() + + Box(modifier = Modifier.weight(1f)) { + LazyColumn(state = scrollState) { + items(viewModel.messages) { + Text( + it, + style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current), + modifier = Modifier.padding(16.dp) + ) + } + } + } + OutlinedTextField( + value = viewModel.message, + onValueChange = { viewModel.updateMessage(it) }, + label = { Text("Message") }, + ) + Row { + Button({ viewModel.send() }) { Text("Send") } + Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") } + Button({ viewModel.clear() }) { Text("Clear") } + Button({ + viewModel.messages.joinToString("\n").let { + clipboard.setPrimaryClip(ClipData.newPlainText("", it)) + } + }) { Text("Copy") } + } + + Column { + for (model in models) { + Downloadable.Button(viewModel, dm, model) + } + } + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt new file mode 100644 index 000000000..be95e2221 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -0,0 +1,104 @@ +package com.example.llama + +import android.util.Log +import androidx.compose.runtime.getValue +import androidx.compose.runtime.mutableStateOf +import androidx.compose.runtime.setValue +import androidx.lifecycle.ViewModel +import androidx.lifecycle.viewModelScope +import kotlinx.coroutines.flow.catch +import kotlinx.coroutines.launch + +class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { + companion object { + @JvmStatic + private val NanosPerSecond = 1_000_000_000.0 + } + + private val tag: String? = this::class.simpleName + + var messages by mutableStateOf(listOf("Initializing...")) + private set + + var message by mutableStateOf("") + private set + + override fun onCleared() { + super.onCleared() + + viewModelScope.launch { + try { + llm.unload() + } catch (exc: IllegalStateException) { + messages += exc.message!! + } + } + } + + fun send() { + val text = message + message = "" + + // Add to messages console. + messages += text + messages += "" + + viewModelScope.launch { + llm.send(text) + .catch { + Log.e(tag, "send() failed", it) + messages += it.message!! + } + .collect { messages = messages.dropLast(1) + (messages.last() + it) } + } + } + + fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) { + viewModelScope.launch { + try { + val start = System.nanoTime() + val warmupResult = llm.bench(pp, tg, pl, nr) + val end = System.nanoTime() + + messages += warmupResult + + val warmup = (end - start).toDouble() / NanosPerSecond + messages += "Warm up time: $warmup seconds, please wait..." + + if (warmup > 5.0) { + messages += "Warm up took too long, aborting benchmark" + return@launch + } + + messages += llm.bench(512, 128, 1, 3) + } catch (exc: IllegalStateException) { + Log.e(tag, "bench() failed", exc) + messages += exc.message!! + } + } + } + + fun load(pathToModel: String) { + viewModelScope.launch { + try { + llm.load(pathToModel) + messages += "Loaded $pathToModel" + } catch (exc: IllegalStateException) { + Log.e(tag, "load() failed", exc) + messages += exc.message!! + } + } + } + + fun updateMessage(newMessage: String) { + message = newMessage + } + + fun clear() { + messages = listOf() + } + + fun log(message: String) { + messages += message + } +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt new file mode 100644 index 000000000..40c30e8d9 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt @@ -0,0 +1,11 @@ +package com.example.llama.ui.theme + +import androidx.compose.ui.graphics.Color + +val Purple80 = Color(0xFFD0BCFF) +val PurpleGrey80 = Color(0xFFCCC2DC) +val Pink80 = Color(0xFFEFB8C8) + +val Purple40 = Color(0xFF6650a4) +val PurpleGrey40 = Color(0xFF625b71) +val Pink40 = Color(0xFF7D5260) diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt new file mode 100644 index 000000000..e742220a8 --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt @@ -0,0 +1,70 @@ +package com.example.llama.ui.theme + +import android.app.Activity +import android.os.Build +import androidx.compose.foundation.isSystemInDarkTheme +import androidx.compose.material3.MaterialTheme +import androidx.compose.material3.darkColorScheme +import androidx.compose.material3.dynamicDarkColorScheme +import androidx.compose.material3.dynamicLightColorScheme +import androidx.compose.material3.lightColorScheme +import androidx.compose.runtime.Composable +import androidx.compose.runtime.SideEffect +import androidx.compose.ui.graphics.toArgb +import androidx.compose.ui.platform.LocalContext +import androidx.compose.ui.platform.LocalView +import androidx.core.view.WindowCompat + +private val DarkColorScheme = darkColorScheme( + primary = Purple80, + secondary = PurpleGrey80, + tertiary = Pink80 +) + +private val LightColorScheme = lightColorScheme( + primary = Purple40, + secondary = PurpleGrey40, + tertiary = Pink40 + + /* Other default colors to override + background = Color(0xFFFFFBFE), + surface = Color(0xFFFFFBFE), + onPrimary = Color.White, + onSecondary = Color.White, + onTertiary = Color.White, + onBackground = Color(0xFF1C1B1F), + onSurface = Color(0xFF1C1B1F), + */ +) + +@Composable +fun LlamaAndroidTheme( + darkTheme: Boolean = isSystemInDarkTheme(), + // Dynamic color is available on Android 12+ + dynamicColor: Boolean = true, + content: @Composable () -> Unit +) { + val colorScheme = when { + dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> { + val context = LocalContext.current + if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context) + } + + darkTheme -> DarkColorScheme + else -> LightColorScheme + } + val view = LocalView.current + if (!view.isInEditMode) { + SideEffect { + val window = (view.context as Activity).window + window.statusBarColor = colorScheme.primary.toArgb() + WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme + } + } + + MaterialTheme( + colorScheme = colorScheme, + typography = Typography, + content = content + ) +} diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt new file mode 100644 index 000000000..0b87946ca --- /dev/null +++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt @@ -0,0 +1,34 @@ +package com.example.llama.ui.theme + +import androidx.compose.material3.Typography +import androidx.compose.ui.text.TextStyle +import androidx.compose.ui.text.font.FontFamily +import androidx.compose.ui.text.font.FontWeight +import androidx.compose.ui.unit.sp + +// Set of Material typography styles to start with +val Typography = Typography( + bodyLarge = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Normal, + fontSize = 16.sp, + lineHeight = 24.sp, + letterSpacing = 0.5.sp + ) + /* Other default text styles to override + titleLarge = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Normal, + fontSize = 22.sp, + lineHeight = 28.sp, + letterSpacing = 0.sp + ), + labelSmall = TextStyle( + fontFamily = FontFamily.Default, + fontWeight = FontWeight.Medium, + fontSize = 11.sp, + lineHeight = 16.sp, + letterSpacing = 0.5.sp + ) + */ +) diff --git a/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml b/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml new file mode 100644 index 000000000..07d5da9cb --- /dev/null +++ b/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml @@ -0,0 +1,170 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml b/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml new file mode 100644 index 000000000..7706ab9e6 --- /dev/null +++ b/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml @@ -0,0 +1,30 @@ + + + + + + + + + + + diff --git a/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml 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UmY2RIYtNPVDpE$%vda+HD#3m z&VuXJ{BK&Qe+rBa7eq}Q(bq|tn(RrJAk|ztj2(i{d>nmQnM?;HF2k&9sA6up5tmjl z7lySlzMbifH17-m-Lwa_F&e7nOH?ESi3#ckR3tsM+jsck3`oG!uMS}|eAwVXv>}qxwq?QY%QJ0}r@^;fhuUA9W z*BVl>TGo&N004@xSiwDUXUvp51sVmqO3m)=B55aPwf@0=e}cN+$-BdKxY`YrT_4)0 z_d10#i44Q*rFr8MC>*)v$EJvz``(pb{e&*6k+b zsMz%($|1+8hn8c2?P(l@;Rb&CsZeYoCI3?2!LqjbwPXW3z4G$Qfj=cT5Yb%vY0(AX oeb?AaKtwrnc|$|zzw9vfvn^aJJ!zd)XFXqqy0000001=f@-~a#s literal 0 HcmV?d00001 diff --git a/examples/llama.android/app/src/main/res/values/colors.xml b/examples/llama.android/app/src/main/res/values/colors.xml new file mode 100644 index 000000000..ca1931bca --- /dev/null +++ b/examples/llama.android/app/src/main/res/values/colors.xml @@ -0,0 +1,10 @@ + + + #FFBB86FC + #FF6200EE + #FF3700B3 + #FF03DAC5 + #FF018786 + #FF000000 + #FFFFFFFF + diff --git a/examples/llama.android/app/src/main/res/values/strings.xml b/examples/llama.android/app/src/main/res/values/strings.xml new file mode 100644 index 000000000..7a9d314e2 --- /dev/null +++ b/examples/llama.android/app/src/main/res/values/strings.xml @@ -0,0 +1,3 @@ + + LlamaAndroid + diff --git a/examples/llama.android/app/src/main/res/values/themes.xml b/examples/llama.android/app/src/main/res/values/themes.xml new file mode 100644 index 000000000..8a24fda56 --- /dev/null +++ b/examples/llama.android/app/src/main/res/values/themes.xml @@ -0,0 +1,5 @@ + + + + + + + + + +

+ +
+
+ + + + +)LITERAL"; +unsigned int index_html_len = sizeof(index_html); diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp index e09b3c8c5..647abe116 100644 --- a/examples/server/index.js.hpp +++ b/examples/server/index.js.hpp @@ -1,1903 +1,4 @@ -unsigned char index_js[] = { - 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x74, 0x28, 0x29, - 0x7b, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x45, - 0x72, 0x72, 0x6f, 0x72, 0x28, 0x22, 0x43, 0x79, 0x63, 0x6c, 0x65, 0x20, - 0x64, 0x65, 0x74, 0x65, 0x63, 0x74, 0x65, 0x64, 0x22, 0x29, 0x7d, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x53, 0x79, 0x6d, 0x62, 0x6f, - 0x6c, 0x2e, 0x66, 0x6f, 0x72, 0x28, 0x22, 0x70, 0x72, 0x65, 0x61, 0x63, - 0x74, 0x2d, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x73, 0x22, 0x29, 0x3b, - 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x65, 0x28, 0x29, - 0x7b, 0x69, 0x66, 0x28, 0x66, 0x3e, 0x31, 0x29, 0x7b, 0x66, 0x2d, 0x2d, - 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', + integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', + string: ` "\\"" ( + [^"\\\\] | + "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\\"" space`, + null: '"null" space', }; -unsigned int json_schema_to_grammar_mjs_len = 3695; + +const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; +const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; +const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; + +export class SchemaConverter { + constructor(propOrder) { + this._propOrder = propOrder || {}; + this._rules = new Map(); + this._rules.set('space', SPACE_RULE); + } + + _formatLiteral(literal) { + const escaped = JSON.stringify(literal).replace( + GRAMMAR_LITERAL_ESCAPE_RE, + m => GRAMMAR_LITERAL_ESCAPES[m] + ); + return `"${escaped}"`; + } + + _addRule(name, rule) { + let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); + let key = escName; + + if (this._rules.has(escName)) { + if (this._rules.get(escName) === rule) { + return key; + } + + let i = 0; + while (this._rules.has(`${escName}${i}`)) { + i += 1; + } + key = `${escName}${i}`; + } + + this._rules.set(key, rule); + return key; + } + + visit(schema, name) { + const schemaType = schema.type; + const ruleName = name || 'root'; + + if (schema.oneOf || schema.anyOf) { + const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => + this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) + ).join(' | '); + + return this._addRule(ruleName, rule); + } else if ('const' in schema) { + return this._addRule(ruleName, this._formatLiteral(schema.const)); + } else if ('enum' in schema) { + const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); + return this._addRule(ruleName, rule); + } else if (schemaType === 'object' && 'properties' in schema) { + // TODO: `required` keyword (from python implementation) + const propOrder = this._propOrder; + const propPairs = Object.entries(schema.properties).sort((a, b) => { + // sort by position in prop_order (if specified) then by key + const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; + const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; + return orderA - orderB || a[0].localeCompare(b[0]); + }); + + let rule = '"{" space'; + propPairs.forEach(([propName, propSchema], i) => { + const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); + if (i > 0) { + rule += ' "," space'; + } + rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; + }); + rule += ' "}" space'; + + return this._addRule(ruleName, rule); + } else if (schemaType === 'array' && 'items' in schema) { + // TODO `prefixItems` keyword (from python implementation) + const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); + const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; + return this._addRule(ruleName, rule); + } else { + if (!PRIMITIVE_RULES[schemaType]) { + throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); + } + return this._addRule( + ruleName === 'root' ? 'root' : schemaType, + PRIMITIVE_RULES[schemaType] + ); + } + } + + formatGrammar() { + let grammar = ''; + this._rules.forEach((rule, name) => { + grammar += `${name} ::= ${rule}\n`; + }); + return grammar; + } +} +)LITERAL"; +unsigned int json_schema_to_grammar_mjs_len = sizeof(json_schema_to_grammar_mjs); From e6f291d15844398f8326940fe5ad7f2e02b5aa56 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 20:17:30 +0200 Subject: [PATCH 192/334] server : fix context shift (#5195) * server : fix context shift + simplify self-extend * server : take system_tokens into account * server : more n_past fixes * server : rever n_past_se changes --- examples/server/chat.sh | 1 + examples/server/server.cpp | 109 ++++++++++++++++++++----------------- 2 files changed, 60 insertions(+), 50 deletions(-) diff --git a/examples/server/chat.sh b/examples/server/chat.sh index 014360121..da0a6ca68 100755 --- a/examples/server/chat.sh +++ b/examples/server/chat.sh @@ -48,6 +48,7 @@ chat_completion() { top_p: 0.9, n_keep: $n_keep, n_predict: 256, + cache_prompt: true, stop: ["\n### Human:"], stream: true }')" diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 11dd82c33..21bdce8ed 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -185,7 +185,7 @@ struct llama_client_slot llama_sampling_context *ctx_sampling = nullptr; int32_t ga_i = 0; // group-attention state - int32_t ga_n = 1;// group-attention factor + int32_t ga_n = 1; // group-attention factor int32_t ga_w = 512; // group-attention width int32_t n_past_se = 0; // self-extend @@ -219,7 +219,8 @@ struct llama_client_slot sent_token_probs_index = 0; infill = false; ga_i = 0; - n_past_se = 0; + n_past_se = 0; + generated_token_probs.clear(); for (slot_image & img : images) @@ -1227,7 +1228,7 @@ struct llama_server_context std::vector append_tokens = tokenize(json_prompt, false); // has next image for (int i = 0; i < (int) append_tokens.size(); ++i) { - llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); + llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true); slot.n_past += 1; } } @@ -1295,6 +1296,8 @@ struct llama_server_context for (llama_client_slot &slot : slots) { slot.cache_tokens.clear(); + slot.n_past = 0; + slot.n_past_se = 0; } } @@ -1364,26 +1367,26 @@ struct llama_server_context kv_cache_clear(); } return true; - } else { - task_server task; - task.type = TASK_TYPE_NEXT_RESPONSE; - task.target_id = -1; - queue_tasks.post(task); } + task_server task; + task.type = TASK_TYPE_NEXT_RESPONSE; + task.target_id = -1; + queue_tasks.post(task); + for (llama_client_slot &slot : slots) { if (slot.ga_n == 1) { - if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) + if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) { // Shift context - const int n_left = slot.n_past - slot.params.n_keep - 1; + const int n_left = system_tokens.size() + slot.n_past - slot.params.n_keep - 1; const int n_discard = n_left / 2; LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard); for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1429,8 +1432,10 @@ struct llama_server_context slot.i_batch = batch.n_tokens; const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + // TODO: we always have to take into account the "system_tokens" + // this is not great and needs to be improved somehow + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); slot.n_past += 1; } @@ -1481,8 +1486,8 @@ struct llama_server_context prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } @@ -1582,8 +1587,8 @@ struct llama_server_context } LOG_VERBOSE("prompt ingested", { - {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"n_past", slot.n_past}, + {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); @@ -1591,10 +1596,13 @@ struct llama_server_context // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; + int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; - int ga_i = slot.ga_i; + + int32_t ga_i = slot.ga_i; int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { if (slot.ga_n != 1) @@ -1606,7 +1614,7 @@ struct llama_server_context } } llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); - slot_npast += 1; + slot_npast++; } if (has_images && !ingest_images(slot, n_batch)) @@ -1666,6 +1674,7 @@ struct llama_server_context slot.n_past_se += n_tokens; } } + llama_batch batch_view = { n_tokens, @@ -1782,51 +1791,51 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); + printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" --numa attempt optimizations that help on some NUMA systems\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); + printf(" number of layers to store in VRAM\n"); printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); #endif printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); + printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); - printf(" set an alias for the model, will be added as `model` field in completion response\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); - printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); - printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); - printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); - printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); - printf(" -spf FNAME, --system-prompt-file FNAME\n"); - printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); - printf(" --log-disable disables logging to a file.\n"); + printf(" set an alias for the model, will be added as `model` field in completion response\n"); + printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); + printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); + printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); + printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); + printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); + printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); + printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); + printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); + printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); + printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -spf FNAME, --system-prompt-file FNAME\n"); + printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); + printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); + printf(" --log-disable disables logging to a file.\n"); printf("\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); + printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); printf("\n"); } From e0085fdf7c758f0bc2746fc106fb29dd9df959de Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 21:19:26 +0200 Subject: [PATCH 193/334] Revert "server : change deps.sh xxd files to string literals (#5221)" This reverts commit 4003be0e5feef320f3707786f22722b73cff9356. --- examples/server/completion.js.hpp | 651 ++- examples/server/deps.sh | 11 +- examples/server/index.html.hpp | 3829 ++++++++++++----- examples/server/index.js.hpp | 1907 +++++++- .../server/json-schema-to-grammar.mjs.hpp | 424 +- 5 files changed, 5454 insertions(+), 1368 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 5609ee3bf..fe5f81228 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -1,204 +1,449 @@ -const char completion_js[] = R"LITERAL( -const paramDefaults = { - stream: true, - n_predict: 500, - temperature: 0.2, - stop: [""] +unsigned char completion_js[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72, + 0x75, 0x65, 0x2c, 0x0a, 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0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x67, 0x65, 0x6e, 0x65, + 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, + 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f, 0x6d, 0x6f, 0x64, 0x65, + 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22, 0x29, 0x2e, 0x74, 0x68, 0x65, + 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72, 0x2e, 0x6a, 0x73, 0x6f, + 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, + 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, + 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; - -let generation_settings = null; - - -// Completes the prompt as a generator. Recommended for most use cases. -// -// Example: -// -// import { llama } from '/completion.js' -// -// const request = llama("Tell me a joke", {n_predict: 800}) -// for await (const chunk of request) { -// document.write(chunk.data.content) -// } -// -export async function* llama(prompt, params = {}, config = {}) { - let controller = config.controller; - - if (!controller) { - controller = new AbortController(); - } - - const completionParams = { ...paramDefaults, ...params, prompt }; - - const response = await fetch("/completion", { - method: 'POST', - body: JSON.stringify(completionParams), - headers: { - 'Connection': 'keep-alive', - 'Content-Type': 'application/json', - 'Accept': 'text/event-stream', - ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) - }, - signal: controller.signal, - }); - - const reader = response.body.getReader(); - const decoder = new TextDecoder(); - - let content = ""; - let leftover = ""; // Buffer for partially read lines - - try { - let cont = true; - - while (cont) { - const result = await reader.read(); - if (result.done) { - break; - } - - // Add any leftover data to the current chunk of data - const text = leftover + decoder.decode(result.value); - - // Check if the last character is a line break - const endsWithLineBreak = text.endsWith('\n'); - - // Split the text into lines - let lines = text.split('\n'); - - // If the text doesn't end with a line break, then the last line is incomplete - // Store it in leftover to be added to the next chunk of data - if (!endsWithLineBreak) { - leftover = lines.pop(); - } else { - leftover = ""; // Reset leftover if we have a line break at the end - } - - // Parse all sse events and add them to result - const regex = /^(\S+):\s(.*)$/gm; - for (const line of lines) { - const match = regex.exec(line); - if (match) { - result[match[1]] = match[2] - // since we know this is llama.cpp, let's just decode the json in data - if (result.data) { - result.data = JSON.parse(result.data); - content += result.data.content; - - // yield - yield result; - - // if we got a stop token from server, we will break here - if (result.data.stop) { - if (result.data.generation_settings) { - generation_settings = result.data.generation_settings; - } - cont = false; - break; - } - } - if (result.error) { - result.error = JSON.parse(result.error); - if (result.error.content.includes('slot unavailable')) { - // Throw an error to be caught by upstream callers - throw new Error('slot unavailable'); - } else { - console.error(`llama.cpp error: ${result.error.content}`); - } - } - if (result.error) { - result.error = JSON.parse(result.error); - console.error(`llama.cpp error: ${result.error.content}`); - } - } - } - } - } catch (e) { - if (e.name !== 'AbortError') { - console.error("llama error: ", e); - } - throw e; - } - finally { - controller.abort(); - } - - return content; -} - -// Call llama, return an event target that you can subscribe to -// -// Example: -// -// import { llamaEventTarget } from '/completion.js' -// -// const conn = llamaEventTarget(prompt) -// conn.addEventListener("message", (chunk) => { -// document.write(chunk.detail.content) -// }) -// -export const llamaEventTarget = (prompt, params = {}, config = {}) => { - const eventTarget = new EventTarget(); - (async () => { - let content = ""; - for await (const chunk of llama(prompt, params, config)) { - if (chunk.data) { - content += chunk.data.content; - eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); - } - if (chunk.data.generation_settings) { - eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); - } - if (chunk.data.timings) { - eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); - } - } - eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); - })(); - return eventTarget; -} - -// Call llama, return a promise that resolves to the completed text. This does not support streaming -// -// Example: -// -// llamaPromise(prompt).then((content) => { -// document.write(content) -// }) -// -// or -// -// const content = await llamaPromise(prompt) -// document.write(content) -// -export const llamaPromise = (prompt, params = {}, config = {}) => { - return new Promise(async (resolve, reject) => { - let content = ""; - try { - for await (const chunk of llama(prompt, params, config)) { - content += chunk.data.content; - } - resolve(content); - } catch (error) { - reject(error); - } - }); -}; - -/** - * (deprecated) - */ -export const llamaComplete = async (params, controller, callback) => { - for await (const chunk of llama(params.prompt, params, { controller })) { - callback(chunk); - } -} - -// Get the model info from the server. This is useful for getting the context window and so on. -export const llamaModelInfo = async () => { - if (!generation_settings) { - generation_settings = await fetch("/model.json").then(r => r.json()); - } - return generation_settings; -} -)LITERAL"; -unsigned int completion_js_len = sizeof(completion_js); +unsigned int completion_js_len = 5346; diff --git a/examples/server/deps.sh b/examples/server/deps.sh index c0a9de9f9..ea23e6450 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -15,13 +15,6 @@ cd $PUBLIC for FILE in $FILES; do echo "generate $FILE.hpp" - # Use C++11 string literals instead of ugly xxd. - f=$(echo $FILE | sed 's/\./_/g' -e 's/-/_/g') - echo "const char $f[] = R\"LITERAL(" > $DIR/$FILE.hpp - cat $FILE >> $DIR/$FILE.hpp - echo ")LITERAL\";" >> $DIR/$FILE.hpp - echo "unsigned int ${f}_len = sizeof($f);" >> $DIR/$FILE.hpp - - #Deprecated old xxd - #xxd -i $FILE > $DIR/$FILE.hpp + # use simple flag for old version of xxd + xxd -i $FILE > $DIR/$FILE.hpp done diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 603d12068..20551520e 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -1,1038 +1,2791 @@ -const char index_html[] = R"LITERAL( - - - - - - - llama.cpp - chat - - - - - - - -
- -
-
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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', - string: ` "\\"" ( - [^"\\\\] | - "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\\"" space`, - null: '"null" space', +unsigned char json_schema_to_grammar_mjs[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x53, 0x50, 0x41, 0x43, 0x45, 0x5f, + 0x52, 0x55, 0x4c, 0x45, 0x20, 0x3d, 0x20, 0x27, 0x22, 0x20, 0x22, 0x3f, + 0x27, 0x3b, 0x0a, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x50, 0x52, + 0x49, 0x4d, 0x49, 0x54, 0x49, 0x56, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, + 0x53, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x62, 0x6f, 0x6f, 0x6c, + 0x65, 0x61, 0x6e, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x74, 0x72, 0x75, 0x65, + 0x22, 0x20, 0x7c, 0x20, 0x22, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x22, 0x29, + 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x6e, + 0x75, 0x6d, 0x62, 0x65, 0x72, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x2d, 0x22, + 0x3f, 0x20, 0x28, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x20, 0x7c, 0x20, 0x5b, + 0x31, 0x2d, 0x39, 0x5d, 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0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a }; - -const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; -const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; -const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; - -export class SchemaConverter { - constructor(propOrder) { - this._propOrder = propOrder || {}; - this._rules = new Map(); - this._rules.set('space', SPACE_RULE); - } - - _formatLiteral(literal) { - const escaped = JSON.stringify(literal).replace( - GRAMMAR_LITERAL_ESCAPE_RE, - m => GRAMMAR_LITERAL_ESCAPES[m] - ); - return `"${escaped}"`; - } - - _addRule(name, rule) { - let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); - let key = escName; - - if (this._rules.has(escName)) { - if (this._rules.get(escName) === rule) { - return key; - } - - let i = 0; - while (this._rules.has(`${escName}${i}`)) { - i += 1; - } - key = `${escName}${i}`; - } - - this._rules.set(key, rule); - return key; - } - - visit(schema, name) { - const schemaType = schema.type; - const ruleName = name || 'root'; - - if (schema.oneOf || schema.anyOf) { - const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => - this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) - ).join(' | '); - - return this._addRule(ruleName, rule); - } else if ('const' in schema) { - return this._addRule(ruleName, this._formatLiteral(schema.const)); - } else if ('enum' in schema) { - const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); - return this._addRule(ruleName, rule); - } else if (schemaType === 'object' && 'properties' in schema) { - // TODO: `required` keyword (from python implementation) - const propOrder = this._propOrder; - const propPairs = Object.entries(schema.properties).sort((a, b) => { - // sort by position in prop_order (if specified) then by key - const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; - const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; - return orderA - orderB || a[0].localeCompare(b[0]); - }); - - let rule = '"{" space'; - propPairs.forEach(([propName, propSchema], i) => { - const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); - if (i > 0) { - rule += ' "," space'; - } - rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; - }); - rule += ' "}" space'; - - return this._addRule(ruleName, rule); - } else if (schemaType === 'array' && 'items' in schema) { - // TODO `prefixItems` keyword (from python implementation) - const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); - const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; - return this._addRule(ruleName, rule); - } else { - if (!PRIMITIVE_RULES[schemaType]) { - throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); - } - return this._addRule( - ruleName === 'root' ? 'root' : schemaType, - PRIMITIVE_RULES[schemaType] - ); - } - } - - formatGrammar() { - let grammar = ''; - this._rules.forEach((rule, name) => { - grammar += `${name} ::= ${rule}\n`; - }); - return grammar; - } -} -)LITERAL"; -unsigned int json_schema_to_grammar_mjs_len = sizeof(json_schema_to_grammar_mjs); +unsigned int json_schema_to_grammar_mjs_len = 3695; From e8dc55d0065d076d4c20f3c4bfca562701b4edfe Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 30 Jan 2024 19:04:37 -0500 Subject: [PATCH 194/334] kompute : llama-bench support and ggml_cpu_has_kompute() (#5226) --- common/common.cpp | 1 + examples/llama-bench/llama-bench.cpp | 15 +++++++++++---- ggml.c | 11 ++++++++++- ggml.h | 1 + llama.cpp | 5 ----- 5 files changed, 23 insertions(+), 10 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 288013676..0dd1c50cf 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1521,6 +1521,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); + fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index f239415d3..542cc7bb8 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -563,6 +563,7 @@ struct test { static const bool cuda; static const bool opencl; static const bool vulkan; + static const bool kompute; static const bool metal; static const bool gpu_blas; static const bool blas; @@ -647,6 +648,9 @@ struct test { if (vulkan) { return "Vulkan"; } + if (kompute) { + return "Kompute"; + } if (metal) { return "Metal"; } @@ -662,7 +666,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", @@ -686,8 +690,9 @@ struct test { field == "avg_ns" || field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "opencl" || field == "vulkan"|| field == "metal" || field == "gpu_blas" || field == "blas" || - field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") { + if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || + field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || + field == "mul_mat_q") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -714,7 +719,8 @@ struct test { } std::vector values = { build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), + std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), @@ -743,6 +749,7 @@ const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cublas(); const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::vulkan = !!ggml_cpu_has_vulkan(); +const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); diff --git a/ggml.c b/ggml.c index a7a9ea319..b2c8baaa8 100644 --- a/ggml.c +++ b/ggml.c @@ -20473,6 +20473,14 @@ int ggml_cpu_has_vulkan(void) { #endif } +int ggml_cpu_has_kompute(void) { +#if defined(GGML_USE_KOMPUTE) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_sycl(void) { #if defined(GGML_USE_SYCL) return 1; @@ -20482,7 +20490,8 @@ int ggml_cpu_has_sycl(void) { } int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_sycl(); + return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || + ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index bf782e6ad..afc87b843 100644 --- a/ggml.h +++ b/ggml.h @@ -2266,6 +2266,7 @@ extern "C" { GGML_API int ggml_cpu_has_cublas (void); GGML_API int ggml_cpu_has_clblast (void); GGML_API int ggml_cpu_has_vulkan (void); + GGML_API int ggml_cpu_has_kompute (void); GGML_API int ggml_cpu_has_gpublas (void); GGML_API int ggml_cpu_has_sse3 (void); GGML_API int ggml_cpu_has_ssse3 (void); diff --git a/llama.cpp b/llama.cpp index 7b9a5c079..a490eeab2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6878,11 +6878,6 @@ static int llama_decode_internal( n_threads = std::min(4, n_threads); } - const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1; - if ((ggml_cpu_has_cublas() || ggml_cpu_has_vulkan()) && fully_offloaded) { - n_threads = 1; - } - #ifdef GGML_USE_MPI const int64_t n_layer = hparams.n_layer; ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); From 01684139c352561840ae55ec627ab58abc3e06ab Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 31 Jan 2024 10:38:07 +0800 Subject: [PATCH 195/334] support SYCL backend windows build (#5208) * support SYCL backend windows build * add windows build in CI * add for win build CI * correct install oneMKL * fix install issue * fix ci * fix install cmd * fix install cmd * fix install cmd * fix install cmd * fix install cmd * fix win build * fix win build * fix win build * restore other CI part * restore as base * rm no new line * fix no new line issue, add -j * fix grammer issue * allow to trigger manually, fix format issue * fix format * add newline * fix format * fix format * fix format issuse --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- .github/workflows/build.yml | 25 ++++ .github/workflows/editorconfig.yml | 6 + .gitignore | 1 + CMakeLists.txt | 6 +- README_sycl.md => README-sycl.md | 198 +++++++++++++++++++++++++++-- README.md | 4 +- examples/sycl/win-build-sycl.bat | 23 ++++ examples/sycl/win-run-llama2.bat | 13 ++ scripts/install-oneapi.bat | 19 +++ 9 files changed, 281 insertions(+), 14 deletions(-) rename README_sycl.md => README-sycl.md (58%) create mode 100644 examples/sycl/win-build-sycl.bat create mode 100644 examples/sycl/win-run-llama2.bat create mode 100644 scripts/install-oneapi.bat diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index fb719a550..c6db1666e 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -565,6 +565,31 @@ jobs: path: | cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip + windows-latest-cmake-sycl: + runs-on: windows-latest + defaults: + run: + shell: bash + + env: + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_offline.exe + WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel + + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + with: + fetch-depth: 0 + + - name: Install + run: scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + + - name: Build + id: cmake_build + run: examples/sycl/win-build-sycl.bat + ios-xcode-build: runs-on: macos-latest diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index b4e535acf..0e0993cd4 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -1,6 +1,12 @@ name: EditorConfig Checker on: + workflow_dispatch: # allows manual triggering + inputs: + create_release: + description: 'Create new release' + required: true + type: boolean push: branches: - master diff --git a/.gitignore b/.gitignore index cb0069bfb..b84459b92 100644 --- a/.gitignore +++ b/.gitignore @@ -89,3 +89,4 @@ examples/jeopardy/results.txt poetry.lock poetry.toml +nppBackup diff --git a/CMakeLists.txt b/CMakeLists.txt index 65a6f3971..15a1101aa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -507,7 +507,11 @@ if (LLAMA_SYCL) set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h) set(GGML_SOURCES_SYCL ggml-sycl.cpp) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + if (WIN32) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib) + else() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + endif() endif() if (LLAMA_KOMPUTE) diff --git a/README_sycl.md b/README-sycl.md similarity index 58% rename from README_sycl.md rename to README-sycl.md index d5a1818f5..2b2cfe03a 100644 --- a/README_sycl.md +++ b/README-sycl.md @@ -8,10 +8,14 @@ [Linux](#linux) +[Windows](#windows) + [Environment Variable](#environment-variable) [Known Issue](#known-issue) +[Q&A](#q&a) + [Todo](#todo) ## Background @@ -33,7 +37,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |OS|Status|Verified| |-|-|-| |Linux|Support|Ubuntu 22.04| -|Windows|Ongoing| | +|Windows|Support|Windows 11| ## Intel GPU @@ -42,7 +46,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |-|-|-| |Intel Data Center Max Series| Support| Max 1550| |Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770| +|Intel Arc Series| Support| Arc 770, 730M| |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| @@ -131,6 +135,7 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx #build all binary cmake --build . --config Release -v +cd .. ``` or @@ -195,7 +200,7 @@ GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building or run by script: ``` -./examples/sycl/run_llama2.sh +./examples/sycl/run-llama2.sh ``` Note: @@ -205,11 +210,175 @@ Note: 5. Check the device ID in output -Like： +Like: ``` Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ``` +## Windows + +### Setup Environment + +1. Install Intel GPU driver. + +Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). + +2. Install Intel® oneAPI Base toolkit. + +a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). + +Recommend to install to default folder: **/opt/intel/oneapi**. + +Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder. + +b. Enable oneAPI running environment: + +- In Search, input 'oneAPI'. + +Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" + +- In Run: + +In CMD: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +c. Check GPU + +In oneAPI command line: + +``` +sycl-ls +``` + +There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. + +Output (example): +``` +[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] +[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] + +``` + +3. Install cmake & make + +a. Download & install cmake for windows: https://cmake.org/download/ + +b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/ + + +### Build locally: + +In oneAPI command line window: + +``` +mkdir -p build +cd build +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +:: for FP16 +:: faster for long-prompt inference +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON + +:: for FP32 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + + +:: build example/main only +:: make main + +:: build all binary +make -j +cd .. +``` + +or + +``` +.\examples\sycl\win-build-sycl.bat +``` + +Note: + +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +### Run + +1. Put model file to folder **models** + +2. Enable oneAPI running environment + +- In Search, input 'oneAPI'. + +Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" + +- In Run: + +In CMD: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +3. List device ID + +Run without parameter: + +``` +build\bin\ls-sycl-device.exe + +or + +build\bin\main.exe +``` + +Check the ID in startup log, like: + +``` +found 4 SYCL devices: + Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, + max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 + Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, + max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 + Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + +``` + +|Attribute|Note| +|-|-| +|compute capability 1.3|Level-zero running time, recommended | +|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| + +4. Set device ID and execute llama.cpp + +Set device ID = 0 by **set GGML_SYCL_DEVICE=0** + +``` +set GGML_SYCL_DEVICE=0 +build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 +``` +or run by script: + +``` +.\examples\sycl\win-run-llama2.bat +``` + +Note: + +- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. + + +5. Check the device ID in output + +Like: +``` +Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device +``` ## Environment Variable @@ -220,7 +389,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device |LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.
For FP32/FP16, LLAMA_SYCL=ON is mandatory.| |LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference.
For FP32, not set it.| |CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path| -|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path| +|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path| #### Running @@ -232,19 +401,24 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ## Known Issue -- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. - - Miss to enable oneAPI running environment. - - Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. - - - Hang during startup llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. Solution: add **--no-mmap**. +## Q&A + +- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. + + Miss to enable oneAPI running environment. + + Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. + +- In Windows, no result, not error. + + Miss to enable oneAPI running environment. + ## Todo - Support to build in Windows. diff --git a/README.md b/README.md index b37348a74..7746cb510 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 + - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 @@ -604,7 +606,7 @@ Building the program with BLAS support may lead to some performance improvements llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). - For detailed info, please refer to [llama.cpp for SYCL](README_sycl.md). + For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). ### Prepare Data & Run diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat new file mode 100644 index 000000000..f9d43f8ed --- /dev/null +++ b/examples/sycl/win-build-sycl.bat @@ -0,0 +1,23 @@ + +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + +mkdir -p build +cd build +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +:: for FP16 +:: faster for long-prompt inference +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON + +:: for FP32 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + + +:: build example/main only +:: make main + +:: build all binary +make -j +cd .. diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat new file mode 100644 index 000000000..28d935541 --- /dev/null +++ b/examples/sycl/win-run-llama2.bat @@ -0,0 +1,13 @@ +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + +INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + + +set GGML_SYCL_DEVICE=0 +rem set GGML_SYCL_DEBUG=1 +.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0 + + diff --git a/scripts/install-oneapi.bat b/scripts/install-oneapi.bat new file mode 100644 index 000000000..e99bef14a --- /dev/null +++ b/scripts/install-oneapi.bat @@ -0,0 +1,19 @@ +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + + +set URL=%1 +set COMPONENTS=%2 + +curl.exe --output %TEMP%\webimage.exe --url %URL% --retry 5 --retry-delay 5 +start /b /wait %TEMP%\webimage.exe -s -x -f webimage_extracted --log extract.log +del %TEMP%\webimage.exe +if "%COMPONENTS%"=="" ( + webimage_extracted\bootstrapper.exe -s --action install --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=. +) else ( + webimage_extracted\bootstrapper.exe -s --action install --components=%COMPONENTS% --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=. +) +set installer_exit_code=%ERRORLEVEL% +rd /s/q "webimage_extracted" +exit /b %installer_exit_code% From d62520eb2cc1d7168a30edec6110e1daefbd959f Mon Sep 17 00:00:00 2001 From: Yiming Cui Date: Wed, 31 Jan 2024 11:04:21 +0800 Subject: [PATCH 196/334] Fix typos of IQ2_XXS and IQ3_XXS in llama.cpp (#5231) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index a490eeab2..bb23689fa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2713,10 +2713,10 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small"; case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; - case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; - case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XSS - 3.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; default: return "unknown, may not work"; } From f8e9140cb46eebaa867e1184a9946e4840eec772 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 31 Jan 2024 11:44:19 +0100 Subject: [PATCH 197/334] Vulkan Fixes (#5223) * Fix Vulkan F16 models * Fix Vulkan context shift crash * Add Vulkan to common.cpp dump_non_result_info_yaml function * Fix bug in Vulkan CPY op * Fix small matrix multiplication errors in AMD GPUs on Windows or with amdvlk Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> --------- Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> --- common/common.cpp | 1 + ggml-vulkan-shaders.hpp | 1952 +++++++++++++---------------------- ggml-vulkan.cpp | 14 +- ggml_vk_generate_shaders.py | 4 +- 4 files changed, 704 insertions(+), 1267 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 0dd1c50cf..9d976c7c8 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1520,6 +1520,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); + fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 321e36383..e2e9be22c 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -890,7 +890,7 @@ const uint64_t cpy_f32_f32_len = 2472; unsigned char dequant_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x87,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x81,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -898,7 +898,7 @@ unsigned char dequant_f16_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x4f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -910,23 +910,23 @@ unsigned char dequant_f16_data[] = { 0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00, 0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x4e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x5c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5d,0x00,0x00,0x00, +0x5a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x5b,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5b,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x5d,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x5b,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x80,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x7e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -945,330 +945,109 @@ unsigned char dequant_f16_data[] = { 0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x1b,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00, -0x24,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 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-0xc8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x60,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -1280,23 +1059,23 @@ unsigned char dequant_f16_fp32_data[] = { 0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 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0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -15341,22 +14820,184 @@ unsigned char get_rows_f16_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x55,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x53,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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+0x61,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x61,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x63,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, @@ -15388,198 +15029,28 @@ unsigned char get_rows_f16_data[] = { 0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x16,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 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-0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x00,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_f16_len = 1940; - -unsigned char get_rows_f16_f32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x65,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -15613,51 +15084,51 @@ unsigned char get_rows_f16_f32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x67,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15676,23 +15147,23 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, 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+15194,28 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x53,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x56,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x64,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x65,0x00,0x00,0x00, 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+0x79,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, @@ -15770,7 +15237,7 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x7b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00, 0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00, @@ -15783,51 +15250,51 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x67,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15846,23 +15313,23 @@ unsigned char get_rows_f16_fp32_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x55,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x57,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x64,0x00,0x00,0x00, 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+0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -15893,31 +15360,27 @@ unsigned char get_rows_f16_fp32_data[] = { 0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x53,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00, 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-0xf8,0x00,0x02,0x00,0x7d,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0xf7,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -15938,7 +15401,7 @@ unsigned char get_rows_f16_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -15951,42 +15414,42 @@ unsigned char get_rows_f16_fp32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, -0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5a,0x00,0x00,0x00, -0x5b,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00, -0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x73,0x00,0x04,0x00, 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+0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x75,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x58,0x00,0x00,0x00, +0x76,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x72,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x76,0x00,0x00,0x00, +0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_fp32_len = 1996; +const uint64_t get_rows_f16_fp32_len = 1948; unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -52701,7 +52164,7 @@ const uint64_t mul_f32_len = 1456; unsigned char mul_mat_vec_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xba,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xb6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -52709,9 +52172,9 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x13,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0xad,0x00,0x00,0x00, +0x51,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xaa,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -52729,23 +52192,23 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x63,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x64,0x00,0x00,0x00, +0x62,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xaa,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00, +0xa7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa8,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0xab,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0xa8,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xb5,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0xb2,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -52760,7 +52223,7 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x17,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00, @@ -52775,7 +52238,7 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00, +0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0x30,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, @@ -52784,26 +52247,22 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x50,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x54,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x64,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x65,0x00,0x00,0x00, -0x66,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00, 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+0x3b,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x09,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x89,0x00,0x00,0x00, +0x08,0x01,0x00,0x00,0x1d,0x00,0x03,0x00,0xa7,0x00,0x00,0x00, +0x17,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xa8,0x00,0x00,0x00, +0xa7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xa9,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xa9,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x2c,0x00,0x06,0x00,0x0a,0x00,0x00,0x00,0xb2,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00, 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0e,0x00,0x00,0x00, @@ -52819,122 +52278,91 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 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+0x2e,0x00,0x00,0x00,0x8b,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x87,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x82,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x38,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x54,0x00,0x00,0x00, +0x55,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00, +0x56,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x73,0x00,0x04,0x00, +0x17,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x54,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x51,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x4d,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, +0x73,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x5c,0x00,0x00,0x00, +0x5b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00, +0x66,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00, +0x66,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x69,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6b,0x00,0x00,0x00, +0x69,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x17,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x72,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00, +0x73,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, +0x57,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x72,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0x73,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, +0x6b,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x17,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x83,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x17,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, +0x5c,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x83,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x1f,0x00,0x00,0x00,0x84,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x00,0x00,0x00, +0xb5,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x22,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00, +0xe0,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x88,0x00,0x00,0x00, +0x89,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x30,0x00,0x00,0x00, +0xa4,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0xf7,0x00,0x03,0x00,0xa6,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfa,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0xa6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x00,0x00,0x00, +0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x2a,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00, +0xac,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x1e,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x1b,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xaa,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xa6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t mul_mat_vec_f16_f32_len = 2788; +const uint64_t mul_mat_vec_f16_f32_len = 2372; unsigned char mul_mat_vec_nc_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 1d93ec6bb..bccc40bf5 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -817,7 +817,7 @@ static void ggml_vk_load_shaders() { // mulmat std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; - std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 8, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, vk_device.subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -2873,7 +2873,8 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (op == GGML_OP_CPY) { GGML_ASSERT(!transfer_src0); GGML_ASSERT(!transfer_src1); - d_sz = dst->ne[1] * dst->nb[1]; + x_sz = ggml_nbytes(src0); + d_sz = ggml_nbytes(dst); if (extra->offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; @@ -4556,8 +4557,15 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } ggml_vk_preallocate_buffers(); + int last_node = cgraph->n_nodes - 1; + + // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly + while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) { + last_node -= 1; + } + for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + ggml_vk_build_graph(cgraph->nodes[i], i == last_node); } ggml_compute_params params = {}; diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index d0861fde4..6b1b82bf3 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -19,8 +19,8 @@ shader_int8_ext = """ # Type-specific defines shader_f16_defines = """ -#define QUANT_K 32 -#define QUANT_R 2 +#define QUANT_K 1 +#define QUANT_R 1 #define A_TYPE float16_t """ From dabcc5b471348e4ae03ddacc41e19ad75fb2f041 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 31 Jan 2024 13:43:03 +0100 Subject: [PATCH 198/334] ggml : limit n_threads to the max n_tasks (#5238) --- ggml.c | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index b2c8baaa8..afd9c6c61 100644 --- a/ggml.c +++ b/ggml.c @@ -16985,12 +16985,16 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa struct ggml_cplan cplan; memset(&cplan, 0, sizeof(struct ggml_cplan)); + int max_tasks = 1; + // thread scheduling for the different operations + work buffer size estimation for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; const int n_tasks = ggml_get_n_tasks(node, n_threads); + max_tasks = MAX(max_tasks, n_tasks); + size_t cur = 0; switch (node->op) { @@ -17157,7 +17161,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa work_size += CACHE_LINE_SIZE*(n_threads - 1); } - cplan.n_threads = n_threads; + cplan.n_threads = MIN(max_tasks, n_threads); cplan.work_size = work_size; cplan.work_data = NULL; From b2b9f025e7821e78bd501d75d01838c26de07a57 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 31 Jan 2024 21:04:46 +0800 Subject: [PATCH 199/334] format license text, restore apache license by legal suggestion (#5233) --- examples/sycl/ls-sycl-device.cpp | 10 ++++++---- ggml-sycl.cpp | 15 +++++++++++---- ggml-sycl.h | 9 +++++---- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 42847154a..52442e4ca 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -1,7 +1,9 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + #include "ggml-sycl.h" diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 3fc346975..1cc55ef52 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -1,7 +1,14 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// #include #include diff --git a/ggml-sycl.h b/ggml-sycl.h index 0eabb53cc..ba0c61473 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -1,7 +1,8 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// #pragma once From 15606309a05ccf7fadbaad5538cb7c32acb1e06b Mon Sep 17 00:00:00 2001 From: JidongZhang-THU <1119708529@qq.com> Date: Wed, 31 Jan 2024 21:10:15 +0800 Subject: [PATCH 200/334] llava : add MobileVLM support (#5132) * New Feature: 1. Sum_Rows: fix cuda kernel overflow fix block shape error when nrows too big 2. Im2Col: Support Batch in cuda Support f32 to f32 both in cpu && cuda 3. DepthWiseConv: Support by Im2Col && MulMat 4. Pool_2d: Supoort avg pooling in cuda 5. HardSigmoid: Imp in cuda 6. HardSwish: Imp in cuda * fix tabs instead of spaces * code clean * CUDA POOL2D * ADD POOL2D test case in test-backend-ops.cpp * code clean * fix pool2d_kernel nits * fix bug in pool2d kernel * fix avg pooling, count_include_pad nits * test-backend-ops : add more pool_2d tests * cuda : fix warnings and formatting * ggml : check types in release builds too in pool_2d * test-backend-ops : remove f16 pool_2d tests * cuda : more style fixes * Add assert in ggml_cuda_op_pool2d * pool2d float padding fallback * test-backend-ops : add dst_type to im2col --------- Co-authored-by: slaren --- examples/llava/MobileVLM-README.md | 58 +++++++- ggml-cuda.cu | 209 ++++++++++++++++++++++++++--- ggml.c | 118 +++++++++++++--- ggml.h | 3 +- tests/test-backend-ops.cpp | 74 +++++++++- 5 files changed, 421 insertions(+), 41 deletions(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index c6258eba6..9eba791da 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -111,17 +111,71 @@ llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 m llama_print_timings: total time = 34570.79 ms ``` +## Orin compile and run +### compile +```sh +make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 +``` + +### run on Orin +### case 1 +**input** +```sh +./llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + --image /data/local/tmp/demo.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" \ + --n-gpu-layers 999 +``` +**output** +```sh + +encode_image_with_clip: image encoded in 296.62 ms by CLIP ( 2.06 ms per image patch) + + Susan Wise Bauer + +llama_print_timings: load time = 1067.64 ms +llama_print_timings: sample time = 1.53 ms / 6 runs ( 0.25 ms per token, 3934.43 tokens per second) +llama_print_timings: prompt eval time = 306.84 ms / 246 tokens ( 1.25 ms per token, 801.72 tokens per second) +llama_print_timings: eval time = 91.50 ms / 6 runs ( 15.25 ms per token, 65.58 tokens per second) +llama_print_timings: total time = 1352.63 ms / 252 tokens +``` + +### case 2 +**input** +```sh +./llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" \ + --n-gpu-layers 999 + +``` +**output** +```sh +encode_image_with_clip: image encoded in 302.15 ms by CLIP ( 2.10 ms per image patch) + + The image features a cat lying in the grass. + +llama_print_timings: load time = 1057.07 ms +llama_print_timings: sample time = 3.27 ms / 11 runs ( 0.30 ms per token, 3360.83 tokens per second) +llama_print_timings: prompt eval time = 213.60 ms / 232 tokens ( 0.92 ms per token, 1086.14 tokens per second) +llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 ms per token, 66.01 tokens per second) +llama_print_timings: total time = 1365.47 ms / 243 tokens +``` + ## Minor shortcomings The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost. ## TODO -- [ ] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` +- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` - [ ] Optimize LDP projector performance - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`; - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc. -- [ ] run MobileVLM on `Jetson Orin` +- [x] run MobileVLM on `Jetson Orin` - [ ] Support more model variants, such as `MobileVLM-3B`. diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 949bc8a1c..e56595742 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -524,6 +524,8 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define CUDA_SILU_BLOCK_SIZE 256 #define CUDA_TANH_BLOCK_SIZE 256 #define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_HARDSIGMOID_BLOCK_SIZE 256 +#define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_CPY_BLOCK_SIZE 32 #define CUDA_SCALE_BLOCK_SIZE 256 @@ -540,6 +542,7 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define CUDA_PAD_BLOCK_SIZE 256 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 +#define CUDA_POOL2D_BLOCK_SIZE 256 #define CUDA_Q8_0_NE_ALIGN 2048 @@ -823,6 +826,24 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) { dst[i] = fmaxf(x[i], 0); } +static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +static __global__ void hardswish_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} + static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { @@ -5823,7 +5844,7 @@ static __global__ void alibi_f32(const float * x, float * dst, const int ncols, } static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) { - const int row = blockIdx.y; + const int row = blockIdx.x; const int col = threadIdx.x; float sum = 0.0f; @@ -6145,9 +6166,10 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } -static __global__ void im2col_f32_f16( - const float * x, half * dst, - int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW, +template +static __global__ void im2col_kernel( + const float * x, T * dst, int batch_offset, + int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW, int s0, int s1, int p0, int p1, int d0, int d1) { const int i = threadIdx.x + blockIdx.x * blockDim.x; if (i >= pelements) { @@ -6160,21 +6182,73 @@ static __global__ void im2col_f32_f16( const int ky = (i - kd) / OW; const int ix = i % OW; + const int oh = blockIdx.y; + const int batch = blockIdx.z / IC; + const int ic = blockIdx.z % IC; + const int64_t iiw = ix * s0 + kx * d0 - p0; - const int64_t iih = blockIdx.y * s1 + ky * d1 - p1; + const int64_t iih = oh * s1 + ky * d1 - p1; const int64_t offset_dst = - (blockIdx.y * OW + ix) * CHW + - (blockIdx.z * (KW * KH) + ky * KW + kx); + ((batch * OH + oh) * OW + ix) * CHW + + (ic * (KW * KH) + ky * KW + kx); if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = __float2half(0.0f); + dst[offset_dst] = 0.0f; } else { - const int64_t offset_src = blockIdx.z * offset_delta; - dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]); + const int64_t offset_src = ic * offset_delta + batch * batch_offset; + dst[offset_dst] = x[offset_src + iih * IW + iiw]; } } +template +static __global__ void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx >= parallel_elements) { + return; + } + + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = max(0, start_h); + const int eh = min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = max(0, start_w); + const int ew = min(iw, start_w + kw); + const To scale = 1. / (kh * kw); + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + #if __CUDA_ARCH__ >= 350 + Ti cur = __ldg(i_ptr + i * iw + j); + #else + Ti cur = i_ptr[i * iw + j]; + #endif + switch (op) { + case GGML_OP_POOL_AVG: res += cur * scale; break; + case GGML_OP_POOL_MAX: res = max(res, (To)cur); break; + } + } + } + o_ptr[cur_oh * ow + cur_ow] = res; +} + template static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { @@ -6388,6 +6462,16 @@ static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ relu_f32<<>>(x, dst, k); } +static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; + hardsigmoid_f32<<>>(x, dst, k); +} + +static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE; + hardswish_f32<<>>(x, dst, k); +} + static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) { const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; leaky_relu_f32<<>>(x, dst, k, negative_slope); @@ -7475,7 +7559,7 @@ static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { const dim3 block_dims(WARP_SIZE, 1, 1); - const dim3 block_nums(1, nrows, 1); + const dim3 block_nums(nrows, 1, 1); k_sum_rows_f32<<>>(x, dst, ncols); } @@ -7587,14 +7671,15 @@ static void soft_max_f32_cuda(const float * x, const float * y, float * dst, con } } -static void im2col_f32_f16_cuda(const float* x, half* dst, +template +static void im2col_cuda(const float* x, T* dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, - int offset_delta, + int batch, int batch_offset, int offset_delta, int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { const int parallel_elements = OW * KW * KH; const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; - dim3 block_nums(num_blocks, OH, IC); - im2col_f32_f16<<>>(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); + dim3 block_nums(num_blocks, OH, batch * IC); + im2col_kernel<<>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); } // buffer pool for cuda @@ -8179,6 +8264,34 @@ static void ggml_cuda_op_relu( (void) src1_dd; } +static void ggml_cuda_op_hardsigmoid( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_cuda_op_hardswish( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + static void ggml_cuda_op_leaky_relu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { @@ -8810,13 +8923,46 @@ static void ggml_cuda_op_alibi( (void) src1_dd; } +static void ggml_cuda_op_pool2d( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE; + dim3 block_nums(num_blocks); + pool2d_nchw_kernel<<>>(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_dd, dst_dd, op); + + (void) src1; + (void) src1_dd; +} + static void ggml_cuda_op_im2col( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; @@ -8838,8 +8984,14 @@ static void ggml_cuda_op_im2col( const int64_t OW = dst->ne[1]; const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const int64_t batch = src1->ne[3]; + const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 - im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + if(dst->type == GGML_TYPE_F16) { + im2col_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } else { + im2col_cuda(src1_dd, (float*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } (void) src0; (void) src0_dd; @@ -9435,6 +9587,13 @@ static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); } +static void ggml_cuda_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardsigmoid); +} + +static void ggml_cuda_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardswish); +} static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu); } @@ -10220,6 +10379,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); } +static void ggml_cuda_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pool2d); +} + static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); } @@ -10321,6 +10484,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_UNARY_OP_RELU: func = ggml_cuda_relu; break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_cuda_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_cuda_hardswish; + break; default: return false; } @@ -10395,6 +10564,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_OP_IM2COL: func = ggml_cuda_im2col; break; + case GGML_OP_POOL_2D: + func = ggml_cuda_pool2d; + break; case GGML_OP_SUM_ROWS: func = ggml_cuda_sum_rows; break; @@ -11123,6 +11295,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: return true; @@ -11221,6 +11395,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ROPE: case GGML_OP_ALIBI: case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: diff --git a/ggml.c b/ggml.c index afd9c6c61..ee994c875 100644 --- a/ggml.c +++ b/ggml.c @@ -5349,7 +5349,7 @@ GGML_API struct ggml_tensor * ggml_conv_1d( int s0, int p0, int d0) { - struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K] + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K] struct ggml_tensor * result = ggml_mul_mat(ctx, @@ -5427,16 +5427,15 @@ struct ggml_tensor * ggml_conv_depthwise_2d( int p1, int d0, int d1) { + struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), - s0, s1, p0, p1, d0, d1, true); // [N * IC, OH, OW, KH * KW] - - struct ggml_tensor * result = - ggml_mul_mat(ctx, - ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1), // [OC，1, KH, KW] => [1, OC, 1, KH * KW] - ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3])); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW] + struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] return result; @@ -5457,7 +5456,8 @@ struct ggml_tensor * ggml_im2col( int p1, int d0, int d1, - bool is_2D) { + bool is_2D, + enum ggml_type dst_type) { if(is_2D) { GGML_ASSERT(a->ne[2] == b->ne[2]); @@ -5481,7 +5481,7 @@ struct ggml_tensor * ggml_im2col( is_2D ? b->ne[3] : 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); @@ -5506,7 +5506,7 @@ struct ggml_tensor * ggml_conv_2d( int p1, int d0, int d1) { - struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW] + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW] struct ggml_tensor * result = ggml_mul_mat(ctx, @@ -5632,12 +5632,13 @@ struct ggml_tensor * ggml_pool_2d( is_node = true; } + struct ggml_tensor * result; const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), a->ne[2], }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); + result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); @@ -5645,7 +5646,6 @@ struct ggml_tensor * ggml_pool_2d( result->op = GGML_OP_POOL_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - return result; } @@ -12493,6 +12493,92 @@ static void ggml_compute_forward_conv_transpose_1d( } } +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + int64_t t0 = ggml_perf_time_us(); + UNUSED(t0); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (params->type == GGML_TASK_INIT) { + return; + } + + if (params->type == GGML_TASK_FINALIZE) { + return; + } + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + + // src0: kernel [OC, IC, KH, KW] // src1: image [N, IC, IH, IW] // dst: result [N, OH, OW, IC*KH*KW] @@ -12583,14 +12669,14 @@ static void ggml_compute_forward_im2col( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - switch (src0->type) { + switch (dst->type) { case GGML_TYPE_F16: { ggml_compute_forward_im2col_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - GGML_ASSERT(false); + ggml_compute_forward_im2col_f32(params, src0, src1, dst); } break; default: { @@ -12781,8 +12867,8 @@ static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, const struct ggml_tensor * src, struct ggml_tensor * dst) { - assert(src->type == GGML_TYPE_F32); - assert(params->ith == 0); + GGML_ASSERT(src->type == GGML_TYPE_F32); + GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; diff --git a/ggml.h b/ggml.h index afc87b843..e0a4799f3 100644 --- a/ggml.h +++ b/ggml.h @@ -1495,7 +1495,8 @@ extern "C" { int p1, int d0, int d1, - bool is_2D); + bool is_2D, + enum ggml_type dst_type); GGML_API struct ggml_tensor * ggml_conv_depthwise_2d( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 1d29070b6..eb06123d2 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -227,6 +227,14 @@ static std::string var_to_str(ggml_type type) { return ggml_type_name(type); } +static std::string var_to_str(ggml_op_pool pool) { + switch (pool) { + case GGML_OP_POOL_AVG: return "avg"; + case GGML_OP_POOL_MAX: return "max"; + default: return std::to_string(pool); + } +} + #define VARS_TO_STR1(a) VAR_TO_STR(a) #define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b) #define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c) @@ -238,6 +246,7 @@ static std::string var_to_str(ggml_type type) { #define VARS_TO_STR9(a, b, c, d, e, f, g, h, i) VAR_TO_STR(a) + "," + VARS_TO_STR8(b, c, d, e, f, g, h, i) #define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j) #define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k) +#define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l) #ifdef GGML_USE_SYCL static bool inline _isinf(float f) { @@ -1162,10 +1171,45 @@ struct test_alibi : public test_case { } }; +// GGML_OP_POOL2D +struct test_pool2d : public test_case { + enum ggml_op_pool pool_type; + const ggml_type type_input; + const std::array ne_input; + // kernel size + const int k0; + const int k1; + // stride + const int s0; + const int s1; + // padding + const int p0; + const int p1; + + std::string vars() override { + return VARS_TO_STR9(pool_type, type_input, ne_input, k0, k1, s0, s1, p0, p1); + } + + test_pool2d(ggml_op_pool pool_type = GGML_OP_POOL_AVG, + ggml_type type_input = GGML_TYPE_F32, + std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] + int k0 = 3, int k1 = 3, + int s0 = 1, int s1 = 1, + int p0 = 1, int p1 = 1) + : pool_type(pool_type), type_input(type_input), ne_input(ne_input), k0(k0), k1(k1), s0(s0), s1(s1), p0(p0), p1(p1) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); + ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1); + return out; + } +}; + // GGML_OP_IM2COL struct test_im2col : public test_case { const ggml_type type_input; const ggml_type type_kernel; + const ggml_type dst_type; const std::array ne_input; const std::array ne_kernel; // stride @@ -1181,22 +1225,22 @@ struct test_im2col : public test_case { const bool is_2D; std::string vars() override { - return VARS_TO_STR11(type_input, type_kernel, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D); + return VARS_TO_STR12(type_input, type_kernel, dst_type, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D); } - test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, + test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, ggml_type dst_type = GGML_TYPE_F32, std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] std::array ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1] int s0 = 1, int s1 = 1, int p0 = 1, int p1 = 1, int d0 = 1, int d1 = 1, bool is_2D = true) - : type_input(type_input), type_kernel(type_kernel), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {} + : type_input(type_input), type_kernel(type_kernel), dst_type(dst_type), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data()); - ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D); + ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D, dst_type); return out; } }; @@ -1912,6 +1956,27 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } + for (ggml_type type_input : {GGML_TYPE_F32}) { + for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) { + for (int k0 : {1, 3}) { + for (int k1 : {1, 3}) { + for (int s0 : {1, 2}) { + for (int s1 : {1, 2}) { + for (int p0 : {0, 1}) { + for (int p1 : {0, 1}) { + test_cases.emplace_back(new test_pool2d(pool_type, type_input, {10, 10, 3, 1}, k0, k1, s0, s1, p0, p1)); + } + } + } + } + } + } + } + } + + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1})); @@ -2049,7 +2114,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } test_cases.emplace_back(new test_alibi()); - test_cases.emplace_back(new test_im2col()); test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); From efb7bdbbd061d087c788598b97992c653f992ddd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 15:35:41 +0200 Subject: [PATCH 201/334] metal : add im2col F32 dst support (#5132) --- ggml-metal.m | 13 ++++++++++--- ggml-metal.metal | 33 +++++++++++++++++++++++++++++---- 2 files changed, 39 insertions(+), 7 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index f87859552..5260ed827 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -135,6 +135,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, GGML_METAL_KERNEL_TYPE_IM2COL_F16, + GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, @@ -506,6 +507,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); @@ -630,6 +632,10 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_ALIBI: case GGML_OP_ROPE: case GGML_OP_IM2COL: + return true; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + return false; case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARGSORT: @@ -2015,7 +2021,7 @@ static bool ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; @@ -2023,6 +2029,7 @@ static bool ggml_metal_graph_compute( const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int32_t N = src1->ne[is_2D ? 3 : 2]; @@ -2043,8 +2050,8 @@ static bool ggml_metal_graph_compute( id pipeline = nil; - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + switch (dst->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; default: GGML_ASSERT(false); }; diff --git a/ggml-metal.metal b/ggml-metal.metal index 2614d82e8..efed6ad46 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1775,9 +1775,29 @@ kernel void kernel_rope( template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope; template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope; -kernel void kernel_im2col_f16( +typedef void (im2col_t)( device const float * x, - device half * dst, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template +kernel void kernel_im2col( + device const float * x, + device char * dst, constant int32_t & ofs0, constant int32_t & ofs1, constant int32_t & IW, @@ -1800,14 +1820,19 @@ kernel void kernel_im2col_f16( (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); + device T * pdst = (device T *) (dst); + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = 0.0f; + pdst[offset_dst] = 0.0f; } else { const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; - dst[offset_dst] = x[offset_src + iih * IW + iiw]; + pdst[offset_dst] = x[offset_src + iih * IW + iiw]; } } +template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; +template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; + kernel void kernel_upscale_f32( device const char * src0, device char * dst, From 5cb04dbc16d1da38c8fdcc0111b40e67d00dd1c3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 17:30:17 +0200 Subject: [PATCH 202/334] llama : remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD (#5240) * llama : remove LLAMA_MAX_DEVICES from llama.h ggml-ci * Update llama.cpp Co-authored-by: slaren * server : remove LLAMA_MAX_DEVICES ggml-ci * llama : remove LLAMA_SUPPORTS_GPU_OFFLOAD ggml-ci * train : remove LLAMA_SUPPORTS_GPU_OFFLOAD * readme : add deprecation notice * readme : change deprecation notice to "remove" and fix url * llama : remove gpu includes from llama.h ggml-ci --------- Co-authored-by: slaren --- README.md | 3 +- common/common.cpp | 56 ++++++++++---------- common/common.h | 66 ++++++++++++------------ common/train.cpp | 12 ++--- examples/batched-bench/batched-bench.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 16 +++--- examples/server/server.cpp | 44 ++++++++-------- llama.cpp | 39 +++++++++++--- llama.h | 29 ++++------- 9 files changed, 143 insertions(+), 124 deletions(-) diff --git a/README.md b/README.md index 7746cb510..e6ed1d429 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 +- Remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD: https://github.com/ggerganov/llama.cpp/pull/5240 +- Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: diff --git a/common/common.cpp b/common/common.cpp index 9d976c7c8..ce739b15c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -583,20 +583,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_gpu_layers = std::stoi(argv[i]); -#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { if (++i >= argc) { invalid_param = true; break; } params.n_gpu_layers_draft = std::stoi(argv[i]); -#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; @@ -637,11 +637,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - if (split_arg.size() >= LLAMA_MAX_DEVICES) { + if (split_arg.size() >= llama_max_devices()) { invalid_param = true; break; } - for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { + for (size_t i = 0; i < llama_max_devices(); ++i) { if (i < split_arg.size()) { params.tensor_split[i] = std::stof(split_arg[i]); } else { @@ -989,30 +989,30 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); - if (llama_mlock_supported()) { + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) { + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -ngld N, --n-gpu-layers-draft N\n"); - printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); -#endif // LLAMA_SUPPORTS_GPU_OFFLOAD + if (llama_supports_gpu_offload()) { + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -ngld N, --n-gpu-layers-draft N\n"); + printf(" number of layers to store in VRAM for the draft model\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); + printf(" -ts SPLIT, --tensor-split SPLIT\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); + } printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); @@ -1651,7 +1651,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES); + const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); diff --git a/common/common.h b/common/common.h index 214a379b5..24a99d728 100644 --- a/common/common.h +++ b/common/common.h @@ -43,40 +43,40 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed - int32_t n_threads = get_num_physical_cores(); - int32_t n_threads_draft = -1; - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_threads_batch_draft = -1; - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 8; // number of tokens to draft during speculative decoding - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_parallel = 1; // number of parallel sequences to decode - int32_t n_sequences = 1; // number of sequences to decode - float p_accept = 0.5f; // speculative decoding accept probability - float p_split = 0.1f; // speculative decoding split probability - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_beams = 0; // if non-zero then use beam search of given width. - int32_t grp_attn_n = 1; // group-attention factor - int32_t grp_attn_w = 512; // group-attention width - int32_t n_print = -1; // print token count every n tokens (-1 = disabled) - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor - float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor - float yarn_beta_fast = 32.0f; // YaRN low correction dim - float yarn_beta_slow = 1.0f; // YaRN high correction dim - int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment - // pinging @cebtenzzre + int32_t n_threads = get_num_physical_cores(); + int32_t n_threads_draft = -1; + int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_threads_batch_draft = -1; + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 8; // number of tokens to draft during speculative decoding + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + float p_accept = 0.5f; // speculative decoding accept probability + float p_split = 0.1f; // speculative decoding split probability + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + int32_t n_beams = 0; // if non-zero then use beam search of given width. + int32_t grp_attn_n = 1; // group-attention factor + int32_t grp_attn_w = 512; // group-attention width + int32_t n_print = -1; // print token count every n tokens (-1 = disabled) + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f; // YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment + // pinging @cebtenzzre // // sampling parameters struct llama_sampling_params sparams; diff --git a/common/train.cpp b/common/train.cpp index e6f2f7a2f..e4c3d5df6 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1363,12 +1363,12 @@ bool consume_common_train_arg( *invalid_param = true; return true; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params->n_gpu_layers = std::stoi(argv[i]); -#else - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (llama_supports_gpu_offload()) { + params->n_gpu_layers = std::stoi(argv[i]); + } else { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "-h" || arg == "--help") { params->print_usage = true; return true; diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 7924db267..b52d68457 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -88,7 +88,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); - const std::vector t_split (LLAMA_MAX_DEVICES, 0.0f); + const std::vector t_split(llama_max_devices(), 0.0f); model_params.n_gpu_layers = n_gpu_layers; model_params.tensor_split = t_split.data(); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 542cc7bb8..c5a6f744e 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -160,7 +160,7 @@ struct cmd_params { std::vector main_gpu; std::vector no_kv_offload; std::vector mul_mat_q; - std::vector> tensor_split; + std::vector> tensor_split; int reps; bool verbose; output_formats output_format; @@ -179,7 +179,7 @@ static const cmd_params cmd_params_defaults = { /* main_gpu */ {0}, /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, - /* tensor_split */ {{}}, + /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -380,10 +380,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { const std::regex regex{R"([;/]+)"}; std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - std::array tensor_split; - for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { + std::vector tensor_split(llama_max_devices()); + for (size_t i = 0; i < llama_max_devices(); ++i) { if (i < split_arg.size()) { tensor_split[i] = std::stof(split_arg[i]); } else { @@ -459,7 +459,7 @@ struct cmd_params_instance { int main_gpu; bool no_kv_offload; bool mul_mat_q; - std::array tensor_split; + std::vector tensor_split; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -582,7 +582,7 @@ struct test { int main_gpu; bool no_kv_offload; bool mul_mat_q; - std::array tensor_split; + std::vector tensor_split; int n_prompt; int n_gen; std::string test_time; @@ -704,7 +704,7 @@ struct test { std::vector get_values() const { std::string tensor_split_str; int max_nonzero = 0; - for (int i = 0; i < LLAMA_MAX_DEVICES; i++) { + for (size_t i = 0; i < llama_max_devices(); i++) { if (tensor_split[i] > 0) { max_nonzero = i; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 21bdce8ed..ea77125ea 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1789,28 +1789,28 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); - if (llama_mlock_supported()) + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); -#endif + if (llama_supports_gpu_offload()) { + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); + printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); + } printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); @@ -2066,13 +2066,13 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params.n_gpu_layers = std::stoi(argv[i]); -#else - LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " + if (llama_supports_gpu_offload()) { + params.n_gpu_layers = std::stoi(argv[i]); + } else { + LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); -#endif + } } else if (arg == "--split-mode" || arg == "-sm") { @@ -2115,9 +2115,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) + for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { if (i_device < split_arg.size()) { diff --git a/llama.cpp b/llama.cpp index bb23689fa..9b249ba9c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10090,18 +10090,45 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { return result; } -int32_t llama_max_devices(void) { - return LLAMA_MAX_DEVICES; +size_t llama_max_devices(void) { +#if defined(GGML_USE_METAL) + return 1; +#elif defined(GGML_USE_CUBLAS) + return GGML_CUDA_MAX_DEVICES; +#elif defined(GGML_USE_SYCL) + return GGML_SYCL_MAX_DEVICES; +#else + return 1; +#endif } -bool llama_mmap_supported(void) { +bool llama_supports_mmap(void) { return llama_mmap::SUPPORTED; } -bool llama_mlock_supported(void) { +bool llama_supports_mlock(void) { return llama_mlock::SUPPORTED; } +bool llama_supports_gpu_offload(void) { +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) + // Defined when llama.cpp is compiled with support for offloading model layers to GPU. + return true; +#else + return false; +#endif +} + +// deprecated: +bool llama_mmap_supported(void) { + return llama_supports_mmap(); +} + +bool llama_mlock_supported(void) { + return llama_supports_mlock(); +} + void llama_backend_init(bool numa) { ggml_time_init(); @@ -10133,8 +10160,8 @@ int64_t llama_time_us(void) { } struct llama_model * llama_load_model_from_file( - const char * path_model, - struct llama_model_params params) { + const char * path_model, + struct llama_model_params params) { ggml_time_init(); llama_model * model = new llama_model; diff --git a/llama.h b/llama.h index 17d43d039..9a60e9bfb 100644 --- a/llama.h +++ b/llama.h @@ -3,15 +3,7 @@ #include "ggml.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUBLAS -#include "ggml-cuda.h" -#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES -#elif defined(GGML_USE_SYCL) -#include "ggml-sycl.h" -#define LLAMA_MAX_DEVICES GGML_SYCL_MAX_DEVICES -#else -#define LLAMA_MAX_DEVICES 1 -#endif // GGML_USE_CUBLAS + #include #include #include @@ -49,12 +41,6 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 4 -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) -// Defined when llama.cpp is compiled with support for offloading model layers to GPU. -#define LLAMA_SUPPORTS_GPU_OFFLOAD -#endif - #ifdef __cplusplus extern "C" { #endif @@ -201,7 +187,7 @@ extern "C" { // LLAMA_SPLIT_LAYER: ignored int32_t main_gpu; - // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES + // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() const float * tensor_split; // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. @@ -338,9 +324,14 @@ extern "C" { LLAMA_API int64_t llama_time_us(void); - LLAMA_API int32_t llama_max_devices(void); - LLAMA_API bool llama_mmap_supported (void); - LLAMA_API bool llama_mlock_supported(void); + LLAMA_API size_t llama_max_devices(void); + + LLAMA_API bool llama_supports_mmap (void); + LLAMA_API bool llama_supports_mlock (void); + LLAMA_API bool llama_supports_gpu_offload(void); + + LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead"); + LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead"); LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); From d3bac7d58408c602ec1f1e423695f1df8410bb03 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 18:47:10 +0200 Subject: [PATCH 203/334] llama : reorder build_orion() at correct place (#5118) --- llama.cpp | 239 +++++++++++++++++++++++++++--------------------------- 1 file changed, 119 insertions(+), 120 deletions(-) diff --git a/llama.cpp b/llama.cpp index 9b249ba9c..02b0a485a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4666,126 +4666,6 @@ struct llm_build_context { ctx0 = nullptr; } } - struct ggml_cgraph * build_orion() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); - - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - // norm - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, model.layers[il].attn_norm_b, - LLM_NORM, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - // compute Q and K and RoPE them - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); - // if (model.layers[il].bq) { - // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); - // cb(Qcur, "Qcur", il); - // } - - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); - // if (model.layers[il].bk) { - // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); - // cb(Kcur, "Kcur", il); - // } - - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - // if (model.layers[il].bv) { - // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); - // cb(Vcur, "Vcur", il); - // } - - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Qcur, "Qcur", il); - - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Kcur, "Kcur", il); - - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, - NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, model.output_norm_b, - LLM_NORM, cb, -1); - cb(cur, "result_norm", -1); - - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - - struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -6589,6 +6469,125 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_orion() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + // if (model.layers[il].bq) { + // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + // cb(Qcur, "Qcur", il); + // } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + // if (model.layers[il].bk) { + // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + // cb(Kcur, "Kcur", il); + // } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + // if (model.layers[il].bv) { + // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + // cb(Vcur, "Vcur", il); + // } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( From 1cfb5372cf5707c8ec6dde7c874f4a44a6c4c915 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 31 Jan 2024 19:21:55 +0000 Subject: [PATCH 204/334] Fix broken Vulkan Cmake (properly) (#5230) * build vulkan as object * vulkan ci --- .github/workflows/build.yml | 6 ++++-- CMakeLists.txt | 8 ++------ 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c6db1666e..f4c374ce5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -356,6 +356,8 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' + - build: 'vulkan' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -406,7 +408,7 @@ jobs: - name: Install Vulkan SDK id: get_vulkan - if: ${{ matrix.build == 'kompute' }} + if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} run: | curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install @@ -451,7 +453,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 diff --git a/CMakeLists.txt b/CMakeLists.txt index 15a1101aa..1ee455b3a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -423,10 +423,7 @@ if (LLAMA_VULKAN) if (Vulkan_FOUND) message(STATUS "Vulkan found") - set(GGML_HEADERS_VULKAN ggml-vulkan.h) - set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) - - add_library(ggml-vulkan STATIC ggml-vulkan.cpp ggml-vulkan.h) + add_library(ggml-vulkan OBJECT ggml-vulkan.cpp ggml-vulkan.h) if (BUILD_SHARED_LIBS) set_target_properties(ggml-vulkan PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() @@ -1012,7 +1009,6 @@ add_library(ggml OBJECT ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} @@ -1094,7 +1090,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_VULKAN}" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") From ce32060198b7e2d6a13a9b8e1e1369e3c295ae2a Mon Sep 17 00:00:00 2001 From: Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Date: Thu, 1 Feb 2024 17:19:51 +0800 Subject: [PATCH 205/334] llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair --- convert-hf-to-gguf.py | 152 ++++++++++++++++++++++++ gguf-py/gguf/constants.py | 18 +++ gguf-py/gguf/gguf_writer.py | 3 + gguf-py/gguf/tensor_mapping.py | 14 ++- llama.cpp | 205 ++++++++++++++++++++++++++++++++- 5 files changed, 387 insertions(+), 5 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 6ab7f486e..4ebab07b3 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -203,6 +203,8 @@ class Model: return CodeShellModel if model_architecture == "OrionForCausalLM": return OrionModel + if model_architecture == "InternLM2ForCausalLM": + return InternLM2Model return Model def _is_model_safetensors(self) -> bool: @@ -254,6 +256,8 @@ class Model: return gguf.MODEL_ARCH.CODESHELL if arch == "OrionForCausalLM": return gguf.MODEL_ARCH.ORION + if arch == "InternLM2ForCausalLM": + return gguf.MODEL_ARCH.INTERNLM2 raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1344,6 +1348,154 @@ class CodeShellModel(Model): self.gguf_writer.add_tensor("output.weight", data) print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + +class InternLM2Model(Model): + def set_vocab(self): + # (TODO): Is there a better way? + # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character + # \x00 specially and convert it into an emoji character to prevent it from being mistakenly + # recognized as an empty string in C++. + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + sys.exit(1) + + sentencepiece_model = model.ModelProto() + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + + tokenizer = SentencePieceProcessor(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.id_to_piece(token_id) + text = piece.encode("utf-8") + score = tokenizer.get_score(token_id) + if text == b"\x00": + # (TODO): fixme + # Hack here and replace the \x00 characters. + print(f"InternLM2 convert token '{text}' to '🐉'!") + text = "🐉" + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + self.gguf_writer.add_name("InternLM2") + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + + def post_write_tensors(self, tensor_map, name, data_torch): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + def write_tensors(self): + from einops import rearrange + + num_heads = self.hparams.get("num_attention_heads") + num_kv_heads = self.hparams.get("num_key_value_heads") + hidden_size = self.hparams.get("hidden_size") + q_per_kv = num_heads // num_kv_heads + head_dim = hidden_size // num_heads + num_groups = num_heads // q_per_kv + + block_count = self.hparams["num_hidden_layers"] + model_kv = dict(self.get_tensors()) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv" + for name, data_torch in model_kv.items(): + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + if re.match(qkv_pattern, name): + bid = re.findall(qkv_pattern, name)[0] + qkv = data_torch + qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) + q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] + q = rearrange(q, " o g n i -> o (g n i)").T + k = rearrange(k, " o g n i -> o (g n i)").T + v = rearrange(v, " o g n i -> o (g n i)").T + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v) + else: + self.post_write_tensors(tensor_map, name, data_torch) + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index f5c933a41..ed8e26f83 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -72,6 +72,7 @@ class Keys: PAD_ID = "tokenizer.ggml.padding_token_id" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_PREFIX = "tokenizer.ggml.add_space_prefix" HF_JSON = "tokenizer.huggingface.json" RWKV = "tokenizer.rwkv.world" CHAT_TEMPLATE = "tokenizer.chat_template" @@ -102,6 +103,7 @@ class MODEL_ARCH(IntEnum): PLAMO = auto() CODESHELL = auto() ORION = auto() + INTERNLM2 = auto() class MODEL_TENSOR(IntEnum): @@ -153,6 +155,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", + MODEL_ARCH.INTERNLM2: "internlm2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -446,6 +449,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.INTERNLM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d93aaa877..16808196e 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -411,6 +411,9 @@ class GGUFWriter: def add_add_eos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_EOS, value) + def add_add_space_prefix(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) + def add_chat_template(self, value: str) -> None: self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index de177af13..4f16d8504 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -19,6 +19,7 @@ class TensorNameMap: "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 + "model.tok_embeddings", # internlm2 ), # Token type embeddings @@ -42,7 +43,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen - "output", # llama-pth bloom + "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 ), @@ -51,7 +52,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon - "model.norm", # llama-hf baichuan + "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth "embeddings.LayerNorm", # bert "transformer.norm_f", # mpt @@ -84,6 +85,7 @@ class TensorNameMap: "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo + "model.layers.{bid}.attention_norm", # internlm2 ), # Attention norm 2 @@ -111,6 +113,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.query", # bert "transformer.h.{bid}.attn.q_proj", # gpt-j "model.layers.layers.{bid}.self_attn.q_proj", # plamo + "model.layers.{bid}.attention.wq" # internlm2 ), # Attention key @@ -120,6 +123,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j "model.layers.layers.{bid}.self_attn.k_proj", # plamo + "model.layers.{bid}.attention.wk" # internlm2 ), # Attention value @@ -129,6 +133,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j "model.layers.layers.{bid}.self_attn.v_proj", # plamo + "model.layers.{bid}.attention.wv" # internlm2 ), # Attention output @@ -147,6 +152,7 @@ class TensorNameMap: "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo + "model.layers.{bid}.attention.wo", # internlm2 ), # Rotary embeddings @@ -169,6 +175,7 @@ class TensorNameMap: "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 + "model.layers.{bid}.ffn_norm", # internlm2 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -194,6 +201,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo + "model.layers.{bid}.feed_forward.w3", # internlm2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -212,6 +220,7 @@ class TensorNameMap: "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen "model.layers.layers.{bid}.mlp.gate_proj", # plamo + "model.layers.{bid}.feed_forward.w1", # internlm2 ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -236,6 +245,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo + "model.layers.{bid}.feed_forward.w2", # internlm2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 02b0a485a..e8f44c2cb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -204,6 +204,7 @@ enum llm_arch { LLM_ARCH_PLAMO, LLM_ARCH_CODESHELL, LLM_ARCH_ORION, + LLM_ARCH_INTERNLM2, LLM_ARCH_UNKNOWN, }; @@ -226,6 +227,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_PLAMO, "plamo" }, { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, }; enum llm_kv { @@ -278,6 +280,7 @@ enum llm_kv { LLM_KV_TOKENIZER_PAD_ID, LLM_KV_TOKENIZER_ADD_BOS, LLM_KV_TOKENIZER_ADD_EOS, + LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, }; @@ -332,6 +335,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" }, { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, + { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, }; @@ -669,7 +673,23 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, - + { + LLM_ARCH_INTERNLM2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1377,6 +1397,7 @@ enum e_model { MODEL_13B, MODEL_14B, MODEL_15B, + MODEL_20B, MODEL_30B, MODEL_34B, MODEL_40B, @@ -1618,6 +1639,8 @@ struct llama_vocab { id special_suffix_id = 32008; id special_eot_id = 32010; + bool add_space_prefix = true; + int find_bpe_rank(const std::string & token_left, const std::string & token_right) const { GGML_ASSERT(token_left.find(' ') == std::string::npos); GGML_ASSERT(token_left.find('\n') == std::string::npos); @@ -2731,6 +2754,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; + case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; case MODEL_40B: return "40B"; @@ -2743,6 +2767,14 @@ static const char * llama_model_type_name(e_model type) { default: return "?B"; } } +static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ + switch (type) { + case LLAMA_VOCAB_TYPE_SPM: return "SPM"; + case LLAMA_VOCAB_TYPE_BPE: return "BPE"; + default: return "unknown"; + } +} + static void llm_load_arch(llama_model_loader & ml, llama_model & model) { model.arch = ml.get_arch(); @@ -3006,6 +3038,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_INTERNLM2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_7B; break; + case 48: model.type = e_model::MODEL_20B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3057,6 +3098,11 @@ static void llm_load_vocab( vocab.special_unk_id = 0; vocab.special_sep_id = -1; vocab.special_pad_id = -1; + + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); + if (add_space_prefix_keyidx != -1) { + vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); + } // The default value of add_space_prefix is true. } else if (tokenizer_name == "gpt2") { vocab.type = LLAMA_VOCAB_TYPE_BPE; @@ -3269,7 +3315,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); - LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix + LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); @@ -4018,8 +4064,35 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_INTERNLM2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6588,6 +6661,126 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_internlm2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + }; static struct ggml_cgraph * llama_build_graph( @@ -6746,6 +6939,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_orion(); } break; + case LLM_ARCH_INTERNLM2: + { + result = llm.build_internlm2(); + } break; default: GGML_ASSERT(false); } @@ -7688,7 +7885,9 @@ static std::vector llama_tokenize_internal(const llama_vocab & // auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); if (&fragment == &fragment_buffer.front()) { - raw_text = " " + raw_text; // prefix with space if the first token is not special + if (vocab.add_space_prefix) { + raw_text = " " + raw_text; // prefix with space if the first token is not special + } } #ifdef PRETOKENIZERDEBUG From d71ac90985854b0905e1abba778e407e17f9f887 Mon Sep 17 00:00:00 2001 From: Ali Nehzat Date: Fri, 2 Feb 2024 02:18:53 +1100 Subject: [PATCH 206/334] make : generate .a library for static linking (#5205) --- Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 781f0bf8c..bf9e085de 100644 --- a/Makefile +++ b/Makefile @@ -586,8 +586,11 @@ train.o: common/train.cpp common/train.h libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) +libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) + ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS) + clean: - rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples From 8ca511cadee2c67f0bd8c7034a2513778ee9a1b7 Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 1 Feb 2024 18:30:17 +0100 Subject: [PATCH 207/334] cuda : fix LLAMA_CUDA_F16 (#5262) --- ggml-cuda.