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#include "models.h"
ggml_cgraph * clip_graph_youtuvl::build() {
GGML_ASSERT(model.class_embedding == nullptr);
const int batch_size = 1;
const bool use_window_attn = !hparams.wa_layer_indexes.empty();
const int n_pos = n_patches;
const int num_position_ids = n_pos * 4;
const int m = 2;
const int Wp = n_patches_x;
const int Hp = n_patches_y;
const int Hm = Hp / m;
const int Wm = Wp / m;
norm_type norm_t = NORM_TYPE_NORMAL;
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
ggml_tensor * inp = build_inp_raw();
// change conv3d to linear
// reshape and permute to get patches, permute from (patch_size, m, Wm, patch_size, m, Hm, C) to (C, patch_size, patch_size, m, m, Wm, Hm)
{
inp = ggml_reshape_4d(
ctx0, inp,
Wm * m * patch_size, m * patch_size, Hm, 3);
inp = ggml_permute(ctx0, inp, 1, 2, 3, 0);
inp = ggml_cont_4d(
ctx0, inp,
m * patch_size * 3, Wm, m * patch_size, Hm);
inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
inp = ggml_cont_4d(
ctx0, inp,
m * patch_size * 3, patch_size, m, Hm * Wm);
inp = ggml_permute(ctx0, inp, 1, 0, 2, 3);
inp = ggml_cont_4d(
ctx0, inp,
patch_size, 3, patch_size, Hm * Wm * m * m);
inp = ggml_permute(ctx0, inp, 2, 0, 1, 3);
inp = ggml_cont_3d(
ctx0, inp,
3*patch_size* patch_size, Hm * Wm * m * m, 1);
}
inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp);
if (model.patch_bias) {
inp = ggml_add(ctx0, inp, model.patch_bias);
}
inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches);
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) {
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 ? hparams.wa_layer_indexes.count(il) > 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);
// 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);
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);
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);
}
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, inpL);
inpL = cur; // inpL = residual, cur = hidden_states
// layernorm2
cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
// ffn
cur = build_ffn(cur,
layer.ff_up_w, layer.ff_up_b,
nullptr, nullptr,
layer.ff_down_w, layer.ff_down_b,
hparams.ffn_op, il);
// residual 2
cur = ggml_add(ctx0, inpL, cur);
inpL = cur;
}
ggml_tensor * embeddings = inpL;
if (use_window_attn) {
const int spatial_merge_unit = 4;
window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / spatial_merge_unit);
ggml_set_name(window_idx, "window_idx");
ggml_set_input(window_idx);
GGML_ASSERT(batch_size == 1);
embeddings = ggml_reshape_2d(ctx0, embeddings, n_embd * spatial_merge_unit, n_patches / spatial_merge_unit);
embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd, n_patches, batch_size);
cb(embeddings, "window_order_restored", -1);
}
// post-layernorm (part of Siglip2VisionTransformer, applied after encoder)
if (model.post_ln_w) {
embeddings = build_norm(embeddings, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
}
// Now apply merger (VLPatchMerger):
// 1. Apply RMS norm (ln_q in VLPatchMerger)
embeddings = build_norm(embeddings, model.mm_input_norm_w, nullptr, NORM_TYPE_RMS, 1e-6, -1);
cb(embeddings, "merger_normed", -1);
// 2. First reshape for spatial merge (merge 2x2 patches)
embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
cb(embeddings, "merger_reshaped", -1);
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);
ggml_build_forward_expand(gf, embeddings);
return gf;
}
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