1#include "models.h"
  2
  3
  4llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
  5    const int64_t n_embd_head = hparams.n_embd_head_v;
  6
  7    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
  8    GGML_ASSERT(n_embd_head == hparams.n_rot);
  9
 10    ggml_tensor * cur;
 11    ggml_tensor * inpL;
 12
 13    inpL = build_inp_embd(model.tok_embd);
 14
 15    // inp_pos - contains the positions
 16    ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;
 17
 18    auto * inp_attn = build_attn_inp_kv();
 19
 20    ggml_tensor * inp_out_ids = build_inp_out_ids();
 21
 22    for (int il = 0; il < n_layer; ++il) {
 23        ggml_tensor * inpSA = inpL;
 24
 25        cur = build_norm(inpL,
 26                model.layers[il].attn_norm, NULL,
 27                LLM_NORM_RMS, il);
 28        cb(cur, "attn_norm", il);
 29
 30        // self-attention
 31        {
 32            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
 33            cb(Qcur, "Qcur", il);
 34
 35            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
 36            cb(Kcur, "Kcur", il);
 37
 38            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
 39            cb(Vcur, "Vcur", il);
 40
 41            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
 42            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 43            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 44
 45            switch (model.type) {
 46                case LLM_TYPE_7B:
 47                    Qcur = ggml_rope_ext(
 48                            ctx0, Qcur, inp_pos, nullptr,
 49                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 50                            ext_factor, attn_factor, beta_fast, beta_slow
 51                            );
 52                    Kcur = ggml_rope_ext(
 53                            ctx0, Kcur, inp_pos, nullptr,
 54                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 55                            ext_factor, attn_factor, beta_fast, beta_slow
 56                            );
 57                    break;
 58                case LLM_TYPE_13B:
 59                    break;
 60                default:
 61                    GGML_ABORT("fatal error");
 62            }
 63
 64            cb(Qcur, "Qcur", il);
 65            cb(Kcur, "Kcur", il);
 66            cb(Vcur, "Vcur", il);
 67
 68            cur = build_attn(inp_attn,
 69                    model.layers[il].wo, NULL,
 70                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 71        }
 72
 73        if (il == n_layer - 1 && inp_out_ids) {
 74            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
 75            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
 76        }
 77
 78        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
 79        cb(ffn_inp, "ffn_inp", il);
 80
 81        // feed-forward network
 82        {
 83            cur = build_norm(ffn_inp,
 84                    model.layers[il].ffn_norm, NULL,
 85                    LLM_NORM_RMS, il);
 86            cb(cur, "ffn_norm", il);
 87
 88            cur = build_ffn(cur,
 89                    model.layers[il].ffn_up,   NULL, NULL,
 90                    model.layers[il].ffn_gate, NULL, NULL,
 91                    model.layers[il].ffn_down, NULL, NULL,
 92                    NULL,
 93                    LLM_FFN_SILU, LLM_FFN_PAR, il);
 94            cb(cur, "ffn_out", il);
 95        }
 96
 97        cur = ggml_add(ctx0, cur, ffn_inp);
 98
 99        cur = build_cvec(cur, il);
100        cb(cur, "l_out", il);
101
102        // input for next layer
103        inpL = cur;
104    }
105
106    cur = inpL;
107
108    cur = build_norm(cur,
109            model.output_norm, NULL,
110            LLM_NORM_RMS, -1);
111
112    cb(cur, "result_norm", -1);
113    res->t_embd = cur;
114
115    // lm_head
116    cur = build_lora_mm(model.output, cur);
117
118    cb(cur, "result_output", -1);
119    res->t_logits = cur;
120
121    ggml_build_forward_expand(gf, cur);
122}