423 lines
14 KiB
Python
423 lines
14 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 unittest.mock import Mock, patch
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import pytest
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from torch import nn
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from torch.optim import Adam, SGD
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from pytorch_lightning import Trainer
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from pytorch_lightning.loggers import TensorBoardLogger
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel
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def test_property_current_epoch():
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""" Test that the current_epoch in LightningModule is accessible via the Trainer. """
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model = BoringModel()
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assert model.current_epoch == 0
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trainer = Mock(current_epoch=123)
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model.trainer = trainer
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assert model.current_epoch == 123
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def test_property_global_step():
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""" Test that the global_step in LightningModule is accessible via the Trainer. """
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model = BoringModel()
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assert model.global_step == 0
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trainer = Mock(global_step=123)
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model.trainer = trainer
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assert model.global_step == 123
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def test_property_global_rank():
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""" Test that the global rank in LightningModule is accessible via the Trainer. """
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model = BoringModel()
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assert model.global_rank == 0
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trainer = Mock(global_rank=123)
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model.trainer = trainer
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assert model.global_rank == 123
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def test_property_local_rank():
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""" Test that the local rank in LightningModule is accessible via the Trainer. """
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model = BoringModel()
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assert model.local_rank == 0
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trainer = Mock(local_rank=123)
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model.trainer = trainer
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assert model.local_rank == 123
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def test_property_logger(tmpdir):
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""" Test that the logger in LightningModule is accessible via the Trainer. """
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model = BoringModel()
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assert model.logger is None
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logger = TensorBoardLogger(tmpdir)
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trainer = Mock(logger=logger)
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model.trainer = trainer
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assert model.logger == logger
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def test_automatic_optimization(tmpdir):
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class TestModel(BoringModel):
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def optimizer_step(self, *_, **__):
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pass
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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accumulate_grad_batches=2,
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)
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with pytest.raises(
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MisconfigurationException, match='overriding .* optimizer_step .* `accumulate_grad_batches` .* should be 1'
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):
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trainer.fit(model)
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def test_automatic_optimization_num_calls(tmpdir):
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with patch("torch.optim.SGD.step") as sgd_step, \
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patch("torch.optim.SGD.zero_grad") as sgd_zero_grad, \
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patch("torch.optim.Adam.step") as adam_step, \
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patch("torch.optim.Adam.zero_grad") as adam_zero_grad:
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx, optimizer_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def configure_optimizers(self):
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optimizer = SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = Adam(self.layer.parameters(), lr=0.1)
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return [optimizer, optimizer_2]
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def optimizer_step(
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self,
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epoch,
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batch_idx,
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optimizer,
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optimizer_idx,
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optimizer_closure,
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on_tpu,
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using_native_amp,
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using_lbfgs,
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):
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assert optimizer_closure.__name__ == "train_step_and_backward_closure"
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# update generator opt every 2 steps
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if optimizer_idx == 0:
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if batch_idx % 2 == 0:
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assert isinstance(optimizer, SGD)
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optimizer.step(closure=optimizer_closure)
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# update discriminator opt every 4 steps
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if optimizer_idx == 1:
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if batch_idx % 4 == 0:
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assert isinstance(optimizer, Adam)
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optimizer.step(closure=optimizer_closure)
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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max_epochs=1,
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default_root_dir=tmpdir,
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limit_train_batches=8,
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limit_val_batches=1,
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accumulate_grad_batches=1,
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)
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trainer.fit(model)
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assert sgd_step.call_count == 4
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assert sgd_zero_grad.call_count == 4
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assert adam_step.call_count == 2
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assert adam_zero_grad.call_count == 2
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def test_params_groups_and_state_are_accessible(tmpdir):
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx, optimizer_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def configure_optimizers(self):
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optimizer = SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = Adam(self.layer.parameters(), lr=0.1)
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return [optimizer, optimizer_2]
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def optimizer_step(
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self,
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epoch,
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batch_idx,
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optimizer,
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optimizer_idx,
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optimizer_closure,
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on_tpu=False,
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using_native_amp=False,
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using_lbfgs=False
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):
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# warm up lr
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if self.trainer.global_step < 500:
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lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
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for pg in optimizer.param_groups:
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pg['lr'] = lr_scale * 0.01
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optimizer.step(closure=optimizer_closure)
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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max_epochs=1,
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default_root_dir=tmpdir,
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limit_train_batches=8,
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limit_val_batches=1,
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accumulate_grad_batches=1,
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)
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trainer.fit(model)
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def test_toggle_untoggle_2_optimizers_no_shared_parameters(tmpdir):
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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return super().training_step(batch, batch_idx)
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def __init__(self):
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super().__init__()
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self.layer_1 = nn.Sequential(
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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)
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self.layer_2 = nn.Sequential(
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 2),
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)
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# set some weights to False to check untoggle works as expected.
