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0475af5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 | #include "models.h"
#include <algorithm>
ggml_cgraph * clip_graph_granite_speech::build() {
const int n_frames = img.nx();
const int context_size = hparams.audio_chunk_size;
const int ctc_layer = n_layer / 2;
const int conv_kernel = hparams.audio_conv_kernel_size;
const int conv_pad = conv_kernel / 2;
const int num_blocks = (n_frames + context_size - 1) / context_size;
const int padded_len = num_blocks * context_size;
const int remainder = n_frames % context_size;
// Calculate projector input dimension based on feature layers
const int proj_input_dim = n_embd * (hparams.feature_layers.size() + 1);
const bool use_feature_concat = !hparams.feature_layers.empty();
ggml_tensor * attn_dists = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, context_size * context_size);
ggml_set_name(attn_dists, "attn_dists");
ggml_set_input(attn_dists);
ggml_tensor * attn_mask = nullptr;
if (remainder > 0) {
attn_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32,
context_size, context_size, 1, num_blocks);
ggml_set_name(attn_mask, "attn_mask");
ggml_set_input(attn_mask);
}
ggml_tensor * inp = build_inp_raw(1);
auto * cur = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
cb(cur, "inp_transposed", -1);
cur = build_mm(model.inp_proj_w, cur);
cur = ggml_add(ctx0, cur, model.inp_proj_b);
cb(cur, "inp_linear", -1);
// Capture layer 0 if requested (after input_linear)
ggml_tensor * concat_result = nullptr;
if (use_feature_concat) {
if (std::find(hparams.feature_layers.begin(), hparams.feature_layers.end(), 0) != hparams.feature_layers.end()) {
concat_result = cur;
cb(concat_result, "feature_layer_0", -1);
}
}
for (int il = 0; il < n_layer; il++) {
const auto & layer = model.layers[il];
auto * residual = cur;
// ffn1 (half-step)
{
auto * ffn1 = build_norm(cur, layer.ff_norm_w, layer.ff_norm_b,
NORM_TYPE_NORMAL, eps, il);
cb(ffn1, "ffn1_norm", il);
ffn1 = build_ffn(ffn1,
layer.ff_up_w, layer.ff_up_b,
nullptr, nullptr,
layer.ff_down_w, layer.ff_down_b,
FFN_SILU, il);
cb(ffn1, "ffn1_out", il);
residual = ggml_add(ctx0, residual, ggml_scale(ctx0, ffn1, 0.5f));
cb(residual, "ffn1_residual", il);
}
// build_attn not used here: Shaw RPE needs pos_attn = mul_mat(pos_emb, Q)
// injected between KQ product and softmax, which build_attn doesn't support
{
auto * normed = build_norm(residual, layer.ln_1_w, layer.ln_1_b,
NORM_TYPE_NORMAL, eps, il);
cb(normed, "attn_norm", il);
if (n_frames < padded_len) {
normed = ggml_pad(ctx0, normed, 0, padded_len - n_frames, 0, 0);
}
ggml_tensor * Q = build_mm(layer.q_w, normed);
ggml_tensor * K = build_mm(layer.k_w, normed);
ggml_tensor * V = build_mm(layer.v_w, normed);
Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, context_size, num_blocks);
K = ggml_reshape_4d(ctx0, K, d_head, n_head, context_size, num_blocks);
V = ggml_reshape_4d(ctx0, V, d_head, n_head, context_size, num_blocks);
ggml_tensor * Q_perm = ggml_permute(ctx0, Q, 0, 2, 1, 3);
ggml_tensor * K_perm = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
ggml_tensor * kq = ggml_mul_mat(ctx0, K_perm, Q_perm);
// Shaw RPE: pos_emb ne[2]=1 broadcasts against Q ne[2]=num_blocks in mul_mat
ggml_tensor * pos_emb = ggml_get_rows(ctx0, layer.attn_rel_pos_emb, attn_dists);
pos_emb = ggml_reshape_3d(ctx0, pos_emb, d_head, context_size, context_size);
