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PaddleSpeech/paddlespeech/s2t/models/u2/updater.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from wenet(https://github.com/wenet-e2e/wenet)
from contextlib import nullcontext
import paddle
from paddle import distributed as dist
from paddlespeech.s2t.training.extensions.evaluator import StandardEvaluator
from paddlespeech.s2t.training.reporter import report
from paddlespeech.s2t.training.timer import Timer
from paddlespeech.s2t.training.updaters.standard_updater import StandardUpdater
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
class U2Evaluator(StandardEvaluator):
def __init__(self, model, dataloader):
super().__init__(model, dataloader)
self.msg = ""
self.num_seen_utts = 0
self.total_loss = 0.0
def evaluate_core(self, batch):
self.msg = "Valid: Rank: {}, ".format(dist.get_rank())
losses_dict = {}
loss, attention_loss, ctc_loss = self.model(*batch[1:])
if paddle.isfinite(loss):
num_utts = batch[1].shape[0]
self.num_seen_utts += num_utts
self.total_loss += float(loss) * num_utts
losses_dict['loss'] = float(loss)
if attention_loss:
losses_dict['att_loss'] = float(attention_loss)
if ctc_loss:
losses_dict['ctc_loss'] = float(ctc_loss)
for k, v in losses_dict.items():
report("eval/" + k, v)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
logger.info(self.msg)
return self.total_loss, self.num_seen_utts
class U2Updater(StandardUpdater):
def __init__(self,
model,
optimizer,
scheduler,
dataloader,
init_state=None,
accum_grad=1,
**kwargs):
super().__init__(
model, optimizer, scheduler, dataloader, init_state=init_state)
self.accum_grad = accum_grad
self.forward_count = 0
self.msg = ""
def update_core(self, batch):
"""One Step
Args:
batch (List[Object]): utts, xs, xlens, ys, ylens
"""
losses_dict = {}
self.msg = "Rank: {}, ".format(dist.get_rank())
# forward
batch_size = batch[1].shape[0]
loss, attention_loss, ctc_loss = self.model(*batch[1:])
# loss div by `batch_size * accum_grad`
loss /= self.accum_grad
# loss backward
if (self.forward_count + 1) != self.accum_grad:
# Disable gradient synchronizations across DDP processes.
# Within this context, gradients will be accumulated on module
# variables, which will later be synchronized.
context = self.model.no_sync
else:
# Used for single gpu training and DDP gradient synchronization
# processes.
context = nullcontext
with context():
loss.backward()
layer_tools.print_grads(self.model, print_func=None)
# loss info
losses_dict['loss'] = float(loss) * self.accum_grad
if attention_loss:
losses_dict['att_loss'] = float(attention_loss)
if ctc_loss:
losses_dict['ctc_loss'] = float(ctc_loss)
# report loss
for k, v in losses_dict.items():
report("train/" + k, v)
# loss msg
self.msg += "batch size: {}, ".format(batch_size)
self.msg += "accum: {}, ".format(self.accum_grad)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
# Truncate the graph
loss.detach()
# update parameters
self.forward_count += 1
if self.forward_count != self.accum_grad:
return
self.forward_count = 0
self.optimizer.step()
self.optimizer.clear_grad()
self.scheduler.step()
def update(self):
# model is default in train mode
# training for a step is implemented here
with Timer("data time cost:{}"):
batch = self.read_batch()
with Timer("step time cost:{}"):
self.update_core(batch)
# #iterations with accum_grad > 1
# Ref.: https://github.com/espnet/espnet/issues/777
if self.forward_count == 0:
self.state.iteration += 1
if self.updates_per_epoch is not None:
if self.state.iteration % self.updates_per_epoch == 0:
self.state.epoch += 1