Buckets:
| void llama_model_lfm3::load_arch_hparams(llama_model_loader & ml) { | |
| ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache); | |
| ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); | |
| for (uint32_t il = 0; il < hparams.n_layer(); ++il) { | |
| hparams.is_recr_impl[il] = hparams.n_head_kv(il) == 0; | |
| } | |
| // LFM3 always mixes full and sliding window attention layers | |
| ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl); | |
| if (!hparams.is_swa_any()) { | |
| throw std::runtime_error("LFM3 requires sliding window attention layers"); | |
| } | |
| hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; | |
| ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); | |
| ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); | |
| // d1: full attention layers read the prompt both ways, sliding window layers stay causal | |
| if (!hparams.causal_attn) { | |
| hparams.non_causal_type = LLAMA_NON_CAUSAL_TYPE_DENSE_ONLY; | |
| } | |
| switch (hparams.n_ff()) { | |
| case 7680: type = LLM_TYPE_1_7B; break; | |
| default: type = LLM_TYPE_UNKNOWN; | |
| } | |
| } | |
| void llama_model_lfm3::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); | |
| } | |
| int rope_freqs_flag = 0; | |
| 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.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); | |
| // absent when use_sandwich_norm is false | |
| layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED); | |
| layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED); | |
| if ((layer.attn_post_norm == nullptr) != (layer.ffn_post_norm == nullptr)) { | |
| throw std::runtime_error("LFM3 requires both post norms or neither"); | |
| } | |
| 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); | |
| if (!hparams.is_recr(i)) { | |
| // note: n_embd_head_k * n_head may differ from n_embd | |
| const int64_t n_embd_q = n_embd_head_k * n_head; | |
| const int64_t n_embd_k = hparams.n_embd_k_gqa(i); | |
| const int64_t n_embd_v = hparams.n_embd_v_gqa(i); | |
| if (n_embd_k != n_embd_v) { | |
| throw std::runtime_error("LFM3 requires equal key and value sizes"); | |
| } | |
| 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); | |
| create_tensor_qkv(layer, i, n_embd, n_embd_q, n_embd_k, n_embd_v, 0); | |
| layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q, n_embd}, 0); | |
| layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head}, 0); | |
| if (!hparams.is_swa(i)) { | |
| layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_embd_head_k / 2}, rope_freqs_flag); | |
| rope_freqs_flag = TENSOR_DUPLICATED; | |
| } | |
| } else { | |
| layer.shortconv.conv = create_tensor(tn(LLM_TENSOR_SHORTCONV_CONV, "weight", i), {hparams.n_shortconv_l_cache, n_embd}, 0); | |
| layer.shortconv.in_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_INPROJ, "weight", i), {n_embd, 3 * n_embd}, 0); | |
| layer.shortconv.out_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_OUTPROJ, "weight", i), {n_embd, n_embd}, 0); | |
| } | |
| } | |
| } | |
| std::unique_ptr<llm_graph_context> llama_model_lfm3::build_arch_graph(const llm_graph_params & params) const { | |
| if (!hparams.causal_attn) { | |
| return std::make_unique<graph<true>>(*this, params); | |
| } | |
| return std::make_unique<graph<false>>(*this, params); | |
| } | |
| template <bool decision> | |
| llama_model_lfm3::graph<decision>::graph(const llama_model & model, const llm_graph_params & params) : | |
| llm_build_lfm_base(params) { | |
| auto build_attn_block = [&model, this](ggml_tensor * cur_in, | |
| ggml_tensor * inp_pos, | |
| auto * inp_attn, | |
| int il) -> ggml_tensor * { | |
| const int64_t n_embd_head = hparams.n_embd_head_k(il); | |
| const int64_t n_head_il = hparams.n_head(il); | |
| const int64_t n_head_kv = hparams.n_head_kv(il); | |
| const int n_rot_l = hparams.n_rot(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); | |
