Buckets:
| void llama_model_beam::load_arch_hparams(llama_model_loader & ml) { | |
| ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); | |
| ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); | |
| ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all); | |
| ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); | |
| ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); | |
| ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm); | |
| hparams.f_final_logit_softcapping = 0.0f; // absent key = no softcap (default is 30) | |
| ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false); | |
| if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { | |
| hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID; | |
| } | |
| if (hparams.n_layer_dense_lead > hparams.n_layer()) { | |
| throw std::runtime_error("beam: leading_dense_block_count exceeds the layer count"); | |
| } | |
| ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); | |
| hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; // masked iff p1 - p0 >= n_swa | |
| uint32_t n_pattern = 0; | |
| ml.get_arr_n(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, n_pattern); | |
| if (hparams.n_swa == 0 || n_pattern != hparams.n_layer_all) { | |
| throw std::runtime_error("beam: requires a positive sliding window and a sliding window pattern for each layer"); | |
| } | |
| ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl); | |
| if (!hparams.is_swa_any()) { | |
| throw std::runtime_error("beam: sliding window pattern has no sliding window layer"); | |
| } | |
| // full layers are NoPE | |
| hparams.rope_pattern = hparams.is_swa_impl; | |
| hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; | |
| hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; | |
| ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); | |
| // NoPE query scale uses pos + 1 | |
| hparams.f_attn_temp_scale = 0.1f; | |
| hparams.attn_temp_length = 2048; | |
| ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false); | |
| ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.attn_temp_length, false); | |
| if (hparams.f_attn_temp_scale == 0.0f) { | |
| throw std::runtime_error("beam: attention.temperature_scale must be nonzero"); | |
| } | |
| if (hparams.attn_temp_length == 0) { | |
| throw std::runtime_error("beam: attention.temperature_length must be positive"); | |
| } | |
| hparams.n_attn_temp_floor_scale = hparams.attn_temp_length; | |
| hparams.f_attn_temp_offset = 1.0f; | |
| hparams.attn_temp_pow = true; | |
| switch (hparams.n_layer()) { | |
| case 52: type = LLM_TYPE_UNKNOWN; break; | |
| default: type = LLM_TYPE_UNKNOWN; | |
| } | |
| } | |
| void llama_model_beam::load_arch_tensors(llama_model_loader &) { | |
| LLAMA_LOAD_LOCALS; | |
| tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); | |
| output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); | |
| output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); | |
| if (output == NULL) { | |
| output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); | |
| } | |
| for (int i = 0; i < n_layer; ++i) { | |
| auto & layer = layers[i]; | |
| layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); | |
| layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); | |
| create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0); | |
| layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); | |
| layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); | |
| layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); | |
| layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); | |
| layer.wqkv_gate_b = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "bias", i), {n_head}, 0); | |
| layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); | |
| layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); | |
| if ((uint32_t) i < hparams.n_layer_dense_lead) { | |
| layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); | |
| layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); | |
| layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); | |
| } else { | |
| const int64_t n_ff_exp = hparams.n_ff_exp(i); | |
| layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); | |
| layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); | |
| layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); | |
| layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); | |
| layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); | |
| } | |
| } | |
| } | |
| std::unique_ptr<llm_graph_context> llama_model_beam::build_arch_graph(const llm_graph_params & params) const { | |
| return std::make_unique<graph>(*this, params); | |
| } | |
| static ggml_tensor * beam_residual_add(ggml_context * ctx, ggml_tensor * h, ggml_tensor * r, int i) { | |
| const float h_scale = (float) (1.0 / std::sqrt((double) i)); | |
| const float r_scale = (float) std::sqrt((double) (i - 1) / (double) i); | |
