67 lines
2.2 KiB
Python
67 lines
2.2 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 abc import ABC, abstractmethod
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import torch
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from torch import Tensor
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from pytorch_lightning import _logger as log
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from pytorch_lightning.callbacks import GradientAccumulationScheduler
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from pytorch_lightning.core.lightning import LightningModule
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try:
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from apex import amp
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except ImportError:
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amp = None
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EPSILON = 1e-6
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EPSILON_FP16 = 1e-5
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class TrainerTrainingTricksMixin(ABC):
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# this is just a summary on variables used in this abstract class,
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# the proper values/initialisation should be done in child class
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default_root_dir: str
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progress_bar_callback: ...
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on_gpu: bool
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@abstractmethod
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def get_model(self) -> LightningModule:
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"""Warning: this is just empty shell for code implemented in other class."""
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def print_nan_gradients(self) -> None:
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model = self.get_model()
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for param in model.parameters():
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if (param.grad is not None) and torch.isnan(param.grad.float()).any():
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log.info(param, param.grad)
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def detect_nan_tensors(self, loss: Tensor) -> None:
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model = self.get_model()
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# check if loss is nan
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if not torch.isfinite(loss).all():
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raise ValueError(
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'The loss returned in `training_step` is nan or inf.'
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)
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# check if a network weight is nan
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for name, param in model.named_parameters():
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if not torch.isfinite(param).all():
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self.print_nan_gradients()
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raise ValueError(
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f'Detected nan and/or inf values in `{name}`.'
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' Check your forward pass for numerically unstable operations.'
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)
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