160 lines
6.5 KiB
Python
160 lines
6.5 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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import os
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import platform
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from unittest import mock
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import pytest
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_6
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel, RandomDataset
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if _TORCH_GREATER_EQUAL_1_6:
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from pytorch_lightning.callbacks import StochasticWeightAveraging
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class SwaTestModel(BoringModel):
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def __init__(self, batchnorm: bool = True):
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super().__init__()
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layers = [nn.Linear(32, 32)]
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if batchnorm:
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layers.append(nn.BatchNorm1d(32))
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layers += [nn.ReLU(), nn.Linear(32, 2)]
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self.layer = nn.Sequential(*layers)
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def training_step(self, batch, batch_idx):
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output = self.forward(batch)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=2)
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class SwaTestCallback(StochasticWeightAveraging):
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update_parameters_calls: int = 0
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transfer_weights_calls: int = 0
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def update_parameters(self, *args, **kwargs):
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self.update_parameters_calls += 1
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return StochasticWeightAveraging.update_parameters(*args, **kwargs)
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def transfer_weights(self, *args, **kwargs):
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self.transfer_weights_calls += 1
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return StochasticWeightAveraging.transfer_weights(*args, **kwargs)
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def on_train_epoch_start(self, trainer, *args):
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super().on_train_epoch_start(trainer, *args)
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assert trainer.train_loop._skip_backward == (trainer.current_epoch > self.swa_end)
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def on_train_epoch_end(self, trainer, *args):
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super().on_train_epoch_end(trainer, *args)
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if self.swa_start <= trainer.current_epoch <= self.swa_end:
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swa_epoch = trainer.current_epoch - self.swa_start
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assert self.n_averaged == swa_epoch + 1
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elif trainer.current_epoch > self.swa_end:
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assert self.n_averaged == self._max_epochs - self.swa_start
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def on_train_end(self, trainer, pl_module):
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super().on_train_end(trainer, pl_module)
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# make sure these are correctly set again
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assert not trainer.train_loop._skip_backward
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assert trainer.accumulate_grad_batches == 2
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assert trainer.num_training_batches == 5
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# check backward call count. the batchnorm update epoch should not backward
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assert trainer.dev_debugger.count_events(
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"backward_call"
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) == trainer.max_epochs * trainer.limit_train_batches
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# check call counts
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assert self.update_parameters_calls == trainer.max_epochs - (self._swa_epoch_start - 1)
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assert self.transfer_weights_calls == 1
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@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
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def train_with_swa(tmpdir, batchnorm=True, accelerator=None, gpus=None, num_processes=1):
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model = SwaTestModel(batchnorm=batchnorm)
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swa_start = 2
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max_epochs = 5
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swa_callback = SwaTestCallback(swa_epoch_start=swa_start, swa_lrs=0.1)
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assert swa_callback.update_parameters_calls == 0
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assert swa_callback.transfer_weights_calls == 0
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=max_epochs,
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limit_train_batches=5,
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limit_val_batches=0,
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callbacks=[swa_callback],
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accumulate_grad_batches=2,
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accelerator=accelerator,
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gpus=gpus,
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num_processes=num_processes
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)
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trainer.fit(model)
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# check the model is the expected
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assert trainer.get_model() == model
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@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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@pytest.mark.skipif(
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not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
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)
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def test_swa_callback_ddp(tmpdir):
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train_with_swa(tmpdir, accelerator="ddp", gpus=2)
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@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_swa_callback_ddp_spawn(tmpdir):
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train_with_swa(tmpdir, accelerator="ddp_spawn", gpus=2)
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@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
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@pytest.mark.skipif(platform.system() == "Windows", reason="ddp_cpu is not available on Windows")
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def test_swa_callback_ddp_cpu(tmpdir):
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train_with_swa(tmpdir, accelerator="ddp_cpu", num_processes=2)
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@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires a GPU machine")
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def test_swa_callback_1_gpu(tmpdir):
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train_with_swa(tmpdir, gpus=1)
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@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
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@pytest.mark.parametrize("batchnorm", (True, False))
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def test_swa_callback(tmpdir, batchnorm):
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train_with_swa(tmpdir, batchnorm=batchnorm)
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@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_6, reason="SWA available from PyTorch 1.6.0")
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def test_swa_raises():
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with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
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StochasticWeightAveraging(swa_epoch_start=0, swa_lrs=0.1)
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with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
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StochasticWeightAveraging(swa_epoch_start=1.5, swa_lrs=0.1)
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with pytest.raises(MisconfigurationException, match=">0 integer or a float between 0 and 1"):
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StochasticWeightAveraging(swa_epoch_start=-1, swa_lrs=0.1)
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with pytest.raises(MisconfigurationException, match="positive float or a list of positive float"):
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StochasticWeightAveraging(swa_epoch_start=5, swa_lrs=[0.2, 1])
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