ollama/llm/patches/12-paligemma.diff

diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 7cda5f10..50fbcf08 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -709,9 +709,12 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
             embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
 
-            embeddings = ggml_gelu(ctx0, embeddings);
-            embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
-            embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+            // paligemma missing second linear layer
+            if (model.mm_2_w) {
+                embeddings = ggml_gelu(ctx0, embeddings);
+                embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
+                embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+            }
 
         } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
             embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
@@ -2076,7 +2079,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         return ctx->vision_model.mm_model_peg_0_b->ne[0];
     }
     if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
-        return ctx->vision_model.mm_2_b->ne[0];
+        // paligemma missing second linear layer
+        if (ctx->vision_model.mm_2_b == nullptr) {
+            return ctx->vision_model.mm_0_b->ne[0];
+        }
     }
     if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
         return ctx->vision_model.mm_3_b->ne[0];
diff --git a/include/llama.h b/include/llama.h
index f23355a6..7c6301bf 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -444,6 +444,9 @@ extern "C" {
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
+    // save image embeddings
+    LLAMA_API void set_image_embeds(struct llama_context *ctx, float *data);
+
     LLAMA_API int64_t llama_time_us(void);
 
     LLAMA_API size_t llama_max_devices(void);
diff --git a/src/llama.cpp b/src/llama.cpp
index a7b1c9eb..b0a6bc27 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2668,6 +2668,7 @@ struct llama_context {
 
     const struct llama_model & model;
 
+    float *image_embeds;
     struct llama_cparams        cparams;
     struct llama_sampling       sampling;
     struct llama_kv_cache       kv_self;
@@ -2751,6 +2752,10 @@ struct llama_context {
     struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
 };
 
+void set_image_embeds(llama_context *ctx, float *data) {
+    ctx->image_embeds = data;
+}
+
 struct llama_lora_weight {
     struct ggml_tensor * a = nullptr;
     struct ggml_tensor * b = nullptr;
@@ -11599,6 +11604,15 @@ struct llm_build_context {
 
         inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
+        // set the image embeddings in the input tensor
+        if (lctx.image_embeds) {
+            struct ggml_tensor *image_embeds = ggml_dup_tensor(ctx0, inpL);
+            image_embeds->data = lctx.image_embeds;
+            image_embeds->ne[1] = 256;
+            inpL = ggml_set_2d_inplace(ctx0, inpL, image_embeds, inpL->nb[1], 0);
+            lctx.image_embeds = NULL;
+        }
+
         inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
         cb(inpL, "inp_scaled", -1);
 
@@ -14589,7 +14603,7 @@ static int llama_decode_internal(
         }
 
         // non-causal masks do not use the KV cache
-        if (hparams.causal_attn) {
+        if (hparams.causal_attn || lctx.image_embeds) {
             llama_kv_cache_update(&lctx);
 
             // if we have enough unused cells before the current head ->