350 lines
11 KiB
Python
350 lines
11 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 pytest
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import torch
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from torch.utils.data import DataLoader
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from torch.utils.data._utils.collate import default_collate
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from pytorch_lightning import Trainer
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.loops.optimization.optimizer_loop import Closure
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from pytorch_lightning.trainer.states import RunningStage
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from tests.helpers.boring_model import BoringModel, RandomDataset
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from tests.helpers.deterministic_model import DeterministicModel
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from tests.helpers.utils import no_warning_call
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def test__training_step__flow_scalar(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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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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max_epochs=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert not model.training_step_end_called
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assert not model.training_epoch_end_called
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def test__training_step__tr_step_end__flow_scalar(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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self.out = acc
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return acc
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def training_step_end(self, tr_step_output):
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assert self.out == tr_step_output
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assert self.count_num_graphs({"loss": tr_step_output}) == 1
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self.training_step_end_called = True
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return tr_step_output
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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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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max_epochs=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert model.training_step_end_called
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assert not model.training_epoch_end_called
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def test__training_step__epoch_end__flow_scalar(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def training_epoch_end(self, outputs):
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self.training_epoch_end_called = True
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# verify we saw the current num of batches
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assert len(outputs) == 2
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for b in outputs:
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# time = 1
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assert len(b) == 1
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assert "loss" in b
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assert isinstance(b, dict)
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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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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max_epochs=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert not model.training_step_end_called
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assert model.training_epoch_end_called
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# assert epoch end metrics were added
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assert len(trainer.callback_metrics) == 0
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assert len(trainer.progress_bar_metrics) == 0
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trainer.state.stage = RunningStage.TRAINING
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# make sure training outputs what is expected
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batch_idx, batch = 0, next(iter(model.train_dataloader()))
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train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
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assert len(train_step_out) == 1
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train_step_out = train_step_out[0][0]
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assert isinstance(train_step_out["loss"], torch.Tensor)
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assert train_step_out["loss"].item() == 171
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# make sure the optimizer closure returns the correct things
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opt_closure = trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop._make_closure(
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batch, batch_idx, 0, trainer.optimizers[0]
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)
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opt_closure_result = opt_closure()
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assert opt_closure_result.item() == 171
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def test__training_step__step_end__epoch_end__flow_scalar(tmpdir):
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"""Checks train_step + training_step_end + training_epoch_end (all with scalar return from train_step)."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def training_step_end(self, tr_step_output):
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assert isinstance(tr_step_output, torch.Tensor)
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assert self.count_num_graphs({"loss": tr_step_output}) == 1
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self.training_step_end_called = True
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return tr_step_output
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def training_epoch_end(self, outputs):
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self.training_epoch_end_called = True
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# verify we saw the current num of batches
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assert len(outputs) == 2
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for b in outputs:
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# time = 1
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assert len(b) == 1
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assert "loss" in b
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assert isinstance(b, dict)
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.val_dataloader = None
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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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max_epochs=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.training_step_called
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assert model.training_step_end_called
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assert model.training_epoch_end_called
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# assert epoch end metrics were added
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assert len(trainer.callback_metrics) == 0
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assert len(trainer.progress_bar_metrics) == 0
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trainer.state.stage = RunningStage.TRAINING
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# make sure training outputs what is expected
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batch_idx, batch = 0, next(iter(model.train_dataloader()))
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train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
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assert len(train_step_out) == 1
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train_step_out = train_step_out[0][0]
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assert isinstance(train_step_out["loss"], torch.Tensor)
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assert train_step_out["loss"].item() == 171
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# make sure the optimizer closure returns the correct things
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opt_closure = trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop._make_closure(
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batch, batch_idx, 0, trainer.optimizers[0]
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)
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opt_closure_result = opt_closure()
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assert opt_closure_result.item() == 171
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def test_train_step_no_return(tmpdir):
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"""Tests that only training_step raises a warning when nothing is returned in case of
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automatic_optimization."""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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self.training_step_called = True
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loss = self.step(batch[0])
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self.log("a", loss, on_step=True, on_epoch=True)
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def training_epoch_end(self, outputs) -> None:
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assert len(outputs) == 0, outputs
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def validation_step(self, batch, batch_idx):
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self.validation_step_called = True
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def validation_epoch_end(self, outputs):
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assert len(outputs) == 0, outputs
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model = TestModel()
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trainer_args = dict(default_root_dir=tmpdir, fast_dev_run=2)
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trainer = Trainer(**trainer_args)
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Closure.warning_cache.clear()
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with pytest.warns(UserWarning, match=r"training_step` returned `None"):
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trainer.fit(model)
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assert model.training_step_called
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assert model.validation_step_called
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model = TestModel()
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model.automatic_optimization = False
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trainer = Trainer(**trainer_args)
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Closure.warning_cache.clear()
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with no_warning_call(UserWarning, match=r"training_step` returned `None"):
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trainer.fit(model)
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def test_training_step_no_return_when_even(tmpdir):
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"""Tests correctness when some training steps have been skipped."""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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self.training_step_called = True
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loss = self.step(batch[0])
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self.log("a", loss, on_step=True, on_epoch=True)
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return loss if batch_idx % 2 else None
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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=4,
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limit_val_batches=1,
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max_epochs=4,
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enable_model_summary=False,
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logger=False,
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enable_checkpointing=False,
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)
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Closure.warning_cache.clear()
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with pytest.warns(UserWarning, match=r".*training_step` returned `None.*"):
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trainer.fit(model)
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trainer.state.stage = RunningStage.TRAINING
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# manually check a few batches
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for batch_idx, batch in enumerate(model.train_dataloader()):
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out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
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if not batch_idx % 2:
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assert out == []
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def test_training_step_none_batches(tmpdir):
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"""Tests correctness when the train dataloader gives None for some steps."""
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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.counter = 0
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def collate_none_when_even(self, batch):
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if self.counter % 2 == 0:
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result = None
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else:
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result = default_collate(batch)
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self.counter += 1
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return result
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 4), collate_fn=self.collate_none_when_even)
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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if batch_idx % 2 == 0:
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assert outputs == []
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else:
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assert outputs
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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_val_batches=1,
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max_epochs=4,
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enable_model_summary=False,
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logger=False,
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enable_checkpointing=False,
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)
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with pytest.warns(UserWarning, match=r".*train_dataloader yielded None.*"):
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trainer.fit(model)
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