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train.py
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train.py
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import logging
import math
import os
import pickle as pkl
import random
from argparse import ArgumentParser
from collections import defaultdict
from datetime import datetime
from itertools import chain
from pprint import pformat
import torch
import torch.nn.functional as F
from ignite.contrib.handlers.param_scheduler import PiecewiseLinear
from ignite.contrib.handlers.tensorboard_logger import (
OptimizerParamsHandler, OutputHandler, TensorboardLogger)
from ignite.contrib.handlers.tqdm_logger import ProgressBar
from ignite.engine import Engine, Events
from ignite.handlers import ModelCheckpoint
from ignite.metrics import Accuracy, Loss, MetricsLambda, RunningAverage
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Subset
from transformers import *
from dataset import InferenceDataset, collate_fn, SPECIAL_TOKENS_DICT
logger = logging.getLogger(__file__)
def average_distributed_scalar(scalar, args):
""" Average a scalar over the nodes if we are in distributed training. We use this for distributed evaluation. """
if args.local_rank == -1:
return scalar
scalar_t = torch.tensor(scalar, dtype=torch.float,
device=args.device) / torch.distributed.get_world_size()
torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM)
return scalar_t.item()
def get_data_loaders(args, tokenizer):
dev_dataset = InferenceDataset('dev', tokenizer, args)
train_dataset = InferenceDataset('train', tokenizer, args)
if args.small_data != -1:
logger.info('Using small subset of data')
dev_dataset = Subset(dev_dataset, list(range(args.small_data)))
train_dataset = Subset(train_dataset, list(range(args.small_data)))
dev_dataloader = DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=(not args.distributed),
num_workers=8,
collate_fn=lambda x: collate_fn(x, tokenizer.eos_token_id, args))
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(not args.distributed),
num_workers=8,
collate_fn=lambda x: collate_fn(x, tokenizer.eos_token_id, args))
return train_dataloader, dev_dataloader
def get_args():
parser = ArgumentParser()
parser.add_argument("--data_path", type=str,
default="/home/hdd1/vibhav/VE-SNLI/mycode-vesnli/dataset/e-SNLI-VE", help="Path of the dataset")
parser.add_argument("--no_image", action="store_true",
help="To process image or not")
parser.add_argument("--no_premise", action="store_true",
help="To process premise or not")
parser.add_argument("--with_expl", action="store_true",
help="To use explanations or not")
parser.add_argument("--model_checkpoint", type=str,
default="gpt2", help="Path, url or short name of the model")
parser.add_argument("--batch_size", type=int,
default=4, help="Batch size for training")
parser.add_argument("--gradient_accumulation_steps", type=int,
default=8, help="Accumulate gradients on several steps")
parser.add_argument("--lr", type=float,
default=5e-5, help="Learning rate")
parser.add_argument("--max_norm", type=float,
default=1.0, help="Clipping gradient norm")
parser.add_argument("--n_epochs", type=int, default=4,
help="Number of training epochs")
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--eval_before_start", action='store_true',
help="If true start with a first evaluation before training")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available()
else "cpu", help="Device (cuda or cpu)")
parser.add_argument("--fp16", type=str, default="",
help="Set to O0, O1, O2, O3 for fp16 training (see apex documentation)")
parser.add_argument("--local_rank", type=int, default=-1,
help="Local rank for distributed training (-1: not distributed)")
parser.add_argument("--output_folder", type=str,
default='./output', help="output storage path")
parser.add_argument("--small_data", type=int,