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self.layer_1[2].weight.requires_grad = False
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self.layer_1[4].weight.requires_grad = False
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self.layer_2[1].weight.requires_grad = False
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self.layer_2[3].weight.requires_grad = False
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def configure_optimizers(self):
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optimizer = SGD(self.layer_1.parameters(), lr=0.1)
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optimizer_2 = Adam(self.layer_2.parameters(), lr=0.1)
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return [optimizer, optimizer_2]
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def optimizer_step(
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self,
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current_epoch,
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batch_nb,
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optimizer,
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optimizer_idx,
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closure,
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on_tpu=False,
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using_native_amp=False,
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using_lbfgs=False
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):
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if optimizer_idx == 0:
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assert self.layer_1[0].weight.requires_grad is True
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assert self.layer_1[2].weight.requires_grad is False
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assert self.layer_1[4].weight.requires_grad is False
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assert self.layer_2[1].weight.requires_grad is False
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assert self.layer_2[3].weight.requires_grad is False
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assert self.layer_2[5].weight.requires_grad is False
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if optimizer_idx == 1:
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assert self.layer_1[0].weight.requires_grad is False
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assert self.layer_1[2].weight.requires_grad is False
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assert self.layer_1[4].weight.requires_grad is False
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assert self.layer_2[1].weight.requires_grad is False
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assert self.layer_2[3].weight.requires_grad is False
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assert self.layer_2[5].weight.requires_grad is True
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optimizer.step(closure=closure)
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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max_epochs=1,
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default_root_dir=tmpdir,
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limit_train_batches=8,
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accumulate_grad_batches=1,
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limit_val_batches=0,
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)
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results = trainer.fit(model)
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assert results
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def test_toggle_untoggle_3_optimizers_shared_parameters(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.layer_1 = nn.Sequential(
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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)
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self.layer_2 = nn.Sequential(
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 2),
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)
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self.layer_3 = nn.Sequential(
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 32),
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nn.ReLU(),
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nn.Linear(32, 2),
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)
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# set some weights to False to check untoggle works as expected.
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self.layer_1[2].weight.requires_grad = False
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self.layer_1[4].weight.requires_grad = False
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self.layer_2[1].weight.requires_grad = False
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self.layer_2[3].weight.requires_grad = False
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self.layer_3[1].weight.requires_grad = False
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self.layer_3[5].weight.requires_grad = False
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def optimizer_step(
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self,
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current_epoch,
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batch_nb,
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optimizer,
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optimizer_idx,
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closure,
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on_tpu=False,
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using_native_amp=False,
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using_lbfgs=False
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):
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if optimizer_idx == 0:
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assert self.layer_1[0].weight.requires_grad is True
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assert self.layer_1[2].weight.requires_grad is False
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assert self.layer_1[4].weight.requires_grad is False
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assert self.layer_2[1].weight.requires_grad is False
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assert self.layer_2[3].weight.requires_grad is False
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assert self.layer_2[5].weight.requires_grad is True
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assert self.layer_3[1].weight.requires_grad is False
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assert self.layer_3[3].weight.requires_grad is False
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assert self.layer_3[5].weight.requires_grad is False
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if optimizer_idx == 1:
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assert self.layer_1[0].weight.requires_grad is False
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assert self.layer_1[2].weight.requires_grad is False
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assert self.layer_1[4].weight.requires_grad is False
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assert self.layer_2[1].weight.requires_grad is False
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assert self.layer_2[3].weight.requires_grad is False
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assert self.layer_2[5].weight.requires_grad is True
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assert self.layer_3[1].weight.requires_grad is False
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assert self.layer_3[3].weight.requires_grad is True
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assert self.layer_3[5].weight.requires_grad is False
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if optimizer_idx == 2:
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assert self.layer_1[0].weight.requires_grad is True
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assert self.layer_1[2].weight.requires_grad is False
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assert self.layer_1[4].weight.requires_grad is False
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assert self.layer_2[1].weight.requires_grad is False
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assert self.layer_2[3].weight.requires_grad is False
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assert self.layer_2[5].weight.requires_grad is False
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assert self.layer_3[1].weight.requires_grad is False
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assert self.layer_3[3].weight.requires_grad is True
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assert self.layer_3[5].weight.requires_grad is False
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optimizer.step(closure=closure)
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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return super().training_step(batch, batch_idx)
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@staticmethod
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def combine_generators(gen_1, gen_2):
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for p in gen_1:
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yield p
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for p in gen_2:
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yield p
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def configure_optimizers(self):
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optimizer_1 = SGD(self.combine_generators(
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self.layer_1.parameters(),
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self.layer_2.parameters(),
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), lr=0.1)
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optimizer_2 = Adam(self.combine_generators(
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self.layer_2.parameters(),
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self.layer_3.parameters(),
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), lr=0.1)
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optimizer_3 = SGD(self.combine_generators(
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self.layer_3.parameters(),
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self.layer_1.parameters(),
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), lr=0.1)
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return [optimizer_1, optimizer_2, optimizer_3]
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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max_epochs=1,
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default_root_dir=tmpdir,
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limit_train_batches=8,
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accumulate_grad_batches=1,
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)
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trainer.fit(model)
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