1 files changed, 179 insertions, 0 deletions
diff --git a/llama.cpp/tools/mtmd/models/youtuvl.cpp b/llama.cpp/tools/mtmd/models/youtuvl.cpp
new file mode 100644
index 0000000..ffbf2be
--- /dev/null
+++ b/llama.cpp/tools/mtmd/models/youtuvl.cpp
@@ -0,0 +1,179 @@
+#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;
+}