llm : add Refact model (#3329)
* add refact model * resolve comments * rebase to the latest * solve alibi cpu error --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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4 changed files with 723 additions and 10 deletions
382
llama.cpp
382
llama.cpp
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@ -165,6 +165,7 @@ enum llm_arch {
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LLM_ARCH_GPTNEOX,
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LLM_ARCH_MPT,
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LLM_ARCH_STARCODER,
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LLM_ARCH_REFACT,
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LLM_ARCH_UNKNOWN,
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};
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@ -177,6 +178,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_REFACT, "refact" },
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};
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enum llm_kv {
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@ -397,6 +399,23 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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},
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},
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{
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LLM_ARCH_REFACT,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -1927,6 +1946,14 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_REFACT:
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{
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GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_1B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -2164,6 +2191,7 @@ static void llm_load_tensors(
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const auto tn = LLM_TN(model.arch);
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switch (model.arch) {
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case LLM_ARCH_LLAMA:
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case LLM_ARCH_REFACT:
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{
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model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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@ -3357,6 +3385,353 @@ static struct ggml_cgraph * llm_build_baichaun(
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return gf;
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}
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static struct ggml_cgraph * llm_build_refact(
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llama_context & lctx,
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const llama_batch & batch) {
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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const auto & kv_self = lctx.kv_self;
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GGML_ASSERT(!!kv_self.ctx);
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_layer = hparams.n_layer;
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const int64_t n_ctx = cparams.n_ctx;
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const int64_t n_head = hparams.n_head;
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const int64_t n_head_kv = hparams.n_head_kv;
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const int64_t n_embd_head = hparams.n_embd_head();
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const int64_t n_embd_gqa = hparams.n_embd_gqa();
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const float norm_rms_eps = hparams.f_norm_rms_eps;
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const int n_gpu_layers = model.n_gpu_layers;
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const int32_t n_tokens = batch.n_tokens;
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const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
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const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
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// printf("n_kv = %d\n", n_kv);
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auto & buf_compute = lctx.buf_compute;
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_compute.size,
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/*.mem_buffer =*/ buf_compute.data,
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/*.no_alloc =*/ false,
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};
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params.no_alloc = true;
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struct ggml_context * ctx0 = ggml_init(params);
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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if (batch.token) {
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struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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ggml_allocr_alloc(lctx.alloc, inp_tokens);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
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}
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ggml_set_name(inp_tokens, "inp_tokens");
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inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
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} else {
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#ifdef GGML_USE_MPI
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GGML_ASSERT(false && "not implemented");
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#endif
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inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
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ggml_allocr_alloc(lctx.alloc, inpL);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
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}
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}
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const int i_gpu_start = n_layer - n_gpu_layers;
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(void) i_gpu_start;
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// offload functions set the tensor output backend to GPU
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// tensors are GPU-accelerated if any input or the output has been offloaded
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offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
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offload_func_t offload_func_kq = llama_nop;
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offload_func_t offload_func_v = llama_nop;
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#ifdef GGML_USE_CUBLAS
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if (n_gpu_layers > n_layer) {
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offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
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}
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if (n_gpu_layers > n_layer + 1) {
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offload_func_v = ggml_cuda_assign_buffers_no_alloc;
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}
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if (n_gpu_layers > n_layer + 2) {
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offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
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}
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#endif // GGML_USE_CUBLAS
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// KQ_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
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ggml_allocr_alloc(lctx.alloc, KQ_scale);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
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}
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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offload_func_kq(KQ_mask);
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ggml_set_name(KQ_mask, "KQ_mask");
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ggml_allocr_alloc(lctx.alloc, KQ_mask);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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float * data = (float *) KQ_mask->data;
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memset(data, 0, ggml_nbytes(KQ_mask));
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for (int h = 0; h < 1; ++h) {
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for (int j = 0; j < n_tokens; ++j) {
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const llama_pos pos = batch.pos[j];
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const llama_seq_id seq_id = batch.seq_id[j];
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for (int i = 0; i < n_kv; ++i) {
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if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
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data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
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}
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}
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}
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}
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}
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for (int il = 0; il < n_layer; ++il) {
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ggml_format_name(inpL, "layer_inp_%d", il);
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offload_func_t offload_func = llama_nop;
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#ifdef GGML_USE_CUBLAS
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if (il >= i_gpu_start) {
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offload_func = ggml_cuda_assign_buffers_no_alloc;
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}
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#endif // GGML_USE_CUBLAS
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struct ggml_tensor * inpSA = inpL;
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// norm
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{
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cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
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offload_func(cur);
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ggml_set_name(cur, "rms_norm_0");
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// cur = cur*attn_norm(broadcasted)
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cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
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offload_func(cur);
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ggml_set_name(cur, "attention_norm_0");
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}
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// self-attention
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{
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// compute Q and K
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struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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offload_func_kq(tmpk);
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ggml_set_name(tmpk, "tmpk");
