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[fix] fix eval using part of train #947

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[fix] fix eval using part of train #947

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2 changes: 1 addition & 1 deletion src/lmflow/args.py
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Original file line number Diff line number Diff line change
Expand Up @@ -561,7 +561,7 @@ class DatasetArguments:
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
"help": "The percentage of the train set used as validation set in case there's no eval dataset."
},
)
preprocessing_num_workers: Optional[int] = field(
Expand Down
49 changes: 22 additions & 27 deletions src/lmflow/datasets/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -265,15 +265,16 @@ def from_dict(self, dict_obj: dict, *args, **kwargs):


@classmethod
def create_from_dict(cls, dict_obj, *args, **kwargs):
def create_from_dict(cls, dict_obj, dataset_args: Optional[DatasetArguments] = None, backend: str = "huggingface"):
r"""
Returns
--------

Returns a Dataset object given a dict.
"""
empty_data_args = DatasetArguments(dataset_path=None)
dataset = Dataset(empty_data_args)
if dataset_args is None:
dataset_args = DatasetArguments(dataset_path=None)
dataset = Dataset(dataset_args, backend=backend)
return dataset.from_dict(dict_obj)


Expand Down Expand Up @@ -467,14 +468,12 @@ def sample(self, n: int, seed: int=42):
if self.backend == "huggingface":
sampled_dataset = self.backend_dataset.shuffle(seed=seed).select(range(n))
output_dataset = self.create_from_dict(
{
"type": self.get_type(),
"instances": [
{
col_name: sampled_dataset[col_name][i] for col_name in sampled_dataset.column_names
} for i in range(n)
]
}
dict_obj={
"type": self.get_type(),
"instances": [data_point for data_point in tqdm(sampled_dataset, desc="Train Dataset")]
},
dataset_args=self.data_args,
backend=self.backend,
)
return output_dataset
else:
Expand Down Expand Up @@ -506,24 +505,20 @@ def train_test_split(self, test_size: float=0.2, shuffle: bool=True, seed: int=4
test_size=test_size, shuffle=shuffle, seed=seed
)
train_dataset = self.create_from_dict(
{
"type": self.get_type(),
"instances": [
{
col_name: splited["train"][col_name][i] for col_name in splited["train"].column_names
} for i in range(len(splited["train"]))
]
}
dict_obj={
"type": self.get_type(),
"instances": [data_point for data_point in tqdm(splited["train"], desc="Train Dataset")]
},
dataset_args=self.data_args,
backend=self.backend,
)
test_dataset = self.create_from_dict(
{
"type": self.get_type(),
"instances": [
{
col_name: splited["test"][col_name][i] for col_name in splited["test"].column_names
} for i in range(len(splited["test"]))
]
}
dict_obj={
"type": self.get_type(),
"instances": [data_point for data_point in tqdm(splited["test"], desc="Test Dataset")]
},
dataset_args=self.data_args,
backend=self.backend,
)
return train_dataset, test_dataset
else:
Expand Down
94 changes: 46 additions & 48 deletions src/lmflow/pipeline/finetuner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@
import lmflow.optim.optimizers as optim
from lmflow.args import OptimizerNames, DatasetArguments, ModelArguments, FinetunerArguments
from lmflow.datasets.dataset import Dataset
from lmflow.models.hf_decoder_model import HFDecoderModel
from lmflow.pipeline.base_tuner import BaseTuner
from lmflow.pipeline.utils.peft_trainer import PeftTrainer, PeftSavingCallback

Expand Down Expand Up @@ -415,10 +416,28 @@ def create_optimizer(self):
self.optimizer = smp.DistributedOptimizer(self.optimizer)

return CustomizedOptimTrainer

def __tokenize_dataset(
self,
model: "HFDecoderModel",
dataset: "Dataset",
) -> "Dataset":
# Tokenization and text grouping must be done in the main process
with self.finetuner_args.main_process_first(desc="dataset map tokenization"):
tokenized_dataset = model.tokenize(dataset)
if self.data_args.disable_group_texts:
lm_dataset = tokenized_dataset
else:
lm_dataset = self.group_text(
tokenized_dataset,
model_max_length=model.get_max_length(),
)

return lm_dataset

def tune(self,
model,
dataset,
dataset: "Dataset",
transform_dataset_in_place=True,
data_collator=None):
"""
Expand All @@ -439,57 +458,38 @@ def tune(self,
if not transform_dataset_in_place:
dataset = copy.deepcopy(dataset)

# Tokenization and text grouping must be done in the main process
train_dataset = None
eval_dataset = None

if dataset.backend == "custom_multi_modal":
dataset.backend_dataset.register_tokenizer(
model.tokenizer, model.image_processor)
lm_dataset = dataset
train_dataset = dataset.get_backend_dataset()
else:
with finetuner_args.main_process_first(desc="dataset map tokenization"):
tokenized_dataset = model.tokenize(dataset)
if data_args.disable_group_texts:
lm_dataset = tokenized_dataset
else:
lm_dataset = self.group_text(
tokenized_dataset,
model_max_length=model.get_max_length(),
if finetuner_args.do_eval:
if finetuner_args.eval_dataset_path is None:
assert data_args.validation_split_percentage != 0, (
"You've set `do_eval=True`. If you don't provide an evaluation dataset using"
" `eval_dataset_path`, please set `validation_split_percentage` to a non-zero"
" value."
)

train_dataset = lm_dataset.get_backend_dataset()
logger.info(f"Number of train samples: {len(train_dataset)}")

if finetuner_args.do_eval:
eval_dataset_args = deepcopy(data_args)
eval_dataset_args.dataset_path = finetuner_args.eval_dataset_path
eval_dataset = Dataset(eval_dataset_args)
with finetuner_args.main_process_first(desc="dataset map tokenization"):
tokenized_dataset = model.tokenize(eval_dataset)
if data_args.disable_group_texts:
lm_dataset = tokenized_dataset
else:
lm_dataset = self.group_text(
tokenized_dataset,
model_max_length=model.get_max_length(),
train_dataset_raw, eval_dataset_raw = dataset.train_test_split(
test_size=data_args.validation_split_percentage / 100,
shuffle=True,
seed=finetuner_args.seed,
)
eval_dataset = lm_dataset.get_backend_dataset()
logger.info(f"Number of eval samples: {len(eval_dataset)}")

def preprocess_logits_for_metrics(logits, labels):
if isinstance(logits, tuple):
# Depending on the model and config, logits may contain extra tensors,
# like past_key_values, but logits always come first
logits = logits[0]
return logits.argmax(dim=-1)

metric = evaluate.load("accuracy")

def compute_metrics(eval_preds):
preds, labels = eval_preds
# preds have the same shape as the labels, after the argmax(-1) has been calculated
# by preprocess_logits_for_metrics but we need to shift the labels
labels = labels[:, 1:].reshape(-1)
preds = preds[:, :-1].reshape(-1)
return metric.compute(predictions=preds, references=labels)
train_dataset = self.__tokenize_dataset(model, train_dataset_raw).get_backend_dataset()
eval_dataset = self.__tokenize_dataset(model, eval_dataset_raw).get_backend_dataset()
else:
eval_dataset_args = deepcopy(data_args)
eval_dataset_args.dataset_path = finetuner_args.eval_dataset_path
eval_dataset_raw = Dataset(eval_dataset_args)
eval_dataset = self.__tokenize_dataset(model, eval_dataset_raw).get_backend_dataset()
logger.info(f"Number of eval samples: {len(eval_dataset)}")

else:
train_dataset = self.__tokenize_dataset(model, dataset).get_backend_dataset()
logger.info(f"Number of train samples: {len(train_dataset)}")

if finetuner_args.do_train:
if data_args.max_train_samples is not None:
Expand Down Expand Up @@ -583,8 +583,6 @@ def switch_active_layers(self):
tokenizer=model.get_tokenizer(),
# Data collator will default to DataCollatorWithPadding, so we change it.
data_collator=data_collator,
compute_metrics=compute_metrics if training_args.do_eval else None,
preprocess_logits_for_metrics=preprocess_logits_for_metrics if training_args.do_eval else None,
callbacks=trainer_callbacks
)
# Training
Expand Down
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