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e56595742..3242a0b4a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8657,9 +8657,9 @@ static void ggml_cuda_op_dequantize_mul_mat_vec( if (src1_convert_f16) { src1_dfloat = src1_dfloat_a.alloc(ne00); - ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00, - ne00, 1, sizeof(float), 0, 0, - ne00, 1, sizeof(half), 0, 0, stream); + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); } #else const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion From 4d0924a8902010d31bd737b6f1f594943d120d0f Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Thu, 1 Feb 2024 19:25:24 +0100 Subject: [PATCH 208/334] Vulkan Phi Fix for AMD Proprietary Drivers (#5260) * Replace tanh to avoid NaN in gelu shader on AMD proprietary driver * Fix another Vulkan CPY buffer size bug --- ggml-vulkan-shaders.hpp | 132 +++++++++++++++++++----------------- ggml-vulkan.cpp | 17 +++-- ggml_vk_generate_shaders.py | 3 +- 3 files changed, 83 insertions(+), 69 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index e2e9be22c..195410c02 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -14670,14 +14670,14 @@ const uint64_t f32_to_f16_fp32_len = 1596; unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x45,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x38,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -14696,15 +14696,15 @@ unsigned char gelu_f32_data[] = { 0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x42,0x00,0x00,0x00, +0x36,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -14731,64 +14731,70 @@ unsigned char gelu_f32_data[] = { 0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, 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+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, +0xf7,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00, +0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x11,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, +0x2a,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00, 0x11,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x11,0x00,0x00,0x00, +0x33,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, +0x30,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x85,0x00,0x05,0x00, +0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, -0x34,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x37,0x00,0x00,0x00, -0x35,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00, -0x11,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x32,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x31,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, -0x3b,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x3b,0x00,0x00,0x00, -0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x31,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x85,0x00,0x05,0x00, -0x11,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x30,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00, -0x3f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x43,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, +0x3f,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, +0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x88,0x00,0x05,0x00, +0x11,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x41,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x11,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x42,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x44,0x00,0x00,0x00, +0x3c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x26,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x38,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x49,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t gelu_f32_len = 1408; +const uint64_t gelu_f32_len = 1484; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index bccc40bf5..b1e0006bb 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -2876,6 +2876,9 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm x_sz = ggml_nbytes(src0); d_sz = ggml_nbytes(dst); + if (extra_src0->offset + x_sz >= d_X->size) { + x_sz = VK_WHOLE_SIZE; + } if (extra->offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; } @@ -2911,12 +2914,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm break; } - x_sz *= ne02 * ne03; - if (y_sz != VK_WHOLE_SIZE) { - y_sz *= ne12 * ne13; - } if (op != GGML_OP_CPY) { - d_sz *= ne02 * ne03; + if (x_sz != VK_WHOLE_SIZE) { + x_sz *= ne02 * ne03; + } + if (y_sz != VK_WHOLE_SIZE) { + y_sz *= ne12 * ne13; + } + if (d_sz != VK_WHOLE_SIZE) { + d_sz *= ne02 * ne03; + } } if (!use_src1 && op == GGML_OP_SOFT_MAX) { diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 6b1b82bf3..67981a751 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -1689,7 +1689,8 @@ void main() { } const float xi = float(data_a[i]); - data_d[i] = D_TYPE(0.5f*xi*(1.0f + tanh(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)))); + const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi); + data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1))); } """ From 128dcbd3c9c4b12f42b560a4430427d7b2828628 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 2 Feb 2024 03:48:53 +0800 Subject: [PATCH 209/334] add --no-mmap in llama-bench (#5257) * add --no-mmap, show sycl backend * fix conflict * fix code format, change print for --no-mmap * ren no_mmap to mmap, show mmap when not default value in printer * update guide for mmap * mv position to reduce model reload --- README-sycl.md | 2 +- examples/llama-bench/llama-bench.cpp | 60 +++++++++++++++++++++++++--- ggml-sycl.cpp | 34 +++++++++++++++- ggml-sycl.h | 3 +- 4 files changed, 89 insertions(+), 10 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 2b2cfe03a..b8ee212b8 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -405,7 +405,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. - Solution: add **--no-mmap**. + Solution: add **--no-mmap** or **--mmap 0**. ## Q&A diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c5a6f744e..e36c061a2 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -20,6 +20,7 @@ #include "llama.h" #include "common.h" #include "ggml-cuda.h" +#include "ggml-sycl.h" // utils static uint64_t get_time_ns() { @@ -120,6 +121,22 @@ static std::string get_gpu_info() { id += "/"; } } +#endif +#ifdef GGML_USE_SYCL + int device_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); + + for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + if (device_list[i] >0 ){ + char buf[128]; + ggml_sycl_get_device_description(i, buf, sizeof(buf)); + id += buf; + id += "/"; + } + } + if (id.length() >2 ) { + id.pop_back(); + } #endif // TODO: other backends return id; @@ -161,6 +178,7 @@ struct cmd_params { std::vector no_kv_offload; std::vector mul_mat_q; std::vector> tensor_split; + std::vector use_mmap; int reps; bool verbose; output_formats output_format; @@ -180,6 +198,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, + /* use_mmap */ {true}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -201,6 +220,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); @@ -370,6 +390,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); + } else if (arg == "-mmp" || arg == "--mmap") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; @@ -441,6 +468,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } + if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } return params; @@ -460,6 +488,7 @@ struct cmd_params_instance { bool no_kv_offload; bool mul_mat_q; std::vector tensor_split; + bool use_mmap; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -468,6 +497,7 @@ struct cmd_params_instance { mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); + mparams.use_mmap = use_mmap; return mparams; } @@ -477,6 +507,7 @@ struct cmd_params_instance { n_gpu_layers == other.n_gpu_layers && split_mode == other.split_mode && main_gpu == other.main_gpu && + use_mmap == other.use_mmap && tensor_split == other.tensor_split; } @@ -503,6 +534,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) + for (const auto & mmp : params.use_mmap) for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) @@ -527,6 +559,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, }; instances.push_back(instance); } @@ -549,6 +582,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, }; instances.push_back(instance); } @@ -565,6 +599,7 @@ struct test { static const bool vulkan; static const bool kompute; static const bool metal; + static const bool sycl; static const bool gpu_blas; static const bool blas; static const std::string cpu_info; @@ -583,6 +618,7 @@ struct test { bool no_kv_offload; bool mul_mat_q; std::vector tensor_split; + bool use_mmap; int n_prompt; int n_gen; std::string test_time; @@ -605,6 +641,7 @@ struct test { no_kv_offload = inst.no_kv_offload; mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; + use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; n_gen = inst.n_gen; // RFC 3339 date-time format @@ -654,25 +691,29 @@ struct test { if (metal) { return "Metal"; } + if (sycl) { + return GGML_SYCL_NAME; + } if (gpu_blas) { return "GPU BLAS"; } if (blas) { return "BLAS"; } + return "CPU"; } static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", + "mul_mat_q", "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -691,8 +732,8 @@ struct test { return INT; } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || - field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q") { + field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || + field == "mul_mat_q" || field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -720,13 +761,13 @@ struct test { std::vector values = { build_commit, std::to_string(build_number), std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, + std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -753,6 +794,7 @@ const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); +const bool test::sycl = !!ggml_cpu_has_sycl(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); @@ -895,6 +937,9 @@ struct markdown_printer : public printer { if (field == "no_kv_offload") { return "nkvo"; } + if (field == "use_mmap") { + return "mmap"; + } if (field == "tensor_split") { return "ts"; } @@ -938,6 +983,9 @@ struct markdown_printer : public printer { if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.push_back("tensor_split"); } + if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { + fields.push_back("use_mmap"); + } fields.push_back("test"); fields.push_back("t/s"); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 1cc55ef52..e8ba48353 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2928,7 +2928,6 @@ void ggml_sycl_set_main_device(int main_device); void ggml_sycl_set_mul_mat_q(bool mul_mat_q); void ggml_sycl_set_scratch_size(size_t scratch_size); void ggml_sycl_free_scratch(void); -int ggml_sycl_get_device_count(void); void ggml_sycl_get_device_description(int device, char * description, size_t description_size); bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); @@ -14493,6 +14492,37 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ return true; } +GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { + int max_compute_units = -1; + for(int i=0;i Date: Thu, 1 Feb 2024 23:20:13 -0800 Subject: [PATCH 210/334] llama : fix memory leak in llama_batch_free (#5252) The llama_batch_init allocates memory for a fixed number of tokens. However, the llama_batch_free only frees memory for the number of tokens that were added to the batch. This change-set uses a null terminated array for the batch seq_id, and frees all the elements until the nullptr is reached. This change-set also changes the name of the first parameter from `n_tokens` to `n_tokens_alloc` to more clearly indicate that this value is the number of tokens allocated to the batch, not the number of tokens in the batch. --- llama.cpp | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/llama.cpp b/llama.cpp index e8f44c2cb..6bf7f9efb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11377,22 +11377,24 @@ struct llama_batch llama_batch_get_one( }; } -struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) { +struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) { llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; if (embd) { - batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd); + batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd); } else { - batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens); + batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc); } - batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens); - batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens); - batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens); - for (int i = 0; i < n_tokens; ++i) { + batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc); + batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc); + batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1)); + for (int i = 0; i < n_tokens_alloc; ++i) { batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max); } - batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens); + batch.seq_id[n_tokens_alloc] = nullptr; + + batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc); return batch; } @@ -11403,7 +11405,7 @@ void llama_batch_free(struct llama_batch batch) { if (batch.pos) free(batch.pos); if (batch.n_seq_id) free(batch.n_seq_id); if (batch.seq_id) { - for (int i = 0; i < batch.n_tokens; ++i) { + for (int i = 0; batch.seq_id[i] != nullptr; ++i) { free(batch.seq_id[i]); } free(batch.seq_id); From af3ba5d94627d337e32a95129e31a3064c459f6b Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 2 Feb 2024 15:53:27 +0800 Subject: [PATCH 211/334] [SYCL] update guide of SYCL backend (#5254) * update guide for make installation, memory, gguf model link, rm todo for windows build * add vs install requirement * update for gpu device check * update help of llama-bench * fix grammer issues --- README-sycl.md | 64 +++++++++++++++++++++++++++----- examples/llama-bench/README.md | 34 ++++++++++------- examples/sycl/win-run-llama2.bat | 2 +- 3 files changed, 77 insertions(+), 23 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index b8ee212b8..f7edc1c3e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -42,6 +42,8 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). ## Intel GPU +### Verified + |Intel GPU| Status | Verified Model| |-|-|-| |Intel Data Center Max Series| Support| Max 1550| @@ -50,6 +52,17 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| +Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. + +### Memory + +The memory is a limitation to run LLM on GPUs. + +When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors: buffer size = 3577.56 MiB`. + +For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+. + +For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. ## Linux @@ -105,7 +118,7 @@ source /opt/intel/oneapi/setvars.sh sycl-ls ``` -There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. +There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. Output (example): ``` @@ -152,6 +165,8 @@ Note: 1. Put model file to folder **models** +You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. + 2. Enable oneAPI running environment ``` @@ -223,7 +238,13 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). -2. Install Intel® oneAPI Base toolkit. +Note: **The driver is mandatory for compute function**. + +2. Install Visual Studio. + +Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows. + +3. Install Intel® oneAPI Base toolkit. a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). @@ -252,7 +273,7 @@ In oneAPI command line: sycl-ls ``` -There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. +There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. Output (example): ``` @@ -260,15 +281,21 @@ Output (example): [opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] [opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] - ``` -3. Install cmake & make +4. Install cmake & make -a. Download & install cmake for windows: https://cmake.org/download/ +a. Download & install cmake for Windows: https://cmake.org/download/ -b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/ +b. Download & install make for Windows provided by mingw-w64 +- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases. + + Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z). + +- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**. + +- Add the **bin** folder path in the Windows system PATH environment. ### Build locally: @@ -309,6 +336,8 @@ Note: 1. Put model file to folder **models** +You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. + 2. Enable oneAPI running environment - In Search, input 'oneAPI'. @@ -419,8 +448,25 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Miss to enable oneAPI running environment. +- Meet compile error. + + Remove folder **build** and try again. + +- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux. + + Please run **sudo sycl-ls**. + + If you see it in result, please add video/render group to your ID: + + ``` + sudo usermod -aG render username + sudo usermod -aG video username + ``` + + Then **relogin**. + + If you do not see it, please check the installation GPU steps again. + ## Todo -- Support to build in Windows. - - Support multiple cards. diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index d02824bfa..374e40a7d 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -23,19 +23,23 @@ usage: ./llama-bench [options] options: -h, --help - -m, --model (default: models/7B/ggml-model-q4_0.gguf) - -p, --n-prompt (default: 512) - -n, --n-gen (default: 128) - -b, --batch-size (default: 512) - --memory-f32 <0|1> (default: 0) - -t, --threads (default: 16) - -ngl N, --n-gpu-layers (default: 99) - -mg i, --main-gpu (default: 0) - -mmq, --mul-mat-q <0|1> (default: 1) - -ts, --tensor_split - -r, --repetitions (default: 5) - -o, --output (default: md) - -v, --verbose (default: 0) + -m, --model (default: models/7B/ggml-model-q4_0.gguf) + -p, --n-prompt (default: 512) + -n, --n-gen (default: 128) + -b, --batch-size (default: 512) + -ctk , --cache-type-k (default: f16) + -ctv , --cache-type-v (default: f16) + -t, --threads (default: 112) + -ngl, --n-gpu-layers (default: 99) + -sm, --split-mode (default: layer) + -mg, --main-gpu (default: 0) + -nkvo, --no-kv-offload <0|1> (default: 0) + -mmp, --mmap <0|1> (default: 1) + -mmq, --mul-mat-q <0|1> (default: 1) + -ts, --tensor_split (default: 0) + -r, --repetitions (default: 5) + -o, --output (default: md) + -v, --verbose (default: 0) Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. ``` @@ -51,6 +55,10 @@ Each test is repeated the number of times given by `-r`, and the results are ave For a description of the other options, see the [main example](../main/README.md). +Note: + +- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`. + ## Examples ### Text generation with different models diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index 28d935541..cf621c675 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -2,7 +2,7 @@ :: Copyright (C) 2024 Intel Corporation :: SPDX-License-Identifier: MIT -INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force From e805f0fa9951081ce0a86378a7aa52b6f636b82d Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Fri, 2 Feb 2024 15:54:14 +0800 Subject: [PATCH 212/334] [SYCL] get MAX_MEM_ALLOC from device property (#5270) * get max alloc size from device prop * fix macro typo --- ggml-sycl.cpp | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index e8ba48353..4ee2eed38 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -337,6 +337,7 @@ namespace dpct } size_t get_global_mem_size() const { return _global_mem_size; } size_t get_local_mem_size() const { return _local_mem_size; } + size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; } /// Returns the maximum clock rate of device's global memory in kHz. If /// compiler does not support this API then returns default value 3200000 kHz. unsigned int get_memory_clock_rate() const { return _memory_clock_rate; } @@ -398,6 +399,10 @@ namespace dpct { _local_mem_size = local_mem_size; } + void set_max_mem_alloc_size(size_t max_mem_alloc_size) + { + _max_mem_alloc_size = max_mem_alloc_size; + } void set_max_work_group_size(int max_work_group_size) { _max_work_group_size = max_work_group_size; @@ -465,6 +470,7 @@ namespace dpct int _max_register_size_per_work_group; size_t _global_mem_size; size_t _local_mem_size; + size_t _max_mem_alloc_size; size_t _max_nd_range_size[3]; int _max_nd_range_size_i[3]; uint32_t _device_id; @@ -516,6 +522,7 @@ namespace dpct dev.get_info()); prop.set_global_mem_size(dev.get_info()); prop.set_local_mem_size(dev.get_info()); + prop.set_max_mem_alloc_size(dev.get_info()); #if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6) if (dev.has(sycl::aspect::ext_intel_memory_clock_rate)) @@ -644,6 +651,11 @@ namespace dpct return get_device_info().get_global_mem_size(); } + size_t get_max_mem_alloc_size() const + { + return get_device_info().get_max_mem_alloc_size(); + } + /// Get the number of bytes of free and total memory on the SYCL device. /// \param [out] free_memory The number of bytes of free memory on the SYCL device. /// \param [out] total_memory The number of bytes of total memory on the SYCL device. @@ -11311,10 +11323,10 @@ void ggml_init_sycl() try { GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); int64_t total_vram = 0; -#if defined(GGML_SYCL_FP16) - fprintf(stderr, "%s: GGML_SYCL_FP16: yes\n", __func__); +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_SYCL_FP16: no\n", __func__); + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif @@ -14788,6 +14800,12 @@ static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_ty UNUSED(buft); } +static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return dpct::get_current_device().get_max_mem_alloc_size(); + + UNUSED(buft); +} + static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); @@ -14818,7 +14836,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .get_name = */ ggml_backend_sycl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength + /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend, /* .is_host = */ nullptr, From 6b91b1e0a92ac2e4e269eec6361ca53a61ced6c6 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 2 Feb 2024 08:56:31 +0100 Subject: [PATCH 213/334] docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL --- .devops/main-intel.Dockerfile | 16 ++--- .devops/main-vulkan.Dockerfile | 29 +++++++++ .devops/server-intel.Dockerfile | 15 +++-- .devops/server-vulkan.Dockerfile | 29 +++++++++ README-sycl.md | 102 +++++++++++++++++++------------ README.md | 64 ++++++++++++++----- 6 files changed, 188 insertions(+), 67 deletions(-) create mode 100644 .devops/main-vulkan.Dockerfile create mode 100644 .devops/server-vulkan.Dockerfile diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile index e1e6acc24..572e5d8ea 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/main-intel.Dockerfile @@ -1,8 +1,8 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 -ARG UBUNTU_VERSION=22.04 -FROM intel/hpckit:$ONEAPI_VERSION as build +FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git @@ -10,16 +10,18 @@ WORKDIR /app COPY . . -# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance RUN mkdir build && \ cd build && \ - cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ - cmake --build . --config Release --target main server + if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ + echo "LLAMA_SYCL_F16 is set" && \ + export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ + fi && \ + cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build . --config Release --target main -FROM ubuntu:$UBUNTU_VERSION as runtime +FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime COPY --from=build /app/build/bin/main /main -COPY --from=build /app/build/bin/server /server ENV LC_ALL=C.utf8 diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile new file mode 100644 index 000000000..bca460365 --- /dev/null +++ b/.devops/main-vulkan.Dockerfile @@ -0,0 +1,29 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION as build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk + +# Build it +WORKDIR /app +COPY . . +RUN mkdir build && \ + cd build && \ + cmake .. -DLLAMA_VULKAN=1 && \ + cmake --build . --config Release --target main + +# Clean up +WORKDIR / +RUN cp /app/build/bin/main /main && \ + rm -rf /app + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/main" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile index e343d278c..312f2df80 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/server-intel.Dockerfile @@ -1,8 +1,8 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 -ARG UBUNTU_VERSION=22.04 -FROM intel/hpckit:$ONEAPI_VERSION as build +FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git @@ -10,13 +10,16 @@ WORKDIR /app COPY . . -# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance RUN mkdir build && \ cd build && \ - cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ - cmake --build . --config Release --target main server + if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ + echo "LLAMA_SYCL_F16 is set" && \ + export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ + fi && \ + cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build . --config Release --target server -FROM ubuntu:$UBUNTU_VERSION as runtime +FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime COPY --from=build /app/build/bin/server /server diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile new file mode 100644 index 000000000..e0add6fc3 --- /dev/null +++ b/.devops/server-vulkan.Dockerfile @@ -0,0 +1,29 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION as build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk + +# Build it +WORKDIR /app +COPY . . +RUN mkdir build && \ + cd build && \ + cmake .. -DLLAMA_VULKAN=1 && \ + cmake --build . --config Release --target server + +# Clean up +WORKDIR / +RUN cp /app/build/bin/server /server && \ + rm -rf /app + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/README-sycl.md b/README-sycl.md index f7edc1c3e..7aa4274a9 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -1,22 +1,15 @@ # llama.cpp for SYCL -[Background](#background) - -[OS](#os) - -[Intel GPU](#intel-gpu) - -[Linux](#linux) - -[Windows](#windows) - -[Environment Variable](#environment-variable) - -[Known Issue](#known-issue) - -[Q&A](#q&a) - -[Todo](#todo) +- [Background](#background) +- [OS](#os) +- [Intel GPU](#intel-gpu) +- [Docker](#docker) +- [Linux](#linux) +- [Windows](#windows) +- [Environment Variable](#environment-variable) +- [Known Issue](#known-issue) +- [Q&A](#q&a) +- [Todo](#todo) ## Background @@ -36,7 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |OS|Status|Verified| |-|-|-| -|Linux|Support|Ubuntu 22.04| +|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| |Windows|Support|Windows 11| @@ -50,7 +43,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |Intel Data Center Flex Series| Support| Flex 170| |Intel Arc Series| Support| Arc 770, 730M| |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| +|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. @@ -64,6 +57,38 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. +## Docker + +Note: +- Only docker on Linux is tested. Docker on WSL may not work. +- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that) + +### Build the image + +You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. + + +```sh +# For F16: +#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile . + +# Or, for F32: +docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile . + +# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example +``` + +### Run + +```sh +# Firstly, find all the DRI cards: +ls -la /dev/dri +# Then, pick the card that you want to use. + +# For example with "/dev/dri/card1" +docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` + ## Linux ### Setup Environment @@ -76,7 +101,7 @@ Note: for iGPU, please install the client GPU driver. b. Add user to group: video, render. -``` +```sh sudo usermod -aG render username sudo usermod -aG video username ``` @@ -85,7 +110,7 @@ Note: re-login to enable it. c. Check -``` +```sh sudo apt install clinfo sudo clinfo -l ``` @@ -103,7 +128,6 @@ Platform #0: Intel(R) OpenCL HD Graphics 2. Install Intel® oneAPI Base toolkit. - a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). Recommend to install to default folder: **/opt/intel/oneapi**. @@ -112,7 +136,7 @@ Following guide use the default folder as example. If you use other folder, plea b. Check -``` +```sh source /opt/intel/oneapi/setvars.sh sycl-ls @@ -131,21 +155,25 @@ Output (example): 2. Build locally: -``` +Note: +- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +```sh mkdir -p build cd build source /opt/intel/oneapi/setvars.sh -#for FP16 -#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference +# For FP16: +#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON -#for FP32 +# Or, for FP32: cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -#build example/main only +# Build example/main only #cmake --build . --config Release --target main -#build all binary +# Or, build all binary cmake --build . --config Release -v cd .. @@ -153,14 +181,10 @@ cd .. or -``` +```sh ./examples/sycl/build.sh ``` -Note: - -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - ### Run 1. Put model file to folder **models** @@ -177,10 +201,10 @@ source /opt/intel/oneapi/setvars.sh Run without parameter: -``` +```sh ./build/bin/ls-sycl-device -or +# or running the "main" executable and look at the output log: ./build/bin/main ``` @@ -209,13 +233,13 @@ found 4 SYCL devices: Set device ID = 0 by **GGML_SYCL_DEVICE=0** -``` +```sh GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 ``` or run by script: -``` -./examples/sycl/run-llama2.sh +```sh +./examples/sycl/run_llama2.sh ``` Note: diff --git a/README.md b/README.md index e6ed1d429..af1f09fa0 100644 --- a/README.md +++ b/README.md @@ -393,28 +393,28 @@ Building the program with BLAS support may lead to some performance improvements Check [BLIS.md](docs/BLIS.md) for more information. +- #### SYCL + SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + + llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + + For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). + - #### Intel oneMKL + Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md). + - Using manual oneAPI installation: By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: ```bash mkdir build cd build - source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-runtime docker image, only required for manual installation + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON cmake --build . --config Release ``` - Using oneAPI docker image: - If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-runtime](https://hub.docker.com/r/intel/oneapi-runtime) - - ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON - cmake --build . --config Release - ``` - - Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. @@ -601,14 +601,48 @@ Building the program with BLAS support may lead to some performance improvements You can get a list of platforms and devices from the `clinfo -l` command, etc. -- #### SYCL +- #### Vulkan - SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + **With docker**: - llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + You don't need to install Vulkan SDK. It will be installed inside the container. - For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). + ```sh + # Build the image + docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile . + # Then, use it: + docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 + ``` + + **Without docker**: + + Firstly, you need to make sure you installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + + For example, on Ubuntu 22.04 (jammy), use the command below: + + ```bash + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + apt update -y + apt-get install -y vulkan-sdk + # To verify the installation, use the command below: + vulkaninfo + ``` + + Then, build llama.cpp using the cmake command below: + + ```bash + mkdir -p build + cd build + cmake .. -DLLAMA_VULKAN=1 + cmake --build . --config Release + # Test the output binary (with "-ngl 33" to offload all layers to GPU) + ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 + + # You should see in the output, ggml_vulkan detected your GPU. For example: + # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 + ``` ### Prepare Data & Run From b05102fe8cfa9893851c6bf6efd15cdc20b6afa2 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 2 Feb 2024 08:39:48 +0000 Subject: [PATCH 214/334] Tidy ggml-sycl (#5261) * Tidy some code in ggml-sycl * Remove blank space * Remove std::printf comments --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 4ee2eed38..ac75f8e16 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -1366,6 +1366,7 @@ namespace dpct } #else return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); #endif // DPCT_USM_LEVEL_NONE } @@ -1667,7 +1668,7 @@ namespace dpct using Ty = typename DataType::T2; Ty s_h; if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only) - detail::dpct_memcpy(q, (void *)&s_h, (void *)s, sizeof(T), device_to_host) + detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host) .wait(); else s_h = *reinterpret_cast(s); @@ -1691,6 +1692,20 @@ namespace dpct int ldb, const void *beta, void *c, int ldc) { #ifndef __INTEL_MKL__ + GGML_UNUSED(q); + GGML_UNUSED(a_trans); + GGML_UNUSED(b_trans); + GGML_UNUSED(m); + GGML_UNUSED(n); + GGML_UNUSED(k); + GGML_UNUSED(alpha); + GGML_UNUSED(a); + GGML_UNUSED(lda); + GGML_UNUSED(b); + GGML_UNUSED(ldb); + GGML_UNUSED(beta); + GGML_UNUSED(c); + GGML_UNUSED(ldc); throw std::runtime_error("The oneAPI Math Kernel Library (oneMKL) Interfaces " "Project does not support this API."); #else @@ -1830,7 +1845,7 @@ namespace dpct template T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask, - int logical_sub_group_size = 32) + unsigned int logical_sub_group_size = 32) { unsigned int id = g.get_local_linear_id(); unsigned int start_index = @@ -2160,6 +2175,7 @@ namespace dpct } #else return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); #endif // DPCT_USM_LEVEL_NONE } @@ -3302,7 +3318,7 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo std::ofstream logfile; logfile.open(filename); // printf("local buf element %d\n", total_elements); - for(int i=0; ibackend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; - const int compute_capability = g_device_caps[id].cc; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check #else @@ -12691,7 +12700,7 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { continue; } - int can_access_peer; + // int can_access_peer; // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other)); // if (can_access_peer) { // if (enable_peer_access) { @@ -12716,7 +12725,6 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; - const int64_t nrows0 = ggml_nrows(src0); const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; @@ -13812,13 +13820,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); - const dpct::memcpy_direction src1_kind = - src1->backend == GGML_BACKEND_CPU ? dpct::host_to_device - : dpct::device_to_device; - const dpct::memcpy_direction dst_kind = dst->backend == GGML_BACKEND_CPU - ? dpct::device_to_host - : dpct::device_to_device; - for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; From 2d40085c26794e29c434480b9e06738e89e5686f Mon Sep 17 00:00:00 2001 From: Mirror Azure <54669636+MirrorAzure@users.noreply.github.com> Date: Fri, 2 Feb 2024 14:39:09 +0300 Subject: [PATCH 215/334] py : add check for '.attn.masked_bias' layers to GPT2model (#5281) --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 4ebab07b3..a6ffd128b 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1138,7 +1138,7 @@ class GPT2Model(Model): for name, data_torch in self.get_tensors(): # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")): + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")): continue if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): From e437b37fd0b2b97e6c6ff1045ec7f901faa6498a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 Feb 2024 14:23:40 +0200 Subject: [PATCH 216/334] scripts : parse wtype in server-llm.sh (#5167) * scripts : parse wtype in server-llm.sh * scripts : fix check for wfile --- scripts/server-llm.sh | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 7bf0929bb..0b83cdbbc 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -141,6 +141,28 @@ for wt in "${wtypes[@]}"; do wfiles+=("") done +# map wtype input to index +if [[ ! -z "$wtype" ]]; then + iw=-1 + is=0 + for wt in "${wtypes[@]}"; do + # uppercase + uwt=$(echo "$wt" | tr '[:lower:]' '[:upper:]') + if [[ "$uwt" == "$wtype" ]]; then + iw=$is + break + fi + is=$((is+1)) + done + + if [[ $iw -eq -1 ]]; then + printf "[-] Invalid weight type: %s\n" "$wtype" + exit 1 + fi + + wtype="$iw" +fi + # sample repos repos=( "https://huggingface.co/TheBloke/Llama-2-7B-GGUF" @@ -252,8 +274,10 @@ for file in $model_files; do printf " %2d) %s %s\n" $iw "$have" "$file" done +wfile="${wfiles[$wtype]}" + # ask for weights type until provided and available -while [[ -z "$wtype" ]]; do +while [[ -z "$wfile" ]]; do printf "\n" read -p "[+] Select weight type: " wtype wfile="${wfiles[$wtype]}" From 191221178f51b6e81122c5bda0fd79620e547d07 Mon Sep 17 00:00:00 2001 From: kalomaze <66376113+kalomaze@users.noreply.github.com> Date: Fri, 2 Feb 2024 08:15:30 -0600 Subject: [PATCH 217/334] perplexity : fix KL divergence calculations on Windows (#5273) --- examples/perplexity/perplexity.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 8d2204969..4b08145cd 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -457,14 +457,14 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par std::ofstream logits_stream; if (!params.logits_file.empty()) { - logits_stream.open(params.logits_file.c_str()); + logits_stream.open(params.logits_file.c_str(), std::ios::binary); if (!logits_stream.is_open()) { fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str()); return {}; } fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str()); logits_stream.write("_logits_", 8); - logits_stream.write((const char *)&n_ctx, sizeof(n_ctx)); + logits_stream.write(reinterpret_cast(&n_ctx), sizeof(n_ctx)); } auto tim1 = std::chrono::high_resolution_clock::now(); From a305dba8ff642e57f538f42010868fe0bc5262a1 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Sat, 3 Feb 2024 08:11:37 +0000 Subject: [PATCH 218/334] Fix im2col with 32fp (#5286) --- ggml-sycl.cpp | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ac75f8e16..51445b5e7 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -8247,7 +8247,8 @@ static void clamp_f32(const float * x, float * dst, const float min, const float dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } -static void im2col_f32_f16(const float *x, sycl::half *dst, int offset_delta, +template +static void im2col_kernel(const float *x, T *dst, int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW, int s0, int s1, int p0, int p1, int d0, int d1, @@ -11019,7 +11020,8 @@ static void soft_max_f32_sycl(const float *x, const float *y, float *dst, }); } -static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, +template +static void im2col_sycl(const float *x, T *dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, int offset_delta, int s0, int s1, int p0, int p1, int d0, int d1, @@ -11036,7 +11038,7 @@ static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - im2col_f32_f16(x, dst, offset_delta, IW, IH, OW, KW, KH, + im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1, item_ct1); }); @@ -12424,7 +12426,7 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; @@ -12447,8 +12449,11 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - im2col_f32_f16_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, - IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + if (dst->type == GGML_TYPE_F16) { + im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } else { + im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } (void) src0; (void) src0_dd; From 6a66c5071a74a96c4f52cf1015a092acd18c3714 Mon Sep 17 00:00:00 2001 From: BADR Date: Sat, 3 Feb 2024 12:20:26 +0100 Subject: [PATCH 219/334] readme : add tenere in the ui tools list (#5284) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index af1f09fa0..4a9bdf314 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,7 @@ as the main playground for developing new features for the [ggml](https://github - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [iohub/collama](https://github.com/iohub/coLLaMA) +- [pythops/tenere](https://github.com/pythops/tenere) --- From 1ec3332ade60aeb1494ace2211cf1a966db6d770 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 3 Feb 2024 06:22:06 -0500 Subject: [PATCH 220/334] YaRN : store rope scaling type as int32_t in memory (#5285) * YaRN : store rope scaling type as int32_t in memory * llama : store mapped names as const char * --- common/common.h | 3 +-- llama.cpp | 24 ++++++++++++------------ llama.h | 2 +- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/common/common.h b/common/common.h index 24a99d728..62de25d6a 100644 --- a/common/common.h +++ b/common/common.h @@ -75,8 +75,7 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment - // pinging @cebtenzzre + int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters struct llama_sampling_params sparams; diff --git a/llama.cpp b/llama.cpp index 6bf7f9efb..4787a92fe 100644 --- a/llama.cpp +++ b/llama.cpp @@ -208,7 +208,7 @@ enum llm_arch { LLM_ARCH_UNKNOWN, }; -static std::map LLM_ARCH_NAMES = { +static std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GPT2, "gpt2" }, @@ -285,7 +285,7 @@ enum llm_kv { LLM_KV_TOKENIZER_RWKV, }; -static std::map LLM_KV_NAMES = { +static std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, @@ -346,7 +346,7 @@ struct LLM_KV { llm_arch arch; std::string operator()(llm_kv kv) const { - return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str()); + return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]); } }; @@ -747,13 +747,13 @@ struct LLM_TN { // gguf helpers // -static std::map LLAMA_ROPE_SCALING_TYPES = { +static std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_NONE, "none" }, { LLAMA_ROPE_SCALING_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_YARN, "yarn" }, }; -static int8_t llama_rope_scaling_type_from_string(const std::string & name) { +static int32_t llama_rope_scaling_type_from_string(const std::string & name) { for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { if (kv.second == name) { return kv.first; @@ -1415,6 +1415,7 @@ static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; + bool rope_finetuned; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; @@ -1434,8 +1435,7 @@ struct llama_hparams { float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; - int8_t rope_scaling_type_train : 3; - bool rope_finetuned : 1; + int32_t rope_scaling_type_train; float f_clamp_kqv; float f_max_alibi_bias; @@ -2701,7 +2701,7 @@ struct llama_model_loader { // load LLaMA models // -static std::string llama_model_arch_name(llm_arch arch) { +static const char * llama_model_arch_name(llm_arch arch) { auto it = LLM_ARCH_NAMES.find(arch); if (it == LLM_ARCH_NAMES.end()) { return "unknown"; @@ -3310,11 +3310,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { const auto & hparams = model.hparams; const auto & vocab = model.vocab; - const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); - LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); + LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch)); LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); @@ -3336,7 +3336,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); - LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str()); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); @@ -10735,7 +10735,7 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { return snprintf(buf, buf_size, "%s %s %s", - llama_model_arch_name(model->arch).c_str(), + llama_model_arch_name(model->arch), llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } diff --git a/llama.h b/llama.h index 9a60e9bfb..cec4158bc 100644 --- a/llama.h +++ b/llama.h @@ -213,7 +213,7 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model From 52bb63c7082c859c3f1dfc527227e6a95b299c7c Mon Sep 17 00:00:00 2001 From: Michael Klimenko Date: Sat, 3 Feb 2024 12:23:37 +0100 Subject: [PATCH 221/334] refactor : switch to emplace_back to avoid extra object (#5291) --- common/common.cpp | 8 ++--- examples/llama-bench/llama-bench.cpp | 34 +++++++++++----------- examples/main/main.cpp | 4 +-- examples/perplexity/perplexity.cpp | 8 ++--- examples/quantize-stats/quantize-stats.cpp | 4 +-- examples/quantize/quantize.cpp | 4 +-- examples/server/server.cpp | 8 ++--- tests/test-llama-grammar.cpp | 2 +- 8 files changed, 36 insertions(+), 36 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index ce739b15c..3302caa20 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -515,7 +515,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); + params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") { if (++i >= argc) { @@ -527,7 +527,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") { if (++i >= argc) { @@ -664,7 +664,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.antiprompt.push_back(argv[i]); + params.antiprompt.emplace_back(argv[i]); } else if (arg == "-ld" || arg == "--logdir") { if (++i >= argc) { invalid_param = true; @@ -880,7 +880,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index e36c061a2..ddb0ba064 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -948,46 +948,46 @@ struct markdown_printer : public printer { void print_header(const cmd_params & params) override { // select fields to print - fields.push_back("model"); - fields.push_back("size"); - fields.push_back("params"); - fields.push_back("backend"); + fields.emplace_back("model"); + fields.emplace_back("size"); + fields.emplace_back("params"); + fields.emplace_back("backend"); bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS"; if (!is_cpu_backend) { - fields.push_back("n_gpu_layers"); + fields.emplace_back("n_gpu_layers"); } if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) { - fields.push_back("n_threads"); + fields.emplace_back("n_threads"); } if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) { - fields.push_back("n_batch"); + fields.emplace_back("n_batch"); } if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) { - fields.push_back("type_k"); + fields.emplace_back("type_k"); } if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) { - fields.push_back("type_v"); + fields.emplace_back("type_v"); } if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) { - fields.push_back("main_gpu"); + fields.emplace_back("main_gpu"); } if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { - fields.push_back("split_mode"); + fields.emplace_back("split_mode"); } if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.push_back("mul_mat_q"); + fields.emplace_back("mul_mat_q"); } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { - fields.push_back("no_kv_offload"); + fields.emplace_back("no_kv_offload"); } if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { - fields.push_back("tensor_split"); + fields.emplace_back("tensor_split"); } if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { - fields.push_back("use_mmap"); + fields.emplace_back("use_mmap"); } - fields.push_back("test"); - fields.push_back("t/s"); + fields.emplace_back("test"); + fields.emplace_back("t/s"); fprintf(fout, "|"); for (const auto & field : fields) { diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 1c6138d23..0ed4d79f9 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -352,12 +352,12 @@ int main(int argc, char ** argv) { // in instruct mode, we inject a prefix and a suffix to each input by the user if (params.instruct) { params.interactive_first = true; - params.antiprompt.push_back("### Instruction:\n\n"); + params.antiprompt.emplace_back("### Instruction:\n\n"); } // similar for chatml mode else if (params.chatml) { params.interactive_first = true; - params.antiprompt.push_back("<|im_start|>user\n"); + params.antiprompt.emplace_back("<|im_start|>user\n"); } // enable interactive mode if interactive start is specified diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 4b08145cd..b2c131d4c 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -881,7 +881,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { size_t li = hs_cur.common_prefix; for (int s = 0; s < 4; ++s) { for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) { - eval_pairs.push_back(std::make_pair(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1])); + eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]); } ++li; } @@ -1159,13 +1159,13 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0; size_t li = n_base1 - 1; for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) { - eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[0][j+1])); + eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]); } const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix; const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0; li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1; for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) { - eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[1][j+1])); + eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]); } } compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); @@ -1524,7 +1524,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params size_t li = cur_task.common_prefix; for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { - eval_pairs.push_back(std::make_pair(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1])); + eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]); } ++li; } diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 6d5f213dc..1d05f1391 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -257,13 +257,13 @@ int main(int argc, char ** argv) { invalid_param = true; break; } - params.include_layers.push_back(argv[i]); + params.include_layers.emplace_back(argv[i]); } else if (arg == "-L" || arg == "--exclude-layer") { if (++i >= argc) { invalid_param = true; break; } - params.exclude_layers.push_back(argv[i]); + params.exclude_layers.emplace_back(argv[i]); } else if (arg == "-t" || arg == "--type") { if (++i >= argc) { invalid_param = true; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index a9673f0d4..85f403ffc 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -208,13 +208,13 @@ int main(int argc, char ** argv) { } } else if (strcmp(argv[arg_idx], "--include-weights") == 0) { if (arg_idx < argc-1) { - included_weights.push_back(argv[++arg_idx]); + included_weights.emplace_back(argv[++arg_idx]); } else { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) { if (arg_idx < argc-1) { - excluded_weights.push_back(argv[++arg_idx]); + excluded_weights.emplace_back(argv[++arg_idx]); } else { usage(argv[0]); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ea77125ea..a9f8cb369 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1884,7 +1884,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - sparams.api_keys.push_back(argv[i]); + sparams.api_keys.emplace_back(argv[i]); } else if (arg == "--api-key-file") { @@ -2160,7 +2160,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); + params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") @@ -2176,7 +2176,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") @@ -2318,7 +2318,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 78fc41117..16ebe753f 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -105,7 +105,7 @@ int main() for (auto rule : expected_rules) { - parsed_grammar.rules.push_back({}); + parsed_grammar.rules.emplace_back(); for (auto element : rule) { parsed_grammar.rules.back().push_back(element); From e920ed393d989ed35625ddaf182ebb52cda07fcd Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 3 Feb 2024 18:15:00 +0100 Subject: [PATCH 222/334] Vulkan Intel Fixes, Optimizations and Debugging Flags (#5301) * Fix Vulkan on Intel ARC Optimize matmul for Intel ARC Add Vulkan dequant test * Add Vulkan debug and validate flags to Make and CMakeLists.txt * Enable asynchronous transfers in Vulkan backend * Fix flake8 * Disable Vulkan async backend functions for now * Also add Vulkan run tests command to Makefile and CMakeLists.txt --- CMakeLists.txt | 20 + Makefile | 12 + ggml-vulkan-shaders.hpp | 10922 +++------------------------------- ggml-vulkan.cpp | 420 +- ggml_vk_generate_shaders.py | 213 +- 5 files changed, 1257 insertions(+), 10330 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 1ee455b3a..c156c4824 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -100,6 +100,10 @@ option(LLAMA_HIPBLAS "llama: use hipBLAS" option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_VULKAN "llama: use Vulkan" OFF) +option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF) +option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF) +option(LLAMA_VULKAN_VALIDATE "llama: enable Vulkan validation" OFF) +option(LLAMA_VULKAN_RUN_TESTS "llama: run Vulkan tests" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) @@ -431,6 +435,22 @@ if (LLAMA_VULKAN) add_compile_definitions(GGML_USE_VULKAN) + if (LLAMA_VULKAN_CHECK_RESULTS) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_CHECK_RESULTS) + endif() + + if (LLAMA_VULKAN_DEBUG) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_DEBUG) + endif() + + if (LLAMA_VULKAN_VALIDATE) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_VALIDATE) + endif() + + if (LLAMA_VULKAN_RUN_TESTS) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_RUN_TESTS) + endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan) else() message(WARNING "Vulkan not found") diff --git a/Makefile b/Makefile index bf9e085de..a55d15888 100644 --- a/Makefile +++ b/Makefile @@ -457,6 +457,18 @@ ifdef LLAMA_VULKAN_CHECK_RESULTS MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS endif +ifdef LLAMA_VULKAN_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_DEBUG +endif + +ifdef LLAMA_VULKAN_VALIDATE + MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE +endif + +ifdef LLAMA_VULKAN_RUN_TESTS + MK_CPPFLAGS += -DGGML_VULKAN_RUN_TESTS +endif + ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h $(CXX) $(CXXFLAGS) -c $< -o $@ endif # LLAMA_VULKAN diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 195410c02..e5e7a8414 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -890,156 +890,6 @@ const uint64_t cpy_f32_f32_len = 2472; unsigned char dequant_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x81,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x4f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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-0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q4_K_len = 5776; - -unsigned char dequant_q4_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xb1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -6669,1134 +3848,10 @@ unsigned char dequant_q4_K_fp32_data[] = { 0x37,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q4_K_fp32_len = 5940; +const uint64_t dequant_q4_K_len = 5940; unsigned char dequant_q5_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7a,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q5_K_len = 5888; - -unsigned char dequant_q5_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xa0,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -12162,366 +6653,10 @@ unsigned char dequant_q5_K_fp32_data[] = { 0x8a,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q5_K_fp32_len = 5988; +const uint64_t dequant_q5_K_len = 5988; unsigned char dequant_q6_K_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x0a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, 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dequant_q6_K_fp32_data[] = { 0x06,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q6_K_fp32_len = 4296; +const uint64_t dequant_q6_K_len = 4296; unsigned char dequant_q8_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -14798,500 +8158,6 @@ const uint64_t gelu_f32_len = 1484; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x68,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x6e,0x00,0x00,0x00, -0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x72,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,0x75,0x00,0x00,0x00, -0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x72,0x00,0x00,0x00, -0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x78,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t get_rows_f16_f32_fp32_len = 1932; - -unsigned char get_rows_f16_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, @@ -15455,21 +8321,19 @@ unsigned char get_rows_f16_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_fp32_len = 1948; +const uint64_t get_rows_f16_len = 1948; -unsigned char get_rows_q4_0_data[] = { +unsigned char get_rows_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x97,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, +0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, -0x7d,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15488,297 +8352,75 @@ unsigned char get_rows_q4_0_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x56,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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-0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_0_len = 2356; - -unsigned char get_rows_q4_0_f32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x9a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, -0x7d,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 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0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00, 0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00, @@ -15813,258 +8455,41 @@ unsigned char get_rows_q4_0_f32_data[] = { 0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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-0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x50,0x00,0x00,0x00, -0x8c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00, -0x8c,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x85,0x00,0x00,0x00, -0x8e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x8a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00, -0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_0_f32_len = 2404; +0x44,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x16,0x00,0x00,0x00, +0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x86,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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char get_rows_q4_0_fp32_data[] = { +unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -16264,425 +8689,11 @@ unsigned char get_rows_q4_0_fp32_data[] = { 0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_0_fp32_len = 2372; +const uint64_t get_rows_q4_0_len = 2372; -unsigned char get_rows_q4_1_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x94,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_1_f32_len = 2456; - -unsigned char get_rows_q4_1_f32_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -16690,7 +8701,7 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x7c,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -16714,91 +8725,92 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x5a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x96,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -17093,503 +9098,11 @@ unsigned char get_rows_q4_1_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_1_fp32_len = 2440; +const uint64_t get_rows_q4_1_len = 2440; -unsigned char get_rows_q5_0_data[] = { +unsigned char get_rows_q4_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 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-0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -17597,7 +9110,7 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -17617,106 +9130,95 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 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0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -17758,80 +9260,54 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5e,0x00,0x00,0x00, -0x5f,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00, +0x5d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 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0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -18074,485 +9550,11 @@ unsigned char get_rows_q5_0_fp32_data[] = { 0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q5_0_fp32_len = 2884; +const uint64_t get_rows_q5_0_len = 2884; -unsigned char get_rows_q5_1_data[] = { +unsigned char get_rows_q5_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xb4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -18560,7 +9562,7 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -18580,101 +9582,106 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -18695,7 +9702,7 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -18716,77 +9723,80 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00, 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-0x2e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, -0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0xb3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q5_1_f32_fp32_len = 2780; +0x52,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00, +0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x20,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x66,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x67,0x00,0x00,0x00, +0x68,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00, +0x6b,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 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get_rows_q8_0_data[] = { +unsigned char get_rows_q5_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00, -0x73,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -19365,247 +10196,81 @@ unsigned char get_rows_q8_0_f32_data[] = { 0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, +0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4d,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5a,0x00,0x00,0x00, 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2296; + +unsigned char get_rows_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x76,0x00,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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matmul_f16_aligned_l_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index b1e0006bb..14fb89e09 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1,6 +1,6 @@ #include "ggml-vulkan.h" -#ifdef VK_RUN_TESTS +#ifdef GGML_VULKAN_RUN_TESTS #include #endif @@ -255,6 +255,7 @@ static size_t vk_staging_offset; static vk_buffer vk_sync_staging; static vk_context * vk_ctx; +static vk_context * vk_transfer_ctx; static bool vk_disable; @@ -264,7 +265,7 @@ size_t vk_output_tensor; #endif static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; #endif GGML_ASSERT(parameter_count > 0); @@ -368,7 +369,7 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s } static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; #endif // Check if gc already contains pipeline before adding it @@ -413,14 +414,14 @@ static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uin } static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; #endif pipeline.descriptor_set_idx = 0; } static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; #endif if (q.cmd_buffers.size() > q.cmd_buffer_idx) { @@ -442,7 +443,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { } static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_submission()" << std::endl; #endif vk_submission s; @@ -453,14 +454,14 @@ static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_sequence_1()" << std::endl; #endif return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; } static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; #endif if (ctx->seqs.empty()) { @@ -536,7 +537,7 @@ static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { } static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_find_queue_family_index()" << std::endl; #endif const uint32_t qfsize = queue_family_props.size(); @@ -578,7 +579,7 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector= vk_gc.tl_semaphores.size()) { @@ -642,7 +643,7 @@ static vk::Event ggml_vk_create_event() { } static void ggml_vk_queue_cleanup(vk_queue& q) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_queue_cleanup()" << std::endl; #endif // Requires command buffers to be done @@ -652,7 +653,7 @@ static void ggml_vk_queue_cleanup(vk_queue& q) { } static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; #endif GGML_ASSERT(size > 0); @@ -743,7 +744,7 @@ static void ggml_vk_destroy_buffer(vk_buffer& buf) { if (buf.size == 0) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; #endif @@ -757,7 +758,7 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { } static void ggml_vk_sync_buffers(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_sync_buffers()" << std::endl; #endif const std::vector mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } }; @@ -773,7 +774,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { } static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_wait_events()" << std::endl; #endif if (events.empty()) { @@ -810,7 +811,7 @@ static bool ggml_vk_build_shader(ggml_type type) { } static void ggml_vk_load_shaders() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_load_shaders()" << std::endl; #endif @@ -849,36 +850,6 @@ static void ggml_vk_load_shaders() { vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - // Build dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - - // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); } else { vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); @@ -901,36 +872,6 @@ static void ggml_vk_load_shaders() { vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - // Build dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_fp32_len, f32_to_f16_fp32_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_fp32_len, dequant_f16_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_fp32_len, dequant_q4_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_fp32_len, dequant_q4_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_fp32_len, dequant_q5_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_fp32_len, dequant_q5_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_fp32_len, dequant_q8_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_fp32_len, dequant_q2_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_fp32_len, dequant_q3_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_fp32_len, dequant_q4_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_fp32_len, dequant_q5_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_fp32_len, dequant_q6_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - - // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_fp32_len, get_rows_f16_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_fp32_len, get_rows_q4_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_fp32_len, get_rows_q4_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_fp32_len, get_rows_q5_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_fp32_len, get_rows_q5_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_fp32_len, get_rows_q8_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_fp32_len, get_rows_f16_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_fp32_len, get_rows_q4_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_fp32_len, get_rows_q4_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_fp32_len, get_rows_q5_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_fp32_len, get_rows_q5_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_fp32_len, get_rows_q8_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); } vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); @@ -945,6 +886,36 @@ static void ggml_vk_load_shaders() { vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + // dequant shaders + vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); + + vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + + // get_rows + vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); @@ -983,7 +954,7 @@ static void ggml_vk_load_shaders() { } void ggml_vk_init() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_init()" << std::endl; #endif static bool initialized = false; @@ -999,17 +970,17 @@ void ggml_vk_init() { vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector layers = { -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE "VK_LAYER_KHRONOS_validation", #endif }; const std::vector extensions = { -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE "VK_EXT_validation_features", #endif }; vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; vk::ValidationFeaturesEXT validation_features = { features_enable, @@ -1120,7 +1091,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions.push_back("VK_KHR_16bit_storage"); -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif @@ -1154,6 +1125,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; vk_fence = vk_device.device.createFence({}); vk_ctx = nullptr; + vk_transfer_ctx = nullptr; vk_disable = false; @@ -1166,7 +1138,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; } static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif switch (type) { @@ -1190,7 +1162,7 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { } static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif switch (type) { @@ -1219,7 +1191,7 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; static vk_buffer ggml_vk_pool_malloc(size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; #endif int best_i = -1; @@ -1253,7 +1225,7 @@ static vk_buffer ggml_vk_pool_malloc(size_t size) { } static void ggml_vk_pool_free(vk_buffer& buffer) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; #endif for (int i = 0; i < MAX_VK_BUFFERS; ++i) { @@ -1286,7 +1258,7 @@ static vk_buffer ggml_vk_create_buffer_temp(size_t size) { } static void * ggml_vk_host_malloc(size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); @@ -1309,7 +1281,7 @@ static void ggml_vk_host_free(void* ptr) { if (ptr == nullptr) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; #endif vk_buffer* buf = nullptr; @@ -1363,7 +1335,7 @@ static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, s const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; #endif std::vector descriptor_buffer_infos; @@ -1398,7 +1370,7 @@ static void ggml_vk_end_submission(vk_submission& s, std::vector w } static void ggml_vk_ctx_end(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl; #endif if (ctx->s == nullptr) { @@ -1410,7 +1382,7 @@ static void ggml_vk_ctx_end(vk_context * ctx) { } static void ggml_vk_ctx_begin(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; #endif if (ctx->s != nullptr) { @@ -1441,7 +1413,7 @@ static void ensure_sync_staging_buffer(size_t size) { } static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; #endif GGML_ASSERT(!ggml_is_contiguous(tensor)); @@ -1548,7 +1520,7 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; #endif // Buffer is already mapped @@ -1582,7 +1554,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "STAGING" << std::endl; #endif @@ -1619,14 +1591,14 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size } static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; #endif return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); } static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; #endif // Buffer is already mapped @@ -1653,14 +1625,14 @@ static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * } static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; #endif ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); } static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; #endif GGML_ASSERT(width > 0); @@ -1693,7 +1665,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "STAGING" << std::endl; #endif @@ -1722,7 +1694,7 @@ static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t o } static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; #endif if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { @@ -1746,7 +1718,7 @@ static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_ } static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; #endif VkBufferCopy bc{ src_offset, dst_offset, size }; @@ -1755,7 +1727,7 @@ static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t } static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; #endif VkBufferCopy bc{ src_offset, dst_offset, size }; @@ -1771,7 +1743,7 @@ static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * } static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; #endif vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); @@ -1785,7 +1757,7 @@ static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, siz } static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; #endif const uint64_t ne0 = src->ne[0]; @@ -1815,7 +1787,7 @@ static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offs } static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; #endif const uint64_t ne0 = dst->ne[0]; @@ -1841,24 +1813,24 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs } static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"; #endif if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " = 4" << std::endl; #endif return 4; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " = 1" << std::endl; #endif return 1; } static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif if (m <= 32 || n <= 32) { @@ -1871,41 +1843,41 @@ static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { } static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; #endif if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; @@ -1914,30 +1886,30 @@ static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, in GGML_ASSERT(false); } - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; } static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif + ggml_vk_sync_buffers(ctx); if (split_k == 1) { - ggml_vk_sync_buffers(ctx); const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); return; @@ -1945,10 +1917,6 @@ static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer GGML_ASSERT(batch_stride_d == m * n); - // Synchronize the two submissions - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.fillBuffer(split_k_buffer.buffer.buffer, 0, split_k_buffer.size, 0); - ggml_vk_sync_buffers(ctx); const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); @@ -1980,7 +1948,7 @@ static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { } static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; #endif @@ -2002,7 +1970,7 @@ static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, } static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2186,7 +2154,7 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co } static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2366,7 +2334,7 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 } static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2455,7 +2423,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor } static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2561,7 +2529,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr } static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { @@ -2774,7 +2742,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * template static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -3095,7 +3063,7 @@ static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor } } -#ifdef VK_RUN_TESTS +#ifdef GGML_VULKAN_RUN_TESTS static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { return; @@ -3129,7 +3097,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 template static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; #endif const size_t x_ne = m * k * batch; @@ -3520,7 +3488,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) } static void ggml_vk_test_transfer(size_t ne, bool pinned) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct @@ -3600,10 +3568,103 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { free(y); } } + +static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; +#endif + const size_t x_sz = sizeof(float) * ne; + const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; + const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); + float * x = (float *) malloc(x_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); + + for (size_t i = 0; i < ne; i++) { + x[i] = rand() / (float)RAND_MAX; + } + + std::vector hist_cur(1 << 4, 0); + + vk_pipeline& p = vk_pipeline_dequant[quant]; + + switch(quant) { + case GGML_TYPE_Q4_0: + ggml_quantize_q4_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_1: + ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_0: + ggml_quantize_q5_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_1: + ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q8_0: + ggml_quantize_q8_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q2_K: + ggml_quantize_q2_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q3_K: + ggml_quantize_q3_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_K: + ggml_quantize_q4_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_K: + ggml_quantize_q5_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q6_K: + ggml_quantize_q6_K(x, qx, ne, ne, hist_cur.data()); + break; + default: + GGML_ASSERT(false); + } + + ggml_vk_pipeline_allocate_descriptor_sets(p, 1); + + ggml_vk_buffer_write(&qx_buf, 0, qx, qx_sz); + + vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + ggml_vk_ctx_begin(ctx); + const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(ctx); + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + auto end = std::chrono::high_resolution_clock::now(); + + double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(&x_buf, 0, x_chk, x_sz_f16); + + double avg_err = 0.0; + for (size_t i = 0; i < ne; i++) { + avg_err += std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + } + + std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err / ne << std::endl; + + ggml_vk_destroy_buffer(x_buf); + ggml_vk_destroy_buffer(qx_buf); + + free(x); + free(qx); + free(x_chk); +} #endif static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl; #endif ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu; @@ -3627,7 +3688,7 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph } void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU @@ -3746,15 +3807,26 @@ void ggml_vk_preallocate_buffers() { if (vk_disable) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; #endif -#if defined(VK_RUN_TESTS) +#if defined(GGML_VULKAN_RUN_TESTS) vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); ggml_vk_test_transfer(8192 * 1000, false); ggml_vk_test_transfer(8192 * 1000, true); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q6_K); + const std::vector vals { 8, 8, 8, 100, 46, 576, @@ -3845,7 +3917,7 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; #endif vk_semaphore_idx = 0; @@ -4068,7 +4140,7 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) return true; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; #endif @@ -4111,7 +4183,7 @@ void ggml_vk_graph_cleanup() { if (vk_disable) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif for (auto& buffer : vk_gc.temp_buffers) { @@ -4150,7 +4222,7 @@ void ggml_vk_graph_cleanup() { } static void ggml_vk_cleanup() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cleanup()" << std::endl; #endif ggml_vk_destroy_buffer(vk_prealloc_x); @@ -4234,7 +4306,7 @@ GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl; #endif ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; @@ -4254,7 +4326,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b } GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -4267,7 +4339,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -4323,7 +4395,7 @@ GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buff } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); @@ -4467,7 +4539,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_t } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -4475,19 +4547,19 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_write_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + ggml_vk_buffer_write_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); UNUSED(backend); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -4495,32 +4567,32 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_read_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + ggml_vk_buffer_read_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); UNUSED(backend); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_copy_async(vk_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + ggml_vk_buffer_copy_async(vk_transfer_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); return true; } @@ -4530,28 +4602,28 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_synchronize()" << std::endl; #endif - if(vk_ctx == nullptr) { + if(vk_transfer_ctx == nullptr) { return; } - ggml_vk_ctx_end(vk_ctx); + ggml_vk_ctx_end(vk_transfer_ctx); - for (auto& cpy : vk_ctx->in_memcpys) { + for (auto& cpy : vk_transfer_ctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(vk_ctx, vk_fence); + ggml_vk_submit(vk_transfer_ctx, vk_fence); VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); vk_device.device.resetFences({ vk_fence }); - for (auto& cpy : vk_ctx->out_memcpys) { + for (auto& cpy : vk_transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - vk_ctx = nullptr; + vk_transfer_ctx = nullptr; UNUSED(backend); } diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 67981a751..4abb0383f 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -157,19 +157,10 @@ struct block_q6_K # Dequant functions shader_f16_dequant_func = """ -#define DEQUANT_FUNC f16vec2 v = f16vec2(data_a[ib + 0], data_a[ib + 1]); -""" -shader_f16_dequant_func_compat = """ #define DEQUANT_FUNC vec2 v = vec2(data_a[ib + 0], data_a[ib + 1]); """ shader_q4_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ -v = (v - 8.0hf)*d; -""" -shader_q4_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const uint vui = uint(data_a[ib].qs[iqs]); \ vec2 v = vec2(vui & 0xF, vui >> 4); \ @@ -177,13 +168,6 @@ v = (v - 8.0f)*d; """ shader_q4_1_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const float16_t m = data_a[ib].m; \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ -v = v*d + m; -""" -shader_q4_1_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ const uint vui = uint(data_a[ib].qs[iqs]); \ @@ -192,14 +176,6 @@ v = v*d + m; """ shader_q5_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ -const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = (v - 16.0hf) * d; -""" -shader_q5_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ @@ -209,14 +185,6 @@ v = (v - 16.0f) * d; """ shader_q5_1_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const float16_t m = data_a[ib].m; \ -const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = v*d + m; -""" -shader_q5_1_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ @@ -226,11 +194,6 @@ v = v*d + m; """ shader_q8_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -f16vec2 v = f16vec2(data_a[ib].qs[iqs], data_a[ib].qs[iqs + 1]); \ -v = v * d; -""" -shader_q8_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \ v = v * d; @@ -2110,7 +2073,7 @@ lock = asyncio.Lock() shader_fnames = [] -async def string_to_spv(name, code, defines, fp16): +async def string_to_spv(name, code, defines, fp16=True): f = NamedTemporaryFile(mode="w", delete=False) f.write(code) f.flush() @@ -2200,64 +2163,6 @@ async def main(): tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - # Build dequant shaders - tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}, fp16)) - - for i in range(0, VK_NUM_TYPES): - stream.clear() - - stream.extend((dequant_head, shader_int8_ext, shader_float_type)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q2_K: - stream.extend((shader_q2_K_defines, dequant_q2_K_body)) - elif i == GGML_TYPE_Q3_K: - stream.extend((shader_q3_K_defines, dequant_q3_K_body)) - elif i == GGML_TYPE_Q4_K: - stream.extend((shader_q4_K_defines, dequant_q4_K_body)) - elif i == GGML_TYPE_Q5_K: - stream.extend((shader_q5_K_defines, dequant_q5_K_body)) - elif i == GGML_TYPE_Q6_K: - stream.extend((shader_q6_K_defines, dequant_q6_K_body)) - else: - continue - - tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}, fp16)) - - # get_rows - for i in range(0, VK_NUM_TYPES): - stream.clear() - stream.extend((generic_head, shader_int8_ext, shader_float_type)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, get_rows_body)) - else: - continue - - tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}, fp16)) - tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # Shaders where precision is needed, so no fp16 version # mul mat vec @@ -2266,17 +2171,17 @@ async def main(): stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_f16_defines, shader_f16_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q2_K: stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body)) elif i == GGML_TYPE_Q3_K: @@ -2290,43 +2195,101 @@ async def main(): else: continue - tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}, fp16)) + tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) - tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) + # Dequant shaders + for i in range(0, VK_NUM_TYPES): + stream.clear() + + stream.extend((dequant_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q2_K: + stream.extend((shader_q2_K_defines, dequant_q2_K_body)) + elif i == GGML_TYPE_Q3_K: + stream.extend((shader_q3_K_defines, dequant_q3_K_body)) + elif i == GGML_TYPE_Q4_K: + stream.extend((shader_q4_K_defines, dequant_q4_K_body)) + elif i == GGML_TYPE_Q5_K: + stream.extend((shader_q5_K_defines, dequant_q5_K_body)) + elif i == GGML_TYPE_Q6_K: + stream.extend((shader_q6_K_defines, dequant_q6_K_body)) + else: + continue + + tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"})) + + tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {})) + + # get_rows + for i in range(0, VK_NUM_TYPES): + stream.clear() + stream.extend((generic_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, get_rows_body)) + else: + continue + + tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"})) + + tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) # Norms - tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}, True)) - tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}, True)) - tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) + tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) await asyncio.gather(*tasks) From 60ecf099eddfe70fec797ef6790572e452054add Mon Sep 17 00:00:00 2001 From: Martin Schwaighofer Date: Sun, 28 Jan 2024 12:59:43 +0100 Subject: [PATCH 223/334] add Vulkan support to Nix flake --- .devops/nix/package.nix | 21 +++++++++++++++++---- flake.nix | 1 + 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index a868a9a61..ad23f7dd7 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -13,18 +13,22 @@ cudaPackages, darwin, rocmPackages, + vulkan-headers, + vulkan-loader, clblast, useBlas ? builtins.all (x: !x) [ useCuda useMetalKit useOpenCL useRocm + useVulkan ], useCuda ? config.cudaSupport, useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, useMpi ? false, # Increases the runtime closure size by ~700M useOpenCL ? false, useRocm ? config.rocmSupport, + useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake }@inputs: @@ -48,7 +52,8 @@ let ++ lib.optionals useMetalKit [ "MetalKit" ] ++ lib.optionals useMpi [ "MPI" ] ++ lib.optionals useOpenCL [ "OpenCL" ] - ++ lib.optionals useRocm [ "ROCm" ]; + ++ lib.optionals useRocm [ "ROCm" ] + ++ lib.optionals useVulkan [ "Vulkan" ]; pnameSuffix = strings.optionalString (suffices != [ ]) @@ -108,6 +113,11 @@ let hipblas rocblas ]; + + vulkanBuildInputs = [ + vulkan-headers + vulkan-loader + ]; in effectiveStdenv.mkDerivation ( @@ -164,7 +174,8 @@ effectiveStdenv.mkDerivation ( ++ optionals useCuda cudaBuildInputs ++ optionals useMpi [ mpi ] ++ optionals useOpenCL [ clblast ] - ++ optionals useRocm rocmBuildInputs; + ++ optionals useRocm rocmBuildInputs + ++ optionals useVulkan vulkanBuildInputs; cmakeFlags = [ @@ -178,6 +189,7 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) (cmakeBool "LLAMA_MPI" useMpi) + (cmakeBool "LLAMA_VULKAN" useVulkan) ] ++ optionals useCuda [ ( @@ -218,6 +230,7 @@ effectiveStdenv.mkDerivation ( useMpi useOpenCL useRocm + useVulkan ; shell = mkShell { @@ -242,11 +255,11 @@ effectiveStdenv.mkDerivation ( # Configurations we don't want even the CI to evaluate. Results in the # "unsupported platform" messages. This is mostly a no-op, because # cudaPackages would've refused to evaluate anyway. - badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + badPlatforms = optionals (useCuda || useOpenCL || useVulkan) lib.platforms.darwin; # Configurations that are known to result in build failures. Can be # overridden by importing Nixpkgs with `allowBroken = true`. - broken = (useMetalKit && !effectiveStdenv.isDarwin); + broken = (useMetalKit && !effectiveStdenv.isDarwin) || (useVulkan && effectiveStdenv.isDarwin); description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; homepage = "https://github.com/ggerganov/llama.cpp/"; diff --git a/flake.nix b/flake.nix index a776ba024..ad2f9b295 100644 --- a/flake.nix +++ b/flake.nix @@ -157,6 +157,7 @@ mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; + vulkan = config.packages.default.override { useVulkan = true; }; } // lib.optionalAttrs (system == "x86_64-linux") { rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; From 3cc5ed353c07201d8d5b98b0a4713ab633da6d04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 3 Feb 2024 20:14:59 +0100 Subject: [PATCH 224/334] make: fix nvcc optimization flags for host code (#5309) --- Makefile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index a55d15888..40b16e0ea 100644 --- a/Makefile +++ b/Makefile @@ -109,6 +109,7 @@ MK_NVCCFLAGS += -O3 else MK_CFLAGS += -O3 MK_CXXFLAGS += -O3 +MK_NVCCFLAGS += -O3 endif # clock_gettime came in POSIX.1b (1993) @@ -365,7 +366,7 @@ ifdef LLAMA_CUBLAS MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o - MK_NVCCFLAGS = -use_fast_math + MK_NVCCFLAGS += -use_fast_math ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT From 3c0d25c4756742ebf15ad44700fabc0700c638bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 3 Feb 2024 20:15:13 +0100 Subject: [PATCH 225/334] make: add nvcc info print (#5310) --- Makefile | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 40b16e0ea..21d5e15ba 100644 --- a/Makefile +++ b/Makefile @@ -553,8 +553,11 @@ $(info I CFLAGS: $(CFLAGS)) $(info I CXXFLAGS: $(CXXFLAGS)) $(info I NVCCFLAGS: $(NVCCFLAGS)) $(info I LDFLAGS: $(LDFLAGS)) -$(info I CC: $(shell $(CC) --version | head -n 1)) -$(info I CXX: $(shell $(CXX) --version | head -n 1)) +$(info I CC: $(shell $(CC) --version | head -n 1)) +$(info I CXX: $(shell $(CXX) --version | head -n 1)) +ifdef LLAMA_CUBLAS +$(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) +endif # LLAMA_CUBLAS $(info ) # From 277fad30c60ef3559dc2d01b19d05e659d40a824 Mon Sep 17 00:00:00 2001 From: Welby Seely Date: Sat, 3 Feb 2024 23:18:51 -0500 Subject: [PATCH 226/334] cmake : use set() for LLAMA_WIN_VER (#5298) option() is specifically for booleans. Fixes #5158 --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c156c4824..8c04e4c19 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -79,7 +79,7 @@ if (NOT MSVC) endif() if (WIN32) - option(LLAMA_WIN_VER "llama: Windows Version" 0x602) + set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version") endif() # 3rd party libs From 5ed26e1fc9fab4ce96ecf2d84183fe45bdcab0d4 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 4 Feb 2024 10:39:58 +0200 Subject: [PATCH 227/334] Adding some imatrix tools (#5302) * imatrix: adding --combine and --continue-from * imatrix: be able to start from a specific chunk --------- Co-authored-by: Iwan Kawrakow --- examples/imatrix/imatrix.cpp | 116 +++++++++++++++++++++++++++++++++-- 1 file changed, 112 insertions(+), 4 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index ea06fcdbf..bc9f6fa68 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -36,6 +36,8 @@ public: void set_parameters(StatParams&& params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); void save_imatrix() const; + bool load_imatrix(const char * file_name, bool add); + static bool load_imatrix(const char * file_name, std::unordered_map& imatrix); private: std::unordered_map m_stats; StatParams m_params; @@ -189,6 +191,57 @@ void IMatrixCollector::save_imatrix(const char * fname) const { } } +bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map& imatrix_data) { + std::ifstream in(imatrix_file, std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__,imatrix_file); + return false; + } + int n_entries; + in.read((char*)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file); + return false; + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file); + return false; + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto& e = imatrix_data[std::move(name)]; + int ncall; + in.read((char*)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n",__func__,i); + imatrix_data = {}; + return false; + } + e.values.resize(nval); + in.read((char*)e.values.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n",__func__,i); + imatrix_data = {}; + return false; + } + e.ncall = ncall; + } + return true; +} + +bool IMatrixCollector::load_imatrix(const char * file_name, bool add) { + if (!add) { + m_stats.clear(); + } + return load_imatrix(file_name, m_stats); +} + static IMatrixCollector g_collector; static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { @@ -269,7 +322,7 @@ static void process_logits( } } -static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl) { +static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) { const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); @@ -282,6 +335,15 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + if (from_chunk > 0) { + if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) { + fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk); + return false; + } + fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx); + tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx); + } + if (int(tokens.size()) < 2*n_ctx) { fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx, n_ctx); @@ -402,7 +464,10 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool int main(int argc, char ** argv) { StatParams sparams; + std::string prev_result_file; + std::string combine_files; bool compute_ppl = true; + int from_chunk = 0; std::vector args; args.push_back(argv[0]); int iarg = 1; @@ -423,6 +488,13 @@ int main(int argc, char ** argv) { compute_ppl = false; } else if (arg == "--keep-imatrix") { sparams.keep_every = std::stoi(argv[++iarg]); + } else if (arg == "--continue-from") { + prev_result_file = argv[++iarg]; + } else if (arg == "--combine") { + combine_files = argv[++iarg]; + } + else if (arg == "--from-chunk") { + from_chunk = std::stoi(argv[++iarg]); } else { args.push_back(argv[iarg]); } @@ -436,14 +508,50 @@ int main(int argc, char ** argv) { } } + g_collector.set_parameters(std::move(sparams)); + + if (!combine_files.empty()) { + std::vector files; + size_t pos = 0; + while (true) { + auto new_pos = combine_files.find(',', pos); + if (new_pos != std::string::npos) { + files.emplace_back(combine_files.substr(pos, new_pos - pos)); + pos = new_pos + 1; + } else { + files.emplace_back(combine_files.substr(pos)); + break; + } + } + if (files.size() < 2) { + fprintf(stderr, "You must provide at least two comma separated files to use --combine\n"); + return 1; + } + printf("Combining the following %d files\n", int(files.size())); + for (auto& file : files) { + printf(" %s\n", file.c_str()); + if (!g_collector.load_imatrix(file.c_str(), true)) { + fprintf(stderr, "Failed to load %s\n", file.c_str()); + return 1; + } + } + g_collector.save_imatrix(); + return 0; + } + + if (!prev_result_file.empty()) { + if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) { + fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str()); + return 1; + } + } + gpt_params params; params.n_batch = 512; if (!gpt_params_parse(args.size(), args.data(), params)) { return 1; } - g_collector.set_parameters(std::move(sparams)); - params.logits_all = true; params.n_batch = std::min(params.n_batch, params.n_ctx); @@ -495,7 +603,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s\n", get_system_info(params).c_str()); } - bool OK = compute_imatrix(ctx, params, compute_ppl); + bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); if (!OK) { return 1; } From 9392ebd49ea5ae236a55b47cbf6a13247e8a3b8c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 4 Feb 2024 00:17:24 +0000 Subject: [PATCH 228/334] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/07f6395285469419cf9d078f59b5b49993198c00' (2024-01-11) → 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/b0d36bd0a420ecee3bc916c91886caca87c894e9?dir=lib' (2023-12-30) → 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/ae5c332cbb5827f6b1f02572496b141021de335f' (2024-01-25) → 'github:NixOS/nixpkgs/b8b232ae7b8b144397fdb12d20f592e5e7c1a64d' (2024-01-31) --- flake.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/flake.lock b/flake.lock index 95e41f333..8cfc78273 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1704982712, - "narHash": "sha256-2Ptt+9h8dczgle2Oo6z5ni5rt/uLMG47UFTR1ry/wgg=", + "lastModified": 1706830856, + "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "07f6395285469419cf9d078f59b5b49993198c00", + "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1706191920, - "narHash": "sha256-eLihrZAPZX0R6RyM5fYAWeKVNuQPYjAkCUBr+JNvtdE=", + "lastModified": 1706732774, + "narHash": "sha256-hqJlyJk4MRpcItGYMF+3uHe8HvxNETWvlGtLuVpqLU0=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "ae5c332cbb5827f6b1f02572496b141021de335f", + "rev": "b8b232ae7b8b144397fdb12d20f592e5e7c1a64d", "type": "github" }, "original": { @@ -37,11 +37,11 @@ "nixpkgs-lib": { "locked": { "dir": "lib", - "lastModified": 1703961334, - "narHash": "sha256-M1mV/Cq+pgjk0rt6VxoyyD+O8cOUiai8t9Q6Yyq4noY=", + "lastModified": 1706550542, + "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "b0d36bd0a420ecee3bc916c91886caca87c894e9", + "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652", "type": "github" }, "original": { From 4833ac209da6a427de64f97e8f403dcdc5de6bc3 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 5 Feb 2024 07:08:24 +0000 Subject: [PATCH 229/334] [SYCL] Fix cpy with dims of 3 (#5289) * Fix cpy with dims of 3 * rm asserts --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 194 +++++++++++++++++++++++++++++--------------------- 1 file changed, 114 insertions(+), 80 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 51445b5e7..a03df4c65 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -7693,6 +7693,13 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) { *dsti = *xi; } +static void cpy_1_f16_f32(const char * cxi, char * cdsti) { + const sycl::half *xi = (const sycl::half *)cxi; + float *dsti = (float *)cdsti; + + *dsti = *xi; +} + static void cpy_1_i16_i16(const char * cxi, char * cdsti) { const int16_t *xi = (const int16_t *)cxi; int16_t *dsti = (int16_t *)cdsti; @@ -7709,9 +7716,9 @@ static void cpy_1_i32_i32(const char * cxi, char * cdsti) { template static void cpy_f32_f16(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, - const sycl::nd_item<3> &item_ct1) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2); @@ -7721,15 +7728,17 @@ static void cpy_f32_f16(const char * cx, char * cdst, const int ne, // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = i - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = i - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -7823,9 +7832,9 @@ static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { template static void cpy_f32_q(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, - const sycl::nd_item<3> &item_ct1) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2)) * qk; @@ -7834,15 +7843,17 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne, return; } - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = (i - i02*ne01*ne00 - i01*ne00); - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; cpy_blck(cx + x_offset, cdst + dst_offset); } @@ -10599,10 +10610,12 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10615,8 +10628,8 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10624,10 +10637,12 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10640,8 +10655,8 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10649,10 +10664,12 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK8_0 == 0); @@ -10661,17 +10678,20 @@ static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK4_0 == 0); @@ -10680,17 +10700,20 @@ static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK4_1 == 0); @@ -10699,17 +10722,20 @@ static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10722,8 +10748,8 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10731,10 +10757,12 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10747,8 +10775,8 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10756,10 +10784,12 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10772,8 +10802,8 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -13910,19 +13940,23 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - GGML_ASSERT(src0->ne[3] == 1); + const int64_t ne02 = src0->ne[2]; + const int64_t nb00 = src0->nb[0]; const int64_t nb01 = src0->nb[1]; const int64_t nb02 = src0->nb[2]; + const int64_t nb03 = src0->nb[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; - GGML_ASSERT(src1->ne[3] == 1); + const int64_t ne12 = src1->ne[2]; + const int64_t nb10 = src1->nb[0]; const int64_t nb11 = src1->nb[1]; const int64_t nb12 = src1->nb[2]; + const int64_t nb13 = src1->nb[3]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; @@ -13934,21 +13968,21 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { - ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { - ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); From 5d55b0cd827bb0fcfedfa329a82bd5d6ef2c93ca Mon Sep 17 00:00:00 2001 From: chiranko <96988916+chiranko@users.noreply.github.com> Date: Mon, 5 Feb 2024 15:41:38 +0800 Subject: [PATCH 230/334] readme : add CodeShell models to the supported models list (#5330) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 4a9bdf314..a6fe34629 100644 --- a/README.md +++ b/README.md @@ -107,6 +107,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** From 4be04c8965578edc09194fab769b4b922b8444f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9D=D0=B8=D1=8F=D0=B7=20=D0=93=D0=B0=D1=80=D0=B8=D1=84?= =?UTF-8?q?=D0=B7=D1=8F=D0=BD=D0=BE=D0=B2?= <112617865+garrnizon@users.noreply.github.com> Date: Mon, 5 Feb 2024 10:43:57 +0300 Subject: [PATCH 231/334] scripts : add non-interactive server-llm.sh (#5303) * Update server-llm.sh Add flag --non-interactive that allows run script without asking a permission * Update scripts/server-llm.sh --------- Co-authored-by: Georgi Gerganov --- scripts/server-llm.sh | 73 ++++++++++++++++++++++++------------------- 1 file changed, 40 insertions(+), 33 deletions(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 0b83cdbbc..062b70496 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -47,6 +47,7 @@ if ! command -v make &> /dev/null; then fi # parse arguments +is_interactive=1 port=8888 repo="" wtype="" @@ -66,15 +67,16 @@ verbose=0 function print_usage { printf "Usage:\n" - printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" - printf " --port: port number, default is 8888\n" - printf " --repo: path to a repo containing GGUF model files\n" - printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" - printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" - printf " --gpu-id: gpu id, default is 0\n" - printf " --n-parallel: number of parallel requests, default is 8\n" - printf " --n-kv: KV cache size, default is 4096\n" - printf " --verbose: verbose output\n\n" + printf " ./server-llm.sh [-interactive] [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" + printf " --non-interactive: run without asking a permision to run\n" + printf " --port: port number, default is 8888\n" + printf " --repo: path to a repo containing GGUF model files\n" + printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" + printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" + printf " --gpu-id: gpu id, default is 0\n" + printf " --n-parallel: number of parallel requests, default is 8\n" + printf " --n-kv: KV cache size, default is 4096\n" + printf " --verbose: verbose output\n\n" printf "Example:\n\n" printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' } @@ -82,6 +84,10 @@ function print_usage { while [[ $# -gt 0 ]]; do key="$1" case $key in + --non-interactive) + is_interactive=0 + shift + ;; --port) port="$2" shift @@ -176,31 +182,32 @@ repos=( "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF" "https://huggingface.co/TheBloke/CausalLM-7B-GGUF" ) +if [ $is_interactive -eq 1 ]; then + printf "\n" + printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" + printf " Based on the options that follow, the script might download a model file\n" + printf " from the internet, which can be a few GBs in size. The script will also\n" + printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" + printf "\n" + printf " Upon success, an HTTP server will be started and it will serve the selected\n" + printf " model using llama.cpp for demonstration purposes.\n" + printf "\n" + printf " Please note:\n" + printf "\n" + printf " - All new data will be stored in the current folder\n" + printf " - The server will be listening on all network interfaces\n" + printf " - The server will run with default settings which are not always optimal\n" + printf " - Do not judge the quality of a model based on the results from this script\n" + printf " - Do not use this script to benchmark llama.cpp\n" + printf " - Do not use this script in production\n" + printf " - This script is only for demonstration purposes\n" + printf "\n" + printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" + printf "\n" + printf " Press Enter to continue ...\n\n" -printf "\n" -printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" -printf " Based on the options that follow, the script might download a model file\n" -printf " from the internet, which can be a few GBs in size. The script will also\n" -printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" -printf "\n" -printf " Upon success, an HTTP server will be started and it will serve the selected\n" -printf " model using llama.cpp for demonstration purposes.\n" -printf "\n" -printf " Please note:\n" -printf "\n" -printf " - All new data will be stored in the current folder\n" -printf " - The server will be listening on all network interfaces\n" -printf " - The server will run with default settings which are not always optimal\n" -printf " - Do not judge the quality of a model based on the results from this script\n" -printf " - Do not use this script to benchmark llama.cpp\n" -printf " - Do not use this script in production\n" -printf " - This script is only for demonstration purposes\n" -printf "\n" -printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" -printf "\n" -printf " Press Enter to continue ...\n\n" - -read + read +fi if [[ -z "$repo" ]]; then printf "[+] No repo provided from the command line\n" From 30679d438d5225b3aecf5cec6482cbc9f8f87ba5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 5 Feb 2024 09:48:03 +0200 Subject: [PATCH 232/334] scripts : fix typos, cleanup (#5303) --- scripts/server-llm.sh | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 062b70496..30bbac321 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -14,16 +14,17 @@ # - Might be unstable! # # Usage: -# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] +# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive] # -# --port: port number, default is 8888 -# --repo: path to a repo containing GGUF model files -# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input -# --backend: cpu, cuda, metal, opencl, depends on the OS -# --gpu-id: gpu id, default is 0 -# --n-parallel: number of parallel requests, default is 8 -# --n-kv: KV cache size, default is 4096 -# --verbose: verbose output +# --port: port number, default is 8888 +# --repo: path to a repo containing GGUF model files +# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input +# --backend: cpu, cuda, metal, opencl, depends on the OS +# --gpu-id: gpu id, default is 0 +# --n-parallel: number of parallel requests, default is 8 +# --n-kv: KV cache size, default is 4096 +# --verbose: verbose output +# --non-interactive: run without asking a permission to run # # Example: # @@ -67,8 +68,7 @@ verbose=0 function print_usage { printf "Usage:\n" - printf " ./server-llm.sh [-interactive] [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" - printf " --non-interactive: run without asking a permision to run\n" + printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n" printf " --port: port number, default is 8888\n" printf " --repo: path to a repo containing GGUF model files\n" printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" @@ -77,6 +77,7 @@ function print_usage { printf " --n-parallel: number of parallel requests, default is 8\n" printf " --n-kv: KV cache size, default is 4096\n" printf " --verbose: verbose output\n\n" + printf " --non-interactive: run without asking a permission to run\n" printf "Example:\n\n" printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' } From e6f81775323f6f4e4a30abf022a6028fa86b79ac Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Mon, 5 Feb 2024 17:00:47 +0900 Subject: [PATCH 233/334] common : add dynamic temperature parameters to main example cli (#5295) * added dynamic temp params in main * added help text --- common/common.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 3302caa20..8c1a60583 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -399,6 +399,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } sparams.penalty_present = std::stof(argv[i]); + } else if (arg == "--dynatemp-range") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.dynatemp_range = std::stof(argv[i]); + } else if (arg == "--dynatemp-exp") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.dynatemp_exponent = std::stof(argv[i]); } else if (arg == "--mirostat") { if (++i >= argc) { invalid_param = true; @@ -942,6 +954,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat); printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present); printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq); + printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range); + printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent); printf(" --mirostat N use Mirostat sampling.\n"); printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat); From a2d60c9158435ae9a6f14632f07f1acf7a3becef Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Mon, 5 Feb 2024 08:10:22 +0000 Subject: [PATCH 234/334] server : allow to get default generation settings for completion (#5307) --- examples/server/README.md | 16 +++++++++++++++- examples/server/server.cpp | 7 ++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index fe934dab1..d8e7c313e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -264,7 +264,21 @@ Notice that each `probs` is an array of length `n_probs`. It also accepts all the options of `/completion` except `stream` and `prompt`. -- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- **GET** `/props`: Return current server settings. + +### Result JSON + +```json +{ + "assistant_name": "", + "user_name": "", + "default_generation_settings": { ... } +} +``` + +- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. +- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a9f8cb369..8000fee5c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -334,6 +334,7 @@ struct llama_server_context // slots / clients std::vector slots; + json default_generation_settings_for_props; llama_server_queue queue_tasks; llama_server_response queue_results; @@ -430,6 +431,9 @@ struct llama_server_context slots.push_back(slot); } + default_generation_settings_for_props = get_formated_generation(slots.front()); + default_generation_settings_for_props["seed"] = -1; + batch = llama_batch_init(n_ctx, 0, params.n_parallel); // empty system prompt @@ -2614,7 +2618,8 @@ int main(int argc, char **argv) res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", llama.name_user.c_str() }, - { "assistant_name", llama.name_assistant.c_str() } + { "assistant_name", llama.name_assistant.c_str() }, + { "default_generation_settings", llama.default_generation_settings_for_props } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); From 6fdfa2ecc684000a25a4ad91823bc82a6652b645 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 10:46:06 +0200 Subject: [PATCH 235/334] iq2_xxs: tune quantization (#5320) We get slightly better PPL, and we cut quantization time in nearly half. The trick is to 1st quantize without forcing points onto the E8-lattice. We can then use a narrower search range around the block scale that we got that way. Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 58 ++++++--------------------------------------------- 1 file changed, 6 insertions(+), 52 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 8236385bc..014c0525a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -9048,8 +9048,6 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict int8_t L[32]; int8_t Laux[32]; float waux[32]; - bool is_on_grid[4]; - bool is_on_grid_aux[4]; uint8_t block_signs[4]; uint32_t q2[2*(QK_K/32)]; @@ -9099,10 +9097,11 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict memset(L, 0, 32); continue; } + float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight); + float eff_max = scale*kMaxQ; float best = 0; - float scale = max/(2*kMaxQ-1); - for (int is = -9; is <= 9; ++is) { - float id = (2*kMaxQ-1+is*0.1f)/max; + for (int is = -6; is <= 6; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/eff_max; float this_scale = 1/id; for (int k = 0; k < 4; ++k) { for (int i = 0; i < 8; ++i) { @@ -9112,9 +9111,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict uint16_t u = 0; for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); int grid_index = kmap_q2xs[u]; - is_on_grid_aux[k] = true; if (grid_index < 0) { - is_on_grid_aux[k] = false; const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); } @@ -9128,16 +9125,12 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { scale = sumqx/sumq2; best = scale*sumqx; - for (int i = 0; i < 32; ++i) L[i] = Laux[i]; - for (int k = 0; k < 4; ++k) is_on_grid[k] = is_on_grid_aux[k]; + memcpy(L, Laux, 32); } } - int n_not_ongrid = 0; - for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid; - if (n_not_ongrid > 0 && scale > 0) { + if (scale > 0) { float id = 1/scale; for (int k = 0; k < 4; ++k) { - if (is_on_grid[k]) continue; uint16_t u = 0; for (int i = 0; i < 8; ++i) { int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); @@ -9193,49 +9186,10 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict float d = max_scale/31; y[ibl].d = GGML_FP32_TO_FP16(d); float id = 1/d; - float sumqx = 0, sumq2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { int l = nearest_int(0.5f*(id*scales[ib]-1)); l = MAX(0, MIN(15, l)); q2[2*ib+1] |= ((uint32_t)l << 28); - const float * xb = xbl + 32*ib; - const float * qw = quant_weights + QK_K*ibl + 32*ib; - for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); - const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib); - const float db = d * (1 + 2*l); - uint32_t u = 0; - for (int k = 0; k < 4; ++k) { - const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127); - const float * xk = xb + 8*k; - const float * wk = weight + 8*k; - const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); - float best_mse = 0; int best_index = aux8[k]; - for (int j = 0; j < 8; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - best_mse += wk[j] * diff * diff; - } - for (int idx = 0; idx < 256; ++idx) { - grid = (const uint8_t *)(kgrid_q2xs + idx); - float mse = 0; - for (int j = 0; j < 8; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - mse += wk[j] * diff * diff; - } - if (mse < best_mse) { - best_mse = mse; best_index = idx; - } - } - u |= (best_index << 8*k); - grid = (const uint8_t *)(kgrid_q2xs + best_index); - //grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); - for (int j = 0; j < 8; ++j) { - float q = db * grid[j] * signs[j]; - sumqx += wk[j] * q * xk[j]; - sumq2 += wk[j] * q * q; - } - } - q2[2*ib] = u; - if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); } memcpy(y[ibl].qs, q2, QK_K/4); } From 7e1ae372f36d98fa66b1d778c5862904b4d80c88 Mon Sep 17 00:00:00 2001 From: Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Date: Mon, 5 Feb 2024 17:04:06 +0800 Subject: [PATCH 236/334] py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci --- convert-hf-to-gguf.py | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a6ffd128b..5e343742d 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1416,8 +1416,32 @@ class InternLM2Model(Model): self.gguf_writer.add_add_space_prefix(add_prefix) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + old_eos = special_vocab.special_token_ids["eos"] + if "chat" in os.path.basename(self.dir_model.absolute()): + # For the chat model, we replace the eos with '<|im_end|>'. + special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer) + print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ +in chat mode so that the conversation can end normally.") + special_vocab.add_to_gguf(self.gguf_writer) + def _try_get_sft_eos(self, tokenizer): + unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]') + im_end_list = tokenizer.encode('<|im_end|>') + assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1) + if len(unused_145_list) == 1: + eos_token = unused_145_list[0] + if len(im_end_list) == 1: + eos_token = im_end_list[0] + return eos_token + + def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + def set_gguf_parameters(self): self.gguf_writer.add_name("InternLM2") self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) @@ -1486,8 +1510,9 @@ class InternLM2Model(Model): qkv = data_torch qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] - q = rearrange(q, " o g n i -> o (g n i)").T - k = rearrange(k, " o g n i -> o (g n i)").T + # The model weights of q and k equire additional reshape. + q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads) + k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads) v = rearrange(v, " o g n i -> o (g n i)").T self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) From 89503dcb5f764a5cc7093db1f395f5121876a2cc Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 12:32:27 +0200 Subject: [PATCH 237/334] iq3_xxs: quards for the no-imatrix situation (#5334) Co-authored-by: Iwan Kawrakow --- llama.