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84 lines
3.2 KiB
84 lines
3.2 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 dataclasses import dataclass
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import paddle
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from deepspeech.utils.log import Log
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__all__ = ["UpdaterBase", "UpdaterState"]
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logger = Log(__name__).getlog()
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@dataclass
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class UpdaterState:
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iteration: int = 0
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epoch: int = 0
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class UpdaterBase():
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"""An updater is the abstraction of how a model is trained given the
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dataloader and the optimizer.
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The `update_core` method is a step in the training loop with only necessary
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operations (get a batch, forward and backward, update the parameters).
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Other stuffs are made extensions. Visualization, saving, loading and
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periodical validation and evaluation are not considered here.
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But even in such simplist case, things are not that simple. There is an
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attempt to standardize this process and requires only the model and
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dataset and do all the stuffs automatically. But this may hurt flexibility.
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If we assume a batch yield from the dataloader is just the input to the
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model, we will find that some model requires more arguments, or just some
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keyword arguments. But this prevents us from over-simplifying it.
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From another perspective, the batch may includes not just the input, but
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also the target. But the model's forward method may just need the input.
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We can pass a dict or a super-long tuple to the model and let it pick what
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it really needs. But this is an abuse of lazy interface.
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After all, we care about how a model is trained. But just how the model is
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used for inference. We want to control how a model is trained. We just
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don't want to be messed up with other auxiliary code.
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So the best practice is to define a model and define a updater for it.
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"""
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def __init__(self, init_state=None):
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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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def update(self, batch):
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raise NotImplementedError(
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"Implement your own `update` method for training a step.")
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def state_dict(self):
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state_dict = {
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"epoch": self.state.epoch,
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"iteration": self.state.iteration,
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}
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return state_dict
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def set_state_dict(self, state_dict):
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self.state.epoch = state_dict["epoch"]
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self.state.iteration = state_dict["iteration"]
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def save(self, path):
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logger.debug(f"Saving to {path}.")
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archive = self.state_dict()
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paddle.save(archive, str(path))
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def load(self, path):
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logger.debug(f"Loading from {path}.")
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archive = paddle.load(str(path))
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self.set_state_dict(archive)
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