[FX] _generate_dummy_input supports audio-classification models for l…

…abels (huggingface#18580)

* Support audio classification architectures for labels generation, as well as provides a flag to print warnings or not

* Use ENV_VARS_TRUE_VALUES

src/transformers/utils/fx.py

@@ -19,6 +19,7 @@
 import inspect
 import math
 import operator
+import os
 import random
 import warnings
 from typing import Any, Callable, Dict, List, Optional, Type, Union
@@ -48,11 +49,12 @@
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
     MODEL_MAPPING_NAMES,
 )
-from ..utils import TORCH_FX_REQUIRED_VERSION, is_torch_fx_available
+from ..utils import ENV_VARS_TRUE_VALUES, TORCH_FX_REQUIRED_VERSION, is_torch_fx_available
 from ..utils.versions import importlib_metadata
 
 
 logger = logging.get_logger(__name__)
+_IS_IN_DEBUG_MODE = os.environ.get("FX_DEBUG_MODE", "").upper() in ENV_VARS_TRUE_VALUES
 
 
 def _generate_supported_model_class_names(
@@ -678,7 +680,12 @@ def _generate_dummy_input(
         if input_name in ["labels", "start_positions", "end_positions"]:
 
             batch_size = shape[0]
-            if model_class_name in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
+            if model_class_name in [
+                *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
+                *get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES),
+                *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
+                *get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES),
+            ]:
                 inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
             elif model_class_name in [
                 *get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
@@ -710,11 +717,6 @@ def _generate_dummy_input(
                     )
                 inputs_dict["labels"] = torch.zeros(*labels_shape, dtype=labels_dtype, device=device)
 
-            elif model_class_name in [
-                *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
-                *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
-            ]:
-                inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
             elif model_class_name in [
                 *get_values(MODEL_FOR_PRETRAINING_MAPPING_NAMES),
                 *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
@@ -725,7 +727,9 @@ def _generate_dummy_input(
             ]:
                 inputs_dict["labels"] = torch.zeros(shape, dtype=torch.long, device=device)
             else:
-                raise NotImplementedError(f"{model_class_name} not supported yet.")
+                raise NotImplementedError(
+                    f"Generating the dummy input named {input_name} for {model_class_name} is not supported yet."
+                )
         elif "pixel_values" in input_name:
             batch_size = shape[0]
             image_size = getattr(model.config, "image_size", None)
@@ -846,7 +850,8 @@ def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, pr
                 raise ValueError("Don't support composite output yet")
             rv.install_metadata(meta_out)
         except Exception as e:
-            warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")
+            if _IS_IN_DEBUG_MODE:
+                warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")
 
         return rv