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index 4787a92fe..65e399adc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9456,8 +9456,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q4_K; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) { - new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; @@ -9496,9 +9496,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } - //else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { - // if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K; - //} + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) { + new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K From abb61944a5f64dec62c893ed0db10790169b672a Mon Sep 17 00:00:00 2001 From: "Dr. Tom Murphy VII Ph.D" <499244+tom7@users.noreply.github.com> Date: Mon, 5 Feb 2024 06:13:57 -0500 Subject: [PATCH 238/334] ggml : avoid duplicating function calls using MIN/MAX macros (#5325) * Avoid duplicating function calls when using MIN/MAX macros. Since these copy "a" and "b" they ask the compiler to evaluate one of them twice. The compiler doesn't have a problem with removing the duplication in something like MAX(0, x + 2), but in some cases we're calling functions, and those calls just happen twice. By explicitly evaluating at the expression we get smaller and faster code without duplicate calls. See ggml_rope_yarn_corr_dims in Compiler Explorer: https://godbolt.org/z/Ee4KMrvKh Code behaves exactly the same. * Update ggml.c --------- Co-authored-by: Georgi Gerganov --- ggml.c | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/ggml.c b/ggml.c index ee994c875..b9ec0c981 100644 --- a/ggml.c +++ b/ggml.c @@ -2470,7 +2470,8 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { size_t max_size = 0; for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) { - max_size = MAX(max_size, ggml_nbytes(tensor)); + size_t bytes = ggml_nbytes(tensor); + max_size = MAX(max_size, bytes); } return max_size; @@ -11887,8 +11888,10 @@ GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims - dims[0] = MAX(0, floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base))); - dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base))); + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)); + dims[0] = MAX(0, start); + dims[1] = MIN(n_dims - 1, end); } static void ggml_compute_forward_rope_f32( From c6b395535a6874d749ef47c33eacd466cb252cd5 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 14:09:47 +0200 Subject: [PATCH 239/334] ggml : make use of ggml-quants.h possible in C++ code (#5338) * Make use of ggml-quants.h possible in C++ code * One cannot possibly be defining static_assert in a C++ compilation --------- Co-authored-by: Iwan Kawrakow --- ggml-impl.h | 2 + ggml-quants.h | 117 +++++++++++++++++++++++++++----------------------- 2 files changed, 65 insertions(+), 54 deletions(-) diff --git a/ggml-impl.h b/ggml-impl.h index 2c58075ac..19df66bce 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -19,6 +19,7 @@ extern "C" { // fall back to the _Static_assert C11 keyword. // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 +#ifndef __cplusplus #ifndef static_assert #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) #define static_assert(cond, msg) _Static_assert(cond, msg) @@ -26,6 +27,7 @@ extern "C" { #define static_assert(cond, msg) struct global_scope_noop_trick #endif #endif +#endif // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) diff --git a/ggml-quants.h b/ggml-quants.h index 5c9f63bd9..bfdf3c997 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -191,70 +191,74 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +#ifdef __cplusplus +extern "C" { +#endif + // Quantization -void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); -void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k); -void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k); -void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k); -void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k); -void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k); +void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k); +void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k); +void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k); +void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k); +void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k); +void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k); -void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k); -void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k); -void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k); -void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); -void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); -void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); -void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k); +void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k); +void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k); +void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k); +void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k); +void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); +void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); -void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_1(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_1(const float * restrict x, void * restrict y, int k); +void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q2_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q3_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); -void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k); +void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization -void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); -void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k); -void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k); -void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k); -void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k); -//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k); +void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k); -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k); -void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k); -void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k); -void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k); -void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); -void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k); -void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k); -void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k); +void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") @@ -276,3 +280,8 @@ void iq2xs_init_impl(int grid_size); void iq2xs_free_impl(int grid_size); void iq3xs_init_impl(int grid_size); void iq3xs_free_impl(int grid_size); + +#ifdef __cplusplus +} +#endif + From 78b00dda6c0d62c34f5371d47718defff6ed2b22 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 Feb 2024 15:55:10 +0100 Subject: [PATCH 240/334] README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov --- README.md | 49 ++++++++++++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index a6fe34629..bb6c49338 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) -Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ +Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ ### Hot topics @@ -58,18 +58,20 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ## Description -The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook +The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide +variety of hardware - locally and in the cloud. -- Plain C/C++ implementation without dependencies -- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks +- Plain C/C++ implementation without any dependencies +- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks - AVX, AVX2 and AVX512 support for x86 architectures -- Mixed F16 / F32 precision -- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support -- CUDA, Metal, OpenCL, SYCL GPU backend support +- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use +- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) +- Vulkan, SYCL, and (partial) OpenCL backend support +- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity -The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022). -Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves -as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library. +Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has +improved significantly thanks to many contributions. It is the main playground for developing new features for the +[ggml](https://github.com/ggerganov/ggml) library. **Supported platforms:** @@ -77,11 +79,14 @@ as the main playground for developing new features for the [ggml](https://github - [X] Linux - [X] Windows (via CMake) - [X] Docker +- [X] FreeBSD **Supported models:** - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 +- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [X] Falcon - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) @@ -95,7 +100,6 @@ as the main playground for developing new features for the [ggml](https://github - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) -- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) @@ -104,15 +108,14 @@ as the main playground for developing new features for the [ggml](https://github - [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) -- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [GPT-2](https://huggingface.co/gpt2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** -- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) -- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava) +- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) +- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) @@ -137,14 +140,22 @@ as the main playground for developing new features for the [ggml](https://github **UI:** +Unless otherwise noted these projects are open-source with permissive licensing: + +- [iohub/collama](https://github.com/iohub/coLLaMA) +- [janhq/jan](https://github.com/janhq/jan) (AGPL) - [nat/openplayground](https://github.com/nat/openplayground) -- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) -- [withcatai/catai](https://github.com/withcatai/catai) -- [semperai/amica](https://github.com/semperai/amica) +- [LMStudio](https://lmstudio.ai/) (proprietary) +- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) +- [ollama/ollama](https://github.com/ollama/ollama) +- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) -- [iohub/collama](https://github.com/iohub/coLLaMA) -- [pythops/tenere](https://github.com/pythops/tenere) +- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [semperai/amica](https://github.com/semperai/amica) +- [withcatai/catai](https://github.com/withcatai/catai) --- From 098f6d737b65134cf220d12b9b706e8cfc5e4610 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 Feb 2024 19:33:00 +0100 Subject: [PATCH 241/334] make: Use ccache for faster compilation (#5318) * make: Use ccache for faster compilation --- CMakeLists.txt | 4 +- Makefile | 169 ++++++++++++++++++++++++++++++++++--------------- 2 files changed, 121 insertions(+), 52 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 8c04e4c19..427015be5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -809,9 +809,9 @@ if (LLAMA_CCACHE) if (LLAMA_CCACHE_FOUND) set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache) set(ENV{CCACHE_SLOPPINESS} time_macros) - message(STATUS "Using ccache") + message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.") else() - message(STATUS "Warning: ccache not found - consider installing it or use LLAMA_CCACHE=OFF") + message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF") endif () endif() diff --git a/Makefile b/Makefile index 21d5e15ba..ba73f0637 100644 --- a/Makefile +++ b/Makefile @@ -112,6 +112,18 @@ MK_CXXFLAGS += -O3 MK_NVCCFLAGS += -O3 endif +ifndef LLAMA_NO_CCACHE +CCACHE := $(shell which ccache) +ifdef CCACHE +export CCACHE_SLOPPINESS = time_macros +$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.) +CC := $(CCACHE) $(CC) +CXX := $(CCACHE) $(CXX) +else +$(info I ccache not found. Consider installing it for faster compilation.) +endif # CCACHE +endif # LLAMA_NO_CCACHE + # clock_gettime came in POSIX.1b (1993) # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional # posix_memalign came in POSIX.1-2001 / SUSv3 @@ -374,9 +386,9 @@ ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG ifdef LLAMA_CUDA_NVCC - NVCC = $(LLAMA_CUDA_NVCC) + NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) else - NVCC = nvcc + NVCC = $(CCACHE) nvcc endif #LLAMA_CUDA_NVCC ifdef CUDA_DOCKER_ARCH MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) @@ -483,7 +495,7 @@ ifdef LLAMA_HIPBLAS ROCM_PATH ?= /opt/rocm GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif - HIPCC ?= $(ROCM_PATH)/bin/hipcc + HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 @@ -607,97 +619,135 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + find examples pocs -type f -name "*.o" -delete # # Examples # +# $< is the first prerequisite, i.e. the source file. +# Explicitly compile this to an object file so that it can be cached with ccache. +# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead. + +# Helper function that replaces .c, .cpp, and .cu file endings with .o: +GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) + main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @echo infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp) + $(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS) baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift @@ -705,7 +755,7 @@ swift: examples/batched.swift endif common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh - @sh scripts/build-info.sh $(CC) > $@.tmp + @sh scripts/build-info.sh "$(CC)" > $@.tmp @if ! cmp -s $@.tmp $@; then \ mv $@.tmp $@; \ else \ @@ -722,7 +772,8 @@ build-info.o: common/build-info.cpp tests: $(TEST_TARGETS) benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) run-benchmark-matmult: benchmark-matmult ./$@ @@ -730,58 +781,76 @@ run-benchmark-matmult: benchmark-matmult .PHONY: run-benchmark-matmult swift vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-c.o: tests/test-c.c llama.h $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) From 906cff55c2848fda091d888a1585915ec0c9ea9e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 6 Feb 2024 07:47:22 +0200 Subject: [PATCH 242/334] py : handle byte tokens in `get_token_type` (#5341) * py : handle byte tokens in `get_token_type` * py : fix empty bytes arg --- convert.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/convert.py b/convert.py index 75c100118..4a2847a27 100755 --- a/convert.py +++ b/convert.py @@ -515,10 +515,14 @@ class HfVocab: # Yield token text, score, and type yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, self.special_ids # Reuse already stored special IDs + token_id, token_text, self.special_ids # Reuse already stored special IDs ) - def get_token_type(self, token_id: int, special_ids: set[int]) -> gguf.TokenType: + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + # Determine token type based on whether it's a special token return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL @@ -530,7 +534,7 @@ class HfVocab: def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: if text in self.specials: - toktype = self.get_token_type(self.specials[text], self.special_ids) + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) score = self.get_token_score(self.specials[text]) else: toktype = gguf.TokenType.USER_DEFINED From 4ffc7a17d4e80c5f3f905139cb570ed9b6934fcb Mon Sep 17 00:00:00 2001 From: Niall Coates <1349685+Niall-@users.noreply.github.com> Date: Tue, 6 Feb 2024 08:16:23 +0000 Subject: [PATCH 243/334] server : various fixes for the prompt field in /completion (#5300) server : fix deadlock when prompt array contains strings and numbers server : removed an unnecessary generation when generating multi-prompts server : removed an unnecessary assert --- examples/server/server.cpp | 34 +++++++++++++++++++++++++++------- 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8000fee5c..fc7e723a1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1163,13 +1163,30 @@ struct llama_server_context task.multitask_id = multitask_id; // when a completion task's prompt array is not a singleton, we split it into multiple requests - if (task.data.count("prompt") && task.data.at("prompt").size() > 1) - { - split_multiprompt_task(task_id, task); - } - // otherwise, it's a single-prompt task, we actually queue it - queue_tasks.post(task); + // if there's numbers in the prompt array it will be treated as an array of tokens + if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { + bool numbers = false; + for (const auto& e : task.data.at("prompt")) { + if (e.is_number()) { + numbers = true; + break; + } + } + + // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers, + // it will completely stall the server. I don't know where the bug for this is. + // + // if there are numbers, it needs to be treated like a single prompt, + // queue_tasks handles a mix of strings and numbers just fine. + if (numbers) { + queue_tasks.post(task); + } else { + split_multiprompt_task(task_id, task); + } + } else { + queue_tasks.post(task); + } } // for multiple images processing @@ -1251,7 +1268,10 @@ struct llama_server_context void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) { int prompt_count = multiprompt_task.data.at("prompt").size(); - assert(prompt_count > 1); + if (prompt_count <= 1) { + send_error(multiprompt_task, "error while handling multiple prompts"); + return; + } // generate all the ID for subtask std::vector subtask_ids(prompt_count); From 31e790322133a4b1d0684527ea446e765e8a96cf Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Tue, 6 Feb 2024 04:20:00 -0500 Subject: [PATCH 244/334] server : add `dynatemp_range` and `dynatemp_exponent` (#5352) * server: added `dynatemp_range` and `dynatemp_exponent` * Update README.md --------- Co-authored-by: Michael Coppola --- examples/server/README.md | 4 ++++ examples/server/server.cpp | 46 +++++++++++++++++++++----------------- 2 files changed, 29 insertions(+), 21 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index d8e7c313e..46d8f85ae 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -137,6 +137,10 @@ node index.js `temperature`: Adjust the randomness of the generated text (default: 0.8). + `dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled). + + `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0). + `top_k`: Limit the next token selection to the K most probable tokens (default: 40). `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fc7e723a1..e48a1da75 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -524,27 +524,29 @@ struct llama_server_context slot->oaicompat_model = ""; } - slot->params.stream = json_value(data, "stream", false); - slot->params.cache_prompt = json_value(data, "cache_prompt", false); - slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); - slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); - slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); - slot->params.seed = json_value(data, "seed", default_params.seed); - slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); - slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot->params.stream = json_value(data, "stream", false); + slot->params.cache_prompt = json_value(data, "cache_prompt", false); + slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); + slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); + slot->params.seed = json_value(data, "seed", default_params.seed); + slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); // infill if (data.count("input_prefix") != 0) @@ -1002,6 +1004,8 @@ struct llama_server_context {"model", params.model_alias}, {"seed", slot.params.seed}, {"temperature", slot.sparams.temp}, + {"dynatemp_range", slot.sparams.dynatemp_range}, + {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, From 8a79c591de9b7ff3242a94f68b7fb5a17ed8c2be Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Tue, 6 Feb 2024 04:20:59 -0500 Subject: [PATCH 245/334] server : include total "num_slots" in props endpoint (#5349) --- examples/server/server.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e48a1da75..d86d7e04a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -432,6 +432,7 @@ struct llama_server_context } default_generation_settings_for_props = get_formated_generation(slots.front()); + default_generation_settings_for_props["num_slots"] = params.n_parallel; default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); From 2c516611f1d0f1e5e9754f8ea1cf97cb1b17bf2c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 Feb 2024 14:44:06 +0100 Subject: [PATCH 246/334] CUDA: mul_mat_vec_q for batch sizes > 1 (#5351) --- ggml-cuda.cu | 240 +++++++++++++++++++++------------------------------ 1 file changed, 98 insertions(+), 142 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 3242a0b4a..95161b3f4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5310,41 +5310,50 @@ template static __global__ void #endif // __CUDA_ARCH__ >= CC_VOLTA } -template -static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) { +template +static __global__ void mul_mat_vec_q( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) { + + const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; + const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row >= nrows) { + if (row >= nrows_x) { return; } - const int blocks_per_row = ncols / qk; + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; const int blocks_per_warp = vdr * WARP_SIZE / qi; // partial sum for each thread - float tmp = 0.0f; + float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f}; const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) { - const int ibx = row*blocks_per_row + i; // x block index + for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) { + const int ibx = row*blocks_per_row_x + i; // x block index const int iby = i * (qk/QK8_1); // y block index that aligns with ibx const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int - tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs); +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { + tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs); + } } // sum up partial sums and write back result #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + for (int j = 0; j < ncols_y; ++j) { + tmp[j] = warp_reduce_sum(tmp[j]); - if (threadIdx.x == 0) { - dst[row] = tmp; + if (threadIdx.x == 0) { + dst[j*nrows_x + row] = tmp[j]; + } } } @@ -6816,121 +6825,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa <<>>(vx, y, dst, ncols, nrows); } -static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK4_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} +template +static void mul_mat_vec_q_cuda( + const void * vx, const void * vy, float * dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { -static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK4_1 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} + GGML_ASSERT(ncols_x % qk == 0); + GGML_ASSERT(ncols_y <= 8); -static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK5_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK5_1 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK8_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} - -static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); + switch (ncols_y) { + case 1: + mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 2: + mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 3: + mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 4: + mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 5: + mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 6: + mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 7: + mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 8: + mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + default: + GGML_ASSERT(false); + // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + } } static void ggml_mul_mat_q4_0_q8_1_cuda( @@ -8578,50 +8522,61 @@ static void ggml_cuda_op_mul_mat_vec_q( const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, cudaStream_t stream) { - GGML_ASSERT(ggml_nrows(src1) == 1); - const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q4_1: - mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_0: - mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_1: - mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q8_0: - mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q2_K: - mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q3_K: - mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q4_K: - mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_K: - mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q6_K: - mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ2_XXS: - mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ2_XS: - mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ3_XXS: - mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; default: GGML_ASSERT(false); @@ -9945,17 +9900,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 #ifdef GGML_CUDA_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else - const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1; + const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); #endif // GGML_CUDA_FORCE_DMMV if (use_mul_mat_vec_q) { - // NOTE: this kernel does not support ggml_nrows(src1) > 1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (use_mul_mat_q) { + if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); } else { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); From 2e9c0bd6b301155ce749e162527fc55e9fb5b832 Mon Sep 17 00:00:00 2001 From: BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:06:48 -0500 Subject: [PATCH 247/334] readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362) --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index bb6c49338..cc87ac797 100644 --- a/README.md +++ b/README.md @@ -105,11 +105,14 @@ improved significantly thanks to many contributions. It is the main playground f - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) -- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) +- [X] [StableLM models](https://huggingface.co/stabilityai) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) +- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) - [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118) +- [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** @@ -119,6 +122,7 @@ improved significantly thanks to many contributions. It is the main playground f - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) +- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) **Bindings:** From f57fadc009cbff741a1961cb7896c47d73978d2c Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 6 Feb 2024 17:28:02 +0200 Subject: [PATCH 248/334] Slight quantization improvement for Q4_K and Q5_K (#5361) * Q4_K: slightly better quantization * Q5_K: slightly better quantization --------- Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 75 +++++++++++++++++++++++---------------------------- 1 file changed, 33 insertions(+), 42 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 014c0525a..101d3e783 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -2381,19 +2381,20 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri uint8_t L[QK_K]; uint8_t Laux[32]; + uint8_t Ls[QK_K/32]; + uint8_t Lm[QK_K/32]; float weights[32]; - float mins[QK_K/32]; - float scales[QK_K/32]; + float sw[QK_K/32]; + float mins[QK_K/32]; + float scales[QK_K/32]; for (int i = 0; i < nb; i++) { float sum_x2 = 0; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; - float sigma2 = sum_x2/QK_K; + float sigma2 = 2*sum_x2/QK_K; float av_x = sqrtf(sigma2); - float max_scale = 0; // as we are deducting the min, scales are always positive - float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { if (quant_weights) { const float * qw = quant_weights + QK_K*i + 32*j; @@ -2401,25 +2402,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri } else { for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); } + float sumw = 0; + for (int l = 0; l < 32; ++l) sumw += weights[l]; + sw[j] = sumw; scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); - //scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false); - float scale = scales[j]; - if (scale > max_scale) { - max_scale = scale; - } - float min = mins[j]; - if (min > max_min) { - max_min = min; - } } - float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; - float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw); + float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw); for (int j = 0; j < QK_K/32; ++j) { - uint8_t ls = nearest_int(inv_scale*scales[j]); - uint8_t lm = nearest_int(inv_min*mins[j]); - ls = MIN(63, ls); - lm = MIN(63, lm); + uint8_t ls = Ls[j]; + uint8_t lm = Lm[j]; if (j < 4) { y[i].scales[j] = ls; y[i].scales[j+4] = lm; @@ -2429,8 +2422,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri y[i].scales[j-0] |= ((lm >> 4) << 6); } } - y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); - y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + y[i].d = GGML_FP32_TO_FP16(d_block); + y[i].dmin = GGML_FP32_TO_FP16(m_block); uint8_t sc, m; for (int j = 0; j < QK_K/32; ++j) { @@ -2688,20 +2681,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri const int nb = n_per_row / QK_K; uint8_t L[QK_K]; - float mins[QK_K/32]; - float scales[QK_K/32]; - float weights[32]; uint8_t Laux[32]; + uint8_t Ls[QK_K/32]; + uint8_t Lm[QK_K/32]; + float mins[QK_K/32]; + float scales[QK_K/32]; + float sw[QK_K/32]; + float weights[32]; for (int i = 0; i < nb; i++) { float sum_x2 = 0; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; - float sigma2 = sum_x2/QK_K; + float sigma2 = 2*sum_x2/QK_K; float av_x = sqrtf(sigma2); - float max_scale = 0; // as we are deducting the min, scales are always positive - float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { if (quant_weights) { const float * qw = quant_weights + QK_K*i + 32*j; @@ -2709,22 +2703,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri } else { for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); } + float sumw = 0; + for (int l = 0; l < 32; ++l) sumw += weights[l]; + sw[j] = sumw; + scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); - float scale = scales[j]; - if (scale > max_scale) { - max_scale = scale; - } - float min = mins[j]; - if (min > max_min) { - max_min = min; - } } - float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; - float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw); + float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw); + for (int j = 0; j < QK_K/32; ++j) { - uint8_t ls = nearest_int(inv_scale*scales[j]); - uint8_t lm = nearest_int(inv_min*mins[j]); + uint8_t ls = Ls[j]; + uint8_t lm = Lm[j]; ls = MIN(63, ls); lm = MIN(63, lm); if (j < 4) { @@ -2736,8 +2727,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri y[i].scales[j-0] |= ((lm >> 4) << 6); } } - y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); - y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + y[i].d = GGML_FP32_TO_FP16(d_block); + y[i].dmin = GGML_FP32_TO_FP16(m_block); uint8_t sc, m; for (int j = 0; j < QK_K/32; ++j) { From b08f22c882a1443e6b97081f3ce718a4d1a741f8 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 6 Feb 2024 19:00:16 +0200 Subject: [PATCH 249/334] Update README.md (#5366) Add some links to quantization related PRs --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index cc87ac797..34f2021f9 100644 --- a/README.md +++ b/README.md @@ -736,9 +736,21 @@ Several quantization methods are supported. They differ in the resulting model d | 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) -- recent k-quants improvements +- recent k-quants improvements and new i-quants - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) + - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) + - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) + - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) + - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) + - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) + - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) + - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) + - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) + - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) + - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) + - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) + - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) ### Perplexity (measuring model quality) From 17c97fb0620448b37516a3f53fea6c482b0a30a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 Feb 2024 18:43:06 +0100 Subject: [PATCH 250/334] CUDA: mul_mat_vec_q max. batch size 8 -> 4 (#5370) --- ggml-cuda.cu | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 95161b3f4..3b828375e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6831,7 +6831,7 @@ static void mul_mat_vec_q_cuda( const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); - GGML_ASSERT(ncols_y <= 8); + GGML_ASSERT(ncols_y <= 4); const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); @@ -6853,22 +6853,22 @@ static void mul_mat_vec_q_cuda( mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); break; - case 5: - mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 6: - mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 7: - mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 8: - mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; + // case 5: + // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 6: + // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 7: + // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 8: + // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; default: GGML_ASSERT(false); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> @@ -9909,7 +9909,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); From 213d1439fadefe182f69c5f7e8dd3b4b6572ebcb Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Tue, 6 Feb 2024 18:08:38 +0000 Subject: [PATCH 251/334] server : remove model.json endpoint (#5371) --- examples/server/completion.js.hpp | 448 +++++++++++++++------------ examples/server/public/completion.js | 3 +- examples/server/server.cpp | 11 - 3 files changed, 244 insertions(+), 218 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index fe5f81228..f5e696e17 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -236,214 +236,250 @@ unsigned char completion_js[] = { 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 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0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, + 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, + 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, + 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; -unsigned int completion_js_len = 5346; +unsigned int completion_js_len = 5782; diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index baaec1d60..ab38a7b40 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => { // Get the model info from the server. This is useful for getting the context window and so on. export const llamaModelInfo = async () => { if (!generation_settings) { - generation_settings = await fetch("/model.json").then(r => r.json()); + const props = await fetch("/props").then(r => r.json()); + generation_settings = props.default_generation_settings; } return generation_settings; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d86d7e04a..9481ce6b1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -990,11 +990,6 @@ struct llama_server_context queue_results.send(res); } - json get_model_props() - { - return get_formated_generation(slots[0]); - } - json get_formated_generation(llama_client_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); @@ -2895,12 +2890,6 @@ int main(int argc, char **argv) } }); - svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) - { - const json data = llama.get_model_props(); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json; charset=utf-8"); }); From f68664ac241a6b5c233d8f1051eef20929b06008 Mon Sep 17 00:00:00 2001 From: Sang-Kil Park Date: Wed, 7 Feb 2024 13:28:00 +0900 Subject: [PATCH 252/334] convert : fix TypeError on GPT-2 vocab.json (#5288) --- convert.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index 4a2847a27..323e8058d 100755 --- a/convert.py +++ b/convert.py @@ -334,9 +334,9 @@ class Params: class BpeVocab: def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) - try: + if isinstance(self.bpe_tokenizer.get('model'), dict): self.vocab = self.bpe_tokenizer["model"]["vocab"] - except KeyError: + else: self.vocab = self.bpe_tokenizer added_tokens: dict[str, int] if fname_added_tokens is not None: From f3e2b4fa3f81a410ecb7dec929c259ef8d8dbb7d Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Wed, 7 Feb 2024 01:15:19 -0500 Subject: [PATCH 253/334] server : update `/props` with "total_slots" value (#5373) * include total "num_slots" in default_generation_settings_for_props * cleanup total_slots return value in /props endpoint * update /props endpoint docs with total_slots * remove num_slots from default_generation_settings_for_props * update /props endpoint section --- examples/server/README.md | 4 +++- examples/server/server.cpp | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 46d8f85ae..1db7cdf21 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -276,13 +276,15 @@ Notice that each `probs` is an array of length `n_probs`. { "assistant_name": "", "user_name": "", - "default_generation_settings": { ... } + "default_generation_settings": { ... }, + "total_slots": 1 } ``` - `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. +- `total_slots` - the total number of slots for process requests (defined by `--parallel` option) - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9481ce6b1..eceda30d0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -432,7 +432,6 @@ struct llama_server_context } default_generation_settings_for_props = get_formated_generation(slots.front()); - default_generation_settings_for_props["num_slots"] = params.n_parallel; default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); @@ -2639,7 +2638,8 @@ int main(int argc, char **argv) json data = { { "user_name", llama.name_user.c_str() }, { "assistant_name", llama.name_assistant.c_str() }, - { "default_generation_settings", llama.default_generation_settings_for_props } + { "default_generation_settings", llama.default_generation_settings_for_props }, + { "total_slots", llama.params.n_parallel } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); From 316c7faf7740fa98ea68f1445f4505810f706b9e Mon Sep 17 00:00:00 2001 From: runfuture Date: Wed, 7 Feb 2024 14:15:56 +0800 Subject: [PATCH 254/334] llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm --- convert-hf-to-gguf.py | 49 ++++++++++ gguf-py/gguf/constants.py | 21 +++++ llama.cpp | 190 +++++++++++++++++++++++++++++++++++++- 3 files changed, 259 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5e343742d..829d68368 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -22,6 +22,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf +from convert import HfVocab + # check for any of the given keys in the dictionary and return the value of the first key found def get_key_opts(d, keys): @@ -205,6 +207,8 @@ class Model: return OrionModel if model_architecture == "InternLM2ForCausalLM": return InternLM2Model + if model_architecture == "MiniCPMForCausalLM": + return MiniCPMModel return Model def _is_model_safetensors(self) -> bool: @@ -258,6 +262,8 @@ class Model: return gguf.MODEL_ARCH.ORION if arch == "InternLM2ForCausalLM": return gguf.MODEL_ARCH.INTERNLM2 + if arch == "MiniCPMForCausalLM": + return gguf.MODEL_ARCH.MINICPM raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -402,6 +408,31 @@ class Model: special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_hf(self): + path = self.dir_model + added_tokens_path = self.dir_model + vocab = HfVocab( + path, added_tokens_path if added_tokens_path.exists() else None + ) + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + class GPTNeoXModel(Model): def set_gguf_parameters(self): @@ -1041,6 +1072,24 @@ class MixtralModel(Model): self._set_vocab_sentencepiece() +class MiniCPMModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + self.gguf_writer.add_name("MiniCPM") + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + + def set_vocab(self): + self._set_vocab_hf() + + class QwenModel(Model): @staticmethod def token_bytes_to_string(b): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index ed8e26f83..1cfd41c0b 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -104,6 +104,7 @@ class MODEL_ARCH(IntEnum): CODESHELL = auto() ORION = auto() INTERNLM2 = auto() + MINICPM = auto() class MODEL_TENSOR(IntEnum): @@ -156,6 +157,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", MODEL_ARCH.INTERNLM2: "internlm2", + MODEL_ARCH.MINICPM: "minicpm", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -464,6 +466,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.MINICPM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], # TODO } diff --git a/llama.cpp b/llama.cpp index 65e399adc..f3c5146d1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -205,6 +205,7 @@ enum llm_arch { LLM_ARCH_CODESHELL, LLM_ARCH_ORION, LLM_ARCH_INTERNLM2, + LLM_ARCH_MINICPM, LLM_ARCH_UNKNOWN, }; @@ -228,6 +229,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, }; enum llm_kv { @@ -690,6 +692,29 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_MINICPM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1390,6 +1415,7 @@ enum e_model { MODEL_UNKNOWN, MODEL_0_5B, MODEL_1B, + MODEL_2B, MODEL_3B, MODEL_4B, MODEL_7B, @@ -2748,6 +2774,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { case MODEL_1B: return "1B"; + case MODEL_2B: return "2B"; case MODEL_3B: return "3B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; @@ -2887,6 +2914,13 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_MINICPM: + { + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_2B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_FALCON: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -3524,13 +3558,16 @@ static bool llm_load_tensors( switch (model.arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: + case LLM_ARCH_MINICPM: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + if (model.arch != LLM_ARCH_MINICPM){ + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } } for (int i = 0; i < n_layer; ++i) { @@ -6781,6 +6818,153 @@ struct llm_build_context { return gf; } + // ref: https://arxiv.org/abs/2203.03466 + // https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738 + // based on the original build_llama() function + struct ggml_cgraph * build_minicpm() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + const int64_t n_embd = hparams.n_embd; + //TODO: if the model varies, these parameters need to be read from the model + const int64_t n_embd_base = 256; + const float scale_embd = 12.0f; + const float scale_depth = 1.4f; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // scale the input embeddings + inpL = ggml_scale(ctx0, inpL, scale_embd); + cb(inpL, "inp_scaled", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + // scale_res - scale the hidden states for residual connection + const float scale_res = scale_depth/sqrtf(float(n_layer)); + cur = ggml_scale(ctx0, cur, scale_res); + cb(cur, "hidden_scaled", -1); + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + // scale the hidden states for residual connection + cur = ggml_scale(ctx0, cur, scale_res); + cb(cur, "hidden_scaled_ffn", -1); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head scaling + const float scale_lmhead = float(n_embd_base)/float(n_embd); + cur = ggml_scale(ctx0, cur, scale_lmhead); + cb(cur, "lmhead_scaling", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.tok_embd, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( @@ -6943,6 +7127,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_internlm2(); } break; + case LLM_ARCH_MINICPM: + { + result = llm.build_minicpm(); + } break; default: GGML_ASSERT(false); } From 9a697d842bc0cfce8268ebd2ba703ffc1c904f98 Mon Sep 17 00:00:00 2001 From: Ben Williams Date: Tue, 6 Feb 2024 22:16:48 -0800 Subject: [PATCH 255/334] readme : update ui list (#5354) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 34f2021f9..672512d18 100644 --- a/README.md +++ b/README.md @@ -149,6 +149,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [iohub/collama](https://github.com/iohub/coLLaMA) - [janhq/jan](https://github.com/janhq/jan) (AGPL) - [nat/openplayground](https://github.com/nat/openplayground) +- [Faraday](https://faraday.dev/) (proprietary) - [LMStudio](https://lmstudio.ai/) (proprietary) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) From ed0bf32290ee5b30ffad5becd99cbecef74aedd7 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 7 Feb 2024 06:21:30 +0000 Subject: [PATCH 256/334] readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md --- README.md | 127 +++++++++++++++-------------------------------------- SHA256SUMS | 40 ----------------- 2 files changed, 36 insertions(+), 131 deletions(-) delete mode 100644 SHA256SUMS diff --git a/README.md b/README.md index 672512d18..0509b0ba1 100644 --- a/README.md +++ b/README.md @@ -33,17 +33,14 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
  • Get the Code
  • Build
  • BLAS Build
    • -
  • Prepare Data & Run
    • +
  • Prepare and Quantize
    • +
  • Run the quantized model
  • Memory/Disk Requirements
  • Quantization
  • Interactive mode
  • Constrained output with grammars
    • -
  • Instruction mode with Alpaca
    • -
  • Using OpenLLaMA
    • -
  • Using GPT4All
    • -
  • Using Pygmalion 7B & Metharme 7B
    • -
  • Obtaining the Facebook LLaMA original model and Stanford Alpaca model data
    • -
  • Verifying the model files
    • +
  • Instruct mode
    • +
  • Obtaining and using the Facebook LLaMA 2 model
  • Seminal papers and background on the models
  • Perplexity (measuring model quality)
  • Android
    • @@ -83,20 +80,16 @@ improved significantly thanks to many contributions. It is the main playground f **Supported models:** +Typically finetunes of the base models below are supported as well. + - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 -- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [X] Falcon -- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) -- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) -- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) -- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy) -- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b) -- [X] [WizardLM](https://github.com/nlpxucan/WizardLM) - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) @@ -166,7 +159,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: Here is a typical run using LLaMA v2 13B on M2 Ultra: -```java +``` $ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e I llama.cpp build info: I UNAME_S: Darwin @@ -250,7 +243,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8 ## Usage -Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model. +Here are the end-to-end binary build and model conversion steps for most supported models. ### Get the Code @@ -635,7 +628,7 @@ Building the program with BLAS support may lead to some performance improvements **Without docker**: - Firstly, you need to make sure you installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) For example, on Ubuntu 22.04 (jammy), use the command below: @@ -648,6 +641,8 @@ Building the program with BLAS support may lead to some performance improvements vulkaninfo ``` + Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. + Then, build llama.cpp using the cmake command below: ```bash @@ -662,34 +657,42 @@ Building the program with BLAS support may lead to some performance improvements # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 ``` -### Prepare Data & Run +### Prepare and Quantize + +To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. ```bash -# obtain the original LLaMA model weights and place them in ./models +# obtain the official LLaMA model weights and place them in ./models ls ./models -65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model +llama-2-7b tokenizer_checklist.chk tokenizer.model # [Optional] for models using BPE tokenizers ls ./models -65B 30B 13B 7B vocab.json + vocab.json +# [Optional] for PyTorch .bin models like Mistral-7B +ls ./models + # install Python dependencies python3 -m pip install -r requirements.txt -# convert the 7B model to ggml FP16 format -python3 convert.py models/7B/ +# convert the model to ggml FP16 format +python3 convert.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/7B/ --vocabtype bpe +python convert.py models/mymodel/ --vocabtype bpe -# quantize the model to 4-bits (using q4_0 method) -./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0 +# quantize the model to 4-bits (using Q4_K_M method) +./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M -# update the gguf filetype to current if older version is unsupported by another application -./quantize ./models/7B/ggml-model-q4_0.gguf ./models/7B/ggml-model-q4_0-v2.gguf COPY +# update the gguf filetype to current version if older version is now unsupported +./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +``` +### Run the quantized model -# run the inference -./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 +```bash +# start inference on a gguf model +./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 ``` When running the larger models, make sure you have enough disk space to store all the intermediate files. @@ -710,7 +713,7 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. -| Model | Original size | Quantized size (4-bit) | +| Model | Original size | Quantized size (Q4_0) | |------:|--------------:|-----------------------:| | 7B | 13 GB | 3.9 GB | | 13B | 24 GB | 7.8 GB | @@ -826,9 +829,9 @@ The `grammars/` folder contains a handful of sample grammars. To write your own, For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. -### Instruction mode with Alpaca +### Instruct mode -1. First, download the `ggml` Alpaca model into the `./models` folder +1. First, download and place the `ggml` model into the `./models` folder 2. Run the `main` tool like this: ``` @@ -854,50 +857,6 @@ cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach. > ``` -### Using [OpenLLaMA](https://github.com/openlm-research/open_llama) - -OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights. - -- Download the [3B](https://huggingface.co/openlm-research/open_llama_3b), [7B](https://huggingface.co/openlm-research/open_llama_7b), or [13B](https://huggingface.co/openlm-research/open_llama_13b) model from Hugging Face. -- Convert the model to ggml FP16 format using `python convert.py ` - -### Using [GPT4All](https://github.com/nomic-ai/gpt4all) - -*Note: these instructions are likely obsoleted by the GGUF update* - -- Obtain the `tokenizer.model` file from LLaMA model and put it to `models` -- Obtain the `added_tokens.json` file from Alpaca model and put it to `models` -- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B` -- It is distributed in the old `ggml` format which is now obsoleted -- You have to convert it to the new format using `convert.py`: - -```bash -python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin -``` - -- You can now use the newly generated `models/gpt4all-7B/ggml-model-q4_0.bin` model in exactly the same way as all other models - -- The newer GPT4All-J model is not yet supported! - -### Using Pygmalion 7B & Metharme 7B - -- Obtain the [LLaMA weights](#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data) -- Obtain the [Pygmalion 7B](https://huggingface.co/PygmalionAI/pygmalion-7b/) or [Metharme 7B](https://huggingface.co/PygmalionAI/metharme-7b) XOR encoded weights -- Convert the LLaMA model with [the latest HF convert script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) -- Merge the XOR files with the converted LLaMA weights by running the [xor_codec](https://huggingface.co/PygmalionAI/pygmalion-7b/blob/main/xor_codec.py) script -- Convert to `ggml` format using the `convert.py` script in this repo: -```bash -python3 convert.py pygmalion-7b/ --outtype q4_1 -``` -> The Pygmalion 7B & Metharme 7B weights are saved in [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) precision. If you wish to convert to `ggml` without quantizating, please specify the `--outtype` as `f32` instead of `f16`. - - -### Obtaining the Facebook LLaMA original model and Stanford Alpaca model data - -- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.** -- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository. -- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data. - ### Obtaining and using the Facebook LLaMA 2 model - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. @@ -909,20 +868,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1 - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) -### Verifying the model files - -Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files. -- The following python script will verify if you have all possible latest files in your self-installed `./models` subdirectory: - -```bash -# run the verification script -./scripts/verify-checksum-models.py -``` - -- On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed `./models` subdirectory: - - On Linux: `sha256sum --ignore-missing -c SHA256SUMS` - - on macOS: `shasum -a 256 --ignore-missing -c SHA256SUMS` - ### Seminal papers and background on the models If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: diff --git a/SHA256SUMS b/SHA256SUMS deleted file mode 100644 index ca4d5a4a5..000000000 --- a/SHA256SUMS +++ /dev/null @@ -1,40 +0,0 @@ -700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth -666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847 models/7B/ggml-model-f16.bin -ec2f2d1f0dfb73b72a4cbac7fa121abbe04c37ab327125a38248f930c0f09ddf models/7B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_1.bin -7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json -745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth -d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth -2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808 models/13B/ggml-model-f16.bin -fad169e6f0f575402cf75945961cb4a8ecd824ba4da6be2af831f320c4348fa5 models/13B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_1.bin -4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f models/13B/params.json -e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/consolidated.00.pth -4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff models/30B/consolidated.01.pth -24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378 models/30B/consolidated.02.pth -1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b models/30B/consolidated.03.pth -7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37 models/30B/ggml-model-f16.bin -d2a441403944819492ec8c2002cc36fa38468149bfb4b7b4c52afc7bd9a7166d models/30B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_1.bin -2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb models/30B/params.json -135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe models/65B/consolidated.00.pth -9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde models/65B/consolidated.01.pth -e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770 models/65B/consolidated.02.pth -73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e models/65B/consolidated.03.pth -882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225 models/65B/consolidated.04.pth -a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/consolidated.05.pth -72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth -d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth -60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0 models/65B/ggml-model-f16.bin -cde053439fa4910ae454407e2717cc46cc2c2b4995c00c93297a2b52e790fa92 models/65B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_1.bin -999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json -9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 models/tokenizer.model From ee1628bdfea8b0079fed0140ac2f00ef1b465b57 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 7 Feb 2024 07:54:50 +0100 Subject: [PATCH 257/334] Basic Vulkan Multi-GPU implementation (#5321) * Initial Vulkan multi-gpu implementation Move most global variables into backend context * Add names to backend device functions * Add further missing cleanup code * Reduce code duplication in tensor split layer assignment * generalize LLAMA_SPLIT_LAYER for all backends, do not expose device count and memory in llama.h * Only do device info print in the beginning and initialize one backend for cpu assist Add missing cleanup code * Rework backend memory management to make sure devices and buffers get properly allocated and freed * Rename cpu assist free function --------- Co-authored-by: slaren --- common/common.cpp | 8 +- ggml-vulkan.cpp | 2639 ++++++++++++++++++++++++++------------------- ggml-vulkan.h | 23 +- ggml.c | 14 +- llama.cpp | 69 +- 5 files changed, 1587 insertions(+), 1166 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 8c1a60583..e0082a823 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -46,6 +46,10 @@ #define GGML_USE_CUBLAS_SYCL #endif +#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN) +#define GGML_USE_CUBLAS_SYCL_VULKAN +#endif + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -660,8 +664,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.tensor_split[i] = 0.0f; } } -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n"); +#ifndef GGML_USE_CUBLAS_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL } else if (arg == "--no-mmap") { params.use_mmap = false; diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 14fb89e09..9e2846ee4 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -15,6 +15,7 @@ #include #include #include +#include #include "ggml.h" #include "ggml-backend-impl.h" @@ -37,6 +38,8 @@ #define GGML_VK_MAX_NODES 8192 +#define MAX_VK_BUFFERS 256 + #ifndef K_QUANTS_PER_ITERATION #define K_QUANTS_PER_ITERATION 1 #else @@ -53,15 +56,68 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA } \ } while (0) -struct vk_buffer { +struct ggml_backend_vk_context; + +struct vk_queue { + uint32_t queue_family_index; + vk::Queue queue; + vk::CommandPool pool; + uint32_t cmd_buffer_idx; + std::vector cmd_buffers; + + vk::PipelineStageFlags stage_flags; +}; + +struct vk_device { + vk::PhysicalDevice physical_device; + vk::PhysicalDeviceProperties properties; + std::string name; + uint64_t max_memory_allocation_size; + bool fp16; + vk::Device device; + uint32_t vendor_id; + vk_queue compute_queue; + vk_queue transfer_queue; + bool single_queue; + uint32_t descriptor_set_mode; + uint32_t subgroup_size; + bool uma; + + ~vk_device() { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "destroy device " << name << std::endl; +#endif + device.destroy(); + } +}; + +struct vk_buffer_struct { vk::Buffer buffer; vk::DeviceMemory device_memory; vk::MemoryPropertyFlags memory_property_flags; void * ptr; size_t size = 0; - uint32_t qf_owner; + + ggml_backend_vk_context * ctx; + + std::shared_ptr device; + + ~vk_buffer_struct() { + if (size == 0) { + return; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl; +#endif + + device->device.freeMemory(device_memory); + device->device.destroyBuffer(buffer); + } }; +typedef std::shared_ptr vk_buffer; +typedef std::weak_ptr vk_buffer_ref; + struct vk_subbuffer { vk_buffer buffer; uint64_t offset; @@ -70,6 +126,7 @@ struct vk_subbuffer { struct vk_pipeline { std::string name; + vk::ShaderModule shader_module; vk::DescriptorSetLayout dsl; std::vector descriptor_pools; std::vector descriptor_sets; @@ -82,16 +139,6 @@ struct vk_pipeline { uint32_t align; }; -struct vk_queue { - uint32_t queue_family_index; - vk::Queue queue; - vk::CommandPool pool; - uint32_t cmd_buffer_idx; - std::vector cmd_buffers; - - vk::PipelineStageFlags stage_flags; -}; - struct vk_semaphore { vk::Semaphore s; uint64_t value; @@ -105,20 +152,6 @@ struct vk_submission { typedef std::vector vk_sequence; -struct vk_device { - vk::PhysicalDevice physical_device; - vk::PhysicalDeviceProperties properties; - uint64_t max_memory_allocation_size; - bool fp16; - vk::Device device; - uint32_t vendor_id; - vk_queue compute_queue; - vk_queue transfer_queue; - uint32_t descriptor_set_mode; - uint32_t subgroup_size; - bool uma; -}; - struct vk_op_push_constants { uint32_t KX; uint32_t KY; @@ -190,13 +223,13 @@ struct ggml_tensor_extra_gpu { size_t ctx_idx; - vk_buffer buffer_gpu; + vk_buffer_ref buffer_gpu; uint64_t offset; void reset() { ready = false; ctx_idx = 0; - buffer_gpu.size = 0; + buffer_gpu.reset(); offset = 0; } }; @@ -210,69 +243,96 @@ struct ggml_vk_garbage_collector { std::vector contexts; }; -typedef void (*ggml_vk_func_t)(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); +struct ggml_backend_vk_context { + std::string name; -vk::Instance vk_instance; -vk_device vk_device; -vk_pipeline vk_pipeline_matmul_f32_l, vk_pipeline_matmul_f32_m, vk_pipeline_matmul_f32_s; -vk_pipeline vk_pipeline_matmul_f32_aligned_l, vk_pipeline_matmul_f32_aligned_m, vk_pipeline_matmul_f32_aligned_s; -vk_pipeline vk_pipeline_matmul_f16_l, vk_pipeline_matmul_f16_m, vk_pipeline_matmul_f16_s; -vk_pipeline vk_pipeline_matmul_f16_aligned_l, vk_pipeline_matmul_f16_aligned_m, vk_pipeline_matmul_f16_aligned_s; -vk_pipeline vk_pipeline_matmul_f16_f32_l, vk_pipeline_matmul_f16_f32_m, vk_pipeline_matmul_f16_f32_s; -vk_pipeline vk_pipeline_matmul_f16_f32_aligned_l, vk_pipeline_matmul_f16_f32_aligned_m, vk_pipeline_matmul_f16_f32_aligned_s; -vk_pipeline vk_pipeline_matmul_split_k_reduce; -vk_pipeline vk_pipeline_dequant[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_mul_mat_vec_p021_f16_f32; -vk_pipeline vk_pipeline_mul_mat_vec_nc_f16_f32; -vk_pipeline vk_pipeline_get_rows[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_get_rows_f32[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_mul_f32; -vk_pipeline vk_pipeline_add_f32; -vk_pipeline vk_pipeline_scale_f32; -vk_pipeline vk_pipeline_sqr_f32; -vk_pipeline vk_pipeline_clamp_f32; -vk_pipeline vk_pipeline_cpy_f32_f32, vk_pipeline_cpy_f32_f16, vk_pipeline_cpy_f16_f16; -vk_pipeline vk_pipeline_norm_f32; -vk_pipeline vk_pipeline_rms_norm_f32; -vk_pipeline vk_pipeline_gelu_f32; -vk_pipeline vk_pipeline_silu_f32; -vk_pipeline vk_pipeline_relu_f32; -vk_pipeline vk_pipeline_diag_mask_inf_f32; -vk_pipeline vk_pipeline_soft_max_f32; -vk_pipeline vk_pipeline_rope_f32, vk_pipeline_rope_f16; -vk_pipeline vk_pipeline_rope_neox_f32, vk_pipeline_rope_neox_f16; + std::weak_ptr device; + vk_pipeline pipeline_matmul_f32_l, pipeline_matmul_f32_m, pipeline_matmul_f32_s; + vk_pipeline pipeline_matmul_f32_aligned_l, pipeline_matmul_f32_aligned_m, pipeline_matmul_f32_aligned_s; + vk_pipeline pipeline_matmul_f16_l, pipeline_matmul_f16_m, pipeline_matmul_f16_s; + vk_pipeline pipeline_matmul_f16_aligned_l, pipeline_matmul_f16_aligned_m, pipeline_matmul_f16_aligned_s; + vk_pipeline pipeline_matmul_f16_f32_l, pipeline_matmul_f16_f32_m, pipeline_matmul_f16_f32_s; + vk_pipeline pipeline_matmul_f16_f32_aligned_l, pipeline_matmul_f16_f32_aligned_m, pipeline_matmul_f16_f32_aligned_s; + vk_pipeline pipeline_matmul_split_k_reduce; + vk_pipeline pipeline_dequant[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; + vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; + vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; + vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_add_f32; + vk_pipeline pipeline_scale_f32; + vk_pipeline pipeline_sqr_f32; + vk_pipeline pipeline_clamp_f32; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_norm_f32; + vk_pipeline pipeline_rms_norm_f32; + vk_pipeline pipeline_gelu_f32; + vk_pipeline pipeline_silu_f32; + vk_pipeline pipeline_relu_f32; + vk_pipeline pipeline_diag_mask_inf_f32; + vk_pipeline pipeline_soft_max_f32; + vk_pipeline pipeline_rope_f32, pipeline_rope_f16; + vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; -static size_t vk_semaphore_idx, vk_event_idx; -static ggml_vk_garbage_collector vk_gc; -static std::vector> vk_pinned_memory; -static size_t vk_prealloc_size_qx, vk_prealloc_size_qy, vk_prealloc_size_x, vk_prealloc_size_y, vk_prealloc_size_split_k; -static vk_buffer vk_prealloc_qx, vk_prealloc_qy, vk_prealloc_x, vk_prealloc_y, vk_prealloc_split_k; -static vk::Fence vk_fence; -static vk_buffer vk_staging; -static size_t vk_staging_size; -static size_t vk_staging_offset; -static vk_buffer vk_sync_staging; + size_t semaphore_idx, event_idx; + ggml_vk_garbage_collector gc; + std::vector> pinned_memory; + size_t prealloc_size_qx, prealloc_size_qy, prealloc_size_x, prealloc_size_y, prealloc_size_split_k; + vk_buffer prealloc_qx, prealloc_qy, prealloc_x, prealloc_y, prealloc_split_k; + vk::Fence fence; + vk_buffer staging; + size_t staging_size; + size_t staging_offset; + vk_buffer sync_staging; -static vk_context * vk_ctx; -static vk_context * vk_transfer_ctx; + vk_buffer buffer_pool[MAX_VK_BUFFERS]; -static bool vk_disable; + vk_context * compute_ctx; + vk_context * transfer_ctx; + + bool disable; + bool initialized; + + size_t idx; +}; + +struct vk_instance { + vk::Instance instance; + + std::vector device_indices; + + std::shared_ptr devices[GGML_VK_MAX_DEVICES]; + ggml_backend_t backends[GGML_VK_MAX_DEVICES]; + ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES]; + ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES]; + bool initialized[GGML_VK_MAX_DEVICES]; +}; #ifdef GGML_VULKAN_CHECK_RESULTS -size_t vk_skip_checks; -size_t vk_output_tensor; +static size_t vk_skip_checks; +static size_t vk_output_tensor; + +static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name); +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor); +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor); #endif -static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { +typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +static bool vk_instance_initialized = false; +static vk_instance vk_instance; + +GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); + +static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; #endif GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - vk_pipeline pipeline; - pipeline.name = name; pipeline.parameter_count = parameter_count; pipeline.push_constant_size = push_constant_size; @@ -280,7 +340,7 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s pipeline.align = align; vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); - vk::ShaderModule shader_module = vk_device.device.createShaderModule(shader_module_create_info); + pipeline.shader_module = ctx->device.lock()->device.createShaderModule(shader_module_create_info); std::vector dsl_binding; std::vector dsl_binding_flags; @@ -301,17 +361,17 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s {}, dsl_binding); descriptor_set_layout_create_info.setPNext(&dslbfci); - pipeline.dsl = vk_device.device.createDescriptorSetLayout(descriptor_set_layout_create_info); + pipeline.dsl = ctx->device.lock()->device.createDescriptorSetLayout(descriptor_set_layout_create_info); // Check if device supports multiple descriptors per pool - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { const uint32_t alloc_count = 2; // Try allocating multiple sets from one pool // This fails on AMD for some reason, so add a fall back to allocating one pool per set vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); - vk::DescriptorPool pool = vk_device.device.createDescriptorPool(descriptor_pool_create_info); + vk::DescriptorPool pool = ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { @@ -319,24 +379,24 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s } try { vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); } catch(vk::OutOfPoolMemoryError const&) { - vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; + ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; } - vk_device.device.destroyDescriptorPool(pool); + ctx->device.lock()->device.destroyDescriptorPool(pool); } - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); - pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); } pipeline.descriptor_set_idx = 0; vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr); - pipeline.layout = vk_device.device.createPipelineLayout(pipeline_layout_create_info); + pipeline.layout = ctx->device.lock()->device.createPipelineLayout(pipeline_layout_create_info); std::vector specialization_entries(specialization_constants.size()); @@ -356,41 +416,45 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( vk::PipelineShaderStageCreateFlags(), vk::ShaderStageFlagBits::eCompute, - shader_module, + pipeline.shader_module, entrypoint.c_str(), &specialization_info); vk::ComputePipelineCreateInfo compute_pipeline_create_info( vk::PipelineCreateFlags(), pipeline_shader_create_info, pipeline.layout); - pipeline.pipeline = vk_device.device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + pipeline.pipeline = ctx->device.lock()->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - return pipeline; + ctx->gc.pipelines.push_back(&pipeline); } -static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { +static void ggml_vk_destroy_pipeline(ggml_backend_vk_context * ctx, vk_pipeline * pipeline) { + for (auto& pool : pipeline->descriptor_pools) { + ctx->device.lock()->device.destroyDescriptorPool(pool); + } + pipeline->descriptor_pools.clear(); + pipeline->descriptor_sets.clear(); + pipeline->descriptor_set_idx = 0; + + ctx->device.lock()->device.destroyDescriptorSetLayout(pipeline->dsl); + + ctx->device.lock()->device.destroyPipelineLayout(pipeline->layout); + + ctx->device.lock()->device.destroyShaderModule(pipeline->shader_module); + + ctx->device.lock()->device.destroyPipeline(pipeline->pipeline); +} + +static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; + std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; #endif - // Check if gc already contains pipeline before adding it - bool gc_found = false; - for (auto * pl : vk_gc.pipelines) { - if (&pipeline == pl) { - gc_found = true; - break; - } - } - - if (!gc_found) { - vk_gc.pipelines.push_back(&pipeline); - } - if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) { // Enough descriptors are available return; } - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size(); std::vector layouts(alloc_count); @@ -398,29 +462,29 @@ static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uin layouts[i] = pipeline.dsl; } vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data()); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end()); } else { for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) { vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); - pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); pipeline.descriptor_sets.push_back(sets[0]); } } } -static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { +static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; + std::cerr << "ggml_pipeline_cleanup(" << pipeline.name << ")" << std::endl; #endif pipeline.descriptor_set_idx = 0; } -static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { +static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; #endif @@ -433,7 +497,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { q.pool, vk::CommandBufferLevel::ePrimary, 1); - const std::vector cmd_buffers = vk_device.device.allocateCommandBuffers(command_buffer_alloc_info); + const std::vector cmd_buffers = ctx->device.lock()->device.allocateCommandBuffers(command_buffer_alloc_info); auto buf = cmd_buffers.front(); q.cmd_buffers.push_back(buf); @@ -442,24 +506,17 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { return buf; } -static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { +static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_submission()" << std::endl; #endif vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(q); + s.buffer = ggml_vk_create_cmd_buffer(ctx, q); s.wait_semaphores = std::move(wait_semaphores); s.signal_semaphores = std::move(signal_semaphores); return s; } -static vk_sequence ggml_vk_create_sequence_1(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_sequence_1()" << std::endl; -#endif - return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; -} - static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; @@ -578,89 +635,89 @@ static uint32_t ggml_vk_find_queue_family_index(std::vectordevice.lock()->device.createCommandPool(command_pool_create_info_compute); q.cmd_buffer_idx = 0; - q.queue = vk_device.device.getQueue(queue_family_index, queue_index); + q.queue = ctx->device.lock()->device.getQueue(queue_family_index, queue_index); q.stage_flags = stage_flags; - - return q; } -static vk_context * ggml_vk_create_context(vk_queue& q) { +static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_context()" << std::endl; #endif - vk_gc.contexts.emplace_back(); - vk_context * result = &vk_gc.contexts[vk_gc.contexts.size() - 1]; + ctx->gc.contexts.emplace_back(); + vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1]; memset((void *) result, 0, sizeof(vk_context)); - result->idx = vk_gc.contexts.size() - 1; + result->idx = ctx->gc.contexts.size() - 1; result->q = &q; return result; } -static vk_semaphore * ggml_vk_create_binary_semaphore() { +static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; #endif vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); - vk_gc.semaphores.push_back({ semaphore, 0 }); - return &vk_gc.semaphores[vk_gc.semaphores.size() - 1]; + vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + ctx->gc.semaphores.push_back({ semaphore, 0 }); + return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; } -static vk_semaphore * ggml_vk_create_timeline_semaphore() { +static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; #endif - if (vk_semaphore_idx >= vk_gc.tl_semaphores.size()) { + if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) { vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); - vk_gc.tl_semaphores.push_back({ semaphore, 0 }); + vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); } - return &vk_gc.tl_semaphores[vk_semaphore_idx++]; + return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; } -static vk::Event ggml_vk_create_event() { - if (vk_event_idx >= vk_gc.events.size()) { - vk_gc.events.push_back(vk_device.device.createEvent({})); +static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { + if (ctx->event_idx >= ctx->gc.events.size()) { + ctx->gc.events.push_back(ctx->device.lock()->device.createEvent({})); } - return vk_gc.events[vk_event_idx++]; + return ctx->gc.events[ctx->event_idx++]; } -static void ggml_vk_queue_cleanup(vk_queue& q) { +static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_queue_cleanup()" << std::endl; #endif // Requires command buffers to be done - vk_device.device.resetCommandPool(q.pool); + ctx->device.lock()->device.resetCommandPool(q.pool); q.cmd_buffer_idx = 0; } -static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; #endif - GGML_ASSERT(size > 0); + vk_buffer buf = std::make_shared(); - vk_buffer buf; + if (size == 0) { + buf->size = 0; + return buf; + } - buf.size = size; + buf->size = size; vk::BufferCreateInfo buffer_create_info{ vk::BufferCreateFlags(), size, @@ -670,11 +727,11 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ nullptr, }; - buf.buffer = vk_device.device.createBuffer(buffer_create_info); + buf->buffer = ctx->device.lock()->device.createBuffer(buffer_create_info); - vk::MemoryRequirements mem_req = vk_device.device.getBufferMemoryRequirements(buf.buffer); + vk::MemoryRequirements mem_req = ctx->device.lock()->device.getBufferMemoryRequirements(buf->buffer); - vk::PhysicalDeviceMemoryProperties mem_props = vk_device.physical_device.getMemoryProperties(); + vk::PhysicalDeviceMemoryProperties mem_props = ctx->device.lock()->physical_device.getMemoryProperties(); uint32_t memory_type_index = UINT32_MAX; @@ -691,30 +748,36 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ } try { - buf.device_memory = vk_device.device.allocateMemory({ mem_req.size, memory_type_index }); + buf->device_memory = ctx->device.lock()->device.allocateMemory({ mem_req.size, memory_type_index }); } catch (const vk::SystemError& e) { // Out of Host/Device memory, clean up buffer - vk_device.device.destroyBuffer(buf.buffer); - buf.size = 0; + ctx->device.lock()->device.destroyBuffer(buf->buffer); + buf->size = 0; throw e; } - buf.memory_property_flags = req_flags; - buf.ptr = nullptr; + buf->memory_property_flags = req_flags; + buf->ptr = nullptr; if (req_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - buf.ptr = vk_device.device.mapMemory(buf.device_memory, 0, VK_WHOLE_SIZE); + buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); } - vk_device.device.bindBufferMemory(buf.buffer, buf.device_memory, 0); + ctx->device.lock()->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); - buf.qf_owner = VK_QUEUE_FAMILY_IGNORED; + buf->ctx = ctx; + + buf->device = ctx->device.lock(); + +#ifdef GGML_VULKAN_DEBUG + std::cerr << "Created buffer " << buf->buffer << std::endl; +#endif return buf; } -static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { try { - return ggml_vk_create_buffer(size, req_flags); + return ggml_vk_create_buffer(ctx, size, req_flags); } catch (const vk::SystemError& e) { std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -722,14 +785,14 @@ static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlag } } -static vk_buffer ggml_vk_create_buffer_device(size_t size) { +static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) { vk_buffer buf; try { - buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } catch (const vk::SystemError& e) { - if (vk_device.uma) { + if (ctx->device.lock()->uma) { // Fall back to host memory type - buf = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + buf = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -741,16 +804,7 @@ static vk_buffer ggml_vk_create_buffer_device(size_t size) { } static void ggml_vk_destroy_buffer(vk_buffer& buf) { - if (buf.size == 0) { - return; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; -#endif - - buf.size = 0; - vk_device.device.freeMemory(buf.device_memory); - vk_device.device.destroyBuffer(buf.buffer); + buf.reset(); } static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { @@ -773,7 +827,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { ); } -static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { +static void ggml_vk_wait_events(vk_context * ctx, std::vector&& events) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_wait_events()" << std::endl; #endif @@ -781,10 +835,10 @@ static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vectors->buffer.waitEvents( events, - src_stages, - dst_stages, + ctx->q->stage_flags, + ctx->q->stage_flags, {}, {}, {} @@ -810,15 +864,15 @@ static bool ggml_vk_build_shader(ggml_type type) { } } -static void ggml_vk_load_shaders() { +static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_load_shaders()" << std::endl; + std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl; #endif // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; - std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + std::initializer_list warptile_l = { 128, 128, 128, 16, ctx->device.lock()->subgroup_size * 2, 64, 2, 4, 4, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_m = { 128, 64, 64, 16, ctx->device.lock()->subgroup_size, 32, 2, 4, 2, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_s = { ctx->device.lock()->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, ctx->device.lock()->subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -828,145 +882,208 @@ static void ggml_vk_load_shaders() { uint32_t m_align = 64; uint32_t s_align = 32; - if (vk_device.fp16) { - vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + if (ctx->device.lock()->fp16) { + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); } else { - vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); } - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); // dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), { 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F16 ], "dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); - vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - vk_pipeline_mul_mat_vec_nc_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - vk_pipeline_norm_f32 = ggml_vk_create_pipeline("norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_rms_norm_f32 = ggml_vk_create_pipeline("rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_cpy_f32_f32 = ggml_vk_create_pipeline("cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_cpy_f32_f16 = ggml_vk_create_pipeline("cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_cpy_f16_f16 = ggml_vk_create_pipeline("cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_add_f32 = ggml_vk_create_pipeline("add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_mul_f32 = ggml_vk_create_pipeline("mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_scale_f32 = ggml_vk_create_pipeline("scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_sqr_f32 = ggml_vk_create_pipeline("sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_clamp_f32 = ggml_vk_create_pipeline("clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_gelu_f32 = ggml_vk_create_pipeline("gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_silu_f32 = ggml_vk_create_pipeline("silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_relu_f32 = ggml_vk_create_pipeline("relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_diag_mask_inf_f32 = ggml_vk_create_pipeline("diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_soft_max_f32 = ggml_vk_create_pipeline("soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_rope_f32 = ggml_vk_create_pipeline("rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_f16 = ggml_vk_create_pipeline("rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_neox_f32 = ggml_vk_create_pipeline("rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_neox_f16 = ggml_vk_create_pipeline("rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); } -void ggml_vk_init() { +static void ggml_vk_print_gpu_info(size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_init()" << std::endl; + std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl; #endif - static bool initialized = false; + GGML_ASSERT(vk_instance.initialized); - if (initialized) { - return; + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); } - initialized = true; + vk::PhysicalDevice physical_device = devices[dev_num]; + std::vector ext_props = physical_device.enumerateDeviceExtensionProperties(); - const char* GGML_VULKAN_DEVICE = getenv("GGML_VULKAN_DEVICE"); - int dev_num = (GGML_VULKAN_DEVICE == NULL ? 0 : atoi(GGML_VULKAN_DEVICE)); + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + physical_device.getProperties2(&props2); + + const size_t subgroup_size = subgroup_props.subgroupSize; + const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + + bool fp16_storage = false; + bool fp16_compute = false; + + for (auto properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } + } + + const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + + bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + + vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures(); + + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + vkGetPhysicalDeviceFeatures2(physical_device, &device_features2); + + fp16 = fp16 && vk12_features.shaderFloat16; + + std::string device_name = props2.properties.deviceName.data(); + std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; + + if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { + std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; + } +} + +void ggml_vk_instance_init() { + if (vk_instance_initialized) { + return; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_instance_init()" << std::endl; +#endif vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector layers = { @@ -989,12 +1106,55 @@ void ggml_vk_init() { validation_features.setPNext(nullptr); instance_create_info.setPNext(&validation_features); -std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; + std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; #endif - vk_instance = vk::createInstance(instance_create_info); + vk_instance.instance = vk::createInstance(instance_create_info); - vk_device.physical_device = vk_instance.enumeratePhysicalDevices()[dev_num]; - std::vector ext_props = vk_device.physical_device.enumerateDeviceExtensionProperties(); + memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES); + + size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size(); + + // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan + char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES"); + if (devices_env != nullptr) { + std::string devices(devices_env); + std::replace(devices.begin(), devices.end(), ',', ' '); + + std::stringstream ss(devices); + size_t tmp; + while (ss >> tmp) { + if(tmp >= num_available_devices) { + std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl; + throw std::runtime_error("Invalid Vulkan device index"); + } + vk_instance.device_indices.push_back(tmp); + } + } else { + vk_instance.device_indices.push_back(0); + } + + vk_instance_initialized = true; +} + +void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl; +#endif + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); + } + + vk_instance.devices[idx] = std::make_shared(); + ctx->device = vk_instance.devices[idx]; + ctx->device.lock()->physical_device = devices[dev_num]; + std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); bool maintenance4_support = false; @@ -1014,18 +1174,18 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; if (maintenance4_support) { subgroup_props.pNext = &props4; } - vk_device.physical_device.getProperties2(&props2); - vk_device.properties = props2.properties; + ctx->device.lock()->physical_device.getProperties2(&props2); + ctx->device.lock()->properties = props2.properties; if (maintenance4_support) { - vk_device.max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + ctx->device.lock()->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); } else { - vk_device.max_memory_allocation_size = props3.maxMemoryAllocationSize; + ctx->device.lock()->max_memory_allocation_size = props3.maxMemoryAllocationSize; } - vk_device.vendor_id = vk_device.properties.vendorID; - vk_device.subgroup_size = subgroup_props.subgroupSize; - vk_device.uma = vk_device.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + ctx->device.lock()->vendor_id = ctx->device.lock()->properties.vendorID; + ctx->device.lock()->subgroup_size = subgroup_props.subgroupSize; + ctx->device.lock()->uma = ctx->device.lock()->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; bool fp16_storage = false; bool fp16_compute = false; @@ -1039,31 +1199,31 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; } const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != NULL; + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; - vk_device.fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + ctx->device.lock()->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - std::vector queue_family_props = vk_device.physical_device.getQueueFamilyProperties(); + std::vector queue_family_props = ctx->device.lock()->physical_device.getQueueFamilyProperties(); // Try to find a non-graphics compute queue and transfer-focused queues const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); const float priorities[] = { 1.0f, 1.0f }; - const bool single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + ctx->device.lock()->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; std::vector device_queue_create_infos; if (compute_queue_family_index != transfer_queue_family_index) { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!single_queue) { + } else if(!ctx->device.lock()->single_queue) { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); } else { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); } vk::DeviceCreateInfo device_create_info; std::vector device_extensions; - vk::PhysicalDeviceFeatures device_features = vk_device.physical_device.getFeatures(); + vk::PhysicalDeviceFeatures device_features = ctx->device.lock()->physical_device.getFeatures(); VkPhysicalDeviceFeatures2 device_features2; device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; @@ -1080,13 +1240,13 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; vk11_features.pNext = &vk12_features; - vkGetPhysicalDeviceFeatures2(vk_device.physical_device, &device_features2); + vkGetPhysicalDeviceFeatures2(ctx->device.lock()->physical_device, &device_features2); - vk_device.fp16 = vk_device.fp16 && vk12_features.shaderFloat16; + ctx->device.lock()->fp16 = ctx->device.lock()->fp16 && vk12_features.shaderFloat16; if (!vk11_features.storageBuffer16BitAccess) { - std::cerr << "ggml_vulkan: device does not support 16-bit storage" << std::endl; - GGML_ASSERT(false); + std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; + throw std::runtime_error("Unsupported device"); } device_extensions.push_back("VK_KHR_16bit_storage"); @@ -1095,10 +1255,11 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif - if (vk_device.fp16) { + if (ctx->device.lock()->fp16) { device_extensions.push_back("VK_KHR_shader_float16_int8"); } - std::cerr << "ggml_vulkan: Using " << vk_device.properties.deviceName << " | uma: " << vk_device.uma << " | fp16: " << vk_device.fp16 << " | warp size: " << vk_device.subgroup_size << std::endl; + ctx->device.lock()->name = ctx->device.lock()->properties.deviceName.data(); + device_create_info = { vk::DeviceCreateFlags(), device_queue_create_infos, @@ -1106,28 +1267,32 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions }; device_create_info.setPNext(&device_features2); - vk_device.device = vk_device.physical_device.createDevice(device_create_info); + ctx->device.lock()->device = ctx->device.lock()->physical_device.createDevice(device_create_info); - vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; + ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; // Shaders - ggml_vk_load_shaders(); + ggml_vk_load_shaders(ctx); // Queues - vk_device.compute_queue = ggml_vk_create_queue(compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - if (!single_queue) { + ggml_vk_create_queue(ctx, ctx->device.lock()->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); + if (!ctx->device.lock()->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - vk_device.transfer_queue = ggml_vk_create_queue(transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + ggml_vk_create_queue(ctx, ctx->device.lock()->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); } else { - vk_device.transfer_queue = vk_device.compute_queue; + // TODO: Use pointer or reference to avoid copy + ctx->device.lock()->transfer_queue = ctx->device.lock()->compute_queue; } - vk_fence = vk_device.device.createFence({}); + ctx->fence = ctx->device.lock()->device.createFence({}); - vk_ctx = nullptr; - vk_transfer_ctx = nullptr; + ctx->compute_ctx = nullptr; + ctx->transfer_ctx = nullptr; - vk_disable = false; + ctx->disable = false; + ctx->initialized = true; + + ctx->idx = idx; #ifdef GGML_VULKAN_CHECK_RESULTS const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); @@ -1137,7 +1302,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; #endif } -static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { +static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif @@ -1158,10 +1323,10 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { return nullptr; } - return &vk_pipeline_dequant[type]; + return &ctx->pipeline_dequant[type]; } -static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { +static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif @@ -1182,15 +1347,10 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { return nullptr; } - return &vk_pipeline_dequant_mul_mat_vec_f32[type]; + return &ctx->pipeline_dequant_mul_mat_vec_f32[type]; } -// buffer pool for vulkan -#define MAX_VK_BUFFERS 256 - -static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; - -static vk_buffer ggml_vk_pool_malloc(size_t size) { +static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; #endif @@ -1199,98 +1359,95 @@ static vk_buffer ggml_vk_pool_malloc(size_t size) { int worst_i = -1; size_t worst_size = 0; //largest unused buffer seen so far for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer &b = g_vk_buffer_pool[i]; - if (b.size > 0 && b.size >= size && b.size < best_size) { + vk_buffer &b = ctx->buffer_pool[i]; + if (b != nullptr && b->size >= size && b->size < best_size) { best_i = i; - best_size = b.size; + best_size = b->size; } - if (b.size > 0 && b.size > worst_size) { + if (b != nullptr && b->size > worst_size) { worst_i = i; - worst_size = b.size; + worst_size = b->size; } } if(best_i != -1) { //found the smallest buffer that fits our needs - vk_buffer b = g_vk_buffer_pool[best_i]; - g_vk_buffer_pool[best_i].size = 0; + vk_buffer b = ctx->buffer_pool[best_i]; + ctx->buffer_pool[best_i].reset(); return b; } if(worst_i != -1) { //no buffer that fits our needs, resize largest one to save memory - vk_buffer& b = g_vk_buffer_pool[worst_i]; + vk_buffer& b = ctx->buffer_pool[worst_i]; ggml_vk_destroy_buffer(b); } - return ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } -static void ggml_vk_pool_free(vk_buffer& buffer) { +static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; + std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl; #endif for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer& b = g_vk_buffer_pool[i]; - if (b.size == 0) { + vk_buffer& b = ctx->buffer_pool[i]; + if (b == nullptr) { b = buffer; - // Set owning queue family index to ignored to avoid synchronization on next use - b.qf_owner = VK_QUEUE_FAMILY_IGNORED; return; } } - fprintf(stderr, "WARNING: vk buffer pool full, increase MAX_VK_BUFFERS\n"); + std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl; ggml_vk_destroy_buffer(buffer); } // Returns an available temporary buffer that may only be used temporarily, it will be reused -static vk_buffer ggml_vk_create_buffer_temp(size_t size) { +static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) { // Try to find existing temp buffer with enough capacity - for (auto& buffer : vk_gc.temp_buffers) { - if (buffer.size >= size) { + for (auto& buffer : ctx->gc.temp_buffers) { + if (buffer->size >= size) { return buffer; } } // Otherwise create new buffer - vk_buffer buf = ggml_vk_pool_malloc(size); - vk_gc.temp_buffers.push_back(buf); + vk_buffer buf = ggml_vk_pool_malloc(ctx, size); + ctx->gc.temp_buffers.push_back(buf); return buf; } -static void * ggml_vk_host_malloc(size_t size) { +static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif - vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + vk_buffer buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - if(!(buf.memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { + if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", size/1024.0/1024.0); - buf.size = 0; - vk_device.device.freeMemory(buf.device_memory); - vk_device.device.destroyBuffer(buf.buffer); + ctx->device.lock()->device.freeMemory(buf->device_memory); + ctx->device.lock()->device.destroyBuffer(buf->buffer); return nullptr; } - vk_pinned_memory.push_back(std::make_tuple(buf.ptr, size, buf)); + ctx->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf)); - return buf.ptr; + return buf->ptr; } -static void ggml_vk_host_free(void* ptr) { +static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) { if (ptr == nullptr) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; #endif - vk_buffer* buf = nullptr; + vk_buffer buf; size_t index; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]); if (ptr >= addr && ptr < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); + buf = std::get<2>(ctx->pinned_memory[i]); index = i; break; } @@ -1300,28 +1457,28 @@ static void ggml_vk_host_free(void* ptr) { return; } - ggml_vk_destroy_buffer(*buf); + ggml_vk_destroy_buffer(buf); - vk_pinned_memory.erase(vk_pinned_memory.begin() + index); + ctx->pinned_memory.erase(ctx->pinned_memory.begin() + index); } -static void ggml_vk_host_get(const void * ptr, vk_buffer *& buf, size_t& buf_offset) { +static void ggml_vk_host_get(ggml_backend_vk_context * ctx, const void * ptr, vk_buffer& buf, size_t& buf_offset) { buf = nullptr; buf_offset = 0; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]); if (ptr >= addr && ptr < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); + buf = std::get<2>(ctx->pinned_memory[i]); buf_offset = ((const uint8_t *)ptr) - addr; break; } } } -static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) { +static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_queue& q, bool one_time = true) { vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(q); + s.buffer = ggml_vk_create_cmd_buffer(ctx, q); if (one_time) { s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); } else { @@ -1331,7 +1488,7 @@ static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) return s; } -static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { +static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); @@ -1344,22 +1501,22 @@ static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, s GGML_ASSERT(buffers.size() == pipeline.parameter_count); vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++]; for (uint32_t i = 0; i < pipeline.parameter_count; i++) { - descriptor_buffer_infos.push_back({buffers[i].buffer.buffer, buffers[i].offset, buffers[i].size}); + descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size}); } for (uint32_t i = 0; i < pipeline.parameter_count; i++) { write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); } - vk_device.device.updateDescriptorSets(write_descriptor_sets, {}); + ctx->device.lock()->device.updateDescriptorSets(write_descriptor_sets, {}); - ctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); - ctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); - ctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, + subctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); + subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); + subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, pipeline.layout, 0, { descriptor_set }, {}); - ctx->s->buffer.dispatch(wg0, wg1, wg2); + subctx->s->buffer.dispatch(wg0, wg1, wg2); } static void ggml_vk_end_submission(vk_submission& s, std::vector wait_semaphores, std::vector signal_semaphores) { @@ -1381,16 +1538,16 @@ static void ggml_vk_ctx_end(vk_context * ctx) { ctx->s = nullptr; } -static void ggml_vk_ctx_begin(vk_context * ctx) { +static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; #endif - if (ctx->s != nullptr) { - ggml_vk_ctx_end(ctx); + if (subctx->s != nullptr) { + ggml_vk_ctx_end(subctx); } - ctx->seqs.push_back({ ggml_vk_begin_submission(*ctx->q) }); - ctx->s = ctx->seqs[ctx->seqs.size() - 1].data(); + subctx->seqs.push_back({ ggml_vk_begin_submission(ctx, *subctx->q) }); + subctx->s = subctx->seqs[subctx->seqs.size() - 1].data(); } static size_t ggml_vk_align_size(size_t width, size_t align) { @@ -1405,14 +1562,14 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect } } -static void ensure_sync_staging_buffer(size_t size) { - if (vk_sync_staging.size < size) { - ggml_vk_destroy_buffer(vk_sync_staging); - vk_sync_staging = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); +static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) { + if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) { + ggml_vk_destroy_buffer(ctx->sync_staging); + ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } -static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { +static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; #endif @@ -1423,9 +1580,9 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf; size_t buf_offset; - ggml_vk_host_get(tensor->data, buf, buf_offset); + ggml_vk_host_get(ctx, tensor->data, buf, buf_offset); const uint64_t ne0 = tensor->ne[0]; const uint64_t ne1 = tensor->ne[1]; @@ -1471,21 +1628,21 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } // Staging buffer required - vk_buffer * staging = &vk_staging; - size_t staging_offset = vk_staging_offset; + vk_buffer staging = ctx->staging; + size_t staging_offset = ctx->staging_offset; const size_t copy_size = ts*ne/bs; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; staging_offset = 0; } else { GGML_ASSERT(false); @@ -1494,23 +1651,23 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size VkBufferCopy buf_copy{ staging_offset, offset, copy_size }; - ggml_vk_sync_buffers(ctx); - vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + ggml_vk_sync_buffers(subctx); + vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); for (uint64_t i3 = 0; i3 < ne3; i3++) { for (uint64_t i2 = 0; i2 < ne2; i2++) { // Find longest contiguous slice if (ne1*nb1 == dstnb2) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); } else { for (uint64_t i1 = 0; i1 < ne1; i1++) { if (ne0*nb0/bs == dstnb1) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); } else { const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; for (uint64_t i0 = 0; i0 < ne0; i0++) { - deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys); } } } @@ -1519,19 +1676,22 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } } -static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { +static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; #endif + // Make sure ctx owns the buffer + GGML_ASSERT(dst->ctx == ctx); + // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf = nullptr; size_t buf_offset; - ggml_vk_host_get(src, buf, buf_offset); + ggml_vk_host_get(ctx, src, buf, buf_offset); if (buf != nullptr) { // Memory is pinned, use as staging buffer @@ -1550,8 +1710,8 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } #ifdef GGML_VULKAN_DEBUG @@ -1559,14 +1719,14 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size #endif // Staging buffer required - vk_buffer * staging = &vk_staging; - size_t staging_offset = vk_staging_offset; + vk_buffer staging = ctx->staging; + size_t staging_offset = ctx->staging_offset; const size_t copy_size = width*height; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; staging_offset = 0; } else { GGML_ASSERT(false); @@ -1578,26 +1738,26 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size offset, copy_size}; - ggml_vk_sync_buffers(ctx); - vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + ggml_vk_sync_buffers(subctx); + vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); if (width == spitch) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &subctx->in_memcpys); } else { for (size_t i = 0; i < height; i++) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); } } } -static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { +static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; #endif - return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); + return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging); } -static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { +static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; #endif @@ -1609,39 +1769,42 @@ static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); } } else { - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, spitch, width, height, true); - ggml_vk_ctx_end(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true); + ggml_vk_ctx_end(subctx); - for (auto& cpy : ctx->in_memcpys) { + for (auto& cpy : subctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); } } -static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { +static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; #endif - ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); + ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1); } -static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { +static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; #endif GGML_ASSERT(width > 0); GGML_ASSERT(height > 0); - GGML_ASSERT(src->size > 0); + GGML_ASSERT(src != nullptr); + // Make sure ctx owns the buffer + GGML_ASSERT(src->ctx == ctx); + // Check if dst is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf = nullptr; size_t buf_offset; - ggml_vk_host_get(dst, buf, buf_offset); + ggml_vk_host_get(ctx, dst, buf, buf_offset); std::vector slices(1); if (width == spitch && width == dpitch) { @@ -1660,8 +1823,8 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ if (buf != nullptr) { // Memory is pinned, use as staging buffer - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); return; } @@ -1670,30 +1833,30 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ #endif // Fall back to staging buffer - vk_buffer * staging = &vk_staging; + vk_buffer staging = ctx->staging; const size_t copy_size = dpitch * height; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; } else { GGML_ASSERT(false); } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); - deferred_memcpy(dst, staging->ptr, copy_size, &ctx->out_memcpys); + deferred_memcpy(dst, staging->ptr, copy_size, &subctx->out_memcpys); } -static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t size, bool sync_staging = false) { - return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst, size, size, size, 1, sync_staging); +static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) { + return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst, size, size, size, 1, sync_staging); } -static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { +static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; #endif @@ -1702,61 +1865,88 @@ static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_ memcpy(dst, (uint8_t *) src->ptr + offset, size); } else { - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ggml_vk_buffer_read_async(ctx, src, offset, dst, size, true); - ggml_vk_ctx_end(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true); + ggml_vk_ctx_end(subctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : ctx->out_memcpys) { + for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } } } -static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; #endif + // Make sure both buffers are on same ctx + GGML_ASSERT(src->ctx == dst->ctx); + VkBufferCopy bc{ src_offset, dst_offset, size }; vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); } -static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { + if (src->ctx == dst->ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; + std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl; #endif - VkBufferCopy bc{ src_offset, dst_offset, size }; + // Copy within the device + ggml_backend_vk_context * ctx = src->ctx; - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); - ggml_vk_buffer_copy_async(ctx, dst, dst_offset, src, src_offset, size); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); - vk_device.device.resetFences({ vk_fence }); + VkBufferCopy bc{ src_offset, dst_offset, size }; + + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); + } else { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl; +#endif + // Copy device to device + ggml_backend_vk_context * src_ctx = src->ctx; + ggml_backend_vk_context * dst_ctx = dst->ctx; + + ggml_vk_ensure_sync_staging_buffer(src_ctx, size); + ggml_vk_ensure_sync_staging_buffer(dst_ctx, size); + + // Copy to src staging buffer + ggml_vk_buffer_copy(src_ctx->sync_staging, 0, src, src_offset, size); + // memcpy to dst staging buffer + memcpy(dst_ctx->sync_staging->ptr, src_ctx->sync_staging->ptr, size); + // Copy to dst buffer + ggml_vk_buffer_copy(dst, dst_offset, dst_ctx->sync_staging, 0, size); + } } -static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { +static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; #endif - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); - ggml_vk_ctx_end(ctx); + // Make sure ctx owns the buffer + GGML_ASSERT(dst->ctx == ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_memset waitForFences"); - vk_device.device.resetFences({ vk_fence }); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); + ggml_vk_ctx_end(subctx); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); } -static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { +static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; #endif @@ -1773,20 +1963,20 @@ static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offs const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3); if (nb0 == ts && nb1 == row_length) { - return ggml_vk_buffer_write_async(ctx, dst, offset, x, i1*nb1); + return ggml_vk_buffer_write_async(ctx, subctx, dst, offset, x, i1*nb1); } if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) { - return ggml_vk_buffer_write_2d_async(ctx, dst, offset, x, nb1, row_length, i1); + return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, x, nb1, row_length, i1); } GGML_ASSERT(i3 == 0); GGML_ASSERT(i2 == 0); GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src)); - return ggml_vk_buffer_write_nc_async(ctx, dst, offset, src); + return ggml_vk_buffer_write_nc_async(ctx, subctx, dst, offset, src); } -static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { +static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; #endif @@ -1804,10 +1994,10 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs const size_t row_length = ts*ne0/bs; if (ggml_is_contiguous(dst)) { - return ggml_vk_buffer_read_async(ctx, src, offset, dst->data, ne1*nb1*ne2*ne3); + return ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst->data, ne1*nb1*ne2*ne3); } if (nb0 == ts) { - return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); + return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); } GGML_ASSERT(false); } @@ -1829,89 +2019,89 @@ static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { return 1; } -static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { +static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, int m, int n) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif if (m <= 32 || n <= 32) { - return vk_pipeline_matmul_f32_aligned_s.align; + return ctx->pipeline_matmul_f32_aligned_s.align; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { - return vk_pipeline_matmul_f32_aligned_m.align; + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { + return ctx->pipeline_matmul_f32_aligned_m.align; } - return vk_pipeline_matmul_f32_aligned_l.align; + return ctx->pipeline_matmul_f32_aligned_l.align; } -static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { +static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; #endif if (bit16_x && bit16_y) { - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; + return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; + return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; + return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_l : &ctx->pipeline_matmul_f16_f32_l; } if (!bit16_x && bit16_y) { GGML_ASSERT(false); } - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; + return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; + return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; + return aligned ? &ctx->pipeline_matmul_f32_aligned_l : &ctx->pipeline_matmul_f32_l; } -static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { +static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif - ggml_vk_sync_buffers(ctx); + ggml_vk_sync_buffers(subctx); if (split_k == 1) { const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; - ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); return; } @@ -1919,10 +2109,10 @@ static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); - ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); + ggml_vk_sync_buffers(subctx); const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -1932,32 +2122,32 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { +static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return &vk_pipeline_cpy_f32_f32; + return &ctx->pipeline_cpy_f32_f32; } if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return &vk_pipeline_cpy_f32_f16; + return &ctx->pipeline_cpy_f32_f16; } if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return &vk_pipeline_cpy_f16_f16; + return &ctx->pipeline_cpy_f16_f16; } std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; GGML_ASSERT(false); } -static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { +static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; - std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; + std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); const int dst_type_size = ggml_type_size(buffer_type); const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2]; - const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; + const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; const vk_op_cpy_push_constants pc = { (uint32_t)ne, @@ -1965,11 +2155,11 @@ static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], 1 , (uint32_t)tensor->ne[0] , nb2, 0, }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); } -static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -1998,17 +2188,17 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qx = nullptr; + vk_buffer d_Qx; size_t qx_buf_offset = 0; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; } @@ -2031,12 +2221,12 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ne01, ne11)); + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, ne01, ne11)); const bool aligned = ne10 == kpad; const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(true, !f16_f32_kernel, ne01, ne11, aligned); + vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(ctx, true, !f16_f32_kernel, ne01, ne11, aligned); const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); @@ -2044,30 +2234,30 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); - vk_buffer* d_X; + vk_buffer d_X; uint64_t x_buf_offset = 0; - vk_buffer* d_Y; + vk_buffer d_Y; uint64_t y_buf_offset = 0; if (load_x) { - d_Qx = &vk_prealloc_qx; + d_Qx = ctx->prealloc_qx; } else if (!src0_uma) { - d_Qx = &extra_src0->buffer_gpu; + d_Qx = extra_src0->buffer_gpu.lock(); qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if (!