default=-1, help='small data size')
return parser.parse_args()
def main():
args = get_args()
if 'e-SNLI-VE' in args.data_path:
args.no_image = False
else:
args.no_image = True
if not args.no_image:
args.no_premise = True
args.with_expl = True
'''Setup'''
t = datetime.today()
output_dir = os.path.join(args.output_folder,
f"{t.month}_{t.day}_{t.hour}_{t.minute}_{t.second}")
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
# logging is set to INFO (resp. WARN) for main (resp. auxiliary) process. logger.info => log main process only, logger.warning => log all processes
logging.basicConfig(filename=os.path.join(output_dir, 'app.log'),
filemode='a',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
# This is a logger.warning: it will be printed by all distributed processes
logger.warning(f"Running process {args.local_rank}")
logger.info(f"Arguments: {pformat(args)}")
logger.info(f'Image not used:{args.no_image}')
logger.info(f'Premise not used:{args.no_premise}')
logger.info(f'Explanations used:{args.with_expl}')
'''Initialize distributed training if needed'''
args.distributed = (args.local_rank != -1)
if args.distributed:
torch.cuda.set_device(args.local_rank)
args.device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
logger.info(
"Prepare tokenizer, pretrained model and optimizer - add special tokens for fine-tuning")
tokenizer = GPT2Tokenizer.from_pretrained(args.model_checkpoint)
tokenizer.add_special_tokens(SPECIAL_TOKENS_DICT)
if args.no_image:
model = GPT2LMHeadModel.from_pretrained(args.model_checkpoint)
else:
import image_gpt2_291
model = image_gpt2_291.GPT2LMHeadModel.from_pretrained(
args.model_checkpoint)
model.resize_token_embeddings(len(tokenizer))
model.to(args.device)
optimizer = AdamW(model.parameters(), lr=args.lr)
'''
Prepare model for FP16 and distributed training if needed (order is important, distributed should be the last)
'''
if args.fp16:
from apex import amp # Apex is only required if we use fp16 training
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16)
if args.distributed:
model = DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
model = model.module
logger.info("Prepare datasets")
train_loader, val_loader = get_data_loaders(args, tokenizer)
'''Training function and trainer'''
def train(engine, batch):
model.train()
batch = tuple(input_tensor.to(args.device) for input_tensor in batch)
if args.no_image:
input_ids, lm_label, label, input_mask = batch
else:
image, input_ids, lm_label, label, input_mask = batch
if args.no_image:
output = model(input_ids=input_ids,
# attention_mask=input_mask,
labels=lm_label)
else:
output = model(input_ids=input_ids,
images=image,
# attention_mask=input_mask,
labels=lm_label)
loss, logits, _ = output
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
if engine.state.iteration % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
if not args.with_expl:
lbl_accuracy = torch.eq(label, logits.argmax(
dim=1)).float().sum() / len(label)
return {
'loss': loss.item(),
'lbl_accuracy': lbl_accuracy.item()
}
else:
if engine.state.iteration % (args.gradient_accumulation_steps * 500) == 0:
input_output = list(zip(input_ids, logits))
random_item = random.choice(input_output)
in_sent = tokenizer.decode(list(filter(
lambda x: x != tokenizer.eos_token_id,
random_item[0])))
out_expl = tokenizer.decode(random_item[1].argmax(dim=1),
skip_special_tokens=True)
logger.info(f'MODEL INPUT: {in_sent}')
logger.info(f'GEN. EXPL {out_expl}')
logger.info('--------------------------------')
return {
'loss': loss.item(),
}
'''Validation function and validator (validator output is the input of the metrics)'''
def validation(engine, batch):
model.eval()
with torch.no_grad():
batch = tuple(input_tensor.to(args.device)
for input_tensor in batch)
if args.no_image:
input_ids, lm_label, label, input_mask = batch
