332 lines
11 KiB
Python
332 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 collections
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from copy import deepcopy
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from unittest.mock import patch
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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.accelerators.cpu import CPUAccelerator
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from pytorch_lightning.accelerators.tpu import TPUAccelerator
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from pytorch_lightning.plugins import PrecisionPlugin, TPUPrecisionPlugin, XLACheckpointIO
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from pytorch_lightning.strategies import DDPStrategy, TPUSpawnStrategy
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from pytorch_lightning.utilities import find_shared_parameters
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel, RandomDataset
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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(max_epochs=1, accelerator="tpu", devices=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(max_epochs=1, default_root_dir=tmpdir)
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trainer.fit(model, ckpt_path=model_path)
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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, accelerator="tpu", devices=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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def test_accelerator_cpu_with_tpu_cores_flag():
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assert TPUAccelerator.is_available()
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trainer = Trainer(accelerator="cpu", devices=8)
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assert isinstance(trainer.accelerator, CPUAccelerator)
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trainer = Trainer(accelerator="tpu", devices=8)
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assert isinstance(trainer.accelerator, TPUAccelerator)
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assert isinstance(trainer.strategy, TPUSpawnStrategy)
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@RunIf(tpu=True)
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@pytest.mark.parametrize(["accelerator", "devices"], [("auto", 8), ("auto", "auto"), ("tpu", None)])
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def test_accelerator_tpu(accelerator, devices):
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assert TPUAccelerator.is_available()
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trainer = Trainer(accelerator=accelerator, devices=devices)
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assert isinstance(trainer.accelerator, TPUAccelerator)
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assert isinstance(trainer.strategy, TPUSpawnStrategy)
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assert trainer.num_devices == 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 isinstance(trainer.accelerator, TPUAccelerator)
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assert trainer.num_devices == 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 isinstance(trainer.accelerator, TPUAccelerator)
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assert trainer.num_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):
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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):
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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_start(self):
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opt = self.optimizers()
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self.opt_step_patch = patch.object(opt, "step", wraps=opt.step)
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self.opt_step_mock = self.opt_step_patch.start()
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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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self.opt_step_patch.stop()
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assert self.opt_step_mock.call_count == 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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accelerator="tpu",
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devices=8,
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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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@RunIf(tpu=True)
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def test_strategy_choice_tpu_str_ddp_spawn(tmpdir):
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with pytest.raises(ValueError, match="TPUAccelerator` can only be used with a `SingleTPUStrategy`"):
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Trainer(strategy="ddp_spawn", accelerator="tpu", devices=8)
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@RunIf(tpu=True)
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def test_strategy_choice_tpu_str_tpu_spawn_debug(tmpdir):
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trainer = Trainer(strategy="tpu_spawn_debug", accelerator="tpu", devices=8)
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assert isinstance(trainer.strategy, TPUSpawnStrategy)
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@RunIf(tpu=True)
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def test_strategy_choice_tpu_strategy(tmpdir):
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trainer = Trainer(strategy=TPUSpawnStrategy(), accelerator="tpu", devices=8)
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assert isinstance(trainer.strategy, TPUSpawnStrategy)
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@RunIf(tpu=True)
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def test_auto_parameters_tying_tpus(tmpdir):
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model = WeightSharingModule()
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shared_params = find_shared_parameters(model)
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assert shared_params[0] == ["layer_1.weight", "layer_3.weight"]
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trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, accelerator="tpu", devices=8, max_epochs=1)
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trainer.fit(model)
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assert torch.all(torch.eq(model.layer_1.weight, model.layer_3.weight))
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@RunIf(tpu=True)
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def test_auto_parameters_tying_tpus_nested_module(tmpdir):
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class SubModule(nn.Module):
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def __init__(self, layer):
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super().__init__()
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self.layer = layer
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def forward(self, x):
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return self.layer(x)
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class NestedModule(BoringModel):
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def __init__(self):
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super().__init__()
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self.layer = nn.Linear(32, 10, bias=False)
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self.net_a = SubModule(self.layer)
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self.layer_2 = nn.Linear(10, 32, bias=False)
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self.net_b = SubModule(self.layer)
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def forward(self, x):
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x = self.net_a(x)
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x = self.layer_2(x)
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x = self.net_b(x)
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return x
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model = NestedModule()
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trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, accelerator="tpu", devices=8, max_epochs=1)
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trainer.fit(model)
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assert torch.all(torch.eq(model.net_a.layer.weight, model.net_b.layer.weight))
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@RunIf(tpu=True)
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def test_tpu_invalid_raises():
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strategy = TPUSpawnStrategy(accelerator=TPUAccelerator(), precision_plugin=PrecisionPlugin())
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with pytest.raises(ValueError, match="TPUAccelerator` can only be used with a `TPUPrecisionPlugin"):
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Trainer(strategy=strategy, devices=8)
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strategy = DDPStrategy(accelerator=TPUAccelerator(), precision_plugin=TPUPrecisionPlugin())
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with pytest.raises(ValueError, match="TPUAccelerator` can only be used with a `SingleTPUStrategy`"):
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Trainer(strategy=strategy, devices=8)
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@RunIf(tpu=True)
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def test_tpu_invalid_raises_set_precision_with_strategy():
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accelerator = TPUAccelerator()
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strategy = TPUSpawnStrategy(accelerator=accelerator, precision_plugin=PrecisionPlugin())
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with pytest.raises(ValueError, match="`TPUAccelerator` can only be used with a `TPUPrecisionPlugin`"):
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Trainer(strategy=strategy, devices=8)
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accelerator = TPUAccelerator()
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strategy = DDPStrategy(accelerator=accelerator, precision_plugin=TPUPrecisionPlugin())
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with pytest.raises(
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ValueError, match="The `TPUAccelerator` can only be used with a `SingleTPUStrategy` or `TPUSpawnStrategy"
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):
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Trainer(strategy=strategy, devices=8)
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@RunIf(tpu=True)
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def test_xla_checkpoint_plugin_being_default():
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trainer = Trainer(accelerator="tpu", devices=8)
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assert isinstance(trainer.strategy.checkpoint_io, XLACheckpointIO)
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@RunIf(tpu=True)
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@patch("pytorch_lightning.strategies.tpu_spawn.xm")
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def test_mp_device_dataloader_attribute(_):
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dataset = RandomDataset(32, 64)
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dataloader = TPUSpawnStrategy().process_dataloader(DataLoader(dataset))
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assert dataloader.dataset == dataset
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@RunIf(tpu=True)
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def test_warning_if_tpus_not_used():
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with pytest.warns(UserWarning, match="TPU available but not used. Set `accelerator` and `devices`"):
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Trainer()
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@RunIf(tpu=True)
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@pl_multi_process_test
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@pytest.mark.parametrize(
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["devices", "expected_device_ids"],
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[
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(1, [0]),
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(8, list(range(8))),
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("8", list(range(8))),
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([2], [2]),
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("2,", [2]),
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],
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)
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def test_trainer_config_device_ids(devices, expected_device_ids):
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trainer = Trainer(accelerator="tpu", devices=devices)
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assert trainer.device_ids == expected_device_ids
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assert trainer.num_devices == len(expected_device_ids)
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