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { - d_X = &vk_prealloc_x; + d_X = ctx->prealloc_x; GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); } else { d_X = d_Qx; @@ -2075,7 +2265,7 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co GGML_ASSERT(qx_sz == x_sz); // NOLINT } if (qy_needs_dequant) { - d_Y = &vk_prealloc_y; + d_Y = ctx->prealloc_y; GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); } else { d_Y = d_Qy; @@ -2087,49 +2277,49 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co vk_pipeline * to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(src0->type); + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne12 * ne13); if (qx_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); } if (qy_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } if (split_k > 1) { - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, ne12 * ne13); } if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, dst->type, false); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, dst->type, false); } else if (load_x || qx_needs_dequant) { if (load_x) { // copy data to device - ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); - vk_staging_offset = qx_sz * ne02 * ne03; + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + ctx->staging_offset = qx_sz * ne02 * ne03; } if (qx_needs_dequant) { const std::vector pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_0, { { *d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { *d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } } if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, dst->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, dst->type); } else if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } uint32_t stride_batch_x = ne00*ne01; @@ -2144,16 +2334,16 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co } // compute - ggml_vk_matmul(ctx, *pipeline, { *d_X, x_buf_offset, x_sz * ne02 * ne03 }, { *d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { *d_D, d_buf_offset, d_sz * ne12 * ne13 }, { vk_prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data); - ggml_vk_buffer_read_async(ctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); } } -static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2184,17 +2374,17 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qx = nullptr; + vk_buffer d_Qx; size_t qx_buf_offset = 0; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; } @@ -2214,42 +2404,42 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_ne = ne11 * ne10; const uint64_t d_ne = ne11 * ne01; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_X; + vk_buffer d_X; uint64_t x_buf_offset = 0; - vk_buffer* d_Y; + vk_buffer d_Y; uint64_t y_buf_offset = 0; if (load_x) { - d_Qx = &vk_prealloc_qx; + d_Qx = ctx->prealloc_qx; } else if(!src1_uma) { - d_Qx = &extra_src0->buffer_gpu; + d_Qx = extra_src0->buffer_gpu.lock(); qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if(!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { - d_X = &vk_prealloc_x; + d_X = ctx->prealloc_x; } else { d_X = d_Qx; x_buf_offset = qx_buf_offset; GGML_ASSERT(qx_sz == x_sz); } if (qy_needs_dequant) { - d_Y = &vk_prealloc_y; + d_Y = ctx->prealloc_y; } else { d_Y = d_Qy; y_buf_offset = qy_buf_offset; @@ -2259,39 +2449,39 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 vk_pipeline * to_fp16_vk_0 = nullptr; vk_pipeline* to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(src0->type); + vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); // Allocate descriptor sets if (qx_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } - ggml_vk_pipeline_allocate_descriptor_sets(*dmmv, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *dmmv, ne12 * ne13); if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, src0->type); + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, src0->type); } else if (load_x) { // copy data to device - ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); } if (y_non_contig) { GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, src1->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, src1->type); } else if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } for (uint64_t i13 = 0; i13 < ne13; i13++) { @@ -2306,34 +2496,34 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; - const uint64_t y_buffer_offset = (y_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t y_buffer_offset = (y_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t y_shader_offset = y_offset - y_buffer_offset; - const uint64_t d_buffer_offset = (d_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_offset - d_buffer_offset; if (!y_non_contig && qy_needs_dequant) { const std::vector pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_1, { { *d_Qy, qy_offset, qy_sz }, { *d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); } // compute const std::array pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))}; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *dmmv, { { *d_X, x_offset, x_sz }, { *d_Y, y_buffer_offset, y_sz + y_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne); } } } } -static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2362,13 +2552,13 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2378,51 +2568,51 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor const uint64_t y_ne = ne10 * ne11 * ne12; const uint64_t d_ne = ne01 * ne11 * ne12; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx = &extra_src0->buffer_gpu; + vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); const uint64_t qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if (!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); } // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_p021_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); } // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_p021_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); } } -static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2454,13 +2644,13 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2475,43 +2665,43 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * const uint64_t qy_sz = ggml_nbytes(src1); const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx = &extra_src0->buffer_gpu; + vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); const uint64_t qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); } // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_nc_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); } // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_nc_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); } } @@ -2528,22 +2718,22 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU); } -static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - ggml_vk_mul_mat_vec_p021_f16_f32(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst); } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - ggml_vk_mul_mat_vec_nc_f16_f32(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst); } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_q_f16(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst); } else { - ggml_vk_mul_mat_q_f16(ctx, src0, src1, dst); + ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst); } } -static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat const uint64_t ne0 = dst->ne[0]; const uint64_t ne1 = dst->ne[1]; @@ -2579,9 +2769,9 @@ static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - const vk_buffer* src_buf = &extra_src0->buffer_gpu; + const vk_buffer src_buf = extra_src0->buffer_gpu.lock(); const uint64_t src_offset = extra_src0->offset; - vk_buffer* dst_buf = &extra->buffer_gpu; + vk_buffer dst_buf = extra->buffer_gpu.lock(); const uint64_t dst_offset = extra->offset; std::vector copies; @@ -2606,78 +2796,79 @@ static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); - (void) src1; + GGML_UNUSED(ctx); + GGML_UNUSED(src1); } -static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { +static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_add_f32; + return &ctx->pipeline_add_f32; } return nullptr; case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); if (dst->type == GGML_TYPE_F16) { - return &vk_pipeline_get_rows[src0->type]; + return &ctx->pipeline_get_rows[src0->type]; } if (dst->type == GGML_TYPE_F32) { - return &vk_pipeline_get_rows_f32[src0->type]; + return &ctx->pipeline_get_rows_f32[src0->type]; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_mul_f32; + return &ctx->pipeline_mul_f32; } return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_scale_f32; + return &ctx->pipeline_scale_f32; } return nullptr; case GGML_OP_SQR: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_sqr_f32; + return &ctx->pipeline_sqr_f32; } return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_clamp_f32; + return &ctx->pipeline_clamp_f32; } return nullptr; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - return ggml_vk_get_cpy_pipeline(src0->type, dst->type); + return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_norm_f32; + return &ctx->pipeline_norm_f32; } return nullptr; case GGML_OP_RMS_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rms_norm_f32; + return &ctx->pipeline_rms_norm_f32; } return nullptr; case GGML_OP_UNARY: switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_SILU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_silu_f32; + return &ctx->pipeline_silu_f32; } break; case GGML_UNARY_OP_GELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_gelu_f32; + return &ctx->pipeline_gelu_f32; } break; case GGML_UNARY_OP_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_relu_f32; + return &ctx->pipeline_relu_f32; } break; default: @@ -2686,12 +2877,12 @@ static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml return nullptr; case GGML_OP_DIAG_MASK_INF: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_diag_mask_inf_f32; + return &ctx->pipeline_diag_mask_inf_f32; } return nullptr; case GGML_OP_SOFT_MAX: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_soft_max_f32; + return &ctx->pipeline_soft_max_f32; } return nullptr; case GGML_OP_ROPE: @@ -2706,17 +2897,17 @@ static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rope_neox_f32; + return &ctx->pipeline_rope_neox_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &vk_pipeline_rope_neox_f16; + return &ctx->pipeline_rope_neox_f16; } } else { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rope_f32; + return &ctx->pipeline_rope_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &vk_pipeline_rope_f16; + return &ctx->pipeline_rope_f16; } } return nullptr; @@ -2735,13 +2926,8 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } } -#ifdef GGML_VULKAN_CHECK_RESULTS -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); -static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor); -#endif - template -static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { @@ -2768,7 +2954,7 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm const uint64_t nb2 = dst->nb[2]; const uint64_t nb3 = dst->nb[3]; - vk_pipeline * pipeline = ggml_vk_op_get_pipeline(src0, src1, dst, op); + vk_pipeline * pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -2782,7 +2968,7 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm GGML_ASSERT(false); } - op_func(ctx, src0, src1, dst); + op_func(ctx, subctx, src0, src1, dst); return; } @@ -2790,19 +2976,19 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - vk_buffer * d_X = nullptr; + vk_buffer d_X = nullptr; size_t x_buf_offset = 0; - vk_buffer * d_Y = nullptr; + vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_X, x_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); src0_uma = d_X != nullptr; if (use_src1) { - ggml_vk_host_get(src1->data, d_Y, y_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } } @@ -2810,30 +2996,31 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma; const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma; - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, vk_device.properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, vk_device.properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; + vk_buffer d_D = extra->buffer_gpu.lock(); + // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > extra_src1->buffer_gpu.size) { + if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } - vk_buffer* d_D = &extra->buffer_gpu; GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = (extra->offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + uint64_t d_buf_offset = (extra->offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT if (transfer_src0) { - d_X = &vk_prealloc_qx; + d_X = ctx->prealloc_qx; } else if(!src0_uma) { - d_X = &extra_src0->buffer_gpu; + d_X = extra_src0->buffer_gpu.lock(); x_buf_offset = extra_src0->offset; GGML_ASSERT(d_X != nullptr); } if (transfer_src1) { - d_Y = &vk_prealloc_qy; + d_Y = ctx->prealloc_qy; } else if (use_src1 && !src1_uma) { - d_Y = &extra_src1->buffer_gpu; + d_Y = extra_src1->buffer_gpu.lock(); y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); } @@ -2856,16 +3043,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm // copy src0 to device if (transfer_src0) { - ggml_vk_h2d_tensor_2d(ctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); - vk_staging_offset = x_sz * ne02 * ne03; + ggml_vk_h2d_tensor_2d(ctx, subctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); + ctx->staging_offset = x_sz * ne02 * ne03; } if (transfer_src1) { - ggml_vk_h2d_tensor_2d(ctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); } // Single call if dimension 2 is contiguous if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, 1); switch (dst->op) { case GGML_OP_NORM: @@ -2896,24 +3083,24 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (!use_src1 && op == GGML_OP_SOFT_MAX) { // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_Y, y_buf_offset, y_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) { - ggml_vk_d2h_tensor_2d(ctx, d_D, 0, dst); + ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); } else if(dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_buffer_read_async(ctx, d_D, 0, d, d_sz); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); } } else { - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne02 * ne03); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: @@ -2940,60 +3127,60 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (!use_src1 && op == GGML_OP_SOFT_MAX) { // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_Y, y_buf_offset + y_offset, y_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); } } } } } -static void ggml_vk_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_get_rows(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_add(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_mul(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_scale(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); } -static void ggml_vk_sqr(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } -static void ggml_vk_clamp(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); } -static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; const int src0_type_size = ggml_type_size(src0->type); const int dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = (extra->offset % vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CPY, { + const uint32_t d_offset = (extra->offset % ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, @@ -3001,30 +3188,30 @@ static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor }); } -static void ggml_vk_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); +static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); } -static void ggml_vk_rms_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } -static void ggml_vk_unary(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } -static void ggml_vk_diag_mask_inf(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); } -static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -3047,19 +3234,19 @@ static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_ if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); } else { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); } } -static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { // If backend is CPU, data from src0 has to be copied off the device if (dst->backend == GGML_BACKEND_CPU) { ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - vk_buffer * d_D = &extra_src0->buffer_gpu; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, 0, dst->data, d_D->size); + vk_buffer d_D = extra_src0->buffer_gpu.lock(); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, dst->data, d_D->size); } } @@ -3096,7 +3283,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 } template -static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { +static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; #endif @@ -3108,39 +3295,39 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size std::string shname; if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_s; + p = &ctx->pipeline_matmul_f32_aligned_s; shname = "F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_s; + p = &ctx->pipeline_matmul_f16_f32_aligned_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_s; + p = &ctx->pipeline_matmul_f16_aligned_s; shname = "F16_ALIGNED_S"; } else { GGML_ASSERT(false); } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_m; + p = &ctx->pipeline_matmul_f32_aligned_m; shname = "F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_m; + p = &ctx->pipeline_matmul_f16_f32_aligned_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_m; + p = &ctx->pipeline_matmul_f16_aligned_m; shname = "F16_ALIGNED_M"; } else { GGML_ASSERT(false); } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_l; + p = &ctx->pipeline_matmul_f32_aligned_l; shname = "F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_l; + p = &ctx->pipeline_matmul_f16_f32_aligned_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_l; + p = &ctx->pipeline_matmul_f16_aligned_l; shname = "F16_ALIGNED_L"; } else { GGML_ASSERT(false); @@ -3154,56 +3341,56 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (k != kpad) { if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_s; + p = &ctx->pipeline_matmul_f32_s; shname = "F32_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_s; + p = &ctx->pipeline_matmul_f16_f32_s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_s; + p = &ctx->pipeline_matmul_f16_s; shname = "F16_S"; } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_m; + p = &ctx->pipeline_matmul_f32_m; shname = "F32_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_m; + p = &ctx->pipeline_matmul_f16_f32_m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_m; + p = &ctx->pipeline_matmul_f16_m; shname = "F16_M"; } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_l; + p = &ctx->pipeline_matmul_f32_l; shname = "F32_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_l; + p = &ctx->pipeline_matmul_f16_f32_l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_l; + p = &ctx->pipeline_matmul_f16_l; shname = "F16_L"; } } } - ggml_vk_pipeline_allocate_descriptor_sets(*p, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, *p, num_it); if (split_k > 1) { - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, num_it); - if (vk_prealloc_split_k.size < sizeof(float) * d_ne * split_k) { + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer - if (vk_prealloc_split_k.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_split_k); + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer_check(sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); } } - vk_buffer d_X = ggml_vk_create_buffer_check(sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_Y = ggml_vk_create_buffer_check(sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_D = ggml_vk_create_buffer_check(sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); @@ -3228,26 +3415,26 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size } } - ggml_vk_buffer_write(&d_X, 0, x, sizeof(X_TYPE) * k * m * batch); - ggml_vk_buffer_write(&d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); + ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch); + ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx); - ggml_vk_matmul(ctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(vk_prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); - ggml_vk_ctx_end(ctx); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_matmul(ctx, subctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_ctx_end(subctx); } auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double time = std::chrono::duration_cast(end-begin).count() / 1000.0; // copy dst to host - ggml_vk_buffer_read(&d_D, 0, d, sizeof(float) * d_ne); + ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne); float * d_chk = (float *) malloc(sizeof(float) * d_ne); @@ -3285,14 +3472,14 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size src1_ggml->data = y; tensor_ggml->data = d_chk; - vk_disable = true; + ctx->disable = true; ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - vk_disable = false; + ctx->disable = false; ggml_free(ggml_ctx); @@ -3325,7 +3512,7 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (split_k > 1) { float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); - ggml_vk_buffer_read(&vk_prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); std::cerr << "d_buf0: " << std::endl << std::endl; ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); @@ -3345,15 +3532,15 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size free(d_chk); - ggml_vk_queue_cleanup(vk_device.transfer_queue); - ggml_vk_queue_cleanup(vk_device.compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); ggml_vk_destroy_buffer(d_X); ggml_vk_destroy_buffer(d_Y); ggml_vk_destroy_buffer(d_D); - ggml_vk_pipeline_cleanup(*p); - ggml_vk_pipeline_cleanup(vk_pipeline_matmul_split_k_reduce); + ggml_pipeline_cleanup(*p); + ggml_pipeline_cleanup(ctx->pipeline_matmul_split_k_reduce); free(x); free(y); @@ -3392,7 +3579,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1 } } -static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) { +static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_t ne1, size_t ne2, size_t ne3) { const size_t ne = ne0 * ne1 * ne2 * ne3; ggml_init_params iparams = { @@ -3406,7 +3593,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3); // NOLINT ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3); - float * data = (float *) ggml_vk_host_malloc(ggml_nbytes(tensor)); + float * data = (float *) ggml_vk_host_malloc(ctx, ggml_nbytes(tensor)); tensor->data = data; float * result_data = (float *) malloc(ggml_nbytes(tensor)); @@ -3426,19 +3613,19 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; } - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); - vk_buffer buffer = ggml_vk_create_buffer_check(ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); - ggml_vk_h2d_tensor_2d(ctx, &buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); + ggml_vk_h2d_tensor_2d(ctx, subctx, buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - ggml_vk_buffer_read(&buffer, 0, result_data, ggml_nbytes(tensor)); + ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor)); double avg_err = 0.0; int first_err_i0 = -1; @@ -3483,22 +3670,22 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) ggml_vk_destroy_buffer(buffer); - ggml_vk_host_free(data); + ggml_vk_host_free(ctx, data); free(result_data); } -static void ggml_vk_test_transfer(size_t ne, bool pinned) { +static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct - vk_buffer buffer = ggml_vk_create_buffer_check(sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); float * x; float * y; if (pinned) { - x = (float *) ggml_vk_host_malloc(sizeof(float) * ne); - y = (float *) ggml_vk_host_malloc(sizeof(float) * ne); + x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne); + y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne); } else { x = (float *) malloc(sizeof(float) * ne); y = (float *) malloc(sizeof(float) * ne); @@ -3508,42 +3695,42 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { x[i] = rand() / (float)RAND_MAX; } - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_buffer_write_async(ctx, &buffer, 0, x, sizeof(float) * ne); + ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne); - for (auto& cpy : ctx->in_memcpys) { + for (auto& cpy : subctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx->in_memcpys.clear(); + subctx->in_memcpys.clear(); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_to_gpu = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_ctx_begin(ctx); + ggml_vk_ctx_begin(ctx, subctx); begin = std::chrono::high_resolution_clock::now(); - ggml_vk_buffer_read_async(ctx, &buffer, 0, y, sizeof(float) * ne); + ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : ctx->out_memcpys) { + for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx->out_memcpys.clear(); + subctx->out_memcpys.clear(); end = std::chrono::high_resolution_clock::now(); @@ -3561,15 +3748,15 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { ggml_vk_destroy_buffer(buffer); if (pinned) { - ggml_vk_host_free(x); - ggml_vk_host_free(y); + ggml_vk_host_free(ctx, x); + ggml_vk_host_free(ctx, y); } else { free(x); free(y); } } -static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { +static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; #endif @@ -3578,8 +3765,8 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); float * x = (float *) malloc(x_sz); void * qx = malloc(qx_sz); - vk_buffer qx_buf = ggml_vk_create_buffer_check(qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer x_buf = ggml_vk_create_buffer_check(x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); for (size_t i = 0; i < ne; i++) { @@ -3588,7 +3775,7 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { std::vector hist_cur(1 << 4, 0); - vk_pipeline& p = vk_pipeline_dequant[quant]; + vk_pipeline& p = ctx->pipeline_dequant[quant]; switch(quant) { case GGML_TYPE_Q4_0: @@ -3625,27 +3812,26 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { GGML_ASSERT(false); } - ggml_vk_pipeline_allocate_descriptor_sets(p, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, p, 1); - ggml_vk_buffer_write(&qx_buf, 0, qx, qx_sz); + ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); - ggml_vk_ctx_end(ctx); + ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_buffer_read(&x_buf, 0, x_chk, x_sz_f16); + ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16); double avg_err = 0.0; for (size_t i = 0; i < ne; i++) { @@ -3687,15 +3873,15 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph return nullptr; } -void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ +static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; + std::cerr << "ggml_ctx->preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU)); - if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { return; } @@ -3735,16 +3921,16 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ const uint32_t y_ne = ne10 * ne11; const uint32_t d_ne = ne20 * ne21; - const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; + const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; - if (extra->buffer_gpu.size == 0) { + if (extra->buffer_gpu.expired()) { // Workaround for CPU backend BLAS matmul calls - extra->buffer_gpu = ggml_vk_create_buffer_temp(d_sz); + extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz); } switch (node->op) { @@ -3779,23 +3965,23 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ } break; case GGML_OP_MUL_MAT: - if (vk_prealloc_size_qx < qx_sz) { - vk_prealloc_size_qx = qx_sz; + if (ctx->prealloc_size_qx < qx_sz) { + ctx->prealloc_size_qx = qx_sz; } - if (vk_prealloc_size_qy < qy_sz) { - vk_prealloc_size_qy = qy_sz; + if (ctx->prealloc_size_qy < qy_sz) { + ctx->prealloc_size_qy = qy_sz; } - if (vk_prealloc_size_x < x_sz) { - vk_prealloc_size_x = x_sz; + if (ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; } - if (vk_prealloc_size_y < y_sz) { - vk_prealloc_size_y = y_sz; + if (ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; } - if (vk_prealloc_size_split_k < split_k_size) { - vk_prealloc_size_split_k = split_k_size; + if (ctx->prealloc_size_split_k < split_k_size) { + ctx->prealloc_size_split_k = split_k_size; } - if (vk_staging_size < x_sz + y_sz) { - vk_staging_size = x_sz + y_sz; + if (ctx->staging_size < x_sz + y_sz) { + ctx->staging_size = x_sz + y_sz; } break; default: @@ -3803,29 +3989,29 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ } } -void ggml_vk_preallocate_buffers() { - if (vk_disable) { +static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { + if (ctx->disable) { return; } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; - std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; + std::cerr << "ggml_ctx->preallocate_buffers()" << std::endl; + std::cerr << "qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << std::endl; #endif #if defined(GGML_VULKAN_RUN_TESTS) - vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - ggml_vk_test_transfer(8192 * 1000, false); - ggml_vk_test_transfer(8192 * 1000, true); + ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ggml_vk_test_transfer(ctx, 8192 * 1000, false); + ggml_vk_test_transfer(ctx, 8192 * 1000, true); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_1); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_1); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q8_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q2_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q3_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q6_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q6_K); const std::vector vals { 8, 8, 8, @@ -3852,76 +4038,76 @@ void ggml_vk_preallocate_buffers() { }; const size_t num_it = 1; for (size_t i = 0; i < vals.size(); i += 3) { - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); std::cerr << std::endl; } GGML_ASSERT(false); #endif - if (vk_prealloc_size_qx > 0 && vk_prealloc_qx.size < vk_prealloc_size_qx) { + if (ctx->prealloc_qx == nullptr || (ctx->prealloc_size_qx > 0 && ctx->prealloc_qx->size < ctx->prealloc_size_qx)) { // Resize buffer - if (vk_prealloc_qx.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_qx); + if (ctx->prealloc_qx != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_qx); } - vk_prealloc_qx = ggml_vk_create_buffer_device(vk_prealloc_size_qx); + ctx->prealloc_qx = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qx); } - if (vk_prealloc_size_qy > 0 && vk_prealloc_qy.size < vk_prealloc_size_qy) { + if (ctx->prealloc_qy == nullptr || (ctx->prealloc_size_qy > 0 && ctx->prealloc_qy->size < ctx->prealloc_size_qy)) { // Resize buffer - if (vk_prealloc_qy.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_qy); + if (ctx->prealloc_qy != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_qy); } - vk_prealloc_qy = ggml_vk_create_buffer_device(vk_prealloc_size_qy); + ctx->prealloc_qy = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qy); } - if (vk_prealloc_size_x > 0 && vk_prealloc_x.size < vk_prealloc_size_x) { + if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) { // Resize buffer - if (vk_prealloc_x.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_x); + if (ctx->prealloc_x != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_x); } - vk_prealloc_x = ggml_vk_create_buffer_device(vk_prealloc_size_x); + ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x); } - if (vk_prealloc_size_y > 0 && vk_prealloc_y.size < vk_prealloc_size_y) { + if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) { // Resize buffer - if (vk_prealloc_y.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_y); + if (ctx->prealloc_y != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_y); } - vk_prealloc_y = ggml_vk_create_buffer_device(vk_prealloc_size_y); + ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y); } - if (vk_prealloc_size_split_k > 0 && vk_prealloc_split_k.size < vk_prealloc_size_split_k) { + if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) { // Resize buffer - if (vk_prealloc_split_k.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_split_k); + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer_device(vk_prealloc_size_split_k); + ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k); } - if (vk_staging_size > 0 && vk_staging.size < vk_staging_size) { + if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) { // Resize buffer - if (vk_staging.size > 0) { - ggml_vk_destroy_buffer(vk_staging); + if (ctx->staging != nullptr) { + ggml_vk_destroy_buffer(ctx->staging); } - vk_staging = ggml_vk_create_buffer_check(vk_staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } -void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ +static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU); - if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { + if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; #endif - vk_semaphore_idx = 0; - vk_staging_offset = 0; + ctx->semaphore_idx = 0; + ctx->staging_offset = 0; const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; @@ -3969,44 +4155,44 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ return; } - if (vk_ctx == nullptr) { - vk_ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(vk_ctx); + if (ctx->compute_ctx == nullptr) { + ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, ctx->compute_ctx); } switch (node->op) { case GGML_OP_REPEAT: - ggml_vk_repeat(vk_ctx, src0, src1, node); + ggml_vk_repeat(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_GET_ROWS: - ggml_vk_get_rows(vk_ctx, src0, src1, node); + ggml_vk_get_rows(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ADD: - ggml_vk_add(vk_ctx, src0, src1, node); + ggml_vk_add(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL: - ggml_vk_mul(vk_ctx, src0, src1, node); + ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_SCALE: - ggml_vk_scale(vk_ctx, src0, node); + ggml_vk_scale(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_SQR: - ggml_vk_sqr(vk_ctx, src0, node); + ggml_vk_sqr(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_CLAMP: - ggml_vk_clamp(vk_ctx, src0, node); + ggml_vk_clamp(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - ggml_vk_cpy(vk_ctx, src0, node); + ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_RESHAPE: @@ -4014,15 +4200,15 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NONE: - ggml_vk_nop(vk_ctx, src0, node); + ggml_vk_nop(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_NORM: - ggml_vk_norm(vk_ctx, src0, node); + ggml_vk_norm(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_RMS_NORM: - ggml_vk_rms_norm(vk_ctx, src0, node); + ggml_vk_rms_norm(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_UNARY: @@ -4030,26 +4216,26 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_RELU: - ggml_vk_unary(vk_ctx, src0, node); + ggml_vk_unary(ctx, ctx->compute_ctx, src0, node); break; default: return; } break; case GGML_OP_DIAG_MASK_INF: - ggml_vk_diag_mask_inf(vk_ctx, src0, node); + ggml_vk_diag_mask_inf(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(vk_ctx, src0, src1, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ROPE: - ggml_vk_rope(vk_ctx, src0, src1, node); + ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL_MAT: - ggml_vk_mul_mat(vk_ctx, src0, src1, node); + ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); break; default: @@ -4057,7 +4243,7 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ } extra->ready = true; - extra->ctx_idx = vk_ctx->idx; + extra->ctx_idx = ctx->compute_ctx->idx; #ifdef GGML_VULKAN_CHECK_RESULTS // Force context reset on each node so that each tensor ends up in its own context @@ -4066,18 +4252,18 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ #endif if (node->backend == GGML_BACKEND_CPU || last_node) { - ggml_vk_ctx_end(vk_ctx); - vk_ctx->exit_tensor = node; - vk_ctx = nullptr; + ggml_vk_ctx_end(ctx->compute_ctx); + ctx->compute_ctx->exit_tensor = node; + ctx->compute_ctx = nullptr; } } -bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor){ +static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ const bool any_on_device = tensor->backend == GGML_BACKEND_GPU || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); - if (vk_disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { return false; } @@ -4145,33 +4331,33 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) #endif #ifdef GGML_VULKAN_CHECK_RESULTS - ggml_vk_check_results_0(params, tensor); + ggml_vk_check_results_0(ctx, params, tensor); #endif GGML_ASSERT(extra->ready); - vk_context& ctx = vk_gc.contexts[extra->ctx_idx]; + vk_context& subctx = ctx->gc.contexts[extra->ctx_idx]; // Only run if ctx hasn't been submitted yet - if (!ctx.seqs.empty()) { + if (!subctx.seqs.empty()) { // Do staging buffer copies - for (auto& cpy : ctx.in_memcpys) { + for (auto& cpy : subctx.in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(&ctx, vk_fence); + ggml_vk_submit(&subctx, ctx->fence); } - if (tensor == ctx.exit_tensor) { - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + if (tensor == subctx.exit_tensor) { + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); // Do staging buffer copies - for (auto& cpy : ctx.out_memcpys) { + for (auto& cpy : subctx.out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx.in_memcpys.clear(); - ctx.out_memcpys.clear(); + subctx.in_memcpys.clear(); + subctx.out_memcpys.clear(); } extra->ready = false; @@ -4179,90 +4365,204 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) return true; } -void ggml_vk_graph_cleanup() { - if (vk_disable) { +// Clean up after graph processing is done +static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { + if (ctx->disable) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif - for (auto& buffer : vk_gc.temp_buffers) { - ggml_vk_pool_free(buffer); + for (auto& buffer : ctx->gc.temp_buffers) { + ggml_vk_pool_free(ctx, buffer); } - vk_gc.temp_buffers.clear(); + ctx->gc.temp_buffers.clear(); - for (auto * pipeline : vk_gc.pipelines) { - ggml_vk_pipeline_cleanup(*pipeline); - } - vk_gc.pipelines.clear(); - - ggml_vk_queue_cleanup(vk_device.compute_queue); - ggml_vk_queue_cleanup(vk_device.transfer_queue); - - for (size_t i = 0; i < vk_gc.semaphores.size(); i++) { - vk_device.device.destroySemaphore({ vk_gc.semaphores[i].s }); - } - vk_gc.semaphores.clear(); - - for (size_t i = 0; i < vk_gc.tl_semaphores.size(); i++) { - vk_device.device.destroySemaphore({ vk_gc.tl_semaphores[i].s }); - } - vk_gc.tl_semaphores.clear(); - - vk_event_idx = 0; - - for (auto& event : vk_gc.events) { - vk_device.device.resetEvent(event); + for (auto * pipeline : ctx->gc.pipelines) { + ggml_pipeline_cleanup(*pipeline); } - vk_staging_offset = 0; + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); - vk_ctx = nullptr; - vk_gc.contexts.clear(); + for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { + ctx->device.lock()->device.destroySemaphore({ ctx->gc.semaphores[i].s }); + } + ctx->gc.semaphores.clear(); + + for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { + ctx->device.lock()->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); + } + ctx->gc.tl_semaphores.clear(); + ctx->semaphore_idx = 0; + + ctx->event_idx = 0; + + for (auto& event : ctx->gc.events) { + ctx->device.lock()->device.resetEvent(event); + } + + ctx->staging_offset = 0; + + ctx->compute_ctx = nullptr; + ctx->transfer_ctx = nullptr; + ctx->gc.contexts.clear(); } -static void ggml_vk_cleanup() { +// Clean up on backend free +static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cleanup()" << std::endl; + std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl; #endif - ggml_vk_destroy_buffer(vk_prealloc_x); - ggml_vk_destroy_buffer(vk_prealloc_y); - ggml_vk_destroy_buffer(vk_prealloc_split_k); - ggml_vk_destroy_buffer(vk_staging); - ggml_vk_destroy_buffer(vk_sync_staging); + ggml_vk_graph_cleanup(ctx); - vk_prealloc_size_x = 0; - vk_prealloc_size_y = 0; - vk_prealloc_size_split_k = 0; - vk_staging_size = 0; + ggml_vk_destroy_buffer(ctx->prealloc_qx); + ggml_vk_destroy_buffer(ctx->prealloc_qy); + ggml_vk_destroy_buffer(ctx->prealloc_x); + ggml_vk_destroy_buffer(ctx->prealloc_y); + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + ggml_vk_destroy_buffer(ctx->staging); + ggml_vk_destroy_buffer(ctx->sync_staging); - for (auto& event : vk_gc.events) { - vk_device.device.destroyEvent(event); + for (auto& buffer : ctx->buffer_pool) { + ggml_vk_destroy_buffer(buffer); } - vk_gc.events.clear(); + + ctx->prealloc_size_qx = 0; + ctx->prealloc_size_qy = 0; + ctx->prealloc_size_x = 0; + ctx->prealloc_size_y = 0; + ctx->prealloc_size_split_k = 0; + ctx->staging_size = 0; + + for (auto& event : ctx->gc.events) { + ctx->device.lock()->device.destroyEvent(event); + } + ctx->gc.events.clear(); + + for (auto* pipeline : ctx->gc.pipelines) { + ggml_vk_destroy_pipeline(ctx, pipeline); + } + ctx->gc.pipelines.clear(); + + ctx->device.lock()->device.destroyFence(ctx->fence); + + ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->compute_queue.pool); + if (!ctx->device.lock()->single_queue) { + ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->transfer_queue.pool); + } +} + +GGML_CALL int ggml_vk_get_device_count() { + ggml_vk_instance_init(); + + return vk_instance.device_indices.size(); +} + +GGML_CALL void ggml_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + vk::PhysicalDeviceProperties props; + devices[device].getProperties(&props); + + snprintf(description, description_size, "%s", props.deviceName.data()); +} + +// CPU assist interface + +void ggml_vk_init_cpu_assist() { + ggml_vk_instance_init(); + + std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl; + + for (size_t i = 0; i < ggml_vk_get_device_count(); i++) { + ggml_vk_print_gpu_info(i); + } + // Initialize the first backend to make sure CPU matrix multiplications can be offloaded. + ggml_backend_vk_init(0); +} + +void ggml_vk_preallocate_buffers_graph_cpu_assist(ggml_tensor * node) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_preallocate_buffers_graph(ctx, node); +} + +void ggml_vk_preallocate_buffers_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_preallocate_buffers(ctx); +} + +void ggml_vk_build_graph_cpu_assist(ggml_tensor * node, bool last_node) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_build_graph(ctx, node, last_node); +} + +bool ggml_vk_compute_forward_cpu_assist(ggml_compute_params * params, ggml_tensor * tensor){ + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return false; + } + + return ggml_vk_compute_forward(ctx, params, tensor); +} + +void ggml_vk_graph_cleanup_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_graph_cleanup(ctx); +} + +void ggml_vk_free_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized || vk_instance.backends[0] == nullptr) { + return; + } + + ggml_backend_vk_free(vk_instance.backends[0]); } // backend interface #define UNUSED GGML_UNUSED -struct ggml_backend_vk_context { - std::string name; -}; - // device backend static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT struct ggml_backend_vk_buffer_context { + ggml_backend_vk_context * ctx; vk_buffer dev_buffer; ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; size_t temp_tensor_extra_index = 0; std::string name; - ggml_backend_vk_buffer_context(vk_buffer dev_buffer) : + ggml_backend_vk_buffer_context(ggml_backend_vk_context * ctx, vk_buffer&& dev_buffer, std::string& name) : + ctx(ctx), dev_buffer(dev_buffer), - name(GGML_VK_NAME) { + name(name) { } ~ggml_backend_vk_buffer_context() { @@ -4294,6 +4594,9 @@ GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { } GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl; +#endif ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_vk_destroy_buffer(ctx->dev_buffer); delete ctx; @@ -4313,6 +4616,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra(); if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) { + GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra; extra->buffer_gpu = extra_view->buffer_gpu; extra->offset = extra_view->offset + tensor->view_offs; @@ -4331,11 +4635,13 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_write(&extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(buffer); + ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size); } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { @@ -4344,31 +4650,35 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(buffer); + ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size); } GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_vk_buffer_copy(&src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + vk_buffer src_buf = src_extra->buffer_gpu.lock(); + vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); + + ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src)); return true; } return false; - - UNUSED(buffer); } GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - ggml_vk_buffer_memset(&ctx->dev_buffer, 0, value, buffer->size); + ggml_vk_buffer_memset(ctx->ctx, ctx->dev_buffer, 0, value, buffer->size); } static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { @@ -4386,6 +4696,7 @@ static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { // vk buffer type struct ggml_backend_vk_buffer_type_context { std::string name; + ggml_backend_vk_context * ctx; }; GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -4398,25 +4709,22 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer( #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif - vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size); - ggml_backend_vk_buffer_context * ctx = new ggml_backend_vk_buffer_context(dev_buffer); + ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name); - return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, ctx, size); - - UNUSED(buft); + return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size); } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_device.properties.limits.minStorageBufferOffsetAlignment; - - UNUSED(buft); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - return vk_device.max_memory_allocation_size; - - UNUSED(buft); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->ctx->device.lock()->max_memory_allocation_size; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { @@ -4426,9 +4734,14 @@ GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_ } GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_vk(backend); + if (!ggml_backend_is_vk(backend)) { + return false; + } - UNUSED(buft); + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + return buft_ctx->ctx->idx == ctx->idx; } static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { @@ -4441,20 +4754,16 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { /* .is_host = */ NULL, }; -GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type() { - static ggml_backend_buffer_type ggml_backend_vk_buffer_type; +GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t idx) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_buffer_type(" << idx << ")" << std::endl; +#endif - static bool ggml_backend_vk_buffer_type_initialized = false; + GGML_ASSERT(idx < vk_instance.device_indices.size()); - if (!ggml_backend_vk_buffer_type_initialized) { - ggml_backend_vk_buffer_type = { - /* .iface = */ ggml_backend_vk_buffer_type_interface, - /* .context = */ new ggml_backend_vk_buffer_type_context{GGML_VK_NAME}, - }; - ggml_backend_vk_buffer_type_initialized = true; - } + ggml_backend_vk_init(idx); - return &ggml_backend_vk_buffer_type; + return &vk_instance.buffer_types[idx]; } // host buffer type @@ -4472,13 +4781,19 @@ GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buff } GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_vk_host_free(buffer->context); +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl; +#endif + ggml_vk_host_free(&vk_instance.contexts[0], buffer->context); } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; +#endif void * ptr = nullptr; try { - ptr = ggml_vk_host_malloc(size); + ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size); } catch (vk::SystemError& e) { std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -4495,7 +4810,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu } GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_device.properties.limits.minMemoryMapAlignment; + return vk_instance.contexts[0].device.lock()->properties.limits.minMemoryMapAlignment; UNUSED(buft); } @@ -4514,127 +4829,150 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .context = */ nullptr, }; + if (!vk_instance.contexts[0].initialized) { + // Fall back to CPU + return ggml_backend_cpu_buffer_type(); + } + return &ggml_backend_vk_buffer_type_host; } // backend GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) { - ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - return vk_ctx->name.c_str(); + return ctx->name.c_str(); } GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { - ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl; +#endif - delete vk_ctx; + size_t idx = ctx->idx; + + ggml_vk_cleanup(ctx); + + // Release device + vk_instance.devices[ctx->idx].reset(); + ctx->initialized = false; + + vk_instance.initialized[idx] = false; + vk_instance.backends[idx] = nullptr; + memset(&vk_instance.buffer_types[idx], 0, sizeof(ggml_backend_buffer_type)); delete backend; } GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_vk_buffer_type(); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - UNUSED(backend); + GGML_ASSERT(ctx->initialized); + + return ggml_backend_vk_buffer_type(ctx->idx); } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_write_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(backend); + ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_read_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(backend); + ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif - if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_copy_async(vk_transfer_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + vk_buffer src_buf = src_extra->buffer_gpu.lock(); + vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); + + ggml_vk_buffer_copy_async(ctx->transfer_ctx, src_buf, src_extra->offset, dst_buf, dst_extra->offset, ggml_nbytes(src)); return true; } return false; - - UNUSED(backend); } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_synchronize()" << std::endl; #endif - if(vk_transfer_ctx == nullptr) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if(ctx->transfer_ctx == nullptr) { return; } - ggml_vk_ctx_end(vk_transfer_ctx); + ggml_vk_ctx_end(ctx->transfer_ctx); - for (auto& cpy : vk_transfer_ctx->in_memcpys) { + for (auto& cpy : ctx->transfer_ctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(vk_transfer_ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(ctx->transfer_ctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : vk_transfer_ctx->out_memcpys) { + for (auto& cpy : ctx->transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - vk_transfer_ctx = nullptr; - - UNUSED(backend); + ctx->transfer_ctx = nullptr; } GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - // ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]); } - ggml_vk_preallocate_buffers(); + ggml_vk_preallocate_buffers(ctx); int last_node = cgraph->n_nodes - 1; @@ -4644,7 +4982,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == last_node); + ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node); } ggml_compute_params params = {}; @@ -4657,19 +4995,19 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml continue; } - bool ok = ggml_vk_compute_forward(¶ms, node); + bool ok = ggml_vk_compute_forward(ctx, ¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } #ifdef GGML_VULKAN_CHECK_RESULTS else { - ggml_vk_check_results_1(¶ms, node); + ggml_vk_check_results_1(ctx, ¶ms, node); } #endif GGML_ASSERT(ok); } - ggml_vk_graph_cleanup(); + ggml_vk_graph_cleanup(ctx); return true; @@ -4734,7 +5072,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } return false; } break; - // case GGML_OP_DUP: + case GGML_OP_DUP: // case GGML_OP_REPEAT: // { // ggml_type src0_type = op->src[0]->type; @@ -4786,18 +5124,30 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .supports_op = */ ggml_backend_vk_supports_op, }; -GGML_CALL ggml_backend_t ggml_backend_vk_init() { - ggml_vk_init(); // TODO: remove from ggml.c +GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { + if (vk_instance.initialized[idx]) { + return vk_instance.backends[idx]; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_init(" << idx << ")" << std::endl; +#endif - ggml_backend_vk_context * ctx = new ggml_backend_vk_context { - /* .name = */ GGML_VK_NAME, + ggml_backend_vk_context * ctx = &vk_instance.contexts[idx]; + ggml_vk_init(ctx, idx); + ctx->name = GGML_VK_NAME + std::to_string(idx); + vk_instance.buffer_types[idx] = { + /* .iface = */ ggml_backend_vk_buffer_type_interface, + /* .context = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx }, }; + vk_instance.initialized[idx] = true; ggml_backend_t vk_backend = new ggml_backend { /* .interface = */ ggml_backend_vk_interface, - /* .context = */ ctx + /* .context = */ &vk_instance.contexts[ctx->idx], }; + vk_instance.backends[idx] = vk_backend; + return vk_backend; } @@ -4805,20 +5155,47 @@ GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_vk_name; } +GGML_CALL int ggml_backend_vk_get_device_count() { + return ggml_vk_get_device_count(); +} + +GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_get_device_description(device, description, description_size); +} + +GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { + GGML_ASSERT(device < vk_instance.device_indices.size()); + + vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; + + vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); + + for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { + if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { + *total = heap.size; + *free = heap.size; + break; + } + } +} + // backend registry GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) { - ggml_backend_t vk_backend = ggml_backend_vk_init(); + ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data); return vk_backend; UNUSED(params); - UNUSED(user_data); } extern "C" GGML_CALL int ggml_backend_vk_reg_devices(); GGML_CALL int ggml_backend_vk_reg_devices() { - ggml_backend_register(GGML_VK_NAME, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(), nullptr); - return 1; + for (auto idx : vk_instance.device_indices) { + char name[128]; + snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, idx); + ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(idx), (void *) (intptr_t) idx); + } + return vk_instance.device_indices.size(); } // checks @@ -4874,7 +5251,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d } } -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { +static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; if (tensor->backend == GGML_BACKEND_GPU) { @@ -4883,7 +5260,7 @@ static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -4944,7 +5321,7 @@ void * comp_result; size_t comp_size; size_t comp_nb[GGML_MAX_DIMS]; size_t check_counter = 0; -static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -4966,7 +5343,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * /*.no_alloc =*/ false, }; - struct ggml_context * ctx = ggml_init(iparams); + struct ggml_context * ggml_ctx = ggml_init(iparams); struct ggml_tensor * src0_clone = nullptr; struct ggml_tensor * src1_clone = nullptr; @@ -4979,7 +5356,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * void * src1_buffer; if (src0 != nullptr) { - src0_clone = ggml_dup_tensor(ctx, src0); + src0_clone = ggml_dup_tensor(ggml_ctx, src0); src0_size = ggml_nbytes(src0); @@ -4995,7 +5372,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5005,10 +5382,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu.size) { - src0_size = extra->buffer_gpu.size - offset; + if (offset + src0_size >= extra->buffer_gpu->size) { + src0_size = extra->buffer_gpu->size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5016,13 +5393,13 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src0, "src0"); + ggml_vk_print_tensor(ctx, src0, "src0"); } ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone); } if (src1 != nullptr) { - src1_clone = ggml_dup_tensor(ctx, src1); + src1_clone = ggml_dup_tensor(ggml_ctx, src1); src1_size = ggml_nbytes(src1); @@ -5038,7 +5415,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5048,10 +5425,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu.size) { - src1_size = extra->buffer_gpu.size - offset; + if (offset + src1_size >= extra->buffer_gpu->size) { + src1_size = extra->buffer_gpu->size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5059,7 +5436,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src1, "src1"); + ggml_vk_print_tensor(ctx, src1, "src1"); std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl; std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; if (src1->src[0] != nullptr) { @@ -5082,51 +5459,51 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (tensor->op == GGML_OP_MUL_MAT) { - tensor_clone = ggml_mul_mat(ctx, src0_clone, src1_clone); + tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_MUL) { - tensor_clone = ggml_mul(ctx, src0_clone, src1_clone); + tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_SCALE) { - tensor_clone = ggml_scale(ctx, src0_clone, ((float *)tensor->op_params)[0]); + tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]); } else if (tensor->op == GGML_OP_SQR) { - tensor_clone = ggml_sqr(ctx, src0_clone); + tensor_clone = ggml_sqr(ggml_ctx, src0_clone); } else if (tensor->op == GGML_OP_CLAMP) { - tensor_clone = ggml_clamp(ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else if (tensor->op == GGML_OP_ADD) { - tensor_clone = ggml_add(ctx, src0_clone, src1_clone); + tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_NORM) { - tensor_clone = ggml_norm(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_RMS_NORM) { - tensor_clone = ggml_rms_norm(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ctx, src0_clone, src1_clone, *(float *)tensor->op_params); + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); } else { - tensor_clone = ggml_soft_max(ctx, src0_clone); + tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { - tensor_clone = ggml_diag_mask_inf(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + const int n_ggml_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ggml_ctx = ((int32_t *) tensor->op_params)[4]; float freq_base = ((float *) tensor->op_params)[5]; float freq_scale = ((float *) tensor->op_params)[6]; float ext_factor = ((float *) tensor->op_params)[7]; float attn_factor = ((float *) tensor->op_params)[8]; float beta_fast = ((float *) tensor->op_params)[9]; float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_custom(ctx, src0_clone, src1_clone, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else if (tensor->op == GGML_OP_UNARY) { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_SILU: - tensor_clone = ggml_silu(ctx, src0_clone); + tensor_clone = ggml_silu(ggml_ctx, src0_clone); break; case GGML_UNARY_OP_GELU: - tensor_clone = ggml_gelu(ctx, src0_clone); + tensor_clone = ggml_gelu(ggml_ctx, src0_clone); break; case GGML_UNARY_OP_RELU: - tensor_clone = ggml_relu(ctx, src0_clone); + tensor_clone = ggml_relu(ggml_ctx, src0_clone); break; default: std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; @@ -5134,40 +5511,40 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { if (src1 == nullptr) { - tensor_clone = ggml_dup(ctx, src0_clone); + tensor_clone = ggml_dup(ggml_ctx, src0_clone); tensor_clone->type = tensor->type; } else { - tensor_clone = ggml_cpy(ctx, src0_clone, src1_clone); + tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone); } } else if (tensor->op == GGML_OP_CONT) { - tensor_clone = ggml_cont_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); } else if (tensor->op == GGML_OP_RESHAPE) { - tensor_clone = ggml_reshape_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); } else if (tensor->op == GGML_OP_VIEW) { - tensor_clone = ggml_view_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); + tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); } else if (tensor->op == GGML_OP_PERMUTE) { int32_t * params = (int32_t *)tensor->op_params; - tensor_clone = ggml_permute(ctx, src0_clone, params[0], params[1], params[2], params[3]); + tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]); } else if (tensor->op == GGML_OP_TRANSPOSE) { - tensor_clone = ggml_transpose(ctx, src0_clone); + tensor_clone = ggml_transpose(ggml_ctx, src0_clone); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ASSERT(false); } // Disable vulkan here to avoid the hooks in ggml.c - vk_disable = true; + ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ctx); + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_clone); - ggml_graph_compute_with_ctx(ctx, cgraph, 8); + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - vk_disable = false; + ctx->disable = false; ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(tensor_clone, "tensor_clone"); + ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone"); } comp_size = ggml_nbytes(tensor_clone); @@ -5183,10 +5560,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * free(src1_buffer); } - ggml_free(ctx); + ggml_free(ggml_ctx); } -void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -5208,11 +5585,11 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu.size) { - tensor_size = extra->buffer_gpu.size - (extra->offset); + if (extra->offset + tensor_size >= extra->buffer_gpu->size) { + tensor_size = extra->buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; @@ -5339,4 +5716,10 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) free(tensor_data); } } + +void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + ggml_vk_check_results_0(ctx, params, tensor); +} #endif diff --git a/ggml-vulkan.h b/ggml-vulkan.h index eb8a148e2..9645126b4 100644 --- a/ggml-vulkan.h +++ b/ggml-vulkan.h @@ -8,24 +8,29 @@ extern "C" { #endif #define GGML_VK_NAME "Vulkan" +#define GGML_VK_MAX_DEVICES 16 -GGML_API void ggml_vk_init(void); +GGML_API void ggml_vk_init_cpu_assist(void); -GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node); -GGML_API void ggml_vk_preallocate_buffers(void); -GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node); -GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node); +GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void); +GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node); +GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor); #ifdef GGML_VULKAN_CHECK_RESULTS -void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor); +void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor); #endif -GGML_API void ggml_vk_graph_cleanup(void); +GGML_API void ggml_vk_graph_cleanup_cpu_assist(void); +GGML_API void ggml_vk_free_cpu_assist(void); // backend API -GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void); +GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num); GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend); +GGML_API GGML_CALL int ggml_backend_vk_get_device_count(void); +GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); -GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); diff --git a/ggml.c b/ggml.c index b9ec0c981..f783a6fd3 100644 --- a/ggml.c +++ b/ggml.c @@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { #elif defined(GGML_USE_CLBLAST) ggml_cl_init(); #elif defined(GGML_USE_VULKAN) - ggml_vk_init(); + ggml_vk_init_cpu_assist(); #elif defined(GGML_USE_SYCL) ggml_init_sycl(); #endif @@ -14850,10 +14850,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); #elif defined(GGML_USE_VULKAN) - const bool skip_cpu = ggml_vk_compute_forward(params, tensor); + const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor); #ifdef GGML_VULKAN_CHECK_RESULTS if (skip_cpu) { - ggml_vk_check_results_1(params, tensor); + ggml_vk_check_results_1_cpu_assist(params, tensor); } #endif if (skip_cpu) { @@ -17269,12 +17269,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { #ifdef GGML_USE_VULKAN for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]); } - ggml_vk_preallocate_buffers(); + ggml_vk_preallocate_buffers_cpu_assist(); for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1); } #endif @@ -17330,7 +17330,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { } #ifdef GGML_USE_VULKAN - ggml_vk_graph_cleanup(); + ggml_vk_graph_cleanup_cpu_assist(); #endif // performance stats (graph) diff --git a/llama.cpp b/llama.cpp index f3c5146d1..c45ae1d50 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1355,7 +1355,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { #elif defined(GGML_USE_CUBLAS) buft = ggml_backend_cuda_buffer_type(gpu); #elif defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(); + buft = ggml_backend_vk_buffer_type(gpu); #elif defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(gpu); #elif defined(GGML_USE_CLBLAST) @@ -1392,6 +1392,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g GGML_UNUSED(tensor_split); } +static size_t llama_get_device_count() { +#if defined(GGML_USE_CUBLAS) + return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_VULKAN) + return ggml_backend_vk_get_device_count(); +#else + return 1; +#endif +} + +static size_t llama_get_device_memory(int device) { +#if defined(GGML_USE_CUBLAS) + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(device, &total, &free); + return free; +#elif defined(GGML_USE_VULKAN) + size_t total; + size_t free; + ggml_backend_vk_get_device_memory(device, &total, &free); + return free; +#else + return 1; + GGML_UNUSED(device); +#endif +} + // // globals // @@ -1763,6 +1790,10 @@ struct llama_context { ggml_backend_free(backend); } +#ifdef GGML_USE_VULKAN + ggml_vk_free_cpu_assist(); +#endif + ggml_backend_buffer_free(buf_input); ggml_free(ctx_input); } @@ -3436,22 +3467,18 @@ static bool llm_load_tensors( model.buft_layer[i] = llama_default_buffer_type_cpu(true); } -#ifdef GGML_USE_CUBLAS if (split_mode == LLAMA_SPLIT_LAYER) { // calculate the split points - int device_count = ggml_backend_cuda_get_device_count(); + int device_count = llama_get_device_count(); bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); - float splits[GGML_CUDA_MAX_DEVICES]; + std::vector splits(device_count); if (all_zero) { // default split, by free memory for (int i = 0; i < device_count; ++i) { - size_t total; - size_t free; - ggml_backend_cuda_get_device_memory(i, &total, &free); - splits[i] = free; + splits[i] = llama_get_device_memory(i); } } else { - std::copy(tensor_split, tensor_split + device_count, splits); + std::copy(tensor_split, tensor_split + device_count, splits.begin()); } // sum and normalize the splits to get the split points @@ -3467,19 +3494,17 @@ static bool llm_load_tensors( // assign the repeating layers to the devices according to the splits int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); for (int64_t i = i_gpu_start; i < n_layer; ++i) { - int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits; + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); } // assign the output layer if (n_gpu_layers > n_layer) { - int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits; + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); model.buft_output = llama_default_buffer_type_offload(layer_gpu); } else { model.buft_output = llama_default_buffer_type_cpu(true); } - } else -#endif - { + } else { ggml_backend_buffer_type_t split_buft; if (split_mode == LLAMA_SPLIT_ROW) { split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); @@ -10483,6 +10508,8 @@ size_t llama_max_devices(void) { return GGML_CUDA_MAX_DEVICES; #elif defined(GGML_USE_SYCL) return GGML_SYCL_MAX_DEVICES; +#elif defined(GGML_USE_VULKAN) + return GGML_VK_MAX_DEVICES; #else return 1; #endif @@ -10690,13 +10717,15 @@ struct llama_context * llama_new_context_with_model( } #elif defined(GGML_USE_VULKAN) if (model->n_gpu_layers > 0) { - ggml_backend_t backend = ggml_backend_vk_init(); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; + for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) { + ggml_backend_t backend = ggml_backend_vk_init(device); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); } - ctx->backends.push_back(backend); } #elif defined(GGML_USE_SYCL) if (model->n_gpu_layers > 0) { From 0ef46da632c32faa1a538e5dc180994e8bbb46e1 Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Wed, 7 Feb 2024 02:17:25 -0600 Subject: [PATCH 258/334] llava-cli : always tokenize special tokens (#5382) * llava-cli: tokenize special tokens in prompt * llava-cli: use the escape CLI argument, remove incomplete separate escaping process --- examples/llava/llava-cli.cpp | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 6ac70ba69..031e9806d 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -34,7 +34,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); + std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true); eval_tokens(ctx_llama, embd_inp, n_batch, n_past); return true; } @@ -152,20 +152,8 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ size_t image_pos = prompt.find(""); if (image_pos != std::string::npos) { // new templating mode: Provide the full prompt including system message and use as a placeholder for the image - system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); - // We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string - size_t pos = 0; - while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) { - user_prompt.replace(pos, 2, "\n"); - pos += 1; // Advance past the replaced newline - } - while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) { - system_prompt.replace(pos, 2, "\n"); - pos += 1; // Advance past the replaced newline - } - printf("system_prompt: %s\n", system_prompt.c_str()); printf("user_prompt: %s\n", user_prompt.c_str()); } else { From 10afa6f1d11ebc9fcc1085f468170002cbf6e2b5 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 7 Feb 2024 18:16:55 +0800 Subject: [PATCH 259/334] [SYCL] update install make by w64devkit (#5297) --- README-sycl.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 7aa4274a9..e3a8e726e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -311,15 +311,13 @@ Output (example): a. Download & install cmake for Windows: https://cmake.org/download/ -b. Download & install make for Windows provided by mingw-w64 +b. Download & install mingw-w64 make for Windows provided by w64devkit -- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases. +- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z). +- Extract `w64devkit` on your pc. -- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**. - -- Add the **bin** folder path in the Windows system PATH environment. +- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`. ### Build locally: From aa7ab99be29b633263803f2e185265734c2d9427 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 7 Feb 2024 12:40:26 +0100 Subject: [PATCH 260/334] CUDA: fixed mmvq kernel for bs 2,3,4 and -sm row (#5386) --- ggml-cuda.cu | 66 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 39 insertions(+), 27 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 3b828375e..db9da2459 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5313,7 +5313,7 @@ template static __global__ void template static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) { + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) { const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; @@ -5352,7 +5352,7 @@ static __global__ void mul_mat_vec_q( tmp[j] = warp_reduce_sum(tmp[j]); if (threadIdx.x == 0) { - dst[j*nrows_x + row] = tmp[j]; + dst[j*nrows_dst + row] = tmp[j]; } } } @@ -6828,7 +6828,7 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa template static void mul_mat_vec_q_cuda( const void * vx, const void * vy, float * dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_y <= 4); @@ -6839,40 +6839,40 @@ static void mul_mat_vec_q_cuda( switch (ncols_y) { case 1: mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 2: mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 3: mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 4: mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; // case 5: // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 6: // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 7: // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 8: // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; default: GGML_ASSERT(false); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; } } @@ -8391,7 +8391,7 @@ static void ggml_cuda_op_mul_mat_q( CUDA_CHECK(cudaGetDevice(&id)); // the main device has a larger memory buffer to hold the results from all GPUs - // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into + // nrows_dst == nrows of the matrix that the kernel writes into const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; switch (src0->type) { @@ -8525,58 +8525,70 @@ static void ggml_cuda_op_mul_mat_vec_q( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + const int64_t ne10 = src1->ne[0]; + GGML_ASSERT(ne10 % QK8_1 == 0); + + const int64_t ne0 = dst->ne[0]; + + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the kernel writes into + const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; + switch (src0->type) { case GGML_TYPE_Q4_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q4_1: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_1: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q8_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q2_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q3_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q4_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q6_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ2_XXS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ2_XS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ3_XXS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; default: GGML_ASSERT(false); @@ -9909,7 +9921,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); From b906596bb775b17656c2e51d5ab1b347faab6860 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kamil=20Tom=C5=A1=C3=ADk?= Date: Wed, 7 Feb 2024 19:44:52 +0100 Subject: [PATCH 261/334] Add Ava in the list of llama.cpp UIs (#4362) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0509b0ba1..7e1187349 100644 --- a/README.md +++ b/README.md @@ -150,6 +150,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [ollama/ollama](https://github.com/ollama/ollama) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) - [semperai/amica](https://github.com/semperai/amica) From 8c933b70c21e05b685d476d0a1f36b34cbda7365 Mon Sep 17 00:00:00 2001 From: Ebey Abraham Date: Wed, 7 Feb 2024 21:11:30 +0000 Subject: [PATCH 262/334] fix typo in readme (#5399) Co-authored-by: Ebey Abraham --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7e1187349..66166c01b 100644 --- a/README.md +++ b/README.md @@ -680,7 +680,7 @@ python3 -m pip install -r requirements.txt python3 convert.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocabtype bpe +python convert.py models/mymodel/ --vocab-type bpe # quantize the model to 4-bits (using Q4_K_M method) ./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M From c4fbb6717c684196bd13b72d21747557130914e8 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Wed, 7 Feb 2024 22:39:23 +0100 Subject: [PATCH 263/334] CMAKE_OSX_ARCHITECTURES for MacOS cross compilation (#5393) Co-authored-by: Jared Van Bortel --- CMakeLists.txt | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 427015be5..a544f2da6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -850,7 +850,9 @@ endif() set(ARCH_FLAGS "") -if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) +if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) message(STATUS "ARM detected") if (MSVC) add_compile_definitions(__ARM_NEON) @@ -876,7 +878,9 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) +elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) message(STATUS "x86 detected") if (MSVC) # instruction set detection for MSVC only From 8504d2d0da8cc7a1f2eee0e9e56949f960510b75 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 8 Feb 2024 09:46:47 +0200 Subject: [PATCH 264/334] tests : .gitignore obj files --- tests/.gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/.gitignore b/tests/.gitignore index 092dce742..9427cf13d 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -1,3 +1,3 @@ * !*.* -test-c.o +*.o From 26d4efd11e48908e14e2ee9471a7fc4c57079a1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 8 Feb 2024 09:46:30 +0100 Subject: [PATCH 265/334] sampling: fix top_k <= 0 (#5388) * sampling: fix top_k <= 0 * Update llama.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- common/sampling.cpp | 2 +- llama.cpp | 4 ++++ tests/test-sampling.cpp | 2 ++ 3 files changed, 7 insertions(+), 1 deletion(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index e8675a8c0..844ad7c53 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -132,7 +132,7 @@ static void sampler_queue( const float temp = params.temp; const float dynatemp_range = params.dynatemp_range; const float dynatemp_exponent = params.dynatemp_exponent; - const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const int32_t top_k = params.top_k; const float top_p = params.top_p; const float min_p = params.min_p; const float tfs_z = params.tfs_z; diff --git a/llama.cpp b/llama.cpp index c45ae1d50..f8f5796a4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8585,6 +8585,10 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can // } const int64_t t_start_sample_us = ggml_time_us(); + + if (k <= 0) { + k = candidates->size; + } k = std::max(k, (int) min_keep); k = std::min(k, (int) candidates->size); diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index c3b3d6629..6374958fe 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -235,6 +235,8 @@ int main(void) { test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1); test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3); + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4); + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0); test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0); test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f); From a6e514a85f0fda38ff78ec91782877ea3d19ed98 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 8 Feb 2024 09:58:19 +0100 Subject: [PATCH 266/334] llava: fix typo/formatting in README.md (#5405) This commit fixes a typo in the README.md file for the llava example which is causing the formatting to look a little off: Clone llava-v15-7b`` and clip-vit-large-patch14-336`` locally Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 323c5fdd0..295181a34 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -21,7 +21,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## Model conversion -- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally: +- Clone `llava-v15-7b` and `clip-vit-large-patch14-336` locally: ```sh git clone https://huggingface.co/liuhaotian/llava-v1.5-7b From 4aa43fab569215a13495a7f1a0f8afc541b16d03 Mon Sep 17 00:00:00 2001 From: runfuture Date: Thu, 8 Feb 2024 18:36:19 +0800 Subject: [PATCH 267/334] llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint --- convert-hf-to-gguf.py | 63 +++++++++++++++++++++++++++++++++++++++++-- llama.cpp | 2 ++ 2 files changed, 63 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 829d68368..0d4ea03b4 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1078,17 +1078,76 @@ class MiniCPMModel(Model): self.gguf_writer.add_name("MiniCPM") self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) def set_vocab(self): self._set_vocab_hf() + def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return ( + weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape) + ) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + n_head = self.hparams.get("num_attention_heads") + n_kv_head = self.hparams.get("num_key_value_heads") + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # HF models permute some of the tensors, so we need to undo that + if name.endswith(("q_proj.weight")): + data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight")): + data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + class QwenModel(Model): @staticmethod diff --git a/llama.cpp b/llama.cpp index f8f5796a4..552e0d02e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2947,6 +2947,8 @@ static void llm_load_hparams( } break; case LLM_ARCH_MINICPM: { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { case 40: model.type = e_model::MODEL_2B; break; default: model.type = e_model::MODEL_UNKNOWN; From b7b74cef36a93ae01e0b9af8986d131761742d0e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 8 Feb 2024 11:36:54 +0100 Subject: [PATCH 268/334] fix trailing whitespace (#5407) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 552e0d02e..89acafbc3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8587,7 +8587,7 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can // } const int64_t t_start_sample_us = ggml_time_us(); - + if (k <= 0) { k = candidates->size; } From ff4ff05c5ff4311c05a8ce1f984c7d8def4f07a5 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 8 Feb 2024 15:20:03 +0100 Subject: [PATCH 269/334] llava : add missing .py, and fix paths in README.md (#5414) This commit adds the missing .py extension to the convert-image-encoder-to-gguf script. It also fixes the paths for the `model` and `mmproj` options in the example llava-cli command. Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 295181a34..721d5e613 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -14,7 +14,7 @@ Build with cmake or run `make llava-cli` to build it. After building, run: `./llava-cli` to see the usage. For example: ```sh -./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg +./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. @@ -38,7 +38,7 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b 3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: From 6e99f2a04f1871d637dd77eb4d81de31a5510253 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Thu, 8 Feb 2024 22:39:10 +0530 Subject: [PATCH 270/334] Fix f16_sycl cpy call from Arc (#5411) * fix f16_sycl cpy call * rm old logic * add fp16 build CI * use macro * format fix --- .github/workflows/build.yml | 41 +++++++++++++++++++++++++++++++++++++ ggml-sycl.cpp | 8 +++++--- 2 files changed, 46 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f4c374ce5..ed292d6b8 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -184,6 +184,47 @@ jobs: cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx .. cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-sycl-fp16: + runs-on: ubuntu-22.04 + + continue-on-error: true + + steps: + - uses: actions/checkout@v2 + + - name: add oneAPI to apt + shell: bash + run: | + cd /tmp + wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main" + + - name: install oneAPI dpcpp compiler + shell: bash + run: | + sudo apt update + sudo apt install intel-oneapi-compiler-dpcpp-cpp + + - name: install oneAPI MKL library + shell: bash + run: | + sudo apt install intel-oneapi-mkl-devel + + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Build + id: cmake_build + run: | + source /opt/intel/oneapi/setvars.sh + mkdir build + cd build + cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON .. + cmake --build . --config Release -j $(nproc) + # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know # how to debug it. # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124 diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a03df4c65..dd562a898 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -12148,7 +12148,8 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( const int64_t src1_ncols, const int64_t src1_padded_row_size, const dpct::queue_ptr &stream) { - const int64_t ne00 = src0->ne[0]; + GGML_TENSOR_BINARY_OP_LOCALS + const int64_t row_diff = row_high - row_low; // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics @@ -12167,8 +12168,9 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( } else { src1_dfloat = src1_dfloat_a.alloc(ne00); ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat, - ne00, ne00, 1, sizeof(float), 0, 0, ne00, 1, - sizeof(sycl::half), 0, 0, stream); + ne00, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, + nb13, stream); } } #else From 41f308f58edc2a04bcf9e245100b0a9b10e9a0fb Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 8 Feb 2024 21:33:03 +0100 Subject: [PATCH 271/334] llama : do not print "offloading layers" message in CPU-only builds (#5416) --- llama.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index 89acafbc3..db7d1c1cd 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4209,8 +4209,7 @@ static bool llm_load_tensors( ctx_bufs.emplace_back(ctx, buf); } - // print memory requirements - { + if (llama_supports_gpu_offload()) { const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); @@ -4222,10 +4221,11 @@ static bool llm_load_tensors( const int max_offloadable_layers = hparams.n_layer + 1; LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); + } - for (ggml_backend_buffer_t buf : model.bufs) { - LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); - } + // print memory requirements + for (ggml_backend_buffer_t buf : model.bufs) { + LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); } // populate tensors_by_name From 8e6a9d2de0096af7120606c74ee2f26684e87b41 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 8 Feb 2024 21:56:40 +0100 Subject: [PATCH 272/334] CUDA: more warps for mmvq on NVIDIA (#5394) --- ggml-cuda.cu | 133 +++++++++++++++++++++++++++++++++------------------ 1 file changed, 86 insertions(+), 47 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index db9da2459..5053757e6 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5310,22 +5310,26 @@ template static __global__ void #endif // __CUDA_ARCH__ >= CC_VOLTA } -template +#define MMVQ_NWARPS_NVIDIA 4 +#define MMVQ_NWARPS_AMD_RDNA2 1 +#define MMVQ_NWARPS_AMD_OLD 4 + +template +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) // tells the compiler to use as many registers as it wants +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) { const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; - const int row = blockIdx.x*blockDim.y + threadIdx.y; - - if (row >= nrows_x) { - return; - } + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + const int row = blockIdx.x; const int blocks_per_row_x = ncols_x / qk; const int blocks_per_col_y = nrows_y / QK8_1; - const int blocks_per_warp = vdr * WARP_SIZE / qi; + const int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi; // partial sum for each thread float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f}; @@ -5333,12 +5337,12 @@ static __global__ void mul_mat_vec_q( const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) { + for (int i = tid / (qi/vdr); i < blocks_per_row_x; i += blocks_per_iter) { const int ibx = row*blocks_per_row_x + i; // x block index const int iby = i * (qk/QK8_1); // y block index that aligns with ibx - const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int + const int iqs = vdr * (tid % (qi/vdr)); // x block quant index when casting the quants to int #pragma unroll for (int j = 0; j < ncols_y; ++j) { @@ -5346,9 +5350,25 @@ static __global__ void mul_mat_vec_q( } } + __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y_template != 0 ? ncols_y_template : 8][WARP_SIZE]; + if (threadIdx.y > 0) { +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { + tmp_shared[threadIdx.y-1][j][threadIdx.x] = tmp[j]; + } + } + __syncthreads(); + if (threadIdx.y > 0) { + return; + } + // sum up partial sums and write back result #pragma unroll for (int j = 0; j < ncols_y; ++j) { +#pragma unroll + for (int i = 0; i < nwarps-1; ++i) { + tmp[j] += tmp_shared[i][j][threadIdx.x]; + } tmp[j] = warp_reduce_sum(tmp[j]); if (threadIdx.x == 0) { @@ -6833,46 +6853,65 @@ static void mul_mat_vec_q_cuda( GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_y <= 4); - const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - switch (ncols_y) { - case 1: - mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 2: - mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 3: - mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 4: - mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - // case 5: - // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; - // case 6: - // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; - // case 7: - // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; - // case 8: - // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - // break; + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + int nwarps; + if (g_device_caps[id].cc >= CC_OFFSET_AMD) { + nwarps = g_device_caps[id].cc >= CC_RDNA2 ? MMVQ_NWARPS_AMD_RDNA2 : MMVQ_NWARPS_AMD_OLD; + } else { + nwarps = MMVQ_NWARPS_NVIDIA; + } + + const dim3 block_nums(nrows_x, 1, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); + + switch (nwarps) { + case 1: switch(ncols_y) { + case 1: + mul_mat_vec_q<1, 1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 2: + mul_mat_vec_q<1, 2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 3: + mul_mat_vec_q<1, 3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 4: + mul_mat_vec_q<1, 4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + default: + GGML_ASSERT(false); + break; + } break; + case 4: switch(ncols_y) { + case 1: + mul_mat_vec_q<4, 1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 2: + mul_mat_vec_q<4, 2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 3: + mul_mat_vec_q<4, 3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + case 4: + mul_mat_vec_q<4, 4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); + break; + default: + GGML_ASSERT(false); + break; + } break; + default: GGML_ASSERT(false); - // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; } } From 44fbe34360dd760f9e68b4271f21533436397f84 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Fri, 9 Feb 2024 06:52:33 +0100 Subject: [PATCH 273/334] Fix Vulkan crash on APUs with very little device memory (#5424) * Fix Vulkan crash on APUs with very little device memory * Fix debug output function names --- ggml-vulkan.cpp | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 9e2846ee4..254f648a6 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -744,6 +744,8 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz } if (memory_type_index >= mem_props.memoryTypeCount) { + ctx->device.lock()->device.destroyBuffer(buf->buffer); + buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); } @@ -3875,7 +3877,7 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_ctx->preallocate_buffers_graph(" << node << ")" << std::endl; + std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) @@ -3994,8 +3996,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { return; } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_ctx->preallocate_buffers()" << std::endl; - std::cerr << "qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << std::endl; + std::cerr << "ggml_vk_preallocate_buffers(qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; #endif #if defined(GGML_VULKAN_RUN_TESTS) ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); From b2f87cb64db47d799b6f3656855c9caf9792ab2a Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Fri, 9 Feb 2024 10:56:43 +0100 Subject: [PATCH 274/334] ggml : fix `error C2078: too many initializers` for MSVC ARM64 (#5404) --- ggml-quants.c | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 101d3e783..1031e3761 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -268,6 +268,17 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) #if defined(__ARM_NEON) + +#ifdef _MSC_VER + +#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) } + +#else + +#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) } + +#endif + #if !defined(__aarch64__) // 64-bit compatibility @@ -8698,10 +8709,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(const int n, float * restrict s, const void * res for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { q8b = ggml_vld1q_s8_x4(q8); q8 += 64; memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); - const uint32x4_t aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; - const uint32x4_t aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; - const uint32x4_t aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; - const uint32x4_t aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); q3 += 16; q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); From e4124c24775f2cb5b3d7acc93bf9dc5471c172ef Mon Sep 17 00:00:00 2001 From: Marko Tasic Date: Fri, 9 Feb 2024 11:17:00 +0100 Subject: [PATCH 275/334] readme : add JavaScript/Wasm repo (#5415) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 66166c01b..0b4efdd33 100644 --- a/README.md +++ b/README.md @@ -124,6 +124,7 @@ Typically finetunes of the base models below are supported as well. - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) +- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) From e5ca3937c685d6e012ac4db40555d6ec100ff03c Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Fri, 9 Feb 2024 10:48:06 +0000 Subject: [PATCH 276/334] llama : do not cap thread count when MoE on CPU (#5419) * Not capping thread count when MoE inference is running on CPU * Whitespace --- llama.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index db7d1c1cd..0566b087b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7285,7 +7285,9 @@ static int llama_decode_internal( // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well // we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering // with the BLAS calls. need a better solution - if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) { + // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is + // being processed then Accelerate/BLAS will not be involved, so capping would limit performance. + if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) { n_threads = std::min(4, n_threads); } From 7c777fcd5dd4af7079e33390cf6a19c328a2666f Mon Sep 17 00:00:00 2001 From: Riley Stewart Date: Fri, 9 Feb 2024 02:49:49 -0800 Subject: [PATCH 277/334] server : fix prompt caching for repeated prompts (#5420) --- examples/server/server.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index eceda30d0..8d668f798 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1592,10 +1592,6 @@ struct llama_server_context LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } - LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); - - llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); - slot.cache_tokens = prompt_tokens; if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) @@ -1609,6 +1605,10 @@ struct llama_server_context } } + LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past); + + llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1); + LOG_VERBOSE("prompt ingested", { {"n_past", slot.n_past}, {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, From e00d2a62dd1441e3b089570ec06d05c18800d368 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 9 Feb 2024 14:00:59 +0100 Subject: [PATCH 278/334] llava : add requirements.txt and update README.md (#5428) * llava: add requirements.txt and update README.md This commit adds a `requirements.txt` file to the `examples/llava` directory. This file contains the required Python packages to run the scripts in the `examples/llava` directory. The motivation of this to make it easier for users to run the scripts in `examples/llava`. This will avoid users from having to possibly run into missing package issues if the packages are not installed on their system. Signed-off-by: Daniel Bevenius * llava: fix typo in llava-surgery.py output Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 12 +++++++++--- examples/llava/llava-surgery.py | 2 +- examples/llava/requirements.txt | 3 +++ 3 files changed, 13 insertions(+), 4 deletions(-) create mode 100644 examples/llava/requirements.txt diff --git a/examples/llava/README.md b/examples/llava/README.md index 721d5e613..19f1a50a2 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -29,19 +29,25 @@ git clone https://huggingface.co/liuhaotian/llava-v1.5-7b git clone https://huggingface.co/openai/clip-vit-large-patch14-336 ``` -2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: +2. Install the required Python packages: + +```sh +pip install -r examples/llava/requirements.txt +``` + +3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b ``` -3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: +4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: ```sh python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: ```sh python ./convert.py ../llava-v1.5-7b diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava-surgery.py index 515f6b58d..0a61efdfe 100644 --- a/examples/llava/llava-surgery.py +++ b/examples/llava/llava-surgery.py @@ -42,5 +42,5 @@ if len(clip_tensors) > 0: torch.save(checkpoint, path) print("Done!") -print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.") +print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.") diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt new file mode 100644 index 000000000..f80f727a7 --- /dev/null +++ b/examples/llava/requirements.txt @@ -0,0 +1,3 @@ +-r ../../requirements/requirements-convert.txt +pillow~=10.2.0 +torch~=2.1.1 From 4b7b38bef5addbd31f453871d79647fbae6bec8a Mon Sep 17 00:00:00 2001 From: Neuman Vong Date: Sat, 10 Feb 2024 05:30:19 +1100 Subject: [PATCH 279/334] vulkan: Set limit for task concurrency (#5427) A common default for the maximum number of open files is 256, which can lead to `asyncio.gather(*tasks)` failing with Too many open files. $ python ggml_vk_generate_shaders.py --glslc=$ANDROID_NDK_PATH/shader-tools/darwin-x86_64/glslc ggml_vulkan: Generating and compiling shaders to SPIR-V Traceback (most recent call last): File "/Users/neuman/Code.noindex/github/llama.cpp/ggml_vk_generate_shaders.py", line 2326, in asyncio.run(main()) File "/Users/neuman/Code.noindex/miniforge3/lib/python3.10/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/Users/neuman/Code.noindex/miniforge3/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete return future.result() File "/Users/neuman/Code.noindex/github/llama.cpp/ggml_vk_generate_shaders.py", line 2294, in main await asyncio.gather(*tasks) [...snip...] OSError: [Errno 24] Too many open files This change sets a reasonable concurrency limit for tasks (and therefore open files), without significant impact on run time. --- ggml_vk_generate_shaders.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 4abb0383f..b2e86e182 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2067,6 +2067,8 @@ type_names = { K_QUANTS_PER_ITERATION = 2 +ASYNCIO_CONCURRENCY = 64 + output_dir = gettempdir() lock = asyncio.Lock() @@ -2291,7 +2293,14 @@ async def main(): tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - await asyncio.gather(*tasks) + # Helper to decorate tasks with semaphore acquisition. + async def withSemaphore(sem, task): + async with sem: + return await task + + # Run tasks concurrently guarded by a concurrency limit. + sem = asyncio.Semaphore(ASYNCIO_CONCURRENCY) + await asyncio.gather(*(withSemaphore(sem, task) for task in tasks)) with open("ggml-vulkan-shaders.hpp", "w") as f: f.write("#include \n\n") From 4633d93af08d890ecd00fa6e4f61d76f21cded4c Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Fri, 9 Feb 2024 10:42:27 +0100 Subject: [PATCH 280/334] ggml : add abort_callback for cpu backend (ggml/725) * a way to use abort_callback with the cpu backend * whisper update --- ggml-backend.c | 26 ++++++++++++++++++++++---- ggml-backend.h | 5 +++-- ggml.c | 2 +- ggml.h | 9 +++++++-- 4 files changed, 33 insertions(+), 9 deletions(-) diff --git a/ggml-backend.c b/ggml-backend.c index 0764dfebc..532da8eda 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -653,6 +653,9 @@ struct ggml_backend_cpu_context { int n_threads; void * work_data; size_t work_size; + + ggml_abort_callback abort_callback; + void * abort_callback_data; }; GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) { @@ -691,6 +694,9 @@ GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(gg cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size); } + cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; + cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; + return cpu_plan; } @@ -721,9 +727,11 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size); cpu_ctx->work_size = cplan.work_size; } - cplan.work_data = cpu_ctx->work_data; + cplan.abort_callback = cpu_ctx->abort_callback; + cplan.abort_callback_data = cpu_ctx->abort_callback_data; + ggml_graph_compute(cgraph, &cplan); return true; } @@ -759,9 +767,11 @@ static struct ggml_backend_i cpu_backend_i = { ggml_backend_t ggml_backend_cpu_init(void) { struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context)); - ctx->n_threads = GGML_DEFAULT_N_THREADS; - ctx->work_data = NULL; - ctx->work_size = 0; + ctx->n_threads = GGML_DEFAULT_N_THREADS; + ctx->work_data = NULL; + ctx->work_size = 0; + ctx->abort_callback = NULL; + ctx->abort_callback_data = NULL; ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend)); @@ -783,6 +793,14 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { ctx->n_threads = n_threads; } +void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->abort_callback = abort_callback; + ctx->abort_callback_data = abort_callback_data; +} + GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size); } diff --git a/ggml-backend.h b/ggml-backend.h index 8b8160fcf..282b3a9b7 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -83,8 +83,9 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_cpu_init(void); - GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); + GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend); + GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads); + GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data); // Create a backend buffer from an existing pointer GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); diff --git a/ggml.c b/ggml.c index f783a6fd3..86cd65862 100644 --- a/ggml.c +++ b/ggml.c @@ -16649,7 +16649,7 @@ struct ggml_compute_state_shared { atomic_int node_n; // active graph node atomic_int node_task; // active graph node task phase - bool (*abort_callback)(void * data); // abort ggml_graph_compute when true + ggml_abort_callback abort_callback; // abort ggml_graph_compute when true void * abort_callback_data; }; diff --git a/ggml.h b/ggml.h index e0a4799f3..1360cd8ee 100644 --- a/ggml.h +++ b/ggml.h @@ -567,6 +567,11 @@ extern "C" { static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor); + // Abort callback + // If not NULL, called before ggml computation + // If it returns true, the computation is aborted + typedef bool (*ggml_abort_callback)(void * data); + // the compute plan that needs to be prepared for ggml_graph_compute() // since https://github.com/ggerganov/ggml/issues/287 struct ggml_cplan { @@ -576,8 +581,8 @@ extern "C" { int n_threads; // abort ggml_graph_compute when true - bool (*abort_callback)(void * data); - void * abort_callback_data; + ggml_abort_callback abort_callback; + void * abort_callback_data; }; enum ggml_cgraph_eval_order { From 43b65f5eb85e8741aba573a8f65bb8efad245d31 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 10 Feb 2024 09:30:36 +0200 Subject: [PATCH 281/334] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 7b6c17915..6ae75bc31 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -475cbad5c1c834e31e26a2283bc1413181644360 +2c7cf49810d523b9632da393a9e8270b60bf3b24 From cd9aea63b577a83def84dbd6dcd90a6fa02af745 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 10 Feb 2024 09:53:05 +0200 Subject: [PATCH 282/334] scripts : update sync scripts with new backends --- scripts/sync-ggml-am.sh | 12 ++++++++++++ scripts/sync-ggml.sh | 6 ++++++ 2 files changed, 18 insertions(+) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 6b2514a11..2c391e641 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -97,6 +97,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-cuda.cu -> ggml-cuda.cu # src/ggml-cuda.h -> ggml-cuda.h # src/ggml-impl.h -> ggml-impl.h + # src/ggml-kompute.cpp -> ggml-kompute.cpp + # src/ggml-kompute.h -> ggml-kompute.h # src/ggml-metal.h -> ggml-metal.h # src/ggml-metal.m -> ggml-metal.m # src/ggml-mpi.h -> ggml-mpi.h @@ -105,6 +107,10 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-opencl.h -> ggml-opencl.h # src/ggml-quants.c -> ggml-quants.c # src/ggml-quants.h -> ggml-quants.h + # src/ggml-sycl.cpp -> ggml-sycl.cpp + # src/ggml-sycl.h -> ggml-sycl.h + # src/ggml-vulkan.cpp -> ggml-vulkan.cpp + # src/ggml-vulkan.h -> ggml-vulkan.h # include/ggml/ggml.h -> ggml.h # include/ggml/ggml-alloc.h -> ggml-alloc.h # include/ggml/ggml-backend.h -> ggml-backend.h @@ -123,6 +129,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \ -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \ -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \ + -e 's/src\/ggml-kompute\.cpp/ggml-kompute.cpp/g' \ + -e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \ -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \ -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \ -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \ @@ -131,6 +139,10 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \ -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \ -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \ + -e 's/src\/ggml-sycl\.cpp/ggml-sycl.cpp/g' \ + -e 's/src\/ggml-sycl\.h/ggml-sycl.h/g' \ + -e 's/src\/ggml-vulkan\.cpp/ggml-vulkan.cpp/g' \ + -e 's/src\/ggml-vulkan\.h/ggml-vulkan.h/g' \ -e 's/include\/ggml\/ggml\.h/ggml.h/g' \ -e 's/include\/ggml\/ggml-alloc\.h/ggml-alloc.h/g' \ -e 's/include\/ggml\/ggml-backend\.h/ggml-backend.h/g' \ diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index 0097db435..feb34bbc8 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -7,6 +7,8 @@ cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h cp -rpv ../ggml/src/ggml-impl.h ./ggml-impl.h +cp -rpv ../ggml/src/ggml-kompute.cpp ./ggml-kompute.cpp +cp -rpv ../ggml/src/ggml-kompute.h ./ggml-kompute.h cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal @@ -16,6 +18,10 @@ cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c cp -rpv ../ggml/src/ggml-quants.h ./ggml-quants.h +cp -rpv ../ggml/src/ggml-sycl.cpp ./ggml-sycl.cpp +cp -rpv ../ggml/src/ggml-sycl.h ./ggml-sycl.h +cp -rpv ../ggml/src/ggml-vulkan.cpp ./ggml-vulkan.cpp +cp -rpv ../ggml/src/ggml-vulkan.h ./ggml-vulkan.h cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h From f026f8120f97090d34a52b3dc023c82e0ede3f7d Mon Sep 17 00:00:00 2001 From: Ian Bull Date: Sat, 10 Feb 2024 02:53:28 -0800 Subject: [PATCH 283/334] metal : use autoreleasepool to avoid memory leaks (#5437) There appears to be a known memory leak when using the `MLTCommandBuffer`. It is suggested to use `@autoreleasepool` in [1,2] [1] https://developer.apple.com/forums/thread/662721 [2] https://forums.developer.apple.com/forums/thread/120931 This change-set wraps the `ggml_metal_graph_compute` in a `@autoreleasepool`. This commit addresses https://github.com/ggerganov/llama.cpp/issues/5436 --- ggml-metal.m | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 5260ed827..c1d8e2de8 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -687,6 +687,7 @@ static bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { + @autoreleasepool { MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor; edesc.dispatchType = MTLDispatchTypeSerial; @@ -2272,6 +2273,7 @@ static bool ggml_metal_graph_compute( [[MTLCaptureManager sharedCaptureManager] stopCapture]; } + } return true; } From 907e08c1109f498b01036367804cff3082c44524 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sun, 11 Feb 2024 11:16:22 +0100 Subject: [PATCH 284/334] server : add llama2 chat template (#5425) * server: add mistral chat template * server: fix typo * server: rename template mistral to llama2 * server: format_llama2: remove BOS * server: validate "--chat-template" argument * server: clean up using_chatml variable Co-authored-by: Jared Van Bortel --------- Co-authored-by: Jared Van Bortel --- examples/server/oai.hpp | 8 ++++++-- examples/server/server.cpp | 22 ++++++++++++++++++++-- examples/server/utils.hpp | 30 ++++++++++++++++++++++++++++++ 3 files changed, 56 insertions(+), 4 deletions(-) diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp index 43410f803..2eca8a9fb 100644 --- a/examples/server/oai.hpp +++ b/examples/server/oai.hpp @@ -15,9 +15,13 @@ using json = nlohmann::json; inline static json oaicompat_completion_params_parse( - const json &body /* openai api json semantics */) + const json &body, /* openai api json semantics */ + const std::string &chat_template) { json llama_params; + std::string formatted_prompt = chat_template == "chatml" + ? format_chatml(body["messages"]) // OpenAI 'messages' to chatml (with <|im_start|>,...) + : format_llama2(body["messages"]); // OpenAI 'messages' to llama2 (with [INST],...) llama_params["__oaicompat"] = true; @@ -30,7 +34,7 @@ inline static json oaicompat_completion_params_parse( // https://platform.openai.com/docs/api-reference/chat/create llama_sampling_params default_sparams; llama_params["model"] = json_value(body, "model", std::string("unknown")); - llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' + llama_params["prompt"] = formatted_prompt; llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); llama_params["temperature"] = json_value(body, "temperature", 0.0); llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8d668f798..4d212f1f0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -36,6 +36,7 @@ struct server_params std::string hostname = "127.0.0.1"; std::vector api_keys; std::string public_path = "examples/server/public"; + std::string chat_template = "chatml"; int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; @@ -1859,6 +1860,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" --chat-template FORMAT_NAME"); + printf(" set chat template, possible valus is: llama2, chatml (default %s)", sparams.chat_template.c_str()); printf("\n"); } @@ -2290,6 +2293,21 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, log_set_target(stdout); LOG_INFO("logging to file is disabled.", {}); } + else if (arg == "--chat-template") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + std::string value(argv[i]); + if (value != "chatml" && value != "llama2") { + fprintf(stderr, "error: chat template can be \"llama2\" or \"chatml\", but got: %s\n", value.c_str()); + invalid_param = true; + break; + } + sparams.chat_template = value; + } else if (arg == "--override-kv") { if (++i >= argc) { @@ -2743,13 +2761,13 @@ int main(int argc, char **argv) // TODO: add mount point without "/v1" prefix -- how? - svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) + svr.Post("/v1/chat/completions", [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); if (!validate_api_key(req, res)) { return; } - json data = oaicompat_completion_params_parse(json::parse(req.body)); + json data = oaicompat_completion_params_parse(json::parse(req.body), sparams.chat_template); const int task_id = llama.queue_tasks.get_new_id(); llama.queue_results.add_waiting_task_id(task_id); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 70cce0721..548548962 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -167,6 +167,34 @@ static T json_value(const json &body, const std::string &key, const T &default_v : default_value; } +inline std::string format_llama2(std::vector messages) +{ + std::ostringstream output; + bool is_inside_turn = false; + + for (auto it = messages.begin(); it != messages.end(); ++it) { + if (!is_inside_turn) { + output << "[INST] "; + } + std::string role = json_value(*it, "role", std::string("user")); + std::string content = json_value(*it, "content", std::string("")); + if (role == "system") { + output << "<>\n" << content << "\n<>\n\n"; + is_inside_turn = true; + } else if (role == "user") { + output << content << " [/INST]"; + is_inside_turn = true; + } else { + output << " " << content << " "; + is_inside_turn = false; + } + } + + LOG_VERBOSE("format_llama2", {{"text", output.str()}}); + + return output.str(); +} + inline std::string format_chatml(std::vector messages) { std::ostringstream chatml_msgs; @@ -180,6 +208,8 @@ inline std::string format_chatml(std::vector messages) chatml_msgs << "<|im_start|>assistant" << '\n'; + LOG_VERBOSE("format_chatml", {{"text", chatml_msgs.str()}}); + return chatml_msgs.str(); } From e4640d8fdf56f14a6db3d092bcd3d2d315cb5d04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 11 Feb 2024 12:44:51 +0100 Subject: [PATCH 285/334] lookup: add print for drafting performance (#5450) --- examples/lookup/lookup.cpp | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index d8de7dd38..18235b8a1 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -1,7 +1,9 @@ #include "common.h" +#include "ggml.h" #include "llama.h" #include +#include #include #include #include @@ -73,6 +75,8 @@ int main(int argc, char ** argv){ int n_drafted = 0; int n_accept = 0; + int64_t t_draft_us = 0; + int n_past = inp.size(); bool has_eos = false; @@ -160,7 +164,7 @@ int main(int argc, char ** argv){ // generate n_pred tokens through prompt lookup auto prompt_lookup = [&]() -> void { - int inp_size = inp.size(); + const int inp_size = inp.size(); for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){ const llama_token * ngram = &inp[inp_size - ngram_size]; @@ -191,8 +195,12 @@ int main(int argc, char ** argv){ return; }; + const int64_t t_start_draft_us = ggml_time_us(); + prompt_lookup(); + t_draft_us += ggml_time_us() - t_start_draft_us; + llama_decode(ctx, batch_tgt); ++n_past; @@ -210,6 +218,8 @@ int main(int argc, char ** argv){ LOG_TEE("n_draft = %d\n", n_draft); LOG_TEE("n_predict = %d\n", n_predict); LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n", + t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us)); LOG_TEE("n_accept = %d\n", n_accept); LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); From a07d0fee1f05c5c1dc49948ae1a3293db017275f Mon Sep 17 00:00:00 2001 From: snadampal <87143774+snadampal@users.noreply.github.com> Date: Sun, 11 Feb 2024 07:22:33 -0600 Subject: [PATCH 286/334] ggml : add mmla kernels for quantized GEMM (#4966) * ggml: aarch64: implement smmla kernel for q8_0_q8_0 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q8_0_q8_0 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: aarch64: implement smmla kernel for q4_0_q8_0 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q4_0_q8_0 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: aarch64: implement smmla kernel for q4_1_q8_1 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q4_1_q8_1 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: update unit tests for the new vec_dot interface * llama.cpp: add MATMUL_INT8 capability to system_info --- common/common.cpp | 1 + ggml-quants.c | 320 +++++++++++++++++++++++++++++++++-- ggml-quants.h | 26 +-- ggml.c | 164 ++++++++++++------ ggml.h | 5 +- llama.cpp | 1 + pocs/vdot/q8dot.cpp | 4 +- pocs/vdot/vdot.cpp | 4 +- tests/test-quantize-fns.cpp | 2 +- tests/test-quantize-perf.cpp | 2 +- 10 files changed, 441 insertions(+), 88 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index e0082a823..9a489a553 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1550,6 +1550,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); + fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); #ifdef NDEBUG fprintf(stream, "debug: false\n"); diff --git a/ggml-quants.c b/ggml-quants.c index 1031e3761..6c122dd2a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -49,6 +49,8 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +#define UNUSED GGML_UNUSED + #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) @@ -3677,15 +3679,88 @@ static inline __m128i get_scale_shuffle(int i) { } #endif -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif const block_q4_0 * restrict x = vx; const block_q8_0 * restrict y = vy; +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q4_0 * restrict vx0 = vx; + const block_q4_0 * restrict vx1 = vx + bx; + + const block_q8_0 * restrict vy0 = vy; + const block_q8_0 * restrict vy1 = vy + by; + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q4_0 * restrict b_x0 = &vx0[i]; + const block_q4_0 * restrict b_x1 = &vx1[i]; + const block_q8_0 * restrict b_y0 = &vy0[i]; + const block_q8_0 * restrict b_y1 = &vy1[i]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); + const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t x0_l = vsubq_s8(v0_0l, s8b); + const int8x16_t x0_h = vsubq_s8(v0_0h, s8b); + const int8x16_t x1_l = vsubq_s8(v0_1l, s8b); + const int8x16_t x1_h = vsubq_s8(v0_1h, s8b); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + return; + } +#endif #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -3967,15 +4042,89 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, #endif } -void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_1; const int nb = n / qk; assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif const block_q4_1 * restrict x = vx; const block_q8_1 * restrict y = vy; +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q4_1 * restrict vx0 = vx; + const block_q4_1 * restrict vx1 = vx + bx; + const block_q8_1 * restrict vy0 = vy; + const block_q8_1 * restrict vy1 = vy + by; + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t summs0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q4_1 * restrict b_x0 = &vx0[i]; + const block_q4_1 * restrict b_x1 = &vx1[i]; + const block_q8_1 * restrict b_y0 = &vy0[i]; + const block_q8_1 * restrict b_y1 = &vy1[i]; + + float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * b_y0->s, + GGML_FP16_TO_FP32(b_x1->m) * b_y0->s, + GGML_FP16_TO_FP32(b_x0->m) * b_y1->s, + GGML_FP16_TO_FP32(b_x1->m) * b_y1->s}; + summs0 += summs_t; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); + const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); + + // 4-bit -> 8-bit + const int8x16_t x0_l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t x0_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t x1_l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t x1_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + // mmla into int32x4_t + float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + + float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + sumv2 = sumv2 + summs0; + + vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + return; + } +#endif // TODO: add WASM SIMD #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -4107,12 +4256,17 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri #endif } -void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; assert(n % qk == 0); assert(qk == QK5_0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q5_0 * restrict x = vx; const block_q8_0 * restrict y = vy; @@ -4393,12 +4547,17 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri #endif } -void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_1; const int nb = n / qk; assert(n % qk == 0); assert(qk == QK5_1); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q5_1 * restrict x = vx; const block_q8_1 * restrict y = vy; @@ -4692,15 +4851,75 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri #endif } -void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif const block_q8_0 * restrict x = vx; const block_q8_0 * restrict y = vy; +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q8_0 * restrict vx0 = vx; + const block_q8_0 * restrict vx1 = vx + bx; + const block_q8_0 * restrict vy0 = vy; + const block_q8_0 * restrict vy1 = vy + by; + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q8_0 * restrict b_x0 = &vx0[i]; + const block_q8_0 * restrict b_y0 = &vy0[i]; + + const block_q8_0 * restrict b_x1 = &vx1[i]; + const block_q8_0 * restrict b_y1 = &vy1[i]; + + const int8x16_t x0_l = vld1q_s8(b_x0->qs); + const int8x16_t x0_h = vld1q_s8(b_x0->qs + 16); + const int8x16_t x1_l = vld1q_s8(b_x1->qs); + const int8x16_t x1_h = vld1q_s8(b_x1->qs + 16); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + return; + } +#endif #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -4795,7 +5014,12 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri } #if QK_K == 256 -void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q2_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -5171,7 +5395,12 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #else -void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q2_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -5429,8 +5658,13 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #endif #if QK_K == 256 -void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const uint32_t kmask1 = 0x03030303; const uint32_t kmask2 = 0x0f0f0f0f; @@ -5949,8 +6183,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri #else -void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q3_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -6292,8 +6531,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri #endif #if QK_K == 256 -void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q4_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -6648,8 +6892,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri #endif } #else -void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q4_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -6891,8 +7140,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri #endif #if QK_K == 256 -void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q5_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -7311,8 +7565,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #else -void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q5_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -7577,8 +7836,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #if QK_K == 256 -void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q6_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -8009,8 +8273,13 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri #else -void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q6_K * restrict x = vx; const block_q8_K * restrict y = vy; @@ -8339,8 +8608,13 @@ static const int8_t keven_signs_q2xs[1024] = { 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, }; -void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_iq2_xxs * restrict x = vx; const block_q8_K * restrict y = vy; @@ -8462,8 +8736,13 @@ void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * res #endif } -void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_iq2_xs * restrict x = vx; const block_q8_K * restrict y = vy; @@ -8682,8 +8961,13 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest } // TODO -void ggml_vec_dot_iq3_xxs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_iq3_xxs * restrict x = vx; const block_q8_K * restrict y = vy; diff --git a/ggml-quants.h b/ggml-quants.h index bfdf3c997..68f09b1e1 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -245,20 +245,20 @@ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_ void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product -void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") diff --git a/ggml.c b/ggml.c index 86cd65862..e45b78d7e 100644 --- a/ggml.c +++ b/ggml.c @@ -428,8 +428,8 @@ int64_t ggml_cycles_per_ms(void) { static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); -static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y); -static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y); +static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); +static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { [GGML_TYPE_I8] = { @@ -457,6 +457,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .is_quantized = false, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, .vec_dot_type = GGML_TYPE_F32, + .nrows = 1, }, [GGML_TYPE_F16] = { .type_name = "f16", @@ -468,6 +469,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, .vec_dot_type = GGML_TYPE_F16, + .nrows = 1, }, [GGML_TYPE_Q4_0] = { .type_name = "q4_0", @@ -479,6 +481,11 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference, .vec_dot = ggml_vec_dot_q4_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif }, [GGML_TYPE_Q4_1] = { .type_name = "q4_1", @@ -490,6 +497,11 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference, .