Set inference_parallelize_kwargs in IPUConfig

huggingface · Jul 17, 2023 · ee5282c · ee5282c
1 parent 53742af
commit ee5282c
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Showing 2 changed files with 21 additions and 22 deletions.
diff --git a/examples/speech-recognition/run_speech_recognition_seq2seq.py b/examples/speech-recognition/run_speech_recognition_seq2seq.py
@@ -411,6 +411,11 @@ def main():
         training_args.ipu_config_name if training_args.ipu_config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_auth_token=True if model_args.use_auth_token else None,
+        inference_parallelize_kwargs={
+            "use_cache": True,
+            "use_cross_cache": True,
+            "max_length": training_args.generation_max_length,
+        },
     )
 
     if model.config.decoder_start_token_id is None:
@@ -546,11 +551,6 @@ def compute_metrics(pred):
         eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
         data_collator=data_collator,
         compute_metrics=compute_metrics if training_args.predict_with_generate else None,
-        inference_parallelize_kwargs={
-            "use_cache": True,
-            "use_cross_cache": True,
-            "max_length": training_args.generation_max_length,
-        },
     )
 
     # 12. Training

diff --git a/tests/examples/run_speech_recognition_seq2seq.txt b/tests/examples/run_speech_recognition_seq2seq.txt
@@ -72,26 +72,31 @@
 >     # Whisper does not have a layer_norm_eps option, remains to be seen if this is a problem
 >     # config.update({"layer_norm_eps": 0.0001})
 > 
-401a410,414
+401a410,419
 >     ipu_config = IPUConfig.from_pretrained(
 >         training_args.ipu_config_name if training_args.ipu_config_name else model_args.model_name_or_path,
 >         cache_dir=model_args.cache_dir,
 >         use_auth_token=True if model_args.use_auth_token else None,
+>         inference_parallelize_kwargs={
+>             "use_cache": True,
+>             "use_cross_cache": True,
+>             "max_length": training_args.generation_max_length,
+>         },
 >     )
-446c459
+446c464
 <     def prepare_dataset(batch):
 ---
 >     def prepare_dataset(batch, feature_extractor, tokenizer):
-452c465
+452c470
 <         # process audio length
 ---
 > 
-457a471,474
+457a476,479
 >         if not training_args.fp32:
 >             # Cast audio inputs to FP16
 >             batch[model_input_name] = batch[model_input_name].astype(np.float16)
 > 
-463,469c480,485
+463,469c485,490
 <     with training_args.main_process_first(desc="dataset map pre-processing"):
 <         vectorized_datasets = raw_datasets.map(
 <             prepare_dataset,
@@ -106,7 +111,7 @@
 >         num_proc=data_args.preprocessing_num_workers,
 >         desc="preprocess train dataset",
 >     )
-509,516c525,528
+509,516c530,533
 <     # make sure all processes wait until data is saved
 <     with training_args.main_process_first():
 <         # only the main process saves them
@@ -120,23 +125,17 @@
 >     feature_extractor.save_pretrained(training_args.output_dir)
 >     tokenizer.save_pretrained(training_args.output_dir)
 >     config.save_pretrained(training_args.output_dir)
-518c530
+518c535
 <     processor = AutoProcessor.from_pretrained(training_args.output_dir)
 ---
 >     processor = WhisperProcessor(feature_extractor, tokenizer)
-524a537
+524a542
 >         pad_to_multiple_of_labels=training_args.generation_max_length,
-528c541
+528c546
 <     trainer = Seq2SeqTrainer(
 ---
 >     trainer = IPUSeq2SeqTrainer(
-529a543
+529a548
 >         ipu_config=ipu_config,
-533d546
+533d551
 <         tokenizer=feature_extractor,
-535a549,553
->         inference_parallelize_kwargs={
->             "use_cache": True,
->             "use_cross_cache": True,
->             "max_length": training_args.generation_max_length,
->         },