else:
image, input_ids, lm_label, label, input_mask = batch
if args.no_image:
output = model(input_ids=input_ids,
# attention_mask=input_mask
)
else:
output = model(input_ids=input_ids,
images=image,
# attention_mask=input_mask
)
logits, _ = output
logits_shifted = logits[..., :-1, :].contiguous().view(-1,
logits.size(-1))
labels_shifted = lm_label[..., 1:].contiguous().view(-1)
return logits_shifted, labels_shifted
'''Engines'''
trainer = Engine(train)
validator = Engine(validation)
# t_total = len(
# train_loader) // args.gradient_accumulation_steps * args.n_epochs
# scheduler = get_linear_schedule_with_warmup(
# optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
'''Linearly decrease the learning rate from lr to zero'''
scheduler = PiecewiseLinear(optimizer, "lr",
[(0, args.lr), (args.n_epochs * len(train_loader), 0.0)])
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
'''
Attach validation to trainer: we evaluate when we start the training and at the end of each epoch
'''
trainer.add_event_handler(Events.EPOCH_COMPLETED,
lambda _: validator.run(val_loader))
if args.eval_before_start:
trainer.add_event_handler(Events.STARTED,
lambda _: validator.run(val_loader))
'''Prepare metrics - note how we compute distributed metrics'''
RunningAverage(output_transform=lambda x: x['loss']).attach(
trainer, "loss")
RunningAverage(output_transform=lambda x: math.exp(
average_distributed_scalar(x['loss'], args))).attach(trainer, "ppl")
if not args.with_expl:
RunningAverage(output_transform=lambda x: 100 * x['lbl_accuracy']).attach(
trainer, "lbl_accuracy")
metrics = {}
metrics["lbl_loss"] = Loss(torch.nn.CrossEntropyLoss(),
output_transform=lambda x: (x[0], x[1]))
metrics["loss"] = MetricsLambda(
lambda l, a: average_distributed_scalar(
l / a.gradient_accumulation_steps, a), metrics["lbl_loss"], args)
metrics["ppl"] = MetricsLambda(math.exp, metrics["loss"])
if not args.with_expl:
metrics["lbl_accuracy"] = 100 * \
Accuracy(output_transform=lambda x: (x[0], x[1]))
for name, metric in metrics.items():
metric.attach(validator, name)
'''
On the main process: add progress bar, tensorboard, checkpoints and save model, configuration and tokenizer before we start to train
'''
if args.local_rank in [-1, 0]:
pbar = ProgressBar(persist=True)
pbar.attach(trainer,
metric_names=["loss", 'ppl'] if args.with_expl else ["loss", 'lbl_accuracy', 'ppl'])
validator.add_event_handler(Events.COMPLETED,
lambda _: pbar.log_message(
"Validation: %s" % pformat(validator.state.metrics)))
tb_logger = TensorboardLogger(log_dir=output_dir)
tb_logger.attach(trainer,
log_handler=OptimizerParamsHandler(optimizer),
event_name=Events.ITERATION_STARTED)
tb_logger.attach(trainer,
log_handler=OutputHandler(
tag="training",
metric_names=["loss"]),
event_name=Events.ITERATION_COMPLETED)
tb_logger.attach(trainer,
log_handler=OutputHandler(
tag="training",
metric_names=["ppl"] if args.with_expl else ["lbl_accuracy", "ppl"]),
event_name=Events.EPOCH_COMPLETED)
tb_logger.attach(validator,
log_handler=OutputHandler(
tag="validation",
metric_names=[
'ppl', 'loss'] if args.with_expl else['ppl', 'loss', 'lbl_accuracy'],
global_step_transform=lambda *args, **kwargs: trainer.state.iteration),
event_name=Events.EPOCH_COMPLETED)
checkpoint_handler = ModelCheckpoint(output_dir,
'checkpoint',
n_saved=8,
require_empty=False)
trainer.add_event_handler(Events.EPOCH_COMPLETED(every=1),
checkpoint_handler,
{'mymodel': getattr(model, 'module', model)})
# "getattr" take care of distributed encapsulation
torch.save(args, os.path.join(output_dir, 'model_training_args.bin'))
getattr(model, 'module', model).config.to_json_file(
os.path.join(output_dir, CONFIG_NAME))
tokenizer.save_vocabulary(output_dir)
'''Run the training'''
trainer.run(train_loader, max_epochs=args.n_epochs)
if __name__ == "__main__":
main()