echodict/llama.cpp

1#include "models.h"2 3llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {4    const int64_t n_embd_head = hparams.n_embd_head_v();5 6    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());7    GGML_ASSERT(n_embd_head == n_rot);8 9    ggml_tensor * cur;10    ggml_tensor * inpL;11 12    inpL = build_inp_embd(model.tok_embd);13 14    // inp_pos - contains the positions15    ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;16 17    auto * inp_attn = build_attn_inp_kv();18 19    ggml_tensor * inp_out_ids = build_inp_out_ids();20 21    for (int il = 0; il < n_layer; ++il) {22        ggml_tensor * inpSA = inpL;23 24        cur = build_norm(inpL,25                model.layers[il].attn_norm, NULL,26                LLM_NORM_RMS, il);27        cb(cur, "attn_norm", il);28 29        // self-attention30        {31            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,32                    n_embd_head, n_head, n_head_kv, il);33 34            switch (model.type) {35                case LLM_TYPE_7B:36                    Qcur = ggml_rope_ext(37                            ctx0, Qcur, inp_pos, nullptr,38                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,39                            ext_factor, attn_factor, beta_fast, beta_slow40                            );41                    Kcur = ggml_rope_ext(42                            ctx0, Kcur, inp_pos, nullptr,43                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,44                            ext_factor, attn_factor, beta_fast, beta_slow45                            );46                    break;47                case LLM_TYPE_13B:48                case LLM_TYPE_UNKNOWN:49                    break;50                default:51                    GGML_ABORT("fatal error");52            }53 54            cb(Qcur, "Qcur", il);55            cb(Kcur, "Kcur", il);56            cb(Vcur, "Vcur", il);57 58            cur = build_attn(inp_attn,59                    model.layers[il].wo, NULL, model.layers[il].wo_s,60                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);61        }62 63        if (il == n_layer - 1 && inp_out_ids) {64            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);65            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);66        }67 68        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);69        cb(ffn_inp, "ffn_inp", il);70 71        // feed-forward network72        {73            cur = build_norm(ffn_inp,74                    model.layers[il].ffn_norm, NULL,75                    LLM_NORM_RMS, il);76            cb(cur, "ffn_norm", il);77 78            cur = build_ffn(cur,79                    model.layers[il].ffn_up,   NULL, NULL,80                    model.layers[il].ffn_gate, NULL, NULL,81                    model.layers[il].ffn_down, NULL, NULL,82                    NULL,83                    LLM_FFN_SILU, LLM_FFN_PAR, il);84            cb(cur, "ffn_out", il);85        }86 87        cur = ggml_add(ctx0, cur, ffn_inp);88 89        cur = build_cvec(cur, il);90        cb(cur, "l_out", il);91 92        // input for next layer93        inpL = cur;94    }95 96    cur = inpL;97 98    cur = build_norm(cur,99            model.output_norm, NULL,100            LLM_NORM_RMS, -1);101 102    cb(cur, "result_norm", -1);103    res->t_embd = cur;104 105    // lm_head106    cur = build_lora_mm(model.output, cur);107 108    cb(cur, "result_output", -1);109    res->t_logits = cur;110 111    ggml_build_forward_expand(gf, cur);112}113