include llama.cpp since there were some edits

llama.cpp/tools/mtmd/models/sensenova-u1.cpp

@@ -0,0 +1,40 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_sensenova_u1::build() {
+    GGML_ASSERT(model.patch_embeddings_0 != nullptr);
+    GGML_ASSERT(model.patch_bias != nullptr);
+    GGML_ASSERT(model.mm_0_w != nullptr);
+    GGML_ASSERT(model.mm_0_b != nullptr);
+    GGML_ASSERT(hparams.n_merge == 2);
+
+    ggml_tensor * pos_x = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+    ggml_set_name(pos_x, "pos_x");
+    ggml_set_input(pos_x);
+
+    ggml_tensor * pos_y = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+    ggml_set_name(pos_y, "pos_y");
+    ggml_set_input(pos_y);
+
+    ggml_tensor * inp = build_inp();
+    inp = ggml_gelu(ctx0, inp);
+
+    // Apply adjacent-pair 2D RoPE to patch embeddings before downsampling.
+    inp = ggml_reshape_4d(ctx0, inp, n_embd, 1, n_patches, n_batch);
+    inp = build_rope_2d(ctx0, inp, pos_x, pos_y, hparams.rope_theta, false);
+    inp = ggml_reshape_3d(ctx0, inp, n_embd, n_patches, n_batch);
+    cb(inp, "patch_embd_rope", -1);
+
+    inp = ggml_reshape_4d(ctx0, inp, n_embd, n_patches_x, n_patches_y, n_batch);
+    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3));
+    inp = ggml_conv_2d(ctx0, model.mm_0_w, inp,
+            hparams.n_merge, hparams.n_merge, 0, 0, 1, 1);
+
+    const int n_output_patches = n_patches / (hparams.n_merge * hparams.n_merge);
+    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));
+    inp = ggml_reshape_3d(ctx0, inp, n_mmproj_embd, n_output_patches, n_batch);
+    inp = ggml_add(ctx0, inp, model.mm_0_b);
+    cb(inp, "proj_out", -1);
+
+    ggml_build_forward_expand(gf, inp);
+    return gf;
+}