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