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#include "models.h"
ggml_cgraph * clip_graph_qwen3vl::build() {
GGML_ASSERT(model.patch_bias != nullptr);
GGML_ASSERT(model.position_embeddings != nullptr);
GGML_ASSERT(model.class_embedding == nullptr);
const int batch_size = 1;
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 = NORM_TYPE_NORMAL;
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);
}
// add patch bias
if (model.patch_bias != nullptr) {
inp = ggml_add(ctx0, inp, model.patch_bias);
cb(inp, "patch_bias", -1);
}
// calculate absolute position embedding and apply
ggml_tensor * learned_pos_embd = resize_position_embeddings();
learned_pos_embd = ggml_cont_4d(
ctx0, learned_pos_embd,
n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
learned_pos_embd = ggml_reshape_4d(
ctx0, learned_pos_embd,
n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
learned_pos_embd = ggml_cont_3d(
ctx0, learned_pos_embd,
n_embd, n_patches_x * n_patches_y, batch_size);
inp = ggml_add(ctx0, inp, learned_pos_embd);
cb(inp, "inp_pos_emb", -1);
ggml_tensor * inpL = inp;
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);
}
// deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size]
ggml_tensor * deepstack_features = nullptr;
const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl
// loop over layers
for (int il = 0; il < n_layer; il++) {
auto & layer = model.layers[il];
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
{
cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
cur = ggml_add(ctx0, cur, layer.qkv_b);
ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
/* nb1 */ ggml_row_size(cur->type, d_head),
/* nb2 */ cur->nb[1],
/* offset */ 0);
ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
/* nb1 */ ggml_row_size(cur->type, d_head),
/* nb2 */ cur->nb[1],
/* offset */ ggml_row_size(cur->type, n_embd));
ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
/* nb1 */ ggml_row_size(cur->type, d_head),
/* nb2 */ cur->nb[1],
/* offset */ ggml_row_size(cur->type, 2 * n_embd));
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);
cur = build_attn(layer.o_w, layer.o_b,
Qcur, Kcur, Vcur, nullptr, 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);
if (layer.has_deepstack()) {
ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size);
feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il);
feat = build_ffn(feat,
layer.deepstack_fc1_w, layer.deepstack_fc1_b,
nullptr, nullptr,
layer.deepstack_fc2_w, layer.deepstack_fc2_b,
ffn_op_type::FFN_GELU, il);
if(!deepstack_features) {
deepstack_features = feat;
} else {
// concat along the feature dimension
deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0);
}
}
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_op_type::FFN_GELU, -1);
embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension
// build the graph
ggml_build_forward_expand(gf, embeddings);
return gf;
}
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