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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
|
#include "models.h"
ggml_cgraph * clip_graph_qwen2vl::build() {
GGML_ASSERT(model.patch_bias == nullptr);
GGML_ASSERT(model.class_embedding == nullptr);
const int batch_size = 1;
const bool use_window_attn = hparams.n_wa_pattern > 0;
const int n_wa_pattern = hparams.n_wa_pattern;
const int n_pos = n_patches;
const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
norm_type norm_t = proj_type == PROJECTOR_TYPE_QWEN25VL
? NORM_TYPE_RMS // qwen 2.5 vl
: NORM_TYPE_NORMAL; // qwen 2 vl
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
GGML_ASSERT(img.nx % (patch_size * 2) == 0);
GGML_ASSERT(img.ny % (patch_size * 2) == 0);
// second conv dimension
{
auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
inp = ggml_add(ctx0, inp, inp_1);
inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b]
inp = ggml_cont_4d(
ctx0, inp,
n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
inp = ggml_reshape_4d(
ctx0, inp,
n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
inp = ggml_cont_3d(
ctx0, inp,
n_embd, n_patches_x * n_patches_y, batch_size);
}
ggml_tensor * inpL = inp;
ggml_tensor * window_mask = nullptr;
ggml_tensor * window_idx = nullptr;
ggml_tensor * inv_window_idx = nullptr;
ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
ggml_set_name(positions, "positions");
ggml_set_input(positions);
// pre-layernorm
if (model.pre_ln_w) {
inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
}
if (use_window_attn) {
// handle window attention inputs
inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
ggml_set_name(inv_window_idx, "inv_window_idx");
ggml_set_input(inv_window_idx);
// mask for window attention
window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
ggml_set_name(window_mask, "window_mask");
ggml_set_input(window_mask);
// if flash attn is used, we need to pad the mask and cast to f16
if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16);
}
// inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size]
GGML_ASSERT(batch_size == 1);
inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4);
inpL = ggml_get_rows(ctx0, inpL, inv_window_idx);
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size);
}
// loop over layers
for (int il = 0; il < n_layer; il++) {
const auto & layer = model.layers[il];
const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true;
ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
// layernorm1
cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
cb(cur, "ln1", il);
// self-attention
{
ggml_tensor * Qcur = ggml_add(ctx0,
ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b);
ggml_tensor * Kcur = ggml_add(ctx0,
ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b);
ggml_tensor * Vcur = ggml_add(ctx0,
ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b);
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
// apply M-RoPE
Qcur = ggml_rope_multi(
ctx0, Qcur, positions, nullptr,
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
Kcur = ggml_rope_multi(
ctx0, Kcur, positions, nullptr,
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
cb(Qcur, "Qcur_rope", il);
cb(Kcur, "Kcur_rope", il);
ggml_tensor * attn_mask = full_attn ? nullptr : window_mask;
cur = build_attn(layer.o_w, layer.o_b,
Qcur, Kcur, Vcur, attn_mask, kq_scale, il);
cb(cur, "attn_out", il);
}
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, inpL);
inpL = cur; // inpL = residual, cur = hidden_states
cb(cur, "ffn_inp", il);
// layernorm2
cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
cb(cur, "ffn_inp_normed", il);
// ffn
cur = build_ffn(cur,
layer.ff_up_w, layer.ff_up_b,
layer.ff_gate_w, layer.ff_gate_b,
layer.ff_down_w, layer.ff_down_b,
hparams.ffn_op, il);
cb(cur, "ffn_out", il);
// residual 2
cur = ggml_add(ctx0, inpL, cur);
cb(cur, "layer_out", il);
inpL = cur;
}
// post-layernorm
if (model.post_ln_w) {
inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
}
// multimodal projection
ggml_tensor * embeddings = inpL;
embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
embeddings = build_ffn(embeddings,
model.mm_0_w, model.mm_0_b,
nullptr, nullptr,
model.mm_1_w, model.mm_1_b,
FFN_GELU,
-1);
if (use_window_attn) {
window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
ggml_set_name(window_idx, "window_idx");
ggml_set_input(window_idx);
// embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size]
GGML_ASSERT(batch_size == 1);
embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4);
embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size);
}
// build the graph
ggml_build_forward_expand(gf, embeddings);
return gf;
}
|
