clip : suppress unused variable warnings (ggml-org#8105)

* 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 <[email protected]>

* squash! clip : suppress unused variable warnings

Remove e (/*e*/) instead instead of using GGML_UNUSED.

---------

Signed-off-by: Daniel Bevenius <[email protected]>

Author: danbev

examples/llava/clip.cpp

@@ -1121,20 +1121,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             }
             if (n < 32)
                 hparams.image_grid_pinpoints[n] = 0;
-        } catch (std::runtime_error & e) {
+        } catch (std::runtime_error & /*e*/) {
             hparams.image_grid_pinpoints[0]=0;
         }
 
         try {
             int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
             strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
-        } catch (std::runtime_error & e) {
+        } catch (std::runtime_error & /*e*/) {
             strcpy(hparams.mm_patch_merge_type, "flat");
         }
 
         try {
             hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
-        } catch(const std::exception& e) {
+        } catch(const std::exception& /*e*/) {
             hparams.image_crop_resolution = hparams.image_size;
         }
 
@@ -1173,37 +1173,37 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         try {
             vision_model.class_embedding  = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
             new_clip->has_class_embedding = true;
-        } catch (const std::exception& e) {
+        } catch (const std::exception& /*e*/) {
             new_clip->has_class_embedding = false;
         }
 
         try {
             vision_model.pre_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
             vision_model.pre_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
             new_clip->has_pre_norm = true;
-        } catch (std::exception & e) {
+        } catch (std::exception & /*e*/) {
             new_clip->has_pre_norm = false;
         }
 
         try {
             vision_model.post_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
             vision_model.post_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
             new_clip->has_post_norm = true;
-        } catch (std::exception & e) {
+        } catch (std::exception & /*e*/) {
             new_clip->has_post_norm = false;
         }
 
         try {
             vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
             new_clip->has_patch_bias = true;
-        } catch (std::exception & e) {
+        } catch (std::exception & /*e*/) {
             new_clip->has_patch_bias = false;
         }
 
         try {
             vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
-        } catch(const std::exception& e) {
+        } catch(const std::exception& /*e*/) {
             LOG_TEE("%s: failed to load vision model tensors\n", __func__);
         }
 
@@ -1215,26 +1215,26 @@ 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*/) { }
             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*/) { }
             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*/) { }
             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*/) { }
             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*/) { }
         } 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"));