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif }, [4] = { // GGML_TYPE_Q4_2 .type_name = "DEPRECATED", @@ -501,6 +513,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = NULL, .vec_dot = NULL, .vec_dot_type = GGML_TYPE_COUNT, + .nrows = 1, }, [5] = { // GGML_TYPE_Q4_3 .type_name = "DEPRECATED", @@ -512,6 +525,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = NULL, .vec_dot = NULL, .vec_dot_type = GGML_TYPE_COUNT, + .nrows = 1, }, [GGML_TYPE_Q5_0] = { .type_name = "q5_0", @@ -523,6 +537,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference, .vec_dot = ggml_vec_dot_q5_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, }, [GGML_TYPE_Q5_1] = { .type_name = "q5_1", @@ -534,6 +549,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference, .vec_dot = ggml_vec_dot_q5_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, + .nrows = 1, }, [GGML_TYPE_Q8_0] = { .type_name = "q8_0", @@ -545,6 +561,11 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference, .vec_dot = ggml_vec_dot_q8_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif }, [GGML_TYPE_Q8_1] = { .type_name = "q8_1", @@ -554,6 +575,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float = quantize_row_q8_1, .from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference, .vec_dot_type = GGML_TYPE_Q8_1, + .nrows = 1, }, [GGML_TYPE_Q2_K] = { .type_name = "q2_K", @@ -565,6 +587,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference, .vec_dot = ggml_vec_dot_q2_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_Q3_K] = { .type_name = "q3_K", @@ -576,6 +599,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference, .vec_dot = ggml_vec_dot_q3_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_Q4_K] = { .type_name = "q4_K", @@ -587,6 +611,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference, .vec_dot = ggml_vec_dot_q4_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_Q5_K] = { .type_name = "q5_K", @@ -598,6 +623,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference, .vec_dot = ggml_vec_dot_q5_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_Q6_K] = { .type_name = "q6_K", @@ -609,6 +635,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference, .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_IQ2_XXS] = { .type_name = "iq2_xxs", @@ -620,6 +647,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_IQ2_XS] = { .type_name = "iq2_xs", @@ -631,6 +659,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_IQ3_XXS] = { .type_name = "iq3_xxs", @@ -642,6 +671,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .from_float_reference = (ggml_from_float_t)quantize_row_iq3_xxs_reference, .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", @@ -1212,7 +1242,13 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } -static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { +static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + #ifdef GGML_SIMD float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); @@ -1249,7 +1285,13 @@ static void ggml_vec_dot_f32(const int n, float * restrict s, const float * rest *s = sumf; } -static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { +static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + ggml_float sumf = 0.0; #if defined(GGML_SIMD) @@ -1455,7 +1497,7 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #endif } -inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s); } +inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } @@ -9992,6 +10034,7 @@ static void ggml_compute_forward_mul_mat( ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; + int64_t const vec_dot_num_rows = type_traits[type].nrows; GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); @@ -10159,12 +10202,23 @@ static void ggml_compute_forward_mul_mat( const int64_t blck_0 = 16; const int64_t blck_1 = 16; + // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols + int64_t nrc = vec_dot_num_rows; + // TODO: currently the mmla kernels support only even numbered rows/cols. + // this check can be removed once they are extended to support odd numbered rows/cols too + if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { + nrc = 1; + } + + const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + // attempt to reduce false-sharing (does not seem to make a difference) - float tmp[16]; + // 16 * 2, accounting for mmla kernels + float tmp[32]; for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ir1 += nrc) { const int64_t i13 = (ir1/(ne12*ne1)); const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1; const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1); @@ -10187,17 +10241,19 @@ static void ggml_compute_forward_mul_mat( (src1_cont || src1->type != vec_dot_type ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size : (i11*nb11 + i12*nb12 + i13*nb13)); - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); //} - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col); + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ir0 += nrc) { + vec_dot(ne00, &tmp[ir0 - iir0], (nrc>1 ? 16 : 0), src0_row + ir0*nb01, (nrc>1 ? nb01 : 0), src1_col, (nrc>1 ? src1_col_stride : 0), nrc); + } + + for (int cn = 0; cn < nrc; ++cn) { + memcpy(&dst_col[iir0 + cn*nb1/nb0], tmp + (cn*16), (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); } - memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); } } } @@ -10386,7 +10442,7 @@ static void ggml_compute_forward_mul_mat_id( //} for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col); + vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_row + ir0*nb01, 0, src1_col, 0, 1); } memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); } @@ -11568,7 +11624,7 @@ static void ggml_compute_forward_soft_max_back_f32( // linear runtime, no additional memory float dot_y_dy = 0; - ggml_vec_dot_f32 (nc, &dot_y_dy, y, dy); + ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); ggml_vec_cpy_f32 (nc, dx, dy); ggml_vec_acc1_f32(nc, dx, -dot_y_dy); ggml_vec_mul_f32 (nc, dx, dx, y); @@ -12369,9 +12425,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32( const int i1n = i10*ne11; for (int i00 = 0; i00 < ne00; i00++) { float v = 0; - ggml_vec_dot_f16(ne02, &v, - (ggml_fp16_t *) wdata_src + i1n, - (ggml_fp16_t *) wdata_kernel + i00*ne02); + ggml_vec_dot_f16(ne02, &v, 0, + (ggml_fp16_t *) wdata_src + i1n, 0, + (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); dst_data[i10*s0 + i00] += v; } } @@ -12466,9 +12522,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32( const int i1n = i10*ne11; for (int i00 = 0; i00 < ne00; i00++) { float v = 0; - ggml_vec_dot_f32(ne02, &v, - wdata_src + i1n, - wdata_kernel + i00*ne02); + ggml_vec_dot_f32(ne02, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i00*ne02, 0, 1); dst_data[i10*s0 + i00] += v; } } @@ -12783,9 +12839,9 @@ static void ggml_compute_forward_conv_transpose_2d( for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { float v = 0; - ggml_vec_dot_f16(ne03, &v, - wdata_src + i1n, - wdata_kernel + i01*ne00*ne03 + i00*ne03); + ggml_vec_dot_f16(ne03, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; } } @@ -13214,9 +13270,9 @@ static void ggml_compute_forward_flash_attn_f32( const int i1 = ik1; ggml_vec_dot_f32(neq0, - S + i1, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); + S + i1, 0, + (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); } // scale @@ -13299,9 +13355,9 @@ static void ggml_compute_forward_flash_attn_f32( const int iv3 = iq3; ggml_vec_dot_f32(masked_begin, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - S); + (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, + (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, + S, 0, 1); } } } @@ -13404,9 +13460,9 @@ static void ggml_compute_forward_flash_attn_f16( const int i1 = ik1; ggml_vec_dot_f16(neq0, - S + i1, - (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); + S + i1, 0, + (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); } } else { for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { @@ -13508,9 +13564,9 @@ static void ggml_compute_forward_flash_attn_f16( const int iv3 = iq3; ggml_vec_dot_f16(nev0, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - S16); + (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, + (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, + S16, 0, 1); } } else { for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { @@ -13652,9 +13708,9 @@ static void ggml_compute_forward_flash_ff_f16( const int i1 = ib01; ggml_vec_dot_f16(nea0, - S + i1, - (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), - (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3))); + S + i1, 0, + (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), 0, + (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3)), 0, 1); } ggml_vec_add_f32(neb01, S, S, (float *) b1->data); @@ -13677,9 +13733,9 @@ static void ggml_compute_forward_flash_ff_f16( for (int64_t ic = 0; ic < nec01; ++ic) { ggml_vec_dot_f16(neb01, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), - S16); + (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, + (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), 0, + S16, 0, 1); } ggml_vec_add_f32(nec01, @@ -13866,9 +13922,9 @@ static void ggml_compute_forward_flash_attn_back_f32( const int i1 = ik1; ggml_vec_dot_f32(neq0, - S + i1, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); + S + i1, 0, + (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); } // scale @@ -14013,7 +14069,7 @@ static void ggml_compute_forward_flash_attn_back_f32( // S = SM * (S - dot(SM, S)) float dot_SM_gradSM = 0; - ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, SM, S); + ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); ggml_vec_mul_f32 (masked_begin, S, S, SM); @@ -18382,7 +18438,7 @@ static enum ggml_opt_result linesearch_backtracking( } // compute the initial gradient in the search direction - ggml_vec_dot_f32(nx, &dginit, g, d); + ggml_vec_dot_f32(nx, &dginit, 0, g, 0, d, 0, 1); // make sure that d points to a descent direction if (0 < dginit) { @@ -18432,7 +18488,7 @@ static enum ggml_opt_result linesearch_backtracking( return count; } - ggml_vec_dot_f32(nx, &dg, g, d); + ggml_vec_dot_f32(nx, &dg, 0, g, 0, d, 0, 1); // check the Wolfe condition if (dg < params->lbfgs.wolfe * dginit) { @@ -18693,8 +18749,8 @@ static enum ggml_opt_result ggml_opt_lbfgs( // ys = y^t \cdot s -> 1 / \rho. // yy = y^t \cdot y. // - ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0]*nx]); - ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]); + ggml_vec_dot_f32(nx, &ys, 0, &lm_y[end[0]*nx], 0, &lm_s[end[0]*nx], 0, 1); + ggml_vec_dot_f32(nx, &yy, 0, &lm_y[end[0]*nx], 0, &lm_y[end[0]*nx], 0, 1); lm_ys[end[0]] = ys; @@ -18713,7 +18769,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( for (int i = 0; i < bound; ++i) { j[0] = (j[0] + m - 1) % m; // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} - ggml_vec_dot_f32(nx, &lm_alpha[j[0]], &lm_s[j[0]*nx], d); + ggml_vec_dot_f32(nx, &lm_alpha[j[0]], 0, &lm_s[j[0]*nx], 0, d, 0, 1); lm_alpha[j[0]] /= lm_ys[j[0]]; // q_{i} = q_{i+1} - \alpha_{i} y_{i} ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]); @@ -18723,7 +18779,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( for (int i = 0; i < bound; ++i) { // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} - ggml_vec_dot_f32(nx, &beta, &lm_y[j[0]*nx], d); + ggml_vec_dot_f32(nx, &beta, 0, &lm_y[j[0]*nx], 0, d, 0, 1); beta /= lm_ys[j[0]]; // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta); @@ -20611,4 +20667,12 @@ int ggml_cpu_has_vsx(void) { #endif } +int ggml_cpu_has_matmul_int8(void) { +#if defined(__ARM_FEATURE_MATMUL_INT8) + return 1; +#else + return 0; +#endif +} + //////////////////////////////////////////////////////////////////////////////// diff --git a/ggml.h b/ggml.h index 1360cd8ee..9cfec5bac 100644 --- a/ggml.h +++ b/ggml.h @@ -2278,6 +2278,7 @@ extern "C" { GGML_API int ggml_cpu_has_ssse3 (void); GGML_API int ggml_cpu_has_sycl (void); GGML_API int ggml_cpu_has_vsx (void); + GGML_API int ggml_cpu_has_matmul_int8(void); // // Internal types and functions exposed for tests and benchmarks @@ -2291,7 +2292,8 @@ extern "C" { #endif typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); - typedef void (*ggml_vec_dot_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y); + typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, + const void * GGML_RESTRICT y, size_t by, int nrc); typedef struct { const char * type_name; @@ -2303,6 +2305,7 @@ extern "C" { ggml_from_float_t from_float_reference; ggml_vec_dot_t vec_dot; enum ggml_type vec_dot_type; + int64_t nrows; // number of rows to process simultaneously; } ggml_type_traits_t; GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type); diff --git a/llama.cpp b/llama.cpp index 0566b087b..3f39a67fb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11869,6 +11869,7 @@ const char * llama_print_system_info(void) { s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | "; s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | "; s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | "; + s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | "; return s.c_str(); } diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp index 111770d55..1a52ff5e9 100644 --- a/pocs/vdot/q8dot.cpp +++ b/pocs/vdot/q8dot.cpp @@ -156,8 +156,8 @@ int main(int argc, char** argv) { t1 = std::chrono::high_resolution_clock::now(); float fs; - if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data()); - else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data()); + if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, 0, x40.data(), 0, y.data(), 0, 1); + else funcs.vec_dot(kVecSize * QK4_1, &fs, 0, x41.data(), 0, y.data(), 0, 1); t2 = std::chrono::high_resolution_clock::now(); t = 1e-3*std::chrono::duration_cast(t2-t1).count(); if (iloop > 3) ggml.addResult(fs, t); diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index 73ffcd1ca..17e9e4482 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -284,8 +284,8 @@ int main(int argc, char** argv) { else { auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type); vdot.from_float(y1.data(), q8.data(), kVecSize); - if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data()); - else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data()); + if (useQ4_1) funcs.vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1); + else funcs.vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1); } sumq += result; t2 = std::chrono::high_resolution_clock::now(); diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 43df8022d..5e92d5742 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -87,7 +87,7 @@ static float dot_product_error( vdot.from_float(test_data2, tmp_q2.data(), test_size); float result = INFINITY; - qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data()); + qfns.vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1); const float dot_ref = dot_product(test_data1, test_data2, test_size); diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index 8ec817344..48d9fae3d 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -346,7 +346,7 @@ int main(int argc, char * argv[]) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { float result; - qfns.vec_dot(size, &result, test_q1, test_q2); + qfns.vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1); return result; }; size_t quantized_size = ggml_row_size(type, size); From 0f2411f154db46780d3aaa3a0664691b2170c83f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 11 Feb 2024 15:33:01 +0200 Subject: [PATCH 287/334] ggml : fix compile warnings (unused vars) (#4966) --- ggml-quants.c | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/ggml-quants.c b/ggml-quants.c index 6c122dd2a..b2a309bf8 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3689,6 +3689,10 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #else assert(nrc == 1); #endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q4_0 * restrict x = vx; const block_q8_0 * restrict y = vy; @@ -4052,6 +4056,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #else assert(nrc == 1); #endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q4_1 * restrict x = vx; const block_q8_1 * restrict y = vy; @@ -4861,6 +4869,10 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #else assert(nrc == 1); #endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); const block_q8_0 * restrict x = vx; const block_q8_0 * restrict y = vy; From 139b62a839825ef20084ed75ed624db7a5ad554a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 11 Feb 2024 15:33:43 +0200 Subject: [PATCH 288/334] common : fix compile warning --- common/sampling.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 844ad7c53..82cbdecea 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -127,8 +127,6 @@ static void sampler_queue( const llama_sampling_params & params, llama_token_data_array & cur_p, size_t & min_keep) { - const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); - const float temp = params.temp; const float dynatemp_range = params.dynatemp_range; const float dynatemp_exponent = params.dynatemp_exponent; From 85910c5b30f6e268321be8df044f5528a6efac52 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 11 Feb 2024 15:35:50 +0200 Subject: [PATCH 289/334] main : ctrl+C print timing in non-interactive mode (#3873) --- examples/main/main.cpp | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 0ed4d79f9..e8ab8cbae 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -98,7 +98,7 @@ static void write_logfile( #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { - if (!is_interacting) { + if (!is_interacting && g_params->interactive) { is_interacting = true; } else { console::cleanup(); @@ -392,7 +392,8 @@ int main(int argc, char ** argv) { LOG_TEE("\n"); } - if (params.interactive) { + // ctrl+C handling + { #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; sigint_action.sa_handler = sigint_handler; @@ -405,7 +406,9 @@ int main(int argc, char ** argv) { }; SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif + } + if (params.interactive) { LOG_TEE("%s: interactive mode on.\n", __func__); if (!params.antiprompt.empty()) { From 684780141a08200ec98eba3e982dbafd1d0b5000 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sun, 11 Feb 2024 13:38:14 +0000 Subject: [PATCH 290/334] server : allow to specify tokens as strings in logit_bias (#5003) * server: allow to specify tokens as strings in logit_bias * Apply suggestions from code review Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 2 +- examples/server/server.cpp | 32 +++++++++++++++++++++++++------- 2 files changed, 26 insertions(+), 8 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 1db7cdf21..0f7373ae8 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -185,7 +185,7 @@ node index.js `ignore_eos`: Ignore end of stream token and continue generating (default: false). - `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []). + `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. (default: []). `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 4d212f1f0..1699eb76b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -626,18 +626,36 @@ struct llama_server_context const int n_vocab = llama_n_vocab(model); for (const auto &el : *logit_bias) { - if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) + if (el.is_array() && el.size() == 2) { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) + float bias; + if (el[1].is_number()) { - if (el[1].is_number()) + bias = el[1].get(); + } + else if (el[1].is_boolean() && !el[1].get()) + { + bias = -INFINITY; + } + else + { + continue; + } + + if (el[0].is_number_integer()) + { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) { - slot->sparams.logit_bias[tok] = el[1].get(); + slot->sparams.logit_bias[tok] = bias; } - else if (el[1].is_boolean() && !el[1].get()) + } + else if (el[0].is_string()) + { + auto toks = llama_tokenize(model, el[0].get(), false); + for (auto tok : toks) { - slot->sparams.logit_bias[tok] = -INFINITY; + slot->sparams.logit_bias[tok] = bias; } } } From a803333a4e6fc534c93afe90d741bc2388bdec87 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sun, 11 Feb 2024 13:43:31 +0000 Subject: [PATCH 291/334] common : use enums for sampler types (#5418) * common: use enums for sampler types * Apply suggestions from code review Co-authored-by: Georgi Gerganov * minor : spaces --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 117 +++++++++++++++++++++++++++++++------------- common/common.h | 7 ++- common/sampling.cpp | 31 +++++------- common/sampling.h | 20 +++++++- 4 files changed, 120 insertions(+), 55 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 9a489a553..f64da2cb6 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -340,13 +340,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - sparams.samplers_sequence = parse_samplers_input(argv[i]); + const auto sampler_names = string_split(argv[i], ';'); + sparams.samplers_sequence = sampler_types_from_names(sampler_names); } else if (arg == "--sampling-seq") { if (++i >= argc) { invalid_param = true; break; } - sparams.samplers_sequence = argv[i]; + sparams.samplers_sequence = sampler_types_from_chars(argv[i]); } else if (arg == "--top-p") { if (++i >= argc) { invalid_param = true; @@ -906,6 +907,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { const llama_sampling_params & sparams = params.sparams; + std::string sampler_type_chars; + std::string sampler_type_names; + for (const auto sampler_type : sparams.samplers_sequence) { + sampler_type_chars += static_cast(sampler_type); + sampler_type_names += sampler_type_to_name_string(sampler_type) + ";"; + } + sampler_type_names.pop_back(); + printf("\n"); printf("usage: %s [options]\n", argv[0]); printf("\n"); @@ -947,8 +956,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - printf(" --samplers samplers that will be used for generation in the order, separated by \';\', for example: \"top_k;tfs;typical;top_p;min_p;temp\"\n"); - printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sparams.samplers_sequence.c_str()); + printf(" --samplers samplers that will be used for generation in the order, separated by \';\' (default: %s)\n", sampler_type_names.c_str()); + printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str()); printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k); printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p); printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p); @@ -1097,45 +1106,85 @@ std::string gpt_random_prompt(std::mt19937 & rng) { } // -// String parsing +// String utils // -std::string parse_samplers_input(std::string input) { - std::string output = ""; +std::vector string_split(std::string input, char separator) { + std::vector parts; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(0, separator_pos); + parts.emplace_back(part); + input = input.substr(separator_pos + 1); + separator_pos = input.find(separator); + } + parts.emplace_back(input); + return parts; +} + +std::vector sampler_types_from_names(const std::vector & names) { // since samplers names are written multiple ways // make it ready for both system names and input names - std::unordered_map samplers_symbols { - {"top_k", 'k'}, - {"top-k", 'k'}, - {"top_p", 'p'}, - {"top-p", 'p'}, - {"nucleus", 'p'}, - {"typical_p", 'y'}, - {"typical-p", 'y'}, - {"typical", 'y'}, - {"min_p", 'm'}, - {"min-p", 'm'}, - {"tfs_z", 'f'}, - {"tfs-z", 'f'}, - {"tfs", 'f'}, - {"temp", 't'}, - {"temperature",'t'} + std::unordered_map sampler_name_map { + {"top_k", llama_sampler_type::TOP_K}, + {"top-k", llama_sampler_type::TOP_K}, + {"top_p", llama_sampler_type::TOP_P}, + {"top-p", llama_sampler_type::TOP_P}, + {"nucleus", llama_sampler_type::TOP_P}, + {"typical_p", llama_sampler_type::TYPICAL_P}, + {"typical-p", llama_sampler_type::TYPICAL_P}, + {"typical", llama_sampler_type::TYPICAL_P}, + {"min_p", llama_sampler_type::MIN_P}, + {"min-p", llama_sampler_type::MIN_P}, + {"tfs_z", llama_sampler_type::TFS_Z}, + {"tfs-z", llama_sampler_type::TFS_Z}, + {"tfs", llama_sampler_type::TFS_Z}, + {"temp", llama_sampler_type::TEMP}, + {"temperature", llama_sampler_type::TEMP} }; - // expected format example: "temp;top_k;tfs_z;typical_p;top_p;min_p" - size_t separator = input.find(';'); - while (separator != input.npos) { - std::string name = input.substr(0,separator); - input = input.substr(separator+1); - separator = input.find(';'); - if (samplers_symbols.find(name) != samplers_symbols.end()) { - output += samplers_symbols[name]; + std::vector sampler_types; + sampler_types.reserve(names.size()); + for (const auto& name : names) { + const auto sampler_item = sampler_name_map.find(name); + if (sampler_item != sampler_name_map.end()) { + sampler_types.push_back(sampler_item->second); } } - if (samplers_symbols.find(input) != samplers_symbols.end()) { - output += samplers_symbols[input]; + return sampler_types; +} + +std::vector sampler_types_from_chars(const std::string & names_string) { + std::unordered_map sampler_name_map { + {'k', llama_sampler_type::TOP_K}, + {'p', llama_sampler_type::TOP_P}, + {'y', llama_sampler_type::TYPICAL_P}, + {'m', llama_sampler_type::MIN_P}, + {'f', llama_sampler_type::TFS_Z}, + {'t', llama_sampler_type::TEMP} + }; + + std::vector sampler_types; + sampler_types.reserve(names_string.size()); + for (const auto & c : names_string) { + const auto sampler_item = sampler_name_map.find(c); + if (sampler_item != sampler_name_map.end()) { + sampler_types.push_back(sampler_item->second); + } + } + return sampler_types; +} + +std::string sampler_type_to_name_string(llama_sampler_type sampler_type) { + switch (sampler_type) { + case llama_sampler_type::TOP_K: return "top_k"; + case llama_sampler_type::TFS_Z: return "tfs_z"; + case llama_sampler_type::TYPICAL_P: return "typical_p"; + case llama_sampler_type::TOP_P: return "top_p"; + case llama_sampler_type::MIN_P: return "min_p"; + case llama_sampler_type::TEMP: return "temp"; + default : return ""; } - return output; } // diff --git a/common/common.h b/common/common.h index 62de25d6a..9bdd45cf9 100644 --- a/common/common.h +++ b/common/common.h @@ -162,10 +162,13 @@ std::string gpt_random_prompt(std::mt19937 & rng); void process_escapes(std::string& input); // -// String parsing +// String utils // -std::string parse_samplers_input(std::string input); +std::vector sampler_types_from_names(const std::vector & names); +std::vector sampler_types_from_chars(const std::string & names_string); +std::vector string_split(std::string input, char separator); +std::string sampler_type_to_name_string(llama_sampler_type sampler_type); // // Model utils diff --git a/common/sampling.cpp b/common/sampling.cpp index 82cbdecea..a001750da 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -103,15 +103,10 @@ std::string llama_sampling_print(const llama_sampling_params & params) { std::string llama_sampling_order_print(const llama_sampling_params & params) { std::string result = "CFG -> Penalties "; if (params.mirostat == 0) { - for (auto s : params.samplers_sequence) { - switch (s) { - case 'k': result += "-> top_k "; break; - case 'f': result += "-> tfs_z "; break; - case 'y': result += "-> typical_p "; break; - case 'p': result += "-> top_p "; break; - case 'm': result += "-> min_p "; break; - case 't': result += "-> temp "; break; - default : break; + for (auto sampler_type : params.samplers_sequence) { + const auto sampler_type_name = sampler_type_to_name_string(sampler_type); + if (!sampler_type_name.empty()) { + result += "-> " + sampler_type_name + " "; } } } else { @@ -135,16 +130,16 @@ static void sampler_queue( const float min_p = params.min_p; const float tfs_z = params.tfs_z; const float typical_p = params.typical_p; - const std::string & samplers_sequence = params.samplers_sequence; + const std::vector & samplers_sequence = params.samplers_sequence; - for (auto s : samplers_sequence) { - switch (s){ - case 'k': llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break; - case 'f': llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break; - case 'y': llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break; - case 'p': llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break; - case 'm': llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break; - case 't': + for (auto sampler_type : samplers_sequence) { + switch (sampler_type) { + case llama_sampler_type::TOP_K : llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break; + case llama_sampler_type::TFS_Z : llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break; + case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break; + case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break; + case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break; + case llama_sampler_type::TEMP: if (dynatemp_range > 0) { float dynatemp_min = std::max(0.0f, temp - dynatemp_range); float dynatemp_max = std::max(0.0f, temp + dynatemp_range); diff --git a/common/sampling.h b/common/sampling.h index 88899c094..2bd6a75d2 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -8,6 +8,16 @@ #include #include +// sampler types +enum class llama_sampler_type : char { + TOP_K = 'k', + TOP_P = 'p', + MIN_P = 'm', + TFS_Z = 'f', + TYPICAL_P = 'y', + TEMP = 't' +}; + // sampling parameters typedef struct llama_sampling_params { int32_t n_prev = 64; // number of previous tokens to remember @@ -28,7 +38,15 @@ typedef struct llama_sampling_params { float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate bool penalize_nl = true; // consider newlines as a repeatable token - std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp + + std::vector samplers_sequence = { + llama_sampler_type::TOP_K, + llama_sampler_type::TFS_Z, + llama_sampler_type::TYPICAL_P, + llama_sampler_type::TOP_P, + llama_sampler_type::MIN_P, + llama_sampler_type::TEMP + }; std::string grammar; // optional BNF-like grammar to constrain sampling From c88c74f967028ae3d5ebade40ae586d20a961abc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sergio=20L=C3=B3pez?= Date: Sun, 11 Feb 2024 15:12:00 +0100 Subject: [PATCH 292/334] vulkan: only use M-sized matmul on Apple GPUs (#5412) * vulkan: refactor guess_matmul_pipeline for vendor Refactor ggml_vk_guess_matmul_pipeline to simplify adding per-vendor conditionals. Signed-off-by: Sergio Lopez * vulkan: only use M-sized matmul on Apple GPUs L-sized and S-sized matmuls are broken on Apple GPUs, force using M-size with this vendor. Signed-off-by: Sergio Lopez --------- Signed-off-by: Sergio Lopez --- ggml-vulkan.cpp | 103 +++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 93 insertions(+), 10 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 254f648a6..7834e635c 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -27,6 +27,7 @@ #define CEIL_DIV(M, N) (((M) + (N)-1) / (N)) #define VK_VENDOR_ID_AMD 0x1002 +#define VK_VENDOR_ID_APPLE 0x106b #define VK_VENDOR_ID_INTEL 0x8086 #define VK_VENDOR_ID_NVIDIA 0x10de @@ -2034,18 +2035,100 @@ static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ct return ctx->pipeline_matmul_f32_aligned_l.align; } -static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; -#endif +static vk_pipeline* ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { if (bit16_x && bit16_y) { - if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; } - if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << " M" << std::endl; +#endif + return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; + } + if (bit16_x && !bit16_y) { + if (m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << " S" << std::endl; +#endif + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << " M" << std::endl; +#endif + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; + } + if (!bit16_x && bit16_y) { + GGML_ASSERT(false); + } + + if (m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << " S" << std::endl; +#endif + return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << " M" << std::endl; +#endif + return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; +} + +static vk_pipeline* ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << " M" << std::endl; +#endif + if (bit16_x && bit16_y) { + return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; + } + if (bit16_x && !bit16_y) { + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; + } + if (!bit16_x && bit16_y) { + GGML_ASSERT(false); + } + return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; +} + +static vk_pipeline* ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, bool aligned) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << " S" << std::endl; +#endif + if (bit16_x && bit16_y) { + return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; + } + if (bit16_x && !bit16_y) { + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; + } + if (!bit16_x && bit16_y) { + GGML_ASSERT(false); + } + return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; +} + +static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; +#endif + switch (ctx->device.lock()->vendor_id) { + case VK_VENDOR_ID_AMD: + return ggml_vk_guess_matmul_pipeline_amd(ctx, bit16_x, bit16_y, m, n, aligned); + case VK_VENDOR_ID_APPLE: + return ggml_vk_guess_matmul_pipeline_apple(ctx, bit16_x, bit16_y, aligned); + case VK_VENDOR_ID_INTEL: + return ggml_vk_guess_matmul_pipeline_intel(ctx, bit16_x, bit16_y, aligned); + } + + if (bit16_x && bit16_y) { + if (m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << " S" << std::endl; +#endif + return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; + } + if (m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif @@ -2057,13 +2140,13 @@ static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; } - if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { + if (m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif @@ -2078,13 +2161,13 @@ static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, GGML_ASSERT(false); } - if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; } - if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { + if (m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif From 97a336507ed9b971d72262bec7e2b8b7016a054a Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 11 Feb 2024 00:17:31 +0000 Subject: [PATCH 293/334] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/b8b232ae7b8b144397fdb12d20f592e5e7c1a64d' (2024-01-31) → 'github:NixOS/nixpkgs/f8e2ebd66d097614d51a56a755450d4ae1632df1' (2024-02-07) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 8cfc78273..239d0686c 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1706732774, - "narHash": "sha256-hqJlyJk4MRpcItGYMF+3uHe8HvxNETWvlGtLuVpqLU0=", + "lastModified": 1707268954, + "narHash": "sha256-2en1kvde3cJVc3ZnTy8QeD2oKcseLFjYPLKhIGDanQ0=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "b8b232ae7b8b144397fdb12d20f592e5e7c1a64d", + "rev": "f8e2ebd66d097614d51a56a755450d4ae1632df1", "type": "github" }, "original": { From 2891c8aa9af17f4ff636ff3868bc34ff72b56e25 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Sun, 11 Feb 2024 10:21:38 -0600 Subject: [PATCH 294/334] Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel Co-authored-by: Jared Van Bortel Co-authored-by: Georgi Gerganov --- .flake8 | 1 + convert-hf-to-gguf.py | 94 ++++++ examples/embedding/embedding.cpp | 12 +- gguf-py/gguf/constants.py | 43 +-- gguf-py/gguf/gguf_writer.py | 6 + gguf-py/gguf/tensor_mapping.py | 13 +- llama.cpp | 498 +++++++++++++++++++++++++++++-- llama.h | 1 + 8 files changed, 616 insertions(+), 52 deletions(-) diff --git a/.flake8 b/.flake8 index 113ca5fd3..18fba2c15 100644 --- a/.flake8 +++ b/.flake8 @@ -1,2 +1,3 @@ [flake8] max-line-length = 125 +ignore = W503 diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 0d4ea03b4..cae1551a2 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -209,6 +209,8 @@ class Model: return InternLM2Model if model_architecture == "MiniCPMForCausalLM": return MiniCPMModel + if model_architecture == "BertModel": + return BertModel return Model def _is_model_safetensors(self) -> bool: @@ -264,6 +266,8 @@ class Model: return gguf.MODEL_ARCH.INTERNLM2 if arch == "MiniCPMForCausalLM": return gguf.MODEL_ARCH.MINICPM + if arch == "BertModel": + return gguf.MODEL_ARCH.BERT raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1629,6 +1633,96 @@ in chat mode so that the conversation can end normally.") self.post_write_tensors(tensor_map, name, data_torch) +class BertModel(Model): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.block_count = self.hparams["num_hidden_layers"] + + def set_gguf_parameters(self): + # TODO(cebtenzzre): merge with parent class + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + self.gguf_writer.add_causal_attention(False) + self.gguf_writer.add_file_type(self.ftype) + + def set_vocab(self): + path = self.dir_model + added_tokens_path = self.dir_model if self.dir_model.exists() else None + + # use huggingface vocab to get all tokens + vocab = HfVocab(path, added_tokens_path) + tokens, scores, toktypes = zip(*vocab.all_tokens()) + assert len(tokens) == vocab.vocab_size + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + n_token_types = len(set(toktypes)) + self.gguf_writer.add_token_type_count(n_token_types) + + # convert to phantom space vocab + def phantom(tok, typ): + if tok.startswith(b"[") and tok.endswith(b"]"): + return tok + if tok.startswith(b"##"): + return tok[2:] + return b"\xe2\x96\x81" + tok + tokens = [phantom(t, y) for t, y in zip(tokens, toktypes)] + + # set up bos and eos tokens (cls and sep) + self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id) + self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id) + + # add vocab to gguf + self.gguf_writer.add_tokenizer_model("bert") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + # handle special tokens + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def write_tensors(self): + tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + tensors = dict(self.get_tensors()) + for name, data_torch in tensors.items(): + # we are only using BERT for embeddings so we don't need the pooling layer + if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): + continue # we don't need these + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + data = data_torch.squeeze().numpy() + n_dims = len(data.shape) + new_dtype: type[np.floating[Any]] + + if ( + self.ftype == 1 and name.endswith(".weight") and n_dims == 2 + and name != "embeddings.token_type_embeddings.weight" # not used with get_rows, must be F32 + ): + # if f16 desired, convert any float32 2-dim weight tensors to float16 + new_dtype = np.float16 + else: + # if f32 desired, convert any float16 to float32 + new_dtype = np.float32 + + print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}") + + if data.dtype != new_dtype: + data = data.astype(new_dtype) + + self.gguf_writer.add_tensor(new_name, data) + + ###### CONVERSION LOGIC ###### diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 3295cd240..27376c8f0 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -87,7 +87,17 @@ int main(int argc, char ** argv) { } const int n_embd = llama_n_embd(model); - const auto * embeddings = llama_get_embeddings(ctx); + auto * embeddings = llama_get_embeddings(ctx); + + // l2-normalize embeddings + float norm = 0; + for (int i = 0; i < n_embd; i++) { + norm += embeddings[i] * embeddings[i]; + } + norm = sqrt(norm); + for (int i = 0; i < n_embd; i++) { + embeddings[i] /= norm; + } for (int i = 0; i < n_embd; i++) { printf("%f ", embeddings[i]); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 1cfd41c0b..a9c13dd38 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -50,6 +50,7 @@ class Keys: VALUE_LENGTH = "{arch}.attention.value_length" LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + CAUSAL = "{arch}.attention.causal" class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" @@ -60,22 +61,23 @@ class Keys: SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" class Tokenizer: - MODEL = "tokenizer.ggml.model" - LIST = "tokenizer.ggml.tokens" - TOKEN_TYPE = "tokenizer.ggml.token_type" - SCORES = "tokenizer.ggml.scores" - MERGES = "tokenizer.ggml.merges" - BOS_ID = "tokenizer.ggml.bos_token_id" - EOS_ID = "tokenizer.ggml.eos_token_id" - UNK_ID = "tokenizer.ggml.unknown_token_id" - SEP_ID = "tokenizer.ggml.seperator_token_id" - PAD_ID = "tokenizer.ggml.padding_token_id" - ADD_BOS = "tokenizer.ggml.add_bos_token" - ADD_EOS = "tokenizer.ggml.add_eos_token" - ADD_PREFIX = "tokenizer.ggml.add_space_prefix" - HF_JSON = "tokenizer.huggingface.json" - RWKV = "tokenizer.rwkv.world" - CHAT_TEMPLATE = "tokenizer.chat_template" + MODEL = "tokenizer.ggml.model" + LIST = "tokenizer.ggml.tokens" + TOKEN_TYPE = "tokenizer.ggml.token_type" + TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count" # for BERT-style token types + SCORES = "tokenizer.ggml.scores" + MERGES = "tokenizer.ggml.merges" + BOS_ID = "tokenizer.ggml.bos_token_id" + EOS_ID = "tokenizer.ggml.eos_token_id" + UNK_ID = "tokenizer.ggml.unknown_token_id" + SEP_ID = "tokenizer.ggml.seperator_token_id" + PAD_ID = "tokenizer.ggml.padding_token_id" + ADD_BOS = "tokenizer.ggml.add_bos_token" + ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_PREFIX = "tokenizer.ggml.add_space_prefix" + HF_JSON = "tokenizer.huggingface.json" + RWKV = "tokenizer.rwkv.world" + CHAT_TEMPLATE = "tokenizer.chat_template" # @@ -122,6 +124,7 @@ class MODEL_TENSOR(IntEnum): ATTN_OUT = auto() ATTN_NORM = auto() ATTN_NORM_2 = auto() + ATTN_OUT_NORM = auto() ATTN_ROT_EMBD = auto() FFN_GATE_INP = auto() FFN_NORM = auto() @@ -134,6 +137,7 @@ class MODEL_TENSOR(IntEnum): FFN_UP_EXP = auto() ATTN_Q_NORM = auto() ATTN_K_NORM = auto() + LAYER_OUT_NORM = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -178,6 +182,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", @@ -187,6 +192,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate.{xid}", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}", + MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -262,17 +268,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { ], MODEL_ARCH.BERT: [ MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.TOKEN_TYPES, MODEL_TENSOR.POS_EMBD, MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, MODEL_TENSOR.ATTN_V, MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, ], MODEL_ARCH.MPT: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 16808196e..7af58a46c 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -357,6 +357,9 @@ class GGUFWriter: def add_layer_norm_rms_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + def add_causal_attention(self, value: bool) -> None: + self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) @@ -387,6 +390,9 @@ class GGUFWriter: def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) + def add_token_type_count(self, value: int) -> None: + self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value) + def add_token_scores(self, scores: Sequence[float]) -> None: self.add_array(Keys.Tokenizer.SCORES, scores) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 4f16d8504..c7ba1420e 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -30,6 +30,7 @@ class TensorNameMap: # Normalization of token embeddings MODEL_TENSOR.TOKEN_EMBD_NORM: ( "word_embeddings_layernorm", # bloom + "embeddings.LayerNorm", # bert ), # Position embeddings @@ -54,7 +55,6 @@ class TensorNameMap: "transformer.ln_f", # gpt2 gpt-j falcon "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth - "embeddings.LayerNorm", # bert "transformer.norm_f", # mpt "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon @@ -79,7 +79,6 @@ class TensorNameMap: "transformer.h.{bid}.ln_mlp", # falcon40b "model.layers.{bid}.input_layernorm", # llama-hf "layers.{bid}.attention_norm", # llama-pth - "encoder.layer.{bid}.attention.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi "h.{bid}.ln_1", # gpt2 @@ -155,6 +154,11 @@ class TensorNameMap: "model.layers.{bid}.attention.wo", # internlm2 ), + # Attention output norm + MODEL_TENSOR.ATTN_OUT_NORM: ( + "encoder.layer.{bid}.attention.output.LayerNorm", # bert + ), + # Rotary embeddings MODEL_TENSOR.ATTN_ROT_EMBD: ( "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf @@ -171,7 +175,6 @@ class TensorNameMap: "transformer.blocks.{bid}.norm_2", # mpt "model.layers.{bid}.post_attention_layernorm", # llama-hf "layers.{bid}.ffn_norm", # llama-pth - "encoder.layer.{bid}.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 @@ -266,6 +269,10 @@ class TensorNameMap: MODEL_TENSOR.ROPE_FREQS: ( "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon ), + + MODEL_TENSOR.LAYER_OUT_NORM: ( + "encoder.layer.{bid}.output.LayerNorm", # bert + ) } mapping: dict[str, tuple[MODEL_TENSOR, str]] diff --git a/llama.cpp b/llama.cpp index 3f39a67fb..d1ee26ce2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -196,6 +196,7 @@ enum llm_arch { LLM_ARCH_STARCODER, LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, + LLM_ARCH_BERT, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, @@ -220,6 +221,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_STARCODER, "starcoder" }, { LLM_ARCH_PERSIMMON, "persimmon" }, { LLM_ARCH_REFACT, "refact" }, + { LLM_ARCH_BERT, "bert" }, { LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, @@ -261,6 +263,7 @@ enum llm_kv { LLM_KV_ATTENTION_VALUE_LENGTH, LLM_KV_ATTENTION_LAYERNORM_EPS, LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, + LLM_KV_ATTENTION_CAUSAL, LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, @@ -273,6 +276,7 @@ enum llm_kv { LLM_KV_TOKENIZER_MODEL, LLM_KV_TOKENIZER_LIST, LLM_KV_TOKENIZER_TOKEN_TYPE, + LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, LLM_KV_TOKENIZER_SCORES, LLM_KV_TOKENIZER_MERGES, LLM_KV_TOKENIZER_BOS_ID, @@ -316,6 +320,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" }, { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, + { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, @@ -328,6 +333,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, { LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" }, + { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" }, { LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" }, { LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" }, { LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" }, @@ -355,6 +361,7 @@ struct LLM_KV { enum llm_tensor { LLM_TENSOR_TOKEN_EMBD, LLM_TENSOR_TOKEN_EMBD_NORM, + LLM_TENSOR_TOKEN_TYPES, LLM_TENSOR_POS_EMBD, LLM_TENSOR_OUTPUT, LLM_TENSOR_OUTPUT_NORM, @@ -536,6 +543,23 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_BERT, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, + { LLM_TENSOR_TOKEN_TYPES, "token_types" }, + { LLM_TENSOR_POS_EMBD, "position_embd" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_BLOOM, { @@ -1440,6 +1464,11 @@ static llama_state g_state; // available llama models enum e_model { MODEL_UNKNOWN, + MODEL_17M, + MODEL_22M, + MODEL_33M, + MODEL_109M, + MODEL_335M, MODEL_0_5B, MODEL_1B, MODEL_2B, @@ -1481,6 +1510,7 @@ struct llama_hparams { uint32_t n_ff; uint32_t n_expert = 0; uint32_t n_expert_used = 0; + uint32_t n_vocab_type = 0; // for BERT-style token types float f_norm_eps; float f_norm_rms_eps; @@ -1493,6 +1523,8 @@ struct llama_hparams { float f_clamp_kqv; float f_max_alibi_bias; + bool causal_attn = true; + bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; @@ -1720,6 +1752,7 @@ struct llama_model { llama_vocab vocab; struct ggml_tensor * tok_embd; + struct ggml_tensor * type_embd; struct ggml_tensor * pos_embd; struct ggml_tensor * tok_norm; struct ggml_tensor * tok_norm_b; @@ -1850,6 +1883,7 @@ struct llama_context { struct ggml_tensor * inp_pos; // I32 [n_batch] struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] struct ggml_tensor * inp_K_shift; // I32 [n_ctx] + struct ggml_tensor * inp_sum; // F32 [1, n_batch] #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -2829,6 +2863,7 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ switch (type) { case LLAMA_VOCAB_TYPE_SPM: return "SPM"; case LLAMA_VOCAB_TYPE_BPE: return "BPE"; + case LLAMA_VOCAB_TYPE_WPM: return "WPM"; default: return "unknown"; } } @@ -3000,6 +3035,26 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_BERT: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); + + switch (hparams.n_layer) { + case 3: + model.type = e_model::MODEL_17M; break; // bge-micro + case 6: + model.type = e_model::MODEL_22M; break; // MiniLM-L6 + case 12: + switch (hparams.n_embd) { + case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small + case 768: model.type = e_model::MODEL_109M; break; // bge-base + } break; + case 24: + model.type = e_model::MODEL_335M; break; // bge-large + } + } break; case LLM_ARCH_BLOOM: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -3204,6 +3259,16 @@ static void llm_load_vocab( vocab.special_unk_id = -1; vocab.special_sep_id = -1; vocab.special_pad_id = -1; + } else if (tokenizer_name == "bert") { + vocab.type = LLAMA_VOCAB_TYPE_WPM; + + // default special tokens + vocab.special_bos_id = 101; + vocab.special_eos_id = 102; + vocab.special_unk_id = 100; + vocab.special_sep_id = -1; + vocab.special_pad_id = -1; + vocab.add_space_prefix = false; } else { LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str()); LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); @@ -3232,6 +3297,8 @@ static void llm_load_vocab( // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { vocab.linefeed_id = llama_byte_to_token(vocab, '\n'); + } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) { + vocab.linefeed_id = vocab.special_pad_id; } else { const std::vector ids = llama_tokenize_internal(vocab, "\u010A", false); GGML_ASSERT(!ids.empty() && "model vocab missing newline token"); @@ -3569,6 +3636,7 @@ static bool llm_load_tensors( const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); const int64_t n_embd_gqa = n_embd_v_gqa; const int64_t n_vocab = hparams.n_vocab; + const int64_t n_vocab_type = hparams.n_vocab_type; const int64_t n_ff = hparams.n_ff; GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); @@ -3783,11 +3851,50 @@ static bool llm_load_tensors( layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}); } } break; - case LLM_ARCH_BLOOM: + case LLM_ARCH_BERT: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.tok_norm = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); + model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); + model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + } + } break; + case LLM_ARCH_BLOOM: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); + model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); // output { @@ -4739,6 +4846,7 @@ struct llm_build_context { const int32_t n_orig_ctx; const bool do_rope_shift; + const bool causal_attn; const llm_build_cb & cb; @@ -4782,6 +4890,7 @@ struct llm_build_context { kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), do_rope_shift (worst_case || kv_self.has_shift), + causal_attn (hparams.causal_attn), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { // all initializations should be done in init() @@ -5625,6 +5734,100 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_bert() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + // get input vectors with right size + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + struct ggml_tensor * inp_sum = ggml_view_1d(ctx0, lctx.inp_sum, n_tokens, 0); + + // construct input embeddings (token, type, position) + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + // token types are hardcoded to zero ("Sentence A") + struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); + inpL = ggml_add(ctx0, inpL, type_row0); + inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL); + cb(inpL, "inp_embd", -1); + + // embed layer norm + inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1); + cb(inpL, "inp_norm", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); // [n_kv, n_tokens] + + // iterate layers + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * cur = inpL; + + // self-attention + { + struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); + cb(Vcur, "Vcur", il); + + // seems like we just need to do this for Q? + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + // re-add the layer input + cur = ggml_add(ctx0, cur, inpL); + + // attention layer norm + cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, cb, il); + + struct ggml_tensor * ffn_inp = cur; + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + // attentions bypass the intermediate layer + cur = ggml_add(ctx0, cur, ffn_inp); + + // output layer norm + cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, cb, il); + + // input for next layer + inpL = cur; + } + + // final output + cur = inpL; + + // pooling + cur = ggml_mul_mat(ctx0, inp_sum, ggml_cont(ctx0, ggml_transpose(ctx0, cur))); + cb(cur, "result_embed", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + struct ggml_cgraph * build_bloom() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -7060,7 +7263,8 @@ static struct ggml_cgraph * llama_build_graph( for (int i = 0; i < n_kv; ++i) { float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || + (llm.causal_attn && lctx.kv_self.cells[i].pos > pos)) { f = -INFINITY; } else { f = 0; @@ -7081,6 +7285,15 @@ static struct ggml_cgraph * llama_build_graph( data[i] = lctx.kv_self.cells[i].delta; } } + + { + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); + float * data = (float *) lctx.inp_sum->data; + + for (int i = 0; i < batch.n_tokens; ++i) { + data[i] = 1.0f/float(batch.n_tokens); + } + } } llm.init(); @@ -7110,6 +7323,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_refact(); } break; + case LLM_ARCH_BERT: + { + result = llm.build_bert(); + } break; case LLM_ARCH_BLOOM: { result = llm.build_bloom(); @@ -7269,13 +7486,18 @@ static int llama_decode_internal( // the output is always the last tensor in the graph struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - GGML_ASSERT(strcmp(res->name, "result_output") == 0); - - // the embeddings could be the second to last tensor, or the third to last tensor struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; - if (strcmp(embeddings->name, "result_norm") != 0) { - embeddings = gf->nodes[gf->n_nodes - 3]; - GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); + if (strcmp(res->name, "result_output") == 0) { + // the embeddings could be the second to last tensor, or the third to last tensor + if (strcmp(embeddings->name, "result_norm") != 0) { + embeddings = gf->nodes[gf->n_nodes - 3]; + GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); + } + } else if (strcmp(res->name, "result_embed") == 0) { + embeddings = res; + res = nullptr; + } else { + GGML_ASSERT(false); } // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); @@ -7344,7 +7566,7 @@ static int llama_decode_internal( // extract logits // TODO: do not compute and extract logits if only embeddings are needed // need to update the graphs to skip "result_output" - { + if (res) { auto & logits_out = lctx.logits; #ifndef NDEBUG @@ -7388,9 +7610,11 @@ static int llama_decode_internal( if (!lctx.embedding.empty()) { auto & embedding_out = lctx.embedding; + const int64_t embed_pos = res ? n_embd * (n_tokens-1) : 0; + embedding_out.resize(n_embd); ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings); - ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float)); + ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embed_pos*sizeof(float), n_embd*sizeof(float)); ggml_backend_synchronize(embeddings_backend); } @@ -7454,6 +7678,9 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { GGML_ASSERT(false); return unicode_to_bytes_bpe(token_data.text); } + case LLAMA_VOCAB_TYPE_WPM: { + GGML_ASSERT(false); + } default: GGML_ASSERT(false); } @@ -7466,6 +7693,7 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 }; return vocab.token_to_id.at(buf); } + case LLAMA_VOCAB_TYPE_WPM: case LLAMA_VOCAB_TYPE_BPE: { return vocab.token_to_id.at(bytes_to_unicode_bpe(ch)); } @@ -7936,12 +8164,212 @@ private: llm_bigram_bpe::queue work_queue; }; -typedef enum FRAGMENT_BUFFER_VARIANT_TYPE{ +struct llm_tokenizer_wpm { + llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {} + + void tokenize(const std::string & text, std::vector & output) { + auto * token_map = &vocab.token_to_id; + + // normalize and split by whitespace + std::vector words = preprocess(text); + + // bos token prepended already + + // find the longest tokens that form the words + for (const std::string &word : words) { + // skip empty words + if (word.size() == 0) { + continue; + } + + // prepend phantom space + std::string word1 = "\xe2\x96\x81" + word; + int n = word1.size(); + + // we're at the start of a new word + int i = 0; + bool match_any = false; + + // move through character position in word + while (i < n) { + // loop through possible match length + bool match = false; + for (int j = n; j > i; j--) { + auto it = token_map->find(word1.substr(i, j - i)); + if (it != token_map->end()) { + output.push_back(it->second); + match = true; + match_any = true; + i = j; + break; + } + } + + // must be an unknown character + if (!match) { + i++; + } + } + + // we didn't find any matches for this word + if (!match_any) { + output.push_back(vocab.special_unk_id); + } + } + + // append eos token + output.push_back(vocab.special_eos_id); + } + + std::vector preprocess(const std::string & text) { + std::string ori_str = normalize(text); + uint64_t ori_size = ori_str.size(); + + // single punct / single symbol / single digit + // baseline: add whitespace on the left and right of punct and chinese characters + std::vector words; + std::string new_str = ""; + uint64_t i = 0; + while (i < ori_size) { + int utf_char_len = utf8_len(ori_str[i]); + if ((utf_char_len == 1) && ispunct(ori_str[i])) { + new_str += " "; + new_str += ori_str[i]; + new_str += " "; + i += 1; + } + else if ((utf_char_len == 3) && is_chinese_char(ori_str.substr(i, 3))) { + new_str += " "; + new_str += ori_str.substr(i, 3); + new_str += " "; + i += 3; + } + else { + new_str += ori_str[i]; + i += 1; + } + } + + // split by whitespace + uint64_t l = 0; + uint64_t r = 0; + while (r < new_str.size()) { + // if is whitespace + if (isspace(new_str[r])) { + if (r > l) words.push_back(new_str.substr(l, (r - l))); + l = r + 1; + r = l; + } + else { + r += 1; + } + } + if (r > l) { + words.push_back(new_str.substr(l, (r - l))); + } + return words; + } + + std::string normalize(const std::string & text) { + // TODO: handle chinese characters? https://github.com/huggingface/tokenizers/blob/ef5f50605ddf9f8caef1598c0e4853862b9707a7/tokenizers/src/normalizers/bert.rs#L98 + std::string text2 = strip_accents(text); + for (size_t i = 0; i < text2.size(); i += utf8_len(text2[i])) { + char c = text2[i]; + if (c >= 'A' && c <= 'Z') { + text2[i] = c - 'A' + 'a'; + } + } + return text2; + } + + bool is_chinese_char(const std::string & str) { + int len = str.length(); + unsigned int codepoint = 0; + int num_bytes = 0; + int i = 0; + unsigned char ch = static_cast(str[i]); + if (ch <= 0x7f) { + codepoint = ch; + num_bytes = 1; + } else if ((ch >> 5) == 0x06) { + codepoint = ch & 0x1f; + num_bytes = 2; + } else if ((ch >> 4) == 0x0e) { + codepoint = ch & 0x0f; + num_bytes = 3; + } else if ((ch >> 3) == 0x1e) { + codepoint = ch & 0x07; + num_bytes = 4; + } + for (int j = 1; j < num_bytes; ++j) { + if (i + j >= len) { + return false; // incomplete UTF-8 character + } + unsigned char next_ch = static_cast(str[i + j]); + if ((next_ch >> 6) != 0x02) { + return false; // invalid trailing byte + } + codepoint = (codepoint << 6) | (next_ch & 0x3f); + } + if ((codepoint >= 0x4E00 && codepoint <= 0x9FFF) || + (codepoint >= 0x3400 && codepoint <= 0x4DBF) || + (codepoint >= 0x20000 && codepoint <= 0x2A6DF) || + (codepoint >= 0x2A700 && codepoint <= 0x2B73F) || + (codepoint >= 0x2B740 && codepoint <= 0x2B81F) || + (codepoint >= 0x2B920 && codepoint <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920 + (codepoint >= 0xF900 && codepoint <= 0xFAFF) || + (codepoint >= 0x2F800 && codepoint <= 0x2FA1F) || + (codepoint >= 0x3000 && codepoint <= 0x303F) || + (codepoint >= 0xFF00 && codepoint <= 0xFFEF)) { + return true; // NOLINT + } + return false; + } + + std::string strip_accents(const std::string & input_string) { + std::string resultString; + std::map accent_map = { + {"À", 'A'}, {"Á", 'A'}, {"Â", 'A'}, {"Ã", 'A'}, {"Ä", 'A'}, {"Å", 'A'}, + {"à", 'a'}, {"á", 'a'}, {"â", 'a'}, {"ã", 'a'}, {"ä", 'a'}, {"å", 'a'}, + {"È", 'E'}, {"É", 'E'}, {"Ê", 'E'}, {"Ë", 'E'}, {"è", 'e'}, {"é", 'e'}, + {"ê", 'e'}, {"ë", 'e'}, {"Ì", 'I'}, {"Í", 'I'}, {"Î", 'I'}, {"Ï", 'I'}, + {"ì", 'i'}, {"í", 'i'}, {"î", 'i'}, {"ï", 'i'}, {"Ò", 'O'}, {"Ó", 'O'}, + {"Ô", 'O'}, {"Õ", 'O'}, {"Ö", 'O'}, {"ò", 'o'}, {"ó", 'o'}, {"ô", 'o'}, + {"õ", 'o'}, {"ö", 'o'}, {"Ù", 'U'}, {"Ú", 'U'}, {"Û", 'U'}, {"Ü", 'U'}, + {"ù", 'u'}, {"ú", 'u'}, {"û", 'u'}, {"ü", 'u'}, {"Ý", 'Y'}, {"ý", 'y'}, + {"Ç", 'C'}, {"ç", 'c'}, {"Ñ", 'N'}, {"ñ", 'n'}, + }; + + for (size_t i = 0; i < input_string.length();) { + int len = utf8_len(input_string[i]); + std::string curChar = input_string.substr(i, len); + auto iter = accent_map.find(curChar); + if (iter != accent_map.end()) { + resultString += iter->second; + } else { + resultString += curChar; + } + i += len; + } + + return resultString; + } + + static size_t utf8_len(char src) { + const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4}; + uint8_t highbits = static_cast(src) >> 4; + return lookup[highbits]; + } + + const llama_vocab & vocab; +}; + +typedef enum FRAGMENT_BUFFER_VARIANT_TYPE { FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN, FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT } FRAGMENT_BUFFER_VARIANT_TYPE; -struct fragment_buffer_variant{ +struct fragment_buffer_variant { fragment_buffer_variant(llama_vocab::id _token) : type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN), @@ -7971,8 +8399,7 @@ struct fragment_buffer_variant{ // #define PRETOKENIZERDEBUG -static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) -{ +static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) { // for each special token for (const auto & st: vocab.special_tokens_cache) { const auto & special_token = st.first; @@ -8090,10 +8517,8 @@ static std::vector llama_tokenize_internal(const llama_vocab & switch (vocab.type) { case LLAMA_VOCAB_TYPE_SPM: { - for (const auto & fragment: fragment_buffer) - { - if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) - { + for (const auto & fragment: fragment_buffer) { + if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { // without adding this leading whitespace, we do not get the same results as the original tokenizer // TODO: It's likely possible to get rid of this string copy entirely @@ -8113,19 +8538,15 @@ static std::vector llama_tokenize_internal(const llama_vocab & llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); tokenizer.tokenize(raw_text, output); - } - else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) - { + } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); } } } break; case LLAMA_VOCAB_TYPE_BPE: { - for (const auto & fragment: fragment_buffer) - { - if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) - { + for (const auto & fragment: fragment_buffer) { + if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); #ifdef PRETOKENIZERDEBUG @@ -8133,9 +8554,23 @@ static std::vector llama_tokenize_internal(const llama_vocab & #endif llm_tokenizer_bpe tokenizer(vocab); tokenizer.tokenize(raw_text, output); + } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) + output.push_back(fragment.token); } - else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) - { + } + } break; + case LLAMA_VOCAB_TYPE_WPM: + { + for (const auto & fragment: fragment_buffer) { + if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { + auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); + +#ifdef PRETOKENIZERDEBUG + LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); +#endif + llm_tokenizer_wpm tokenizer(vocab); + tokenizer.tokenize(raw_text, output); + } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); } } @@ -10799,7 +11234,7 @@ struct llama_context * llama_new_context_with_model( // graph inputs { ggml_init_params init_params = { - /* .mem_size */ ggml_tensor_overhead()*5, + /* .mem_size */ ggml_tensor_overhead()*7, /* .mem_buffer */ nullptr, /* .no_alloc */ true, }; @@ -10810,12 +11245,14 @@ struct llama_context * llama_new_context_with_model( ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); + ctx->inp_sum = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, 1, cparams.n_batch); ggml_set_name(ctx->inp_tokens, "inp_tokens"); ggml_set_name(ctx->inp_embd, "inp_embd"); ggml_set_name(ctx->inp_pos, "inp_pos"); ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask"); ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); + ggml_set_name(ctx->inp_sum, "inp_sum"); ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); @@ -11746,6 +12183,7 @@ static std::string llama_decode_text(const std::string & text) { int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) { if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { + case LLAMA_VOCAB_TYPE_WPM: case LLAMA_VOCAB_TYPE_SPM: { // NOTE: we accept all unsupported token types, // suppressing them like CONTROL tokens. diff --git a/llama.h b/llama.h index cec4158bc..367e8f1a1 100644 --- a/llama.h +++ b/llama.h @@ -61,6 +61,7 @@ extern "C" { enum llama_vocab_type { LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding + LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece }; enum llama_token_type { From 3bdc4cd0f595a6096cca4a64aa75ffa8a3503465 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 11 Feb 2024 19:08:39 +0100 Subject: [PATCH 295/334] CUDA: mul_mat_vec_q tiling, refactor mul mat logic (#5434) * CUDA: mul_mat_vec_q tiling, refactor mul mat logic Co-authored-by: slaren --------- Co-authored-by: slaren --- ggml-cuda.cu | 265 +++++++++++++++++++++++++++++---------------------- 1 file changed, 149 insertions(+), 116 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5053757e6..96976f248 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -150,8 +150,8 @@ #define CUDA_USE_TENSOR_CORES #endif -// max batch size to use MMQ kernels when tensor cores are available -#define MMQ_MAX_BATCH_SIZE 32 +#define MMVQ_MAX_BATCH_SIZE 8 // max batch size to use MMVQ kernels +#define MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -5310,51 +5310,59 @@ template static __global__ void #endif // __CUDA_ARCH__ >= CC_VOLTA } -#define MMVQ_NWARPS_NVIDIA 4 -#define MMVQ_NWARPS_AMD_RDNA2 1 -#define MMVQ_NWARPS_AMD_OLD 4 - -template +template #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(nwarps*WARP_SIZE, 1) // tells the compiler to use as many registers as it wants +// tell the compiler to use as many registers as it wants, see nwarps definition below +__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1) #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) { + const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) { - const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) + constexpr int nwarps = 1; + constexpr int rows_per_cuda_block = 1; +#else + constexpr int nwarps = ncols_y <= 4 ? 4 : 2; + constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - const int row = blockIdx.x; - - const int blocks_per_row_x = ncols_x / qk; - const int blocks_per_col_y = nrows_y / QK8_1; - const int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + const int row0 = rows_per_cuda_block*blockIdx.x; + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; + constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi; // partial sum for each thread - float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f}; + float tmp[ncols_y][rows_per_cuda_block] = {0.0f}; const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = tid / (qi/vdr); i < blocks_per_row_x; i += blocks_per_iter) { - const int ibx = row*blocks_per_row_x + i; // x block index + for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) { + const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx - const int iby = i * (qk/QK8_1); // y block index that aligns with ibx - - const int iqs = vdr * (tid % (qi/vdr)); // x block quant index when casting the quants to int + // x block quant index when casting the quants to int + const int kqs = vdr * (tid % (qi/vdr)); #pragma unroll for (int j = 0; j < ncols_y; ++j) { - tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs); +#pragma unroll + for (int i = 0; i < rows_per_cuda_block; ++i) { + tmp[j][i] += vec_dot_q_cuda( + &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs); + } } } - __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y_template != 0 ? ncols_y_template : 8][WARP_SIZE]; + __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE]; if (threadIdx.y > 0) { #pragma unroll for (int j = 0; j < ncols_y; ++j) { - tmp_shared[threadIdx.y-1][j][threadIdx.x] = tmp[j]; +#pragma unroll + for (int i = 0; i < rows_per_cuda_block; ++i) { + tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i]; + } } } __syncthreads(); @@ -5366,13 +5374,16 @@ static __global__ void mul_mat_vec_q( #pragma unroll for (int j = 0; j < ncols_y; ++j) { #pragma unroll - for (int i = 0; i < nwarps-1; ++i) { - tmp[j] += tmp_shared[i][j][threadIdx.x]; + for (int i = 0; i < rows_per_cuda_block; ++i) { +#pragma unroll + for (int l = 0; l < nwarps-1; ++l) { + tmp[j][i] += tmp_shared[l][j][i][threadIdx.x]; + } + tmp[j][i] = warp_reduce_sum(tmp[j][i]); } - tmp[j] = warp_reduce_sum(tmp[j]); - if (threadIdx.x == 0) { - dst[j*nrows_dst + row] = tmp[j]; + if (threadIdx.x < rows_per_cuda_block) { + dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x]; } } } @@ -6851,65 +6862,75 @@ static void mul_mat_vec_q_cuda( const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); - GGML_ASSERT(ncols_y <= 4); + GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE); int id; CUDA_CHECK(cudaGetDevice(&id)); - int nwarps; - if (g_device_caps[id].cc >= CC_OFFSET_AMD) { - nwarps = g_device_caps[id].cc >= CC_RDNA2 ? MMVQ_NWARPS_AMD_RDNA2 : MMVQ_NWARPS_AMD_OLD; - } else { - nwarps = MMVQ_NWARPS_NVIDIA; - } + int64_t nwarps = 1; + int64_t rows_per_cuda_block = 1; - const dim3 block_nums(nrows_x, 1, 1); + if (g_device_caps[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2 + switch(ncols_y) { + case 1: + nwarps = 4; + rows_per_cuda_block = 1; + break; + case 2: + case 3: + case 4: + nwarps = 4; + rows_per_cuda_block = 2; + break; + case 5: + case 6: + case 7: + case 8: + nwarps = 2; + rows_per_cuda_block = 2; + break; + default: + GGML_ASSERT(false); + break; + } + } + const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block; + const dim3 block_nums(nblocks, 1, 1); const dim3 block_dims(WARP_SIZE, nwarps, 1); - switch (nwarps) { - case 1: switch(ncols_y) { - case 1: - mul_mat_vec_q<1, 1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 2: - mul_mat_vec_q<1, 2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 3: - mul_mat_vec_q<1, 3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 4: - mul_mat_vec_q<1, 4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - default: - GGML_ASSERT(false); - break; - } break; - case 4: switch(ncols_y) { - case 1: - mul_mat_vec_q<4, 1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 2: - mul_mat_vec_q<4, 2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 3: - mul_mat_vec_q<4, 3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - case 4: - mul_mat_vec_q<4, 4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); - break; - default: - GGML_ASSERT(false); - break; - } break; - + switch (ncols_y) { + case 1: + mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 2: + mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 3: + mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 4: + mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 5: + mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 6: + mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 7: + mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; + case 8: + mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + break; default: GGML_ASSERT(false); break; @@ -9735,7 +9756,7 @@ static __global__ void k_compute_batched_ptrs( ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } -static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); @@ -9893,39 +9914,69 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 int64_t min_compute_capability = INT_MAX; + bool any_pascal_with_slow_fp16 = false; if (split) { ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; auto & tensor_split = buft_ctx->tensor_split; for (int id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_device_caps[id].cc && tensor_split[id] < (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { + // skip devices that are not going to do any work: + if (tensor_split[id] >= (id + 1 < g_device_count ? tensor_split[id + 1] : 1.0f)) { + continue; + } + + if (min_compute_capability > g_device_caps[id].cc) { min_compute_capability = g_device_caps[id].cc; } + if (g_device_caps[id].cc == 610) { + any_pascal_with_slow_fp16 = true; + } } } else { - min_compute_capability = g_device_caps[g_main_device].cc; + min_compute_capability = g_device_caps[g_main_device].cc; + any_pascal_with_slow_fp16 = g_device_caps[g_main_device].cc == 610; } + // check data types and tensor shapes for custom matrix multiplication kernels: + bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->ne[1] == 1; + + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; + + bool use_mul_mat_q = ggml_cuda_supports_mmq(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) const bool fp16_performance_good = min_compute_capability >= CC_RDNA1; - bool use_mul_mat_q = ggml_is_quantized(src0->type); + #ifdef CUDA_USE_TENSOR_CORES use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3; #endif // CUDA_USE_TENSOR_CORES #else - const bool fp16_performance_good = min_compute_capability >= CC_VOLTA; - bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); + // fp16 performance is good on Volta or newer and on P100 (compute capability 6.0) + const bool fp16_performance_good = min_compute_capability >= CC_PASCAL && !any_pascal_with_slow_fp16; + + // mmvq and mmq need the __dp4a instruction which on NVIDIA is only available for CC >= 6.1 + use_mul_mat_vec_q = use_mul_mat_vec_q && min_compute_capability >= MIN_CC_DP4A; + use_mul_mat_q = use_mul_mat_q && min_compute_capability >= MIN_CC_DP4A; + #ifdef CUDA_USE_TENSOR_CORES // when tensor cores are available, use them for large batch size // ref: https://github.com/ggerganov/llama.cpp/pull/3776 - use_mul_mat_q = use_mul_mat_q && !(fp16_performance_good && src1->ne[1] > MMQ_MAX_BATCH_SIZE); + use_mul_mat_q = use_mul_mat_q && (!fp16_performance_good || src1->ne[1] <= MMQ_MAX_BATCH_SIZE); #endif // CUDA_USE_TENSOR_CORES #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - use_mul_mat_q = use_mul_mat_q && ggml_cuda_supports_mmq(src0->type); + // if mmvq is available it's a better choice than dmmv: +#ifndef GGML_CUDA_FORCE_DMMV + use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; +#endif // GGML_CUDA_FORCE_DMMV // debug helpers //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); @@ -9943,33 +9994,15 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_mul_mat_vec_nc(src0, src1, dst); } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch - ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); - } else if (src0->type == GGML_TYPE_F32) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); - } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { - if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->type == GGML_TYPE_F32) { -#ifdef GGML_CUDA_FORCE_DMMV - const bool use_mul_mat_vec_q = false; -#else - const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); -#endif // GGML_CUDA_FORCE_DMMV - - if (use_mul_mat_vec_q) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); - } else { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); - } - } else { - if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); - } else if (use_mul_mat_q) { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); - } else { - ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); - } - } + ggml_cuda_mul_mat_batched_cublas(src0, src1, dst); + } else if (use_dequantize_mul_mat_vec) { + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); + } else if (use_mul_mat_vec_q) { + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); } else { - GGML_ASSERT(false); + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); } } From 3b169441dfe8e420f88d1592708cc2a871daadb9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 12 Feb 2024 09:16:06 +0200 Subject: [PATCH 296/334] sync : ggml (#5452) * ggml-alloc : v3 (ggml/727) * ggml-alloc v3 ggml-ci * fix ci ggml-ci * whisper : check for backend buffer allocation failures * whisper : avoid leaks when initialization fails * cleanup ggml-ci * style fixes ggml-ci * sync : ggml * update llama.cpp, clip.cpp, export-lora.cpp * update finetune.cpp, train-text-from-scratch.cpp ggml-ci * ggml-backend : reduce alignment to 32 to match gguf and fix mmap --------- Co-authored-by: slaren --- examples/export-lora/export-lora.cpp | 19 +- examples/finetune/finetune.cpp | 147 +- examples/llava/clip.cpp | 152 +- .../train-text-from-scratch.cpp | 112 +- ggml-alloc.c | 1373 +++++++++-------- ggml-alloc.h | 110 +- ggml-backend.c | 492 +++--- ggml-backend.h | 15 +- ggml.c | 28 +- ggml.h | 18 +- llama.cpp | 181 +-- scripts/sync-ggml.last | 2 +- 12 files changed, 1287 insertions(+), 1362 deletions(-) diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 4cd5d99bb..2f7be8a13 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -337,24 +337,14 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int params.mem_buffer = NULL; params.no_alloc = true; struct ggml_context * ctx = NULL; - struct ggml_allocr * alloc = NULL; - struct ggml_cgraph * gf = NULL; + struct ggml_gallocr * alloc = NULL; + struct ggml_cgraph * gf = NULL; ctx = ggml_init(params); - alloc = ggml_allocr_new_measure(tensor_alignment); + alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling); - size_t alloc_size = ggml_allocr_alloc_graph(alloc, gf); - ggml_allocr_free(alloc); - ggml_free(ctx); - static std::vector data_compute; - data_compute.resize(alloc_size + tensor_alignment); - - ctx = ggml_init(params); - alloc = ggml_allocr_new(data_compute.data(), data_compute.size(), tensor_alignment); - gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling); - ggml_allocr_alloc_graph(alloc, gf); - ggml_allocr_free(alloc); + ggml_gallocr_alloc_graph(alloc, gf); struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads); static std::vector data_work; @@ -363,6 +353,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int ggml_graph_compute(gf, &cplan); + ggml_gallocr_free(alloc); ggml_free(ctx); return true; } diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index b7e19c5fe..b11c56020 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1,5 +1,6 @@ #include "ggml.h" #include "ggml-alloc.h" +#include "ggml-backend.h" #include "llama.h" #include "common.h" #include "train.h" @@ -13,8 +14,6 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static const size_t tensor_alignment = 32; - struct my_llama_hparams { uint32_t n_vocab = 32000; uint32_t n_ctx = 512; @@ -128,7 +127,7 @@ struct my_llama_lora_layer { struct my_llama_lora { struct ggml_context * ctx = NULL; - std::vector data; + ggml_backend_buffer_t data; my_llama_lora_hparams hparams; @@ -372,63 +371,6 @@ static void set_param_lora(struct my_llama_lora * lora) { } } -static void alloc_lora(struct ggml_allocr * alloc, struct my_llama_lora * lora) { - ggml_allocr_alloc(alloc, lora->tok_embeddings_a); - ggml_allocr_alloc(alloc, lora->tok_embeddings_b); - ggml_allocr_alloc(alloc, lora->norm_a); - ggml_allocr_alloc(alloc, lora->norm_b); - ggml_allocr_alloc(alloc, lora->output_a); - ggml_allocr_alloc(alloc, lora->output_b); - for (uint32_t i = 0; i < lora->layers.size(); ++i) { - auto & layer = lora->layers[i]; - ggml_allocr_alloc(alloc, layer.attention_norm_a); - ggml_allocr_alloc(alloc, layer.attention_norm_b); - ggml_allocr_alloc(alloc, layer.wq_a); - ggml_allocr_alloc(alloc, layer.wq_b); - ggml_allocr_alloc(alloc, layer.wk_a); - ggml_allocr_alloc(alloc, layer.wk_b); - ggml_allocr_alloc(alloc, layer.wv_a); - ggml_allocr_alloc(alloc, layer.wv_b); - ggml_allocr_alloc(alloc, layer.wo_a); - ggml_allocr_alloc(alloc, layer.wo_b); - ggml_allocr_alloc(alloc, layer.ffn_norm_a); - ggml_allocr_alloc(alloc, layer.ffn_norm_b); - ggml_allocr_alloc(alloc, layer.w1_a); - ggml_allocr_alloc(alloc, layer.w1_b); - ggml_allocr_alloc(alloc, layer.w2_a); - ggml_allocr_alloc(alloc, layer.w2_b); - ggml_allocr_alloc(alloc, layer.w3_a); - ggml_allocr_alloc(alloc, layer.w3_b); - } - ggml_allocr_alloc(alloc, lora->tok_embeddings_a->grad); - ggml_allocr_alloc(alloc, lora->tok_embeddings_b->grad); - ggml_allocr_alloc(alloc, lora->norm_a->grad); - ggml_allocr_alloc(alloc, lora->norm_b->grad); - ggml_allocr_alloc(alloc, lora->output_a->grad); - ggml_allocr_alloc(alloc, lora->output_b->grad); - for (uint32_t i = 0; i < lora->layers.size(); ++i) { - auto & layer = lora->layers[i]; - ggml_allocr_alloc(alloc, layer.attention_norm_a->grad); - ggml_allocr_alloc(alloc, layer.attention_norm_b->grad); - ggml_allocr_alloc(alloc, layer.wq_a->grad); - ggml_allocr_alloc(alloc, layer.wq_b->grad); - ggml_allocr_alloc(alloc, layer.wk_a->grad); - ggml_allocr_alloc(alloc, layer.wk_b->grad); - ggml_allocr_alloc(alloc, layer.wv_a->grad); - ggml_allocr_alloc(alloc, layer.wv_b->grad); - ggml_allocr_alloc(alloc, layer.wo_a->grad); - ggml_allocr_alloc(alloc, layer.wo_b->grad); - ggml_allocr_alloc(alloc, layer.ffn_norm_a->grad); - ggml_allocr_alloc(alloc, layer.ffn_norm_b->grad); - ggml_allocr_alloc(alloc, layer.w1_a->grad); - ggml_allocr_alloc(alloc, layer.w1_b->grad); - ggml_allocr_alloc(alloc, layer.w2_a->grad); - ggml_allocr_alloc(alloc, layer.w2_b->grad); - ggml_allocr_alloc(alloc, layer.w3_a->grad); - ggml_allocr_alloc(alloc, layer.w3_b->grad); - } -} - static void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) { const auto & lparams = lora->hparams; @@ -522,18 +464,8 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora set_param_lora(lora); - // measure data size - size_t size = 0; - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - size += GGML_PAD(ggml_nbytes(t), tensor_alignment); - } - - // allocate data - struct ggml_allocr * alloc = NULL; - lora->data.resize(size + tensor_alignment); - alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment); - alloc_lora(alloc, lora); - ggml_allocr_free(alloc); + // allocate data for lora tensors + lora->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type()); } static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) { @@ -579,7 +511,7 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl static struct ggml_tensor * llama_build_lora_finetune_graphs( struct my_llama_model * model, struct my_llama_lora * lora, - struct ggml_allocr * alloc, + ggml_gallocr_t alloc, struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, @@ -590,7 +522,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( const int n_tokens, const int n_batch, const bool enable_flash_attn, - const bool enable_checkpointing) { + const bool enable_checkpointing, + const bool measure_only) { ggml_set_scratch(ctx, { 0, 0, nullptr, }); const int n_past = 0; @@ -622,13 +555,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // KQ_pos - contains the positions struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N); - ggml_allocr_alloc(alloc, KQ_pos); - if (!ggml_allocr_is_measure(alloc)) { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } + ggml_set_input(KQ_pos); // rope has so much parameters that we make a custom function for it auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale] @@ -780,7 +707,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // input gradient ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f)); GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL); - ggml_allocr_alloc(alloc, t36->grad); + ggml_set_input(t36->grad); // KQ_pos ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f)); @@ -805,11 +732,23 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // note: they will be freed in reverse order for (unsigned int i = 0; i < checkpoints.size(); ++i) { if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) { - ggml_allocr_alloc(alloc, checkpoints[i]); + ggml_set_input(checkpoints[i]); } } - ggml_allocr_alloc_graph(alloc, gb); + if (measure_only) { + ggml_gallocr_reserve(alloc, gb); + } else { + ggml_gallocr_alloc_graph(alloc, gb); + + // set KQ_pos + { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + } // remove the additional nodes and leafs for (int i = n_leafs_before; i < gb->n_leafs; ++i) { @@ -1663,7 +1602,7 @@ int main(int argc, char ** argv) { printf("%s: seen train_samples %llu\n", __func__, (long long unsigned) train->train_samples); printf("%s: seen train_tokens %llu\n", __func__, (long long unsigned) train->train_tokens); printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs); - printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + lora.data.size()), (float) (ggml_used_mem(lora.ctx) + lora.data.size()) / (1024.0f*1024.0f)); + printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)), (float) (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)) / (1024.0f*1024.0f)); if (params.only_write_lora) { save_train_files_data save_data; @@ -1690,10 +1629,6 @@ int main(int argc, char ** argv) { int n_vocab = model.hparams.n_vocab; int n_batch = params.common.n_batch; - - std::vector mem_input_data; - std::vector mem_compute_data; - // context for input tensors without their data struct ggml_init_params ctx_input_params = { ggml_tensor_overhead() * 2, // mem_size @@ -1706,17 +1641,11 @@ int main(int argc, char ** argv) { struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch); struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); - // measure required memory for input tensors - size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) + - GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) + - tensor_alignment; - printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); - // allocate input tensors - mem_input_data.resize(max_input_size); - ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment); - ggml_allocr_alloc(alloc_inps, tokens_input); - ggml_allocr_alloc(alloc_inps, target_probs); + // measure required memory for input tensors + ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type()); + size_t max_input_size = ggml_backend_buffer_get_size(input_data); + printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); // context for compute tensors without their data const size_t estimated_compute_size_wo_data = ( @@ -1743,7 +1672,7 @@ int main(int argc, char ** argv) { // find best evaluation order for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); - ggml_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment); + ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); @@ -1756,14 +1685,15 @@ int main(int argc, char ** argv) { &logits, tokens_input, target_probs, n_tokens, n_batch, params.common.use_flash, - params.common.use_checkpointing + params.common.use_checkpointing, + true ); - size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment; + size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer if (max_compute_size < best_compute_size) { best_compute_size = max_compute_size; best_order = gf->order; } - ggml_allocr_free(alloc); + ggml_gallocr_free(alloc); ggml_free(ctx_compute); } size_t max_compute_size = best_compute_size; @@ -1774,9 +1704,8 @@ int main(int argc, char ** argv) { "invalid"); // allocate compute tensors - mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); - ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); + ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); @@ -1789,11 +1718,9 @@ int main(int argc, char ** argv) { &logits, tokens_input, target_probs, n_tokens, n_batch, params.common.use_flash, - params.common.use_checkpointing + params.common.use_checkpointing, + false ); - ggml_allocr_free(alloc); - ggml_allocr_free(alloc_inps); - // tokenize data std::vector train_tokens; @@ -1908,6 +1835,8 @@ int main(int argc, char ** argv) { ggml_free(ctx_work); ggml_free(ctx_compute); ggml_free(ctx_input); + ggml_gallocr_free(alloc); + int64_t t1 = ggml_time_ms(); printf("%s: total training time: ", __func__); diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 9129052a2..ccd0d85ad 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -367,7 +367,7 @@ struct clip_ctx { ggml_backend_buffer_t params_buffer = NULL; ggml_backend_buffer_t compute_buffer = NULL; ggml_backend_t backend = NULL; - ggml_allocr * compute_alloc = NULL; + ggml_gallocr_t compute_alloc = NULL; }; static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) { @@ -405,31 +405,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 struct ggml_cgraph * gf = ggml_new_graph(ctx0); struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size); - ggml_allocr_alloc(ctx->compute_alloc, inp_raw); - - if (!ggml_allocr_is_measure(ctx->compute_alloc)) { - float * data = (float *)malloc(ggml_nbytes(inp_raw)); - - for (size_t i = 0; i < imgs->size; i++) { - const int nx = imgs->data[i].nx; - const int ny = imgs->data[i].ny; - GGML_ASSERT(nx == image_size && ny == image_size); - - const int n = nx * ny; - - for (int b = 0; b < batch_size; b++) { - for (int k = 0; k < 3; k++) { - for (int y = 0; y < ny; y++) { - for (int x = 0; x < nx; x++) { - data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k]; - } - } - } - } - } - ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); - free(data); - } + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1); @@ -438,13 +415,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // concat class_embeddings and patch_embeddings struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_allocr_alloc(ctx->compute_alloc, embeddings); - if (!ggml_allocr_is_measure(ctx->compute_alloc)) { - void* zero_mem = malloc(ggml_nbytes(embeddings)); - memset(zero_mem, 0, ggml_nbytes(embeddings)); - ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); - free(zero_mem); - } + ggml_set_name(embeddings, "embeddings"); + ggml_set_input(embeddings); embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); @@ -453,15 +425,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); - ggml_allocr_alloc(ctx->compute_alloc, positions); - if (!ggml_allocr_is_measure(ctx->compute_alloc)) { - int* positions_data = (int*)malloc(ggml_nbytes(positions)); - for (int i = 0; i < num_positions; i++) { - positions_data[i] = i; - } - ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); - free(positions_data); - } + ggml_set_name(positions, "positions"); + ggml_set_input(positions); embeddings = ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); @@ -560,15 +525,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); - ggml_allocr_alloc(ctx->compute_alloc, patches); - if (!ggml_allocr_is_measure(ctx->compute_alloc)) { - int* patches_data = (int*)malloc(ggml_nbytes(patches)); - for (int i = 0; i < num_patches; i++) { - patches_data[i] = i + 1; - } - ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); - free(patches_data); - } + ggml_set_name(patches, "patches"); + ggml_set_input(patches); // shape [1, 576, 1024] // ne is whcn, ne = [1024, 576, 1, 1] @@ -809,7 +767,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } // data - size_t buffer_size = 0; + size_t model_size = 0; { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); @@ -817,7 +775,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { enum ggml_type type = gguf_get_tensor_type(ctx, i); struct ggml_tensor * cur = ggml_get_tensor(meta, name); size_t tensor_size = ggml_nbytes(cur); - buffer_size += tensor_size; + model_size += tensor_size; if (verbosity >= 3) { printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); @@ -825,8 +783,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } } - buffer_size += n_tensors * 128 /* CLIP PADDING */; - clip_ctx * new_clip = new clip_ctx; // update projector type @@ -886,12 +842,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - printf("%s: model size: %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0); + printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } } - printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors); + printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); // load tensors { @@ -925,12 +881,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } // alloc memory and offload data - new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size); - ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer); + new_clip->params_buffer = ggml_backend_alloc_ctx_tensors(new_clip->ctx_data, new_clip->backend); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name); - ggml_allocr_alloc(alloc, cur); const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); if (!fin) { @@ -949,7 +903,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes); } } - ggml_allocr_free(alloc); fin.close(); } @@ -1077,15 +1030,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // measure mem requirement and allocate { new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead()); - new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend); + new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend)); clip_image_f32_batch batch; batch.size = 1; ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch); - size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf); - ggml_allocr_free(new_clip->compute_alloc); - new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size); - new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer); - + ggml_gallocr_reserve(new_clip->compute_alloc, gf); + size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0); printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); } @@ -1267,12 +1217,72 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima GGML_ASSERT(batch_size == 1); // TODO: support multiple images } - // reset alloc buffer to clean the memory from previous invocations - ggml_allocr_reset(ctx->compute_alloc); - // build the inference graph ggml_cgraph * gf = clip_image_build_graph(ctx, imgs); - ggml_allocr_alloc_graph(ctx->compute_alloc, gf); + ggml_gallocr_alloc_graph(ctx->compute_alloc, gf); + + // set inputs + const auto & model = ctx->vision_model; + const auto & hparams = model.hparams; + const int image_size = hparams.image_size; + const int patch_size = hparams.patch_size; + const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); + const int num_positions = num_patches + 1; + + { + struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); + float * data = (float *)malloc(ggml_nbytes(inp_raw)); + + for (size_t i = 0; i < imgs->size; i++) { + const int nx = imgs->data[i].nx; + const int ny = imgs->data[i].ny; + GGML_ASSERT(nx == image_size && ny == image_size); + + const int n = nx * ny; + + for (int b = 0; b < batch_size; b++) { + for (int k = 0; k < 3; k++) { + for (int y = 0; y < ny; y++) { + for (int x = 0; x < nx; x++) { + data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k]; + } + } + } + } + } + ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); + free(data); + } + + { + struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); + + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); + } + + { + struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + + int* positions_data = (int*)malloc(ggml_nbytes(positions)); + for (int i = 0; i < num_positions; i++) { + positions_data[i] = i; + } + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); + } + + { + struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); + int* patches_data = (int*)malloc(ggml_nbytes(patches)); + for (int i = 0; i < num_patches; i++) { + patches_data[i] = i + 1; + } + ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); + free(patches_data); + } if (ggml_backend_is_cpu(ctx->backend)) { ggml_backend_cpu_set_n_threads(ctx->backend, n_threads); diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index eee9d4de3..2e2a8ce08 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -1,5 +1,6 @@ #include "ggml.h" #include "ggml-alloc.h" +#include "ggml-backend.h" #include "common.h" #include "train.h" #include "llama.h" @@ -19,8 +20,6 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static const size_t tensor_alignment = 32; - struct my_llama_hparams { uint32_t n_vocab = 32000; uint32_t n_ctx = 512; @@ -58,7 +57,7 @@ struct my_llama_layer { struct my_llama_model { struct ggml_context * ctx = NULL; - std::vector data; + ggml_backend_buffer_t data = NULL; my_llama_hparams hparams; @@ -147,39 +146,6 @@ static void set_param_model(struct my_llama_model * model) { } } -static void alloc_model(struct ggml_allocr * alloc, struct my_llama_model * model) { - ggml_allocr_alloc(alloc, model->tok_embeddings); - ggml_allocr_alloc(alloc, model->norm); - ggml_allocr_alloc(alloc, model->output); - for (uint32_t i = 0; i < model->layers.size(); ++i) { - auto & layer = model->layers[i]; - ggml_allocr_alloc(alloc, layer.attention_norm); - ggml_allocr_alloc(alloc, layer.wq); - ggml_allocr_alloc(alloc, layer.wk); - ggml_allocr_alloc(alloc, layer.wv); - ggml_allocr_alloc(alloc, layer.wo); - ggml_allocr_alloc(alloc, layer.ffn_norm); - ggml_allocr_alloc(alloc, layer.w1); - ggml_allocr_alloc(alloc, layer.w2); - ggml_allocr_alloc(alloc, layer.w3); - } - ggml_allocr_alloc(alloc, model->tok_embeddings->grad); - ggml_allocr_alloc(alloc, model->norm->grad); - ggml_allocr_alloc(alloc, model->output->grad); - for (uint32_t i = 0; i < model->layers.size(); ++i) { - auto & layer = model->layers[i]; - ggml_allocr_alloc(alloc, layer.attention_norm->grad); - ggml_allocr_alloc(alloc, layer.wq->grad); - ggml_allocr_alloc(alloc, layer.wk->grad); - ggml_allocr_alloc(alloc, layer.wv->grad); - ggml_allocr_alloc(alloc, layer.wo->grad); - ggml_allocr_alloc(alloc, layer.ffn_norm->grad); - ggml_allocr_alloc(alloc, layer.w1->grad); - ggml_allocr_alloc(alloc, layer.w2->grad); - ggml_allocr_alloc(alloc, layer.w3->grad); - } -} - static void init_model(struct my_llama_model * model) { const auto & hparams = model->hparams; @@ -252,17 +218,8 @@ static void init_model(struct my_llama_model * model) { set_param_model(model); - // measure data size - size_t size = 0; - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - size += GGML_PAD(ggml_nbytes(t), tensor_alignment); - } - // allocate data - struct ggml_allocr * alloc = NULL; - model->data.resize(size + tensor_alignment); - alloc = ggml_allocr_new(model->data.data(), model->data.size(), tensor_alignment); - alloc_model(alloc, model); + model->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type()); } static void randomize_model(struct my_llama_model * model, int seed, float mean, float std, float min, float max) { @@ -297,7 +254,7 @@ static void randomize_model(struct my_llama_model * model, int seed, float mean, static struct ggml_tensor * llama_build_train_graphs( struct my_llama_model * model, - struct ggml_allocr * alloc, + ggml_gallocr_t alloc, struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, @@ -308,7 +265,8 @@ static struct ggml_tensor * llama_build_train_graphs( const int n_tokens, const int n_batch, const bool enable_flash_attn, - const bool enable_checkpointing) { + const bool enable_checkpointing, + const bool measure_only) { ggml_set_scratch(ctx, { 0, 0, nullptr, }); const int n_past = 0; @@ -334,13 +292,7 @@ static struct ggml_tensor * llama_build_train_graphs( // KQ_pos - contains the positions struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N); - ggml_allocr_alloc(alloc, KQ_pos); - if (!ggml_allocr_is_measure(alloc)) { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } + ggml_set_input(KQ_pos); // rope has so much parameters that we make a custom function for it auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale] @@ -448,21 +400,31 @@ static struct ggml_tensor * llama_build_train_graphs( // KQ_pos ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f)); GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL); - - ggml_allocr_alloc(alloc, t36->grad); + ggml_set_input(t36->grad); // allocating checkpoints in one block to reduce memory fragmentation // note: they will be freed in reverse order for (int i = 0; i < (int) checkpoints.size(); ++i) { if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) { - ggml_allocr_alloc(alloc, checkpoints[i]); + ggml_set_input(checkpoints[i]); } } //int n_leafs_after = gb->n_leafs; //int n_nodes_after = gb->n_nodes; + if (measure_only) { + // FIXME: will still allocate + ggml_gallocr_reserve(alloc, gb); + } else { + ggml_gallocr_alloc_graph(alloc, gb); - ggml_allocr_alloc_graph(alloc, gb); + if (!measure_only) { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + } // remove the additional nodes and leafs for (int i = n_leafs_before; i < gb->n_leafs; ++i) { @@ -1046,7 +1008,7 @@ int main(int argc, char ** argv) { printf("%s: seen train_samples %llu\n", __func__, (long long unsigned) train->train_samples); printf("%s: seen train_tokens %llu\n", __func__, (long long unsigned) train->train_tokens); printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs); - printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + model.data.size()), (float) (ggml_used_mem(model.ctx) + model.data.size()) / (1024.0f*1024.0f)); + printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)), (float) (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)) / (1024.0f*1024.0f)); if (params.only_write_model) { save_train_files_data save_data; @@ -1073,11 +1035,6 @@ int main(int argc, char ** argv) { int n_vocab = model.hparams.n_vocab; int n_batch = params.common.n_batch; - std::vector mem_input_data; - std::vector mem_compute_data; - - ggml_allocr * alloc = NULL; - // context for input tensors without their data struct ggml_init_params ctx_input_params = { ggml_tensor_overhead() * 2, // mem_size @@ -1091,16 +1048,10 @@ int main(int argc, char ** argv) { struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); // measure required memory for input tensors - size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) + - GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) + - tensor_alignment; - printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); - // allocate input tensors - mem_input_data.resize(max_input_size); - alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment); - ggml_allocr_alloc(alloc, tokens_input); - ggml_allocr_alloc(alloc, target_probs); + ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type()); + size_t max_input_size = ggml_backend_buffer_get_size(input_data); + printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); // context for compute tensors without their data const size_t estimated_compute_size_wo_data = ( @@ -1127,7 +1078,7 @@ int main(int argc, char ** argv) { // find best evaluation order for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); - alloc = ggml_allocr_new_measure(tensor_alignment); + ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); @@ -1140,9 +1091,10 @@ int main(int argc, char ** argv) { &logits, tokens_input, target_probs, n_tokens, n_batch, params.common.use_flash, - params.common.use_checkpointing + params.common.use_checkpointing, + true ); - size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment; + size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer if (max_compute_size < best_compute_size) { best_compute_size = max_compute_size; best_order = gf->order; @@ -1157,9 +1109,8 @@ int main(int argc, char ** argv) { "invalid"); // allocate compute tensors - mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); - alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); + ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); @@ -1172,7 +1123,8 @@ int main(int argc, char ** argv) { &logits, tokens_input, target_probs, n_tokens, n_batch, params.common.use_flash, - params.common.use_checkpointing + params.common.use_checkpointing, + false ); std::vector train_tokens; diff --git a/ggml-alloc.c b/ggml-alloc.c index f9be6e1cb..c28c37c4f 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -17,397 +17,11 @@ //#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__) #define AT_PRINTF(...) -// TODO: GGML_PAD ? -static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) { - assert(alignment && !