1#include "models.h"
 2
 3llm_build_t5_enc::llm_build_t5_enc(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    ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();
14
15    auto * inp_attn = build_attn_inp_no_cache();
16
17    ggml_tensor * inp_out_ids = build_inp_out_ids();
18
19    for (int il = 0; il < n_layer; ++il) {
20        ggml_tensor * inpSA = inpL;
21
22        // norm
23        cur = build_norm(inpL,
24                model.layers[il].attn_norm_enc, NULL,
25                LLM_NORM_RMS, il);
26        cb(cur, "attn_norm", il);
27
28        // self-attention
29        {
30            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);
31            cb(Qcur, "Qcur", il);
32
33            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);
34            cb(Kcur, "Kcur", il);
35
36            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_enc, cur);
37            cb(Vcur, "Vcur", il);
38
39            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
40            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
41            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
42
43            ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
44            ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);
45
46            cur = build_attn(inp_attn,
47                    model.layers[il].wo_enc, nullptr,
48                    Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
49            cb(cur, "kqv_out", il);
50        }
51        if (il == n_layer - 1 && inp_out_ids) {
52            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
53            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
54        }
55        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
56        cb(ffn_inp, "ffn_inp", il);
57
58        // feed-forward network
59        {
60            cur = build_norm(ffn_inp,
61                    model.layers[il].ffn_norm_enc, NULL,
62                    LLM_NORM_RMS, il);
63            cb(cur, "ffn_norm", il);
64
65            // T5 uses relu, flan-T5 uses gelu-gated
66            cur = build_ffn(cur,
67                    model.layers[il].ffn_up_enc,   NULL, NULL,
68                    model.layers[il].ffn_gate_enc, NULL, NULL,
69                    model.layers[il].ffn_down_enc, NULL, NULL,
70                    NULL,
71                    model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
72                    model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,
73                    il);
74            cb(cur, "ffn_out", il);
75        }
76        cur = ggml_add(ctx0, cur, ffn_inp);
77        cb(cur, "ffn_out", il);
78
79        cur = build_cvec(cur, il);
80        cb(cur, "l_out", il);
81
82        // input for next layer
83        inpL = cur;
84    }
85    cur = inpL;
86    cb(cur, "result_embd", -1);
87
88    cur = build_norm(cur,
89            model.output_norm_enc, NULL,
90            LLM_NORM_RMS, -1);
91
92    cb(cur, "result_norm", -1);
93    res->t_embd = cur;
94
95    ggml_build_forward_expand(gf, cur);
96}