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struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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offload_func_kq(tmpq);
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ggml_set_name(tmpq, "tmpq");
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struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
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offload_func_kq(Kcur);
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ggml_set_name(Kcur, "Kcur");
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struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
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offload_func_kq(Qcur);
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ggml_set_name(Qcur, "Qcur");
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// store key and value to memory
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{
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// compute the transposed [n_tokens, n_embd] V matrix
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struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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offload_func_v(tmpv);
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ggml_set_name(tmpv, "tmpv");
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
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offload_func_v(Vcur);
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ggml_set_name(Vcur, "Vcur");
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struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
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offload_func_kq(k);
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ggml_set_name(k, "k");
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struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
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( n_ctx)*ggml_element_size(kv_self.v),
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(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
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offload_func_v(v);
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ggml_set_name(v, "v");
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
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}
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struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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offload_func_kq(Q);
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ggml_set_name(Q, "Q");
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struct ggml_tensor * K =
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ggml_view_3d(ctx0, kv_self.k,
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n_embd_head, n_kv, n_head_kv,
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ggml_element_size(kv_self.k)*n_embd_gqa,
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ggml_element_size(kv_self.k)*n_embd_head,
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ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
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offload_func_kq(K);
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ggml_set_name(K, "K");
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// K * Q
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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offload_func_kq(KQ);
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ggml_set_name(KQ, "KQ");
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// KQ_scaled = KQ / sqrt(n_embd_head)
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// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
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struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
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offload_func_kq(KQ_scaled);
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ggml_set_name(KQ_scaled, "KQ_scaled");
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
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ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
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struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
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offload_func_kq(KQ_masked);
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ggml_set_name(KQ_masked, "KQ_masked");
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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offload_func_v(KQ_soft_max);
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ggml_set_name(KQ_soft_max, "KQ_soft_max");
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// split cached V into n_head heads
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v,
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n_kv, n_embd_head, n_head_kv,
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ggml_element_size(kv_self.v)*n_ctx,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
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offload_func_v(V);
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ggml_set_name(V, "V");
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#if 1
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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offload_func_v(KQV);
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ggml_set_name(KQV, "KQV");
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#else
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// make V contiguous in memory to speed up the matmul, however we waste time on the copy
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// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
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// is there a better way?
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struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
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#endif
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// KQV_merged = KQV.permute(0, 2, 1, 3)
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struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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offload_func_v(KQV_merged);
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ggml_set_name(KQV_merged, "KQV_merged");
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// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
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cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
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offload_func_v(cur);
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ggml_set_name(cur, "KQV_merged_contiguous");
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// projection (no bias)
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cur = ggml_mul_mat(ctx0,
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model.layers[il].wo,
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cur);
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offload_func(cur);
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ggml_set_name(cur, "result_wo");
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}
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struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
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offload_func(inpFF);
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ggml_set_name(inpFF, "inpFF");
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// feed-forward network
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{
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// norm
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{
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cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
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offload_func(cur);
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ggml_set_name(cur, "rms_norm_1");
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// cur = cur*ffn_norm(broadcasted)
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cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
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offload_func(cur);
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ggml_set_name(cur, "ffn_norm");
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}
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struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
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model.layers[il].w3,
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cur);
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offload_func(tmp);
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ggml_set_name(tmp, "result_w3");
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cur = ggml_mul_mat(ctx0,
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model.layers[il].w1,
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cur);
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offload_func(cur);
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ggml_set_name(cur, "result_w1");
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// SILU activation
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cur = ggml_silu(ctx0, cur);
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offload_func(cur);
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ggml_set_name(cur, "silu");
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cur = ggml_mul(ctx0, cur, tmp);
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offload_func(cur);
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ggml_set_name(cur, "silu_x_result_w3");
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cur = ggml_mul_mat(ctx0,
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model.layers[il].w2,
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||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "inpFF_+_result_w2");
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
|
||||
offload_func_nr(cur);
|
||||
ggml_set_name(cur, "rms_norm_2");
|
||||
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.output_norm);
|
||||
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
||||
ggml_set_name(cur, "result_norm");
|
||||
}
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
ggml_set_name(cur, "result_output");
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
static struct ggml_cgraph * llm_build_falcon(
|
||||
llama_context & lctx,
|
||||
const llama_batch & batch) {
|
||||
|
|
@ -3997,6 +4372,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm_build_starcoder(lctx, batch);
|
||||
} break;
|
||||
case LLM_ARCH_REFACT:
|
||||
{
|
||||
result = llm_build_refact(lctx, batch);
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -4130,7 +4509,8 @@ static int llama_decode_internal(
|
|||
// If all tensors can be run on the GPU then using more than 1 thread is detrimental.
|
||||
const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
|
||||
model.arch == LLM_ARCH_BAICHUAN ||
|
||||
model.arch == LLM_ARCH_FALCON;
|
||||
model.arch == LLM_ARCH_FALCON ||
|
||||
model.arch == LLM_ARCH_REFACT;
|
||||
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
|
||||
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
|
||||
n_threads = 1;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue