263 lines
8.0 KiB
Python
263 lines
8.0 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 collections
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from copy import deepcopy
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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 pytorch_lightning import Trainer
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from pytorch_lightning.accelerators.cpu import CPUAccelerator
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from pytorch_lightning.accelerators.tpu import TPUAccelerator
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.plugins import TPUSpawnPlugin
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.runif import RunIf
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from tests.helpers.utils import pl_multi_process_test
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class WeightSharingModule(BoringModel):
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def __init__(self):
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super().__init__()
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self.layer_1 = nn.Linear(32, 10, bias=False)
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self.layer_2 = nn.Linear(10, 32, bias=False)
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self.layer_3 = nn.Linear(32, 10, bias=False)
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self.layer_3.weight = self.layer_1.weight
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def forward(self, x):
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x = self.layer_1(x)
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x = self.layer_2(x)
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x = self.layer_3(x)
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return x
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@RunIf(tpu=True)
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@pl_multi_process_test
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def test_resume_training_on_cpu(tmpdir):
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"""Checks if training can be resumed from a saved checkpoint on CPU"""
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# Train a model on TPU
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model = BoringModel()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=8)
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trainer.fit(model)
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model_path = trainer.checkpoint_callback.best_model_path
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# Verify saved Tensors are on CPU
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ckpt = torch.load(model_path)
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weight_tensor = list(ckpt["state_dict"].values())[0]
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assert weight_tensor.device == torch.device("cpu")
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# Verify that training is resumed on CPU
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trainer = Trainer(
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resume_from_checkpoint=model_path, checkpoint_callback=True, max_epochs=1, default_root_dir=tmpdir
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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@RunIf(tpu=True)
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@pl_multi_process_test
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def test_if_test_works_after_train(tmpdir):
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"""Ensure that .test() works after .fit()"""
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# Train a model on TPU
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model = BoringModel()
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trainer = Trainer(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True)
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trainer.fit(model)
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assert len(trainer.test(model)) == 1
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@RunIf(tpu=True)
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@pl_multi_process_test
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def test_weight_tying_warning(tmpdir, capsys=None):
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"""
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Ensure a warning is thrown if model parameter lengths do not match
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post moving to device.
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"""
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model = WeightSharingModule()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
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with pytest.warns(UserWarning, match=r"The model layers do not match after moving to the target device."):
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trainer.fit(model)
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@RunIf(tpu=True)
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@pl_multi_process_test
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def test_if_weights_tied(tmpdir, capsys=None):
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"""
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Test if weights are properly tied on `on_post_move_to_device`.
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Ensure no warning for parameter mismatch is thrown.
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"""
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class Model(WeightSharingModule):
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def on_post_move_to_device(self):
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self.layer_3.weight = self.layer_1.weight
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model = Model()
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trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
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with pytest.warns(UserWarning, match="The model layers do not match"):
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trainer.fit(model)
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@RunIf(tpu=True)
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def test_accelerator_tpu():
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trainer = Trainer(accelerator="tpu", tpu_cores=8)
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assert trainer._device_type == "tpu"
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assert isinstance(trainer.accelerator, TPUAccelerator)
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with pytest.raises(
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MisconfigurationException, match="You passed `accelerator='tpu'`, but you didn't pass `tpu_cores` to `Trainer`"
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):
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trainer = Trainer(accelerator="tpu")
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@RunIf(tpu=True)
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def test_accelerator_cpu_with_tpu_cores_flag():
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trainer = Trainer(accelerator="cpu", tpu_cores=8)
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assert trainer._device_type == "cpu"
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assert isinstance(trainer.accelerator, CPUAccelerator)
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@RunIf(tpu=True)
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def test_accelerator_tpu_with_auto():
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trainer = Trainer(accelerator="auto", tpu_cores=8)
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assert trainer._device_type == "tpu"
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assert isinstance(trainer.accelerator, TPUAccelerator)
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@RunIf(tpu=True)
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def test_accelerator_tpu_with_devices():
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trainer = Trainer(accelerator="tpu", devices=8)
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assert trainer.tpu_cores == 8
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assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
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assert isinstance(trainer.accelerator, TPUAccelerator)
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@RunIf(tpu=True)
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def test_accelerator_auto_with_devices_tpu():
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trainer = Trainer(accelerator="auto", devices=8)
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assert trainer._device_type == "tpu"
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assert trainer.tpu_cores == 8
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@RunIf(tpu=True)
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def test_accelerator_tpu_with_tpu_cores_priority():
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"""Test for checking `tpu_cores` flag takes priority over `devices`."""
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tpu_cores = 8
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with pytest.warns(UserWarning, match="The flag `devices=1` will be ignored,"):
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trainer = Trainer(accelerator="tpu", devices=1, tpu_cores=tpu_cores)
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assert trainer.tpu_cores == tpu_cores
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@RunIf(tpu=True)
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def test_set_devices_if_none_tpu():
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trainer = Trainer(accelerator="tpu", tpu_cores=8)
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assert trainer.devices == 8
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@RunIf(tpu=True)
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def test_manual_optimization_tpus(tmpdir):
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class ManualOptimizationModel(BoringModel):
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count = 0
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called = collections.defaultdict(int)
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def __init__(self):
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super().__init__()
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self.automatic_optimization = False
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@property
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def should_update(self):
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return self.count % 2 == 0
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def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
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self.called["on_train_batch_start"] += 1
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self.weight_before = self.layer.weight.clone()
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def training_step(self, batch, batch_idx):
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self.called["training_step"] += 1
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opt = self.optimizers()
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output = self.layer(batch)
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loss = self.loss(batch, output)
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if self.should_update:
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self.manual_backward(loss)
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opt.step()
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opt.zero_grad()
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return loss
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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self.called["on_train_batch_end"] += 1
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after_before = self.layer.weight.clone()
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if self.should_update:
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assert not torch.equal(self.weight_before, after_before), self.count
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else:
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assert torch.equal(self.weight_before, after_before)
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assert torch.all(self.layer.weight.grad == 0)
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self.count += 1
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def on_train_end(self):
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assert self.called["training_step"] == 5
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assert self.called["on_train_batch_start"] == 5
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assert self.called["on_train_batch_end"] == 5
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class TestManualOptimizationCallack(Callback):
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def on_train_end(self, trainer, pl_module):
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opt = pl_module.optimizers()
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assert opt._total_optimizer_step_calls == 3
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model = ManualOptimizationModel()
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model_copy = deepcopy(model)
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model.training_step_end = None
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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=5,
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limit_test_batches=0,
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limit_val_batches=0,
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tpu_cores=8,
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callbacks=[TestManualOptimizationCallack()],
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)
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trainer.fit(model)
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for param, param_copy in zip(model.parameters(), model_copy.parameters()):
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assert not torch.equal(param.cpu().data, param_copy.data)
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@RunIf(tpu=True)
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def test_ddp_cpu_not_supported_on_tpus():
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with pytest.raises(MisconfigurationException, match="`accelerator='ddp_cpu'` is not supported on TPU machines"):
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Trainer(accelerator="ddp_cpu")
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