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hybrid_pretrain.py
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hybrid_pretrain.py
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import argparse
import collections
import torch
import numpy as np
import data_loader.data_loaders as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
from parse_config import ConfigParser
from trainer.trainer import HybridTrainer
from transformers import TrainingArguments
from transformers import AutoModel
import transformers
import os
import pdb
import random
from tqdm import tqdm
def main(index=0):
args = argparse.ArgumentParser(description="PyTorch Template")
args.add_argument(
"-c",
"--config",
default=None,
type=str,
help="config file path (default: None)",
)
args.add_argument(
"-r",
"--resume",
default=None,
type=str,
help="path to latest checkpoint (default: None)",
)
args.add_argument(
"-d",
"--device",
default=None,
type=str,
help="indices of GPUs to enable (default: all)",
)
args.add_argument("--run_id", default=0, type=str, help="run id")
# custom cli options to modify configuration from default values given in json file.
CustomArgs = collections.namedtuple("CustomArgs", "flags type target")
options = [
CustomArgs(
["--lr", "--learning_rate"], type=float, target="trainer;learning_rate"
),
CustomArgs(
["--bs", "--batch_size"], type=int, target="train_dataset;args;batch_size"
),
]
config = ConfigParser.from_args(args, options, index=index)
# fix random seeds for reproducibility
seed = config["seed"]
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(seed)
random.seed(seed)
transformers.logging.set_verbosity_info()
# setup data_loader instances
config["train_dataset"]["args"]["length"] = (
config["train_dataset"]["args"]["length"]
// config["train_dataset"]["args"]["batch_size"]
)
train_dataset = config.init_obj(
"train_dataset",
module_data,
n_gpus=config["n_gpus"],
tokenizer=config["tokenizer"],
mlm_probability=config["mlm_probability"],
index=index,
)
# build model architecture, then print to console
model = config.init_obj(
"arch", module_arch, vocab_size=len(train_dataset.tokenizer)
)
if config.resume is not None:
last_trained = torch.load(
os.path.join(config.resume.resolve().as_posix(), "pytorch_model.bin")
)
model.load_state_dict(last_trained)
del last_trained
elif "roberta" in config["tokenizer"]:
# init the model with roberta
roberta_model = AutoModel.from_pretrained("roberta-base")
roberta_model.resize_token_embeddings(len(train_dataset.tokenizer))
model_state_dict = model.state_dict()
roberta_state_dict = roberta_model.state_dict()
state_dict_to_load = {
"bert.tapas." + k: v
for k, v in roberta_state_dict.items()
if "bert.tapas." + k in model_state_dict
}
model_state_dict.update(state_dict_to_load)
model.load_state_dict(model_state_dict)
del roberta_model
del model_state_dict
del roberta_state_dict
del state_dict_to_load
config["trainer"].update(
{
"per_device_train_batch_size": config["train_dataset"]["args"][
"batch_size"
],
"dataloader_num_workers": config["n_gpus"] * 2,
"seed": seed,
"output_dir": config.save_dir.resolve().as_posix(),
"logging_dir": config.log_dir.resolve().as_posix(),
}
)
training_args = TrainingArguments(**config["trainer"])
trainer = HybridTrainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
)
if config.resume is None:
trainer.train()
else:
trainer.train(config.resume.resolve().as_posix())
def _mp_fn(index):
# For xla_spawn (TPUs)
main(index)
if __name__ == "__main__":
main()