fix --gradient_checkpointing (#13964)

examples/research_projects/wav2vec2/run_asr.py

@@ -54,12 +54,6 @@ class ModelArguments:
     freeze_feature_extractor: Optional[bool] = field(
         default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
     )
-    gradient_checkpointing: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
-        },
-    )
     verbose_logging: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether to log verbose messages or not."},
@@ -352,7 +346,7 @@ def main():
     model = Wav2Vec2ForCTC.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
-        gradient_checkpointing=model_args.gradient_checkpointing,
+        gradient_checkpointing=training_args.gradient_checkpointing,
         vocab_size=len(processor.tokenizer),
     )
 

examples/research_projects/wav2vec2/run_common_voice.py

@@ -84,12 +84,6 @@ class ModelArguments:
             "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
         },
     )
-    gradient_checkpointing: Optional[bool] = field(
-        default=True,
-        metadata={
-            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
-        },
-    )
     layerdrop: Optional[float] = field(default=0.0, metadata={"help": "The LayerDrop probability."})
 
 
@@ -373,7 +367,7 @@ def main():
         hidden_dropout=model_args.hidden_dropout,
         feat_proj_dropout=model_args.feat_proj_dropout,
         mask_time_prob=model_args.mask_time_prob,
-        gradient_checkpointing=model_args.gradient_checkpointing,
+        gradient_checkpointing=training_args.gradient_checkpointing,
         layerdrop=model_args.layerdrop,
         ctc_loss_reduction="mean",
         pad_token_id=processor.tokenizer.pad_token_id,

examples/research_projects/wav2vec2/run_pretrain.py

@@ -50,12 +50,6 @@ class ModelArguments:
     freeze_feature_extractor: Optional[bool] = field(
         default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
     )
-    gradient_checkpointing: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
-        },
-    )
     verbose_logging: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether to log verbose messages or not."},
@@ -370,7 +364,7 @@ def main():
     config = Wav2Vec2Config.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
-        gradient_checkpointing=model_args.gradient_checkpointing,
+        gradient_checkpointing=training_args.gradient_checkpointing,
     )
 
     if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":