vbatts/llama.cpp
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squash! clip : suppress unused variable warnings

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Remove e (/*e*/) instead instead of using GGML_UNUSED.
This commit is contained in:
Daniel Bevenius 2024-06-25 11:50:16 +01:00
parent c32bad7e65
commit bc9c9a8a82
1 changed files with 13 additions and 21 deletions
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34
examples/llava/clip.cpp
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@ -1121,24 +1121,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
} }
if (n < 32) if (n < 32)
hparams.image_grid_pinpoints[n] = 0; hparams.image_grid_pinpoints[n] = 0;
} catch (std::runtime_error & e) { } catch (std::runtime_error & /*e*/) {
hparams.image_grid_pinpoints[0]=0; hparams.image_grid_pinpoints[0]=0;
GGML_UNUSED(e);
} }
try { try {
int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE); int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx)); 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"); strcpy(hparams.mm_patch_merge_type, "flat");
GGML_UNUSED(e);
} }
try { try {
hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6 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; hparams.image_crop_resolution = hparams.image_size;
GGML_UNUSED(e);
} }
int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
@ -1176,43 +1173,38 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
try { try {
vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
new_clip->has_class_embedding = true; new_clip->has_class_embedding = true;
} catch (const std::exception& e) { } catch (const std::exception& /*e*/) {
new_clip->has_class_embedding = false; new_clip->has_class_embedding = false;
GGML_UNUSED(e);
} }
try { try {
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); 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")); vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
new_clip->has_pre_norm = true; new_clip->has_pre_norm = true;
} catch (std::exception & e) { } catch (std::exception & /*e*/) {
new_clip->has_pre_norm = false; new_clip->has_pre_norm = false;
GGML_UNUSED(e);
} }
try { try {
vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); 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")); vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
new_clip->has_post_norm = true; new_clip->has_post_norm = true;
} catch (std::exception & e) { } catch (std::exception & /*e*/) {
new_clip->has_post_norm = false; new_clip->has_post_norm = false;
GGML_UNUSED(e);
} }
try { try {
vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
new_clip->has_patch_bias = true; new_clip->has_patch_bias = true;
} catch (std::exception & e) { } catch (std::exception & /*e*/) {
new_clip->has_patch_bias = false; new_clip->has_patch_bias = false;
GGML_UNUSED(e);
} }
try { try {
vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); 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")); 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__); LOG_TEE("%s: failed to load vision model tensors\n", __func__);
GGML_UNUSED(e);
} }
// LLaVA projection // LLaVA projection
@ -1223,26 +1215,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// Yi-type llava // Yi-type llava
vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight")); 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")); vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
} catch (std::runtime_error & e) { GGML_UNUSED(e); } } catch (std::runtime_error & /*e*/) { }
try { try {
// missing in Yi-type llava // 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_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")); vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
} catch (std::runtime_error & e) { GGML_UNUSED(e); } } catch (std::runtime_error & /*e*/) { }
try { try {
// Yi-type llava // Yi-type llava
vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight")); 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")); vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
} catch (std::runtime_error & e) { GGML_UNUSED(e); } } catch (std::runtime_error & /*e*/) { }
try { try {
// Yi-type llava // Yi-type llava
vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight")); 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")); vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
} catch (std::runtime_error & e) { GGML_UNUSED(e); } } catch (std::runtime_error & /*e*/) { }
try { try {
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE); 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__); // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
} catch (std::runtime_error & e) { GGML_UNUSED(e); } } catch (std::runtime_error & /*e*/) { }
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projection // MobileVLM projection
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight")); vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));