1#include "models.h"
2
3
4
5llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) :
6 llm_graph_context(params) {
7 const int64_t 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 // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
15 if (ubatch.token) {
16 inpL = ggml_scale(ctx: ctx0, a: inpL, s: sqrtf(x: n_embd));
17 cb(cur: inpL, name: "inp_scaled", il: -1);
18 }
19
20 // inp_pos - contains the positions
21 ggml_tensor * inp_pos = build_inp_pos();
22
23 auto * inp_attn = build_attn_inp_no_cache();
24
25 ggml_tensor * inp_out_ids = build_inp_out_ids();
26
27 for (int il = 0; il < n_layer; ++il) {
28 const float freq_base_l = model.get_rope_freq_base(cparams, il);
29 const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
30
31 // norm
32 cur = build_norm(cur: inpL, mw: model.layers[il].attn_norm, NULL, type: LLM_NORM_RMS, il);
33 cb(cur, name: "attn_norm", il);
34
35 // self-attention
36 {
37 // compute Q and K and RoPE them
38 ggml_tensor * Qcur = build_lora_mm(w: model.layers[il].wq, cur);
39 cb(cur: Qcur, name: "Qcur", il);
40
41 ggml_tensor * Kcur = build_lora_mm(w: model.layers[il].wk, cur);
42 cb(cur: Kcur, name: "Kcur", il);
43
44 ggml_tensor * Vcur = build_lora_mm(w: model.layers[il].wv, cur);
45 cb(cur: Vcur, name: "Vcur", il);
46
47 Qcur = ggml_reshape_3d(ctx: ctx0, a: Qcur, ne0: n_embd_head, ne1: n_head, ne2: n_tokens);
48 Kcur = ggml_reshape_3d(ctx: ctx0, a: Kcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
49 Vcur = ggml_reshape_3d(ctx: ctx0, a: Vcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
50
51 Qcur = build_norm(cur: Qcur, mw: model.layers[il].attn_q_norm, NULL, type: LLM_NORM_RMS, il);
52 cb(cur: Qcur, name: "Qcur_normed", il);
53
54 Qcur = ggml_rope_ext(ctx: ctx0, a: Qcur, b: inp_pos, c: nullptr, n_dims: n_rot, mode: rope_type, n_ctx_orig, freq_base: freq_base_l, freq_scale: freq_scale_l,
55 ext_factor, attn_factor, beta_fast, beta_slow);
56
57 Kcur = build_norm(cur: Kcur, mw: model.layers[il].attn_k_norm, NULL, type: LLM_NORM_RMS, il);
58 cb(cur: Kcur, name: "Kcur_normed", il);
59
60 Kcur = ggml_rope_ext(ctx: ctx0, a: Kcur, b: inp_pos, c: nullptr, n_dims: n_rot, mode: rope_type, n_ctx_orig, freq_base: freq_base_l, freq_scale: freq_scale_l,
61 ext_factor, attn_factor, beta_fast, beta_slow);
62
63 cb(cur: Qcur, name: "Qcur", il);
64 cb(cur: Kcur, name: "Kcur", il);
65 cb(cur: Vcur, name: "Vcur", il);
66
67 // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
68 Qcur = ggml_scale(ctx: ctx0, a: Qcur, s: hparams.f_attention_scale);
69
70 cur =
71 build_attn(inp: inp_attn,
72 wo: model.layers[il].wo, NULL,
73 q_cur: Qcur, k_cur: Kcur, v_cur: Vcur, kq_b: nullptr, sinks: nullptr, v_mla: nullptr, kq_scale: 1.0f, il);
74 }
75
76 if (il == n_layer - 1 && inp_out_ids) {
77 cur = ggml_get_rows(ctx: ctx0, a: cur, b: inp_out_ids);
78 inpL = ggml_get_rows(ctx: ctx0, a: inpL, b: inp_out_ids);
79 }
80
81 cur = build_norm(cur, mw: model.layers[il].attn_post_norm, NULL, type: LLM_NORM_RMS, il);
82 cb(cur, name: "attn_post_norm", il);
83
84 ggml_tensor * sa_out = ggml_add(ctx: ctx0, a: cur, b: inpL);
85 cb(cur: sa_out, name: "sa_out", il);
86
87 cur = build_norm(cur: sa_out, mw: model.layers[il].ffn_norm, NULL, type: LLM_NORM_RMS, il);
88 cb(cur, name: "ffn_norm", il);
89
90 // feed-forward network
91 {
92 cur = build_ffn(cur,
93 up: model.layers[il].ffn_up, NULL, NULL,
94 gate: model.layers[il].ffn_gate, NULL, NULL,
95 down: model.layers[il].ffn_down, NULL, NULL,
96 NULL, type_op: LLM_FFN_GELU, type_gate: LLM_FFN_PAR, il);
97 cb(cur, name: "ffn_out", il);
98 }
99
100 cur = build_norm(cur, mw: model.layers[il].ffn_post_norm, NULL, type: LLM_NORM_RMS, il: -1);
101 cb(cur, name: "ffn_post_norm", il: -1);
102
103 cur = ggml_add(ctx: ctx0, a: cur, b: sa_out);
104
105 cur = build_cvec(cur, il);
106 cb(cur, name: "l_out", il);
107
108 // input for next layer
109 inpL = cur;
110 }
111
112 cur = inpL;
113
114 cur = build_norm(cur, mw: model.output_norm, NULL, type: LLM_NORM_RMS, il: -1);
115
116 cb(cur, name: "result_norm", il: -1);
117 res->t_embd = cur;
118
119 ggml_build_forward_expand(cgraph: gf, tensor: cur);
120}
121