clip : suppress unused variable warnings

This commit suppresses unused variable warnings for the variables e in
the catch blocks.

The motivation for this change is to suppress the warnings that are
generated on Windows when using the MSVC compiler. The warnings are
not displayed when using GCC because GCC will mark all catch parameters
as used.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>

examples/llava/clip.cpp

@@ -1123,6 +1123,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 hparams.image_grid_pinpoints[n] = 0;
         } catch (std::runtime_error & e) {
             hparams.image_grid_pinpoints[0]=0;
+            GGML_UNUSED(e);
         }
 
         try {
@@ -1130,12 +1131,14 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
         } catch (std::runtime_error & e) {
             strcpy(hparams.mm_patch_merge_type, "flat");
+            GGML_UNUSED(e);
         }
 
         try {
             hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
         } catch(const std::exception& e) {
             hparams.image_crop_resolution = hparams.image_size;
+            GGML_UNUSED(e);
         }
 
         int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
@@ -1175,6 +1178,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->has_class_embedding = true;
         } catch (const std::exception& e) {
             new_clip->has_class_embedding = false;
+            GGML_UNUSED(e);
         }
 
         try {
@@ -1183,6 +1187,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->has_pre_norm = true;
         } catch (std::exception & e) {
             new_clip->has_pre_norm = false;
+            GGML_UNUSED(e);
         }
 
         try {
@@ -1191,6 +1196,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->has_post_norm = true;
         } catch (std::exception & e) {
             new_clip->has_post_norm = false;
+            GGML_UNUSED(e);
         }
 
         try {
@@ -1198,6 +1204,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->has_patch_bias = true;
         } catch (std::exception & e) {
             new_clip->has_patch_bias = false;
+            GGML_UNUSED(e);
         }
 
         try {
@@ -1205,6 +1212,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
         } catch(const std::exception& e) {
             LOG_TEE("%s: failed to load vision model tensors\n", __func__);
+            GGML_UNUSED(e);
         }
 
         // LLaVA projection
@@ -1215,26 +1223,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 // Yi-type llava
                 vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
                 vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & e) { GGML_UNUSED(e); }
             try {
                 // missing in Yi-type llava
                 vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
                 vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & e) { GGML_UNUSED(e); }
             try {
                 // Yi-type llava
                 vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
                 vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & e) { GGML_UNUSED(e); }
             try {
                 // Yi-type llava
                 vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
                 vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & e) { GGML_UNUSED(e); }
             try {
                 vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
                 // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & e) { GGML_UNUSED(e); }
         } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
             // MobileVLM projection
             vision_model.mm_model_mlp_1_w               = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));