From c421ac072d46172ab18924e1e8be53680b54ed3b Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 22 Oct 2024 13:08:41 +0200 Subject: [PATCH 1/5] lora : warn user if new token is added in the adapter (#9948) --- convert_lora_to_gguf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index 439a78de1..bc68f68af 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -348,6 +348,9 @@ if __name__ == '__main__': if ".base_layer.weight" in name: continue logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor") + if ".embed_tokens.weight" in name or ".lm_head.weight" in name: + logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning") + logger.error("Hint: if you are using TRL, make sure not to call setup_chat_format()") sys.exit(1) if base_name in tensor_map: From 11d47057a51f3d9b9231e6b57d0ca36020c0ee99 Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Tue, 22 Oct 2024 21:22:26 +0800 Subject: [PATCH 2/5] Rwkv chat template fix (#10001) * llama: remove useless template matching for rwkv-world Signed-off-by: Molly Sophia * converter: Add comment about the hack for rwkv models Signed-off-by: Molly Sophia * Update src/llama.cpp Co-authored-by: Xuan Son Nguyen --------- Signed-off-by: Molly Sophia Co-authored-by: Xuan Son Nguyen --- convert_hf_to_gguf.py | 1 + src/llama.cpp | 3 ++- tests/test-chat-template.cpp | 4 ---- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index e0b1b2bf9..7e552a71b 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2865,6 +2865,7 @@ class Rwkv6Model(Model): self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) special_vocab.chat_template = "rwkv-world" + # hack: Add '\n\n' as the EOT token to make it chat normally special_vocab._set_special_token("eot", 261) special_vocab.add_to_gguf(self.gguf_writer) diff --git a/src/llama.cpp b/src/llama.cpp index 73190c88f..6a5c56a77 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21697,7 +21697,8 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "[|assistant|]"; } - } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world") || tmpl_contains("'User: ' + message['content'] + '\n\nAssistant:'")) { + } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world")) { + // this template requires the model to have "\n\n" as EOT token for (auto message : chat) { std::string role(message->role); if (role == "user") { diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index fdc4a9bc3..6f046249f 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -65,8 +65,6 @@ int main(void) { u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", // DeepSeek-V2 "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", - // RWKV-World - "{% for message in messages %}{% if message['role'] == 'user' %}{{'User: ' + message['content'] + '\n\nAssistant:'}}{% else %}{{message['content'] + '\n\n'}}{% endif %}{% endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -111,8 +109,6 @@ int main(void) { u8"You are a helpful assistant<用户>HelloHi there<用户>Who are youI am an assistant<用户>Another question", // DeepSeek-V2 u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:", - // RWKV-World - "You are a helpful assistant\n\nUser: Hello\n\nAssistant:Hi there\n\nUser: Who are you\n\nAssistant: I am an assistant \n\nUser: Another question\n\nAssistant:", }; std::vector formatted_chat(1024); int32_t res; From 19d900a7565b8f6b0a708836a57d26966cb9efe2 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 22 Oct 2024 15:31:06 +0200 Subject: [PATCH 3/5] llama : rename batch to ubatch (#9950) This commit renames the member field batch in llm_build_context to ubatch, and also the parameter batch in llama_build_graph, and llama_set_inputs to ubatch. The motivation for this change is to make the code more readable (considering there are the structs llama_batch and llama_sbatch), and consistent with other parts of the code base where parameters/fields of type llama_ubatch are named ubatch. --- src/llama.cpp | 218 +++++++++++++++++++++++++------------------------- 1 file changed, 109 insertions(+), 109 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 6a5c56a77..7a5a46dce 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -10017,7 +10017,7 @@ struct llm_build_context { llama_context & lctx; const llama_hparams & hparams; const llama_cparams & cparams; - const llama_ubatch & batch; + const llama_ubatch & ubatch; const llama_kv_cache & kv_self; const int64_t n_embd; @@ -10063,14 +10063,14 @@ struct llm_build_context { // TODO: consider making the entire interface noexcept llm_build_context( llama_context & lctx, - const llama_ubatch & batch, + const llama_ubatch & ubatch, const llm_build_cb & cb, bool worst_case) : model (lctx.model), lctx (lctx), hparams (model.hparams), cparams (lctx.cparams), - batch (batch), + ubatch (ubatch), kv_self (lctx.kv_self), n_embd (hparams.n_embd), n_layer (hparams.n_layer), @@ -10092,7 +10092,7 @@ struct llm_build_context { beta_slow (cparams.yarn_beta_slow), norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), - n_tokens (batch.n_tokens), + n_tokens (ubatch.n_tokens), n_kv (worst_case ? kv_self.size : kv_self.n), n_outputs (worst_case ? n_tokens : lctx.n_outputs), n_outputs_enc (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd), @@ -10461,7 +10461,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -10621,7 +10621,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr; @@ -10736,7 +10736,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -10840,7 +10840,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -10962,7 +10962,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // multiply by embedding_multiplier_scale of 78.38367176906169 inpL = ggml_scale(ctx0, inpL, 78.38367176906169f); @@ -11120,7 +11120,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11242,7 +11242,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11345,7 +11345,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11447,7 +11447,7 @@ struct llm_build_context { } // construct input embeddings (token, type, position) - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_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); @@ -11634,7 +11634,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11736,7 +11736,7 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11874,7 +11874,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12024,7 +12024,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12137,7 +12137,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12252,7 +12252,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12397,7 +12397,7 @@ struct llm_build_context { struct ggml_tensor * ffn_output; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12516,7 +12516,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12644,7 +12644,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12749,7 +12749,7 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12854,7 +12854,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12964,7 +12964,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13082,7 +13082,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13209,7 +13209,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); @@ -13353,7 +13353,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); @@ -13554,7 +13554,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -13662,7 +13662,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -13800,7 +13800,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13916,7 +13916,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); @@ -13928,7 +13928,7 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "attn_norm", il); - cur = llm_build_mamba(ctx0, lctx, batch, gf, cur, + cur = llm_build_mamba(ctx0, lctx, ubatch, gf, cur, state_copy, state_mask, kv_head, n_kv, cb, il); @@ -13974,7 +13974,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14131,7 +14131,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14259,7 +14259,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14378,7 +14378,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14505,7 +14505,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14650,7 +14650,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14791,7 +14791,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15006,7 +15006,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15160,7 +15160,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); GGML_ASSERT(lctx.is_encoding); struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false); @@ -15292,7 +15292,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); GGML_ASSERT(!lctx.is_encoding); GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first"); @@ -15494,7 +15494,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -15586,7 +15586,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15700,7 +15700,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15824,7 +15824,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15944,11 +15944,11 @@ struct llm_build_context { // Token shift state dimensions should be 2 * n_emb GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2); - const int64_t n_seqs = batch.n_seqs; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_seqs = ubatch.n_seqs; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(n_seqs != 0); - GGML_ASSERT(batch.equal_seqs); + GGML_ASSERT(ubatch.equal_seqs); GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs); struct ggml_tensor * cur; @@ -15956,7 +15956,7 @@ struct llm_build_context { struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1); for (int il = 0; il < n_layer; ++il) { @@ -16070,7 +16070,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -16266,7 +16266,7 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { static struct ggml_cgraph * llama_build_graph( llama_context & lctx, - const llama_ubatch & batch, + const llama_ubatch & ubatch, bool worst_case) { const auto & model = lctx.model; @@ -16288,7 +16288,7 @@ static struct ggml_cgraph * llama_build_graph( // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends // FIXME: fix in ggml_backend_sched const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer; - if (batch.n_tokens < 32 || full_offload) { + if (ubatch.n_tokens < 32 || full_offload) { if (il != -1 && strcmp(name, "norm") == 0) { for (auto * backend : lctx.backends) { if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) && @@ -16303,7 +16303,7 @@ static struct ggml_cgraph * llama_build_graph( struct ggml_cgraph * result = NULL; - struct llm_build_context llm(lctx, batch, cb, worst_case); + struct llm_build_context llm(lctx, ubatch, cb, worst_case); llm.init(); @@ -16554,7 +16554,7 @@ static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t return relative_bucket; } -static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { +static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) { // // set input data // @@ -16563,28 +16563,28 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const auto & cparams = lctx.cparams; const auto & kv_self = lctx.kv_self; - if (batch.token) { - const int64_t n_tokens = batch.n_tokens; + if (ubatch.token) { + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); + ggml_backend_tensor_set(lctx.inp_tokens, ubatch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); } - if (batch.embd) { + if (ubatch.embd) { const int64_t n_embd = hparams.n_embd; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); + ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); } - if (batch.pos && lctx.inp_pos) { - const int64_t n_tokens = batch.n_tokens; + if (ubatch.pos && lctx.inp_pos) { + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); + ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); } if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) { GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs"); - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer)); int32_t * data = (int32_t *) lctx.inp_out_ids->data; @@ -16593,10 +16593,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int i = 0; i < n_tokens; ++i) { data[i] = i; } - } else if (batch.output) { + } else if (ubatch.output) { int32_t n_outputs = 0; for (int i = 0; i < n_tokens; ++i) { - if (batch.output[i]) { + if (ubatch.output[i]) { data[n_outputs++] = i; } } @@ -16621,9 +16621,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. if (cparams.causal_attn && !lctx.is_encoding) { const int64_t n_kv = kv_self.n; - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; float * data = nullptr; @@ -16640,14 +16640,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } // For causal attention, use only the previous KV cells - // of the correct sequence for each token of the batch. + // of the correct sequence for each token of the ubatch. // It's assumed that if a token in the batch has multiple sequences, they are equivalent. for (int h = 0; h < 1; ++h) { for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; for (int j = 0; j < n_seq_tokens; ++j) { - const llama_pos pos = batch.pos[s*n_seq_tokens + j]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + j]; for (int i = 0; i < n_kv; ++i) { float f; @@ -16693,9 +16693,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } } } else { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; // when using kv cache, the mask needs to match the kv cache size const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens; @@ -16705,7 +16705,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int s1 = 0; s1 < n_seqs; ++s1) { - const llama_seq_id seq_id = batch.seq_id[s1][0]; + const llama_seq_id seq_id = ubatch.seq_id[s1][0]; for (int j = 0; j < n_seq_tokens; ++j) { const int32_t tj = s1*n_seq_tokens + j; @@ -16715,10 +16715,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const int32_t ti = s0*n_seq_tokens + i; float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[s0]; ++s) { - if (batch.seq_id[s0][s] == seq_id) { + for (int s = 0; s < ubatch.n_seq_id[s0]; ++s) { + if (ubatch.seq_id[s0][s] == seq_id) { if (hparams.use_alibi) { - f = -std::abs(batch.pos[ti] - batch.pos[tj]); + f = -std::abs(ubatch.pos[ti] - ubatch.pos[tj]); } else { f = 0.0f; } @@ -16740,9 +16740,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_mean); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -16753,12 +16753,12 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { std::vector sum(n_tokens, 0); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); - sum[seq_id] += batch.n_seq_tokens; + sum[seq_id] += ubatch.n_seq_tokens; } std::vector div(n_tokens, 0.0f); @@ -16770,7 +16770,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; for (int i = 0; i < n_seq_tokens; ++i) { data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id]; @@ -16781,9 +16781,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { if (cparams.embeddings && ( cparams.pooling_type == LLAMA_POOLING_TYPE_CLS || cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -16792,13 +16792,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK"); for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + i]; if (pos == 0) { data[seq_id] = s*n_seq_tokens + i; @@ -16808,9 +16808,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -16822,13 +16822,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { std::vector last_row(n_tokens, -1); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST"); for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + i]; if (pos >= last_pos[seq_id]) { last_pos[seq_id] = pos; @@ -16890,10 +16890,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (lctx.inp_pos_bucket) { - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer)); - GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing + GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing int32_t * data = (int32_t *) lctx.inp_pos_bucket->data; @@ -16902,7 +16902,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_kv; ++i) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); } } } @@ -16910,7 +16910,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_tokens; ++i) { - data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch.pos[i], ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); } } } @@ -16926,10 +16926,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) { const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer)); - GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing + GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing float * data = (float *) lctx.inp_KQ_mask_cross->data; @@ -16937,8 +16937,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_output_enc; ++i) { float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[j]; ++s) { - const llama_seq_id seq_id = batch.seq_id[j][s]; + for (int s = 0; s < ubatch.n_seq_id[j]; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[j][s]; if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) { f = 0.0f; } From c8c07d658a6cefc5a50cfdf6be7d726503612303 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 22 Oct 2024 16:59:02 +0200 Subject: [PATCH 4/5] llama : fix empty batch causing llama_batch_allocr to crash (#9966) * llama : fix empty batch cause llama_batch_allocr to crash * move batch_allocr inside decode/encode_internal * fix build * add GGML_ASSERT * Apply suggestions from code review Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- src/llama.cpp | 128 ++++++++++++++++++++++++++------------------------ 1 file changed, 67 insertions(+), 61 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 7a5a46dce..24e1f1f01 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -5177,6 +5177,57 @@ struct llama_model_loader { } }; +// temporary allocate memory for the input batch if needed +static const llama_seq_id batch_default_seq_id = 0; +struct llama_batch_allocr { + std::array seq_id_0 = {batch_default_seq_id}; + std::vector pos; + std::vector n_seq_id; + std::vector seq_id; + std::vector logits; + struct llama_batch batch; + // optionally fulfill the batch returned by llama_batch_get_one + llama_batch_allocr(llama_context & ctx, struct llama_batch in_batch) { + batch = in_batch; + GGML_ASSERT(batch.n_tokens > 0); + if (!batch.pos) { + // determine the last position in KV cache + llama_pos last_pos = -1; + for (const auto & cell : ctx.kv_self.cells) { + if (cell.has_seq_id(batch_default_seq_id)) { + last_pos = std::max(last_pos, cell.pos); + } + } + last_pos++; // next position + pos.