| return ggml_add(ctx, ggml_scale(ctx, h, h_scale), ggml_scale(ctx, r, r_scale)); | |
| } | |
| llama_model_beam::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { | |
| const int64_t n_embd_head = hparams.n_embd_head_v(); | |
| GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); | |
| ggml_tensor * cur; | |
| ggml_tensor * inpL; | |
| inpL = build_inp_embd(model.tok_embd); | |
| ggml_tensor * inp_pos = build_inp_pos(); | |
| bool has_nope = false; | |
| for (int il = 0; il < n_layer; ++il) { | |
| has_nope = has_nope || !hparams.is_swa(il); | |
| } | |
| ggml_tensor * inp_attn_scale = has_nope ? build_inp_attn_scale() : nullptr; | |
| auto * inp_attn = build_attn_inp_kv_iswa(); | |
| ggml_tensor * inp_out_ids = build_inp_out_ids(); | |
| const float kq_scale = 1.0f / sqrtf(float(n_embd_head)); | |
| for (int il = 0; il < n_layer; ++il) { | |
| const auto & layer = model.layers[il]; | |
| const bool is_swa = hparams.is_swa(il); | |
| ggml_tensor * inpSA = inpL; | |
| cur = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "attn_norm", il); | |
| { | |
| ggml_tensor * attn_inp = cur; | |
| auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il); | |
| Qcur = build_norm(Qcur, layer.attn_q_norm, NULL, LLM_NORM_RMS, il); | |
| Kcur = build_norm(Kcur, layer.attn_k_norm, NULL, LLM_NORM_RMS, il); | |
| cb(Qcur, "Qcur_normed", il); | |
| cb(Kcur, "Kcur_normed", il); | |
| if (is_swa) { | |
| const float freq_base_l = model.get_rope_freq_base (cparams, il); | |
| const float freq_scale_l = model.get_rope_freq_scale(cparams, il); | |
| Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, | |
| n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, | |
| ext_factor, attn_factor, beta_fast, beta_slow); | |
| Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, | |
| n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, | |
| ext_factor, attn_factor, beta_fast, beta_slow); | |
| cb(Qcur, "Qcur_rope", il); | |
| cb(Kcur, "Kcur_rope", il); | |
| } else { | |
| Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale); | |
| cb(Qcur, "Qcur_scaled", il); | |
| } | |
| cur = build_attn(inp_attn, | |
| NULL, NULL, NULL, // o_proj is applied after the gate | |
| Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); | |
| cb(cur, "attn_out", il); | |
| ggml_tensor * gate = build_lora_mm(layer.wqkv_gate, attn_inp); | |
| cb(gate, "attn_gate_proj", il); | |
| gate = ggml_reshape_3d(ctx0, gate, n_embd_head, n_head, n_tokens); | |
| gate = ggml_add(ctx0, gate, ggml_reshape_3d(ctx0, layer.wqkv_gate_b, 1, n_head, 1)); | |
| gate = ggml_sigmoid(ctx0, gate); | |
| gate = ggml_reshape_2d(ctx0, gate, n_embd_head * n_head, n_tokens); | |
| cb(gate, "attn_gate_sig", il); | |
| cur = ggml_mul(ctx0, cur, gate); | |
| cb(cur, "attn_gated", il); | |
| cur = build_lora_mm(layer.wo, cur, layer.wo_s); | |
| cb(cur, "attn_o_proj", il); | |
| } | |
| cur = build_norm(cur, layer.attn_post_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "attn_post_norm", il); | |
| if (il == n_layer - 1 && inp_out_ids) { | |
| cur = ggml_get_rows(ctx0, cur, inp_out_ids); | |
| inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); | |
| } | |
| const int i_attn = 2 * (il + 1); | |
| ggml_tensor * ffn_inp = beam_residual_add(ctx0, cur, inpSA, i_attn); | |
| cb(ffn_inp, "ffn_inp", il); | |
| cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "ffn_norm", il); | |
| if ((uint32_t) il < hparams.n_layer_dense_lead) { | |
| cur = build_ffn(cur, | |
| layer.ffn_up, NULL, NULL, | |
| layer.ffn_gate, NULL, NULL, | |
| layer.ffn_down, NULL, NULL, | |
| NULL, | |
| LLM_FFN_SILU, LLM_FFN_PAR, il); | |
| cb(cur, "ffn_out", il); | |
| } else { | |
| cur = build_moe_ffn(cur, | |
| layer.ffn_gate_inp, | |
| layer.ffn_up_exps, | |
| layer.ffn_gate_exps, | |
| layer.ffn_down_exps, | |
| layer.ffn_exp_probs_b, | |
| n_expert, n_expert_used, | |
| LLM_FFN_SILU, | |
| hparams.expert_weights_norm, | |
| hparams.expert_weights_scale, | |
| (llama_expert_gating_func_type) hparams.expert_gating_func, | |
| il); | |
| cb(cur, "ffn_moe_out", il); | |
| } | |
| cur = build_norm(cur, layer.ffn_post_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "ffn_post_norm", il); | |
| cur = beam_residual_add(ctx0, cur, ffn_inp, i_attn + 1); | |
| cur = build_cvec(cur, il); | |
| cb(cur, "l_out", il); | |
| inpL = cur; | |
| } | |
| cur = inpL; | |
| cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); | |
| cb(cur, "result_norm", -1); | |
| res->t_embd = cur; | |
| cur = build_lora_mm(model.output, cur, model.output_s); | |
| if (hparams.f_final_logit_softcapping != 0.0f) { | |
| cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping); | |
| cur = ggml_tanh(ctx0, cur); | |
| cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping); | |
| } | |
| cb(cur, "result_output", -1); | |
| res->t_logits = cur; | |
| } | |
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