(alignment & (alignment - 1))); // power of 2 - size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment; - return offset + align; -} -struct free_block { - void * addr; - size_t size; -}; - -struct ggml_tallocr { - struct ggml_backend_buffer * buffer; - bool buffer_owned; - void * base; - size_t alignment; - - int n_free_blocks; - struct free_block free_blocks[MAX_FREE_BLOCKS]; - - size_t max_size; - - bool measure; - -#ifdef GGML_ALLOCATOR_DEBUG - struct ggml_tensor * allocated_tensors[1024]; -#endif -}; - -#ifdef GGML_ALLOCATOR_DEBUG -static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { - for (int i = 0; i < 1024; i++) { - if (alloc->allocated_tensors[i] == NULL) { - alloc->allocated_tensors[i] = tensor; - return; - } - } - GGML_ASSERT(!"out of allocated_tensors"); -} -static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { - for (int i = 0; i < 1024; i++) { - if (alloc->allocated_tensors[i] == tensor || - (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) { - alloc->allocated_tensors[i] = NULL; - return; - } - } - printf("tried to free tensor %s not found\n", tensor->name); - GGML_ASSERT(!"tensor not found"); -} -#endif - -// check if a tensor is allocated by this buffer -static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) { - return tensor->buffer == alloc->buffer && (!tensor->view_src || tensor->view_src->buffer == alloc->buffer); -} - -static bool ggml_is_view(struct ggml_tensor * t) { +static bool ggml_is_view(const struct ggml_tensor * t) { return t->view_src != NULL; } -void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { - GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources - GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated - - size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor); - size = aligned_offset(NULL, size, alloc->alignment); - - AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size); - - size_t max_avail = 0; - - // find the best fitting free block besides the last block - int best_fit_block = -1; - size_t best_fit_size = SIZE_MAX; - for (int i = 0; i < alloc->n_free_blocks - 1; i++) { - struct free_block * block = &alloc->free_blocks[i]; - max_avail = MAX(max_avail, block->size); - if (block->size >= size && block->size <= best_fit_size) { - best_fit_block = i; - best_fit_size = block->size; - } - } - - if (best_fit_block == -1) { - // the last block is our last resort - struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1]; - max_avail = MAX(max_avail, block->size); - if (block->size >= size) { - best_fit_block = alloc->n_free_blocks - 1; - } else { - fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, largest block available %zu)\n", - __func__, tensor->name, size, max_avail); - GGML_ASSERT(!"not enough space in the buffer"); - return; - } - } - - struct free_block * block = &alloc->free_blocks[best_fit_block]; - void * addr = block->addr; - block->addr = (char*)block->addr + size; - block->size -= size; - if (block->size == 0) { - // remove block if empty - alloc->n_free_blocks--; - for (int j = best_fit_block; j < alloc->n_free_blocks; j++) { - alloc->free_blocks[j] = alloc->free_blocks[j+1]; - } - } - - AT_PRINTF("block %d, addr %p\n", best_fit_block, addr); - - tensor->data = addr; - tensor->buffer = alloc->buffer; - if (!alloc->measure) { - ggml_backend_buffer_init_tensor(alloc->buffer, tensor); - } - -#ifdef GGML_ALLOCATOR_DEBUG - add_allocated_tensor(alloc, tensor); - size_t cur_max = (char*)addr - (char*)alloc->base + size; - if (cur_max > alloc->max_size) { - printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0); - for (int i = 0; i < 1024; i++) { - if (alloc->allocated_tensors[i]) { - printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0); - } - } - printf("\n"); - } -#endif - - alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->base + size); -} - -// this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { - if (ggml_tallocr_is_own(alloc, tensor) == false) { - // the tensor was not allocated in this buffer - // this can happen because the graph allocator will try to free weights and other tensors from different buffers - // the easiest way to deal with this is just to ignore it - // AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer); - return; - } - - void * ptr = tensor->data; - - size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor); - size = aligned_offset(NULL, size, alloc->alignment); - AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks); - -#ifdef GGML_ALLOCATOR_DEBUG - remove_allocated_tensor(alloc, tensor); -#endif - - // see if we can merge with an existing block - for (int i = 0; i < alloc->n_free_blocks; i++) { - struct free_block * block = &alloc->free_blocks[i]; - // check if ptr is at the end of the block - if ((char*)block->addr + block->size == ptr) { - block->size += size; - // check if we can merge with the next block - if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) { - block->size += alloc->free_blocks[i+1].size; - alloc->n_free_blocks--; - for (int j = i+1; j < alloc->n_free_blocks; j++) { - alloc->free_blocks[j] = alloc->free_blocks[j+1]; - } - } - return; - } - // check if ptr is at the beginning of the block - if ((char*)ptr + size == block->addr) { - block->addr = ptr; - block->size += size; - // check if we can merge with the previous block - if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) { - alloc->free_blocks[i-1].size += block->size; - alloc->n_free_blocks--; - for (int j = i; j < alloc->n_free_blocks; j++) { - alloc->free_blocks[j] = alloc->free_blocks[j+1]; - } - } - return; - } - } - // otherwise, add a new block - GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks"); - // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster) - int insert_pos = 0; - while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) { - insert_pos++; - } - // shift all blocks from insert_pos onward to make room for the new block - for (int i = alloc->n_free_blocks; i > insert_pos; i--) { - alloc->free_blocks[i] = alloc->free_blocks[i-1]; - } - // insert the new block - alloc->free_blocks[insert_pos].addr = ptr; - alloc->free_blocks[insert_pos].size = size; - alloc->n_free_blocks++; -} - -void ggml_tallocr_reset(ggml_tallocr_t alloc) { - alloc->n_free_blocks = 1; - size_t align_offset = aligned_offset(alloc->base, 0, alloc->alignment); - alloc->free_blocks[0].addr = (char *)alloc->base + align_offset; - - if (alloc->measure) { - alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows - } else { - alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; - ggml_backend_buffer_reset(alloc->buffer); - } -} - -ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) { - struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(data, size); - - ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); - - *alloc = (struct ggml_tallocr) { - /*.buffer = */ buffer, - /*.buffer_owned = */ true, - /*.base = */ ggml_backend_buffer_get_base(buffer), - /*.alignment = */ alignment, - /*.n_free_blocks = */ 0, - /*.free_blocks = */ {{0}}, - /*.max_size = */ 0, - /*.measure = */ false, -#ifdef GGML_ALLOCATOR_DEBUG - /*.allocated_tensors = */ {0}, -#endif - }; - - ggml_tallocr_reset(alloc); - - return alloc; -} - -ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) { - ggml_tallocr_t alloc = ggml_tallocr_new((void *)0x1000, SIZE_MAX/2, alignment); - alloc->measure = true; - - return alloc; -} - -ggml_tallocr_t ggml_tallocr_new_measure_from_buft(struct ggml_backend_buffer_type * buft) { - // create a backend buffer to get the correct tensor allocation sizes - ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, 1); - - // TODO: move alloc initialization to a common ggml_tallocr_new_impl function - ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); - alloc->buffer_owned = true; - alloc->measure = true; - ggml_tallocr_reset(alloc); - return alloc; -} - -ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { - return ggml_tallocr_new_measure_from_buft(ggml_backend_get_default_buffer_type(backend)); -} - -ggml_tallocr_t ggml_tallocr_new_from_buft(struct ggml_backend_buffer_type * buft, size_t size) { - // create a backend buffer to get the correct tensor allocation sizes - ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); - ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); - alloc->buffer_owned = true; - return alloc; -} - -ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { - return ggml_tallocr_new_from_buft(ggml_backend_get_default_buffer_type(backend), size); -} - -ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) { - ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); - - *alloc = (struct ggml_tallocr) { - /*.buffer = */ buffer, - /*.buffer_owned = */ false, - /*.base = */ ggml_backend_buffer_get_base(buffer), - /*.alignment = */ ggml_backend_buffer_get_alignment(buffer), - /*.n_free_blocks = */ 0, - /*.free_blocks = */ {{0}}, - /*.max_size = */ 0, - /*.measure = */ false, -#ifdef GGML_ALLOCATOR_DEBUG - /*.allocated_tensors = */ {0}, -#endif - }; - - ggml_tallocr_reset(alloc); - - return alloc; -} - -struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t alloc) { - return alloc->buffer; -} - -void ggml_tallocr_free(ggml_tallocr_t alloc) { - if (alloc == NULL) { - return; - } - - if (alloc->buffer_owned) { - ggml_backend_buffer_free(alloc->buffer); - } - free(alloc); -} - -bool ggml_tallocr_is_measure(ggml_tallocr_t alloc) { - return alloc->measure; -} - -size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) { - // FIXME: changes in the tensor sizes compared to the measure graph may cause allocations to fail - // to avoid this, we add a 10% margin to the buffer size - return alloc->max_size + alloc->max_size/10; -} - -// graph allocator - -struct hash_node { - int n_children; - int n_views; -}; - -struct ggml_gallocr { - ggml_tallocr_t talloc; - struct ggml_hash_set hash_set; - struct hash_node * hash_values; - size_t hash_values_size; - ggml_tallocr_t * hash_allocs; - int * parse_seq; - int parse_seq_len; -}; - -ggml_gallocr_t ggml_gallocr_new(void) { - ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr)); - - *galloc = (struct ggml_gallocr) { - /*.talloc = */ NULL, - /*.hash_set = */ {0}, - /*.hash_values = */ NULL, - /*.hash_values_size = */ 0, - /*.hash_allocs = */ NULL, - /*.parse_seq = */ NULL, - /*.parse_seq_len = */ 0, - }; - - return galloc; -} - -void ggml_gallocr_free(ggml_gallocr_t galloc) { - if (galloc == NULL) { - return; - } - - if (galloc->hash_set.keys != NULL) { - free(galloc->hash_set.keys); - } - if (galloc->hash_values != NULL) { - free(galloc->hash_values); - } - if (galloc->hash_allocs != NULL) { - free(galloc->hash_allocs); - } - if (galloc->parse_seq != NULL) { - free(galloc->parse_seq); - } - free(galloc); -} - -void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n) { - free(galloc->parse_seq); - galloc->parse_seq = malloc(sizeof(int) * n); - - for (int i = 0; i < n; i++) { - galloc->parse_seq[i] = list[i]; - } - galloc->parse_seq_len = n; -} - -static struct hash_node * hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) { - size_t i = ggml_hash_find_or_insert(galloc->hash_set, t); - return &galloc->hash_values[i]; -} - static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { if (a->type != b->type) { return false; @@ -447,106 +61,511 @@ static bool ggml_op_can_inplace(enum ggml_op op) { } } -static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * node) { - if (galloc->talloc != NULL) { - return galloc->talloc; - } - - return galloc->hash_allocs[ggml_hash_find_or_insert(galloc->hash_set, node)]; +// TODO: GGML_PAD ? +static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) { + assert(alignment && !(alignment & (alignment - 1))); // power of 2 + size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment; + return offset + align; } -static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) { - ggml_tallocr_t alloc = node_tallocr(galloc, view); +// tallocr +struct ggml_tallocr { + ggml_backend_buffer_t buffer; + void * base; + size_t alignment; + size_t offset; +}; - GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL); - if (update_backend) { - view->backend = view->view_src->backend; +ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) { + ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr)); + if (talloc == NULL) { + return NULL; } - // views are initialized in the alloc buffer rather than the view_src buffer - view->buffer = alloc->buffer; - view->data = (char *)view->view_src->data + view->view_offs; - assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft); + void * base = ggml_backend_buffer_get_base(buffer); + size_t align = ggml_backend_buffer_get_alignment(buffer); - if (!alloc->measure) { - ggml_backend_buffer_init_tensor(alloc->buffer, view); - } + assert(align && !(align & (align - 1))); // power of 2 + + *talloc = (struct ggml_tallocr) { + /*.buffer = */ buffer, + /*.base = */ base, + /*.alignment = */ align, + /*.offset = */ aligned_offset(base, 0, align), + }; + return talloc; } -static void allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { - ggml_tallocr_t alloc = node_tallocr(galloc, node); +void ggml_tallocr_free(ggml_tallocr_t talloc) { + free(talloc); +} - if (node->data == NULL) { - if (ggml_is_view(node)) { - init_view(galloc, node, true); +void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) { + size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor); + size = GGML_PAD(size, talloc->alignment); + + if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) { + fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n", + __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset); + GGML_ASSERT(!"not enough space in the buffer"); + return; + } + + void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset; + talloc->offset += size; + + assert(((uintptr_t)addr % talloc->alignment) == 0); + + ggml_backend_tensor_alloc(talloc->buffer, tensor, addr); +} + +// dynamic tensor allocator + +struct free_block { + size_t offset; + size_t size; +}; + +struct ggml_dyn_tallocr { + size_t alignment; + int n_free_blocks; + struct free_block free_blocks[MAX_FREE_BLOCKS]; + size_t max_size; + +#ifdef GGML_ALLOCATOR_DEBUG + struct { + const struct ggml_tensor * tensor; + size_t offset; + } allocated_tensors[1024]; +#endif +}; + +#ifdef GGML_ALLOCATOR_DEBUG +static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) { + for (int i = 0; i < 1024; i++) { + if (alloc->allocated_tensors[i].tensor == NULL) { + alloc->allocated_tensors[i].tensor = tensor; + alloc->allocated_tensors[i].offset = offset; + return; + } + } + GGML_ASSERT(!"out of allocated_tensors"); +} +static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) { + for (int i = 0; i < 1024; i++) { + if (alloc->allocated_tensors[i].offset == offset) { + alloc->allocated_tensors[i].tensor = NULL; + return; + } + } + fprintf(stderr, "tried to free tensor %s not found\n", tensor->name); + GGML_ASSERT(!"tensor not found"); +} +#endif + +static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) { + size = aligned_offset(NULL, size, alloc->alignment); + + AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size); + + size_t max_avail = 0; + + // find the best fitting free block besides the last block + int best_fit_block = -1; + size_t best_fit_size = SIZE_MAX; + for (int i = 0; i < alloc->n_free_blocks - 1; i++) { + struct free_block * block = &alloc->free_blocks[i]; + max_avail = MAX(max_avail, block->size); + if (block->size >= size && block->size <= best_fit_size) { + best_fit_block = i; + best_fit_size = block->size; + } + } + + if (best_fit_block == -1) { + // the last block is our last resort + struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1]; + max_avail = MAX(max_avail, block->size); + if (block->size >= size) { + best_fit_block = alloc->n_free_blocks - 1; } else { - // see if we can reuse a parent's buffer (inplace) - if (ggml_op_can_inplace(node->op)) { - for (int i = 0; i < GGML_MAX_SRC; i++) { - struct ggml_tensor * parent = node->src[i]; - if (parent == NULL) { - break; - } + // this should never happen + fprintf(stderr, "%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n", + __func__, size, max_avail); + GGML_ASSERT(!"not enough space in the buffer"); + GGML_UNREACHABLE(); + } + } - // if the node's data is external, then we cannot re-use it - if (ggml_tallocr_is_own(alloc, parent) == false) { - AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); - continue; - } + struct free_block * block = &alloc->free_blocks[best_fit_block]; + size_t offset = block->offset; + block->offset = offset + size; + block->size -= size; + if (block->size == 0) { + // remove block if empty + alloc->n_free_blocks--; + for (int j = best_fit_block; j < alloc->n_free_blocks; j++) { + alloc->free_blocks[j] = alloc->free_blocks[j+1]; + } + } - struct hash_node * p_hn = hash_get(galloc, parent); - if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) { - if (ggml_is_view(parent)) { - struct ggml_tensor * view_src = parent->view_src; - struct hash_node * view_src_hn = hash_get(galloc, view_src); - if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { - // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite - // the parent's data that it will need later (same layout requirement). the problem is that then - // we cannot free the tensor because the original address of the allocation is lost. - // adding a view_src pointer to the tensor would solve this and simplify the code dealing with views - // for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data) - AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); - node->view_src = view_src; - view_src_hn->n_views += 1; - init_view(galloc, node, false); - return; - } - } else { - AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); - node->view_src = parent; - p_hn->n_views += 1; - init_view(galloc, node, false); + AT_PRINTF("block %d, offset %zu\n", best_fit_block, offset); + +#ifdef GGML_ALLOCATOR_DEBUG + add_allocated_tensor(alloc, offset, tensor); + size_t cur_max = offset + size; + if (cur_max > alloc->max_size) { + // sort allocated_tensors by offset + for (int i = 0; i < 1024; i++) { + for (int j = i + 1; j < 1024; j++) { + if (alloc->allocated_tensors[i].offset > alloc->allocated_tensors[j].offset) { + const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor; + size_t tmp_offset = alloc->allocated_tensors[i].offset; + alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor; + alloc->allocated_tensors[i].offset = alloc->allocated_tensors[j].offset; + alloc->allocated_tensors[j].tensor = tmp_tensor; + alloc->allocated_tensors[j].offset = tmp_offset; + } + } + } + fprintf(stderr, "max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0); + for (int i = 0; i < 1024; i++) { + if (alloc->allocated_tensors[i].tensor) { + fprintf(stderr, "%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name, + alloc->allocated_tensors[i].offset, + alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor), + ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0); + } + } + fprintf(stderr, "\n"); + } +#endif + + alloc->max_size = MAX(alloc->max_size, offset + size); + + return offset; + + GGML_UNUSED(tensor); +} + +// this is a very naive implementation, but for our case the number of free blocks should be very small +static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, size_t size, const struct ggml_tensor * tensor) { + size = aligned_offset(NULL, size, alloc->alignment); + + AT_PRINTF("%s: freeing %s at %zu (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, offset, size, alloc->n_free_blocks); + +#ifdef GGML_ALLOCATOR_DEBUG + remove_allocated_tensor(alloc, offset, tensor); +#endif + + // see if we can merge with an existing block + for (int i = 0; i < alloc->n_free_blocks; i++) { + struct free_block * block = &alloc->free_blocks[i]; + // check if ptr is at the end of the block + if (block->offset + block->size == offset) { + block->size += size; + // check if we can merge with the next block + if (i < alloc->n_free_blocks - 1 && block->offset + block->size == alloc->free_blocks[i+1].offset) { + block->size += alloc->free_blocks[i+1].size; + alloc->n_free_blocks--; + for (int j = i+1; j < alloc->n_free_blocks; j++) { + alloc->free_blocks[j] = alloc->free_blocks[j+1]; + } + } + return; + } + // check if ptr is at the beginning of the block + if (offset + size == block->offset) { + block->offset = offset; + block->size += size; + // check if we can merge with the previous block + if (i > 0 && alloc->free_blocks[i-1].offset + alloc->free_blocks[i-1].size == block->offset) { + alloc->free_blocks[i-1].size += block->size; + alloc->n_free_blocks--; + for (int j = i; j < alloc->n_free_blocks; j++) { + alloc->free_blocks[j] = alloc->free_blocks[j+1]; + } + } + return; + } + } + // otherwise, add a new block + GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks"); + // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster) + int insert_pos = 0; + while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].offset < offset) { + insert_pos++; + } + // shift all blocks from insert_pos onward to make room for the new block + for (int i = alloc->n_free_blocks; i > insert_pos; i--) { + alloc->free_blocks[i] = alloc->free_blocks[i-1]; + } + // insert the new block + alloc->free_blocks[insert_pos].offset = offset; + alloc->free_blocks[insert_pos].size = size; + alloc->n_free_blocks++; + + GGML_UNUSED(tensor); +} + +static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) { + alloc->n_free_blocks = 1; + alloc->free_blocks[0].offset = 0; + alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows + alloc->max_size = 0; +} + +static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) { + struct ggml_dyn_tallocr * alloc = (struct ggml_dyn_tallocr *)malloc(sizeof(struct ggml_dyn_tallocr)); + + *alloc = (struct ggml_dyn_tallocr) { + /*.alignment = */ alignment, + /*.n_free_blocks = */ 0, + /*.free_blocks = */ {{0}}, + /*.max_size = */ 0, +#ifdef GGML_ALLOCATOR_DEBUG + /*.allocated_tensors = */ {{0}}, +#endif + }; + + ggml_dyn_tallocr_reset(alloc); + + return alloc; +} + +static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) { + free(alloc); +} + +static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc) { + return alloc->max_size; +} + + +///////////////////////////////////// + +// graph allocator + +struct hash_node { + int n_children; + int n_views; + int buffer_id; + size_t offset; // offset within the buffer + bool allocated; +}; + +// +struct tensor_alloc { + size_t offset; + size_t size_max; // 0 = pre-allocated, unused, or view +}; + +struct node_alloc { + int buffer_id; + struct tensor_alloc dst; + struct tensor_alloc src[GGML_MAX_SRC]; +}; + +struct ggml_gallocr { + ggml_backend_buffer_type_t * bufts; // [n_buffers] + ggml_backend_buffer_t * buffers; // [n_buffers] + struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers] + int n_buffers; + + struct ggml_hash_set hash_set; + struct hash_node * hash_values; // [hash_set.size] + + struct node_alloc * node_allocs; // [n_nodes] + int n_nodes; +}; + +ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) { + ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1); + GGML_ASSERT(galloc != NULL); + + galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1); + GGML_ASSERT(galloc->bufts != NULL); + + galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1); + GGML_ASSERT(galloc->buffers != NULL); + + galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1); + GGML_ASSERT(galloc->buf_tallocs != NULL); + + for (int i = 0; i < n_bufs; i++) { + galloc->bufts[i] = bufts[i]; + galloc->buffers[i] = NULL; + size_t alignment = ggml_backend_buft_get_alignment(bufts[i]); + galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment); + } + galloc->n_buffers = n_bufs; + + return galloc; +} + +ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft) { + return ggml_gallocr_new_n(&buft, 1); +} + +void ggml_gallocr_free(ggml_gallocr_t galloc) { + if (galloc == NULL) { + return; + } + + for (int i = 0; i < galloc->n_buffers; i++) { + if (galloc->buffers != NULL) { + ggml_backend_buffer_free(galloc->buffers[i]); + } + if (galloc->buf_tallocs != NULL) { + ggml_dyn_tallocr_free(galloc->buf_tallocs[i]); + } + } + + free(galloc->hash_set.keys); + free(galloc->hash_values); + free(galloc->bufts); + free(galloc->buffers); + free(galloc->buf_tallocs); + free(galloc->node_allocs); + free(galloc); +} + +typedef struct ggml_gallocr * ggml_gallocr_t; + +static struct hash_node * ggml_gallocr_hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) { + size_t i = ggml_hash_find_or_insert(galloc->hash_set, t); + return &galloc->hash_values[i]; +} + +static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) { + return ggml_gallocr_hash_get(galloc, t)->allocated; +} + +static void ggml_gallocr_set_node_offset(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, size_t offset) { + struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); + hn->buffer_id = buffer_id; + hn->offset = offset; + hn->allocated = true; +} + +static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) { + return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated; +} + +static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) { + struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); + + if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) { + hn->allocated = true; + assert(hn->offset == 0); + + // try to reuse a parent's buffer (inplace) + if (ggml_op_can_inplace(node->op)) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + struct ggml_tensor * parent = node->src[i]; + if (parent == NULL) { + break; + } + + // if the node's data is external, then we cannot re-use it + if (!ggml_gallocr_is_own(galloc, parent)) { + AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); + continue; + } + + // outputs cannot be reused + if (parent->flags & GGML_TENSOR_FLAG_OUTPUT || (parent->view_src != NULL && parent->view_src->flags & GGML_TENSOR_FLAG_OUTPUT)) { + AT_PRINTF("not reusing parent %s for %s as it is an output\n", parent->name, node->name); + continue; + } + + if (!ggml_are_same_layout(node, parent)) { + AT_PRINTF("not reusing parent %s for %s as layouts are different\n", parent->name, node->name); + continue; + } + + struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent); + if (p_hn->n_children == 1 && p_hn->n_views == 0) { + if (ggml_is_view(parent)) { + struct ggml_tensor * view_src = parent->view_src; + struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src); + if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { + AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); + assert(view_src_hn->offset == p_hn->offset); + hn->buffer_id = p_hn->buffer_id; + hn->offset = p_hn->offset; + p_hn->allocated = false; // avoid freeing the parent + view_src_hn->allocated = false; return; } + } else { + AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); + hn->buffer_id = p_hn->buffer_id; + hn->offset = p_hn->offset; + p_hn->allocated = false; // avoid freeing the parent + return; } } } - ggml_tallocr_alloc(alloc, node); } + // allocate tensor from the buffer + struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; + ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; + size_t size = ggml_backend_buft_get_alloc_size(buft, node); + size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node); + hn->buffer_id = buffer_id; + hn->offset = offset; + return; } } -static void free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { - ggml_tallocr_t alloc = node_tallocr(galloc, node); +static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) { + // graph outputs are never freed + if (node->flags & GGML_TENSOR_FLAG_OUTPUT) { + AT_PRINTF("not freeing output %s\n", node->name); + return; + } - ggml_tallocr_free_tensor(alloc, node); + struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; + ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; + struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); + size_t offset = hn->offset; + size_t size = ggml_backend_buft_get_alloc_size(buft, node); + ggml_dyn_tallocr_free_tensor(alloc, offset, size, node); + hn->allocated = false; } -static void ggml_tallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * gf) { - const int * parse_seq = galloc->parse_seq; - int parse_seq_len = galloc->parse_seq_len; +static int get_node_buffer_id(const int * node_buffer_ids, int i) { + return node_buffer_ids ? node_buffer_ids[i] : 0; +} + +static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) { + // clear hash tables + memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *)); + memset(galloc->hash_values, 0, galloc->hash_set.size * sizeof(struct hash_node)); + + // allocate all graph inputs first to avoid overwriting them + for (int i = 0; i < graph->n_nodes; i++) { + if (graph->nodes[i]->flags & GGML_TENSOR_FLAG_INPUT) { + ggml_gallocr_allocate_node(galloc, graph->nodes[i], get_node_buffer_id(node_buffer_ids, i)); + } + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (graph->nodes[i]->src[j] == NULL) { + break; + } + if (graph->nodes[i]->src[j]->flags & GGML_TENSOR_FLAG_INPUT) { + ggml_gallocr_allocate_node(galloc, graph->nodes[i]->src[j], get_node_buffer_id(node_buffer_ids, i)); + } + } + } // count number of children and views - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view(node)) { struct ggml_tensor * view_src = node->view_src; - hash_get(galloc, view_src)->n_views += 1; - if (node->buffer == NULL && node->data != NULL) { - // view of a pre-allocated tensor, didn't call init_view() yet - init_view(galloc, node, true); - } + ggml_gallocr_hash_get(galloc, view_src)->n_views += 1; } for (int j = 0; j < GGML_MAX_SRC; j++) { @@ -554,227 +573,283 @@ static void ggml_tallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr if (parent == NULL) { break; } - hash_get(galloc, parent)->n_children += 1; - if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { - init_view(galloc, parent, true); - } + ggml_gallocr_hash_get(galloc, parent)->n_children += 1; } } // allocate tensors - // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers - int last_barrier_pos = 0; - int n_nodes = parse_seq_len ? parse_seq_len : gf->n_nodes; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + int buffer_id = get_node_buffer_id(node_buffer_ids, i); - for (int ind = 0; ind < n_nodes; ind++) { - // allocate a node if there is no parse_seq or this is not a barrier - if (parse_seq_len == 0 || parse_seq[ind] != -1) { - int i = parse_seq_len ? parse_seq[ind] : ind; - struct ggml_tensor * node = gf->nodes[i]; - - // allocate parents (leafs) - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; - } - allocate_node(galloc, parent); + // allocate parents (only leafs need to be allocated at this point) + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; } - - // allocate node - allocate_node(galloc, node); - - AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name); - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; - } - AT_PRINTF("%s", parent->name); - if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { - AT_PRINTF(", "); - } - } - AT_PRINTF("\n"); + ggml_gallocr_allocate_node(galloc, parent, buffer_id); } + // allocate node + ggml_gallocr_allocate_node(galloc, node, buffer_id); + + AT_PRINTF("exec: %s (%s) <= ", ggml_op_desc(node), node->name); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; + } + AT_PRINTF("%s", parent->name); + if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { + AT_PRINTF(", "); + } + } + AT_PRINTF("\n"); + // update parents - // update immediately if there is no parse_seq - // update only at barriers if there is parse_seq - if ((parse_seq_len == 0) || parse_seq[ind] == -1) { - int update_start = parse_seq_len ? last_barrier_pos : ind; - int update_end = parse_seq_len ? ind : ind + 1; - for (int i = update_start; i < update_end; i++) { - int node_i = parse_seq_len ? parse_seq[i] : i; - struct ggml_tensor * node = gf->nodes[node_i]; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; + } + struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent); + p_hn->n_children -= 1; - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; - } - struct hash_node * p_hn = hash_get(galloc, parent); - p_hn->n_children -= 1; - - //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views); - - if (p_hn->n_children == 0 && p_hn->n_views == 0) { - if (ggml_is_view(parent)) { - struct ggml_tensor * view_src = parent->view_src; - struct hash_node * view_src_hn = hash_get(galloc, view_src); - view_src_hn->n_views -= 1; - AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); - if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0) { - free_node(galloc, view_src); - } - } - else { - free_node(galloc, parent); - } + AT_PRINTF("parent %s: %d children, %d views, allocated: %d\n", + parent->name, p_hn->n_children, p_hn->n_views, p_hn->allocated); + + if (p_hn->n_children == 0 && p_hn->n_views == 0) { + if (ggml_is_view(parent)) { + struct ggml_tensor * view_src = parent->view_src; + struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src); + view_src_hn->n_views -= 1; + AT_PRINTF("view_src %s: %d children, %d views\n", + view_src->name, view_src_hn->n_children, view_src_hn->n_views); + if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src_hn->allocated) { + ggml_gallocr_free_node(galloc, view_src, buffer_id); } } + else if (p_hn->allocated) { + ggml_gallocr_free_node(galloc, parent, buffer_id); + } } AT_PRINTF("\n"); - if (parse_seq_len) { - last_barrier_pos = ind + 1; + } + } +} + +bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) { + size_t hash_size = graph->visited_hash_table.size; + + // initialize hash table + if (galloc->hash_set.size < hash_size) { + free(galloc->hash_set.keys); + free(galloc->hash_values); + galloc->hash_set.size = hash_size; + galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size); + galloc->hash_values = calloc(sizeof(struct hash_node), hash_size); + GGML_ASSERT(galloc->hash_set.keys != NULL); + GGML_ASSERT(galloc->hash_values != NULL); + } else { + // reset hash table + memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * galloc->hash_set.size); + memset(galloc->hash_values, 0, sizeof(struct hash_node) * galloc->hash_set.size); + } + + // reset allocators + for (int i = 0; i < galloc->n_buffers; i++) { + ggml_dyn_tallocr_reset(galloc->buf_tallocs[i]); + } + + // allocate in hash table + ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids); + + // set the node_allocs from the hash table + if (galloc->n_nodes < graph->n_nodes) { + free(galloc->node_allocs); + galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes); + GGML_ASSERT(galloc->node_allocs != NULL); + } + galloc->n_nodes = graph->n_nodes; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + struct node_alloc * node_alloc = &galloc->node_allocs[i]; + node_alloc->buffer_id = get_node_buffer_id(node_buffer_ids, i); + if (node->view_src || node->data) { + node_alloc->dst.offset = SIZE_MAX; + node_alloc->dst.size_max = 0; + } else { + struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); + node_alloc->dst.offset = hn->offset; + node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node); + } + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (!src || src->view_src || src->data) { + node_alloc->src[j].offset = SIZE_MAX; + node_alloc->src[j].size_max = 0; + } else { + struct hash_node * hn = ggml_gallocr_hash_get(galloc, src); + node_alloc->src[j].offset = hn->offset; + node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src); } } } -} -size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph) { - size_t hash_size = graph->visited_hash_table.size; + // reallocate buffers if needed + for (int i = 0; i < galloc->n_buffers; i++) { + size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0; + size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]); - // check if the hash table is initialized and large enough - if (galloc->hash_set.size < hash_size) { - if (galloc->hash_set.keys != NULL) { - free(galloc->hash_set.keys); + if (new_size > cur_size) { +#ifndef NDEBUG + fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); +#endif + ggml_backend_buffer_free(galloc->buffers[i]); + galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size); + if (galloc->buffers[i] == NULL) { + fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size); + return false; + } } - if (galloc->hash_values != NULL) { - free(galloc->hash_values); + } + + return true; +} + +bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { + return ggml_gallocr_reserve_n(galloc, graph, NULL); +} + +static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, struct node_alloc * node_alloc, struct tensor_alloc * tensor_alloc) { + assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[node_alloc->buffer_id], node) <= tensor_alloc->size_max); + + if (node->view_src != NULL) { + if (node->buffer == NULL) { + assert(tensor_alloc->offset == SIZE_MAX); + if (node->view_src->buffer == NULL) { + // this tensor was allocated without ggml-backend + return; + } + ggml_backend_view_init(galloc->buffers[node_alloc->buffer_id], node); } - galloc->hash_set.keys = malloc(sizeof(struct ggml_tensor *) * hash_size); - galloc->hash_set.size = hash_size; - galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size); + } else { + if (node->data == NULL) { + assert(tensor_alloc->offset != SIZE_MAX); + assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[node_alloc->buffer_id], node) <= tensor_alloc->size_max); + void * base = ggml_backend_buffer_get_base(galloc->buffers[node_alloc->buffer_id]); + void * addr = (char *)base + tensor_alloc->offset; + ggml_backend_tensor_alloc(galloc->buffers[node_alloc->buffer_id], node, addr); + } else { + if (node->buffer == NULL) { + // this tensor was allocated without ggml-backend + return; + } + +#ifndef NDEBUG + size_t offset = + (char *)node->data - + (char *)ggml_backend_buffer_get_base(node->buffer); + size_t size = ggml_backend_buffer_get_alloc_size(node->buffer, node); + assert(tensor_alloc->offset == SIZE_MAX || offset == tensor_alloc->offset); + assert(tensor_alloc->offset == SIZE_MAX || size <= tensor_alloc->size_max); +#endif + } + } +} + +static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct node_alloc * nalloc, struct tensor_alloc * talloc) { + ggml_backend_buffer_type_t buft = galloc->bufts[nalloc->buffer_id]; + size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(buft, node); + return talloc->size_max >= node_size; +} + +static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) { + if (galloc->n_nodes != graph->n_nodes) { +#ifndef NDEBUG + fprintf(stderr, "%s: graph has different number of nodes\n", __func__); +#endif + return true; } - // reset hash table - memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * hash_size); - memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size); + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + struct node_alloc * node_alloc = &galloc->node_allocs[i]; - galloc->talloc = talloc; - ggml_tallocr_alloc_graph_impl(galloc, graph); - galloc->talloc = NULL; + if (!ggml_gallocr_node_needs_realloc(galloc, node, node_alloc, &node_alloc->dst)) { +#ifndef NDEBUG + fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name); +#endif + return true; + } - size_t max_size = ggml_tallocr_max_size(talloc); - - return max_size; -} - -void ggml_gallocr_alloc_graph_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, struct ggml_hash_set hash_set, ggml_tallocr_t * hash_node_talloc) { - const size_t hash_size = hash_set.size; - - GGML_ASSERT(hash_size >= (size_t)(graph->n_nodes + graph->n_leafs)); - - galloc->talloc = NULL; - - // alloc hash_values if needed - if (galloc->hash_values == NULL || galloc->hash_values_size < hash_size) { - free(galloc->hash_values); - galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size); - galloc->hash_values_size = hash_size; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + if (!ggml_gallocr_node_needs_realloc(galloc, src, node_alloc, &node_alloc->src[j])) { +#ifndef NDEBUG + fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name); +#endif + return true; + } + } } - // free hash_set.keys if needed - if (galloc->hash_set.keys != NULL) { - free(galloc->hash_set.keys); - } - galloc->hash_set = hash_set; - - // reset hash values - memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size); - - galloc->hash_allocs = hash_node_talloc; - - ggml_tallocr_alloc_graph_impl(galloc, graph); - - // remove unowned resources - galloc->hash_set.keys = NULL; - galloc->hash_allocs = NULL; + return false; } -// legacy API wrapper - -struct ggml_allocr { - ggml_tallocr_t talloc; - ggml_gallocr_t galloc; -}; - -static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) { - ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr)); - *alloc = (struct ggml_allocr) { - /*.talloc = */ talloc, - /*.galloc = */ ggml_gallocr_new(), - }; - return alloc; -} - -ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment) { - return ggml_allocr_new_impl(ggml_tallocr_new(data, size, alignment)); -} - -ggml_allocr_t ggml_allocr_new_measure(size_t alignment) { - return ggml_allocr_new_impl(ggml_tallocr_new_measure(alignment)); -} - -ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) { - return ggml_allocr_new_impl(ggml_tallocr_new_from_buffer(buffer)); -} - -ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size) { - return ggml_allocr_new_impl(ggml_tallocr_new_from_backend(backend, size)); -} - -ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend) { - return ggml_allocr_new_impl(ggml_tallocr_new_measure_from_backend(backend)); -} - -struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc) { - return ggml_tallocr_get_buffer(alloc->talloc); -} - -void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) { - ggml_gallocr_set_parse_seq(alloc->galloc, list, n); -} - -void ggml_allocr_free(ggml_allocr_t alloc) { - if (alloc == NULL) { - return; +bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) { + if (ggml_gallocr_needs_realloc(galloc, graph)) { + if (galloc->n_buffers == 1) { +#ifndef NDEBUG + fprintf(stderr, "%s: reallocating buffers automatically\n", __func__); +#endif + if (!ggml_gallocr_reserve(galloc, graph)) { + return false; + } + } else { +#ifndef NDEBUG + fprintf(stderr, "%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__); +#endif + return false; + } } - ggml_gallocr_free(alloc->galloc); - ggml_tallocr_free(alloc->talloc); - free(alloc); + // reset buffers + for (int i = 0; i < galloc->n_buffers; i++) { + // zero size buffers are not allocated + if (galloc->buffers[i] != NULL) { + ggml_backend_buffer_reset(galloc->buffers[i]); + } + } + + // allocate the graph tensors from the previous assignments + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + struct node_alloc * node_alloc = &galloc->node_allocs[i]; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_gallocr_init_tensor(galloc, src, node_alloc, &node_alloc->src[j]); + } + ggml_gallocr_init_tensor(galloc, node, node_alloc, &node_alloc->dst); + } + + return true; } -bool ggml_allocr_is_measure(ggml_allocr_t alloc) { - return ggml_tallocr_is_measure(alloc->talloc); -} +size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) { + GGML_ASSERT(buffer_id >= 0 && buffer_id < galloc->n_buffers); -void ggml_allocr_reset(ggml_allocr_t alloc) { - ggml_tallocr_reset(alloc->talloc); -} - -void ggml_allocr_alloc(ggml_allocr_t alloc, struct ggml_tensor * tensor) { - ggml_tallocr_alloc(alloc->talloc, tensor); -} - -size_t ggml_allocr_max_size(ggml_allocr_t alloc) { - return ggml_tallocr_max_size(alloc->talloc); -} - -size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) { - return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph); + if (galloc->buffers[buffer_id] == NULL) { + return 0; + } + return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]); } // utils @@ -795,17 +870,17 @@ static bool alloc_tensor_range(struct ggml_context * ctx, return false; } - ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer); + struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer); for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) { if (t->data == NULL) { if (t->view_src == NULL) { ggml_tallocr_alloc(tallocr, t); - } else { + } else if (t->buffer == NULL) { ggml_backend_view_init(buffer, t); } } else { - if (t->view_src != NULL) { + if (t->view_src != NULL && t->buffer == NULL) { // view of a pre-allocated tensor ggml_backend_view_init(buffer, t); } @@ -838,7 +913,6 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte } if (this_size > max_size) { - // tensor is too large to fit in a single buffer fprintf(stderr, "%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n", __func__, t->name, ggml_backend_buft_name(buft), @@ -870,7 +944,6 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte } if (n_buffers == 0) { - // all the tensors in the context are already allocated #ifndef NDEBUG fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__); #endif diff --git a/ggml-alloc.h b/ggml-alloc.h index 4e5997521..1d9085d15 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -6,88 +6,62 @@ extern "C" { #endif -struct ggml_backend; -struct ggml_backend_buffer; -struct ggml_backend_buffer_type; - -// -// Legacy API -// - -typedef struct ggml_allocr * ggml_allocr_t; - -// initialize allocator for use with CPU backend only -GGML_API ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment); -GGML_API ggml_allocr_t ggml_allocr_new_measure(size_t alignment); - -// initialize allocator for use with ggml-backend -GGML_API ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer); -GGML_API ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer -GGML_API ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend); - -GGML_API struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc); - -// tell the allocator to parse nodes following the order described in the list -// you should call this if your graph are optimized to execute out-of-order -GGML_API void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n); - -GGML_API void ggml_allocr_free (ggml_allocr_t alloc); -GGML_API bool ggml_allocr_is_measure (ggml_allocr_t alloc); -GGML_API void ggml_allocr_reset (ggml_allocr_t alloc); -GGML_API void ggml_allocr_alloc (ggml_allocr_t alloc, struct ggml_tensor * tensor); -GGML_API size_t ggml_allocr_max_size (ggml_allocr_t alloc); - -GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph); - -// -// ggml-backend v2 API -// - -// Separate tensor and graph allocator objects -// This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators -// The original API is kept as a wrapper around the new API +typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t; +typedef struct ggml_backend_buffer * ggml_backend_buffer_t; +typedef struct ggml_backend * ggml_backend_t; // Tensor allocator typedef struct ggml_tallocr * ggml_tallocr_t; -GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment); -GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment); -GGML_API ggml_tallocr_t ggml_tallocr_new_from_buft(struct ggml_backend_buffer_type * buft, size_t size); -GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer -GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); -GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_buft(struct ggml_backend_buffer_type * buft); -GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend); - -GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc); - -GGML_API void ggml_tallocr_free (ggml_tallocr_t talloc); -GGML_API bool ggml_tallocr_is_measure (ggml_tallocr_t talloc); -GGML_API void ggml_tallocr_reset (ggml_tallocr_t talloc); -GGML_API void ggml_tallocr_alloc (ggml_tallocr_t talloc, struct ggml_tensor * tensor); -GGML_API size_t ggml_tallocr_max_size (ggml_tallocr_t talloc); - +GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer); +GGML_API void ggml_tallocr_free(ggml_tallocr_t talloc); +GGML_API void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor); // Graph allocator +/* + Example usage: + ggml_gallocr_t galloc = ggml_gallocr_new(ggml_bacckend_cpu_buffer_type()); + + // optional: create a worst-case graph and reserve the buffers to avoid reallocations + ggml_gallocr_reserve(galloc, build_graph(max_batch)); + + // allocate the graph + struct ggml_cgraph * graph = build_graph(batch); + ggml_gallocr_alloc_graph(galloc, graph); + + printf("compute buffer size: %zu bytes\n", ggml_gallocr_get_buffer_size(galloc, 0)); + + // evaluate the graph + ggml_backend_graph_compute(backend, graph); +*/ + +// special tensor flags for use with the graph allocator: +// ggml_set_input(): all input tensors are allocated at the beginning of the graph in non-overlapping addresses +// ggml_set_output(): output tensors are never freed and never overwritten + typedef struct ggml_gallocr * ggml_gallocr_t; -GGML_API ggml_gallocr_t ggml_gallocr_new(void); -GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); +GGML_API ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft); +GGML_API ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs); +GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); -GGML_API void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n); -GGML_API size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph); +// pre-allocate buffers from a measure graph - does not allocate or modify the graph +// call with a worst-case graph to avoid buffer reallocations +// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed +// returns false if the buffer allocation failed +GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph); +GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids); -// Allocate tensors from the allocators given by the hash table -GGML_API void ggml_gallocr_alloc_graph_n( - ggml_gallocr_t galloc, - struct ggml_cgraph * graph, - struct ggml_hash_set hash_set, - ggml_tallocr_t * hash_node_talloc); +// automatic reallocation if the topology changes when using a single buffer +// returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers) +GGML_API bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph); +GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id); // Utils // Create a buffer and allocate all the tensors in a ggml_context -GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, struct ggml_backend_buffer_type * buft); -GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, struct ggml_backend * backend); +GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft); +GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend); #ifdef __cplusplus } diff --git a/ggml-backend.c b/ggml-backend.c index 532da8eda..9ee81b766 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -475,6 +475,8 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) { // backend CPU +static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment + GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { return "CPU"; @@ -482,7 +484,14 @@ GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t } GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { - return (void *)buffer->context; + uintptr_t data = (uintptr_t)buffer->context; + + // align the buffer + if (data % TENSOR_ALIGNMENT != 0) { + data = GGML_PAD(data, TENSOR_ALIGNMENT); + } + + return (void *)data; } GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { @@ -540,8 +549,6 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { /* .reset = */ NULL, }; -static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 - GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU"; @@ -550,9 +557,11 @@ GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned - void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? - - GGML_ASSERT(data != NULL && "failed to allocate buffer"); + void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h) + if (data == NULL) { + fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size); } @@ -766,6 +775,9 @@ static struct ggml_backend_i cpu_backend_i = { ggml_backend_t ggml_backend_cpu_init(void) { struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context)); + if (ctx == NULL) { + return NULL; + } ctx->n_threads = GGML_DEFAULT_N_THREADS; ctx->work_data = NULL; @@ -774,6 +786,10 @@ ggml_backend_t ggml_backend_cpu_init(void) { ctx->abort_callback_data = NULL; ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend)); + if (cpu_backend == NULL) { + free(ctx); + return NULL; + } *cpu_backend = (struct ggml_backend) { /* .interface = */ cpu_backend_i, @@ -802,6 +818,7 @@ void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_ } GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { + GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size); } @@ -865,6 +882,8 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_back ctx->n_buffers = n_buffers; ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t)); + GGML_ASSERT(ctx->buffers != NULL); + size_t total_size = 0; for (size_t i = 0; i < n_buffers; i++) { ctx->buffers[i] = buffers[i]; @@ -886,6 +905,18 @@ GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, } } +// creates a copy of the tensor with the same memory layout +static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) { + struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor); + for (int i = 0; i < GGML_MAX_DIMS; i++) { + dup->nb[i] = tensor->nb[i]; + } + return dup; +} + +static bool ggml_is_view_op(enum ggml_op op) { + return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE; +} // scheduler @@ -894,7 +925,7 @@ GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, #define GGML_MAX_SPLIT_INPUTS 16 struct ggml_backend_sched_split { - ggml_tallocr_t tallocr; + int backend_id; int i_start; int i_end; struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; @@ -909,15 +940,17 @@ struct ggml_backend_sched { int n_backends; ggml_backend_t backends[GGML_MAX_BACKENDS]; ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS]; - ggml_tallocr_t tallocs[GGML_MAX_BACKENDS]; ggml_gallocr_t galloc; // hash keys of the nodes in the graph struct ggml_hash_set hash_set; - // hash values (arrays of [hash_set.size]) - ggml_tallocr_t * node_talloc; // tallocr assigned to each node (indirectly this is the backend) - struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // copies of each node for each destination backend + // hash values + int * tensor_backend_id; + struct ggml_tensor * (* tensor_copies)[GGML_MAX_BACKENDS]; + + int * node_backend_ids; // [n_nodes] + int n_nodes; // copy of the graph with modified inputs struct ggml_cgraph * graph; @@ -927,77 +960,46 @@ struct ggml_backend_sched { struct ggml_context * ctx; + ggml_backend_sched_eval_callback callback_eval; + void * callback_eval_user_data; + // align context_buffer to GGML_MEM_ALIGN #ifdef _MSC_VER __declspec(align(GGML_MEM_ALIGN)) #else __attribute__((aligned(GGML_MEM_ALIGN))) #endif - char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)]; - - ggml_backend_sched_eval_callback callback_eval; - void * callback_eval_user_data; + char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)]; }; #define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node) -#define node_allocr(node) sched->node_talloc[hash_id(node)] +#define tensor_backend_id(node) sched->tensor_backend_id[hash_id(node)] +#define tensor_backend(node) (tensor_backend_id(node) == -1 ? NULL : sched->backends[tensor_backend_id(node)]) -static bool ggml_is_view_op(enum ggml_op op) { - return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE; -} - -// returns the priority of the backend, lower is better -static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) { +// returns the priority of the backend, lower id is higher priority +static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) { for (int i = 0; i < sched->n_backends; i++) { if (sched->backends[i] == backend) { return i; } } - return INT_MAX; + return -1; } -static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) { - for (int i = 0; i < sched->n_backends; i++) { - if (sched->tallocs[i] == allocr) { - return i; - } - } - return INT_MAX; -} - -static ggml_tallocr_t sched_allocr_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { +static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) { if (buffer == NULL) { - return NULL; - } - - // check if this is already allocate in a allocr buffer (from user manual allocations) - for (int i = 0; i < sched->n_backends; i++) { - if (ggml_tallocr_get_buffer(sched->tallocs[i]) == buffer) { - return sched->tallocs[i]; - } + return -1; } // find highest prio backend that supports the buffer type for (int i = 0; i < sched->n_backends; i++) { if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) { - return sched->tallocs[i]; + return i; } } GGML_ASSERT(false && "tensor buffer type not supported by any backend"); } -static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_tallocr_t allocr) { - if (allocr == NULL) { - return NULL; - } - for (int i = 0; i < sched->n_backends; i++) { - if (sched->tallocs[i] == allocr) { - return sched->backends[i]; - } - } - GGML_UNREACHABLE(); -} - #if 0 static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) @@ -1008,37 +1010,39 @@ static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_I #endif // returns the backend that should be used for the node based on the current locations -static ggml_tallocr_t sched_allocr_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { +static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) { + // TODO: use supports_op to check if the backend supports the op + // assign pre-allocated nodes to their backend // dst - ggml_tallocr_t cur_allocr = sched_allocr_from_buffer(sched, node->buffer); - if (cur_allocr != NULL) { + int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->buffer); + if (cur_backend != -1) { SET_CAUSE(node, "1.dst"); - return cur_allocr; + return cur_backend; } // view_src - if (node->view_src != NULL) { - cur_allocr = sched_allocr_from_buffer(sched, node->view_src->buffer); - if (cur_allocr != NULL) { + if (tensor->view_src != NULL) { + cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src->buffer); + if (cur_backend != -1) { SET_CAUSE(node, "1.vsrc"); - return cur_allocr; + return cur_backend; } } // assign nodes that use weights to the backend of the weights for (int i = 0; i < GGML_MAX_SRC; i++) { - const struct ggml_tensor * src = node->src[i]; + const struct ggml_tensor * src = tensor->src[i]; if (src == NULL) { break; } if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { - ggml_tallocr_t src_allocr = sched_allocr_from_buffer(sched, src->buffer); + int src_backend = ggml_backend_sched_backend_from_buffer(sched, src->buffer); // operations with weights are always run on the same backend as the weights SET_CAUSE(node, "1.wgt%d", i); - return src_allocr; + return src_backend; } } - return NULL; + return -1; } static char * fmt_size(size_t size) { @@ -1051,11 +1055,11 @@ static char * fmt_size(size_t size) { return buffer; } -static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { int cur_split = 0; for (int i = 0; i < graph->n_nodes; i++) { if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) { - ggml_backend_t split_backend = get_allocr_backend(sched, sched->splits[cur_split].tallocr); + ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id]; fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend), sched->splits[cur_split].n_inputs); for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) { @@ -1069,17 +1073,15 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra if (ggml_is_view_op(node->op)) { continue; } - ggml_tallocr_t node_allocr = node_allocr(node); - ggml_backend_t node_backend = node_allocr ? get_allocr_backend(sched, node_allocr) : NULL; // FIXME: + ggml_backend_t tensor_backend = tensor_backend(node); fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, - fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", GET_CAUSE(node)); + fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { break; } - ggml_tallocr_t src_allocr = node_allocr(src); - ggml_backend_t src_backend = src_allocr ? get_allocr_backend(sched, src_allocr) : NULL; + ggml_backend_t src_backend = tensor_backend(src); fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } @@ -1087,23 +1089,13 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra } } -// creates a copy of the tensor with the same memory layout -static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) { - struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor); - for (int i = 0; i < GGML_MAX_DIMS; i++) { - dup->nb[i] = tensor->nb[i]; - } - return dup; -} - - //#define DEBUG_PASS1 //#define DEBUG_PASS2 //#define DEBUG_PASS3 //#define DEBUG_PASS4 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend -static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { // reset splits sched->n_splits = 0; sched->is_reset = false; @@ -1125,28 +1117,28 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // pass 1: assign backends to ops with pre-allocated inputs for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; - if (node_allocr(leaf) != NULL) { + if (tensor_backend_id(leaf) != -1) { // do not overwrite user assignments continue; } - node_allocr(leaf) = sched_allocr_from_cur(sched, leaf); + tensor_backend_id(leaf) = ggml_backend_sched_backend_id_from_cur(sched, leaf); } for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - if (node_allocr(node) != NULL) { + if (tensor_backend_id(node) != -1) { // do not overwrite user assignments continue; } - node_allocr(node) = sched_allocr_from_cur(sched, node); + tensor_backend_id(node) = ggml_backend_sched_backend_id_from_cur(sched, node); // src for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { break; } - if (node_allocr(src) == NULL) { - node_allocr(src) = sched_allocr_from_cur(sched, src); + if (tensor_backend_id(src) == -1) { + tensor_backend_id(src) = ggml_backend_sched_backend_id_from_cur(sched, src); } } } @@ -1161,22 +1153,22 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // pass 2.1 expand gpu up { - ggml_tallocr_t cur_allocr = NULL; + int cur_backend_id = -1; for (int i = graph->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; } - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr != NULL) { - if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) { + int tensor_backend_id = tensor_backend_id(node); + if (tensor_backend_id != -1) { + if (tensor_backend_id == sched->n_backends - 1) { // skip cpu (lowest prio backend) - cur_allocr = NULL; + cur_backend_id = -1; } else { - cur_allocr = node_allocr; + cur_backend_id = tensor_backend_id; } } else { - node_allocr(node) = cur_allocr; + tensor_backend_id(node) = cur_backend_id; SET_CAUSE(node, "2.1"); } } @@ -1184,22 +1176,22 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // pass 2.2 expand gpu down { - ggml_tallocr_t cur_allocr = NULL; + int cur_backend_id = -1; for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; } - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr != NULL) { - if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) { + int tensor_backend_id = tensor_backend_id(node); + if (tensor_backend_id != -1) { + if (tensor_backend_id == sched->n_backends - 1) { // skip cpu (lowest prio backend) - cur_allocr = NULL; + cur_backend_id = -1; } else { - cur_allocr = node_allocr; + cur_backend_id = tensor_backend_id; } } else { - node_allocr(node) = cur_allocr; + tensor_backend_id(node) = cur_backend_id; SET_CAUSE(node, "2.2"); } } @@ -1207,17 +1199,17 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // pass 2.3 expand rest up { - ggml_tallocr_t cur_allocr = NULL; + int cur_backend_id = -1; for (int i = graph->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; } - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr != NULL) { - cur_allocr = node_allocr; + int tensor_backend_id = tensor_backend_id(node); + if (tensor_backend_id != -1) { + cur_backend_id = tensor_backend_id; } else { - node_allocr(node) = cur_allocr; + tensor_backend_id(node) = cur_backend_id; SET_CAUSE(node, "2.3"); } } @@ -1225,17 +1217,17 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // pass 2.4 expand rest down { - ggml_tallocr_t cur_allocr = NULL; + int cur_backend_id = -1; for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; if (ggml_is_view_op(node->op)) { continue; } - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr != NULL) { - cur_allocr = node_allocr; + int tensor_backend_id = tensor_backend_id(node); + if (tensor_backend_id != -1) { + cur_backend_id = tensor_backend_id; } else { - node_allocr(node) = cur_allocr; + tensor_backend_id(node) = cur_backend_id; SET_CAUSE(node, "2.4"); } } @@ -1247,9 +1239,9 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // pass 3: assign backends to remaining src from dst and view_src for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t cur_allocr = node_allocr(node); - if (node->view_src != NULL && cur_allocr == NULL) { - cur_allocr = node_allocr(node) = node_allocr(node->view_src); + int cur_backend_id = tensor_backend_id(node); + if (node->view_src != NULL && cur_backend_id == -1) { + cur_backend_id = tensor_backend_id(node) = tensor_backend_id(node->view_src); SET_CAUSE(node, "3.vsrc"); } for (int j = 0; j < GGML_MAX_SRC; j++) { @@ -1257,14 +1249,14 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g if (src == NULL) { break; } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr == NULL) { + int src_backend_id = tensor_backend_id(src); + if (src_backend_id == -1) { if (src->view_src != NULL) { // views are always on the same backend as the source - node_allocr(src) = node_allocr(src->view_src); + tensor_backend_id(src) = tensor_backend_id(src->view_src); SET_CAUSE(src, "3.vsrc"); } else { - node_allocr(src) = cur_allocr; + tensor_backend_id(src) = cur_backend_id; SET_CAUSE(src, "3.cur"); } } @@ -1281,15 +1273,14 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; if (!ggml_is_view_op(node->op)) { - sched->splits[0].tallocr = node_allocr(node); + sched->splits[0].backend_id = tensor_backend_id(node); break; } } sched->splits[0].i_start = 0; sched->splits[0].n_inputs = 0; memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK - ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; - size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); + int cur_backend_id = sched->splits[0].backend_id; for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; @@ -1297,19 +1288,18 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g continue; } - ggml_tallocr_t node_allocr = node_allocr(node); + int tensor_backend_id = tensor_backend_id(node); - GGML_ASSERT(node_allocr != NULL); // all nodes should be assigned by now + GGML_ASSERT(tensor_backend_id != -1); // all nodes should be assigned by now - if (node_allocr != cur_allocr) { + if (tensor_backend_id != cur_backend_id) { sched->splits[cur_split].i_end = i; cur_split++; GGML_ASSERT(cur_split < GGML_MAX_SPLITS); - sched->splits[cur_split].tallocr = node_allocr; + sched->splits[cur_split].backend_id = tensor_backend_id; sched->splits[cur_split].i_start = i; sched->splits[cur_split].n_inputs = 0; - cur_allocr = node_allocr; - cur_backend_id = sched_allocr_prio(sched, cur_allocr); + cur_backend_id = tensor_backend_id; } // find inputs that are not on the same backend @@ -1318,43 +1308,25 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g if (src == NULL) { break; } - ggml_tallocr_t src_allocr = node_allocr(src); - GGML_ASSERT(src_allocr != NULL); // all inputs should be assigned by now - if (src_allocr != node_allocr) { + int src_backend_id = tensor_backend_id(src); + assert(src_backend_id != -1); // all inputs should be assigned by now + if (src_backend_id != tensor_backend_id) { // create a copy of the input in the split's backend size_t id = hash_id(src); - if (sched->node_copies[id][cur_backend_id] == NULL) { - ggml_backend_t backend = get_allocr_backend(sched, cur_allocr); + if (sched->tensor_copies[id][cur_backend_id] == NULL) { + ggml_backend_t backend = sched->backends[cur_backend_id]; struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); - sched->node_copies[id][cur_backend_id] = tensor_copy; - node_allocr(tensor_copy) = cur_allocr; + sched->tensor_copies[id][cur_backend_id] = tensor_copy; + tensor_backend_id(tensor_copy) = cur_backend_id; SET_CAUSE(tensor_copy, "4.cpy"); int n_inputs = sched->splits[cur_split].n_inputs++; GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); sched->splits[cur_split].inputs[n_inputs] = src; } - node->src[j] = sched->node_copies[id][cur_backend_id]; - -#if 0 - // check if the input is already in the split - bool found = false; - for (int k = 0; k < sched->splits[cur_split].n_inputs; k++) { - if (sched->splits[cur_split].inputs[k] == src) { - found = true; - break; - } - } - - if (!found) { - int n_inputs = sched->splits[cur_split].n_inputs++; - //printf("split %d input %d: %s (%s)\n", cur_split, n_inputs, src->name, ggml_backend_name(get_allocr_backend(sched, src_allocr))); - GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); - sched->splits[cur_split].inputs[n_inputs] = src; - } -#endif + node->src[j] = sched->tensor_copies[id][cur_backend_id]; } } } @@ -1369,30 +1341,30 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g // sanity check: all sources should have the same backend as the node for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr == NULL) { + ggml_backend_t tensor_backend = tensor_backend(node); + if (tensor_backend == NULL) { fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); } - if (node->view_src != NULL && node_allocr != node_allocr(node->view_src)) { + if (node->view_src != NULL && tensor_backend != tensor_backend(node->view_src)) { fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n", - node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL", - node->view_src->name, node_allocr(node->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(node->view_src))) : "NULL"); + node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", + node->view_src->name, tensor_backend(node->view_src) ? ggml_backend_name(tensor_backend(node->view_src)) : "NULL"); } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { break; } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now + ggml_backend_t src_backend = tensor_backend(src); + if (src_backend != tensor_backend /* && src_backend != NULL */) { fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n", - node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL", - j, src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL"); + node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", + j, src->name, src_backend ? ggml_backend_name(src_backend) : "NULL"); } - if (src->view_src != NULL && src_allocr != node_allocr(src->view_src)) { + if (src->view_src != NULL && src_backend != tensor_backend(src->view_src)) { fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n", - src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL", - src->view_src->name, node_allocr(src->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(src->view_src))) : "NULL"); + src->name, src_backend ? ggml_backend_name(src_backend) : "NULL", + src->view_src->name, tensor_backend(src->view_src) ? ggml_backend_name(tensor_backend(src->view_src)) : "NULL"); } } } @@ -1406,32 +1378,45 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g struct ggml_backend_sched_split * split = &sched->splits[i]; split->graph = ggml_graph_view(graph, split->i_start, split->i_end); - // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)]; + struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id]; + // add a dependency to the input source so that it is not freed before the copy is done - GGML_ASSERT(input_cpy->src[0] == NULL || input_cpy->src[0] == input); - input_cpy->src[0] = input; + struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input); + sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(input); + graph_copy->nodes[graph_copy->n_nodes++] = input_dep; + + // add a dependency to the input copy so that it is allocated at the start of the split + sched->node_backend_ids[graph_copy->n_nodes] = split->backend_id; graph_copy->nodes[graph_copy->n_nodes++] = input_cpy; } for (int j = split->i_start; j < split->i_end; j++) { + sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]); graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j]; } } sched->graph = graph_copy; } -static void sched_alloc_splits(ggml_backend_sched_t sched) { - ggml_gallocr_alloc_graph_n( - sched->galloc, - sched->graph, - sched->hash_set, - sched->node_talloc); +static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { + // ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids); + if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { +#ifndef NDEBUG + fprintf(stderr, "ggml_backend_sched: failed to allocate graph, reserving\n"); +#endif + ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids); + if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { + fprintf(stderr, "ggml_backend_sched: failed to allocate graph\n"); + return false; + } + } + + return true; } -static void sched_compute_splits(ggml_backend_sched_t sched) { +static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; @@ -1439,20 +1424,18 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { for (int i = 0; i < sched->n_splits; i++) { struct ggml_backend_sched_split * split = &splits[i]; - ggml_backend_t split_backend = get_allocr_backend(sched, split->tallocr); - int split_backend_id = sched_backend_prio(sched, split_backend); + int split_backend_id = split->backend_id; + ggml_backend_t split_backend = sched->backends[split_backend_id]; // copy the input tensors to the split backend uint64_t copy_start_us = ggml_time_us(); for (int j = 0; j < split->n_inputs; j++) { struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][split_backend_id]; + struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id]; GGML_ASSERT(input->buffer != NULL); GGML_ASSERT(input_cpy->buffer != NULL); - // TODO: avoid this copy if it was already copied in a previous split, and the input didn't change - // this is important to avoid copying constants such as KQ_mask and inp_pos multiple times ggml_backend_tensor_copy_async(split_backend, input, input_cpy); } //ggml_backend_synchronize(split_backend); // necessary to measure copy time @@ -1468,7 +1451,9 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { uint64_t compute_start_us = ggml_time_us(); if (!sched->callback_eval) { - ggml_backend_graph_compute(split_backend, &split->graph); + if (!ggml_backend_graph_compute(split_backend, &split->graph)) { + return false; + } //ggml_backend_synchronize(split_backend); // necessary to measure compute time } else { // similar to ggml_backend_compare_graph_backend @@ -1488,7 +1473,9 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1); - ggml_backend_graph_compute(split_backend, &gv); + if (!ggml_backend_graph_compute(split_backend, &gv)) { + return false; + } if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) { break; @@ -1510,19 +1497,8 @@ static void sched_compute_splits(ggml_backend_sched_t sched) { } } #endif -} -static void sched_reset(ggml_backend_sched_t sched) { - for (int i = 0; i < sched->n_backends; i++) { - ggml_tallocr_reset(sched->tallocs[i]); - } - // reset state for the next run - size_t hash_size = sched->hash_set.size; - memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); - memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); - memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); - - sched->is_reset = true; + return true; } ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { @@ -1532,9 +1508,10 @@ ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_back struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); // initialize hash table - sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); - sched->node_talloc = calloc(sizeof(sched->node_talloc[0]) * sched->hash_set.size, 1); - sched->node_copies = calloc(sizeof(sched->node_copies[0]) * sched->hash_set.size, 1); + sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size); + sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size); + sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), graph_size); sched->n_backends = n_backends; for (int i = 0; i < n_backends; i++) { @@ -1542,14 +1519,9 @@ ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_back sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]); } - sched->galloc = ggml_gallocr_new(); + sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends); - // init measure allocs for each backend - for (int i = 0; i < n_backends; i++) { - sched->tallocs[i] = ggml_tallocr_new_measure_from_buft(sched->bufts[i]); - } - - sched_reset(sched); + ggml_backend_sched_reset(sched); return sched; } @@ -1558,49 +1530,54 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { if (sched == NULL) { return; } - for (int i = 0; i < sched->n_backends; i++) { - ggml_tallocr_free(sched->tallocs[i]); - } ggml_gallocr_free(sched->galloc); ggml_free(sched->ctx); free(sched->hash_set.keys); - free(sched->node_talloc); - free(sched->node_copies); + free(sched->tensor_backend_id); + free(sched->tensor_copies); + free(sched->node_backend_ids); free(sched); } -void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { - GGML_ASSERT(ggml_tallocr_is_measure(sched->tallocs[0])); // can only be initialized once +void ggml_backend_sched_reset(ggml_backend_sched_t sched) { + // reset state for the next run + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); // NOLINT + memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size); + memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size); - sched_split_graph(sched, measure_graph); - sched_alloc_splits(sched); - - // allocate buffers and reset allocators - for (int i = 0; i < sched->n_backends; i++) { - size_t size = ggml_tallocr_max_size(sched->tallocs[i]); - ggml_tallocr_free(sched->tallocs[i]); - sched->tallocs[i] = ggml_tallocr_new_from_buft(sched->bufts[i], size); - } - - sched_reset(sched); + sched->is_reset = true; } -void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { + ggml_backend_sched_split_graph(sched, measure_graph); + + if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids)) { + return false; + } + + ggml_backend_sched_reset(sched); + return true; +} + +bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); if (!sched->is_reset) { - sched_reset(sched); + ggml_backend_sched_reset(sched); } - sched_split_graph(sched, graph); - sched_alloc_splits(sched); - sched_compute_splits(sched); -} + ggml_backend_sched_split_graph(sched, graph); + if (!ggml_backend_sched_alloc_splits(sched)) { + return false; + } -void ggml_backend_sched_reset(ggml_backend_sched_t sched) { - sched_reset(sched); -} + if (!ggml_backend_sched_compute_splits(sched)) { + return false; + } + return true; +} void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) { sched->callback_eval = callback; @@ -1611,37 +1588,30 @@ int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) { return sched->n_splits; } -ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) { - int backend_index = sched_backend_prio(sched, backend); +size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) { + int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); - return sched->tallocs[backend_index]; -} - -ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) { - int backend_index = sched_backend_prio(sched, backend); - GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); - return ggml_tallocr_get_buffer(sched->tallocs[backend_index]); + return ggml_gallocr_get_buffer_size(sched->galloc, backend_index); } void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { - int backend_index = sched_backend_prio(sched, backend); + int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); - node_allocr(node) = sched->tallocs[backend_index]; + tensor_backend_id(node) = backend_index; } ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { - ggml_tallocr_t allocr = node_allocr(node); - if (allocr == NULL) { + int backend_index = tensor_backend_id(node); + if (backend_index == -1) { return NULL; } - return get_allocr_backend(sched, allocr); + return sched->backends[backend_index]; } // utils void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { GGML_ASSERT(tensor->buffer == NULL); - //GGML_ASSERT(tensor->data == NULL); // views of pre-allocated tensors may have the data set in ggml_new_tensor, but still need to be initialized by the backend GGML_ASSERT(tensor->view_src != NULL); GGML_ASSERT(tensor->view_src->buffer != NULL); GGML_ASSERT(tensor->view_src->data != NULL); @@ -1665,7 +1635,7 @@ void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor ggml_backend_buffer_init_tensor(buffer, tensor); } -static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, +static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, struct ggml_context * ctx_allocated, struct ggml_context * ctx_unallocated, struct ggml_tensor * src) { GGML_ASSERT(src != NULL); @@ -1678,7 +1648,7 @@ static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, stru struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src); if (src->view_src != NULL) { - dst->view_src = graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src); + dst->view_src = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src); dst->view_offs = src->view_offs; } dst->op = src->op; @@ -1691,14 +1661,14 @@ static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, stru if (s == NULL) { break; } - dst->src[i] = graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s); + dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s); } node_copies[id] = dst; return dst; } -static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) { +static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) { size_t id = ggml_hash_find(hash_set, src); if (node_init[id]) { return; @@ -1707,7 +1677,7 @@ static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor struct ggml_tensor * dst = node_copies[id]; if (dst->view_src != NULL) { - graph_init_tensor(hash_set, node_copies, node_init, src->view_src); + graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src); ggml_backend_view_init(dst->view_src->buffer, dst); } else { @@ -1720,17 +1690,17 @@ static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor if (s == NULL) { break; } - graph_init_tensor(hash_set, node_copies, node_init, s); + graph_copy_init_tensor(hash_set, node_copies, node_init, s); } } struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) { struct ggml_hash_set hash_set = { /* .size = */ graph->visited_hash_table.size, - /* .keys = */ calloc(sizeof(hash_set.keys[0]) * graph->visited_hash_table.size, 1) + /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT }; - struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]) * hash_set.size, 1); - bool * node_init = calloc(sizeof(node_init[0]) * hash_set.size, 1); + struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT + bool * node_init = calloc(sizeof(node_init[0]), hash_set.size); struct ggml_init_params params = { /* .mem_size = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false), @@ -1759,7 +1729,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s // dup nodes for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node); + graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node); } // allocate nodes @@ -1784,7 +1754,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s // copy data and init views for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - graph_init_tensor(hash_set, node_copies, node_init, node); + graph_copy_init_tensor(hash_set, node_copies, node_init, node); } // build graph copy diff --git a/ggml-backend.h b/ggml-backend.h index 282b3a9b7..f13c69bff 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -130,11 +130,7 @@ extern "C" { // in build_graph: build_graph(...) { - // allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer) - alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu); - ggml_allocr_alloc(alloc_cpu, tensor); - - // manually assigning nodes to a backend (optional, shouldn't be needed in most cases) + // manually assign nodes to a backend (optional, should not be needed in most cases) struct ggml_tensor * node = ggml_mul_mat(ctx, ...); ggml_backend_sched_set_node_backend(sched, node, backend_gpu); } @@ -164,20 +160,19 @@ extern "C" { GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph - GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // Get the number of splits of the last graph GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); - GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend); + GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); // Allocate and compute graph on the backend scheduler - GGML_API void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + GGML_API bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); - // Reset all assignments and allocators - must be called before using the sched allocators to allocate inputs + // Reset all assignments and allocators - must be called before changing the node backends GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // Set a callback to be called for each resulting node during graph compute diff --git a/ggml.c b/ggml.c index e45b78d7e..d921d82fe 100644 --- a/ggml.c +++ b/ggml.c @@ -2649,7 +2649,7 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.nb =*/ { 0, 0, 0, 0 }, /*.op =*/ GGML_OP_NONE, /*.op_params =*/ { 0 }, - /*.is_param =*/ false, + /*.flags =*/ 0, /*.grad =*/ NULL, /*.src =*/ { NULL }, /*.perf_runs =*/ 0, @@ -6551,7 +6551,7 @@ struct ggml_tensor * ggml_cross_entropy_loss_back( void ggml_set_param( struct ggml_context * ctx, struct ggml_tensor * tensor) { - tensor->is_param = true; + tensor->flags |= GGML_TENSOR_FLAG_PARAM; GGML_ASSERT(tensor->grad == NULL); tensor->grad = ggml_dup_tensor(ctx, tensor); @@ -15367,7 +15367,7 @@ static struct ggml_tensor * ggml_recompute_graph_node( return NULL; } - if (node->is_param) { + if (node->flags & GGML_TENSOR_FLAG_PARAM) { return node; } @@ -15401,7 +15401,7 @@ static struct ggml_tensor * ggml_recompute_graph_node( clone->op = node->op; clone->grad = node->grad; - clone->is_param = node->is_param; + clone->flags = node->flags; clone->extra = node->extra; for (int k = 0; k < GGML_MAX_DIMS; ++k) { clone->nb[k] = node->nb[k]; @@ -16433,7 +16433,7 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * for (int i = 0; i < gf->n_nodes; i++) { struct ggml_tensor * node = gf->nodes[i]; - if (node->is_param) { + if (node->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); ggml_build_forward_expand(gb, node->grad); } @@ -17918,7 +17918,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) { GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n", i, node->ne[0], node->ne[1], node->ne[2], - ggml_op_name(node->op), node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs, + ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " ", node->perf_runs, (double) node->perf_cycles / (double) ggml_cycles_per_ms(), (double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs, (double) node->perf_time_us / 1000.0, @@ -18011,7 +18011,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph continue; } - if (node->is_param) { + if (node->flags & GGML_TENSOR_FLAG_PARAM) { snprintf(color, sizeof(color), "yellow"); } else if (node->grad) { if (ggml_graph_find(gf, node)) { @@ -18185,7 +18185,7 @@ static enum ggml_opt_result ggml_opt_adam( int np = 0; int64_t nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { - if (gf->nodes[i]->is_param) { + if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); @@ -18548,7 +18548,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { - if (gf->nodes[i]->is_param) { + if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); @@ -19023,6 +19023,16 @@ enum ggml_opt_result ggml_opt_resume_g( //////////////////////////////////////////////////////////////////////////////// +void ggml_set_input(struct ggml_tensor * tensor) { + tensor->flags |= GGML_TENSOR_FLAG_INPUT; +} + +void ggml_set_output(struct ggml_tensor * tensor) { + tensor->flags |= GGML_TENSOR_FLAG_OUTPUT; +} + +//////////////////////////////////////////////////////////////////////////////// + void ggml_quantize_init(enum ggml_type type) { ggml_critical_section_start(); diff --git a/ggml.h b/ggml.h index 9cfec5bac..01cecc1e1 100644 --- a/ggml.h +++ b/ggml.h @@ -505,11 +505,17 @@ extern "C" { enum ggml_log_level { GGML_LOG_LEVEL_ERROR = 2, - GGML_LOG_LEVEL_WARN = 3, - GGML_LOG_LEVEL_INFO = 4, + GGML_LOG_LEVEL_WARN = 3, + GGML_LOG_LEVEL_INFO = 4, GGML_LOG_LEVEL_DEBUG = 5 }; + enum ggml_tensor_flag { + GGML_TENSOR_FLAG_INPUT = 1, + GGML_TENSOR_FLAG_OUTPUT = 2, + GGML_TENSOR_FLAG_PARAM = 4, + }; + // ggml object struct ggml_object { size_t offs; @@ -543,7 +549,7 @@ extern "C" { // op params - allocated as int32_t for alignment int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; - bool is_param; + int32_t flags; struct ggml_tensor * grad; struct ggml_tensor * src[GGML_MAX_SRC]; @@ -2092,6 +2098,12 @@ extern "C" { ggml_opt_callback callback, void * callback_data); + // + // tensor flags + // + GGML_API void ggml_set_input(struct ggml_tensor * tensor); + GGML_API void ggml_set_output(struct ggml_tensor * tensor); + // // quantization // diff --git a/llama.cpp b/llama.cpp index d1ee26ce2..a5b873a7b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1872,8 +1872,6 @@ struct llama_context { // memory buffers used to evaluate the model std::vector buf_compute_meta; ggml_backend_sched_t sched = nullptr; - // allocator for the input tensors - ggml_tallocr * alloc = nullptr; // input tensors ggml_backend_buffer_t buf_input = nullptr; @@ -7199,12 +7197,10 @@ struct llm_build_context { static struct ggml_cgraph * llama_build_graph( llama_context & lctx, - const llama_batch & batch) { + const llama_batch & batch, + bool worst_case) { const auto & model = lctx.model; - // check if we should build the worst-case graph (for memory measurement) - const bool worst_case = ggml_tallocr_is_measure(lctx.alloc); - // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) { if (il >= 0) { @@ -7225,77 +7221,6 @@ static struct ggml_cgraph * llama_build_graph( struct llm_build_context llm(lctx, batch, cb, worst_case); - // - // set input data - // - - if (!ggml_tallocr_is_measure(lctx.alloc)) { - if (batch.token) { - const int64_t n_tokens = batch.n_tokens; - - ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); - } - - if (batch.embd) { - const int64_t n_embd = llm.n_embd; - const int64_t n_tokens = batch.n_tokens; - - ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); - } - - if (batch.pos) { - const int64_t n_tokens = batch.n_tokens; - - ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); - } - - { - const int64_t n_kv = llm.n_kv; - const int64_t n_tokens = batch.n_tokens; - - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); - float * data = (float *) lctx.inp_KQ_mask->data; - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || - (llm.causal_attn && lctx.kv_self.cells[i].pos > pos)) { - f = -INFINITY; - } else { - f = 0; - } - data[h*(n_kv*n_tokens) + j*n_kv + i] = f; - } - } - } - } - - if (llm.do_rope_shift) { - const int64_t n_ctx = llm.n_ctx; - - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); - int32_t * data = (int32_t *) lctx.inp_K_shift->data; - - for (int i = 0; i < n_ctx; ++i) { - data[i] = lctx.kv_self.cells[i].delta; - } - } - - { - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); - float * data = (float *) lctx.inp_sum->data; - - for (int i = 0; i < batch.n_tokens; ++i) { - data[i] = 1.0f/float(batch.n_tokens); - } - } - } - llm.init(); switch (model.arch) { @@ -7384,6 +7309,83 @@ static struct ggml_cgraph * llama_build_graph( return result; } +static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { + // + // set input data + // + + const auto & hparams = lctx.model.hparams; + const auto & cparams = lctx.cparams; + const auto & kv_self = lctx.kv_self; + + if (batch.token) { + const int64_t n_tokens = batch.n_tokens; + + ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); + } + + if (batch.embd) { + const int64_t n_embd = hparams.n_embd; + const int64_t n_tokens = batch.n_tokens; + + ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); + } + + if (batch.pos) { + const int64_t n_tokens = batch.n_tokens; + + ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); + } + + { + const int64_t n_kv = kv_self.n; + const int64_t n_tokens = batch.n_tokens; + + assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); + + float * data = (float *) lctx.inp_KQ_mask->data; + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_pos pos = batch.pos[j]; + const llama_seq_id seq_id = batch.seq_id[j][0]; + + for (int i = 0; i < n_kv; ++i) { + float f; + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { + f = -INFINITY; + } else { + f = 0; + } + data[h*(n_kv*n_tokens) + j*n_kv + i] = f; + } + } + } + } + + + { + assert(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); + float * data = (float *) lctx.inp_sum->data; + + for (int i = 0; i < batch.n_tokens; ++i) { + data[i] = 1.0f/float(batch.n_tokens); + } + } + + if (kv_self.has_shift) { + const int64_t n_ctx = cparams.n_ctx; + + assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); + + int32_t * data = (int32_t *) lctx.inp_K_shift->data; + + for (int i = 0; i < n_ctx; ++i) { + data[i] = lctx.kv_self.cells[i].delta; + } + } +} + // decode a batch of tokens by evaluating the transformer // // - lctx: llama context @@ -7482,7 +7484,7 @@ static int llama_decode_internal( ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); - ggml_cgraph * gf = llama_build_graph(lctx, batch); + ggml_cgraph * gf = llama_build_graph(lctx, batch, false); // the output is always the last tensor in the graph struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; @@ -7527,6 +7529,9 @@ static int llama_decode_internal( if (lctx.backend_cpu != nullptr) { ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); } + + llama_set_inputs(lctx, batch); + ggml_backend_sched_graph_compute(lctx.sched, gf); // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); @@ -11278,23 +11283,27 @@ struct llama_context * llama_new_context_with_model( ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES); - ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); // build worst-case graph int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch); int n_past = cparams.n_ctx - n_tokens; llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph - ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0)); + ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true); // initialize scheduler with the worst-case graph - ggml_backend_sched_init_measure(ctx->sched, gf); - ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); + if (!ggml_backend_sched_reserve(ctx->sched, gf)) { + LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); + llama_free(ctx); + return nullptr; + } - for (ggml_backend_t backend : ctx->backends) { - ggml_backend_buffer_t buf = ggml_backend_sched_get_buffer(ctx->sched, backend); + for (size_t i = 0; i < ctx->backends.size(); i++) { + ggml_backend_t backend = ctx->backends[i]; + ggml_backend_buffer_type_t buft = backend_buft[i]; + size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend); LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, - ggml_backend_buffer_name(buf), - ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); + ggml_backend_buft_name(buft), + size / 1024.0 / 1024.0); } // note: the number of splits during measure is higher than during inference due to the kv shift diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 6ae75bc31..7a23ab162 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -2c7cf49810d523b9632da393a9e8270b60bf3b24 +5070f078a67c18c11736e78316ab715ca9afde16 From 4a46d2b7923be83d6019251671ee63aa1fa0d6bc Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 12 Feb 2024 09:38:44 +0100 Subject: [PATCH 297/334] llava : remove prog parameter from ArgumentParser (#5457) * llava: remove prog parameter from ArgumentParser This commit removes the `prog` parameter from `ArgumentParser` so that it uses the default value which is the name of the script. The motivation for this change is that currently the usage output looks like this: ```console $ python examples/llava/convert-image-encoder-to-gguf.py --help usage: convert_hf_to_gguf.py [-h] ... ``` And with this change it will look like this: ```console $ python examples/llava/convert-image-encoder-to-gguf.py --help usage: convert-image-encoder-to-gguf.py [-h] ... ``` Signed-off-by: Daniel Bevenius * ci: add W503 to flake8 ignore list This commit adds W503 to the ignore list for flake8. This is done to avoid the following error: W503 line break before binary operator Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- .github/workflows/python-lint.yml | 2 +- examples/llava/convert-image-encoder-to-gguf.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index 56d17b66c..ea0a05ea1 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -16,5 +16,5 @@ jobs: - name: flake8 Lint uses: py-actions/flake8@v2 with: - ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704" + ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503" exclude: "examples/*,examples/*/**,*/**/__init__.py" diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index f5a3c9b46..e204b56be 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -71,7 +71,7 @@ def bytes_to_unicode(): return dict(zip(bs, cs)) -ap = argparse.ArgumentParser(prog="convert_hf_to_gguf.py") +ap = argparse.ArgumentParser() ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True) ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16") ap.add_argument("--text-only", action="store_true", required=False, From 43fe07c1a4f3a58612e1d9543f7c6b556710f5d0 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Mon, 12 Feb 2024 20:22:05 +0530 Subject: [PATCH 298/334] ggml-sycl: Replace 3d ops with macro (#5458) * use macro * use macro * fix format --- ggml-sycl.cpp | 75 ++++++++++----------------------------------------- 1 file changed, 14 insertions(+), 61 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index dd562a898..cd4b3a1e1 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -11578,11 +11578,8 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, } char * dst_ptr = (char *) dst; - const int64_t ne0 = src->ne[0]; - const int64_t nb0 = src->nb[0]; - const int64_t nb1 = src->nb[1]; - const int64_t nb2 = src->nb[2]; - const int64_t nb3 = src->nb[3]; + GGML_TENSOR_LOCALS_1(int64_t, ne, src, ne); + GGML_TENSOR_LOCALS(int64_t, nb, src, nb); const enum ggml_type type = src->type; const int64_t ts = ggml_type_size(type); const int64_t bs = ggml_blck_size(type); @@ -12426,9 +12423,7 @@ inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; + GGML_TENSOR_LOCALS_3(int64_t, ne0, src0, ne); const int64_t nrows = ggml_nrows(src0); //const int n_past = ((int32_t *) dst->op_params)[0]; @@ -12758,15 +12753,9 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, ggml_sycl_op_mul_mat_t op, const bool convert_src1_to_q8_1) try { - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); const int64_t nrows1 = ggml_nrows(src1); GGML_ASSERT(ne03 == ne13); @@ -13337,23 +13326,13 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00); - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02); - const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03); + GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb); - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); - const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); - const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); + GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); const int64_t ne1 = ggml_nelements(src1); const int64_t ne = ggml_nelements(dst); @@ -13655,23 +13634,15 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src1->type == GGML_TYPE_F32); - const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00); - const int64_t ne01 = src00->ne[1]; - const int64_t ne02 = src00->ne[2]; - const int64_t ne03 = src00->ne[3]; + GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne); //const int64_t nb01 = src00->nb[1]; - const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02); - const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03); + GGML_TENSOR_LOCALS(int64_t, nb0, src00, nb); - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); + GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); //const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); - const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); const int64_t ne1 = ggml_nelements(src1); const int64_t ne = ggml_nelements(dst); @@ -13940,25 +13911,7 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - - const int64_t nb00 = src0->nb[0]; - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; - const int64_t nb03 = src0->nb[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - - - const int64_t nb10 = src1->nb[0]; - const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; - const int64_t nb13 = src1->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; From dbd8828eb03b9aa8d0af7e4c533d3c2f5b38aba6 Mon Sep 17 00:00:00 2001 From: Lee <44310445+lx200916@users.noreply.github.com> Date: Tue, 13 Feb 2024 01:29:57 +0800 Subject: [PATCH 299/334] py : fix persimmon `n_rot` conversion (#5460) * convert : fix persimmon offical weight conversion to write correct n_rot. * Update convert-persimmon-to-gguf.py --------- Co-authored-by: Georgi Gerganov --- convert-persimmon-to-gguf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index d2be805d1..def210531 100755 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -88,7 +88,8 @@ def main(): gguf_writer.add_embedding_length(hidden_size) gguf_writer.add_block_count(block_count) gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size) - gguf_writer.add_rope_dimension_count(hidden_size // head_count) + # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443 + gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) gguf_writer.add_head_count(head_count) gguf_writer.add_head_count_kv(head_count_kv) gguf_writer.add_rope_freq_base(hparams.rotary_emb_base) From df334a11251b81fd0b6a0e51e7146e0ba9e973f2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 12 Feb 2024 19:54:29 +0200 Subject: [PATCH 300/334] swift : package no longer use ggml dependency (#5465) * Revert "swift : update Package.swift to use ggml as dependency (#4691)" This reverts commit ece9a45e8ffb73ad461c792720c2fec28b0137bc. * spm : add ggml headers --- Package.swift | 24 +++++++++++++++++++----- spm-headers/ggml-alloc.h | 1 + spm-headers/ggml-backend.h | 1 + spm-headers/ggml.h | 1 + 4 files changed, 22 insertions(+), 5 deletions(-) create mode 120000 spm-headers/ggml-alloc.h create mode 120000 spm-headers/ggml-backend.h create mode 120000 spm-headers/ggml.h diff --git a/Package.swift b/Package.swift index 37524edee..b24c9204a 100644 --- a/Package.swift +++ b/Package.swift @@ -13,17 +13,31 @@ let package = Package( products: [ .library(name: "llama", targets: ["llama"]), ], - dependencies: [ - .package(url: "https://github.com/ggerganov/ggml.git", .branch("release")) - ], targets: [ .target( name: "llama", - dependencies: ["ggml"], path: ".", - exclude: ["ggml-metal.metal"], + exclude: [ + "cmake", + "examples", + "scripts", + "models", + "tests", + "CMakeLists.txt", + "ggml-cuda.cu", + "ggml-cuda.h", + "Makefile" + ], sources: [ + "ggml.c", "llama.cpp", + "ggml-alloc.c", + "ggml-backend.c", + "ggml-quants.c", + "ggml-metal.m", + ], + resources: [ + .process("ggml-metal.metal") ], publicHeadersPath: "spm-headers", cSettings: [ diff --git a/spm-headers/ggml-alloc.h b/spm-headers/ggml-alloc.h new file mode 120000 index 000000000..a49d385a1 --- /dev/null +++ b/spm-headers/ggml-alloc.h @@ -0,0 +1 @@ +../ggml-alloc.h \ No newline at end of file diff --git a/spm-headers/ggml-backend.h b/spm-headers/ggml-backend.h new file mode 120000 index 000000000..17c2cf14f --- /dev/null +++ b/spm-headers/ggml-backend.h @@ -0,0 +1 @@ +../ggml-backend.h \ No newline at end of file diff --git a/spm-headers/ggml.h b/spm-headers/ggml.h new file mode 120000 index 000000000..39215298f --- /dev/null +++ b/spm-headers/ggml.h @@ -0,0 +1 @@ +../ggml.h \ No newline at end of file From 099afc6274c859ca67146e725839f2d97a5ef313 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 12 Feb 2024 20:14:39 +0200 Subject: [PATCH 301/334] llama : fix quantization when tensors are missing (#5423) --- llama.cpp | 32 ++++++++++++++++++++++++-------- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/llama.cpp b/llama.cpp index a5b873a7b..d316d067b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -772,22 +772,37 @@ struct LLM_TN { llm_arch arch; std::string operator()(llm_tensor tensor) const { + if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + return "__missing__"; + } return LLM_TENSOR_NAMES[arch].at(tensor); } std::string operator()(llm_tensor tensor, const std::string & suffix) const { + if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + return "__missing__"; + } return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix; } std::string operator()(llm_tensor tensor, int bid) const { + if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + return "__missing__"; + } return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid); } std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const { + if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + return "__missing__"; + } return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix; } std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const { + if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) { + return "__missing__"; + } return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix; } }; @@ -10227,6 +10242,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } ++qs.i_ffn_up; } + // if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; //} // IK: let's remove this, else Q2_K is almost the same as Q3_K_S @@ -10286,19 +10302,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // K-quants case LLAMA_FTYPE_MOSTLY_Q2_K_S: - case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; + case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break; case LLAMA_FTYPE_MOSTLY_Q3_K_XS: case LLAMA_FTYPE_MOSTLY_Q3_K_S: case LLAMA_FTYPE_MOSTLY_Q3_K_M: - case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; + case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break; case LLAMA_FTYPE_MOSTLY_Q4_K_S: - case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break; + case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break; case LLAMA_FTYPE_MOSTLY_Q5_K_S: - case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; - case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; - case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_XS :quantized_type = GGML_TYPE_IQ2_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ3_XXS:quantized_type = GGML_TYPE_IQ3_XXS; break; + case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break; + case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break; + case LLAMA_FTYPE_MOSTLY_IQ2_XS: quantized_type = GGML_TYPE_IQ2_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } From 895407f31b358e3d9335e847d13f033491ec8a5b Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 13 Feb 2024 09:07:57 +0200 Subject: [PATCH 302/334] ggml-quants : fix compiler warnings (shadow variable) (#5472) Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index b2a309bf8..f44377f45 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3819,15 +3819,15 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r /* Compute combined scale for the block */ const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); - __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i qx = bytes_from_nibbles_32(x[i].qs); // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. const __m256i off = _mm256_set1_epi8( 8 ); - bx = _mm256_sub_epi8( bx, off ); + qx = _mm256_sub_epi8( qx, off ); - __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256 q = mul_sum_i8_pairs_float(bx, by); + const __m256 q = mul_sum_i8_pairs_float(qx, qy); /* Multiply q with scale and accumulate */ acc = _mm256_fmadd_ps( d, q, acc ); @@ -4196,10 +4196,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - const __m256i bx = bytes_from_nibbles_32(x[i].qs); - const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs ); + const __m256i qx = bytes_from_nibbles_32(x[i].qs); + const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[i].qs ); - const __m256 xy = mul_sum_us8_pairs_float(bx, by); + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); // Accumulate d0*d1*x*y #if defined(__AVX2__) @@ -4418,14 +4418,14 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r /* Compute combined scale for the block */ const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); - __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i qx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); - bx = _mm256_or_si256(bx, bxhi); + qx = _mm256_or_si256(qx, bxhi); - __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256 q = mul_sum_i8_pairs_float(bx, by); + const __m256 q = mul_sum_i8_pairs_float(qx, qy); /* Multiply q with scale and accumulate */ acc = _mm256_fmadd_ps(d, q, acc); @@ -4722,15 +4722,15 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; - __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i qx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); - bx = _mm256_or_si256(bx, bxhi); + qx = _mm256_or_si256(qx, bxhi); const __m256 dy = _mm256_set1_ps(y[i].d); - const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + const __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256 q = mul_sum_us8_pairs_float(bx, by); + const __m256 q = mul_sum_us8_pairs_float(qx, qy); acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); } @@ -4973,10 +4973,10 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { // Compute combined scale for the block const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); - __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs); - __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + __m256i qx = _mm256_loadu_si256((const __m256i *)x[i].qs); + __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256 q = mul_sum_i8_pairs_float(bx, by); + const __m256 q = mul_sum_i8_pairs_float(qx, qy); // Multiply q with scale and accumulate #if defined(__AVX2__) From 99b8b43d7b185a6483f28cf798a2d968b2e16ca7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 Feb 2024 11:20:24 +0200 Subject: [PATCH 303/334] tests : disable moe test (#5473) --- tests/test-backend-ops.cpp | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index eb06123d2..9af8517d9 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2129,14 +2129,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_pad()); test_cases.emplace_back(new test_leaky_relu()); + // these tests are disabled to save execution time, but they can be handy for debugging +#if 0 #if !defined(__SANITIZE_THREAD__) // FIXME: these tests use too much memory with thread sanitizer test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024)); //test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336)); #endif - - // these tests are disabled to save execution time, but they can be handy for debugging -#if 0 test_cases.emplace_back(new test_llama(1)); test_cases.emplace_back(new test_llama(2)); test_cases.emplace_back(new test_falcon(1)); From 49cc1f7d67de2da99f3ac185f9ff1319b7bf35f8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 Feb 2024 13:01:29 +0200 Subject: [PATCH 304/334] bert : add tests + fix quantization (#5475) * llama : do not quantize pos embd and token type tensors * ci : add BERT tests ggml-ci * ci : do not do BERT tests on low-perf nodes ggml-ci --- ci/run.sh | 46 ++++++++++++++++++++++++++++++++++++++++++++++ llama.cpp | 6 +++++- 2 files changed, 51 insertions(+), 1 deletion(-) diff --git a/ci/run.sh b/ci/run.sh index 82fe247a5..a4264d775 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -568,6 +568,50 @@ function gg_sum_open_llama_7b_v2 { #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" } +# bge-small + +function gg_run_embd_bge_small { + cd ${SRC} + + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/tokenizer.model + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/sentence_bert_config.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/vocab.txt + + path_models="../models-mnt/bge-small" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert-hf-to-gguf.py ${path_models} + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + + ./bin/quantize ${model_f16} ${model_q8_0} q8_0 + + (time ./bin/embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + + set +e +} + +function gg_sum_embd_bge_small { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'BGE Small (BERT):\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" +} + ## main if [ -z ${GG_BUILD_LOW_PERF} ]; then @@ -591,6 +635,8 @@ test $ret -eq 0 && gg_run ctest_debug test $ret -eq 0 && gg_run ctest_release if [ -z ${GG_BUILD_LOW_PERF} ]; then + test $ret -eq 0 && gg_run embd_bge_small + if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then test $ret -eq 0 && gg_run open_llama_3b_v2 diff --git a/llama.cpp b/llama.cpp index d316d067b..6dce392df 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10444,7 +10444,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s quantize &= !params->only_copy; // do not quantize expert gating tensors - quantize &= name.find("ffn_gate_inp.weight") == std::string::npos; + quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_FFN_GATE_INP, "weight"); + + // do not quantize positional embeddings and token types (BERT) + quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight"); + quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight"); enum ggml_type new_type; void * new_data; From ad014bba97ef6ef6c3e2f78b2fc463e91ae94579 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 13 Feb 2024 12:38:37 +0100 Subject: [PATCH 305/334] make: add error message for bad CUDA version (#5444) * make: add error message for bad CUDA version * Update Makefile Co-authored-by: Jared Van Bortel --------- Co-authored-by: Jared Van Bortel --- Makefile | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/Makefile b/Makefile index ba73f0637..0a2070b53 100644 --- a/Makefile +++ b/Makefile @@ -569,6 +569,14 @@ $(info I CC: $(shell $(CC) --version | head -n 1)) $(info I CXX: $(shell $(CXX) --version | head -n 1)) ifdef LLAMA_CUBLAS $(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) +CUDA_VERSION := $(shell nvcc --version | grep -oP 'release (\K[0-9]+\.