pos_emb = ggml_reshape_4d(ctx0, pos_emb, d_head, context_size, 1, context_size);
ggml_tensor * Q_shaw = ggml_permute(ctx0, Q, 0, 1, 3, 2);
ggml_tensor * pos_attn = ggml_mul_mat(ctx0, pos_emb, Q_shaw);
pos_attn = ggml_cont(ctx0, ggml_permute(ctx0, pos_attn, 0, 2, 3, 1));
ggml_tensor * scores = ggml_add(ctx0, kq, pos_attn);
ggml_tensor * attn_weights = ggml_soft_max_ext(ctx0, scores, attn_mask,
kq_scale, 0.0f);
ggml_tensor * V_perm = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
ggml_tensor * attn_out = ggml_mul_mat(ctx0, V_perm, attn_weights);
attn_out = ggml_permute(ctx0, attn_out, 0, 2, 1, 3);
attn_out = ggml_cont_2d(ctx0, attn_out, n_embd, padded_len);
if (n_frames < padded_len) {
attn_out = ggml_view_2d(ctx0, attn_out,
n_embd, n_frames, attn_out->nb[1], 0);
}
cur = build_mm(layer.o_w, attn_out);
cur = ggml_add(ctx0, cur, layer.o_b);
cb(cur, "attn_out", il);
}
residual = ggml_add(ctx0, residual, cur);
// conv module
{
cur = build_norm(residual, layer.norm_conv_w, layer.norm_conv_b,
NORM_TYPE_NORMAL, eps, il);
cb(cur, "conv_norm", il);
auto * x = build_mm(layer.conv_pw1_w, cur);
x = ggml_add(ctx0, x, layer.conv_pw1_b);
cb(x, "conv_pw1", il);
// GLU: ggml has no fused op, manual split + sigmoid gate
{
int64_t d = x->ne[0] / 2;
ggml_tensor * gate = ggml_sigmoid(ctx0,
ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], d * x->nb[0]));
x = ggml_mul(ctx0,
ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], 0), gate);
x = ggml_cont(ctx0, ggml_transpose(ctx0, x));
}
cb(x, "conv_glu", il);
x = ggml_pad(ctx0, x, conv_pad, 0, 0, 0);
x = ggml_roll(ctx0, x, conv_pad, 0, 0, 0);
x = ggml_pad(ctx0, x, conv_pad, 0, 0, 0);
x = ggml_ssm_conv(ctx0, x, layer.conv_dw_w);
cb(x, "conv_dw", il);
// folded batch norm
x = ggml_add(ctx0, ggml_mul(ctx0, x, layer.conv_norm_w), layer.conv_norm_b);
x = ggml_silu(ctx0, x);
cb(x, "conv_bn_silu", il);
x = build_mm(layer.conv_pw2_w, x);
x = ggml_add(ctx0, x, layer.conv_pw2_b);
cb(x, "conv_pw2", il);
cur = x;
}
residual = ggml_add(ctx0, residual, cur);
// ffn2 (half-step)
{
auto * ffn2 = build_norm(residual, layer.ff_norm_1_w, layer.ff_norm_1_b,
NORM_TYPE_NORMAL, eps, il);
cb(ffn2, "ffn2_norm", il);
ffn2 = build_ffn(ffn2,
layer.ff_up_1_w, layer.ff_up_1_b,
nullptr, nullptr,
layer.ff_down_1_w, layer.ff_down_1_b,
FFN_SILU, il);
cb(ffn2, "ffn2_out", il);
residual = ggml_add(ctx0, residual, ggml_scale(ctx0, ffn2, 0.5f));
}
cur = build_norm(residual, layer.ln_2_w, layer.ln_2_b,
NORM_TYPE_NORMAL, eps, il);
cb(cur, "layer_out", il);
// Capture intermediate layer (il + 1) if requested
if (use_feature_concat) {
if (hparams.is_feature_layer(il + 1)) {
if (concat_result == nullptr) {
concat_result = cur;
} else {
concat_result = ggml_concat(ctx0, concat_result, cur, 0);
}
cb(concat_result, string_format("feature_layer_%d", il + 1).c_str(), il);
}
}
// CTC branch
if (il + 1 == ctc_layer) {
auto * mid = build_mm(model.ctc_out_w, cur);
mid = ggml_add(ctx0, mid, model.ctc_out_b);
mid = ggml_soft_max(ctx0, mid);
mid = build_mm(model.ctc_out_mid_w, mid);
mid = ggml_add(ctx0, mid, model.ctc_out_mid_b);
cur = ggml_add(ctx0, cur, mid);
cb(cur, "ctc_branch", il);
}
}
// Append final output to concatenated features if using feature concatenation
if (use_feature_concat && concat_result != nullptr) {
concat_result = ggml_concat(ctx0, concat_result, cur, 0);
cb(concat_result, "concat_final", -1);
cur = concat_result;
}
cb(cur, "encoder_out", -1);
// QFormer projector
