1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
|
#include "models.h"
llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
ggml_tensor * inpL;
ggml_tensor * cur;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv();
// check ubatch to see if we have input tokens (text)
// or an input embedding vector (image)
bool is_text;
if (ubatch.token) {
is_text = true;
} else {
is_text = false;
}
for (int il = 0; il < n_layer; ++il) {
// get either the text or image weight tensors
ggml_tensor *wqkv, *wo;
ggml_tensor *ffn_gate, *ffn_down, *ffn_up;
if (is_text) {
wqkv = model.layers[il].wqkv;
wo = model.layers[il].wo;
ffn_gate = model.layers[il].ffn_gate;
ffn_down = model.layers[il].ffn_down;
ffn_up = model.layers[il].ffn_up;
} else {
wqkv = model.layers[il].visexp_attn_wqkv;
wo = model.layers[il].visexp_attn_wo;
ffn_gate = model.layers[il].visexp_ffn_gate;
ffn_down = model.layers[il].visexp_ffn_down;
ffn_up = model.layers[il].visexp_ffn_up;
}
ggml_tensor * inpSA = inpL;
cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
// build self attention
{
ggml_tensor * qkv = build_lora_mm(wqkv, cur);
// split qkv into Q, K, V along the first dimension
ggml_tensor * Qcur =
ggml_view_3d(ctx0, qkv, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), qkv->nb[1], 0);
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
qkv->nb[1], n_embd * ggml_element_size(qkv));
ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
qkv->nb[1], 2 * n_embd * ggml_element_size(qkv));
Qcur = ggml_rope(ctx0, Qcur, inp_pos, n_embd_head, rope_type);
Kcur = ggml_rope(ctx0, Kcur, inp_pos, n_embd_head, rope_type);
cur = build_attn(inp_attn,
wo, nullptr,
Qcur, Kcur, Vcur,
nullptr, nullptr, nullptr,
kq_scale, il);
cb(cur, "attn_out", il);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
ffn_up, NULL, NULL,
ffn_gate, NULL, NULL,
ffn_down, NULL, NULL,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
|
