ACID/train.py at main · deeplearningplus/ACID · GitHub

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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()
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importcopy
importlogging
fromdataclassesimportdataclass, field
fromtypingimportDict, Optional, Sequence

importtorch
importtransformers
fromtorch.utils.dataimportDataset
fromtransformersimportTrainer, DataCollatorForLanguageModeling
fromdatasetsimportload_from_disk

@dataclass
classModelArguments:
model_name_or_path: Optional[str] =field(default="facebook/opt-125m")


@dataclass
classDataArguments:
data_path: str=field(default=None, metadata={"help": "Path to the training data."})


@dataclass
classTrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] =field(default=None)
optim: str=field(default="adamw_torch")
model_max_length: int=field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
 )

defmake_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) ->Dict:
"""Make dataset and collator"""
tokenized_datasets=load_from_disk(data_args.data_path)
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, mlm_probability=0.0)
returndict(train_dataset=tokenized_datasets['train'], eval_dataset=None, data_collator=data_collator)


deftrain():
parser=transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args=parser.parse_args_into_dataclasses()

model=transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
 )

tokenizer=transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
#use_fast=False,
 )

data_module=make_data_module(tokenizer=tokenizer, data_args=data_args)
trainer=Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)


if__name__=="__main__":
train()