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(tok_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(cur: inpL,
24 mw: model.layers[il].attn_norm_enc, NULL,
25 type: LLM_NORM_RMS, il);
26 cb(cur, name: "attn_norm", il);
27
28 // self-attention
29 {
30 ggml_tensor * Qcur = build_lora_mm(w: model.layers[il].wq_enc, cur);
31 cb(cur: Qcur, name: "Qcur", il);
32
33 ggml_tensor * Kcur = build_lora_mm(w: model.layers[il].wk_enc, cur);
34 cb(cur: Kcur, name: "Kcur", il);
35
36 ggml_tensor * Vcur = build_lora_mm(w: model.layers[il].wv_enc, cur);
37 cb(cur: Vcur, name: "Vcur", il);
38
39 Qcur = ggml_reshape_3d(ctx: ctx0, a: Qcur, ne0: n_embd_head, ne1: n_head, ne2: n_tokens);
40 Kcur = ggml_reshape_3d(ctx: ctx0, a: Kcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
41 Vcur = ggml_reshape_3d(ctx: ctx0, a: Vcur, ne0: n_embd_head, ne1: n_head_kv, ne2: 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: pos_bucket_enc, attn_rel_b);
45
46 cur = build_attn(inp: inp_attn,
47 wo: model.layers[il].wo_enc, wo_b: nullptr,
48 q_cur: Qcur, k_cur: Kcur, v_cur: Vcur, kq_b, sinks: nullptr, v_mla: nullptr, kq_scale: 1.0f, il);
49 cb(cur, name: "kqv_out", il);
50 }
51 if (il == n_layer - 1 && inp_out_ids) {
52 cur = ggml_get_rows(ctx: ctx0, a: cur, b: inp_out_ids);
53 inpSA = ggml_get_rows(ctx: ctx0, a: inpSA, b: inp_out_ids);
54 }
55 ggml_tensor * ffn_inp = ggml_add(ctx: ctx0, a: cur, b: inpSA);
56 cb(cur: ffn_inp, name: "ffn_inp", il);
57
58 // feed-forward network
59 {
60 cur = build_norm(cur: ffn_inp,
61 mw: model.layers[il].ffn_norm_enc, NULL,
62 type: LLM_NORM_RMS, il);
63 cb(cur, name: "ffn_norm", il);
64
65 // T5 uses relu, flan-T5 uses gelu-gated
66 cur = build_ffn(cur,
67 up: model.layers[il].ffn_up_enc, NULL, NULL,
68 gate: model.layers[il].ffn_gate_enc, NULL, NULL,
69 down: model.layers[il].ffn_down_enc, NULL, NULL,
70 NULL,
71 type_op: model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
72 type_gate: model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
73 il);
74 cb(cur, name: "ffn_out", il);
75 }
76 cur = ggml_add(ctx: ctx0, a: cur, b: ffn_inp);
77 cb(cur, name: "ffn_out", il);
78
79 cur = build_cvec(cur, il);
80 cb(cur, name: "l_out", il);
81
82 // input for next layer
83 inpL = cur;
84 }
85 cur = inpL;
86 cb(cur, name: "result_embd", il: -1);
87
88 cur = build_norm(cur,
89 mw: model.output_norm_enc, NULL,
90 type: LLM_NORM_RMS, il: -1);
91
92 cb(cur, name: "result_norm", il: -1);
93 res->t_embd = cur;
94
95 ggml_build_forward_expand(cgraph: gf, tensor: cur);
96}
97