diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 307 |
1 files changed, 275 insertions, 32 deletions
@@ -195,6 +195,7 @@ enum llm_arch { LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, + LLM_ARCH_PHI2, LLM_ARCH_UNKNOWN, }; @@ -212,6 +213,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = { { LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, + { LLM_ARCH_PHI2, "phi2" }, }; enum llm_kv { @@ -550,6 +552,19 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_PHI2, + { + { 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_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, @@ -1420,6 +1435,7 @@ struct llama_model { struct ggml_tensor * output_norm; struct ggml_tensor * output_norm_b; struct ggml_tensor * output; + struct ggml_tensor * output_b; std::vector<llama_layer> layers; @@ -2635,6 +2651,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_PHI2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -2987,7 +3012,7 @@ static void llm_load_tensors( (void) main_gpu; - enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; + enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU; enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU; #ifdef GGML_USE_CUBLAS @@ -3630,7 +3655,73 @@ static void llm_load_tensors( } } } 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); + + // 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); + + if (backend_norm == GGML_BACKEND_GPU) { + vram_weights += ggml_nbytes(model.output_norm); + vram_weights += ggml_nbytes(model.output_norm_b); + vram_weights += ggml_nbytes(model.output); + vram_weights += ggml_nbytes(model.output_b); + } + } + + const uint32_t n_ff = hparams.n_ff; + + 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 + + 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); + + if (backend == GGML_BACKEND_GPU) { + vram_weights += + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) + + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) + + ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) + + ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b) + + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b); + } + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -3991,6 +4082,7 @@ static struct ggml_tensor * llm_build_ffn( // if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, + const llama_model & model, const llama_hparams & hparams, const llama_kv_cache & kv, struct ggml_tensor * wo, @@ -4002,6 +4094,7 @@ static struct ggml_tensor * llm_build_kqv( int32_t n_tokens, int32_t n_kv, float max_alibi_bias, + float scale, const llm_build_cb & cb, int il) { const int64_t n_embd = hparams.n_embd; @@ -4024,6 +4117,12 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); + if (model.arch == LLM_ARCH_PHI2) { + // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs + // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 + ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + } + if (max_alibi_bias > 0.0f) { // temporary branch until we figure out how to handle ggml_alibi through ggml_add kq = ggml_scale(ctx, kq, kq_scale); @@ -4043,7 +4142,7 @@ static struct ggml_tensor * llm_build_kqv( kq = ggml_soft_max(ctx, kq); cb(kq, "kq_soft_max", il); } else { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, 1.0f/sqrtf(float(n_embd_head))); + kq = ggml_soft_max_ext(ctx, kq, kq_mask, scale); cb(kq, "kq_soft_max_ext", il); } @@ -4250,9 +4349,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4433,9 +4532,9 @@ struct llm_build_context { // apply ALiBi for 13B model const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f; - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4557,9 +4656,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4657,9 +4756,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4866,9 +4965,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); // TODO: not tested, could be broken - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -4957,9 +5056,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5054,9 +5153,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, model.layers[il].bo, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5148,9 +5247,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5261,9 +5360,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5320,15 +5419,15 @@ struct llm_build_context { 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); + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(inp_pos, "inp_pos", -1); // KQ_scale - struct ggml_tensor * KQ_scale= ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); cb(KQ_scale, "KQ_scale", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask= ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed @@ -5378,9 +5477,9 @@ struct llm_build_context { llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); - cur = llm_build_kqv(ctx0, hparams, kv_self, + cur = llm_build_kqv(ctx0, model, hparams, kv_self, model.layers[il].wo, NULL, - Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); cb(cur, "kqv_out", il); } @@ -5424,6 +5523,122 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_phi2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + struct ggml_tensor * cur; + struct ggml_tensor * attn_norm_output; + struct ggml_tensor * ffn_output; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + 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); + cb(inp_pos, "inp_pos", -1); + + // Q_scale + struct ggml_tensor * Q_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(Q_scale, "Q_scale", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + 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, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + attn_norm_output = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(attn_norm_output, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", 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_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_custom( + 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); + + Qcur = ggml_scale(ctx0, Qcur, Q_scale); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + 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); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, model, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + // FF + { + ffn_output = llm_build_ffn(ctx0, attn_norm_output, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(ffn_output, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_output); + cb(cur, "l_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output_no_bias", -1); + + cur = ggml_add(ctx0, cur, model.output_b); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; // @@ -5437,7 +5652,7 @@ enum llm_offload_func_e { OFFLOAD_FUNC_FRC, // force offload OFFLOAD_FUNC_KQV, OFFLOAD_FUNC_NR, - OFFLOAD_FUNC_EMB, + OFFLOAD_FUNC_EMB, // embeddings OFFLOAD_FUNC_OUT, }; @@ -5522,6 +5737,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map { "pos_embd", OFFLOAD_FUNC_NR }, { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope) + { "Q_scale", OFFLOAD_FUNC_FRC }, { "KQ_scale", OFFLOAD_FUNC_FRC }, { "KQ_mask", OFFLOAD_FUNC_FRC }, { "K_shift", OFFLOAD_FUNC_FRC }, @@ -5606,6 +5822,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map { "l_out", OFFLOAD_FUNC }, { "result_norm", OFFLOAD_FUNC_EMB }, + { "result_output_no_bias", OFFLOAD_FUNC_EMB }, { "result_output", OFFLOAD_FUNC_OUT }, }; @@ -5623,6 +5840,7 @@ static struct ggml_cgraph * llama_build_graph( bool alloc_inp_tokens = false; bool alloc_inp_embd = false; bool alloc_inp_pos = false; + bool alloc_inp_Q_scale = false; bool alloc_inp_KQ_scale = false; bool alloc_inp_KQ_mask = false; bool alloc_inp_K_shift = false; @@ -5690,7 +5908,7 @@ static struct ggml_cgraph * llama_build_graph( alloc_inp_pos = true; } - if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) { + if (!alloc_inp_Q_scale && strcmp(name, "Q_scale") == 0) { ggml_allocr_alloc(lctx.alloc, cur); if (!ggml_allocr_is_measure(lctx.alloc)) { @@ -5698,6 +5916,23 @@ static struct ggml_cgraph * llama_build_graph( ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); } + alloc_inp_Q_scale = true; + } + + if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) { + ggml_allocr_alloc(lctx.alloc, cur); + + if (!ggml_allocr_is_measure(lctx.alloc)) { + const int64_t n_embd_head = model.hparams.n_embd_head(); + if (model.arch == LLM_ARCH_PHI2) { + // with phi2, we scale the Q to avoid precision issues + // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 + ggml_set_f32(cur, 1.0f); + } else { + ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head))); + } + } + alloc_inp_KQ_scale = true; } @@ -5922,6 +6157,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_qwen(); } break; + case LLM_ARCH_PHI2: + { + result = llm.build_phi2(); + } break; default: GGML_ASSERT(false); } @@ -6055,12 +6294,16 @@ static int llama_decode_internal( ggml_allocr_alloc_graph(lctx.alloc, gf); - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2]; - - GGML_ASSERT(strcmp(res->name, "result_output") == 0); - GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0); + // 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); + } #ifdef GGML_USE_CUBLAS for (int i = 0; i < gf->n_leafs; i++) { |