1#include "models.h"
2
3llm_build_qwen3::llm_build_qwen3(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 GGML_ASSERT(n_embd_head == hparams.n_rot);
8
9 ggml_tensor * cur;
10 ggml_tensor * inpL;
11
12 inpL = build_inp_embd(model.tok_embd);
13
14 // inp_pos - contains the positions
15 ggml_tensor * inp_pos = build_inp_pos();
16
17 auto * inp_attn = build_attn_inp_kv();
18
19 ggml_tensor * inp_out_ids = build_inp_out_ids();
20
21 for (int il = 0; il < n_layer; ++il) {
22 ggml_tensor * inpSA = inpL;
23
24 // norm
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 // compute Q and K and RoPE them
33 ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
34 cb(Qcur, "Qcur", il);
35
36 ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
37 cb(Kcur, "Kcur", il);
38
39 ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
40 cb(Vcur, "Vcur", il);
41
42 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
43 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
44 Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
45
46 Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
47 cb(Qcur, "Qcur_normed", il);
48
49 Qcur = ggml_rope_ext(
50 ctx0, Qcur, inp_pos, nullptr,
51 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
52 ext_factor, attn_factor, beta_fast, beta_slow
53 );
54
55 Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
56 cb(Kcur, "Kcur_normed", il);
57
58 Kcur = ggml_rope_ext(
59 ctx0, Kcur, inp_pos, nullptr,
60 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
61 ext_factor, attn_factor, beta_fast, beta_slow
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, model.layers[il].bo,
70 Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
71 }
72 if (il == n_layer - 1 && inp_out_ids) {
73 cur = ggml_get_rows(ctx0, cur, inp_out_ids);
74 inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
75 }
76 ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
77 cb(ffn_inp, "ffn_inp", il);
78
79 // feed-forward network
80 cur = build_norm(ffn_inp,
81 model.layers[il].ffn_norm, NULL,
82 LLM_NORM_RMS, il);
83 cb(cur, "ffn_norm", il);
84
85 cur = build_ffn(cur,
86 model.layers[il].ffn_up, NULL, NULL,
87 model.layers[il].ffn_gate, NULL, NULL,
88 model.layers[il].ffn_down, NULL, NULL,
89 NULL,
90 LLM_FFN_SILU, LLM_FFN_PAR, il);
91 cb(cur, "ffn_out", il);
92
93 cur = ggml_add(ctx0, cur, ffn_inp);
94
95 cur = build_cvec(cur, il);
96 cb(cur, "l_out", il);
97
98 // input for next layer
99 inpL = cur;
100 }
101 cur = inpL;
102
103 cur = build_norm(cur,
104 model.output_norm, NULL,
105 LLM_NORM_RMS, -1);
106
107 cb(cur, "result_norm", -1);
108 res->t_embd = cur;
109
110 // lm_head
111 cur = build_lora_mm(model.output, cur);
112
113 cb(cur, "result_output", -1);
114 res->t_logits = cur;
115
116 ggml_build_forward_expand(gf, cur);
117}