#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; }