Sync from upstream llama.cpp repository

tools/mtmd/models/siglip.cpp

@@ -0,0 +1,86 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_siglip::build() {
+    ggml_tensor * inp = build_inp();
+
+    ggml_tensor * learned_pos_embd = model.position_embeddings;
+    if (proj_type == PROJECTOR_TYPE_LFM2) {
+        learned_pos_embd = resize_position_embeddings();
+    }
+
+    ggml_tensor * cur = build_vit(
+                            inp, n_patches,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            learned_pos_embd,
+                            nullptr);
+
+    if (proj_type == PROJECTOR_TYPE_GEMMA3) {
+        const int batch_size = 1;
+        GGML_ASSERT(n_patches_x == n_patches_y);
+        const int patches_per_image = n_patches_x;
+        const int kernel_size = hparams.n_merge;
+
+        cur = ggml_transpose(ctx0, cur);
+        cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size);
+
+        // doing a pool2d to reduce the number of output tokens
+        cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
+        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size);
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        // apply norm before projection
+        cur = ggml_rms_norm(ctx0, cur, eps);
+        cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w);
+
+        // apply projection
+        cur = ggml_mul_mat(ctx0,
+            ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)),
+            cur);
+
+    } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) {
+        // pixel_shuffle
+        // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578
+        const int scale_factor = model.hparams.n_merge;
+        cur = build_patch_merge_permute(cur, scale_factor);
+        cur = ggml_mul_mat(ctx0, model.projection, cur);
+
+    } else if (proj_type == PROJECTOR_TYPE_LFM2) {
+        // pixel unshuffle block
+        const int scale_factor = model.hparams.n_merge;
+        cur = build_patch_merge_permute(cur, scale_factor);
+
+        // projection, in LFM2-VL input norm is optional
+        if (model.mm_input_norm_w) {
+            cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
+            cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
+        }
+
+        if (model.mm_input_norm_b) {
+            cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
+        }
+
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_2_w, model.mm_2_b,
+            FFN_GELU,
+            -1);
+
+    } else if (proj_type == PROJECTOR_TYPE_JANUS_PRO) {
+        cur = build_ffn(cur,
+            model.mm_0_w, model.mm_0_b,
+            nullptr, nullptr,
+            model.mm_1_w, model.mm_1_b,
+            hparams.ffn_op,
+            -1);
+
+    } else {
+        GGML_ABORT("SigLIP: Unsupported projector type");
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}