{
const int window_size = hparams.audio_proj_window_size;
const int num_queries = window_size / hparams.audio_proj_downsample_rate;
const int proj_n_head = hparams.audio_proj_head_count;
const int proj_d_head = n_embd / proj_n_head;
const float proj_kq_scale = 1.0f / sqrtf((float)proj_d_head);
const float proj_eps = 1e-12f;
const int nblocks_proj = (n_frames + window_size - 1) / window_size;
const int padded_proj = nblocks_proj * window_size;
if (n_frames < padded_proj) {
cur = ggml_pad(ctx0, cur, 0, padded_proj - n_frames, 0, 0);
}
ggml_tensor * enc_windows = ggml_reshape_3d(ctx0, cur, proj_input_dim, window_size, nblocks_proj);
ggml_tensor * queries = build_norm(model.qf_proj_blocks[0].qf_proj_query,
model.qf_proj_blocks[0].qf_proj_norm_w, model.qf_proj_blocks[0].qf_proj_norm_b,
NORM_TYPE_NORMAL, proj_eps, -1);
{
ggml_tensor * q_3d = ggml_reshape_3d(ctx0, queries, n_embd, num_queries, 1);
ggml_tensor * q_shape = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32,
n_embd, num_queries, nblocks_proj);
queries = ggml_repeat(ctx0, q_3d, q_shape);
}
for (int il = 0; il < (int)model.qf_proj_blocks[0].qf_proj_layers.size(); il++) {
const auto & pl = model.qf_proj_blocks[0].qf_proj_layers[il];
// self-attention
{
ggml_tensor * Q = ggml_add(ctx0, build_mm(pl.q_w, queries), pl.q_b);
ggml_tensor * K = ggml_add(ctx0, build_mm(pl.k_w, queries), pl.k_b);
ggml_tensor * V = ggml_add(ctx0, build_mm(pl.v_w, queries), pl.v_b);
Q = ggml_reshape_4d(ctx0, Q, proj_d_head, proj_n_head, num_queries, nblocks_proj);
K = ggml_reshape_4d(ctx0, K, proj_d_head, proj_n_head, num_queries, nblocks_proj);
V = ggml_reshape_4d(ctx0, V, proj_d_head, proj_n_head, num_queries, nblocks_proj);
ggml_tensor * sa_out = build_attn(pl.o_w, pl.o_b,
Q, K, V, nullptr, proj_kq_scale, il);
sa_out = ggml_reshape_3d(ctx0, sa_out, n_embd, num_queries, nblocks_proj);
queries = build_norm(ggml_add(ctx0, sa_out, queries),
pl.ln_1_w, pl.ln_1_b,
NORM_TYPE_NORMAL, proj_eps, il);
}
// cross-attention
{
ggml_tensor * Q = ggml_add(ctx0, build_mm(pl.cross_attn_q_w, queries), pl.cross_attn_q_b);
ggml_tensor * K = ggml_add(ctx0, build_mm(pl.cross_attn_k_w, enc_windows), pl.cross_attn_k_b);
ggml_tensor * V = ggml_add(ctx0, build_mm(pl.cross_attn_v_w, enc_windows), pl.cross_attn_v_b);
Q = ggml_reshape_4d(ctx0, Q, proj_d_head, proj_n_head, num_queries, nblocks_proj);
K = ggml_reshape_4d(ctx0, K, proj_d_head, proj_n_head, window_size, nblocks_proj);
V = ggml_reshape_4d(ctx0, V, proj_d_head, proj_n_head, window_size, nblocks_proj);
ggml_tensor * ca_out = build_attn(pl.cross_attn_o_w, pl.cross_attn_o_b,
Q, K, V, nullptr, proj_kq_scale, il);
ca_out = ggml_reshape_3d(ctx0, ca_out, n_embd, num_queries, nblocks_proj);
queries = build_norm(ggml_add(ctx0, ca_out, queries),
pl.cross_attn_norm_w, pl.cross_attn_norm_b,
NORM_TYPE_NORMAL, proj_eps, il);
}
// ffn
{
ggml_tensor * ffn_out = build_ffn(queries,
pl.ff_up_w, pl.ff_up_b,
nullptr, nullptr,
pl.ff_down_w, pl.ff_down_b,
FFN_GELU, il);
queries = build_norm(ggml_add(ctx0, ffn_out, queries),
pl.ln_2_w, pl.ln_2_b,
NORM_TYPE_NORMAL, proj_eps, il);
}
}
cur = ggml_reshape_2d(ctx0, queries, n_embd, num_queries * nblocks_proj);
cur = ggml_add(ctx0, build_mm(model.qf_proj_blocks[0].qf_proj_linear_w, cur), model.qf_proj_blocks[0].qf_proj_linear_b);
cb(cur, "projector_out", -1);
}
ggml_build_forward_expand(gf, cur);
return gf;
}
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