80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
# Copyright The PyTorch Lightning team.
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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 Union
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import torch
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from torch.optim import Optimizer
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from pytorch_lightning.plugins.precision_plugin import PrecisionPlugin
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class NativeAMPPlugin(PrecisionPlugin):
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def __init__(self, trainer=None):
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"""
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Integrates native amp into Lightning's internals.
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"""
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self.trainer = trainer
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def connect(self, model, optimizers):
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return model, optimizers
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def training_step(self, fx, args):
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with torch.cuda.amp.autocast():
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output = fx(*args)
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return output
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def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
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closure_loss = self.trainer.scaler.scale(closure_loss)
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automatic_optimization = self.trainer.train_loop.automatic_optimization
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# do backward pass
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if automatic_optimization:
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model = self.trainer.get_model()
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model.backward(closure_loss, optimizer, opt_idx)
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else:
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closure_loss.backward(*args, **kwargs)
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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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# unscale gradient to allow analyze within `on_after_backward`
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if not self.trainer.train_loop.should_accumulate() and automatic_optimization:
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self.trainer.scaler.unscale_(optimizer)
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return closure_loss
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def clip_gradients(self, grad_clip_val: Union[int, float], optimizer: Optimizer, norm_type: float):
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model = self.trainer.get_model()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=grad_clip_val, norm_type=norm_type)
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@property
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def scaler(self):
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return torch.cuda.amp.GradScaler()
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def optimizer_step(self, trainer, optimizer, closure):
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# native amp does not yet support closures.
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# TODO: pass the closure to the step ASAP
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with trainer.profiler.profile("closure"):
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closure()
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if not self.trainer.train_loop.automatic_optimization:
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trainer.scaler.unscale_(optimizer)
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trainer.call_hook("on_after_backward")
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with trainer.profiler.profile("optimizer_step"):
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trainer.scaler.step(optimizer)
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trainer.scaler.update()
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