| ggml_tensor * rope_factors = hparams.is_swa(il) ? nullptr : model.layers[il].rope_freqs; | |
| auto [q, k, v] = build_qkv(model.layers[il], cur_in, n_embd_head, n_head_il, n_head_kv, il); | |
| q = build_norm(q, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); | |
| cb(q, "model.layers.{}.self_attn.q_layernorm", il); | |
| k = build_norm(k, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); | |
| cb(k, "model.layers.{}.self_attn.k_layernorm", il); | |
| q = ggml_rope_ext(ctx0, q, inp_pos, rope_factors, n_rot_l, rope_type, n_ctx_orig, | |
| freq_base_l, freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow); | |
| k = ggml_rope_ext(ctx0, k, inp_pos, rope_factors, n_rot_l, rope_type, n_ctx_orig, | |
| freq_base_l, freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow); | |
| ggml_tensor * attn_out = build_attn(inp_attn, | |
| NULL, NULL, NULL, | |
| q, k, v, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il); | |
| cb(attn_out, "attn_out", il); | |
| ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur_in); | |
| gate = ggml_sigmoid(ctx0, gate); | |
| cb(gate, "model.layers.{}.self_attn.gate", il); | |
| ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head, n_head_il, n_tokens); | |
| ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens); | |
| attn_3d = ggml_mul(ctx0, attn_3d, gate_3d); | |
| attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head * n_head_il, n_tokens); | |
| ggml_tensor * out = build_lora_mm(model.layers[il].wo, attn_out, model.layers[il].wo_s); | |
| cb(out, "model.layers.{}.self_attn.out_proj", il); | |
| return out; | |
| }; | |
| ggml_tensor * cur = build_inp_embd(model.tok_embd); | |
| cb(cur, "model.embed_tokens", -1); | |
| ggml_build_forward_expand(gf, cur); | |
| // decision: no memory, the whole prompt is read in one batch | |
| auto * inp_hybrid = decision ? nullptr : build_inp_mem_hybrid_iswa(); | |
| auto * inp_attn = decision ? build_attn_inp_no_cache() : nullptr; | |
| ggml_tensor * inp_pos = build_inp_pos(); | |
| ggml_tensor * inp_out_ids = build_inp_out_ids(); | |
| for (int il = 0; il < n_layer; ++il) { | |
| auto * prev_cur = cur; | |
| cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "model.layers.{}.operator_norm", il); | |
| if (hparams.is_recr(il)) { | |
| if constexpr (decision) { | |
| cur = build_lfm_shortconv(model, cur, il); | |
| } else { | |
| const auto * mctx_recr = static_cast<const llama_memory_hybrid_iswa_context *>(mctx)->get_recr(); | |
| cur = build_lfm_shortconv(model, mctx_recr, inp_hybrid->get_recr(), cur, il); | |
| } | |
| } else if constexpr (decision) { | |
| cur = build_attn_block(cur, inp_pos, inp_attn, il); | |
| } else { | |
| cur = build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il); | |
| } | |
| if (model.layers[il].attn_post_norm) { | |
| cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); | |
| cb(cur, "model.layers.{}.post_operator_norm", il); | |
| } | |
| if (il == n_layer - 1 && inp_out_ids) { | |
| cur = ggml_get_rows(ctx0, cur, inp_out_ids); | |
| prev_cur = ggml_get_rows(ctx0, prev_cur, inp_out_ids); | |
| } | |
| cur = ggml_add(ctx0, prev_cur, cur); | |
| auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); | |
| cb(ffn_norm_out, "model.layers.{}.ffn_norm", il); | |
| ggml_tensor * ffn_out = build_lfm_dense_ffn(model, ffn_norm_out, il); | |
| if (model.layers[il].ffn_post_norm) { | |
| ffn_out = build_norm(ffn_out, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il); | |
| cb(ffn_out, "model.layers.{}.post_ffn_norm", il); | |
| } | |
| cur = ggml_add(ctx0, cur, ffn_out); | |
| cur = build_cvec(cur, il); | |
| cb(cur, "l_out", il); | |
| } | |
| 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); | |
| cb(cur, "result_output", -1); | |
| res->t_logits = cur; | |
| } | |
| template struct llama_model_lfm3::graph<false>; | |
| template struct llama_model_lfm3::graph<true>; | |
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