[0-9])') +ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) +ifndef CUDA_DOCKER_ARCH +ifndef CUDA_POWER_ARCH +$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH) +endif # CUDA_POWER_ARCH +endif # CUDA_DOCKER_ARCH +endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) endif # LLAMA_CUBLAS $(info ) From 03bf161eb6dea6400ee49c6dc6b69bdcfa9fd3fc Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Tue, 13 Feb 2024 06:06:58 -0600 Subject: [PATCH 306/334] llama : support batched embeddings (#5466) * batched embedding: pool outputs by sequence id. updated embedding example * bring back non-causal attention * embd : minor improvements * llama : minor --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 1 + examples/embedding/embedding.cpp | 146 +++++++++++++++++++++++-------- gguf-py/gguf/constants.py | 1 + gguf-py/gguf/gguf_writer.py | 3 + llama.cpp | 61 +++++++++---- llama.h | 5 ++ 6 files changed, 163 insertions(+), 54 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index cae1551a2..5adfdc143 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1648,6 +1648,7 @@ class BertModel(Model): self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) self.gguf_writer.add_causal_attention(False) + self.gguf_writer.add_pooling_layer(True) self.gguf_writer.add_file_type(self.ftype) def set_vocab(self): diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 27376c8f0..b4688cf51 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -7,6 +7,51 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +static std::vector split_lines(const std::string & s) { + std::string line; + std::vector lines; + std::stringstream ss(s); + while (std::getline(ss, line)) { + lines.push_back(line); + } + return lines; +} + +static void batch_add_seq(llama_batch & batch, const std::vector & tokens, int seq_id) { + for (size_t i = 0; i < tokens.size(); i++) { + llama_batch_add(batch, tokens[i], i, { seq_id }, false); + } +} + +static void normalize(float * vec, float * out, int n) { + float norm = 0; + for (int i = 0; i < n; i++) { + norm += vec[i] * vec[i]; + } + norm = sqrt(norm); + for (int i = 0; i < n; i++) { + out[i] = vec[i] / norm; + } +} + +static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) { + // clear previous kv_cache values (irrelevant for embeddings) + llama_kv_cache_clear(ctx); + + // run model + fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq); + if (llama_decode(ctx, batch) < 0) { + fprintf(stderr, "%s : failed to decode\n", __func__); + } + + // normalize on copy + for (int k = 0; k < n_seq; k++) { + float * emb = llama_get_embeddings_ith(ctx, k); + float * out = output + k * n_embd; + normalize(emb, out, n_embd); + } +} + int main(int argc, char ** argv) { gpt_params params; @@ -55,59 +100,84 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s\n", get_system_info(params).c_str()); } - int n_past = 0; + // split the prompt into lines + std::vector prompts = split_lines(params.prompt); - // tokenize the prompt - auto embd_inp = ::llama_tokenize(ctx, params.prompt, true); + // max batch size + const uint64_t n_batch = params.n_batch; + GGML_ASSERT(params.n_batch == params.n_ctx); + // tokenize the prompts and trim + std::vector> inputs; + for (const auto & prompt : prompts) { + auto inp = ::llama_tokenize(ctx, prompt, true); + if (inp.size() > n_batch) { + inp.resize(n_batch); + } + inputs.push_back(inp); + } + + // tokenization stats if (params.verbose_prompt) { - fprintf(stderr, "\n"); - fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); - fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); - for (int i = 0; i < (int) embd_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); + for (int i = 0; i < (int) inputs.size(); i++) { + fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str()); + fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size()); + for (int j = 0; j < (int) inputs[i].size(); j++) { + fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str()); + } + fprintf(stderr, "\n\n"); } - fprintf(stderr, "\n"); } - if (embd_inp.size() > (size_t)n_ctx) { - fprintf(stderr, "%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\n", - __func__, embd_inp.size(), n_ctx); - return 1; - } - - while (!embd_inp.empty()) { - int n_tokens = std::min(params.n_batch, (int) embd_inp.size()); - if (llama_decode(ctx, llama_batch_get_one(embd_inp.data(), n_tokens, n_past, 0))) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return 1; - } - n_past += n_tokens; - embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens); - } + // initialize batch + const int n_prompts = prompts.size(); + struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts); + // allocate output const int n_embd = llama_n_embd(model); - auto * embeddings = llama_get_embeddings(ctx); + std::vector embeddings(n_prompts * n_embd, 0); + float * emb = embeddings.data(); - // l2-normalize embeddings - float norm = 0; - for (int i = 0; i < n_embd; i++) { - norm += embeddings[i] * embeddings[i]; - } - norm = sqrt(norm); - for (int i = 0; i < n_embd; i++) { - embeddings[i] /= norm; + // break into batches + int p = 0; // number of prompts processed already + int s = 0; // number of prompts in current batch + for (int k = 0; k < n_prompts; k++) { + // clamp to n_batch tokens + auto & inp = inputs[k]; + const uint64_t n_toks = inp.size(); + + // encode if at capacity + if (batch.n_tokens + n_toks > n_batch) { + float * out = emb + p * n_embd; + batch_decode(ctx, batch, out, s, n_embd); + llama_batch_clear(batch); + p += s; + s = 0; + } + + // add to batch + batch_add_seq(batch, inp, s); + s += 1; } - for (int i = 0; i < n_embd; i++) { - printf("%f ", embeddings[i]); - } - printf("\n"); + // final batch + float * out = emb + p * n_embd; + batch_decode(ctx, batch, out, s, n_embd); + // print first 3 embeddings + for (int j = 0; j < std::min(3, n_prompts); j++) { + fprintf(stderr, "embedding %d: ", j); + for (int i = 0; i < n_embd; i++) { + fprintf(stderr, "%f ", emb[j * n_embd + i]); + } + fprintf(stderr, "\n\n"); + } + fprintf(stderr, "\n"); + + // clean up llama_print_timings(ctx); llama_free(ctx); llama_free_model(model); - llama_backend_free(); return 0; diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a9c13dd38..644e1589c 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -40,6 +40,7 @@ class Keys: TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" EXPERT_COUNT = "{arch}.expert_count" EXPERT_USED_COUNT = "{arch}.expert_used_count" + POOLING_LAYER = "{arch}.pooling_layer" class Attention: HEAD_COUNT = "{arch}.attention.head_count" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 7af58a46c..d87bd8e88 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -360,6 +360,9 @@ class GGUFWriter: def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + def add_pooling_layer(self, value: bool) -> None: + self.add_bool(Keys.LLM.POOLING_LAYER.format(arch=self.arch), value) + def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) diff --git a/llama.cpp b/llama.cpp index 6dce392df..eb6c46f36 100644 --- a/llama.cpp +++ b/llama.cpp @@ -254,6 +254,7 @@ enum llm_kv { LLM_KV_TENSOR_DATA_LAYOUT, LLM_KV_EXPERT_COUNT, LLM_KV_EXPERT_USED_COUNT, + LLM_KV_POOLING_LAYER, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, @@ -311,6 +312,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, + { LLM_KV_POOLING_LAYER, "%s.pooling_layer" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -1539,6 +1541,7 @@ struct llama_hparams { float f_max_alibi_bias; bool causal_attn = true; + bool pooling_layer = false; bool operator!=(const llama_hparams & other) const { @@ -1601,6 +1604,7 @@ struct llama_cparams { bool mul_mat_q; bool offload_kqv; + bool do_pooling; ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; @@ -1896,7 +1900,7 @@ struct llama_context { struct ggml_tensor * inp_pos; // I32 [n_batch] struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] struct ggml_tensor * inp_K_shift; // I32 [n_ctx] - struct ggml_tensor * inp_sum; // F32 [1, n_batch] + struct ggml_tensor * inp_sum; // F32 [n_batch, n_batch] #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -3053,6 +3057,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); + ml.get_key(LLM_KV_POOLING_LAYER, hparams.pooling_layer); switch (hparams.n_layer) { case 3: @@ -4859,7 +4864,7 @@ struct llm_build_context { const int32_t n_orig_ctx; const bool do_rope_shift; - const bool causal_attn; + const bool do_pooling; const llm_build_cb & cb; @@ -4903,7 +4908,7 @@ struct llm_build_context { kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), do_rope_shift (worst_case || kv_self.has_shift), - causal_attn (hparams.causal_attn), + do_pooling (hparams.pooling_layer && cparams.do_pooling), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { // all initializations should be done in init() @@ -5752,17 +5757,18 @@ struct llm_build_context { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); struct ggml_tensor * cur; struct ggml_tensor * inpL; // get input vectors with right size + const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type); struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - struct ggml_tensor * inp_sum = ggml_view_1d(ctx0, lctx.inp_sum, n_tokens, 0); + struct ggml_tensor * inp_sum = ggml_view_2d(ctx0, lctx.inp_sum, n_tokens, n_tokens, stride1, 0); // construct input embeddings (token, type, position) inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + // token types are hardcoded to zero ("Sentence A") struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); inpL = ggml_add(ctx0, inpL, type_row0); @@ -5832,9 +5838,11 @@ struct llm_build_context { // final output cur = inpL; - // pooling - cur = ggml_mul_mat(ctx0, inp_sum, ggml_cont(ctx0, ggml_transpose(ctx0, cur))); - cb(cur, "result_embed", -1); + // pooling layer + if (do_pooling) { + cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_sum); + } + cb(cur, "result_embd", -1); ggml_build_forward_expand(gf, cur); @@ -7367,7 +7375,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { for (int i = 0; i < n_kv; ++i) { float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || + (hparams.causal_attn && lctx.kv_self.cells[i].pos > pos)) { f = -INFINITY; } else { f = 0; @@ -7378,7 +7387,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - { assert(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); float * data = (float *) lctx.inp_sum->data; @@ -7399,6 +7407,20 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { data[i] = lctx.kv_self.cells[i].delta; } } + + if (hparams.pooling_layer && cparams.do_pooling) { + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); + float * data = (float *) lctx.inp_sum->data; + + memset(lctx.inp_sum->data, 0, batch.n_tokens * batch.n_tokens * ggml_element_size(lctx.inp_sum)); + + for (int i = 0; i < n_tokens; ++i) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + data[seq_id*n_tokens + i] = 1.0f; + } + } } // decode a batch of tokens by evaluating the transformer @@ -7510,7 +7532,7 @@ static int llama_decode_internal( embeddings = gf->nodes[gf->n_nodes - 3]; GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); } - } else if (strcmp(res->name, "result_embed") == 0) { + } else if (strcmp(res->name, "result_embd") == 0) { embeddings = res; res = nullptr; } else { @@ -7630,11 +7652,12 @@ static int llama_decode_internal( if (!lctx.embedding.empty()) { auto & embedding_out = lctx.embedding; - const int64_t embed_pos = res ? n_embd * (n_tokens-1) : 0; + const int64_t embd_pos = res ? n_embd * (n_tokens-1) : 0; + const int64_t embd_size = res ? n_embd : n_embd * n_tokens; - embedding_out.resize(n_embd); + embedding_out.resize(embd_size); ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings); - ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embed_pos*sizeof(float), n_embd*sizeof(float)); + ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embd_pos*sizeof(float), embd_size*sizeof(float)); ggml_backend_synchronize(embeddings_backend); } @@ -10950,6 +10973,7 @@ struct llama_context_params llama_context_default_params() { /*.logits_all =*/ false, /*.embedding =*/ false, /*.offload_kqv =*/ true, + /*.do_pooling =*/ true, }; return result; @@ -11105,6 +11129,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.mul_mat_q = params.mul_mat_q; cparams.offload_kqv = params.offload_kqv; + cparams.do_pooling = params.do_pooling; cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base; @@ -11252,7 +11277,7 @@ struct llama_context * llama_new_context_with_model( // resized during inference, reserve maximum ctx->logits.reserve(hparams.n_vocab*cparams.n_batch); - if (params.embedding){ + if (params.embedding) { ctx->embedding.resize(hparams.n_embd); } @@ -11270,7 +11295,7 @@ struct llama_context * llama_new_context_with_model( ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); - ctx->inp_sum = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, 1, cparams.n_batch); + ctx->inp_sum = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); ggml_set_name(ctx->inp_tokens, "inp_tokens"); ggml_set_name(ctx->inp_embd, "inp_embd"); @@ -12128,6 +12153,10 @@ float * llama_get_embeddings(struct llama_context * ctx) { return ctx->embedding.data(); } +float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) { + return ctx->embedding.data() + i*ctx->model.hparams.n_embd; +} + const char * llama_token_get_text(const struct llama_model * model, llama_token token) { return model->vocab.id_to_token[token].text.c_str(); } diff --git a/llama.h b/llama.h index 367e8f1a1..5ef78ec96 100644 --- a/llama.h +++ b/llama.h @@ -236,6 +236,7 @@ extern "C" { bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU + bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) }; // model quantization parameters @@ -628,6 +629,10 @@ extern "C" { // shape: [n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); + // Get the embeddings for the ith sequence + // llama_get_embeddings(ctx) + i*n_embd + LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); + // // Vocab // From cf45252a7cfcb998bade46a886e20477cecc538a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 13 Feb 2024 15:14:22 +0200 Subject: [PATCH 307/334] tests : multi-thread the tokenizer tests (#5474) * tests : multi-thread the tokenizer tests ggml-ci * unicode : fix data race for unidentified codepoints ggml-ci * unicode : minor style fixes ggml-ci --- llama.cpp | 24 +++++----- tests/test-tokenizer-1-bpe.cpp | 77 ++++++++++++++++---------------- tests/test-tokenizer-1-llama.cpp | 53 ++++++++++++---------- unicode.h | 72 ++++++++++++++++------------- 4 files changed, 124 insertions(+), 102 deletions(-) diff --git a/llama.cpp b/llama.cpp index eb6c46f36..381a03068 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7782,7 +7782,7 @@ struct llm_bigram_spm { }; struct llm_tokenizer_spm { - llm_tokenizer_spm(const llama_vocab & vocab): vocab(vocab) {} + llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {} void tokenize(const std::string & text, std::vector & output) { // split string into utf8 chars @@ -7857,6 +7857,7 @@ private: if (p == rev_merge.end()) { // output any symbols that did not form tokens as bytes. + output.reserve(output.size() + symbol.n); for (int j = 0; j < (int)symbol.n; ++j) { llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]); output.push_back(token_id); @@ -8419,17 +8420,18 @@ struct fragment_buffer_variant { token(_token), raw_text(_dummy), offset(0), - length(0){} + length(0) {} + fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length) : type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT), - token((llama_vocab::id)-1), + token((llama_vocab::id) - 1), raw_text(_raw_text), offset(_offset), length(_length){ - GGML_ASSERT( _offset >= 0 ); - GGML_ASSERT( _length >= 1 ); - GGML_ASSERT( offset + length <= raw_text.length() ); + GGML_ASSERT(_offset >= 0); + GGML_ASSERT(_length >= 1); + GGML_ASSERT(offset + length <= raw_text.length()); } const FRAGMENT_BUFFER_VARIANT_TYPE type; @@ -8553,14 +8555,14 @@ static std::vector llama_tokenize_internal(const llama_vocab & } std::forward_list fragment_buffer; - fragment_buffer.emplace_front( raw_text, 0, raw_text.length() ); + fragment_buffer.emplace_front(raw_text, 0, raw_text.length()); - if (special) tokenizer_st_partition( vocab, fragment_buffer ); + if (special) tokenizer_st_partition(vocab, fragment_buffer); switch (vocab.type) { case LLAMA_VOCAB_TYPE_SPM: { - for (const auto & fragment: fragment_buffer) { + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { // without adding this leading whitespace, we do not get the same results as the original tokenizer @@ -8588,7 +8590,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } break; case LLAMA_VOCAB_TYPE_BPE: { - for (const auto & fragment: fragment_buffer) { + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); @@ -8604,7 +8606,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } break; case LLAMA_VOCAB_TYPE_WPM: { - for (const auto & fragment: fragment_buffer) { + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index 386530f23..3bb629561 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -4,13 +4,13 @@ #include "console.h" #include +#include #include #include -#include -#include -#include -#include #include +#include +#include +#include int main(int argc, char **argv) { if (argc < 2) { @@ -74,45 +74,46 @@ int main(int argc, char **argv) { } } catch (const std::invalid_argument &) { - fprintf(stderr, "%s : info: utf8 conversion %d '%s'\n", __func__, i, str.c_str()); + //fprintf(stderr, "%s : info: utf8 conversion %d '%s'\n", __func__, i, str.c_str()); } } - for (uint32_t cp = 0x0000; cp < 0xffff; ++cp) { - // NOTE: these exceptions seem to be necessary, because the GPT2 tokenizer doesn't want to interfere with some ASCII control characters - if ((cp < 0x03 || cp > 0x05) && cp != 0x0b && cp != 0x11 && (cp < 0x13 || cp > 0x17) && cp != 0x19 && (cp < 0x1c || cp > 0x1e) && (cp < 0xd800 || cp > 0xdfff)) { - std::string str = " " + codepoint_to_utf8(cp); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_bpe(ctx, tokens); - if (str != check) { - fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", - __func__, cp, check.c_str(), check.length(), str.c_str(), str.length()); - return 3; - } - } - } - // Restrict to assigned unicode planes - // for (uint32_t cp = 0x10000; cp < 0x0010ffff; ++cp) { - for (uint32_t cp = 0x10000; cp < 0x00040000; ++cp) { - std::string str = codepoint_to_utf8(cp); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_bpe(ctx, tokens); - if (str != check) { - fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", - __func__, cp, check.c_str(), check.length(), str.c_str(), str.length()); - return 4; - } - } - for (uint32_t cp = 0x000e0000; cp < 0x0010ffff; ++cp) { - std::string str = codepoint_to_utf8(cp); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_bpe(ctx, tokens); - if (str != check) { - fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", - __func__, cp, check.c_str(), check.length(), str.c_str(), str.length()); - return 4; + // unicode + { + const int nthread = std::thread::hardware_concurrency(); + + std::vector threads(nthread); + + for (int i = 0; i < nthread; ++i) { + threads[i] = std::thread([i, nthread, ctx]() { + for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) { + if (!( // NOLINT + (cp < 0x03 || cp > 0x05) && cp != 0x0b && cp != 0x11 && + (cp < 0x13 || cp > 0x17) && cp != 0x19 && + (cp < 0x1c || cp > 0x1e) && + (cp < 0xd800 || cp > 0xdfff) && + (cp < 0x00040000 || cp >= 0x000e0000) + )) { + continue; + } + + std::string str = codepoint_to_utf8(cp); + std::vector tokens = llama_tokenize(ctx, str, false); + std::string check = llama_detokenize_bpe(ctx, tokens); + if (cp != 9601 && str != check) { + fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", + cp, check.c_str(), check.length(), str.c_str(), str.length()); + std::exit(3); + } + } + }); + } + + for (auto & t : threads) { + t.join(); } } + llama_free_model(model); llama_free(ctx); diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-llama.cpp index 4b58fe495..b0d814a41 100644 --- a/tests/test-tokenizer-1-llama.cpp +++ b/tests/test-tokenizer-1-llama.cpp @@ -4,13 +4,13 @@ #include "console.h" #include +#include #include #include -#include -#include -#include -#include #include +#include +#include +#include int main(int argc, char **argv) { if (argc < 2) { @@ -72,26 +72,33 @@ int main(int argc, char **argv) { } } - for (uint32_t cp = 0x0000; cp < 0xffff; ++cp) { - if (cp < 0xd800 || cp > 0xdfff) { - std::string str = codepoint_to_utf8(cp); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_spm(ctx, tokens); - if (cp != 9601 && str != check) { - fprintf(stderr, "%s : error: codepoint %d detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", - __func__, cp, check.c_str(), check.length(), str.c_str(), str.length()); - return 3; - } + // unicode + { + const int nthread = std::thread::hardware_concurrency(); + + std::vector threads(nthread); + + for (int i = 0; i < nthread; ++i) { + threads[i] = std::thread([i, nthread, ctx]() { + for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) { + if (cp >= 0xd800 && cp <= 0xdfff) { + continue; + } + + std::string str = codepoint_to_utf8(cp); + std::vector tokens = llama_tokenize(ctx, str, false); + std::string check = llama_detokenize_spm(ctx, tokens); + if (cp != 9601 && str != check) { + fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", + cp, check.c_str(), check.length(), str.c_str(), str.length()); + std::exit(3); + } + } + }); } - } - for (uint32_t cp = 0x10000; cp < 0x0010ffff; ++cp) { - std::string str = codepoint_to_utf8(cp); - std::vector tokens = llama_tokenize(ctx, str, false); - std::string check = llama_detokenize_spm(ctx, tokens); - if (str != check) { - fprintf(stderr, "%s : error: codepoint %d detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", - __func__, cp, check.c_str(), check.length(), str.c_str(), str.length()); - return 4; + + for (auto & t : threads) { + t.join(); } } diff --git a/unicode.h b/unicode.h index 844eff3da..263260702 100644 --- a/unicode.h +++ b/unicode.h @@ -264,26 +264,29 @@ static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) { offset += 1; return result; } - else if (!(utf8[offset + 0] & 0x40)) { + if (!(utf8[offset + 0] & 0x40)) { throw std::invalid_argument("invalid character"); } - else if (!(utf8[offset + 0] & 0x20)) { - if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) + if (!(utf8[offset + 0] & 0x20)) { + if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) { throw std::invalid_argument("invalid character"); + } auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f); offset += 2; return result; } - else if (!(utf8[offset + 0] & 0x10)) { - if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) + if (!(utf8[offset + 0] & 0x10)) { + if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) { throw std::invalid_argument("invalid character"); + } auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f); offset += 3; return result; } - else if (!(utf8[offset + 0] & 0x08)) { - if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) + if (!(utf8[offset + 0] & 0x08)) { + if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) { throw std::invalid_argument("invalid character"); + } auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f); offset += 4; return result; @@ -331,21 +334,22 @@ static uint32_t codepoint_from_utf16(const std::vector & utf16, size_t offset += 1; return result; } - else { - if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) - throw std::invalid_argument("invalid character"); - auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff)); - offset += 2; - return result; + + if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) { + throw std::invalid_argument("invalid character"); } - throw std::invalid_argument("invalid string"); + + auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff)); + offset += 2; + return result; } static std::vector codepoints_from_utf16(const std::vector & utf16) { std::vector result; size_t offset = 0; - while (offset < utf16.size()) + while (offset < utf16.size()) { result.push_back(codepoint_from_utf16(utf16, offset)); + } return result; } @@ -361,44 +365,52 @@ static std::vector codepoints_from_utf16(const std::vector & static std::unordered_map codepoint_type_map() { std::unordered_map codepoint_types; for (auto p : digit_ranges) { - for(auto i = p.first; i <= p.second; ++ i) + for (auto i = p.first; i <= p.second; ++ i) { codepoint_types[i] = CODEPOINT_TYPE_DIGIT; + } } - for(auto p : letter_ranges) { - for(auto i = p.first; i <= p.second; ++ i) + for (auto p : letter_ranges) { + for (auto i = p.first; i <= p.second; ++ i) { codepoint_types[i] = CODEPOINT_TYPE_LETTER; + } } - for(auto p : whitespace_ranges) { - for(auto i = p.first; i <= p.second; ++ i) + for (auto p : whitespace_ranges) { + for (auto i = p.first; i <= p.second; ++ i) { codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE; + } } - for(auto p : accent_mark_ranges) { - for(auto i = p.first; i <= p.second; ++ i) + for (auto p : accent_mark_ranges) { + for (auto i = p.first; i <= p.second; ++ i) { codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK; + } } - for(auto p : punctuation_ranges) { - for(auto i = p.first; i <= p.second; ++ i) + for (auto p : punctuation_ranges) { + for (auto i = p.first; i <= p.second; ++ i) { codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION; + } } - for (auto p : symbol_ranges) { - for (auto i = p.first; i <= p.second; ++i) + for (auto p : symbol_ranges) { + for (auto i = p.first; i <= p.second; ++i) { codepoint_types[i] = CODEPOINT_TYPE_SYMBOL; + } } - for(auto p : control_ranges) { - for(auto i = p.first; i <= p.second; ++ i) + for (auto p : control_ranges) { + for (auto i = p.first; i <= p.second; ++ i) { codepoint_types[i] = CODEPOINT_TYPE_CONTROL; + } } return codepoint_types; } static int codepoint_type(uint32_t cp) { static std::unordered_map codepoint_types = codepoint_type_map(); - return codepoint_types[cp]; + return codepoint_types.find(cp) == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : codepoint_types.at(cp); } static int codepoint_type(const std::string & utf8) { - if (utf8.length() == 0) + if (utf8.length() == 0) { return CODEPOINT_TYPE_UNIDENTIFIED; + } size_t offset = 0; return codepoint_type(codepoint_from_utf8(utf8, offset)); } From 263978904c7472db1865409a7ff1129599f6a40b Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 13 Feb 2024 14:15:42 +0100 Subject: [PATCH 308/334] finetune : rename feed-forward tensors (w1/w2/w3) (#4839) * finetune: rename feed-forward tensors (w1/w2/w3) This commit renames the feed-forward tensors w1, w2 and w3 to ffn_gate, ffn_down and ffn_up respectively. The motivation for this change is to make it easier to understand the purpose of the tensors. This also seems to be inline with the names used in the llama_layer struct in llama.cpp. Signed-off-by: Daniel Bevenius * train-text-from-scratch: rename ff tensors This commit renames the feed-forward tensors w1, w2 and w3 to ffn_gate, ffn_down and ffn_up respectively. The motivation for this change is to make it easier to understand the purpose of the tensors. This also seems to be inline with the names used in the llama_layer struct in llama.cpp Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- examples/finetune/README.md | 6 +- examples/finetune/finetune.cpp | 242 +++++++++--------- .../train-text-from-scratch.cpp | 54 ++-- 3 files changed, 151 insertions(+), 151 deletions(-) diff --git a/examples/finetune/README.md b/examples/finetune/README.md index a884706c5..2fafd505e 100644 --- a/examples/finetune/README.md +++ b/examples/finetune/README.md @@ -80,9 +80,9 @@ The LORA rank can be configured for each model tensor type separately with these --rank-wk N LORA rank for wk tensor (default 4) --rank-wv N LORA rank for wv tensor (default 4) --rank-wo N LORA rank for wo tensor (default 4) - --rank-w1 N LORA rank for w1 tensor (default 4) - --rank-w2 N LORA rank for w2 tensor (default 4) - --rank-w3 N LORA rank for w3 tensor (default 4) + --rank-ffn_gate N LORA rank for ffn_gate tensor (default 4) + --rank-ffn_down N LORA rank for ffn_down tensor (default 4) + --rank-ffn_up N LORA rank for ffn_up tensor (default 4) ``` The LORA rank of 'norm' tensors should always be 1. diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index b11c56020..98bf5a07a 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -60,9 +60,9 @@ struct my_llama_layer { struct ggml_tensor * ffn_norm; // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; + struct ggml_tensor * ffn_gate; // w1 + struct ggml_tensor * ffn_down; // w2 + struct ggml_tensor * ffn_up; // w3 }; struct my_llama_model { @@ -85,9 +85,9 @@ struct my_llama_lora_hparams { uint32_t n_rank_wv = 4; uint32_t n_rank_wo = 4; uint32_t n_rank_ffn_norm = 1; - uint32_t n_rank_w1 = 4; - uint32_t n_rank_w2 = 4; - uint32_t n_rank_w3 = 4; + uint32_t n_rank_ffn_gate = 4; + uint32_t n_rank_ffn_down = 4; + uint32_t n_rank_ffn_up = 4; uint32_t n_rank_tok_embeddings = 4; uint32_t n_rank_norm = 1; uint32_t n_rank_output = 4; @@ -117,12 +117,12 @@ struct my_llama_lora_layer { struct ggml_tensor * ffn_norm_b; // ff - struct ggml_tensor * w1_a; - struct ggml_tensor * w1_b; - struct ggml_tensor * w2_a; - struct ggml_tensor * w2_b; - struct ggml_tensor * w3_a; - struct ggml_tensor * w3_b; + struct ggml_tensor * ffn_gate_a; + struct ggml_tensor * ffn_gate_b; + struct ggml_tensor * ffn_down_a; + struct ggml_tensor * ffn_down_b; + struct ggml_tensor * ffn_up_a; + struct ggml_tensor * ffn_up_b; }; struct my_llama_lora { @@ -208,9 +208,9 @@ static void print_lora_params(struct my_llama_lora_hparams * params) { printf("%s: n_rank_wv : %u\n", __func__, params->n_rank_wv); printf("%s: n_rank_wo : %u\n", __func__, params->n_rank_wo); printf("%s: n_rank_ffn_norm : %u\n", __func__, params->n_rank_ffn_norm); - printf("%s: n_rank_w1 : %u\n", __func__, params->n_rank_w1); - printf("%s: n_rank_w2 : %u\n", __func__, params->n_rank_w2); - printf("%s: n_rank_w3 : %u\n", __func__, params->n_rank_w3); + printf("%s: n_rank_ffn_gate : %u\n", __func__, params->n_rank_ffn_gate); + printf("%s: n_rank_ffn_down : %u\n", __func__, params->n_rank_ffn_down); + printf("%s: n_rank_ffn_up : %u\n", __func__, params->n_rank_ffn_up); printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings); printf("%s: n_rank_norm : %u\n", __func__, params->n_rank_norm); printf("%s: n_rank_output : %u\n", __func__, params->n_rank_output); @@ -319,9 +319,9 @@ static void init_model(struct llama_model * input, struct my_llama_model * model layer.wv = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i)); layer.wo = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i)); layer.ffn_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i)); - layer.w1 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i)); - layer.w2 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i)); - layer.w3 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i)); + layer.ffn_gate = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i)); + layer.ffn_down = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i)); + layer.ffn_up = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i)); assert_shape_1d(layer.attention_norm, hparams.n_embd); assert_shape_2d(layer.wq, hparams.n_embd, hparams.n_embd); @@ -329,9 +329,9 @@ static void init_model(struct llama_model * input, struct my_llama_model * model assert_shape_2d(layer.wv, hparams.n_embd, hparams.n_embd_gqa()); assert_shape_2d(layer.wo, hparams.n_embd, hparams.n_embd); assert_shape_1d(layer.ffn_norm, hparams.n_embd); - assert_shape_2d(layer.w1, hparams.n_embd, hparams.n_ff); - assert_shape_2d(layer.w2, hparams.n_ff, hparams.n_embd); - assert_shape_2d(layer.w3, hparams.n_embd, hparams.n_ff); + assert_shape_2d(layer.ffn_gate, hparams.n_embd, hparams.n_ff); + assert_shape_2d(layer.ffn_down, hparams.n_ff, hparams.n_embd); + assert_shape_2d(layer.ffn_up, hparams.n_embd, hparams.n_ff); } } @@ -362,12 +362,12 @@ static void set_param_lora(struct my_llama_lora * lora) { ggml_set_param(ctx, layer.wo_b); ggml_set_param(ctx, layer.ffn_norm_a); ggml_set_param(ctx, layer.ffn_norm_b); - ggml_set_param(ctx, layer.w1_a); - ggml_set_param(ctx, layer.w1_b); - ggml_set_param(ctx, layer.w2_a); - ggml_set_param(ctx, layer.w2_b); - ggml_set_param(ctx, layer.w3_a); - ggml_set_param(ctx, layer.w3_b); + ggml_set_param(ctx, layer.ffn_gate_a); + ggml_set_param(ctx, layer.ffn_gate_b); + ggml_set_param(ctx, layer.ffn_down_a); + ggml_set_param(ctx, layer.ffn_down_b); + ggml_set_param(ctx, layer.ffn_up_a); + ggml_set_param(ctx, layer.ffn_up_b); } } @@ -435,12 +435,12 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd); layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1); - layer.w1_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_embd); - layer.w1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_ff); - layer.w2_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_ff); - layer.w2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_embd); - layer.w3_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_embd); - layer.w3_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_ff); + layer.ffn_gate_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_embd); + layer.ffn_gate_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_ff); + layer.ffn_down_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_ff); + layer.ffn_down_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_embd); + layer.ffn_up_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up, n_embd); + layer.ffn_up_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up, n_ff); ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i)); ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i)); @@ -454,12 +454,12 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora ggml_set_name(layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_b", i)); ggml_set_name(layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_a", i)); ggml_set_name(layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_b", i)); - ggml_set_name(layer.w1_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i)); - ggml_set_name(layer.w1_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i)); - ggml_set_name(layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i)); - ggml_set_name(layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i)); - ggml_set_name(layer.w3_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i)); - ggml_set_name(layer.w3_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i)); + ggml_set_name(layer.ffn_gate_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i)); + ggml_set_name(layer.ffn_gate_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i)); + ggml_set_name(layer.ffn_down_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i)); + ggml_set_name(layer.ffn_down_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i)); + ggml_set_name(layer.ffn_up_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i)); + ggml_set_name(layer.ffn_up_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i)); } set_param_lora(lora); @@ -497,12 +497,12 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl randomize_tensor_normal(layer.ffn_norm_a, rnd); ggml_set_zero(layer.ffn_norm_b); - randomize_tensor_normal(layer.w1_a, rnd); - ggml_set_zero(layer.w1_b); - randomize_tensor_normal(layer.w2_a, rnd); - ggml_set_zero(layer.w2_b); - randomize_tensor_normal(layer.w3_a, rnd); - ggml_set_zero(layer.w3_b); + randomize_tensor_normal(layer.ffn_gate_a, rnd); + ggml_set_zero(layer.ffn_gate_b); + randomize_tensor_normal(layer.ffn_down_a, rnd); + ggml_set_zero(layer.ffn_down_b); + randomize_tensor_normal(layer.ffn_up_a, rnd); + ggml_set_zero(layer.ffn_up_b); } free_random_normal_distribution(rnd); @@ -610,13 +610,13 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b)); struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b)); - struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b)); - struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b)); - struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b)); - struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b)); - struct ggml_tensor * w1 = add_to_f32(ctx, layer.w1, ggml_mul_mat(ctx, llayer.w1_a, llayer.w1_b)); - struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b)); - struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b)); + struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b)); + struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b)); + struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b)); + struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b)); + struct ggml_tensor * ffn_gate = add_to_f32(ctx, layer.ffn_gate, ggml_mul_mat(ctx, llayer.ffn_gate_a, llayer.ffn_gate_b)); + struct ggml_tensor * ffn_down = add_to_f32(ctx, layer.ffn_down, ggml_mul_mat(ctx, llayer.ffn_down_a, llayer.ffn_down_b)); + struct ggml_tensor * ffn_up = add_to_f32(ctx, layer.ffn_up, ggml_mul_mat(ctx, llayer.ffn_up_a, llayer.ffn_up_b)); struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch); struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch); @@ -659,11 +659,11 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch); struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch); struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch); - struct ggml_tensor * t25 = ggml_mul_mat (ctx, w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); - struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); + struct ggml_tensor * t25 = ggml_mul_mat (ctx, ffn_up, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); + struct ggml_tensor * t26 = ggml_mul_mat (ctx, ffn_gate, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch); struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch); - struct ggml_tensor * t29 = ggml_mul_mat (ctx, w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); + struct ggml_tensor * t29 = ggml_mul_mat (ctx, ffn_down, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch); cur = t30; if (enable_checkpointing) { @@ -723,9 +723,9 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f)); ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f)); ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_gate, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_down, 1.0f)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_up, 1.0f)); } // allocating checkpoints in one block to reduce memory fragmentation @@ -798,9 +798,9 @@ static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V); GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT); GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_w1, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_w2, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_w3, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP); + GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_gate, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE); + GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_down, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN); + GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_up, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP); init_lora(model, lora); @@ -825,12 +825,12 @@ static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context copy_tensor_by_name(layer.wo_b, f_ggml_ctx, ggml_get_name(layer.wo_b)); copy_tensor_by_name(layer.ffn_norm_a, f_ggml_ctx, ggml_get_name(layer.ffn_norm_a)); copy_tensor_by_name(layer.ffn_norm_b, f_ggml_ctx, ggml_get_name(layer.ffn_norm_b)); - copy_tensor_by_name(layer.w1_a, f_ggml_ctx, ggml_get_name(layer.w1_a)); - copy_tensor_by_name(layer.w1_b, f_ggml_ctx, ggml_get_name(layer.w1_b)); - copy_tensor_by_name(layer.w2_a, f_ggml_ctx, ggml_get_name(layer.w2_a)); - copy_tensor_by_name(layer.w2_b, f_ggml_ctx, ggml_get_name(layer.w2_b)); - copy_tensor_by_name(layer.w3_a, f_ggml_ctx, ggml_get_name(layer.w3_a)); - copy_tensor_by_name(layer.w3_b, f_ggml_ctx, ggml_get_name(layer.w3_b)); + copy_tensor_by_name(layer.ffn_gate_a, f_ggml_ctx, ggml_get_name(layer.ffn_gate_a)); + copy_tensor_by_name(layer.ffn_gate_b, f_ggml_ctx, ggml_get_name(layer.ffn_gate_b)); + copy_tensor_by_name(layer.ffn_down_a, f_ggml_ctx, ggml_get_name(layer.ffn_down_a)); + copy_tensor_by_name(layer.ffn_down_b, f_ggml_ctx, ggml_get_name(layer.ffn_down_b)); + copy_tensor_by_name(layer.ffn_up_a, f_ggml_ctx, ggml_get_name(layer.ffn_up_a)); + copy_tensor_by_name(layer.ffn_up_b, f_ggml_ctx, ggml_get_name(layer.ffn_up_b)); } } @@ -868,9 +868,9 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V, lora->hparams.n_rank_wv); gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, lora->hparams.n_rank_wo); gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM, lora->hparams.n_rank_ffn_norm); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_w1); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_w2); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_w3); + gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_ffn_gate); + gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_ffn_down); + gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_ffn_up); gguf_add_tensor(fctx, lora->tok_embeddings_a); gguf_add_tensor(fctx, lora->tok_embeddings_b); @@ -894,12 +894,12 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod gguf_add_tensor(fctx, layer.wo_b); gguf_add_tensor(fctx, layer.ffn_norm_a); gguf_add_tensor(fctx, layer.ffn_norm_b); - gguf_add_tensor(fctx, layer.w1_a); - gguf_add_tensor(fctx, layer.w1_b); - gguf_add_tensor(fctx, layer.w2_a); - gguf_add_tensor(fctx, layer.w2_b); - gguf_add_tensor(fctx, layer.w3_a); - gguf_add_tensor(fctx, layer.w3_b); + gguf_add_tensor(fctx, layer.ffn_gate_a); + gguf_add_tensor(fctx, layer.ffn_gate_b); + gguf_add_tensor(fctx, layer.ffn_down_a); + gguf_add_tensor(fctx, layer.ffn_down_b); + gguf_add_tensor(fctx, layer.ffn_up_a); + gguf_add_tensor(fctx, layer.ffn_up_b); } } @@ -1104,12 +1104,12 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor write_tensor(&file, layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraB")); write_tensor(&file, layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraA")); write_tensor(&file, layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraB")); - write_tensor(&file, layer.w1_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA")); - write_tensor(&file, layer.w1_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB")); - write_tensor(&file, layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA")); - write_tensor(&file, layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB")); - write_tensor(&file, layer.w3_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA")); - write_tensor(&file, layer.w3_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB")); + write_tensor(&file, layer.ffn_gate_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA")); + write_tensor(&file, layer.ffn_gate_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB")); + write_tensor(&file, layer.ffn_down_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA")); + write_tensor(&file, layer.ffn_down_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB")); + write_tensor(&file, layer.ffn_up_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA")); + write_tensor(&file, layer.ffn_up_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB")); } } @@ -1139,9 +1139,9 @@ struct train_params { uint32_t n_rank_wv; uint32_t n_rank_wo; uint32_t n_rank_ffn_norm; - uint32_t n_rank_w1; - uint32_t n_rank_w2; - uint32_t n_rank_w3; + uint32_t n_rank_ffn_gate; + uint32_t n_rank_ffn_down; + uint32_t n_rank_ffn_up; uint32_t n_rank_tok_embeddings; uint32_t n_rank_norm; uint32_t n_rank_output; @@ -1152,9 +1152,9 @@ struct train_params { bool custom_n_rank_wv; bool custom_n_rank_wo; bool custom_n_rank_ffn_norm; - bool custom_n_rank_w1; - bool custom_n_rank_w2; - bool custom_n_rank_w3; + bool custom_n_rank_ffn_gate; + bool custom_n_rank_ffn_down; + bool custom_n_rank_ffn_up; bool custom_n_rank_tok_embeddings; bool custom_n_rank_norm; bool custom_n_rank_output; @@ -1186,9 +1186,9 @@ static struct train_params get_default_train_params() { params.n_rank_wv = 4; params.n_rank_wo = 4; params.n_rank_ffn_norm = 1; - params.n_rank_w1 = 4; - params.n_rank_w2 = 4; - params.n_rank_w3 = 4; + params.n_rank_ffn_gate = 4; + params.n_rank_ffn_down = 4; + params.n_rank_ffn_up = 4; params.n_rank_tok_embeddings = 4; params.n_rank_norm = 1; params.n_rank_output = 4; @@ -1199,9 +1199,9 @@ static struct train_params get_default_train_params() { params.custom_n_rank_wv = false; params.custom_n_rank_wo = false; params.custom_n_rank_ffn_norm = false; - params.custom_n_rank_w1 = false; - params.custom_n_rank_w2 = false; - params.custom_n_rank_w3 = false; + params.custom_n_rank_ffn_gate = false; + params.custom_n_rank_ffn_down = false; + params.custom_n_rank_ffn_up = false; params.custom_n_rank_tok_embeddings = false; params.custom_n_rank_norm = false; params.custom_n_rank_output = false; @@ -1232,9 +1232,9 @@ static void train_print_usage(int argc, char ** argv, const struct train_params fprintf(stderr, " --rank-wk N LORA rank for wk tensor, overrides default rank.\n"); fprintf(stderr, " --rank-wv N LORA rank for wv tensor, overrides default rank.\n"); fprintf(stderr, " --rank-wo N LORA rank for wo tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-w1 N LORA rank for w1 tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-w2 N LORA rank for w2 tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-w3 N LORA rank for w3 tensor, overrides default rank.\n"); + fprintf(stderr, " --rank-ffn_gate N LORA rank for ffn_gate tensor, overrides default rank.\n"); + fprintf(stderr, " --rank-ffn_down N LORA rank for ffn_down tensor, overrides default rank.\n"); + fprintf(stderr, " --rank-ffn_up N LORA rank for ffn_up tensor, overrides default rank.\n"); print_common_train_usage(argc, argv, ¶ms->common); } @@ -1369,27 +1369,27 @@ static bool train_params_parse(int argc, char ** argv, struct train_params * par } params->n_rank_wo = std::stoi(argv[i]); params->custom_n_rank_wo = true; - } else if (arg == "--rank-w1") { + } else if (arg == "--rank-ffn_gate") { if (++i >= argc) { invalid_param = true; break; } - params->n_rank_w1 = std::stoi(argv[i]); - params->custom_n_rank_w1 = true; - } else if (arg == "--rank-w2") { + params->n_rank_ffn_gate = std::stoi(argv[i]); + params->custom_n_rank_ffn_gate = true; + } else if (arg == "--rank-ffn_down") { if (++i >= argc) { invalid_param = true; break; } - params->n_rank_w2 = std::stoi(argv[i]); - params->custom_n_rank_w2 = true; - } else if (arg == "--rank-w3") { + params->n_rank_ffn_down = std::stoi(argv[i]); + params->custom_n_rank_ffn_down = true; + } else if (arg == "--rank-ffn_up") { if (++i >= argc) { invalid_param = true; break; } - params->n_rank_w3 = std::stoi(argv[i]); - params->custom_n_rank_w3 = true; + params->n_rank_ffn_up = std::stoi(argv[i]); + params->custom_n_rank_ffn_up = true; } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); train_print_usage(argc, argv, &default_params); @@ -1452,12 +1452,12 @@ static int64_t get_parameter_count(struct my_llama_lora* lora) { nx += ggml_nelements(layer.wo_b); nx += ggml_nelements(layer.ffn_norm_a); nx += ggml_nelements(layer.ffn_norm_b); - nx += ggml_nelements(layer.w1_a); - nx += ggml_nelements(layer.w1_b); - nx += ggml_nelements(layer.w2_a); - nx += ggml_nelements(layer.w2_b); - nx += ggml_nelements(layer.w3_a); - nx += ggml_nelements(layer.w3_b); + nx += ggml_nelements(layer.ffn_gate_a); + nx += ggml_nelements(layer.ffn_gate_b); + nx += ggml_nelements(layer.ffn_down_a); + nx += ggml_nelements(layer.ffn_down_b); + nx += ggml_nelements(layer.ffn_up_a); + nx += ggml_nelements(layer.ffn_up_b); } return nx; } @@ -1511,9 +1511,9 @@ int main(int argc, char ** argv) { uint32_t n_rank_wv = params.custom_n_rank_wv ? params.n_rank_wv : params.lora_r; uint32_t n_rank_wo = params.custom_n_rank_wo ? params.n_rank_wo : params.lora_r; uint32_t n_rank_ffn_norm = params.custom_n_rank_ffn_norm ? params.n_rank_ffn_norm : 1; - uint32_t n_rank_w1 = params.custom_n_rank_w1 ? params.n_rank_w1 : params.lora_r; - uint32_t n_rank_w2 = params.custom_n_rank_w2 ? params.n_rank_w2 : params.lora_r; - uint32_t n_rank_w3 = params.custom_n_rank_w3 ? params.n_rank_w3 : params.lora_r; + uint32_t n_rank_ffn_gate = params.custom_n_rank_ffn_gate ? params.n_rank_ffn_gate : params.lora_r; + uint32_t n_rank_ffn_down = params.custom_n_rank_ffn_down ? params.n_rank_ffn_down : params.lora_r; + uint32_t n_rank_ffn_up = params.custom_n_rank_ffn_up ? params.n_rank_ffn_up : params.lora_r; uint32_t n_rank_tok_embeddings = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r; uint32_t n_rank_norm = params.custom_n_rank_norm ? params.n_rank_norm : 1; uint32_t n_rank_output = params.custom_n_rank_output ? params.n_rank_output : params.lora_r; @@ -1523,9 +1523,9 @@ int main(int argc, char ** argv) { lora.hparams.n_rank_wv = n_rank_wv; lora.hparams.n_rank_wo = n_rank_wo; lora.hparams.n_rank_ffn_norm = n_rank_ffn_norm; - lora.hparams.n_rank_w1 = n_rank_w1; - lora.hparams.n_rank_w2 = n_rank_w2; - lora.hparams.n_rank_w3 = n_rank_w3; + lora.hparams.n_rank_ffn_gate = n_rank_ffn_gate; + lora.hparams.n_rank_ffn_down = n_rank_ffn_down; + lora.hparams.n_rank_ffn_up = n_rank_ffn_up; lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings; lora.hparams.n_rank_norm = n_rank_norm; lora.hparams.n_rank_output = n_rank_output; @@ -1566,9 +1566,9 @@ int main(int argc, char ** argv) { || (lora.hparams.n_rank_wv != n_rank_wv) || (lora.hparams.n_rank_wo != n_rank_wo) || (lora.hparams.n_rank_ffn_norm != n_rank_ffn_norm) - || (lora.hparams.n_rank_w1 != n_rank_w1) - || (lora.hparams.n_rank_w2 != n_rank_w2) - || (lora.hparams.n_rank_w3 != n_rank_w3) + || (lora.hparams.n_rank_ffn_gate != n_rank_ffn_gate) + || (lora.hparams.n_rank_ffn_down != n_rank_ffn_down) + || (lora.hparams.n_rank_ffn_up != n_rank_ffn_up) || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings) || (lora.hparams.n_rank_norm != n_rank_norm) || (lora.hparams.n_rank_output != n_rank_output) diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 2e2a8ce08..bfdf124d7 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -50,9 +50,9 @@ struct my_llama_layer { struct ggml_tensor * ffn_norm; // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; + struct ggml_tensor * ffn_gate; // w1 + struct ggml_tensor * ffn_down; // w2 + struct ggml_tensor * ffn_up; // w3 }; struct my_llama_model { @@ -140,9 +140,9 @@ static void set_param_model(struct my_llama_model * model) { ggml_set_param(ctx, layer.wv); ggml_set_param(ctx, layer.wo); ggml_set_param(ctx, layer.ffn_norm); - ggml_set_param(ctx, layer.w1); - ggml_set_param(ctx, layer.w2); - ggml_set_param(ctx, layer.w3); + ggml_set_param(ctx, layer.ffn_gate); + ggml_set_param(ctx, layer.ffn_down); + ggml_set_param(ctx, layer.ffn_up); } } @@ -198,9 +198,9 @@ static void init_model(struct my_llama_model * model) { layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); - layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); - layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); + layer.ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); + layer.ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); + layer.ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i)); @@ -211,9 +211,9 @@ static void init_model(struct my_llama_model * model) { ggml_set_name(layer.ffn_norm, tni(LLM_TENSOR_FFN_NORM, i)); - ggml_set_name(layer.w1, tni(LLM_TENSOR_FFN_GATE, i)); - ggml_set_name(layer.w2, tni(LLM_TENSOR_FFN_DOWN, i)); - ggml_set_name(layer.w3, tni(LLM_TENSOR_FFN_UP, i)); + ggml_set_name(layer.ffn_gate, tni(LLM_TENSOR_FFN_GATE, i)); + ggml_set_name(layer.ffn_down, tni(LLM_TENSOR_FFN_DOWN, i)); + ggml_set_name(layer.ffn_up, tni(LLM_TENSOR_FFN_UP, i)); } set_param_model(model); @@ -244,9 +244,9 @@ static void randomize_model(struct my_llama_model * model, int seed, float mean, randomize_tensor_normal(layer.ffn_norm, rnd); - randomize_tensor_normal(layer.w1, rnd); - randomize_tensor_normal(layer.w2, rnd); - randomize_tensor_normal(layer.w3, rnd); + randomize_tensor_normal(layer.ffn_gate, rnd); + randomize_tensor_normal(layer.ffn_down, rnd); + randomize_tensor_normal(layer.ffn_up, rnd); } free_random_normal_distribution(rnd); @@ -356,11 +356,11 @@ static struct ggml_tensor * llama_build_train_graphs( struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, f_norm_rms_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch); struct ggml_tensor * t23 = ggml_repeat (ctx, layer.ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch); struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch); - struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); - struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); + struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.ffn_up, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); + struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.ffn_gate, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch); struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch); - struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); + struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.ffn_down, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch); cur = t30; checkpoints.push_back(cur); @@ -521,9 +521,9 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex copy_tensor_by_name(layer.wv, f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i)); copy_tensor_by_name(layer.wo, f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i)); copy_tensor_by_name(layer.ffn_norm, f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i)); - copy_tensor_by_name(layer.w1, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i)); - copy_tensor_by_name(layer.w2, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i)); - copy_tensor_by_name(layer.w3, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i)); + copy_tensor_by_name(layer.ffn_gate, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i)); + copy_tensor_by_name(layer.ffn_down, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i)); + copy_tensor_by_name(layer.ffn_up, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i)); } } @@ -664,9 +664,9 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo gguf_add_tensor(fctx, layer.wv); gguf_add_tensor(fctx, layer.wo); gguf_add_tensor(fctx, layer.ffn_norm); - gguf_add_tensor(fctx, layer.w1); - gguf_add_tensor(fctx, layer.w2); - gguf_add_tensor(fctx, layer.w3); + gguf_add_tensor(fctx, layer.ffn_gate); + gguf_add_tensor(fctx, layer.ffn_down); + gguf_add_tensor(fctx, layer.ffn_up); } } @@ -915,9 +915,9 @@ static int64_t get_parameter_count(struct my_llama_model* model) { nx += ggml_nelements(layer.wv); nx += ggml_nelements(layer.wo); nx += ggml_nelements(layer.ffn_norm); - nx += ggml_nelements(layer.w1); - nx += ggml_nelements(layer.w2); - nx += ggml_nelements(layer.w3); + nx += ggml_nelements(layer.ffn_gate); + nx += ggml_nelements(layer.ffn_down); + nx += ggml_nelements(layer.ffn_up); } return nx; } From 037259be689353081e7bae3c1ab4ab18e7fbe8c9 Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Tue, 13 Feb 2024 15:24:50 +0200 Subject: [PATCH 309/334] llama : make load error reporting more granular (#5477) Makes it easier to pinpoint where e.g. `unordered_map::at: key not found` comes from. --- llama.cpp | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 381a03068..61c695187 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4384,9 +4384,21 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam model.hparams.vocab_only = params.vocab_only; - llm_load_arch (ml, model); - llm_load_hparams(ml, model); - llm_load_vocab (ml, model); + try { + llm_load_arch(ml, model); + } catch(const std::exception & e) { + throw std::runtime_error("error loading model architecture: " + std::string(e.what())); + } + try { + llm_load_hparams(ml, model); + } catch(const std::exception & e) { + throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what())); + } + try { + llm_load_vocab(ml, model); + } catch(const std::exception & e) { + throw std::runtime_error("error loading model vocabulary: " + std::string(e.what())); + } llm_load_print_meta(ml, model); From c4e6dd59e45ef7b14f7763fb073b517395dc176c Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Tue, 13 Feb 2024 18:18:16 +0200 Subject: [PATCH 310/334] llama : allow raw byte in SPM vocabs; don't crash on nl 404 (#5478) * common : don't crash if newline token is not found * common : llama_byte_to_token: allow falling back to finding just the token byte in SPM vocabs --- llama.cpp | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 61c695187..8ebbf7628 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3314,7 +3314,12 @@ static void llm_load_vocab( // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { - vocab.linefeed_id = llama_byte_to_token(vocab, '\n'); + try { + vocab.linefeed_id = llama_byte_to_token(vocab, '\n'); + } catch (const std::exception & e) { + LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what()); + vocab.linefeed_id = vocab.special_pad_id; + } } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) { vocab.linefeed_id = vocab.special_pad_id; } else { @@ -7746,7 +7751,13 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { switch (llama_vocab_get_type(vocab)) { case LLAMA_VOCAB_TYPE_SPM: { const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 }; - return vocab.token_to_id.at(buf); + auto token = vocab.token_to_id.find(buf); + if (token != vocab.token_to_id.end()) { + return (*token).second; + } + // Try to fall back to just the byte as a string + const char buf2[2] = { (char)ch, 0 }; + return vocab.token_to_id.at(buf2); } case LLAMA_VOCAB_TYPE_WPM: case LLAMA_VOCAB_TYPE_BPE: { From ea9c8e11436ad50719987fa23a289c74b7b40d40 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 13 Feb 2024 12:03:53 -0500 Subject: [PATCH 311/334] llama : add support for Nomic Embed (#5468) --- convert-hf-to-gguf.py | 117 ++++++++++++------- gguf-py/gguf/constants.py | 56 +++++---- gguf-py/gguf/tensor_mapping.py | 12 +- llama.cpp | 201 ++++++++++++++++++++++++--------- 4 files changed, 273 insertions(+), 113 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5adfdc143..ae471481d 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -10,7 +10,7 @@ import re import sys from enum import IntEnum from pathlib import Path -from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast +from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast import numpy as np import torch @@ -25,15 +25,6 @@ import gguf from convert import HfVocab -# check for any of the given keys in the dictionary and return the value of the first key found -def get_key_opts(d, keys): - for k in keys: - if k in d: - return d[k] - print(f"Could not find any of {keys}") - sys.exit() - - ###### MODEL DEFINITIONS ###### class SentencePieceTokenTypes(IntEnum): @@ -58,6 +49,15 @@ class Model: self.hparams = Model.load_hparams(self.dir_model) self.model_arch = self._get_model_architecture() self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) + self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) + + def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any: + key = next((k for k in keys if k in self.hparams), None) + if key is not None: + return self.hparams[key] + if optional: + return None + raise KeyError(f"could not find any of: {keys}") def set_vocab(self): self._set_vocab_gpt2() @@ -79,28 +79,33 @@ class Model: def set_gguf_parameters(self): self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_block_count(self.hparams.get( - "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")), - )) - if (n_ctx := self.hparams.get("max_position_embeddings")) is not None: + self.gguf_writer.add_block_count(self.block_count) + + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) - if (n_embd := self.hparams.get("hidden_size")) is not None: - self.gguf_writer.add_embedding_length(n_embd) - if (n_ff := self.hparams.get("intermediate_size")) is not None: + + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + self.gguf_writer.add_embedding_length(n_embd) + + if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) - if (n_head := self.hparams.get("num_attention_heads")) is not None: - self.gguf_writer.add_head_count(n_head) + + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + self.gguf_writer.add_head_count(n_head) + if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) - if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None: - self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps) + if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: + self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None: + self.gguf_writer.add_layer_norm_eps(f_norm_eps) if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: self.gguf_writer.add_expert_used_count(n_experts_used) - self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + self.gguf_writer.add_file_type(self.ftype) def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) @@ -211,6 +216,8 @@ class Model: return MiniCPMModel if model_architecture == "BertModel": return BertModel + if model_architecture == "NomicBertModel": + return NomicBertModel return Model def _is_model_safetensors(self) -> bool: @@ -268,6 +275,8 @@ class Model: return gguf.MODEL_ARCH.MINICPM if arch == "BertModel": return gguf.MODEL_ARCH.BERT + if arch == "NomicBertModel": + return gguf.MODEL_ARCH.NOMIC_BERT raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1297,21 +1306,21 @@ class GPT2Model(Model): class Phi2Model(Model): def set_gguf_parameters(self): - block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"]) + block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) - rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"]) - n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"]) - n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"]) + rot_pct = self.find_hparam(["partial_rotary_factor"]) + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) self.gguf_writer.add_name("Phi2") - self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"])) + self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) self.gguf_writer.add_embedding_length(n_embd) self.gguf_writer.add_feed_forward_length(4 * n_embd) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head) - self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"])) + self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"])) self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_add_bos_token(False) @@ -1636,20 +1645,12 @@ in chat mode so that the conversation can end normally.") class BertModel(Model): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.block_count = self.hparams["num_hidden_layers"] + self.vocab_size = None def set_gguf_parameters(self): - # TODO(cebtenzzre): merge with parent class - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + super().set_gguf_parameters() self.gguf_writer.add_causal_attention(False) self.gguf_writer.add_pooling_layer(True) - self.gguf_writer.add_file_type(self.ftype) def set_vocab(self): path = self.dir_model @@ -1659,6 +1660,7 @@ class BertModel(Model): vocab = HfVocab(path, added_tokens_path) tokens, scores, toktypes = zip(*vocab.all_tokens()) assert len(tokens) == vocab.vocab_size + self.vocab_size = vocab.vocab_size # we need this to validate the size of the token_type embeddings # though currently we are passing all zeros to the token_type embeddings @@ -1672,7 +1674,7 @@ class BertModel(Model): if tok.startswith(b"##"): return tok[2:] return b"\xe2\x96\x81" + tok - tokens = [phantom(t, y) for t, y in zip(tokens, toktypes)] + tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes)) # set up bos and eos tokens (cls and sep) self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id) @@ -1724,6 +1726,43 @@ class BertModel(Model): self.gguf_writer.add_tensor(new_name, data) +class NomicBertModel(BertModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # the HF config claims n_ctx=8192, but it uses RoPE scaling + self.hparams["n_ctx"] = 2048 + + # SwigLU activation + assert self.hparams["activation_function"] == "swiglu" + # this doesn't do anything in the HF version + assert self.hparams["causal"] is False + # no bias tensors + assert self.hparams["qkv_proj_bias"] is False + assert self.hparams["mlp_fc1_bias"] is False + assert self.hparams["mlp_fc2_bias"] is False + # norm at end of layer + assert self.hparams["prenorm"] is False + # standard RoPE + assert self.hparams["rotary_emb_fraction"] == 1.0 + assert self.hparams["rotary_emb_interleaved"] is False + assert self.hparams["rotary_emb_scale_base"] is None + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) + + def get_tensors(self): + assert self.vocab_size is not None + for name, data in super().get_tensors(): + # Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly. + if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size: + rounded_vocab_size = (self.vocab_size + 63) // 64 * 64 + assert data.shape == (rounded_vocab_size, self.hparams["n_embd"]) + data = data[:self.vocab_size, :] + yield name, data + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 644e1589c..5fba01714 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -87,27 +87,28 @@ class Keys: class MODEL_ARCH(IntEnum): - LLAMA = auto() - FALCON = auto() - BAICHUAN = auto() - GPT2 = auto() - GPTJ = auto() - GPTNEOX = auto() - MPT = auto() - STARCODER = auto() - PERSIMMON = auto() - REFACT = auto() - BERT = auto() - BLOOM = auto() - STABLELM = auto() - QWEN = auto() - QWEN2 = auto() - PHI2 = auto() - PLAMO = auto() - CODESHELL = auto() - ORION = auto() + LLAMA = auto() + FALCON = auto() + BAICHUAN = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + PERSIMMON = auto() + REFACT = auto() + BERT = auto() + NOMIC_BERT = auto() + BLOOM = auto() + STABLELM = auto() + QWEN = auto() + QWEN2 = auto() + PHI2 = auto() + PLAMO = auto() + CODESHELL = auto() + ORION = auto() INTERNLM2 = auto() - MINICPM = auto() + MINICPM = auto() class MODEL_TENSOR(IntEnum): @@ -153,6 +154,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.PERSIMMON: "persimmon", MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", + MODEL_ARCH.NOMIC_BERT: "nomic-bert", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", @@ -282,6 +284,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, ], + MODEL_ARCH.NOMIC_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], MODEL_ARCH.MPT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index c7ba1420e..861003776 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -15,7 +15,7 @@ class TensorNameMap: "word_embeddings", # bloom "model.embed_tokens", # llama-hf "tok_embeddings", # llama-pth - "embeddings.word_embeddings", # bert + "embeddings.word_embeddings", # bert nomic-bert "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 @@ -24,13 +24,14 @@ class TensorNameMap: # Token type embeddings MODEL_TENSOR.TOKEN_TYPES: ( - "embeddings.token_type_embeddings", # bert + "embeddings.token_type_embeddings", # bert nomic-bert ), # Normalization of token embeddings MODEL_TENSOR.TOKEN_EMBD_NORM: ( "word_embeddings_layernorm", # bloom "embeddings.LayerNorm", # bert + "emb_ln", # nomic-bert ), # Position embeddings @@ -103,6 +104,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.query_key_value", # persimmon "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 + "encoder.layers.{bid}.attn.Wqkv", # nomic-bert ), # Attention query @@ -152,11 +154,13 @@ class TensorNameMap: "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo "model.layers.{bid}.attention.wo", # internlm2 + "encoder.layers.{bid}.attn.out_proj", # nomic-bert ), # Attention output norm MODEL_TENSOR.ATTN_OUT_NORM: ( "encoder.layer.{bid}.attention.output.LayerNorm", # bert + "encoder.layers.{bid}.norm1", # nomic-bert ), # Rotary embeddings @@ -205,6 +209,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 + "encoder.layers.{bid}.mlp.fc11", # nomic-bert ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -224,6 +229,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.w2", # qwen "model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.{bid}.feed_forward.w1", # internlm2 + "encoder.layers.{bid}.mlp.fc12", # nomic-bert ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -249,6 +255,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 + "encoder.layers.{bid}.mlp.fc2", # nomic-bert ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -272,6 +279,7 @@ class TensorNameMap: MODEL_TENSOR.LAYER_OUT_NORM: ( "encoder.layer.{bid}.output.LayerNorm", # bert + "encoder.layers.{bid}.norm2", # nomic-bert ) } diff --git a/llama.cpp b/llama.cpp index 8ebbf7628..14e8821cd 100644 --- a/llama.cpp +++ b/llama.cpp @@ -197,6 +197,7 @@ enum llm_arch { LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, LLM_ARCH_BERT, + LLM_ARCH_NOMIC_BERT, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, @@ -211,27 +212,28 @@ enum llm_arch { }; static std::map LLM_ARCH_NAMES = { - { LLM_ARCH_LLAMA, "llama" }, - { LLM_ARCH_FALCON, "falcon" }, - { LLM_ARCH_GPT2, "gpt2" }, - { LLM_ARCH_GPTJ, "gptj" }, - { LLM_ARCH_GPTNEOX, "gptneox" }, - { LLM_ARCH_MPT, "mpt" }, - { LLM_ARCH_BAICHUAN, "baichuan" }, - { LLM_ARCH_STARCODER, "starcoder" }, - { LLM_ARCH_PERSIMMON, "persimmon" }, - { LLM_ARCH_REFACT, "refact" }, - { LLM_ARCH_BERT, "bert" }, - { LLM_ARCH_BLOOM, "bloom" }, - { LLM_ARCH_STABLELM, "stablelm" }, - { LLM_ARCH_QWEN, "qwen" }, - { LLM_ARCH_QWEN2, "qwen2" }, - { LLM_ARCH_PHI2, "phi2" }, - { LLM_ARCH_PLAMO, "plamo" }, - { LLM_ARCH_CODESHELL, "codeshell" }, - { LLM_ARCH_ORION, "orion" }, - { LLM_ARCH_INTERNLM2, "internlm2" }, - { LLM_ARCH_MINICPM, "minicpm" }, + { LLM_ARCH_LLAMA, "llama" }, + { LLM_ARCH_FALCON, "falcon" }, + { LLM_ARCH_GPT2, "gpt2" }, + { LLM_ARCH_GPTJ, "gptj" }, + { LLM_ARCH_GPTNEOX, "gptneox" }, + { LLM_ARCH_MPT, "mpt" }, + { LLM_ARCH_BAICHUAN, "baichuan" }, + { LLM_ARCH_STARCODER, "starcoder" }, + { LLM_ARCH_PERSIMMON, "persimmon" }, + { LLM_ARCH_REFACT, "refact" }, + { LLM_ARCH_BERT, "bert" }, + { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, + { LLM_ARCH_BLOOM, "bloom" }, + { LLM_ARCH_STABLELM, "stablelm" }, + { LLM_ARCH_QWEN, "qwen" }, + { LLM_ARCH_QWEN2, "qwen2" }, + { LLM_ARCH_PHI2, "phi2" }, + { LLM_ARCH_PLAMO, "plamo" }, + { LLM_ARCH_CODESHELL, "codeshell" }, + { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, }; enum llm_kv { @@ -375,6 +377,7 @@ enum llm_tensor { LLM_TENSOR_ATTN_OUT, LLM_TENSOR_ATTN_NORM, LLM_TENSOR_ATTN_NORM_2, + LLM_TENSOR_ATTN_OUT_NORM, LLM_TENSOR_ATTN_ROT_EMBD, LLM_TENSOR_FFN_GATE_INP, LLM_TENSOR_FFN_NORM, @@ -387,6 +390,7 @@ enum llm_tensor { LLM_TENSOR_FFN_UP_EXP, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, + LLM_TENSOR_LAYER_OUT_NORM, }; static std::map> LLM_TENSOR_NAMES = { @@ -552,12 +556,27 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, { LLM_TENSOR_TOKEN_TYPES, "token_types" }, { LLM_TENSOR_POS_EMBD, "position_embd" }, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, - { LLM_TENSOR_FFN_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, + { + LLM_ARCH_NOMIC_BERT, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, + { LLM_TENSOR_TOKEN_TYPES, "token_types" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, @@ -1485,6 +1504,7 @@ enum e_model { MODEL_22M, MODEL_33M, MODEL_109M, + MODEL_137M, MODEL_335M, MODEL_0_5B, MODEL_1B, @@ -1620,6 +1640,8 @@ struct llama_layer { struct ggml_tensor * attn_q_norm_b; struct ggml_tensor * attn_k_norm; struct ggml_tensor * attn_k_norm_b; + struct ggml_tensor * attn_out_norm; + struct ggml_tensor * attn_out_norm_b; // attention struct ggml_tensor * wq; @@ -1638,6 +1660,8 @@ struct llama_layer { // normalization struct ggml_tensor * ffn_norm; struct ggml_tensor * ffn_norm_b; + struct ggml_tensor * layer_out_norm; + struct ggml_tensor * layer_out_norm_b; // ff struct ggml_tensor * ffn_gate; // w1 @@ -2855,6 +2879,11 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { + case MODEL_22M: return "22M"; + case MODEL_33M: return "33M"; + case MODEL_109M: return "109M"; + case MODEL_137M: return "137M"; + case MODEL_0_5B: return "0.5B"; case MODEL_1B: return "1B"; case MODEL_2B: return "2B"; case MODEL_3B: return "3B"; @@ -3073,6 +3102,17 @@ static void llm_load_hparams( model.type = e_model::MODEL_335M; break; // bge-large } } break; + case LLM_ARCH_NOMIC_BERT: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); + ml.get_key(LLM_KV_POOLING_LAYER, hparams.pooling_layer); + + if (hparams.n_layer == 12 && hparams.n_embd == 768) { + model.type = e_model::MODEL_137M; + } + } break; case LLM_ARCH_BLOOM: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -3875,10 +3915,14 @@ static bool llm_load_tensors( } } break; case LLM_ARCH_BERT: + case LLM_ARCH_NOMIC_BERT: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); + if (model.arch == LLM_ARCH_BERT) { + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); + } + model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); @@ -3888,29 +3932,38 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + if (model.arch == LLM_ARCH_BERT) { + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + } else { + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + } - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + if (model.arch == LLM_ARCH_BERT) { + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + } else { + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + } + + layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); } } break; case LLM_ARCH_BLOOM: @@ -5773,6 +5826,7 @@ struct llm_build_context { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); struct ggml_tensor * cur; @@ -5789,7 +5843,9 @@ struct llm_build_context { // token types are hardcoded to zero ("Sentence A") struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); inpL = ggml_add(ctx0, inpL, type_row0); - inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL); + if (model.arch == LLM_ARCH_BERT) { + inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL); + } cb(inpL, "inp_embd", -1); // embed layer norm @@ -5805,7 +5861,7 @@ struct llm_build_context { struct ggml_tensor * cur = inpL; // self-attention - { + if (model.arch == LLM_ARCH_BERT) { struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); cb(Qcur, "Qcur", il); @@ -5818,6 +5874,37 @@ struct llm_build_context { // seems like we just need to do this for Q? Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } else { + // compute Q and K and RoPE them + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); @@ -5828,25 +5915,34 @@ struct llm_build_context { cur = ggml_add(ctx0, cur, inpL); // attention layer norm - cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, cb, il); + cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il); struct ggml_tensor * ffn_inp = cur; cb(ffn_inp, "ffn_inp", il); // feed-forward network - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - NULL, - LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + if (model.arch == LLM_ARCH_BERT) { + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + } else { + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + } cb(cur, "ffn_out", il); // attentions bypass the intermediate layer cur = ggml_add(ctx0, cur, ffn_inp); // output layer norm - cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, cb, il); + cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il); // input for next layer inpL = cur; @@ -7289,6 +7385,7 @@ static struct ggml_cgraph * llama_build_graph( result = llm.build_refact(); } break; case LLM_ARCH_BERT: + case LLM_ARCH_NOMIC_BERT: { result = llm.build_bert(); } break; From 6c00a066928b0475b865a2e3e709e2166e02d548 Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Tue, 13 Feb 2024 18:56:38 +0100 Subject: [PATCH 312/334] gguf : add python reader example (#5216) * Update CMakeLists.txt * Create reader.py * Update reader.py * Update reader.py another whitespace :| * Update reader.py * lintlintlint --- examples/CMakeLists.txt | 1 + gguf-py/examples/reader.py | 45 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+) create mode 100644 gguf-py/examples/reader.py diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 68ad89964..653abc73a 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -38,6 +38,7 @@ else() add_subdirectory(speculative) add_subdirectory(lookahead) add_subdirectory(lookup) + add_subdirectory(gguf) add_subdirectory(train-text-from-scratch) add_subdirectory(imatrix) if (LLAMA_BUILD_SERVER) diff --git a/gguf-py/examples/reader.py b/gguf-py/examples/reader.py new file mode 100644 index 000000000..62e0769da --- /dev/null +++ b/gguf-py/examples/reader.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 +import sys +from pathlib import Path +from gguf.gguf_reader import GGUFReader + + +sys.path.insert(0, str(Path(__file__).parent.parent)) + + +def read_gguf_file(gguf_file_path): + """ + Reads and prints key-value pairs and tensor information from a GGUF file in an improved format. + + Parameters: + - gguf_file_path: Path to the GGUF file. + """ + + reader = GGUFReader(gguf_file_path) + + # List all key-value pairs in a columnized format + print("Key-Value Pairs:") + max_key_length = max(len(key) for key in reader.fields.keys()) + for key, field in reader.fields.items(): + value = field.parts[field.data[0]] + print(f"{key:{max_key_length}} : {value}") + print("----") + + # List all tensors + print("Tensors:") + tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}" + print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization")) + print("-" * 80) + for tensor in reader.tensors: + shape_str = "x".join(map(str, tensor.shape)) + size_str = str(tensor.n_elements) + quantization_str = tensor.tensor_type.name + print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str)) + + +if __name__ == '__main__': + if len(sys.argv) < 2: + print("Usage: reader.py ") + sys.exit(1) + gguf_file_path = sys.argv[1] + read_gguf_file(gguf_file_path) From f5ca054855dea83f424003162f26de376e5643f6 Mon Sep 17 00:00:00 2001 From: AT Date: Tue, 13 Feb 2024 15:44:25 -0600 Subject: [PATCH 313/334] Early return for zero size calls to get_tensor. (#5482) * Early return for zero size calls to get_tensor. Signed-off-by: Adam Treat * Update ggml-kompute.cpp Co-authored-by: Georgi Gerganov * Update ggml-kompute.cpp Co-authored-by: Georgi Gerganov * Add an early return to the get/set tensor when the size is null. Signed-off-by: Adam Treat * Early return after the assertions. Signed-off-by: Adam Treat * Since we do the early return in the generic backend now no reason to do so here as well. Signed-off-by: Adam Treat --------- Signed-off-by: Adam Treat Co-authored-by: Georgi Gerganov --- ggml-backend.c | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/ggml-backend.c b/ggml-backend.c index 9ee81b766..87eea8440 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -219,6 +219,10 @@ GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); + if (!size) { + return; + } + tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size); } @@ -229,6 +233,10 @@ GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); + if (!size) { + return; + } + tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size); } From aa2341298924ac89778252015efcb792f2df1e20 Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Wed, 14 Feb 2024 08:38:35 +0100 Subject: [PATCH 314/334] llava : support v1.6 (#5267) * Create llava-survery-v2.py * Update convert-image-encoder-to-gguf.py * Update convert-image-encoder-to-gguf.py * Rename llava-survery-v2.py to llava-surgery-v2.py * Update convert-image-encoder-to-gguf.py will now search for projector * Update convert-image-encoder-to-gguf.py whoops * Update llava-surgery-v2.py * Clip: Bugfix for normalization (it did not loat the 3 std and mean values) Clip: bicubic resize function Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6) Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final convert-image-encoder: fixed image-grid flattening * whitespace corrections * ws * Tensors are now properly permuted. Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference. * ws * added verbose_prompt support into cli added stopwords for llava-1.6 into cli * moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed * ws * convert : skip unknown tensors (need for LLaVA) * llava : update readme * llava : fix compile warnings * llava : style * convert : add --skip-unknown CLI arg * server : remove clip structs * bugfix for non llava-1.6 It should now work with llava-1.5 as well * clip : minor code rearrange * llava : update readme a bit --------- Co-authored-by: John Co-authored-by: Georgi Gerganov --- convert.py | 37 +- examples/llava/README.md | 12 +- examples/llava/clip.cpp | 766 +++++++++++++++--- examples/llava/clip.h | 47 +- .../llava/convert-image-encoder-to-gguf.py | 66 +- examples/llava/llava-cli.cpp | 26 +- examples/llava/llava-surgery-v2.py | 167 ++++ examples/llava/llava.cpp | 296 ++++++- examples/llava/llava.h | 2 - examples/server/server.cpp | 15 +- 10 files changed, 1229 insertions(+), 205 deletions(-) create mode 100644 examples/llava/llava-surgery-v2.py diff --git a/convert.py b/convert.py index 323e8058d..63a0a5d78 100755 --- a/convert.py +++ b/convert.py @@ -1173,7 +1173,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM for (name, tensor) in model.items()} -def convert_model_names(model: LazyModel, params: Params) -> LazyModel: +def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel: tmap = gguf.TensorNameMap(ARCH, params.n_layer) should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) @@ -1199,7 +1199,11 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: for name, lazy_tensor in model.items(): tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) if name_new is None: - raise Exception(f"Unexpected tensor name: {name}") + if skip_unknown: + print(f"Unexpected tensor name: {name} - skipping") + continue + else: + raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)") if tensor_type in should_skip: print(f"skipping tensor {name_new}") @@ -1377,19 +1381,20 @@ def main(args_in: list[str] | None = None) -> None: output_choices.append("q8_0") vocab_types = ["spm", "bpe", "hfft"] parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") - parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) - parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") - parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") - parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") - parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") - parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY) - parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine") - parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") + parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) + parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") + parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") + parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") + parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") + parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") + parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY) + parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine") + parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") + parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") args = parser.parse_args(args_in) if args.awq_path: @@ -1461,7 +1466,7 @@ def main(args_in: list[str] | None = None) -> None: print(f"Special vocab info: {special_vocab}") model = model_plus.model - model = convert_model_names(model, params) + model = convert_model_names(model, params, args.skip_unknown) ftype = pick_output_type(model, args.outtype) model = convert_to_output_type(model, ftype) outfile = args.outfile or default_outfile(model_plus.paths, ftype) diff --git a/examples/llava/README.md b/examples/llava/README.md index 19f1a50a2..e2ef0eff1 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -19,9 +19,9 @@ After building, run: `./llava-cli` to see the usage. For example: **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. -## Model conversion +## LLaVA 1.5 -- Clone `llava-v15-7b` and `clip-vit-large-patch14-336` locally: +- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: ```sh git clone https://huggingface.co/liuhaotian/llava-v1.5-7b @@ -55,8 +55,14 @@ python ./convert.py ../llava-v1.5-7b Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory. +## LLaVA 1.6 + +- Use `llava-surgery-v2.py` + +- TODO: add detailed instructions + ## TODO -- [ ] Support non-CPU backend for the image encoding part. +- [x] Support non-CPU backend for the image encoding part. - [ ] Support different sampling methods. - [ ] Support more model variants. diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index ccd0d85ad..9c5091e61 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1,7 +1,7 @@ // NOTE: This is modified from clip.cpp only for LLaVA, // so there might be still unnecessary artifacts hanging around // I'll gradually clean and extend it - +// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" #include "ggml.h" #include "ggml-alloc.h" @@ -30,6 +30,26 @@ #include #include #include +#include + +//#define CLIP_DEBUG_FUNCTIONS + +// RGB uint8 image +struct clip_image_u8 { + int nx; + int ny; + + std::vector buf; +}; + +// RGB float32 image (NHWC) +// Memory layout: RGBRGBRGB... +struct clip_image_f32 { + int nx; + int ny; + + std::vector buf; +}; static std::string format(const char * fmt, ...) { va_list ap; @@ -50,50 +70,56 @@ static std::string format(const char * fmt, ...) { // key constants // -#define KEY_FTYPE "general.file_type" -#define KEY_NAME "general.name" -#define KEY_DESCRIPTION "general.description" -#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" -#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" +#define KEY_FTYPE "general.file_type" +#define KEY_NAME "general.name" +#define KEY_DESCRIPTION "general.description" +#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" +#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" #define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" -#define KEY_USE_GELU "clip.use_gelu" -#define KEY_N_EMBD "clip.%s.embedding_length" -#define KEY_N_FF "clip.%s.feed_forward_length" -#define KEY_N_BLOCK "clip.%s.block_count" -#define KEY_N_HEAD "clip.%s.attention.head_count" +#define KEY_USE_GELU "clip.use_gelu" +#define KEY_N_EMBD "clip.%s.embedding_length" +#define KEY_N_FF "clip.%s.feed_forward_length" +#define KEY_N_BLOCK "clip.%s.block_count" +#define KEY_N_HEAD "clip.%s.attention.head_count" #define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" -#define KEY_PROJ_DIM "clip.%s.projection_dim" -#define KEY_TOKENS "tokenizer.ggml.tokens" -#define KEY_N_POSITIONS "clip.text.context_length" -#define KEY_IMAGE_SIZE "clip.vision.image_size" -#define KEY_PATCH_SIZE "clip.vision.patch_size" -#define KEY_IMAGE_MEAN "clip.vision.image_mean" -#define KEY_IMAGE_STD "clip.vision.image_std" -#define KEY_PROJ_TYPE "clip.projector_type" +#define KEY_PROJ_DIM "clip.%s.projection_dim" +#define KEY_TOKENS "tokenizer.ggml.tokens" +#define KEY_N_POSITIONS "clip.text.context_length" +#define KEY_IMAGE_SIZE "clip.vision.image_size" +#define KEY_PATCH_SIZE "clip.vision.patch_size" +#define KEY_IMAGE_MEAN "clip.vision.image_mean" +#define KEY_IMAGE_STD "clip.vision.image_std" +#define KEY_PROJ_TYPE "clip.projector_type" + +#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" +#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" +#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution" + // // tensor name constants // -#define TN_TOKEN_EMBD "%s.token_embd.weight" -#define TN_POS_EMBD "%s.position_embd.weight" -#define TN_CLASS_EMBD "v.class_embd" -#define TN_PATCH_EMBD "v.patch_embd.weight" -#define TN_ATTN_K "%s.blk.%d.attn_k.%s" -#define TN_ATTN_Q "%s.blk.%d.attn_q.%s" -#define TN_ATTN_V "%s.blk.%d.attn_v.%s" -#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" -#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" -#define TN_FFN_UP "%s.blk.%d.ffn_up.%s" -#define TN_LN_1 "%s.blk.%d.ln1.%s" -#define TN_LN_2 "%s.blk.%d.ln2.%s" -#define TN_LN_PRE "%s.pre_ln.%s" -#define TN_LN_POST "%s.post_ln.%s" -#define TN_TEXT_PROJ "text_projection.weight" -#define TN_VIS_PROJ "visual_projection.weight" -#define TN_LLAVA_PROJ "mm.%d.%s" -#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" +#define TN_TOKEN_EMBD "%s.token_embd.weight" +#define TN_POS_EMBD "%s.position_embd.weight" +#define TN_CLASS_EMBD "v.class_embd" +#define TN_PATCH_EMBD "v.patch_embd.weight" +#define TN_ATTN_K "%s.blk.%d.attn_k.%s" +#define TN_ATTN_Q "%s.blk.%d.attn_q.%s" +#define TN_ATTN_V "%s.blk.%d.attn_v.%s" +#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" +#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" +#define TN_FFN_UP "%s.blk.%d.ffn_up.%s" +#define TN_LN_1 "%s.blk.%d.ln1.%s" +#define TN_LN_2 "%s.blk.%d.ln2.%s" +#define TN_LN_PRE "%s.pre_ln.%s" +#define TN_LN_POST "%s.post_ln.%s" +#define TN_TEXT_PROJ "text_projection.weight" +#define TN_VIS_PROJ "visual_projection.weight" +#define TN_LLAVA_PROJ "mm.%d.%s" +#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" +#define TN_IMAGE_NEWLINE "model.image_newline" enum projector_type { @@ -104,8 +130,8 @@ enum projector_type { }; static std::map PROJECTOR_TYPE_NAMES = { - { PROJECTOR_TYPE_MLP, "mlp" }, - { PROJECTOR_TYPE_LDP, "ldp" }, + { PROJECTOR_TYPE_MLP, "mlp" }, + { PROJECTOR_TYPE_LDP, "ldp" }, }; @@ -165,7 +191,6 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int } } - static void replace_all(std::string & s, const std::string & search, const std::string & replace) { std::string result; for (size_t pos = 0; ; pos += search.length()) { @@ -217,7 +242,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { } } -static void print_tensor_info(const ggml_tensor* tensor, const char* prefix = "") { +static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") { size_t tensor_size = ggml_nbytes(tensor); printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", prefix, ggml_n_dims(tensor), tensor->name, tensor_size, @@ -233,31 +258,136 @@ static projector_type clip_projector_type_from_string(const std::string & name) return PROJECTOR_TYPE_UNKNOWN; } -// -// image data -// +#ifdef CLIP_DEBUG_FUNCTIONS +static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) { + std::ofstream file(filename, std::ios::binary); + if (!file.is_open()) { + std::cerr << "Failed to open file for writing: " << filename << std::endl; + return; + } -// RGB uint8 image -struct clip_image_u8 { - int nx; - int ny; + // PPM header: P6 format, width, height, and max color value + file << "P6\n" << img.nx << " " << img.ny << "\n255\n"; - std::vector buf; -}; + // Write pixel data + for (size_t i = 0; i < img.buf.size(); i += 3) { + // PPM expects binary data in RGB format, which matches our image buffer + file.write(reinterpret_cast(&img.buf[i]), 3); + } -// RGB float32 image (NHWC) -// Memory layout: RGBRGBRGB... -struct clip_image_f32 { - int nx; - int ny; + file.close(); +} + +static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) { + std::ofstream file(filename, std::ios::binary); + if (!file.is_open()) { + std::cerr << "Failed to open file for writing: " << filename << std::endl; + return; + } + + int fileSize = 54 + 3 * img.nx * img.ny; // File header + info header + pixel data + int bytesPerPixel = 3; + int widthInBytes = img.nx * bytesPerPixel; + int paddingAmount = (4 - (widthInBytes % 4)) % 4; + int stride = widthInBytes + paddingAmount; + + // Bitmap file header + unsigned char fileHeader[14] = { + 'B','M', // Signature + 0,0,0,0, // Image file size in bytes + 0,0,0,0, // Reserved + 54,0,0,0 // Start of pixel array + }; + + // Total file size + fileSize = 54 + (stride * img.ny); + fileHeader[2] = (unsigned char)(fileSize); + fileHeader[3] = (unsigned char)(fileSize >> 8); + fileHeader[4] = (unsigned char)(fileSize >> 16); + fileHeader[5] = (unsigned char)(fileSize >> 24); + + // Bitmap information header (BITMAPINFOHEADER) + unsigned char infoHeader[40] = { + 40,0,0,0, // Size of this header (40 bytes) + 0,0,0,0, // Image width + 0,0,0,0, // Image height + 1,0, // Number of color planes + 24,0, // Bits per pixel + 0,0,0,0, // No compression + 0,0,0,0, // Image size (can be 0 for no compression) + 0,0,0,0, // X pixels per meter (not specified) + 0,0,0,0, // Y pixels per meter (not specified) + 0,0,0,0, // Total colors (color table not used) + 0,0,0,0 // Important colors (all are important) + }; + + // Width and height in the information header + infoHeader[4] = (unsigned char)(img.nx); + infoHeader[5] = (unsigned char)(img.nx >> 8); + infoHeader[6] = (unsigned char)(img.nx >> 16); + infoHeader[7] = (unsigned char)(img.nx >> 24); + infoHeader[8] = (unsigned char)(img.ny); + infoHeader[9] = (unsigned char)(img.ny >> 8); + infoHeader[10] = (unsigned char)(img.ny >> 16); + infoHeader[11] = (unsigned char)(img.ny >> 24); + + // Write file headers + file.write(reinterpret_cast(fileHeader), sizeof(fileHeader)); + file.write(reinterpret_cast(infoHeader), sizeof(infoHeader)); + + // Pixel data + std::vector padding(3, 0); // Max padding size to be added to each row + for (int y = img.ny - 1; y >= 0; --y) { // BMP files are stored bottom-to-top + for (int x = 0; x < img.nx; ++x) { + // Each pixel + size_t pixelIndex = (y * img.nx + x) * 3; + unsigned char pixel[3] = { + img.buf[pixelIndex + 2], // BMP stores pixels in BGR format + img.buf[pixelIndex + 1], + img.buf[pixelIndex] + }; + file.write(reinterpret_cast(pixel), 3); + } + // Write padding for the row + file.write(reinterpret_cast(padding.data()), paddingAmount); + } + + file.close(); +} + +// debug function to convert f32 to u8 +static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u8& dst) { + dst.nx = src.nx; + dst.ny = src.ny; + dst.buf.resize(3 * src.nx * src.ny); + for (size_t i = 0; i < src.buf.size(); ++i) { + dst.buf[i] = static_cast(std::min(std::max(int(src.buf[i] * 255.0f), 0), 255)); + } +} +#endif - std::vector buf; -}; // // clip layers // +struct clip_hparams { + int32_t image_size; + int32_t patch_size; + int32_t hidden_size; + int32_t n_intermediate; + int32_t projection_dim; + int32_t n_head; + int32_t n_layer; + + float eps; + + char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default) + + int32_t image_grid_pinpoints[32]; + int32_t image_crop_resolution; +}; + struct clip_layer { // attention struct ggml_tensor * k_w; @@ -287,7 +417,7 @@ struct clip_layer { }; struct clip_vision_model { - struct clip_vision_hparams hparams; + struct clip_hparams hparams; // embeddings struct ggml_tensor * class_embedding; @@ -310,6 +440,8 @@ struct clip_vision_model { struct ggml_tensor * mm_2_w = NULL; struct ggml_tensor * mm_2_b = NULL; + struct ggml_tensor * image_newline = NULL; + // Yi type models with mlp+normalization projection struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4 struct ggml_tensor * mm_1_b = NULL; @@ -364,9 +496,10 @@ struct clip_ctx { std::vector buf_compute_meta; // memory buffers to evaluate the model - ggml_backend_buffer_t params_buffer = NULL; + ggml_backend_buffer_t params_buffer = NULL; ggml_backend_buffer_t compute_buffer = NULL; - ggml_backend_t backend = NULL; + + ggml_backend_t backend = NULL; ggml_gallocr_t compute_alloc = NULL; }; @@ -379,18 +512,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 const auto & model = ctx->vision_model; const auto & hparams = model.hparams; - const int image_size = hparams.image_size; - const int patch_size = hparams.patch_size; - const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); - const int num_positions = num_patches + 1; - const int hidden_size = hparams.hidden_size; - const int n_head = hparams.n_head; - const int d_head = hidden_size / n_head; - const int n_layer = hparams.n_layer; - //const int n_intermediate = hparams.n_intermediate; - //const int projection_dim = hparams.projection_dim; - const float eps = hparams.eps; - int batch_size = imgs->size; + const int image_size = hparams.image_size; + const int patch_size = hparams.patch_size; + const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); + const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side); + const int num_positions = num_patches + 1; + const int hidden_size = hparams.hidden_size; + const int n_head = hparams.n_head; + const int d_head = hidden_size / n_head; + const int n_layer = hparams.n_layer; + const float eps = hparams.eps; + + const int batch_size = imgs->size; + if (ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); } @@ -540,7 +674,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); embeddings = ggml_gelu(ctx0, embeddings); - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); @@ -791,10 +924,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { if (idx != -1) { const std::string proj_type = gguf_get_val_str(ctx, idx); new_clip->proj_type = clip_projector_type_from_string(proj_type); - } - else { + } else { new_clip->proj_type = PROJECTOR_TYPE_MLP; } + if (new_clip->proj_type == PROJECTOR_TYPE_MLP) { if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) { new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM; @@ -920,11 +1053,41 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision")); hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision")); + try { + int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS); + int n = gguf_get_arr_n(ctx, idx); + const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx); + for (int i = 0; i < 32 && i < n && pinpoints[i] != 0; ++i) { + hparams.image_grid_pinpoints[i] = pinpoints[i]; + } + if (n < 32) + hparams.image_grid_pinpoints[n] = 0; + } catch (std::runtime_error & e) { + hparams.image_grid_pinpoints[0]=0; + } + + try { + int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE); + strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx)); + } catch (std::runtime_error & e) { + strcpy(hparams.mm_patch_merge_type, "flat"); + } + + try { + hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6 + } catch(const std::exception& e) { + hparams.image_crop_resolution = hparams.image_size; + } + int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); + + const float * mean_data = (const float *)gguf_get_arr_data(ctx, idx_mean); + const float * std_data = (const float *)gguf_get_arr_data(ctx, idx_std); + for (int i = 0; i < 3; ++i) { - new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean)); - new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); + new_clip->image_mean[i] = mean_data[i]; + new_clip->image_std[i] = std_data[i]; } if (verbosity >= 2) { @@ -936,13 +1099,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("v_projection_dim %d\n", hparams.projection_dim); printf("v_n_head %d\n", hparams.n_head); printf("v_n_layer %d\n", hparams.n_layer); + printf("v_eps %f\n", hparams.eps); + printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); + printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); + printf("v_image_grid_pinpoints: "); + for (int i = 0; i < 32 & hparams.image_grid_pinpoints[i]!=0; ++i) { + printf("%d ", hparams.image_grid_pinpoints[i]); + } + printf("\n"); + printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); + } - vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); - vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + try { + vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + } catch(const std::exception& e) { + fprintf(stderr, "%s: failed to load vision model tensors\n", __func__); + } // LLaVA projection if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) { @@ -968,40 +1145,43 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight")); vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias")); } catch (std::runtime_error & e) { } - } - else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { + try { + vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE); + // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__); + } catch (std::runtime_error & e) { } + } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { // MobileVLM projection - vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight")); - vision_model.mm_model_mlp_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias")); - vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "weight")); - vision_model.mm_model_mlp_3_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "bias")); - vision_model.mm_model_block_1_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight")); - vision_model.mm_model_block_1_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight")); - vision_model.mm_model_block_1_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias")); + vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight")); + vision_model.mm_model_mlp_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias")); + vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "weight")); + vision_model.mm_model_mlp_3_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "bias")); + vision_model.mm_model_block_1_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight")); + vision_model.mm_model_block_1_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight")); + vision_model.mm_model_block_1_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias")); vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight")); vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias")); vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight")); vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias")); - vision_model.mm_model_block_1_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight")); - vision_model.mm_model_block_1_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight")); - vision_model.mm_model_block_1_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias")); - vision_model.mm_model_block_2_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight")); - vision_model.mm_model_block_2_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight")); - vision_model.mm_model_block_2_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias")); + vision_model.mm_model_block_1_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight")); + vision_model.mm_model_block_1_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight")); + vision_model.mm_model_block_1_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias")); + vision_model.mm_model_block_2_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight")); + vision_model.mm_model_block_2_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight")); + vision_model.mm_model_block_2_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias")); vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight")); vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias")); vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight")); vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias")); - vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); - vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); - vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); - } - else { + vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); + vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); + vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); + } else { std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } vision_model.layers.resize(hparams.n_layer); + for (int il = 0; il < hparams.n_layer; ++il) { auto & layer = vision_model.layers[il]; layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight")); @@ -1084,24 +1264,255 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length return true; } -// normalize: x = (x - mean) / std -// TODO: implement bicubic interpolation instead of linear. -bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) { +// Linear interpolation between two points +inline float lerp(float s, float e, float t) { + return s + (e - s) * t; +} +// Bilinear resize function +static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int target_width, int target_height) { + dst.nx = target_width; + dst.ny = target_height; + dst.buf.resize(3 * target_width * target_height); + + float x_ratio = static_cast(src.nx - 1) / target_width; + float y_ratio = static_cast(src.ny - 1) / target_height; + + for (int y = 0; y < target_height; y++) { + for (int x = 0; x < target_width; x++) { + float px = x_ratio * x; + float py = y_ratio * y; + int x_floor = static_cast(px); + int y_floor = static_cast(py); + float x_lerp = px - x_floor; + float y_lerp = py - y_floor; + + for (int c = 0; c < 3; c++) { + float top = lerp( + static_cast(src.buf[3 * (y_floor * src.nx + x_floor) + c]), + static_cast(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]), + x_lerp + ); + float bottom = lerp( + static_cast(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]), + static_cast(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]), + x_lerp + ); + dst.buf[3 * (y * target_width + x) + c] = static_cast(lerp(top, bottom, y_lerp)); + } + } + } +} + +// Normalize image to float32 - careful with pytorch .to(model.device, dtype=torch.float16) - this sometimes reduces precision (32>16>32), sometimes not +static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32* dst, const float mean[3], const float std[3]) { + dst->nx = src->nx; + dst->ny = src->ny; + dst->buf.resize(src->buf.size()); + + for (size_t i = 0; i < src->buf.size(); ++i) { + int c = i % 3; // rgb + dst->buf[i] = (static_cast(src->buf[i]) / 255.0f - mean[c]) / std[c]; + } +} + +inline float clip(float x, float lower, float upper) { + return std::max(lower, std::min(x, upper)); +} + +static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int target_width, int target_height) { + const int nx = img.nx; + const int ny = img.ny; + + dst.nx = target_width; + dst.ny = target_height; + dst.buf.resize(3 * target_width * target_height); + + float Cc; + float C[5]; + float d0, d2, d3, a0, a1, a2, a3; + int i, j, k, jj; + int x, y; + float dx, dy; + float tx, ty; + + tx = (float)nx / (float)target_width; + ty = (float)ny / (float)target_height; + + // Bicubic interpolation; adapted from ViT.cpp, inspired from : + // -> https://github.com/yglukhov/bicubic-interpolation-image-processing/blob/master/libimage.c#L36 + // -> https://en.wikipedia.org/wiki/Bicubic_interpolation + + for (i = 0; i < target_height; i++) { + for (j = 0; j < target_width; j++) { + x = (int)(tx * j); + y = (int)(ty * i); + + dx = tx * j - x; + dy = ty * i - y; + + for (k = 0; k < 3; k++) { + for (jj = 0; jj <= 3; jj++) { + d0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x - 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k]; + d2 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k]; + d3 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 2, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k]; + a0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k]; + + a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3; + a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2; + a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3; + + C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx; + + d0 = C[0] - C[1]; + d2 = C[2] - C[1]; + d3 = C[3] - C[1]; + a0 = C[1]; + a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3; + a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2; + a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3; + Cc = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy; + + const uint8_t Cc2 = std::min(std::max(std::round(Cc), 0.0f), 255.0f); + dst.buf[(i * target_width + j) * 3 + k] = float(Cc2); + } + } + } + } + + return true; +} + +// llava-1.6 type of resize_and_pad (black) +static void resize_and_pad_image(const clip_image_u8& image, clip_image_u8 &image_output, const std::pair& target_resolution) { + int target_width = target_resolution.first; + int target_height = target_resolution.second; + + float scale_w = static_cast(target_width) / image.nx; + float scale_h = static_cast(target_height) / image.ny; + + int new_width, new_height; + + if (scale_w < scale_h) { + new_width = target_width; + new_height = std::min(static_cast(std::ceil(image.ny * scale_w)), target_height); + } else { + new_height = target_height; + new_width = std::min(static_cast(std::ceil(image.nx * scale_h)), target_width); + } + + clip_image_u8 resized_image; + // bilinear_resize(image, resized_image, new_width, new_height); + bicubic_resize(image, resized_image, new_width, new_height); + + clip_image_u8 padded_image; + padded_image.nx = target_width; + padded_image.ny = target_height; + padded_image.buf.resize(3 * target_width * target_height, 0); // Initialize with black + + // Calculate padding offsets + int pad_x = (target_width - new_width) / 2; + int pad_y = (target_height - new_height) / 2; + + // Copy the resized image into the center of the padded buffer + for (int y = 0; y < new_height; ++y) { + for (int x = 0; x < new_width; ++x) { + for (int c = 0; c < 3; ++c) { + padded_image.buf[3 * ((y + pad_y) * target_width + (x + pad_x)) + c] = resized_image.buf[3 * (y * new_width + x) + c]; + } + } + } + image_output = std::move(padded_image); +} + +/** + * Selects the best resolution from a list of possible resolutions based on the original size. + * + * @param original_size The original size of the image in the format (width, height). + * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...]