Buckets:
| void llama_model_maion_coder::load_arch_hparams(llama_model_loader & ml) { | |
| ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); | |
| 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_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step); | |
| ml.get_key(LLM_KV_MOE_LATENT_SIZE, hparams.moe_latent_size); | |
| if (hparams.n_moe_layer_step == 0 || hparams.moe_latent_size == 0 || hparams.n_expert == 0) { | |
| throw std::runtime_error("maion-coder requires a positive MoE layer step, latent size and expert count"); | |
| } | |
| ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); | |
| uint32_t n_swa_layers = 0; | |
| ml.get_arr_n(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, n_swa_layers); | |
| if (hparams.n_swa == 0 || n_swa_layers != hparams.n_layer_all) { | |
| throw std::runtime_error("maion-coder 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); | |
| hparams.rope_pattern = hparams.is_swa_impl; | |
| hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; | |
| hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; | |
| hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; | |
| type = LLM_TYPE_UNKNOWN; | |
| } | |
| void llama_model_maion_coder::load_arch_tensors(llama_model_loader &) { | |
| LLAMA_LOAD_LOCALS; | |
| const int64_t moe_n_embd = hparams.moe_latent_size; | |
| 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}, 0); | |
| for (int i = 0; i < n_layer; ++i) { | |
| const bool is_moe_layer = (i + 1) % hparams.n_moe_layer_step == 0; | |
| auto & layer = layers[i]; | |
| layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_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.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 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 (is_moe_layer) { | |
| 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_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, 0); | |
| layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0); | |
| layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_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, moe_n_embd, n_expert}, 0); | |
| layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_n_embd, n_embd}, 0); | |
| } else { | |
| layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); | |
| layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); | |
| layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); | |
| } | |
| } | |
| } | |
| std::unique_ptr<llm_graph_context> llama_model_maion_coder::build_arch_graph(const llm_graph_params & params) const { | |
| return std::make_unique<graph>(*this, params); | |
| } | |
| llama_model_maion_coder::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_k(); | |
| GGML_ASSERT(n_embd_head == hparams.n_embd_head_v()); | |
| GGML_ASSERT(n_embd_head == n_rot); | |
| ggml_tensor * inpL = build_inp_embd(model.tok_embd); | |
| ggml_tensor * inp_pos = build_inp_pos(); | |
| 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) { | |
| // export per-layer input hidden state so speculative drafts (e.g. dflash/eagle3) can read it | |
| res->t_layer_inp[il] = inpL; | |
| ggml_tensor * inpSA = inpL; | |
| ggml_tensor * cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "attn_norm", il); | |
| auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il); | |
| Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); | |
| Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); | |
| // Full-attention layers do not use RoPE. | |
| if (hparams.is_swa(il)) { | |
| 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); | |
| } | |
| cur = build_attn(inp_attn, model.layers[il].wo, NULL, model.layers[il].wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, 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); | |
| } | |
| cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); | |
| ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); | |
| cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); | |
| if (model.layers[il].ffn_gate_inp == nullptr) { | |
| cur = build_ffn(cur, | |
| model.layers[il].ffn_up, NULL, NULL, | |
| model.layers[il].ffn_gate, NULL, NULL, | |
| model.layers[il].ffn_down, NULL, NULL, | |
| NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); | |
| } else { | |
| ggml_tensor * router_logits = build_lora_mm(model.layers[il].ffn_gate_inp, cur); | |
| ggml_tensor * inp_latent = build_lora_mm(model.layers[il].ffn_latent_down, cur); | |
| cur = build_moe_ffn(inp_latent, | |
| model.layers[il].ffn_gate_inp, | |
| model.layers[il].ffn_up_exps, | |
| model.layers[il].ffn_gate_exps, | |
| model.layers[il].ffn_down_exps, | |
| nullptr, | |
| n_expert, hparams.n_expert_used(il), | |
| LLM_FFN_SILU, true, 0.0f, | |
| LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, | |
| il, router_logits); | |
| cur = build_lora_mm(model.layers[il].ffn_latent_up, cur); | |
| } | |
| cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il); | |
| cur = ggml_add(ctx0, cur, ffn_inp); | |
| cur = build_cvec(cur, il); | |
| inpL = cur; | |
| } | |
| ggml_tensor * cur = build_norm(inpL, 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); | |
| cb(cur, "result_output", -1); | |
| res->t_logits = cur; | |
| } | |
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