1#include "models.h"
2
3llm_build_bloom::llm_build_bloom(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 const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
6
7 GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
8
9 ggml_tensor * cur;
10 ggml_tensor * inpL;
11
12 inpL = build_inp_embd(tok_embd: model.tok_embd);
13
14 auto * inp_attn = build_attn_inp_kv();
15
16 inpL = build_norm(cur: inpL,
17 mw: model.tok_norm,
18 mb: model.tok_norm_b,
19 type: LLM_NORM, il: -1);
20 cb(cur: inpL, name: "inp_norm", il: -1);
21
22 ggml_tensor * inp_out_ids = build_inp_out_ids();
23
24 for (int il = 0; il < n_layer; ++il) {
25 cur = build_norm(cur: inpL,
26 mw: model.layers[il].attn_norm,
27 mb: model.layers[il].attn_norm_b,
28 type: LLM_NORM, il);
29 cb(cur, name: "attn_norm", il);
30
31 // self-attention
32 {
33 cur = build_lora_mm(w: model.layers[il].wqkv, cur);
34 cb(cur, name: "wqkv", il);
35
36 cur = ggml_add(ctx: ctx0, a: cur, b: model.layers[il].bqkv);
37 cb(cur, name: "bqkv", il);
38
39 ggml_tensor * Qcur = ggml_view_3d(ctx: ctx0, a: cur, ne0: n_embd_head, ne1: n_head, ne2: n_tokens, nb1: n_embd_head*sizeof(float), nb2: cur->nb[1], offset: 0*sizeof(float)*(n_embd));
40 ggml_tensor * Kcur = ggml_view_3d(ctx: ctx0, a: cur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens, nb1: n_embd_head*sizeof(float), nb2: cur->nb[1], offset: 1*sizeof(float)*(n_embd));
41 ggml_tensor * Vcur = ggml_view_3d(ctx: ctx0, a: cur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens, nb1: n_embd_head*sizeof(float), nb2: cur->nb[1], offset: 1*sizeof(float)*(n_embd + n_embd_gqa));
42
43 cb(cur: Qcur, name: "Qcur", il);
44 cb(cur: Kcur, name: "Kcur", il);
45 cb(cur: Vcur, name: "Vcur", il);
46
47 cur = build_attn(inp: inp_attn,
48 wo: model.layers[il].wo, wo_b: model.layers[il].bo,
49 q_cur: Qcur, k_cur: Kcur, v_cur: Vcur, kq_b: nullptr, sinks: nullptr, v_mla: nullptr, kq_scale: 1.0f/sqrtf(x: float(n_embd_head)), il);
50 }
51
52 if (il == n_layer - 1 && inp_out_ids) {
53 cur = ggml_get_rows(ctx: ctx0, a: cur, b: inp_out_ids);
54 inpL = ggml_get_rows(ctx: ctx0, a: inpL, b: inp_out_ids);
55 }
56
57 // Add the input
58 ggml_tensor * ffn_inp = ggml_add(ctx: ctx0, a: cur, b: inpL);
59 cb(cur: ffn_inp, name: "ffn_inp", il);
60
61 // FF
62 {
63 cur = build_norm(cur: ffn_inp,
64 mw: model.layers[il].ffn_norm,
65 mb: model.layers[il].ffn_norm_b,
66 type: LLM_NORM, il);
67 cb(cur, name: "ffn_norm", il);
68
69 cur = build_ffn(cur,
70 up: model.layers[il].ffn_up, up_b: model.layers[il].ffn_up_b, NULL,
71 NULL, NULL, NULL,
72 down: model.layers[il].ffn_down, down_b: model.layers[il].ffn_down_b, NULL,
73 NULL,
74 type_op: LLM_FFN_GELU, type_gate: LLM_FFN_SEQ, il);
75 cb(cur, name: "ffn_out", il);
76 }
77
78 cur = ggml_add(ctx: ctx0, a: cur, b: ffn_inp);
79
80 cur = build_cvec(cur, il);
81 cb(cur, name: "l_out", il);
82
83 // input for next layer
84 inpL = cur;
85 }
86
87 cur = build_norm(cur: inpL,
88 mw: model.output_norm,
89 mb: model.output_norm_b,
90 type: LLM_NORM, il: -1);
91
92 cb(cur, name: "result_norm", il: -1);
93 res->t_embd = cur;
94
95 cur = build_lora_mm(w: model.output, cur);
96
97 cb(cur, name: "result_output", il: -1);
98 res->t_logits = cur;
99
100 ggml_build_forward_expand(cgraph: gf, tensor: cur);
101}
102