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[examples template] added max_sample args and metrics changes #10602

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Mar 9, 2021
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[examples template] added max_sample args and metrics changes #10602

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88 changes: 60 additions & 28 deletions ...w_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,6 +144,20 @@ class DataTrainingArguments:
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_val_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
},
)

def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
Expand Down Expand Up @@ -317,13 +331,37 @@ def main():
def tokenize_function(examples):
return tokenizer(examples[text_column_name], padding="max_length", truncation=True)

tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_train:
if "train" not in datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = datasets["train"]
if data_args.max_train_samples is not None:
# Select Sample from Dataset
train_dataset = train_dataset.select(range(data_args.max_train_samples))
# tokenize train dataset in batch
train_dataset = train_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)

if training_args.do_eval:
if "validation" not in datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = datasets["validation"]
# Selecting samples from dataset
if data_args.max_val_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
# tokenize validation dataset
eval_dataset = eval_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)

# Data collator
data_collator=default_data_collator if not training_args.fp16 else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
Expand All @@ -332,8 +370,8 @@ def tokenize_function(examples):
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
Expand All @@ -358,33 +396,27 @@ def tokenize_function(examples):
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload

output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
if trainer.is_world_process_zero():
with open(output_train_file, "w") as writer:
logger.info("***** Train results *****")
for key, value in sorted(train_result.metrics.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))

# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()

# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")

results = trainer.evaluate()
metrics = trainer.evaluate()

output_eval_file = os.path.join(training_args.output_dir, "eval_results_{{cookiecutter.example_shortcut}}.txt")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in sorted(results.items()):
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

return results
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)


def _mp_fn(index):
Expand Down