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + pos[i] = i+last_pos; + } + batch.pos = pos.data(); + } + if (!batch.n_seq_id) { + n_seq_id.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + n_seq_id[i] = seq_id_0.size(); + } + batch.n_seq_id = n_seq_id.data(); + } + if (!batch.seq_id) { + seq_id.resize(batch.n_tokens + 1); + seq_id[batch.n_tokens] = NULL; + for (int32_t i = 0; i < batch.n_tokens; i++) { + seq_id[i] = seq_id_0.data(); + } + batch.seq_id = seq_id.data(); + } + if (!batch.logits) { + logits.resize(batch.n_tokens); + logits[logits.size() - 1] = true; + batch.logits = logits.data(); + } + } +}; + template<> bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { uint32_t tmp; @@ -17095,16 +17146,20 @@ static void llama_graph_compute( // static int llama_decode_internal( llama_context & lctx, - llama_batch batch) { + llama_batch inp_batch) { lctx.is_encoding = false; - const uint32_t n_tokens_all = batch.n_tokens; - if (n_tokens_all == 0) { + if (inp_batch.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + // temporary allocate memory for the input batch if needed + llama_batch_allocr batch_allocr(lctx, inp_batch); + const llama_batch & batch = batch_allocr.batch; + const uint32_t n_tokens_all = batch.n_tokens; + const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -17409,17 +17464,20 @@ static int llama_decode_internal( // static int llama_encode_internal( llama_context & lctx, - llama_batch batch) { + llama_batch inp_batch) { lctx.is_encoding = true; - const uint32_t n_tokens = batch.n_tokens; - - if (n_tokens == 0) { + if (inp_batch.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + // temporary allocate memory for the input batch if needed + llama_batch_allocr batch_allocr(lctx, inp_batch); + const llama_batch & batch = batch_allocr.batch; + const uint32_t n_tokens = batch.n_tokens; + const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -21090,61 +21148,10 @@ void llama_batch_free(struct llama_batch batch) { if (batch.logits) free(batch.logits); } -// temporary allocate memory for the input batch if needed -static const llama_seq_id batch_default_seq_id = 0; -struct llama_batch_allocr { - std::array seq_id_0 = {batch_default_seq_id}; - std::vector pos; - std::vector n_seq_id; - std::vector seq_id; - std::vector logits; - struct llama_batch batch; - // optionally fulfill the batch returned by llama_batch_get_one - llama_batch_allocr(struct llama_context * ctx, struct llama_batch in_batch) { - batch = in_batch; - if (!batch.pos) { - // determine the last position in KV cache - llama_pos last_pos = -1; - for (const auto & cell : ctx->kv_self.cells) { - if (cell.has_seq_id(batch_default_seq_id)) { - last_pos = std::max(last_pos, cell.pos); - } - } - last_pos++; // next position - pos.resize(batch.n_tokens); - for (int32_t i = 0; i < batch.n_tokens; i++) { - pos[i] = i+last_pos; - } - batch.pos = pos.data(); - } - if (!batch.n_seq_id) { - n_seq_id.resize(batch.n_tokens); - for (int32_t i = 0; i < batch.n_tokens; i++) { - n_seq_id[i] = seq_id_0.size(); - } - batch.n_seq_id = n_seq_id.data(); - } - if (!batch.seq_id) { - seq_id.resize(batch.n_tokens + 1); - seq_id[batch.n_tokens] = NULL; - for (int32_t i = 0; i < batch.n_tokens; i++) { - seq_id[i] = seq_id_0.data(); - } - batch.seq_id = seq_id.data(); - } - if (!batch.logits) { - logits.resize(batch.n_tokens); - logits[logits.size() - 1] = true; - batch.logits = logits.data(); - } - } -}; - int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch) { - llama_batch_allocr batch_allocr(ctx, batch); - const int ret = llama_encode_internal(*ctx, batch_allocr.batch); + const int ret = llama_encode_internal(*ctx, batch); if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); } @@ -21155,8 +21162,7 @@ int32_t llama_encode( int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch) { - llama_batch_allocr batch_allocr(ctx, batch); - const int ret = llama_decode_internal(*ctx, batch_allocr.batch); + const int ret = llama_decode_internal(*ctx, batch); if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); } From 873279b1592e433c4d9eb5065091cc98473c7bee Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 20 Oct 2024 00:22:59 +0000 Subject: [PATCH 5/5] 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/5633bcff0c6162b9e4b5f1264264611e950c8ec7?narHash=sha256-9UTxR8eukdg%2BXZeHgxW5hQA9fIKHsKCdOIUycTryeVw%3D' (2024-10-09) → 'github:NixOS/nixpkgs/4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0?narHash=sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c%2BcHUJwA%3D' (2024-10-18) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 702527028..1f8defab7 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1728492678, - "narHash": "sha256-9UTxR8eukdg+XZeHgxW5hQA9fIKHsKCdOIUycTryeVw=", + "lastModified": 1729256560, + "narHash": "sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c+cHUJwA=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "5633bcff0c6162b9e4b5f1264264611e950c8ec7", + "rev": "4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0", "type": "github" }, "original": {