. + * @return The best fit resolution in the format (width, height). + */ +static std::pair select_best_resolution(const std::pair & original_size, const std::vector> & possible_resolutions) { + int original_width = original_size.first; + int original_height = original_size.second; + std::pair best_fit; + int max_effective_resolution = 0; + int min_wasted_resolution = std::numeric_limits::max(); + + for (const auto& resolution : possible_resolutions) { + int width = resolution.first; + int height = resolution.second; + float scale = std::min(static_cast(width) / original_width, static_cast(height) / original_height); + int downscaled_width = static_cast(original_width * scale); + int downscaled_height = static_cast(original_height * scale); + int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); + int wasted_resolution = (width * height) - effective_resolution; + // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { + max_effective_resolution = effective_resolution; + min_wasted_resolution = wasted_resolution; + best_fit = resolution; + } + } + + return best_fit; +} + +static std::vector divide_to_patches_u8(const clip_image_u8 & image, int patch_size) { + std::vector patches; + int width = image.nx; + int height = image.ny; + for (int i = 0; i < height; i += patch_size) { + for (int j = 0; j < width; j += patch_size) { + clip_image_u8 *patch = clip_image_u8_init(); + patch->nx = std::min(patch_size, width - j); + patch->ny = std::min(patch_size, height - i); + patch->buf.resize(3 * patch->nx * patch->ny); + for (int y = 0; y < patch->ny; ++y) { + for (int x = 0; x < patch->nx; ++x) { + for (int c = 0; c < 3; ++c) { + patch->buf[3 * (y * patch->nx + x) + c] = image.buf[3 * ((i + y) * width + (j + x)) + c]; + } + } + } + patches.push_back(patch); + } + } + return patches; +} + +// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector +// res_imgs memory is being allocated here, previous allocations will be freed if found +bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) { + bool pad_to_square = true; if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; } + auto & params = ctx->vision_model.hparams; + // The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing + if (strcmp(params.mm_patch_merge_type, "spatial_unpad") == 0) { + pad_to_square = false; + } + // free the previous res_imgs if any set + if (res_imgs.size > 0 && res_imgs.size < 100) { + for (size_t i = 0; i < res_imgs.size; i++) { + clip_image_f32_free(&(res_imgs.data[i])); + } + delete[] res_imgs.data; + } + res_imgs.data = nullptr; + res_imgs.size = 0; // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily - if (pad2square && img->nx != img->ny) { + if (pad_to_square && img->nx != img->ny) { int longer_side = std::max(img->nx, img->ny); temp->nx = longer_side; temp->ny = longer_side; temp->buf.resize(3 * longer_side * longer_side); - const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA + const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA (this is the mean rgb color * 255) // fill with background color for (size_t i = 0; i < temp->buf.size(); i++) { @@ -1119,18 +1530,63 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli } } } else { - temp->nx = img->nx; - temp->ny = img->ny; - temp->buf.resize(img->buf.size()); - memcpy(temp->buf.data(), img->buf.data(), temp->buf.size()); + if (params.image_grid_pinpoints[0] != 0) { + // "spatial_unpad" with "anyres" processing for llava-1.6 + std::vector> possible_resolutions; + for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) { + possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]}); + } + std::pair best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions); + // clip_image_save_to_bmp(*img, "input.bmp"); + resize_and_pad_image(*img, *temp, best_resolution); // we do not pad with mean-bg color anymore in llava-1.6 + // clip_image_save_to_bmp(*temp, "resized.bmp"); + // visually verify normalized image: + // normalize_image_u8_to_f32(*temp, *res, ctx->image_mean, ctx->image_std); + // { + // clip_image_u8 * temp2 = clip_image_u8_init(); + // clip_image_convert_f32_to_u8(*res, *temp2); + // clip_image_save_to_bmp(*temp2, "resized_normalized_f32.bmp"); + // clip_image_u8_free(temp2); + // } + + std::vector patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6) + + clip_image_u8 *image_original_resize = clip_image_u8_init(); + // bilinear_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square + bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square + patches.insert(patches.begin(), image_original_resize); + // clip_image_f32_batch_init(patches.size()); + res_imgs.size = patches.size(); + res_imgs.data = new clip_image_f32[res_imgs.size]; + int num=0; + for (auto& patch : patches) { + normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std); + num++; + } + + for (size_t i = 0; i < patches.size(); i++) { + // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); + clip_image_u8_free(patches[i]); + } + + clip_image_u8_free(temp); + + return true; + } else { + temp->nx = img->nx; + temp->ny = img->ny; + temp->buf.resize(img->buf.size()); + memcpy(temp->buf.data(), img->buf.data(), temp->buf.size()); + } } const int nx = temp->nx; const int ny = temp->ny; + // clip_image_save_to_bmp(*temp, "resized_vanilla.bmp"); const int nx2 = ctx->vision_model.hparams.image_size; const int ny2 = ctx->vision_model.hparams.image_size; - + clip_image_f32 * res = clip_image_f32_init(); res->nx = nx2; res->ny = ny2; res->buf.resize(3 * nx2 * ny2); @@ -1184,9 +1640,25 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli } clip_image_u8_free(temp); + // { + // clip_image_u8 * temp2 = clip_image_u8_init(); + // clip_image_convert_f32_to_u8(*res, *temp2); + // clip_image_save_to_bmp(*temp2, "resized_normalized_f32_vanilla.bmp"); + // clip_image_u8_free(temp2); + // } + // res_imgs.push_back(res); + + res_imgs.size = 1; + res_imgs.data = new clip_image_f32[res_imgs.size]; + res_imgs.data[0] = std::move(*res); + return true; } +ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) { + return ctx->vision_model.image_newline; +} + void clip_free(clip_ctx * ctx) { ggml_free(ctx->ctx_data); gguf_free(ctx->ctx_gguf); @@ -1194,6 +1666,42 @@ void clip_free(clip_ctx * ctx) { delete ctx; } +size_t clip_embd_nbytes(const struct clip_ctx * ctx) { + return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); +} + +int32_t clip_image_size(const struct clip_ctx * ctx) { + return ctx->vision_model.hparams.image_size; +} + +int32_t clip_patch_size(const struct clip_ctx * ctx) { + return ctx->vision_model.hparams.patch_size; +} + +int32_t clip_hidden_size(const struct clip_ctx * ctx) { + return ctx->vision_model.hparams.hidden_size; +} + +const char * clip_patch_merge_type(const struct clip_ctx * ctx) { + return ctx->vision_model.hparams.mm_patch_merge_type; +} + +const int32_t * clip_image_grid(const struct clip_ctx * ctx) { + return ctx->vision_model.hparams.image_grid_pinpoints; +} + +int clip_n_patches(const struct clip_ctx * ctx) { + const auto & params = ctx->vision_model.hparams; + + int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size); + + if (ctx->proj_type == PROJECTOR_TYPE_LDP) { + n_patches /= 4; + } + + return n_patches; +} + bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); @@ -1213,7 +1721,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } int batch_size = imgs->size; - if(ctx->has_llava_projector) { + if (ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); // TODO: support multiple images } @@ -1224,9 +1732,10 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // set inputs const auto & model = ctx->vision_model; const auto & hparams = model.hparams; - const int image_size = hparams.image_size; - const int patch_size = hparams.patch_size; - const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); + + const int image_size = hparams.image_size; + const int patch_size = hparams.patch_size; + const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); const int num_positions = num_patches + 1; { @@ -1301,11 +1810,11 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // copy the embeddings to the location passed by the user ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); + return true; } bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) { - ggml_type type = GGML_TYPE_Q4_1; assert(itype < GGML_TYPE_COUNT); @@ -1494,26 +2003,13 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { if (ctx->proj_type == PROJECTOR_TYPE_LDP) { return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0]; } - else if (ctx->proj_type == PROJECTOR_TYPE_MLP) { + if (ctx->proj_type == PROJECTOR_TYPE_MLP) { return ctx->vision_model.mm_2_b->ne[0]; - } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { + } + if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { return ctx->vision_model.mm_3_b->ne[0]; } - else { - std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; - throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); - } -} -int clip_n_patches(const struct clip_ctx * ctx) { - auto & params = ctx->vision_model.hparams; - int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size); - if (ctx->proj_type == PROJECTOR_TYPE_LDP) { - n_patches /= 4; - } - return n_patches; -} - -size_t clip_embd_nbytes(const struct clip_ctx * ctx) { - return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); + std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; + throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 458a256a1..cd9a4022f 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -24,25 +24,7 @@ struct clip_ctx; extern "C" { #endif -struct clip_vision_hparams { - int32_t image_size; - int32_t patch_size; - int32_t hidden_size; - int32_t n_intermediate; - int32_t projection_dim; - int32_t n_head; - int32_t n_layer; - float eps; -}; - -CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity); - -CLIP_API void clip_free(struct clip_ctx * ctx); - -CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); - -CLIP_API int clip_n_patches (const struct clip_ctx * ctx); -CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); +struct clip_ctx; struct clip_image_u8_batch { struct clip_image_u8 * data; @@ -54,10 +36,29 @@ struct clip_image_f32_batch { size_t size; }; +CLIP_API struct clip_ctx * clip_model_load (const char * fname, int verbosity); +CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity); + +CLIP_API void clip_free(struct clip_ctx * ctx); + +CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); + +CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx); +CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx); +CLIP_API int32_t clip_hidden_size(const struct clip_ctx * ctx); + +// TODO: should be enum, not string +CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx); + +CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx); + +CLIP_API int clip_n_patches (const struct clip_ctx * ctx); +CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); + CLIP_API struct clip_image_u8 * clip_image_u8_init (); CLIP_API struct clip_image_f32 * clip_image_f32_init(); -CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); +CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); @@ -65,7 +66,11 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); -CLIP_API bool clip_image_preprocess (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square); +/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ +CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs ); + +CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); + CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec); CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec); diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index e204b56be..c69f89ac2 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -78,18 +78,19 @@ ap.add_argument("--text-only", action="store_true", required=False, help="Save a text-only model. It can't be used to encode images") ap.add_argument("--vision-only", action="store_true", required=False, help="Save a vision-only model. It can't be used to encode texts") -ap.add_argument("--clip_model_is_vision", action="store_true", required=False, +ap.add_argument("--clip-model-is-vision", action="store_true", required=False, help="The clip model is a pure vision model (ShareGPT4V vision extract for example)") +ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, + help="The clip model is from openclip (for ViT-SO400M type))") ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp") -ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values") -ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values") ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 +# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 default_image_mean = [0.48145466, 0.4578275, 0.40821073] default_image_std = [0.26862954, 0.26130258, 0.27577711] -ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) -ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) +ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) +ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) # with proper args = ap.parse_args() @@ -105,7 +106,7 @@ if args.use_f32: # output in the same directory as the model if output_dir is None dir_model = args.model_dir -if args.clip_model_is_vision: +if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip: vocab = None tokens = None else: @@ -133,7 +134,7 @@ ftype = 1 if args.use_f32: ftype = 0 -if args.clip_model_is_vision: +if args.clip_model_is_vision or args.clip_model_is_openclip: model = CLIPVisionModel.from_pretrained(dir_model) processor = None else: @@ -202,6 +203,57 @@ if has_vision_encoder: fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"]) block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"] fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count) + # /** + # "image_grid_pinpoints": [ + # [ + # 336, + # 672 + # ], + # [ + # 672, + # 336 + # ], + # [ + # 672, + # 672 + # ], + # [ + # 1008, + # 336 + # ], + # [ + # 336, + # 1008 + # ] + # ], + # Flattened: + # [ + # 336, 672, + # 672, 336, + # 672, 672, + # 1008, 336, + # 336, 1008 + # ] + # * + # */ + if "image_grid_pinpoints" in v_hparams: + # flatten it + image_grid_pinpoints = [] + for pinpoint in v_hparams["image_grid_pinpoints"]: + for p in pinpoint: + image_grid_pinpoints.append(p) + fout.add_array("clip.vision.image_grid_pinpoints", image_grid_pinpoints) + if "image_crop_resolution" in v_hparams: + fout.add_uint32("clip.vision.image_crop_resolution", v_hparams["image_crop_resolution"]) + if "image_aspect_ratio" in v_hparams: + fout.add_string("clip.vision.image_aspect_ratio", v_hparams["image_aspect_ratio"]) + if "image_split_resolution" in v_hparams: + fout.add_uint32("clip.vision.image_split_resolution", v_hparams["image_split_resolution"]) + if "mm_patch_merge_type" in v_hparams: + fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"]) + if "mm_projector_type" in v_hparams: + fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"]) + if processor is not None: image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 031e9806d..bef7f7c95 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -155,11 +155,29 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); printf("system_prompt: %s\n", system_prompt.c_str()); + if (params->verbose_prompt) { + auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } printf("user_prompt: %s\n", user_prompt.c_str()); + if (params->verbose_prompt) { + auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } } else { // llava-1.5 native mode system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:"; user_prompt = prompt + "\nASSISTANT:"; + if (params->verbose_prompt) { + auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } } eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos); @@ -171,13 +189,17 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ fprintf(stderr, "\n"); struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); - + std::string response = ""; for (int i = 0; i < max_tgt_len; i++) { const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); + response += tmp; if (strcmp(tmp, "") == 0) break; if (strstr(tmp, "###")) break; // Yi-VL behavior - printf("%s", tmp); + if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works) + if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6 + if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6 + fflush(stdout); } diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava-surgery-v2.py new file mode 100644 index 000000000..5bc5bc513 --- /dev/null +++ b/examples/llava/llava-surgery-v2.py @@ -0,0 +1,167 @@ +import argparse +import glob +import os +import torch +from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file + +# Function to determine if file is a SafeTensor file +def is_safetensor_file(file_path): + return file_path.endswith('.safetensors') + + +# Unified loading function +def load_model(file_path): + if is_safetensor_file(file_path): + tensors = {} + with safe_open(file_path, framework="pt", device="cpu") as f: + for key in f.keys(): + tensors[key] = f.get_tensor(key).clone() + # output shape + print(f"{key} : {tensors[key].shape}") + return tensors, 'safetensor' + else: + return torch.load(file_path, map_location=torch.device('cpu')), 'pytorch' + + +# Unified saving function +def save_model(model, file_path, file_type): + if file_type == 'safetensor': + # safe_save(model, file_path) + save_file(model, file_path) + else: + torch.save(model, file_path) + + +# Adapted function to clean vision tower from checkpoint +def clean_vision_tower_from_checkpoint(checkpoint_path): + checkpoint, file_type = load_model(checkpoint_path) + # file_type = 'pytorch' + model_path = os.path.dirname(checkpoint_path) + print(f"Searching for vision tower tensors in {checkpoint_path}") + clip_tensors = [k for k, v in checkpoint.items() if (k.startswith("model.vision_tower") or k.startswith("vit."))] + + if len(clip_tensors) > 0: + print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}") + # Adapted for file type + clip_path = os.path.join(model_path, "llava.clip") + + if os.path.exists(clip_path): + print(f"Loading existing llava.clip from {clip_path}") + existing_clip, _ = load_model(clip_path) + else: + print(f"Creating new llava.clip at {clip_path}") + existing_clip = {} + # Update existing_clip with new tensors, avoid duplicates + for name in clip_tensors: + simple_name = name[name.index('vision_model.'):] if 'vision_model.' in name else name + print(f"Adding {simple_name} to llava.clip") + if simple_name not in existing_clip: + existing_clip[simple_name] = checkpoint[name] + + # Save the updated clip tensors back to llava.clip + save_model(existing_clip, clip_path, 'pytorch') + + # Remove the tensors from the original checkpoint + for name in clip_tensors: + del checkpoint[name] + + # Save the updated checkpoint + checkpoint_path = checkpoint_path + save_model(checkpoint, checkpoint_path, file_type) + return True + return False + +def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector): + newline_checkpoint_path = None + projector_checkpoint_path = None + + for path in checkpoint_paths: + checkpoint, _ = load_model(path) + if newline_criteria(checkpoint) and newline_checkpoint_path is None: + newline_checkpoint_path = path + if projector(checkpoint): + projector_checkpoint_path = path + + return newline_checkpoint_path, projector_checkpoint_path + +def newline_criteria(checkpoint): + return any(k.startswith("model.image_newline") for k in checkpoint.keys()) + +def proj_criteria(checkpoint): + return any(k.startswith("model.mm_projector") or k.startswith("vision_proj.") for k in checkpoint.keys()) + + +# Command-line interface setup +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model", required=True, help="Path to LLaVA v1.5+ model") +ap.add_argument("-C", "--clean-vision-tower", action="store_true", help="Remove any vision tower from the model files") +args = ap.parse_args() + +if args.clean_vision_tower: + # Generalized to handle both PyTorch and SafeTensors models + model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True) + # checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and path.startswith('pytorch')) or (path.endswith('.safetensors') and path.startswith('model'))] + checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])] + for projector_checkpoint_path in checkpoint_paths: + print(f"Cleaning {projector_checkpoint_path}") + if not clean_vision_tower_from_checkpoint(projector_checkpoint_path): + print(f"No vision tower found in {projector_checkpoint_path}") + # we break once none is found, so far all models append them at the end + # break + print("Done! All vision tower tensors are removed from the model files and stored in llava.clip file.") + +# Now we look for the projector in the last checkpoint +model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True) +checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])] +# last_checkpoint_path = checkpoint_paths[0] +# first_checkpoint_path = checkpoint_paths[-1] +newline_checkpoint_path, projector_checkpoint_path = find_relevant_checkpoints(checkpoint_paths, newline_criteria, proj_criteria) + +print(f"Taking projector from {projector_checkpoint_path}") +first_mm_tensors = [] +first_checkpoint = None +if newline_checkpoint_path is not None: + print(f"Taking newline from {newline_checkpoint_path}") + first_checkpoint, file_type = load_model(newline_checkpoint_path) + first_mm_tensors = [k for k, v in first_checkpoint.items() if k.startswith("model.image_newline")] + +# Load the checkpoint +mm_tensors = [] +last_checkpoint = None +if projector_checkpoint_path is not None: + last_checkpoint, file_type = load_model(projector_checkpoint_path) + mm_tensors = [k for k, v in last_checkpoint.items() if k.startswith("model.mm_projector") or k.startswith("vision_proj.")] + +if len(mm_tensors) == 0: + if last_checkpoint is not None: + for k, v in last_checkpoint.items(): + print(k) + print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.") + print("No tensors found. Is this a LLaVA model?") + exit() + +print(f"Found {len(mm_tensors)} tensors to extract.") +print(f"Found additional {len(first_mm_tensors)} tensors to extract.") +# projector = {name: checkpoint.[name].float() for name in mm_tensors} +projector = {} +for name in mm_tensors: + projector[name] = last_checkpoint[name].float() +for name in first_mm_tensors: + projector[name] = first_checkpoint[name].float() + +if len(projector) > 0: + save_model(projector, f"{args.model}/llava.projector", 'pytorch') + +for name in mm_tensors: + del last_checkpoint[name] +for name in first_mm_tensors: + del first_checkpoint[name] + +if len(mm_tensors) > 0: + save_model(last_checkpoint, projector_checkpoint_path, file_type) +if len(first_mm_tensors) > 0: + save_model(first_checkpoint, newline_checkpoint_path, file_type) + +print("Done!") +print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.") +print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.") diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index d42e7582e..22953417f 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -2,32 +2,296 @@ #include "common.h" #include "llama.h" #include "llava.h" +#include "base64.hpp" #include #include #include +#include + +// RGB uint8 image +struct clip_image_u8 { + int nx; + int ny; + + std::vector buf; +}; + +// RGB float32 image (NHWC) +// Memory layout: RGBRGBRGB... +struct clip_image_f32 { + int nx; + int ny; + + std::vector buf; +}; + +struct clip_image_grid_shape { + int first; + int second; +}; + +/** + * Selects the best resolution from a list of possible resolutions based on the original size. + * + * @param original_size The original size of the image in the format (width, height). + * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...]. + * @return The best fit resolution in the format (width, height). + */ +static std::pair select_best_resolution(const std::pair& original_size, const std::vector>& possible_resolutions) { + int original_width = original_size.first; + int original_height = original_size.second; + + std::pair best_fit; + int max_effective_resolution = 0; + int min_wasted_resolution = std::numeric_limits::max(); + + for (const auto& resolution : possible_resolutions) { + int width = resolution.first; + int height = resolution.second; + float scale = std::min(static_cast(width) / original_width, static_cast(height) / original_height); + int downscaled_width = static_cast(original_width * scale); + int downscaled_height = static_cast(original_height * scale); + int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); + int wasted_resolution = (width * height) - effective_resolution; + // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { + max_effective_resolution = effective_resolution; + min_wasted_resolution = wasted_resolution; + best_fit = resolution; + } + } + + return best_fit; +} + +/** + * @brief Get the anyres image grid shape object + * + * @param image_size + * @param grid_pinpoints + * @param image_patch_size + * @return + */ +static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair & image_size, const std::vector> & grid_pinpoints, int image_patch_size) { + /** + Conversion from gguf flat array to vector: + std::vector> possible_resolutions; + for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) { + possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]}); + } + */ + auto best_resolution = select_best_resolution(image_size, grid_pinpoints); + return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size}; +} + +// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out) +static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) { + struct { + struct ggml_tensor * newline; + struct ggml_context * ctx; + } model; + + const int32_t image_size = clip_image_size(ctx_clip); + const int32_t patch_size = clip_patch_size(ctx_clip); + + int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches) + + int num_patches_width = grid_shape.first; // grid 1-4 + int num_patches_height = grid_shape.second; // grid 1-4 + + const size_t num_images = num_patches_width + num_patches_height + 1; + + // TODO: size calculation is not calculated - it's only tens of MB + size_t ctx_size = 0; + + { + ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features + ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32); + } + + struct ggml_init_params params { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ false, // NOTE: this should be false when using the legacy API + }; + + // Python reference code for full unpad: + /* + base_image_feature = image_feature[0] + image_feature = image_feature[1:] + image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() + image_feature = image_feature.flatten(1, 2).flatten(2, 3) + image_feature = unpad_image(image_feature, image_sizes[image_idx]) + image_feature = torch.cat(( + image_feature, + self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1) + ), dim=-1) + image_feature = image_feature.flatten(1, 2).transpose(0, 1) + image_feature = torch.cat((base_image_feature, image_feature), dim=0) + */ + // We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval. + // In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet. + // Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them. + // Once all images are processed to prepended the base_image_features without any changes. + + // Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling)) + /* + image_feature = image_feature.view(2, 2, 24, 24, 4096) + image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous() + image_feature = image_feature.view(2, 24, 2, 24, 4096) + image_feature = image_feature.flatten(0, 3) + + // Reshape to 4D tensor by merging the last two dimensions + image_feature = image_feature.view(2, 2, 24, 24*4096) + image_feature = image_feature.permute(0, 2, 1, 3).contiguous() + image_feature = image_feature.view(-1, 4096) + */ + + model.ctx = ggml_init(params); + + ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip); + model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); + if (newline_tmp->backend != GGML_BACKEND_CPU) { + if (newline_tmp->buffer == NULL) { + printf("newline_tmp tensor buffer is NULL\n"); + } + ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp)); + } else { + model.newline->data = newline_tmp->data; + if (model.newline->data == NULL) { + printf("newline_tmp tensor data is NULL\n"); + } + } + + struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4 + // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false); + // fill it with the image embeddings, ignoring the base + for (size_t i = 1; i < num_images; i++) { + size_t offset = (i-1) * clip_embd_nbytes(ctx_clip); + memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip)); + } + + struct ggml_cgraph * gf = ggml_new_graph(model.ctx); + size_t size_ele = ggml_type_size(GGML_TYPE_F32); + + struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features, + num_patches_per_side * clip_n_mmproj_embd(ctx_clip), + num_patches_per_side, + num_patches_width, + num_patches_height, + size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip), + size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side, + size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0); + // ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false); + struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3)); + /** + At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings + image_feature = torch.cat(( + image_feature, + self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device) + ), dim=-1) + * + */ + + // ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false); + struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side, size_ele * clip_n_mmproj_embd(ctx_clip), 0); + // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false); + ggml_build_forward_expand(gf, flatten); + ggml_graph_compute_with_ctx(model.ctx, gf, 1); + struct ggml_tensor* result = gf->nodes[gf->n_nodes - 1]; + + memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context + // append without newline tokens (default behavior in llava_arch when not using unpad ): + memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches + *n_img_pos_out = static_cast(result->ne[1]+clip_n_patches(ctx_clip)); + + // Debug: Test single segments + // Current findings: sending base image, sending a segment embedding all works similar to python + // However, permuted embeddings do not work yet (stride issue?) + // memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context + // memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context + // *n_img_pos_out=576; + + ggml_free(model.ctx); + return true; +} -#include "base64.hpp" static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { - clip_image_f32 * img_res = clip_image_f32_init(); - if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) { + // std::vector img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336 + clip_image_f32_batch img_res_v; + img_res_v.size = 0; + img_res_v.data = nullptr; + if (!clip_image_preprocess(ctx_clip, img, img_res_v)) { fprintf(stderr, "%s: unable to preprocess image\n", __func__); - clip_image_f32_free(img_res); + delete[] img_res_v.data; return false; } - *n_img_pos = clip_n_patches(ctx_clip); - const int64_t t_img_enc_start_us = ggml_time_us(); - bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); - clip_image_f32_free(img_res); - if (!encoded) { - fprintf(stderr, "Unable to encode image\n"); - return false; + const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip); + + if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) { + // flat / default llava-1.5 type embedding + *n_img_pos = clip_n_patches(ctx_clip); + bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096 + delete[] img_res_v.data; + if (!encoded) { + fprintf(stderr, "Unable to encode image\n"); + + return false; + } + } else { + // spatial_unpad llava-1.6 type embedding + // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working + std::vector image_embd_v; + image_embd_v.resize(img_res_v.size); + for (size_t i = 0; i < img_res_v.size; i++) { + image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184 + const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside + if (!encoded) { + fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); + return false; + } + } + const int64_t t_img_enc_batch_us = ggml_time_us(); + printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); + + const int32_t * image_grid = clip_image_grid(ctx_clip); + + std::vector> grid_pinpoints; + for (int i = 0; i < 32 && image_grid[i] != 0; i += 2) { + grid_pinpoints.push_back({image_grid[i], image_grid[i+1]}); + } + + // free all img_res_v - not needed anymore + delete[] img_res_v.data; + img_res_v.size = 0; + img_res_v.data = nullptr; + + const int32_t image_size = clip_image_size(ctx_clip); + + struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size); + + int n_img_pos_out; + clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out); + *n_img_pos = n_img_pos_out; + + for (size_t i = 0; i < image_embd_v.size(); i++) { + free(image_embd_v[i]); + } + image_embd_v.clear(); + + // debug image/segment/normalization content: + // clip_image_u8 * tmp = clip_image_u8_init(); + // clip_image_convert_f32_to_u8(*image_feature, *tmp); + // clip_image_save_to_bmp(*tmp, "image_feature.bmp"); } + printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); + const int64_t t_img_enc_end_us = ggml_time_us(); float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; @@ -48,7 +312,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * } static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { - float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)); + float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model if (!image_embd) { fprintf(stderr, "Unable to allocate memory for image embeddings\n"); free(image_embd); @@ -85,7 +349,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ return true; } -LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { +struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { clip_image_u8 * img = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { clip_image_u8_free(img); @@ -142,7 +406,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long return true; } -LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) { +struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) { unsigned char* image_bytes; long image_bytes_length; auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); @@ -151,13 +415,13 @@ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct return NULL; } - auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length); + llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length); free(image_bytes); return embed; } -LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) { +void llava_image_embed_free(struct llava_image_embed * embed) { free(embed->embed); free(embed); } diff --git a/examples/llava/llava.h b/examples/llava/llava.h index e08ce7883..9e9466a5d 100644 --- a/examples/llava/llava.h +++ b/examples/llava/llava.h @@ -3,7 +3,6 @@ #include "ggml.h" - #ifdef LLAMA_SHARED # if defined(_WIN32) && !defined(__MINGW32__) # ifdef LLAMA_BUILD @@ -42,7 +41,6 @@ LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past); - #ifdef __cplusplus } #endif diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1699eb76b..6e3434030 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -968,13 +968,20 @@ struct llama_server_context { continue; } - clip_image_f32 * img_res = clip_image_f32_init(); - if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true)) + clip_image_f32_batch img_res_v; + img_res_v.size = 0; + img_res_v.data = nullptr; + if (!clip_image_preprocess(clp_ctx, img.img_data, img_res_v)) { LOG_TEE("Error processing the given image"); clip_free(clp_ctx); + clip_image_f32_free(img_res_v.data); return false; } + + // note: assumes only one image was returned by clip_image_preprocess + clip_image_f32 * img_res = img_res_v.data; + img.image_tokens = clip_n_patches(clp_ctx); img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx)); if (!img.image_embedding) @@ -989,7 +996,9 @@ struct llama_server_context LOG_TEE("Unable to encode image\n"); return false; } - clip_image_f32_free(img_res); + + clip_image_f32_free(img_res_v.data); + img.request_encode_image = false; } From 8084d554406b767d36b3250b3b787462d5dd626f Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Wed, 14 Feb 2024 11:49:01 +0300 Subject: [PATCH 315/334] cmake : ARM intrinsics detection for MSVC (#5401) --- CMakeLists.txt | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a544f2da6..f8c7f9978 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -855,11 +855,21 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) message(STATUS "ARM detected") if (MSVC) + add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead add_compile_definitions(__ARM_NEON) add_compile_definitions(__ARM_FEATURE_FMA) - add_compile_definitions(__ARM_FEATURE_DOTPROD) - # add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) # MSVC doesn't support vdupq_n_f16, vld1q_f16, vst1q_f16 - add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead + + set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) + string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) + if (GGML_COMPILER_SUPPORT_DOTPROD) + add_compile_definitions(__ARM_FEATURE_DOTPROD) + endif () + check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) + if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) + add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + endif () + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) else() check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") From ccbb277f4642fc0d84c72dbc0d51ed2df418d6ce Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Wed, 14 Feb 2024 15:49:42 +0100 Subject: [PATCH 316/334] llava : update README.md (#5489) * Update README.md * Update README.md * Update examples/llava/README.md --------- Co-authored-by: Georgi Gerganov --- examples/llava/README.md | 46 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 42 insertions(+), 4 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index e2ef0eff1..1d5374f2a 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -1,10 +1,12 @@ # LLaVA -Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants. +Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants, +as well as llava-1.6 [llava-v1.6](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) variants. The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b) and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b) models are available. +For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](https://huggingface.co/cmp-nct/llava-1.6-gguf) After API is confirmed, more models will be supported / uploaded. @@ -18,6 +20,7 @@ After building, run: `./llava-cli` to see the usage. For example: ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. +**note**: For GPU offloading ensure to use the `-ngl` flag just like usual ## LLaVA 1.5 @@ -55,11 +58,46 @@ python ./convert.py ../llava-v1.5-7b Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory. -## LLaVA 1.6 +## LLaVA 1.6 gguf conversion + +1) Backup your pth/safetensor model files as llava-surgery modifies them +2) Use `python llava-surgery-v2.py -C -m /path/to/hf-model` which also supports llava-1.5 variants pytorch as well as safetensor models: +- you will find a llava.projector and a llava.clip file in your model directory +3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config.json) +4) Create the visual gguf model: `python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip_model_is_vision` +- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP +5) Everything else as usual: convert.py the hf model, quantize as needed +**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) +**note** llava-1.6 greatly benefits from batched prompt processing (defaults work) + +## llava-cli templating and llava-1.6 prompting + +llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."` +For llava-1.5 models which are not vicuna (mistral and Yi) you need to adapt system prompt as well as user prompt, for this purpose llava-cli has a basic templating system: + +**For Mistral and using llava-cli binary:** +Add this: `-p "\nUSER:\nProvide a full description.\nASSISTANT:\n"` +The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role + +**For the 34B this should work:** +Add this: `-e -p <|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n\nProvide a full description.<|im_end|><|im_start|>assistant\n` + + +## How to know if you are running in llava-1.5 or llava-1.6 mode + +When running llava-cli you will see a visual information right before the prompt is being processed: + +**Llava-1.5:** +`encode_image_with_clip: image embedding created: 576 tokens` + +**Llava-1.6 (anything above 576):** +`encode_image_with_clip: image embedding created: 2880 tokens` + + +Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6 + -- Use `llava-surgery-v2.py` -- TODO: add detailed instructions ## TODO From 594fca3fefe27b8e95cfb1656eb0e160ad15a793 Mon Sep 17 00:00:00 2001 From: Rune <43761327+Rune-AI@users.noreply.github.com> Date: Wed, 14 Feb 2024 16:15:49 +0100 Subject: [PATCH 317/334] readme : fix typo (#5490) executabhle -> executable --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0b4efdd33..0c4ee5a27 100644 --- a/README.md +++ b/README.md @@ -958,7 +958,7 @@ We have three Docker images available for this project: 1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) 2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) -3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executabhle file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) Additionally, there the following images, similar to the above: From 704359e29985a06a389337a2617b7f3fa8eff908 Mon Sep 17 00:00:00 2001 From: Neuman Vong Date: Thu, 15 Feb 2024 17:11:15 +1100 Subject: [PATCH 318/334] vulkan: Find optimal memory type but with fallback (#5381) * @0cc4m feedback * More feedback @0cc4m --- ggml-vulkan.cpp | 65 ++++++++++++++++++++++++++++++++----------------- 1 file changed, 42 insertions(+), 23 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 7834e635c..1fad24fd1 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -707,9 +707,21 @@ static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { q.cmd_buffer_idx = 0; } -static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { +static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) { + for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) { + vk::MemoryType memory_type = mem_props->memoryTypes[i]; + if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) && + (flags & memory_type.propertyFlags) == flags && + mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) { + return static_cast(i); + } + } + return UINT32_MAX; +} + +static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; + std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl; #endif vk_buffer buf = std::make_shared(); @@ -736,15 +748,15 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz uint32_t memory_type_index = UINT32_MAX; - for (uint32_t i = 0; i < mem_props.memoryTypeCount; ++i) { - vk::MemoryType memory_type = mem_props.memoryTypes[i]; - if ((mem_req.memoryTypeBits & ((uint64_t)1 << i)) && (req_flags & memory_type.propertyFlags) == req_flags && mem_props.memoryHeaps[memory_type.heapIndex].size >= mem_req.size) { - memory_type_index = i; - break; - } + memory_type_index = find_properties(&mem_props, &mem_req, req_flags); + buf->memory_property_flags = req_flags; + + if (memory_type_index == UINT32_MAX && fallback_flags) { + memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags); + buf->memory_property_flags = fallback_flags; } - if (memory_type_index >= mem_props.memoryTypeCount) { + if (memory_type_index == UINT32_MAX) { ctx->device.lock()->device.destroyBuffer(buf->buffer); buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); @@ -758,10 +770,9 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz buf->size = 0; throw e; } - buf->memory_property_flags = req_flags; buf->ptr = nullptr; - if (req_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); } @@ -778,9 +789,9 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz return buf; } -static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) { try { - return ggml_vk_create_buffer(ctx, size, req_flags); + return ggml_vk_create_buffer(ctx, size, req_flags, fallback_flags); } catch (const vk::SystemError& e) { std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -791,16 +802,16 @@ static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) { vk_buffer buf; try { - buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); - } catch (const vk::SystemError& e) { if (ctx->device.lock()->uma) { // Fall back to host memory type - buf = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { - std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; - std::cerr << "ggml_vulkan: " << e.what() << std::endl; - throw e; + buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } + } catch (const vk::SystemError& e) { + std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; + throw e; } return buf; @@ -1422,7 +1433,9 @@ static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif - vk_buffer buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + vk_buffer buf = ggml_vk_create_buffer(ctx, size, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", @@ -1568,7 +1581,9 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) { if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) { ggml_vk_destroy_buffer(ctx->sync_staging); - ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } } @@ -4082,7 +4097,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { std::cerr << "ggml_vk_preallocate_buffers(qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; #endif #if defined(GGML_VULKAN_RUN_TESTS) - ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); ggml_vk_test_transfer(ctx, 8192 * 1000, false); ggml_vk_test_transfer(ctx, 8192 * 1000, true); @@ -4174,7 +4191,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { if (ctx->staging != nullptr) { ggml_vk_destroy_buffer(ctx->staging); } - ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } } From 7930a8a6e89a04c77c51e3ae5dc1cd8e845b6b8f Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Thu, 15 Feb 2024 08:59:18 +0100 Subject: [PATCH 319/334] llaba : hotfix for llava-1.6 image number (#5495) Co-authored-by: John --- examples/llava/llava.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 22953417f..4ed310a0e 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -100,7 +100,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector int num_patches_width = grid_shape.first; // grid 1-4 int num_patches_height = grid_shape.second; // grid 1-4 - const size_t num_images = num_patches_width + num_patches_height + 1; + const size_t num_images = num_patches_width * num_patches_height + 1; // TODO: size calculation is not calculated - it's only tens of MB size_t ctx_size = 0; From 0d4177126b0556e202efb85bf3f768be81076400 Mon Sep 17 00:00:00 2001 From: Elbios <141279586+Elbios@users.noreply.github.com> Date: Thu, 15 Feb 2024 09:01:57 +0100 Subject: [PATCH 320/334] llava : fix memory management bug (#5491) * Fix memory management in llava and server code Fixes this error: llama_new_context_with_model: graph splits (measure): 3 Available slots: -> Slot 0 - max context: 6000 {"timestamp":1707926446,"level":"INFO","function":"main","line":2623,"message":"model loaded"} all slots are idle and system prompt is empty, clear the KV cache slot 0 - loaded image slot 0 is processing [task id: 0] slot 0 : kv cache rm - [0, end) slot 0 - encoding image [id: 1] munmap_chunk(): invalid pointer Aborted * Make it cleaner by checking size in batch free wrapper --- examples/llava/clip.cpp | 24 +++++++++++++++++------- examples/llava/clip.h | 2 ++ examples/server/server.cpp | 11 +++++++++-- 3 files changed, 28 insertions(+), 9 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 9c5091e61..2cad27e82 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1230,8 +1230,20 @@ struct clip_image_f32 * clip_image_f32_init() { return new clip_image_f32(); } -void clip_image_u8_free (struct clip_image_u8 * img) { delete img; } +void clip_image_u8_free(struct clip_image_u8 * img) { delete img; } void clip_image_f32_free(struct clip_image_f32 * img) { delete img; } +void clip_image_u8_batch_free(struct clip_image_u8_batch & batch) { + if (batch.size > 0) { + delete[] batch.data; + batch.size = 0; + } +} +void clip_image_f32_batch_free(struct clip_image_f32_batch & batch) { + if (batch.size > 0) { + delete[] batch.data; + batch.size = 0; + } +} static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) { img->nx = nx; @@ -1494,11 +1506,8 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli pad_to_square = false; } // free the previous res_imgs if any set - if (res_imgs.size > 0 && res_imgs.size < 100) { - for (size_t i = 0; i < res_imgs.size; i++) { - clip_image_f32_free(&(res_imgs.data[i])); - } - delete[] res_imgs.data; + if (res_imgs.size > 0) { + clip_image_f32_batch_free(res_imgs); } res_imgs.data = nullptr; res_imgs.size = 0; @@ -1650,7 +1659,8 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli res_imgs.size = 1; res_imgs.data = new clip_image_f32[res_imgs.size]; - res_imgs.data[0] = std::move(*res); + res_imgs.data[0] = *res; + clip_image_f32_free(res); return true; } diff --git a/examples/llava/clip.h b/examples/llava/clip.h index cd9a4022f..e5bd54924 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -60,6 +60,8 @@ CLIP_API struct clip_image_f32 * clip_image_f32_init(); CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); +CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch & batch); +CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch); CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6e3434030..2decd7762 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -975,7 +975,12 @@ struct llama_server_context { LOG_TEE("Error processing the given image"); clip_free(clp_ctx); - clip_image_f32_free(img_res_v.data); + clip_image_f32_batch_free(img_res_v); + return false; + } + if (img_res_v.size == 0) + { + LOG_TEE("Error processing the given image"); return false; } @@ -987,6 +992,7 @@ struct llama_server_context if (!img.image_embedding) { LOG_TEE("Unable to allocate memory for image embeddings\n"); + clip_image_f32_batch_free(img_res_v); clip_free(clp_ctx); return false; } @@ -994,10 +1000,11 @@ struct llama_server_context if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding)) { LOG_TEE("Unable to encode image\n"); + clip_image_f32_batch_free(img_res_v); return false; } - clip_image_f32_free(img_res_v.data); + clip_image_f32_batch_free(img_res_v); img.request_encode_image = false; } From 73122473ffd73030146276dbb85da7c8021a3ee4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C3=ABl=20de=20Vries?= Date: Thu, 15 Feb 2024 14:14:37 +0100 Subject: [PATCH 321/334] fix(gguf-py): special tokens are no longer skipped when add__token is set to false (#5487) * fix(gguf-py): special tokens are no longer skipped when add__token is set to false * fix(gguf-py): added missing cls and mask token ids to the gguf metadata --- gguf-py/gguf/constants.py | 4 ++++ gguf-py/gguf/gguf_writer.py | 6 ++++++ gguf-py/gguf/vocab.py | 6 +----- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5fba01714..9986ce9de 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -73,6 +73,8 @@ class Keys: UNK_ID = "tokenizer.ggml.unknown_token_id" SEP_ID = "tokenizer.ggml.seperator_token_id" PAD_ID = "tokenizer.ggml.padding_token_id" + CLS_ID = "tokenizer.ggml.cls_token_id" + MASK_ID = "tokenizer.ggml.mask_token_id" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" ADD_PREFIX = "tokenizer.ggml.add_space_prefix" @@ -685,5 +687,7 @@ KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID +KEY_TOKENIZER_CLS_ID = Keys.Tokenizer.CLS_ID +KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d87bd8e88..26724bf94 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -414,6 +414,12 @@ class GGUFWriter: def add_pad_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.PAD_ID, id) + def add_cls_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.CLS_ID, id) + + def add_mask_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.MASK_ID, id) + def add_add_bos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_BOS, value) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index cd1942975..a23136b18 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -29,7 +29,7 @@ class SpecialVocab: if special_token_types is not None: self.special_token_types = special_token_types else: - self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') + self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask') self._load(Path(path)) def __repr__(self) -> str: @@ -152,10 +152,6 @@ class SpecialVocab: add_entry = tokenizer_config.get(f'add_{typ}_token') if isinstance(add_entry, bool): self.add_special_token[typ] = add_entry - if not added_tokens: - # We will need this to get the content for the token, so if it's empty - # may as well just give up. - continue entry = tokenizer_config.get(f'{typ}_token') if isinstance(entry, str): tc_content = entry From 9350a1cf21b1492c69b20175b73a419b897d6a3a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 15 Feb 2024 15:41:15 +0200 Subject: [PATCH 322/334] scripts : add hf.sh helper script (#5501) * scripts : add hf.sh helper scripts * hf : add error logs * hf : add support for --repo and --file --- scripts/hf.sh | 107 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) create mode 100755 scripts/hf.sh diff --git a/scripts/hf.sh b/scripts/hf.sh new file mode 100755 index 000000000..1e9e5a6ea --- /dev/null +++ b/scripts/hf.sh @@ -0,0 +1,107 @@ +#!/bin/bash +# +# Shortcut for downloading HF models +# +# Usage: +# ./main -m $(./examples/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./examples/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./examples/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf) +# + +# all logs go to stderr +function log { + echo "$@" 1>&2 +} + +function usage { + log "Usage: $0 [[--url] ] [--repo ] [--file ] [-h|--help]" + exit 1 +} + +# check for curl or wget +function has_cmd { + if ! [ -x "$(command -v $1)" ]; then + return 1 + fi +} + +if has_cmd wget; then + cmd="wget -q --show-progress -c -O %s %s" +elif has_cmd curl; then + cmd="curl -C - -f -o %s -L %s" +else + log "[E] curl or wget not found" + exit 1 +fi + +url="" +repo="" +file="" + +# parse args +while [[ $# -gt 0 ]]; do + case "$1" in + --url) + url="$2" + shift 2 + ;; + --repo) + repo="$2" + shift 2 + ;; + --file) + file="$2" + shift 2 + ;; + -h|--help) + usage + ;; + *) + url="$1" + shift + ;; + esac +done + +if [ -n "$repo" ] && [ -n "$file" ]; then + url="https://huggingface.co/$repo/resolve/main/$file" +fi + +if [ -z "$url" ]; then + log "[E] missing --url" + usage +fi + +# check if the URL is a HuggingFace model, and if so, try to download it +is_url=false + +if [[ ${#url} -gt 22 ]]; then + if [[ ${url:0:22} == "https://huggingface.co" ]]; then + is_url=true + fi +fi + +if [ "$is_url" = false ]; then + log "[E] invalid URL, must start with https://huggingface.co" + exit 0 +fi + +# replace "blob/main" with "resolve/main" +url=${url/blob\/main/resolve\/main} + +basename=$(basename $url) + +log "[+] attempting to download $basename" + +if [ -n "$cmd" ]; then + cmd=$(printf "$cmd" "$basename" "$url") + log "[+] $cmd" + if $cmd; then + echo $basename + exit 0 + fi +fi + +log "[-] failed to download" + +exit 1 From 9060a1e9dfca6038906e819be5fa42217f49028c Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 15 Feb 2024 16:49:01 +0100 Subject: [PATCH 323/334] cuda : print message when initialization fails (#5512) * cuda : print message when initialization fails * use CUDA_NAME both times --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 96976f248..b35fcb7fd 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7943,6 +7943,7 @@ GGML_CALL void ggml_init_cublas() { if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) { initialized = true; g_cublas_loaded = false; + fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__); return; } From c06e45d72983d9ace7b1535f7e7ea258d212169e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 15 Feb 2024 18:49:08 +0200 Subject: [PATCH 324/334] clip : fix wrong loop condition --- examples/llava/clip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 2cad27e82..98d512f67 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1103,7 +1103,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); printf("v_image_grid_pinpoints: "); - for (int i = 0; i < 32 & hparams.image_grid_pinpoints[i]!=0; ++i) { + for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) { printf("%d ", hparams.image_grid_pinpoints[i]); } printf("\n"); From 4524290e87b8e107cc2b56e1251751546f4b9051 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Thu, 15 Feb 2024 11:21:49 -0600 Subject: [PATCH 325/334] Use correct type of pooling for embedding models (#5500) Use correct type of pooling for embedding models --- convert-hf-to-gguf.py | 24 ++++++++++- gguf-py/gguf/constants.py | 8 +++- gguf-py/gguf/gguf_writer.py | 5 ++- llama.cpp | 82 +++++++++++++++++++++++++------------ llama.h | 6 +++ 5 files changed, 94 insertions(+), 31 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ae471481d..9771fccf9 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1650,7 +1650,29 @@ class BertModel(Model): def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_causal_attention(False) - self.gguf_writer.add_pooling_layer(True) + + # get pooling path + with open(self.dir_model / "modules.json", encoding="utf-8") as f: + modules = json.load(f) + pooling_path = None + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break + + # get pooling type + pooling_type = gguf.PoolingType.NONE + if pooling_path is not None: + with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: + pooling = json.load(f) + if pooling["pooling_mode_mean_tokens"]: + pooling_type = gguf.PoolingType.MEAN + elif pooling["pooling_mode_cls_token"]: + pooling_type = gguf.PoolingType.CLS + else: + raise NotImplementedError("Only MEAN and CLS pooling types supported") + + self.gguf_writer.add_pooling_type(pooling_type.value) def set_vocab(self): path = self.dir_model diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 9986ce9de..114a9a974 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -40,7 +40,7 @@ class Keys: TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" EXPERT_COUNT = "{arch}.expert_count" EXPERT_USED_COUNT = "{arch}.expert_used_count" - POOLING_LAYER = "{arch}.pooling_layer" + POOLING_TYPE = "{arch}.pooling_type" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -561,6 +561,12 @@ class RopeScalingType(Enum): YARN = 'yarn' +class PoolingType(IntEnum): + NONE = 0 + MEAN = 1 + CLS = 2 + + class GGMLQuantizationType(IntEnum): F32 = 0 F16 = 1 diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 26724bf94..e4681475c 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -19,6 +19,7 @@ from .constants import ( GGUFValueType, Keys, RopeScalingType, + PoolingType, TokenType, ) @@ -360,8 +361,8 @@ class GGUFWriter: def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) - def add_pooling_layer(self, value: bool) -> None: - self.add_bool(Keys.LLM.POOLING_LAYER.format(arch=self.arch), value) + def add_pooling_type(self, value: PoolingType) -> None: + self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value) def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) diff --git a/llama.cpp b/llama.cpp index 14e8821cd..aceb9c25a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -256,7 +256,7 @@ enum llm_kv { LLM_KV_TENSOR_DATA_LAYOUT, LLM_KV_EXPERT_COUNT, LLM_KV_EXPERT_USED_COUNT, - LLM_KV_POOLING_LAYER, + LLM_KV_POOLING_TYPE, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, @@ -314,7 +314,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, - { LLM_KV_POOLING_LAYER, "%s.pooling_layer" }, + { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -1561,7 +1561,7 @@ struct llama_hparams { float f_max_alibi_bias; bool causal_attn = true; - bool pooling_layer = false; + uint32_t pooling_type = LLAMA_POOLING_NONE; bool operator!=(const llama_hparams & other) const { @@ -1924,7 +1924,8 @@ struct llama_context { struct ggml_tensor * inp_pos; // I32 [n_batch] struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] struct ggml_tensor * inp_K_shift; // I32 [n_ctx] - struct ggml_tensor * inp_sum; // F32 [n_batch, n_batch] + struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] + struct ggml_tensor * inp_cls; // I32 [n_batch] #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -3086,7 +3087,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_LAYER, hparams.pooling_layer); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); switch (hparams.n_layer) { case 3: @@ -3107,7 +3108,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_LAYER, hparams.pooling_layer); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); if (hparams.n_layer == 12 && hparams.n_embd == 768) { model.type = e_model::MODEL_137M; @@ -4934,7 +4935,7 @@ struct llm_build_context { const int32_t n_orig_ctx; const bool do_rope_shift; - const bool do_pooling; + const uint32_t pooling_type; const llm_build_cb & cb; @@ -4978,7 +4979,7 @@ struct llm_build_context { kv_head (worst_case ? n_ctx - n_tokens : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), do_rope_shift (worst_case || kv_self.has_shift), - do_pooling (hparams.pooling_layer && cparams.do_pooling), + pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE), cb (cb), buf_compute_meta (lctx.buf_compute_meta) { // all initializations should be done in init() @@ -5835,7 +5836,8 @@ struct llm_build_context { // get input vectors with right size const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type); struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - struct ggml_tensor * inp_sum = ggml_view_2d(ctx0, lctx.inp_sum, n_tokens, n_tokens, stride1, 0); + struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0); + struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0); // construct input embeddings (token, type, position) inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); @@ -5952,8 +5954,12 @@ struct llm_build_context { cur = inpL; // pooling layer - if (do_pooling) { - cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_sum); + if (pooling_type == LLAMA_POOLING_MEAN) { + cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean); + } else if (pooling_type == LLAMA_POOLING_CLS) { + cur = ggml_get_rows(ctx0, cur, inp_cls); + } else { + GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type"); } cb(cur, "result_embd", -1); @@ -7501,15 +7507,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - { - assert(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); - float * data = (float *) lctx.inp_sum->data; - - for (int i = 0; i < batch.n_tokens; ++i) { - data[i] = 1.0f/float(batch.n_tokens); - } - } - if (kv_self.has_shift) { const int64_t n_ctx = cparams.n_ctx; @@ -7522,17 +7519,46 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (hparams.pooling_layer && cparams.do_pooling) { + if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) { const int64_t n_tokens = batch.n_tokens; - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_sum->buffer)); - float * data = (float *) lctx.inp_sum->data; + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); + float * data = (float *) lctx.inp_mean->data; - memset(lctx.inp_sum->data, 0, batch.n_tokens * batch.n_tokens * ggml_element_size(lctx.inp_sum)); + memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean)); + + std::vector sum(n_tokens, 0); + for (int i = 0; i < n_tokens; ++i) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + sum[seq_id] += 1; + } + + std::vector div(n_tokens, 0.0f); + for (int i = 0; i < n_tokens; ++i) { + const uint64_t s = sum[i]; + if (s > 0) { + div[i] = 1.0f/float(s); + } + } for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; - data[seq_id*n_tokens + i] = 1.0f; + data[seq_id*n_tokens + i] = div[seq_id]; + } + } + + if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) { + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); + uint32_t * data = (uint32_t *) lctx.inp_cls->data; + + for (int i = 0; i < n_tokens; ++i) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + const llama_pos pos = batch.pos[i]; + if (pos == 0) { + data[seq_id] = i; + } } } } @@ -11417,14 +11443,16 @@ struct llama_context * llama_new_context_with_model( ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); - ctx->inp_sum = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); + ctx->inp_mean = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch); + ctx->inp_cls = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); ggml_set_name(ctx->inp_tokens, "inp_tokens"); ggml_set_name(ctx->inp_embd, "inp_embd"); ggml_set_name(ctx->inp_pos, "inp_pos"); ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask"); ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); - ggml_set_name(ctx->inp_sum, "inp_sum"); + ggml_set_name(ctx->inp_mean, "inp_mean"); + ggml_set_name(ctx->inp_cls, "inp_cls"); ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); diff --git a/llama.h b/llama.h index 5ef78ec96..4a26bd619 100644 --- a/llama.h +++ b/llama.h @@ -112,6 +112,12 @@ extern "C" { LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, }; + enum llama_pooling_type { + LLAMA_POOLING_NONE = 0, + LLAMA_POOLING_MEAN = 1, + LLAMA_POOLING_CLS = 2, + }; + enum llama_split_mode { LLAMA_SPLIT_NONE = 0, // single GPU LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs From 594845aab1c6775877f6d9545a51dc0f8d0b3d77 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 16 Feb 2024 09:57:55 +0200 Subject: [PATCH 326/334] ci : fix BERT model download and convert --- ci/run.sh | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ci/run.sh b/ci/run.sh index a4264d775..979b4a793 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -580,6 +580,10 @@ function gg_run_embd_bge_small { gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/sentence_bert_config.json gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/vocab.txt + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/modules.json + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json + + gg_wget models-mnt/bge-small/1_Pooling https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/1_Pooling/config.json path_models="../models-mnt/bge-small" From 60ed04cf82dc91ade725dd7ad53f0ee81f76eccf Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 16 Feb 2024 10:24:39 +0100 Subject: [PATCH 327/334] llava : fix clip-model-is-vision flag in README.md (#5509) * llava: fix clip-model-is-vision flag in README.md This commit fixes the flag `--clip_model_is_vision` in README.md which is does not match the actual flag: ```console $ python convert-image-encoder-to-gguf.py --help ... --clip-model-is-vision The clip model is a pure vision model (ShareGPT4V vision extract for example) ``` Signed-off-by: Daniel Bevenius * llava: update link to vit config in README.md Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index 1d5374f2a..57eb42932 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -63,8 +63,8 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director 1) Backup your pth/safetensor model files as llava-surgery modifies them 2) Use `python llava-surgery-v2.py -C -m /path/to/hf-model` which also supports llava-1.5 variants pytorch as well as safetensor models: - you will find a llava.projector and a llava.clip file in your model directory -3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config.json) -4) Create the visual gguf model: `python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip_model_is_vision` +3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config_vit.json) and rename it to config.json. +4) Create the visual gguf model: `python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip-model-is-vision` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP 5) Everything else as usual: convert.py the hf model, quantize as needed **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) From f486f6e1e5e9d01603d9325ab3e05f1edb362a95 Mon Sep 17 00:00:00 2001 From: bmwl Date: Fri, 16 Feb 2024 01:31:07 -0800 Subject: [PATCH 328/334] ggml : add numa options (#5377) * Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverted Makefile * Fixed include * Removed sched.h from ggml.h, moved ggml_get_numa_affinity into ggml.c, removed trailing whitespace and fixed up a few inconsistent variables * removed trailing whitespace * Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverting Makefile * Fixed a number of issues with the move from BOOL to ggml_numa_strategies. Added a note about mirror mode note being implemented yet * Removing MIRROR_MODE code for this PR * Removing last bit of MIRROR_MODE code for this PR * Removing unneeded branch in server.cpp example and moving get_numa_affinity and making it static * Fixed lingering init_llama_backend() bool calls in tests and examples * Remote enum llama_numa_strategies * Revert bad merge with dynatemp flags * add missing enum ggml_numa_strategies declaration and revert sync problem with master * add missing enum ggml_numa_strategies declaration * fixed ggml_init_numa variable * Update ggml.h Co-authored-by: Jared Van Bortel * Update READMEs with info about numa flags, change INTERLEAVE strategy name to DISTRIBUTE everywhere, implement the improved distribution strategy from @rankaiyx, fix a spelling mistake and un-merge some bad merges * split numa init out from llama_backend_init and created llama_numa_init. Updated all code paths and samples * Fix up some boolean vs enum comparisons * Added #ifdefs for non-Linux OS that don't have cpu_set_t datatype * Update ggml.h Align enum values Co-authored-by: Georgi Gerganov * Update ggml.c Remove whitespace Co-authored-by: Georgi Gerganov * Update ggml.c align paremeters Co-authored-by: Georgi Gerganov * Update examples/server/server.cpp remove whitespace and align brace Co-authored-by: Georgi Gerganov * Update common/common.cpp Remove whitespace and align brace Co-authored-by: Georgi Gerganov * unified ggml_numa_strategy enum and fixed text alignment in server.cpp example * Update ggml.c simplified return for platforms without NUMA support Co-authored-by: Jared Van Bortel * removed redundant else from cli argument processing of --numa * whitespace --------- Co-authored-by: root Co-authored-by: Jared Van Bortel Co-authored-by: Georgi Gerganov Co-authored-by: Jared Van Bortel --- common/common.cpp | 20 +++-- common/common.h | 2 +- examples/batched-bench/batched-bench.cpp | 3 +- examples/batched.swift/Sources/main.swift | 2 +- examples/batched/batched.cpp | 3 +- examples/beam-search/beam-search.cpp | 3 +- examples/embedding/embedding.cpp | 3 +- examples/imatrix/imatrix.cpp | 3 +- examples/infill/infill.cpp | 3 +- examples/llama-bench/llama-bench.cpp | 3 +- .../app/src/main/cpp/llama-android.cpp | 4 +- .../llama.cpp.swift/LibLlama.swift | 2 +- examples/llava/llava-cli.cpp | 3 +- examples/lookahead/lookahead.cpp | 3 +- examples/lookup/lookup.cpp | 3 +- examples/main/README.md | 6 +- examples/main/main.cpp | 3 +- examples/parallel/parallel.cpp | 3 +- examples/passkey/passkey.cpp | 3 +- examples/perplexity/perplexity.cpp | 3 +- examples/quantize/quantize.cpp | 2 +- examples/server/README.md | 7 ++ examples/server/server.cpp | 22 +++-- examples/simple/simple.cpp | 3 +- examples/speculative/speculative.cpp | 3 +- examples/tokenize/tokenize.cpp | 2 +- ggml.c | 80 ++++++++++++++++--- ggml.h | 12 ++- llama.cpp | 14 ++-- llama.h | 5 +- tests/test-autorelease.cpp | 2 +- tests/test-model-load-cancel.cpp | 2 +- tests/test-tokenizer-0-falcon.cpp | 2 +- tests/test-tokenizer-0-llama.cpp | 2 +- tests/test-tokenizer-1-bpe.cpp | 2 +- tests/test-tokenizer-1-llama.cpp | 2 +- 36 files changed, 178 insertions(+), 62 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index f64da2cb6..c5e83cc2a 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -671,7 +671,15 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } else if (arg == "--no-mmap") { params.use_mmap = false; } else if (arg == "--numa") { - params.numa = true; + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else { invalid_param = true; break; } } else if (arg == "--verbose-prompt") { params.verbose_prompt = true; } else if (arg == "--no-display-prompt") { @@ -935,7 +943,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -tb N, --threads-batch N\n"); printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n"); printf(" -td N, --threads-draft N"); - printf(" number of threads to use during generation (default: same as --threads)"); + printf(" number of threads to use during generation (default: same as --threads)\n"); printf(" -tbd N, --threads-batch-draft N\n"); printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n"); printf(" -p PROMPT, --prompt PROMPT\n"); @@ -1005,7 +1013,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks); printf(" --multiple-choice compute multiple choice score over random tasks from datafile supplied with -f\n"); printf(" --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks); - printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base"); + printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base\n"); printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); @@ -1022,7 +1030,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); + printf(" - distribute: spread execution evenly over all nodes\n"); + printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); + printf(" - numactl: use the CPU map provided by numactl\n"); printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); if (llama_supports_gpu_offload()) { @@ -1689,7 +1700,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false"); fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false"); - fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false"); fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); diff --git a/common/common.h b/common/common.h index 9bdd45cf9..74c136995 100644 --- a/common/common.h +++ b/common/common.h @@ -76,6 +76,7 @@ struct gpt_params { float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; // // sampling parameters struct llama_sampling_params sparams; @@ -134,7 +135,6 @@ struct gpt_params { bool logits_all = false; // return logits for all tokens in the batch bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory - bool numa = false; // attempt optimizations that help on some NUMA systems bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation bool infill = false; // use infill mode diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index b52d68457..55dfd9784 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -82,7 +82,8 @@ int main(int argc, char ** argv) { // init LLM - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); // initialize the model diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index 4d0005349..d75c503d5 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -17,7 +17,7 @@ let n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(argu let n_len: Int = 32 // init LLM -llama_backend_init(false) +llama_backend_init() defer { llama_backend_free() } diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index b1775e0b0..eab636692 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -50,7 +50,8 @@ int main(int argc, char ** argv) { // init LLM - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); // initialize the model diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp index 679b382e1..866c6d7a6 100644 --- a/examples/beam-search/beam-search.cpp +++ b/examples/beam-search/beam-search.cpp @@ -119,7 +119,8 @@ int main(int argc, char ** argv) // Init LLM : //--------------------------------- - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index b4688cf51..acff715e9 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -74,7 +74,8 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index bc9f6fa68..f21bc48f3 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -568,7 +568,8 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model_params mparams = llama_model_params_from_gpt_params(params); diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 72fb133b4..92c67b7cf 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -202,7 +202,8 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); LOG("%s: llama backend init\n", __func__); - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index ddb0ba064..11410f8ae 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -1151,8 +1151,7 @@ int main(int argc, char ** argv) { if (!params.verbose) { llama_log_set(llama_null_log_callback, NULL); } - bool numa = false; - llama_backend_init(numa); + llama_backend_init(); // initialize printer std::unique_ptr p; diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp index d5e705dce..2beb1e0d5 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp @@ -274,8 +274,8 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) { - llama_backend_init(numa); +Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) { + llama_backend_init(); } extern "C" diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index fc79fd346..58fcf40c6 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -51,7 +51,7 @@ actor LlamaContext { } static func create_context(path: String) throws -> LlamaContext { - llama_backend_init(false) + llama_backend_init() var model_params = llama_model_default_params() #if targetEnvironment(simulator) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index bef7f7c95..e29da6cb2 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -218,7 +218,8 @@ static struct llava_context * llava_init(gpt_params * params) { auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); - llama_backend_init(params->numa); + llama_backend_init(); + llama_numa_init(params->numa); llama_model_params model_params = llama_model_params_from_gpt_params(*params); diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index e55a15a1b..e2551e7a4 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -54,7 +54,8 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS // init llama.cpp - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model = NULL; llama_context * ctx = NULL; diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 18235b8a1..b53fae110 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -31,7 +31,8 @@ int main(int argc, char ** argv){ #endif // LOG_DISABLE_LOGS // init llama.cpp - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model = NULL; llama_context * ctx = NULL; diff --git a/examples/main/README.md b/examples/main/README.md index c7997f665..7f84e4262 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -283,7 +283,11 @@ These options help improve the performance and memory usage of the LLaMA models. ### NUMA support -- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. +- `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes. +- `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node. +- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus. + + These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. ### Memory Float 32 diff --git a/examples/main/main.cpp b/examples/main/main.cpp index e8ab8cbae..f5d2f4893 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -185,7 +185,8 @@ int main(int argc, char ** argv) { } LOG("%s: llama backend init\n", __func__); - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index d2e074d9e..7d11fcd59 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -122,7 +122,8 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS // init llama.cpp - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model = NULL; llama_context * ctx = NULL; diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 5c0022832..e12a1cdf1 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -71,7 +71,8 @@ int main(int argc, char ** argv) { // init LLM - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); // initialize the model diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index b2c131d4c..67d2d3293 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1809,7 +1809,8 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 85f403ffc..4a5c504e3 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -237,7 +237,7 @@ int main(int argc, char ** argv) { params.imatrix = &imatrix_data; } - llama_backend_init(false); + llama_backend_init(); // parse command line arguments const std::string fname_inp = argv[arg_idx]; diff --git a/examples/server/README.md b/examples/server/README.md index 0f7373ae8..8e141d22d 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -16,6 +16,13 @@ Command line options: - `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. - `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. - `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. +- `--numa STRATEGY`: Attempt one of the below optimization strategies that help on some NUMA systems +- `--numa distribute`: Spread execution evenly over all nodes +- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on +- `--numa numactl`: Use the CPU map provided by numactl +if run without this previously, it is recommended to drop the system page cache before using this +see https://github.com/ggerganov/llama.cpp/issues/1437 + - `--numa`: Attempt optimizations that help on some NUMA systems. - `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2decd7762..912c750cc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1855,7 +1855,10 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); + printf(" - distribute: spread execution evenly over all nodes\n"); + printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); + printf(" - numactl: use the CPU map provided my numactl\n"); if (llama_supports_gpu_offload()) { printf(" -ngl N, --n-gpu-layers N\n"); printf(" number of layers to store in VRAM\n"); @@ -2264,9 +2267,17 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, { params.use_mmap = false; } - else if (arg == "--numa") - { - params.numa = true; + else if (arg == "--numa") { + if (++i >= argc) { + invalid_param = true; + break; + } else { + std::string value(argv[i]); + /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else { invalid_param = true; break; } + } } else if (arg == "--embedding") { @@ -2497,7 +2508,8 @@ int main(int argc, char **argv) params.model_alias = params.model; } - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER}, {"commit", LLAMA_COMMIT}}); diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 9cfde8308..39e2d8ea4 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -31,7 +31,8 @@ int main(int argc, char ** argv) { // init LLM - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); // initialize the model diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 7b3af01f3..3848791d4 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -50,7 +50,8 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS // init llama.cpp - llama_backend_init(params.numa); + llama_backend_init(); + llama_numa_init(params.numa); llama_model * model_tgt = NULL; llama_model * model_dft = NULL; diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index 4ff8e3fa7..d95a92475 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -17,7 +17,7 @@ int main(int argc, char ** argv) { const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids"; - llama_backend_init(false); + llama_backend_init(); llama_model_params model_params = llama_model_default_params(); model_params.vocab_only = true; diff --git a/ggml.c b/ggml.c index d921d82fe..4e302fb7d 100644 --- a/ggml.c +++ b/ggml.c @@ -1954,9 +1954,16 @@ struct ggml_numa_node { }; struct ggml_numa_nodes { + enum ggml_numa_strategy numa_strategy; struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; uint32_t n_nodes; uint32_t total_cpus; // hardware threads on system + uint32_t current_node; // node on which main process is execting +#ifdef __linux__ + cpu_set_t cpuset; // cpuset from numactl +#else + uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype +#endif }; // @@ -1990,7 +1997,22 @@ inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } -void ggml_numa_init(void) { +#ifdef __linux__ +static cpu_set_t ggml_get_numa_affinity(void) { + cpu_set_t cpuset; + pthread_t thread; + thread = pthread_self(); + CPU_ZERO(&cpuset); + pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); + return cpuset; +} +#else +static uint32_t ggml_get_numa_affinity(void) { + return 0; // no NUMA support +} +#endif + +void ggml_numa_init(enum ggml_numa_strategy numa_flag) { if (g_state.numa.n_nodes > 0) { fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); @@ -2002,6 +2024,13 @@ void ggml_numa_init(void) { char path[256]; int rv; + // set numa scheme + g_state.numa.numa_strategy = numa_flag; + + GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); + + g_state.numa.cpuset = ggml_get_numa_affinity(); + // enumerate nodes while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); @@ -2020,11 +2049,17 @@ void ggml_numa_init(void) { GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); - if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1) { + // figure out which node we're on + uint current_cpu; + int getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); + + if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { g_state.numa.n_nodes = 0; return; } + GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); + for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { struct ggml_numa_node * node = &g_state.numa.nodes[n]; GGML_PRINT_DEBUG("CPUs on node %u:", n); @@ -16638,26 +16673,46 @@ typedef pthread_t ggml_thread_t; // Android's libc implementation "bionic" does not support setting affinity #if defined(__linux__) && !defined(__BIONIC__) -static void set_numa_thread_affinity(int thread_n, int n_threads) { +static void set_numa_thread_affinity(int thread_n) { if (!ggml_is_numa()) { return; } - // run thread on node_num thread_n / (threads per node) - const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes); - struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; + int node_num; + int rv; size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + switch(g_state.numa.numa_strategy) { + case GGML_NUMA_STRATEGY_DISTRIBUTE: + // run thread on node_num thread_n / (threads per node) + node_num = thread_n % g_state.numa.n_nodes; + break; + case GGML_NUMA_STRATEGY_ISOLATE: + // run thread on current_node + node_num = g_state.numa.current_node; + break; + case GGML_NUMA_STRATEGY_NUMACTL: + // use the cpuset that numactl gave us + rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); + } + return; + default: + return; + } + + struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); CPU_ZERO_S(setsize, cpus); for (size_t i = 0; i < node->n_cpus; ++i) { CPU_SET_S(node->cpus[i], setsize, cpus); } - int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", - strerror(rv)); + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); } CPU_FREE(cpus); @@ -16678,8 +16733,7 @@ static void clear_numa_thread_affinity(void) { int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", - strerror(rv)); + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); } CPU_FREE(cpus); @@ -16687,7 +16741,7 @@ static void clear_numa_thread_affinity(void) { #else // TODO: Windows etc. // (the linux implementation may also work on BSD, someone should test) -static void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads); } +static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } static void clear_numa_thread_affinity(void) {} #endif @@ -16987,7 +17041,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { const int n_threads = state->shared->n_threads; - set_numa_thread_affinity(state->ith, n_threads); + set_numa_thread_affinity(state->ith); int node_n = -1; int task_phase = GGML_TASK_FINALIZE; diff --git a/ggml.h b/ggml.h index 01cecc1e1..270018185 100644 --- a/ggml.h +++ b/ggml.h @@ -658,6 +658,16 @@ extern "C" { void * wdata; }; + // numa strategies + enum ggml_numa_strategy { + GGML_NUMA_STRATEGY_DISABLED = 0, + GGML_NUMA_STRATEGY_DISTRIBUTE = 1, + GGML_NUMA_STRATEGY_ISOLATE = 2, + GGML_NUMA_STRATEGY_NUMACTL = 3, + GGML_NUMA_STRATEGY_MIRROR = 4, + GGML_NUMA_STRATEGY_COUNT + }; + // misc GGML_API void ggml_time_init(void); // call this once at the beginning of the program @@ -668,7 +678,7 @@ extern "C" { GGML_API void ggml_print_backtrace(void); - GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems + GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node GGML_API void ggml_print_object (const struct ggml_object * obj); diff --git a/llama.cpp b/llama.cpp index aceb9c25a..08e7b02b4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1034,7 +1034,7 @@ struct llama_mmap { int fd = fileno(file->fp); int flags = MAP_SHARED; // prefetch/readahead impairs performance on NUMA systems - if (numa) { prefetch = 0; } + if (numa) { prefetch = 0; } #ifdef __linux__ // advise the kernel to read the file sequentially (increases readahead) if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) { @@ -11182,7 +11182,7 @@ bool llama_mlock_supported(void) { return llama_supports_mlock(); } -void llama_backend_init(bool numa) { +void llama_backend_init(void) { ggml_time_init(); // needed to initialize f16 tables @@ -11192,15 +11192,17 @@ void llama_backend_init(bool numa) { ggml_free(ctx); } - if (numa) { - ggml_numa_init(); - } - #ifdef GGML_USE_MPI ggml_mpi_backend_init(); #endif } +void llama_numa_init(enum ggml_numa_strategy numa) { + if (numa != GGML_NUMA_STRATEGY_DISABLED) { + ggml_numa_init(numa); + } +} + void llama_backend_free(void) { #ifdef GGML_USE_MPI ggml_mpi_backend_free(); diff --git a/llama.h b/llama.h index 4a26bd619..f4ec6ea63 100644 --- a/llama.h +++ b/llama.h @@ -312,7 +312,10 @@ extern "C" { // Initialize the llama + ggml backend // If numa is true, use NUMA optimizations // Call once at the start of the program - LLAMA_API void llama_backend_init(bool numa); + LLAMA_API void llama_backend_init(void); + + //optional: + LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa); // Call once at the end of the program - currently only used for MPI LLAMA_API void llama_backend_free(void); diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp index 36a23c0bb..57fa00011 100644 --- a/tests/test-autorelease.cpp +++ b/tests/test-autorelease.cpp @@ -12,7 +12,7 @@ int main(int argc, char ** argv) { auto * model_path = get_model_or_exit(argc, argv); std::thread([&model_path]() { - llama_backend_init(false); + llama_backend_init(); auto * model = llama_load_model_from_file(model_path, llama_model_default_params()); auto * ctx = llama_new_context_with_model(model, llama_context_default_params()); llama_free(ctx); diff --git a/tests/test-model-load-cancel.cpp b/tests/test-model-load-cancel.cpp index 7ea4bbacc..858535c3c 100644 --- a/tests/test-model-load-cancel.cpp +++ b/tests/test-model-load-cancel.cpp @@ -14,7 +14,7 @@ int main(int argc, char *argv[] ) { fprintf(stderr, "using '%s'\n", model_path); fclose(file); - llama_backend_init(false); + llama_backend_init(); auto params = llama_model_params{}; params.use_mmap = false; params.progress_callback = [](float progress, void * ctx){ diff --git a/tests/test-tokenizer-0-falcon.cpp b/tests/test-tokenizer-0-falcon.cpp index a4e9d2b91..472b0b3a8 100644 --- a/tests/test-tokenizer-0-falcon.cpp +++ b/tests/test-tokenizer-0-falcon.cpp @@ -61,7 +61,7 @@ int main(int argc, char **argv) { llama_model * model; llama_context * ctx; - llama_backend_init(false); + llama_backend_init(); // load the vocab { diff --git a/tests/test-tokenizer-0-llama.cpp b/tests/test-tokenizer-0-llama.cpp index 39c8d188c..0a16cd7eb 100644 --- a/tests/test-tokenizer-0-llama.cpp +++ b/tests/test-tokenizer-0-llama.cpp @@ -60,7 +60,7 @@ int main(int argc, char **argv) { llama_model * model; llama_context * ctx; - llama_backend_init(false); + llama_backend_init(); // load the vocab { diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index 3bb629561..3596ce55a 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -25,7 +25,7 @@ int main(int argc, char **argv) { llama_model * model; llama_context * ctx; - llama_backend_init(false); + llama_backend_init(); // load the vocab { diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-llama.cpp index b0d814a41..9333f8686 100644 --- a/tests/test-tokenizer-1-llama.cpp +++ b/tests/test-tokenizer-1-llama.cpp @@ -25,7 +25,7 @@ int main(int argc, char **argv) { llama_model * model; llama_context * ctx; - llama_backend_init(false); + llama_backend_init(); // load the vocab { From 5f5808ca7b7f23a1fa7a77241842bb84a0e55108 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C5=91czey=20Barnab=C3=A1s?= <31726601+An0nie@users.noreply.github.com> Date: Fri, 16 Feb 2024 11:00:56 +0100 Subject: [PATCH 329/334] server : fix system prompt cli (#5516) --- examples/server/server.cpp | 47 ++++++++++++++++++-------------------- 1 file changed, 22 insertions(+), 25 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 912c750cc..0cb802ce8 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -436,10 +436,6 @@ struct llama_server_context default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); - - // empty system prompt - system_prompt = ""; - system_tokens.clear(); } std::vector tokenize(const json & json_prompt, bool add_bos) const @@ -765,27 +761,30 @@ struct llama_server_context } void update_system_prompt() { - system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token); - - llama_batch_clear(batch); - kv_cache_clear(); + system_tokens.clear(); - for (int i = 0; i < (int) system_tokens.size(); ++i) - { - llama_batch_add(batch, system_tokens[i], i, { 0 }, false); - } + if (!system_prompt.empty()) { + system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token); - if (llama_decode(ctx, batch) != 0) - { - LOG_TEE("%s: llama_decode() failed\n", __func__); - return; - } + llama_batch_clear(batch); - // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < params.n_parallel; ++i) - { - llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); + for (int i = 0; i < (int)system_tokens.size(); ++i) + { + llama_batch_add(batch, system_tokens[i], i, { 0 }, false); + } + + if (llama_decode(ctx, batch) != 0) + { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return; + } + + // assign the system KV cache to all parallel sequences + for (int32_t i = 1; i < params.n_parallel; ++i) + { + llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); + } } LOG_TEE("system prompt updated\n"); @@ -807,10 +806,8 @@ struct llama_server_context name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); - if (slots.size() > 0) - { - notify_system_prompt_changed(); - } + + notify_system_prompt_changed(); } static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, From 6dcc02d2444c779c18d49c364c5d5c5728b6b484 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Fri, 16 Feb 2024 11:33:25 +0000 Subject: [PATCH 330/334] server : add "samplers" param to control the samplers order (#5494) --- common/common.cpp | 59 ++++++++++++++++++++++++-------------- common/common.h | 2 +- common/sampling.cpp | 2 +- common/sampling.h | 14 ++++----- examples/server/README.md | 2 ++ examples/server/server.cpp | 25 ++++++++++++++++ 6 files changed, 74 insertions(+), 30 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index c5e83cc2a..3a92d3797 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -341,7 +341,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } const auto sampler_names = string_split(argv[i], ';'); - sparams.samplers_sequence = sampler_types_from_names(sampler_names); + sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); } else if (arg == "--sampling-seq") { if (++i >= argc) { invalid_param = true; @@ -964,7 +964,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - printf(" --samplers samplers that will be used for generation in the order, separated by \';\' (default: %s)\n", sampler_type_names.c_str()); + printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n"); + printf(" (default: %s)\n", sampler_type_names.c_str()); printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str()); printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k); printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p); @@ -1133,34 +1134,50 @@ std::vector string_split(std::string input, char separator) { return parts; } -std::vector sampler_types_from_names(const std::vector & names) { +std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names) { + std::unordered_map sampler_canonical_name_map { + {"top_k", llama_sampler_type::TOP_K}, + {"top_p", llama_sampler_type::TOP_P}, + {"typical_p", llama_sampler_type::TYPICAL_P}, + {"min_p", llama_sampler_type::MIN_P}, + {"tfs_z", llama_sampler_type::TFS_Z}, + {"temperature", llama_sampler_type::TEMPERATURE} + }; + // since samplers names are written multiple ways // make it ready for both system names and input names - std::unordered_map sampler_name_map { - {"top_k", llama_sampler_type::TOP_K}, + std::unordered_map sampler_alt_name_map { {"top-k", llama_sampler_type::TOP_K}, - {"top_p", llama_sampler_type::TOP_P}, {"top-p", llama_sampler_type::TOP_P}, {"nucleus", llama_sampler_type::TOP_P}, - {"typical_p", llama_sampler_type::TYPICAL_P}, {"typical-p", llama_sampler_type::TYPICAL_P}, {"typical", llama_sampler_type::TYPICAL_P}, - {"min_p", llama_sampler_type::MIN_P}, {"min-p", llama_sampler_type::MIN_P}, - {"tfs_z", llama_sampler_type::TFS_Z}, {"tfs-z", llama_sampler_type::TFS_Z}, {"tfs", llama_sampler_type::TFS_Z}, - {"temp", llama_sampler_type::TEMP}, - {"temperature", llama_sampler_type::TEMP} + {"temp", llama_sampler_type::TEMPERATURE} }; std::vector sampler_types; sampler_types.reserve(names.size()); - for (const auto& name : names) { - const auto sampler_item = sampler_name_map.find(name); - if (sampler_item != sampler_name_map.end()) { + for (const auto & name : names) + { + auto sampler_item = sampler_canonical_name_map.find(name); + if (sampler_item != sampler_canonical_name_map.end()) + { sampler_types.push_back(sampler_item->second); } + else + { + if (allow_alt_names) + { + sampler_item = sampler_alt_name_map.find(name); + if (sampler_item != sampler_alt_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + } + } } return sampler_types; } @@ -1172,7 +1189,7 @@ std::vector sampler_types_from_chars(const std::string & nam {'y', llama_sampler_type::TYPICAL_P}, {'m', llama_sampler_type::MIN_P}, {'f', llama_sampler_type::TFS_Z}, - {'t', llama_sampler_type::TEMP} + {'t', llama_sampler_type::TEMPERATURE} }; std::vector sampler_types; @@ -1188,12 +1205,12 @@ std::vector sampler_types_from_chars(const std::string & nam std::string sampler_type_to_name_string(llama_sampler_type sampler_type) { switch (sampler_type) { - case llama_sampler_type::TOP_K: return "top_k"; - case llama_sampler_type::TFS_Z: return "tfs_z"; - case llama_sampler_type::TYPICAL_P: return "typical_p"; - case llama_sampler_type::TOP_P: return "top_p"; - case llama_sampler_type::MIN_P: return "min_p"; - case llama_sampler_type::TEMP: return "temp"; + case llama_sampler_type::TOP_K: return "top_k"; + case llama_sampler_type::TFS_Z: return "tfs_z"; + case llama_sampler_type::TYPICAL_P: return "typical_p"; + case llama_sampler_type::TOP_P: return "top_p"; + case llama_sampler_type::MIN_P: return "min_p"; + case llama_sampler_type::TEMPERATURE: return "temperature"; default : return ""; } } diff --git a/common/common.h b/common/common.h index 74c136995..935771d44 100644 --- a/common/common.h +++ b/common/common.h @@ -165,7 +165,7 @@ void process_escapes(std::string& input); // String utils // -std::vector sampler_types_from_names(const std::vector & names); +std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); std::vector sampler_types_from_chars(const std::string & names_string); std::vector string_split(std::string input, char separator); std::string sampler_type_to_name_string(llama_sampler_type sampler_type); diff --git a/common/sampling.cpp b/common/sampling.cpp index a001750da..53013138a 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -139,7 +139,7 @@ static void sampler_queue( case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break; case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break; case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break; - case llama_sampler_type::TEMP: + case llama_sampler_type::TEMPERATURE: if (dynatemp_range > 0) { float dynatemp_min = std::max(0.0f, temp - dynatemp_range); float dynatemp_max = std::max(0.0f, temp + dynatemp_range); diff --git a/common/sampling.h b/common/sampling.h index 2bd6a75d2..e1279a894 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -10,12 +10,12 @@ // sampler types enum class llama_sampler_type : char { - TOP_K = 'k', - TOP_P = 'p', - MIN_P = 'm', - TFS_Z = 'f', - TYPICAL_P = 'y', - TEMP = 't' + TOP_K = 'k', + TOP_P = 'p', + MIN_P = 'm', + TFS_Z = 'f', + TYPICAL_P = 'y', + TEMPERATURE = 't' }; // sampling parameters @@ -45,7 +45,7 @@ typedef struct llama_sampling_params { llama_sampler_type::TYPICAL_P, llama_sampler_type::TOP_P, llama_sampler_type::MIN_P, - llama_sampler_type::TEMP + llama_sampler_type::TEMPERATURE }; std::string grammar; // optional BNF-like grammar to constrain sampling diff --git a/examples/server/README.md b/examples/server/README.md index 8e141d22d..249368749 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -204,6 +204,8 @@ node index.js `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) + `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. (default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values) + ### Result JSON - Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0cb802ce8..a0b46970b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -672,6 +672,24 @@ struct llama_server_context } } + const auto &samplers_sequence = data.find("samplers"); + if (samplers_sequence != data.end() && samplers_sequence->is_array()) + { + std::vector sampler_names; + for (const auto &sampler_name : *samplers_sequence) + { + if (sampler_name.is_string()) + { + sampler_names.emplace_back(sampler_name); + } + } + slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); + } + else + { + slot->sparams.samplers_sequence = default_sparams.samplers_sequence; + } + if (multimodal) { const auto &images_data = data.find("image_data"); @@ -1026,6 +1044,12 @@ struct llama_server_context const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); + std::vector samplers_sequence; + for (const auto &sampler_type : slot.sparams.samplers_sequence) + { + samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); + } + return json { {"n_ctx", slot.n_ctx}, {"model", params.model_alias}, @@ -1056,6 +1080,7 @@ struct llama_server_context {"logit_bias", slot.sparams.logit_bias}, {"n_probs", slot.sparams.n_probs}, {"grammar", slot.sparams.grammar}, + {"samplers", samplers_sequence} }; } From 65085c713e14f78cdda6abc275b1a5d8c2b8ca15 Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Fri, 16 Feb 2024 11:45:48 +0000 Subject: [PATCH 331/334] llama : minor fixed return int value (#5529) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 08e7b02b4..8966c3e66 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10893,7 +10893,7 @@ static int llama_apply_lora_from_file_internal( { LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n", __func__, ftype); - return false; + return 1; } } From 4cb072769804c77ab466bc8351c76ede9d5ba49d Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Fri, 16 Feb 2024 12:43:23 +0000 Subject: [PATCH 332/334] llava : removed excess free(NULL) operation (#5531) --- examples/llava/llava.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 4ed310a0e..4cb65a07b 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -315,7 +315,6 @@ static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_thre float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model if (!image_embd) { fprintf(stderr, "Unable to allocate memory for image embeddings\n"); - free(image_embd); return false; } From d2819d5577b35507be83d0c3f4d2d3c0ab1488ca Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 16 Feb 2024 15:14:40 +0200 Subject: [PATCH 333/334] scripts : add helpers script for bench comparing commits (#5521) * scripts : add helpers script for bench comparing commits * scripts : detect CUDA * set flags after checking the command line * fix make flags --------- Co-authored-by: slaren --- scripts/compare-commits.sh | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100755 scripts/compare-commits.sh diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh new file mode 100755 index 000000000..331c4b9ce --- /dev/null +++ b/scripts/compare-commits.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +if [ $# -lt 2 ]; then + echo "usage: ./scripts/compare-commits.sh [additional llama-bench arguments]" + exit 1 +fi + +set -e +set -x + +bench_args="${@:3}" + +rm -f llama-bench.sqlite + +backend="cpu" + +if [[ "$OSTYPE" == "darwin"* ]]; then + backend="metal" +elif command -v nvcc &> /dev/null; then + backend="cuda" +fi + +make_opts="" + +if [[ "$backend" == "cuda" ]]; then + make_opts="LLAMA_CUBLAS=1" +fi + +git checkout $1 +make clean && make -j32 $make_opts llama-bench +./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite + +git checkout $2 +make clean && make -j32 $make_opts llama-bench +./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite + +./scripts/compare-llama-bench.py -b $1 -c $2 From 5bf2b94dd4fb74378b78604023b31512fec55f8f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 16 Feb 2024 19:05:56 +0200 Subject: [PATCH 334/334] cmake : fix VULKAN and ROCm builds (#5525) * cmake : fix VULKAN and ROCm builds * cmake : fix (cont) * vulkan : fix compile warnings ggml-ci * cmake : fix ggml-ci * cmake : minor ggml-ci --- CMakeLists.txt | 379 +++++++++++++++++++++++++----------------------- ggml-vulkan.cpp | 12 +- 2 files changed, 205 insertions(+), 186 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f8c7f9978..2a922fdb3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -112,17 +112,14 @@ option(LLAMA_MPI "llama: use MPI" option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) +option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ON) - # add perf arguments option(LLAMA_PERF "llama: enable perf" OFF) -if (LLAMA_PERF) - add_definitions(-DGGML_PERF) -endif() # Required for relocatable CMake package include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) @@ -130,6 +127,7 @@ include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) # # Compile flags # + if (LLAMA_SYCL) set(CMAKE_CXX_STANDARD 17) else() @@ -140,6 +138,7 @@ set(CMAKE_CXX_STANDARD_REQUIRED true) set(CMAKE_C_STANDARD 11) set(CMAKE_C_STANDARD_REQUIRED true) set(THREADS_PREFER_PTHREAD_FLAG ON) + find_package(Threads REQUIRED) include(CheckCXXCompilerFlag) @@ -151,17 +150,17 @@ endif() if (NOT MSVC) if (LLAMA_SANITIZE_THREAD) add_compile_options(-fsanitize=thread) - link_libraries(-fsanitize=thread) + link_libraries (-fsanitize=thread) endif() if (LLAMA_SANITIZE_ADDRESS) add_compile_options(-fsanitize=address -fno-omit-frame-pointer) - link_libraries(-fsanitize=address) + link_libraries (-fsanitize=address) endif() if (LLAMA_SANITIZE_UNDEFINED) add_compile_options(-fsanitize=undefined) - link_libraries(-fsanitize=undefined) + link_libraries (-fsanitize=undefined) endif() endif() @@ -298,14 +297,17 @@ if (LLAMA_BLAS) endif() message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}") + add_compile_options(${BLAS_LINKER_FLAGS}) + add_compile_definitions(GGML_USE_OPENBLAS) + if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${LLAMA_BLAS_VENDOR} MATCHES "Generic" OR ${LLAMA_BLAS_VENDOR} MATCHES "Intel")) add_compile_definitions(GGML_BLAS_USE_MKL) endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES}) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS}) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES}) + set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS}) else() message(WARNING "BLAS not found, please refer to " "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" @@ -330,9 +332,6 @@ if (LLAMA_CUBLAS) set(GGML_SOURCES_CUDA ggml-cuda.cu) add_compile_definitions(GGML_USE_CUBLAS) -# if (LLAMA_CUDA_CUBLAS) -# add_compile_definitions(GGML_CUDA_CUBLAS) -# endif() if (LLAMA_CUDA_FORCE_DMMV) add_compile_definitions(GGML_CUDA_FORCE_DMMV) endif() @@ -387,15 +386,20 @@ if (LLAMA_MPI) find_package(MPI) if (MPI_C_FOUND) message(STATUS "MPI found") + set(GGML_HEADERS_MPI ggml-mpi.h) - set(GGML_SOURCES_MPI ggml-mpi.c ggml-mpi.h) + set(GGML_SOURCES_MPI ggml-mpi.c) + add_compile_definitions(GGML_USE_MPI) add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS}) + if (NOT MSVC) add_compile_options(-Wno-cast-qual) endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES}) set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS}) + # Even if you're only using the C header, C++ programs may bring in MPI # C++ functions, so more linkage is needed if (MPI_CXX_FOUND) @@ -427,31 +431,28 @@ if (LLAMA_VULKAN) if (Vulkan_FOUND) message(STATUS "Vulkan found") - add_library(ggml-vulkan OBJECT ggml-vulkan.cpp ggml-vulkan.h) - if (BUILD_SHARED_LIBS) - set_target_properties(ggml-vulkan PROPERTIES POSITION_INDEPENDENT_CODE ON) - endif() - target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) + set(GGML_HEADERS_VULKAN ggml-vulkan.h) + set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) add_compile_definitions(GGML_USE_VULKAN) if (LLAMA_VULKAN_CHECK_RESULTS) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_CHECK_RESULTS) + add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) endif() if (LLAMA_VULKAN_DEBUG) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_DEBUG) + add_compile_definitions(GGML_VULKAN_DEBUG) endif() if (LLAMA_VULKAN_VALIDATE) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_VALIDATE) + add_compile_definitions(GGML_VULKAN_VALIDATE) endif() if (LLAMA_VULKAN_RUN_TESTS) - target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_RUN_TESTS) + add_compile_definitions(GGML_VULKAN_RUN_TESTS) endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} Vulkan::Vulkan) else() message(WARNING "Vulkan not found") endif() @@ -463,43 +464,45 @@ if (LLAMA_HIPBLAS) if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang") message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang") endif() + if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") endif() - find_package(hip) - find_package(hipblas) - find_package(rocblas) + find_package(hip REQUIRED) + find_package(hipblas REQUIRED) + find_package(rocblas REQUIRED) - if (${hipblas_FOUND} AND ${hip_FOUND}) - message(STATUS "HIP and hipBLAS found") - add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) - if (LLAMA_HIP_UMA) - add_compile_definitions(GGML_HIP_UMA) - endif() - add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h) - if (BUILD_SHARED_LIBS) - set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON) - endif() - if (LLAMA_CUDA_FORCE_DMMV) - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV) - endif() - if (LLAMA_CUDA_FORCE_MMQ) - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_MMQ) - endif() - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) - target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) - target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX) - target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas) + message(STATUS "HIP and hipBLAS found") - if (LLAMA_STATIC) - message(FATAL_ERROR "Static linking not supported for HIP/ROCm") - endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm) - else() - message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm") + set(GGML_HEADERS_ROCM ggml-cuda.h) + set(GGML_SOURCES_ROCM ggml-cuda.cu) + + add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) + + if (LLAMA_HIP_UMA) + add_compile_definitions(GGML_HIP_UMA) endif() + + if (LLAMA_CUDA_FORCE_DMMV) + add_compile_definitions(GGML_CUDA_FORCE_DMMV) + endif() + + if (LLAMA_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) + add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) + add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) + + set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX) + + if (LLAMA_STATIC) + message(FATAL_ERROR "Static linking not supported for HIP/ROCm") + endif() + + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device PUBLIC hip::host roc::rocblas roc::hipblas) endif() if (LLAMA_SYCL) @@ -509,10 +512,14 @@ if (LLAMA_SYCL) #todo: AOT find_package(IntelSYCL REQUIRED) + + message(STATUS "SYCL found") + + add_compile_definitions(GML_USE_SYCL) + if (LLAMA_SYCL_F16) add_compile_definitions(GGML_SYCL_F16) endif() - add_compile_definitions(GGML_USE_SYCL) add_compile_options(-I./) #include DPCT add_compile_options(-I/${SYCL_INCLUDE_DIR}) @@ -521,7 +528,7 @@ if (LLAMA_SYCL) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib") - set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h) + set(GGML_HEADERS_SYCL ggml-sycl.h) set(GGML_SOURCES_SYCL ggml-sycl.cpp) if (WIN32) @@ -540,61 +547,61 @@ if (LLAMA_KOMPUTE) endif() function(compile_shader) - set(options) - set(oneValueArgs) - set(multiValueArgs SOURCES) - cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - foreach(source ${compile_shader_SOURCES}) - get_filename_component(filename ${source} NAME) - set(spv_file ${filename}.spv) - add_custom_command( - OUTPUT ${spv_file} - DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp - COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} - COMMENT "Compiling ${source} to ${spv_file}" - ) + set(options) + set(oneValueArgs) + set(multiValueArgs SOURCES) + cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + foreach(source ${compile_shader_SOURCES}) + get_filename_component(filename ${source} NAME) + set(spv_file ${filename}.spv) + add_custom_command( + OUTPUT ${spv_file} + DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp + COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} + COMMENT "Compiling ${source} to ${spv_file}" + ) - get_filename_component(RAW_FILE_NAME ${spv_file} NAME) - set(FILE_NAME "shader${RAW_FILE_NAME}") - string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) - string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) - string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") - set(OUTPUT_HEADER_FILE "${HEADER_FILE}") - message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") - if(CMAKE_GENERATOR MATCHES "Visual Studio") - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" - ) - else() - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" - ) - endif() - endforeach() + get_filename_component(RAW_FILE_NAME ${spv_file} NAME) + set(FILE_NAME "shader${RAW_FILE_NAME}") + string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) + string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) + string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") + set(OUTPUT_HEADER_FILE "${HEADER_FILE}") + message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") + if(CMAKE_GENERATOR MATCHES "Visual Studio") + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" + ) + else() + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" + ) + endif() + endforeach() endfunction() if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") @@ -604,66 +611,66 @@ if (LLAMA_KOMPUTE) # Compile our shaders compile_shader(SOURCES - kompute-shaders/op_scale.comp - kompute-shaders/op_scale_8.comp - kompute-shaders/op_add.comp - kompute-shaders/op_addrow.comp - kompute-shaders/op_mul.comp - kompute-shaders/op_silu.comp - kompute-shaders/op_relu.comp - kompute-shaders/op_gelu.comp - kompute-shaders/op_softmax.comp - kompute-shaders/op_norm.comp - kompute-shaders/op_rmsnorm.comp - kompute-shaders/op_diagmask.comp - kompute-shaders/op_mul_mat_mat_f32.comp - kompute-shaders/op_mul_mat_f16.comp - kompute-shaders/op_mul_mat_q8_0.comp - kompute-shaders/op_mul_mat_q4_0.comp - kompute-shaders/op_mul_mat_q4_1.comp - kompute-shaders/op_mul_mat_q6_k.comp - kompute-shaders/op_getrows_f16.comp - kompute-shaders/op_getrows_q4_0.comp - kompute-shaders/op_getrows_q4_1.comp - kompute-shaders/op_getrows_q6_k.comp - kompute-shaders/op_rope_f16.comp - kompute-shaders/op_rope_f32.comp - kompute-shaders/op_cpy_f16_f16.comp - kompute-shaders/op_cpy_f16_f32.comp - kompute-shaders/op_cpy_f32_f16.comp - kompute-shaders/op_cpy_f32_f32.comp + kompute-shaders/op_scale.comp + kompute-shaders/op_scale_8.comp + kompute-shaders/op_add.comp + kompute-shaders/op_addrow.comp + kompute-shaders/op_mul.comp + kompute-shaders/op_silu.comp + kompute-shaders/op_relu.comp + kompute-shaders/op_gelu.comp + kompute-shaders/op_softmax.comp + kompute-shaders/op_norm.comp + kompute-shaders/op_rmsnorm.comp + kompute-shaders/op_diagmask.comp + kompute-shaders/op_mul_mat_mat_f32.comp + kompute-shaders/op_mul_mat_f16.comp + kompute-shaders/op_mul_mat_q8_0.comp + kompute-shaders/op_mul_mat_q4_0.comp + kompute-shaders/op_mul_mat_q4_1.comp + kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f16.comp + kompute-shaders/op_getrows_q4_0.comp + kompute-shaders/op_getrows_q4_1.comp + kompute-shaders/op_getrows_q6_k.comp + kompute-shaders/op_rope_f16.comp + kompute-shaders/op_rope_f32.comp + kompute-shaders/op_cpy_f16_f16.comp + kompute-shaders/op_cpy_f16_f32.comp + kompute-shaders/op_cpy_f32_f16.comp + kompute-shaders/op_cpy_f32_f32.comp ) # Create a custom target for our generated shaders add_custom_target(generated_shaders DEPENDS - shaderop_scale.h - shaderop_scale_8.h - shaderop_add.h - shaderop_addrow.h - shaderop_mul.h - shaderop_silu.h - shaderop_relu.h - shaderop_gelu.h - shaderop_softmax.h - shaderop_norm.h - shaderop_rmsnorm.h - shaderop_diagmask.h - shaderop_mul_mat_mat_f32.h - shaderop_mul_mat_f16.h - shaderop_mul_mat_q8_0.h - shaderop_mul_mat_q4_0.h - shaderop_mul_mat_q4_1.h - shaderop_mul_mat_q6_k.h - shaderop_getrows_f16.h - shaderop_getrows_q4_0.h - shaderop_getrows_q4_1.h - shaderop_getrows_q6_k.h - shaderop_rope_f16.h - shaderop_rope_f32.h - shaderop_cpy_f16_f16.h - shaderop_cpy_f16_f32.h - shaderop_cpy_f32_f16.h - shaderop_cpy_f32_f32.h + shaderop_scale.h + shaderop_scale_8.h + shaderop_add.h + shaderop_addrow.h + shaderop_mul.h + shaderop_silu.h + shaderop_relu.h + shaderop_gelu.h + shaderop_softmax.h + shaderop_norm.h + shaderop_rmsnorm.h + shaderop_diagmask.h + shaderop_mul_mat_mat_f32.h + shaderop_mul_mat_f16.h + shaderop_mul_mat_q8_0.h + shaderop_mul_mat_q4_0.h + shaderop_mul_mat_q4_1.h + shaderop_mul_mat_q6_k.h + shaderop_getrows_f16.h + shaderop_getrows_q4_0.h + shaderop_getrows_q4_1.h + shaderop_getrows_q6_k.h + shaderop_rope_f16.h + shaderop_rope_f32.h + shaderop_cpy_f16_f16.h + shaderop_cpy_f16_f32.h + shaderop_cpy_f32_f16.h + shaderop_cpy_f32_f32.h ) # Create a custom command that depends on the generated_shaders @@ -676,8 +683,10 @@ if (LLAMA_KOMPUTE) # Add the stamp to the main sources to ensure dependency tracking set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - set(GGML_HEADERS_KOMPUTE ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) + set(GGML_HEADERS_KOMPUTE ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) + add_compile_definitions(GGML_USE_KOMPUTE) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute) set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR}) else() @@ -685,6 +694,18 @@ if (LLAMA_KOMPUTE) endif() endif() +if (LLAMA_CPU_HBM) + find_library(memkind memkind REQUIRED) + + add_compile_definitions(GGML_USE_CPU_HBM) + + target_link_libraries(ggml PUBLIC memkind) +endif() + +if (LLAMA_PERF) + add_compile_definitions(GGML_PERF) +endif() + function(get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") @@ -821,6 +842,7 @@ execute_process( ERROR_VARIABLE output OUTPUT_QUIET ) + if (output MATCHES "dyld-1015\.7") add_compile_definitions(HAVE_BUGGY_APPLE_LINKER) endif() @@ -830,10 +852,10 @@ endif() # feel free to update the Makefile for your architecture and send a pull request or issue message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}") if (MSVC) - string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR) - message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}") + string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR) + message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}") else () - set(CMAKE_GENERATOR_PLATFORM_LWR "") + set(CMAKE_GENERATOR_PLATFORM_LWR "") endif () if (NOT MSVC) @@ -1027,11 +1049,6 @@ endif() # ggml -if (GGML_USE_CPU_HBM) - add_definitions(-DGGML_USE_CPU_HBM) - find_library(memkind memkind REQUIRED) -endif() - add_library(ggml OBJECT ggml.c ggml.h @@ -1048,16 +1065,17 @@ add_library(ggml OBJECT ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} + ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} + ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) -target_compile_features(ggml PUBLIC c_std_11) # don't bump +target_compile_features (ggml PUBLIC c_std_11) # don't bump + target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) -if (GGML_USE_CPU_HBM) - target_link_libraries(ggml PUBLIC memkind) -endif() add_library(ggml_static STATIC $) + if (BUILD_SHARED_LIBS) set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) add_library(ggml_shared SHARED $) @@ -1073,7 +1091,8 @@ add_library(llama ) target_include_directories(llama PUBLIC .) -target_compile_features(llama PUBLIC cxx_std_11) # don't bump +target_compile_features (llama PUBLIC cxx_std_11) # don't bump + target_link_libraries(llama PRIVATE ggml ${LLAMA_EXTRA_LIBS} @@ -1124,7 +1143,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 1fad24fd1..4a30414df 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1091,7 +1091,7 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } -void ggml_vk_instance_init() { +static void ggml_vk_instance_init() { if (vk_instance_initialized) { return; } @@ -1150,7 +1150,7 @@ void ggml_vk_instance_init() { vk_instance_initialized = true; } -void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { +static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { GGML_ASSERT(idx < vk_instance.device_indices.size()); size_t dev_num = vk_instance.device_indices[idx]; #ifdef GGML_VULKAN_DEBUG @@ -4556,13 +4556,13 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { } } -GGML_CALL int ggml_vk_get_device_count() { +GGML_CALL static int ggml_vk_get_device_count() { ggml_vk_instance_init(); return vk_instance.device_indices.size(); } -GGML_CALL void ggml_vk_get_device_description(int device, char * description, size_t description_size) { +GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) { ggml_vk_instance_init(); std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); @@ -4580,7 +4580,7 @@ void ggml_vk_init_cpu_assist() { std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl; - for (size_t i = 0; i < ggml_vk_get_device_count(); i++) { + for (int i = 0; i < ggml_vk_get_device_count(); i++) { ggml_vk_print_gpu_info(i); } // Initialize the first backend to make sure CPU matrix multiplications can be offloaded. @@ -5267,7 +5267,7 @@ GGML_CALL void ggml_backend_vk_get_device_description(int device, char * descrip } GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { - GGML_ASSERT(device < vk_instance.device_indices.size()); + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];