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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
import logging
from contextlib import nullcontext
# if your python version < 3.7 use the below one
# from contextlib import suppress as nullcontext
import torch
from torch.nn.utils import clip_grad_norm_
class Executor:
def __init__(self):
self.step = 0
def train(
self, model, optimizer, scheduler, data_loader, device, writer, args, scaler
):
"""Train one epoch"""
model.train()
clip = args.get("grad_clip", 50.0)
log_interval = args.get("log_interval", 10)
rank = args.get("rank", 0)
epoch = args.get("epoch", 0)
accum_grad = args.get("accum_grad", 1)
is_distributed = args.get("is_distributed", True)
use_amp = args.get("use_amp", False)
logging.info(
"using accumulate grad, new batch size is {} times"
" larger than before".format(accum_grad)
)
if use_amp:
assert scaler is not None
# A context manager to be used in conjunction with an instance of
# torch.nn.parallel.DistributedDataParallel to be able to train
# with uneven inputs across participating processes.
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
model_context = model.join
else:
model_context = nullcontext
num_seen_utts = 0
with model_context():
for batch_idx, batch in enumerate(data_loader):
key, feats, target, feats_lengths, target_lengths = batch
feats = feats.to(device)
target = target.to(device)
feats_lengths = feats_lengths.to(device)
target_lengths = target_lengths.to(device)
num_utts = target_lengths.size(0)
if num_utts == 0:
continue
context = None
# Disable gradient synchronizations across DDP processes.
# Within this context, gradients will be accumulated on module
# variables, which will later be synchronized.
if is_distributed and batch_idx % accum_grad != 0:
context = model.no_sync
# Used for single gpu training and DDP gradient synchronization
# processes.
else:
context = nullcontext
with context():
# autocast context
# The more details about amp can be found in
# https://pytorch.org/docs/stable/notes/amp_examples.html
with torch.cuda.amp.autocast(scaler is not None):
loss_dict = model(feats, feats_lengths, target, target_lengths)
loss = loss_dict["loss"] / accum_grad
if use_amp:
scaler.scale(loss).backward()
else:
loss.backward()
num_seen_utts += num_utts
if batch_idx % accum_grad == 0:
if rank == 0 and writer is not None:
writer.add_scalar("train_loss", loss, self.step)
# Use mixed precision training
if use_amp:
scaler.unscale_(optimizer)
grad_norm = clip_grad_norm_(model.parameters(), clip)
# Must invoke scaler.update() if unscale_() is used in
# the iteration to avoid the following error:
# RuntimeError: unscale_() has already been called
# on this optimizer since the last update().
# We don't check grad here since that if the gradient
# has inf/nan values, scaler.step will skip
# optimizer.step().
scaler.step(optimizer)
scaler.update()
else:
grad_norm = clip_grad_norm_(model.parameters(), clip)
if torch.isfinite(grad_norm):
optimizer.step()
optimizer.zero_grad()
scheduler.step()
self.step += 1
if batch_idx % log_interval == 0:
lr = optimizer.param_groups[0]["lr"]
log_str = "TRAIN Batch {}/{} loss {:.6f} ".format(
epoch, batch_idx, loss.item() * accum_grad
)
for name, value in loss_dict.items():
if name != "loss" and value is not None:
log_str += "{} {:.6f} ".format(name, value.item())
log_str += "lr {:.8f} rank {}".format(lr, rank)
logging.debug(log_str)
def cv(self, model, data_loader, device, args):
"""Cross validation on"""
model.eval()
rank = args.get("rank", 0)
epoch = args.get("epoch", 0)
log_interval = args.get("log_interval", 10)
# in order to avoid division by 0
num_seen_utts = 1
total_loss = 0.0
with torch.no_grad():
for batch_idx, batch in enumerate(data_loader):
key, feats, target, feats_lengths, target_lengths = batch
feats = feats.to(device)
target = target.to(device)
feats_lengths = feats_lengths.to(device)
target_lengths = target_lengths.to(device)
num_utts = target_lengths.size(0)
if num_utts == 0:
continue
loss_dict = model(feats, feats_lengths, target, target_lengths)
loss = loss_dict["loss"]
if torch.isfinite(loss):
num_seen_utts += num_utts
total_loss += loss.item() * num_utts
if batch_idx % log_interval == 0:
log_str = "CV Batch {}/{} loss {:.6f} ".format(
epoch, batch_idx, loss.item()
)
for name, value in loss_dict.items():
if name != "loss" and value is not None:
log_str += "{} {:.6f} ".format(name, value.item())
log_str += "history loss {:.6f}".format(total_loss / num_seen_utts)
log_str += " rank {}".format(rank)
logging.debug(log_str)
return total_loss, num_seen_utts
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