1#include "models.h"
 2
 3llm_build_refact::llm_build_refact(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
 8    ggml_tensor * cur;
 9    ggml_tensor * inpL;
10
11    inpL = build_inp_embd(model.tok_embd);
12
13    auto * inp_attn = build_attn_inp_kv();
14
15    ggml_tensor * inp_out_ids = build_inp_out_ids();
16
17    for (int il = 0; il < n_layer; ++il) {
18        ggml_tensor * inpSA = inpL;
19
20        cur = build_norm(inpL,
21                model.layers[il].attn_norm, NULL,
22                LLM_NORM_RMS, il);
23        cb(cur, "attn_norm", il);
24
25        // self-attention
26        {
27            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
28            cb(Qcur, "Qcur", il);
29
30            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
31            cb(Kcur, "Kcur", il);
32
33            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
34            cb(Vcur, "Vcur", il);
35
36            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
37            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
38            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
39
40            cb(Qcur, "Qcur", il);
41            cb(Kcur, "Kcur", il);
42            cb(Vcur, "Vcur", il);
43
44            cur = build_attn(inp_attn,
45                    model.layers[il].wo, NULL,
46                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
47        }
48        if (il == n_layer - 1 && inp_out_ids) {
49            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
50            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
51        }
52        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
53        cb(ffn_inp, "ffn_inp", il);
54
55        // feed-forward network
56        {
57            cur = build_norm(ffn_inp,
58                    model.layers[il].ffn_norm, NULL,
59                    LLM_NORM_RMS, il);
60            cb(cur, "ffn_norm", il);
61
62            cur = build_ffn(cur,
63                    model.layers[il].ffn_up,   NULL, NULL,
64                    model.layers[il].ffn_gate, NULL, NULL,
65                    model.layers[il].ffn_down, NULL, NULL,
66                    NULL,
67                    LLM_FFN_SILU, LLM_FFN_PAR, il);
68            cb(cur, "ffn_out", il);
69        }
70        cur = ggml_add(ctx0, cur, ffn_inp);
71
72        cur = build_cvec(cur, il);
73        cb(cur, "l_out", il);
74
75        // input for next layer
76        inpL = cur;
77    }
78    cur = inpL;
79
80    cur = build_norm(cur,
81            model.output_norm, NULL,
82            LLM_NORM_RMS, -1);
83
84    cb(cur, "result_norm", -1);
85    res->t_embd = cur;
86
87    // lm_head
88    cur = build_lora_mm(model.output, cur);
89
90    cb(cur, "result_output", -1);
91    res->t_logits = cur;
92
93    ggml_build_forward_expand(gf, cur);
94}