#include "models.h" llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); ggml_tensor * cur; ggml_tensor * inpL; ggml_tensor * inp_pos = nullptr; if (model.arch != LLM_ARCH_JINA_BERT_V2) { inp_pos = build_inp_pos(); } // construct input embeddings (token, type, position) inpL = build_inp_embd(model.tok_embd); // token types are hardcoded to zero ("Sentence A") if (model.type_embd) { ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); inpL = ggml_add(ctx0, inpL, type_row0); } if (model.arch == LLM_ARCH_BERT) { inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL); } cb(inpL, "inp_embd", -1); // embed layer norm inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1); cb(inpL, "inp_norm", -1); auto * inp_attn = build_attn_inp_no_cache(); ggml_tensor * inp_out_ids = build_inp_out_ids(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * cur = inpL; { ggml_tensor * Qcur; ggml_tensor * Kcur; ggml_tensor * Vcur; // self-attention if (model.layers[il].wqkv) { cur = build_lora_mm(model.layers[il].wqkv, cur); cb(cur, "wqkv", il); if (model.layers[il].bqkv) { cur = ggml_add(ctx0, cur, model.layers[il].bqkv); cb(cur, "bqkv", il); } Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd)); Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd)); Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)); } else { Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq); Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk); Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv); 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); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); } if (model.layers[il].attn_q_norm) { Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head * n_head, n_tokens); Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, model.layers[il].attn_q_norm_b, LLM_NORM, il); Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); } if (model.layers[il].attn_k_norm) { Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head * n_head_kv, n_tokens); Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, model.layers[il].attn_k_norm_b, LLM_NORM, il); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); } // RoPE if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) { Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, 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, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il); cb(cur, "kqv_out", il); } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } // re-add the layer input cur = ggml_add(ctx0, cur, inpL); // attention layer norm cur = build_norm(cur, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, il); if (model.layers[il].attn_norm_2 != nullptr) { cur = ggml_add(ctx0, cur, inpL); // re-add the layer input cur = build_norm(cur, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, il); } ggml_tensor * ffn_inp = cur; cb(ffn_inp, "ffn_inp", il); // feed-forward network if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) { // MoE branch cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr, model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used, LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) { cur = build_ffn(cur, model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL, LLM_FFN_GELU, LLM_FFN_SEQ, il); cb(cur, "ffn_out", il); } else if (model.arch == LLM_ARCH_JINA_BERT_V2) { cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL, model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_GEGLU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); } else { 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); cb(cur, "ffn_out", il); } // attentions bypass the intermediate layer cur = ggml_add(ctx0, cur, ffn_inp); // output layer norm cur = build_norm(cur, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, il); // input for next layer inpL = cur; } cur = inpL; cb(cur, "result_embd", -1); res->t_embd = cur; ggml_build_forward_expand(gf, cur); }