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193 lines
7.6 KiB
193 lines
7.6 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 typing import Dict
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from typing import Optional
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from paddle import Tensor
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from paddle.io import DataLoader
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from paddle.io import DistributedBatchSampler
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from paddle.nn import Layer
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from paddle.optimizer import Optimizer
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from timer import timer
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from deepspeech.training.reporter import report
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from deepspeech.training.updaters.updater import UpdaterBase
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from deepspeech.training.updaters.updater import UpdaterState
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from deepspeech.utils.log import Log
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__all__ = ["StandardUpdater"]
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logger = Log(__name__).getlog()
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class StandardUpdater(UpdaterBase):
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"""An example of over-simplification. Things may not be that simple, but
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you can subclass it to fit your need.
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"""
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def __init__(self,
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model: Layer,
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optimizer: Optimizer,
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dataloader: DataLoader,
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init_state: Optional[UpdaterState]=None):
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# it is designed to hold multiple models
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models = {"main": model}
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self.models: Dict[str, Layer] = models
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self.model = model
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# it is designed to hold multiple optimizers
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optimizers = {"main": optimizer}
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self.optimizer = optimizer
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self.optimizers: Dict[str, Optimizer] = optimizers
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# dataloaders
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self.dataloader = dataloader
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# init state
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if init_state is None:
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self.state = UpdaterState()
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else:
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self.state = init_state
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self.train_iterator = iter(dataloader)
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def update(self):
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# We increase the iteration index after updating and before extension.
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# Here are the reasons.
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# 0. Snapshotting(as well as other extensions, like visualizer) is
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# executed after a step of updating;
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# 1. We decide to increase the iteration index after updating and
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# before any all extension is executed.
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# 3. We do not increase the iteration after extension because we
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# prefer a consistent resume behavior, when load from a
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# `snapshot_iter_100.pdz` then the next step to train is `101`,
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# naturally. But if iteration is increased increased after
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# extension(including snapshot), then, a `snapshot_iter_99` is
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# loaded. You would need a extra increasing of the iteration idex
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# before training to avoid another iteration `99`, which has been
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# done before snapshotting.
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# 4. Thus iteration index represrnts "currently how mant epochs has
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# been done."
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# NOTE: use report to capture the correctly value. If you want to
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# report the learning rate used for a step, you must report it before
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# the learning rate scheduler's step() has been called. In paddle's
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# convention, we do not use an extension to change the learning rate.
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# so if you want to report it, do it in the updater.
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# Then here comes the next question. When is the proper time to
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# increase the epoch index? Since all extensions are executed after
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# updating, it is the time that after updating is the proper time to
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# increase epoch index.
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# 1. If we increase the epoch index before updating, then an extension
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# based ot epoch would miss the correct timing. It could only be
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# triggerd after an extra updating.
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# 2. Theoretically, when an epoch is done, the epoch index should be
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# increased. So it would be increase after updating.
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# 3. Thus, eppoch index represents "currently how many epochs has been
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# done." So it starts from 0.
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# switch to training mode
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for model in self.models.values():
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model.train()
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# training for a step is implemented here
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batch = self.read_batch()
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self.update_core(batch)
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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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def update_core(self, batch):
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"""A simple case for a training step. Basic assumptions are:
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Single model;
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Single optimizer;
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A batch from the dataloader is just the input of the model;
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The model return a single loss, or a dict containing serval losses.
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Parameters updates at every batch, no gradient accumulation.
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"""
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loss = self.model(*batch)
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if isinstance(loss, Tensor):
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loss_dict = {"main": loss}
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else:
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# Dict[str, Tensor]
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loss_dict = loss
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if "main" not in loss_dict:
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main_loss = 0
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for loss_item in loss.values():
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main_loss += loss_item
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loss_dict["main"] = main_loss
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for name, loss_item in loss_dict.items():
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report(name, float(loss_item))
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self.optimizer.clear_gradient()
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loss_dict["main"].backward()
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self.optimizer.update()
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@property
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def updates_per_epoch(self):
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"""Number of updater per epoch, determined by the length of the
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dataloader."""
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length_of_dataloader = None
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try:
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length_of_dataloader = len(self.dataloader)
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except TypeError:
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logger.debug("This dataloader has no __len__.")
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finally:
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return length_of_dataloader
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def new_epoch(self):
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"""Start a new epoch."""
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# NOTE: all batch sampler for distributed training should
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# subclass DistributedBatchSampler and implement `set_epoch` method
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if hasattr(self.dataloader, "batch_sampler"):
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batch_sampler = self.dataloader.batch_sampler
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if isinstance(batch_sampler, DistributedBatchSampler):
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batch_sampler.set_epoch(self.state.epoch)
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self.train_iterator = iter(self.dataloader)
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def read_batch(self):
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"""Read a batch from the data loader, auto renew when data is exhausted."""
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with timer() as t:
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try:
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batch = next(self.train_iterator)
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except StopIteration:
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self.new_epoch()
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batch = next(self.train_iterator)
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logger.debug(
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f"Read a batch takes {t.elapse}s.") # replace it with logger
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return batch
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def state_dict(self):
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"""State dict of a Updater, model, optimizer and updater state are included."""
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state_dict = super().state_dict()
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for name, model in self.models.items():
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state_dict[f"{name}_params"] = model.state_dict()
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for name, optim in self.optimizers.items():
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state_dict[f"{name}_optimizer"] = optim.state_dict()
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return state_dict
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def set_state_dict(self, state_dict):
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"""Set state dict for a Updater. Parameters of models, states for
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optimizers and UpdaterState are restored."""
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for name, model in self.models.items():
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model.set_state_dict(state_dict[f"{name}_params"])
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for name, optim in self.optimizers.items():
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optim.set_state_dict(state_dict[f"{name}_optimizer"])
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super().set_state_dict(state_dict)
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