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Added max_sample_ arguments #10551

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merged 8 commits into from
Mar 8, 2021
Merged

Added max_sample_ arguments #10551

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c2acb5d
reverted changes of logging and saving metrics
bhadreshpsavani Mar 5, 2021
85f2dec
added max_sample arguments
bhadreshpsavani Mar 5, 2021
2f99439
fixed code
bhadreshpsavani Mar 5, 2021
34f1b23
white space diff
bhadreshpsavani Mar 5, 2021
0474538
reformetting code
bhadreshpsavani Mar 6, 2021
b9d9b6f
Merge remote-tracking branch 'origin/master' into argument-addition
bhadreshpsavani Mar 6, 2021
7cf59c4
Merge remote-tracking branch 'origin/master' into argument-addition
bhadreshpsavani Mar 8, 2021
b1f5323
reformatted code
bhadreshpsavani Mar 8, 2021
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69 changes: 53 additions & 16 deletions examples/language-modeling/run_clm.py
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Expand Up @@ -114,6 +114,21 @@ class DataTrainingArguments:
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
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."
},
)

block_size: Optional[int] = field(
default=None,
metadata={
Expand Down Expand Up @@ -346,19 +361,37 @@ def group_texts(examples):
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)

if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = tokenized_datasets["train"].map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
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I think this map can be dome has before (deleted lines 349 to 354 in the diff) since it's the same for training and validation.

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Hi @sgugger,

so we should do it like below

lm_datasets = tokenized_datasets.map(
        group_texts,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        load_from_cache_file=not data_args.overwrite_cache,
    )

and we simply select samples for train and validation?

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Yes. It avoids duplicating the same code this way.

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I did this for almost all the examples, I thought preprocessing will be done only if it will be required.
Shall I do these changes for all the examples or mentioned here only?

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@sgugger sgugger Mar 8, 2021 •
edited

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For the other examples, you are doing the select before doing the map (to avoid preprocessing all the dataset) so it's not possible to group all the preprocessing together.I think it only applies to the three scripts in language_modeling.

if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))

if training_args.do_eval:
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Suggested change
if training_args.do_eval:
if training_args.do_eval:

please add a new line between the ifs so that they don't mesh together (same in all other scripts).

if "validation" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = tokenized_datasets["validation"].map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_val_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_val_samples))

# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=lm_datasets["train"] if training_args.do_train else None,
eval_dataset=lm_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 will default to DataCollatorWithPadding, so we change it.
data_collator=default_data_collator,
Expand All @@ -377,24 +410,28 @@ def group_texts(examples):

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))

trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()

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

eval_output = trainer.evaluate()

perplexity = math.exp(eval_output["eval_loss"])
results["perplexity"] = perplexity
metrics = trainer.evaluate()

trainer.log_metrics("eval", results)
trainer.save_metrics("eval", results)
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))
perplexity = math.exp(metrics["eval_loss"])
metrics["perplexity"] = perplexity

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


def _mp_fn(index):
Expand Down
67 changes: 51 additions & 16 deletions examples/language-modeling/run_mlm.py
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Expand Up @@ -146,6 +146,20 @@ class DataTrainingArguments:
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
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 @@ -380,12 +394,29 @@ def group_texts(examples):
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = tokenized_datasets["train"].map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))

if training_args.do_eval:
if "validation" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = tokenized_datasets["validation"].map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_val_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_val_samples))

# Data collator
# This one will take care of randomly masking the tokens.
Expand All @@ -395,8 +426,8 @@ def group_texts(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 @@ -413,24 +444,28 @@ def group_texts(examples):
trainer.save_model() # Saves the tokenizer too for easy upload
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))

trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()

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

eval_output = trainer.evaluate()

perplexity = math.exp(eval_output["eval_loss"])
results["perplexity"] = perplexity
metrics = trainer.evaluate()

trainer.log_metrics("eval", results)
trainer.save_metrics("eval", results)
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))
perplexity = math.exp(metrics["eval_loss"])
metrics["perplexity"] = perplexity

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


def _mp_fn(index):
Expand Down
67 changes: 51 additions & 16 deletions examples/language-modeling/run_plm.py
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Open in desktop
Expand Up @@ -143,6 +143,20 @@ class DataTrainingArguments:
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
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 @@ -358,12 +372,29 @@ def group_texts(examples):
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_train:
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Same comment on this script too.

if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = tokenized_datasets["train"].map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
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Same here.

if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))

if training_args.do_eval:
if "validation" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = tokenized_datasets["validation"].map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_val_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_val_samples))

# Data collator
data_collator = DataCollatorForPermutationLanguageModeling(
Expand All @@ -376,8 +407,8 @@ def group_texts(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 @@ -394,24 +425,28 @@ def group_texts(examples):
trainer.save_model() # Saves the tokenizer too for easy upload
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))

trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()

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

eval_output = trainer.evaluate()

perplexity = math.exp(eval_output["eval_loss"])
results["perplexity"] = perplexity
metrics = trainer.evaluate()

trainer.log_metrics("eval", results)
trainer.save_metrics("eval", results)
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))
perplexity = math.exp(metrics["eval_loss"])
metrics["perplexity"] = perplexity

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


def _mp_fn(index):
Expand Down