331 lines
10 KiB
Python
331 lines
10 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License
|
|
import collections
|
|
from copy import deepcopy
|
|
from unittest.mock import patch
|
|
|
|
import pytest
|
|
import torch
|
|
from torch import nn
|
|
from torch.utils.data import DataLoader
|
|
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.accelerators.cpu import CPUAccelerator
|
|
from pytorch_lightning.accelerators.tpu import TPUAccelerator
|
|
from pytorch_lightning.plugins import DDPPlugin, TPUPrecisionPlugin, TPUSpawnPlugin, XLACheckpointIO
|
|
from pytorch_lightning.utilities import find_shared_parameters
|
|
from pytorch_lightning.utilities.exceptions import MisconfigurationException
|
|
from tests.helpers.boring_model import BoringModel, RandomDataset
|
|
from tests.helpers.runif import RunIf
|
|
from tests.helpers.utils import pl_multi_process_test
|
|
|
|
|
|
class WeightSharingModule(BoringModel):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer_1 = nn.Linear(32, 10, bias=False)
|
|
self.layer_2 = nn.Linear(10, 32, bias=False)
|
|
self.layer_3 = nn.Linear(32, 10, bias=False)
|
|
self.layer_3.weight = self.layer_1.weight
|
|
|
|
def forward(self, x):
|
|
x = self.layer_1(x)
|
|
x = self.layer_2(x)
|
|
x = self.layer_3(x)
|
|
return x
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@pl_multi_process_test
|
|
def test_resume_training_on_cpu(tmpdir):
|
|
"""Checks if training can be resumed from a saved checkpoint on CPU."""
|
|
# Train a model on TPU
|
|
model = BoringModel()
|
|
trainer = Trainer(max_epochs=1, tpu_cores=8)
|
|
trainer.fit(model)
|
|
|
|
model_path = trainer.checkpoint_callback.best_model_path
|
|
|
|
# Verify saved Tensors are on CPU
|
|
ckpt = torch.load(model_path)
|
|
weight_tensor = list(ckpt["state_dict"].values())[0]
|
|
assert weight_tensor.device == torch.device("cpu")
|
|
|
|
# Verify that training is resumed on CPU
|
|
trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
|
|
trainer.fit(model, ckpt_path=model_path)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@pl_multi_process_test
|
|
def test_if_test_works_after_train(tmpdir):
|
|
"""Ensure that .test() works after .fit()"""
|
|
|
|
# Train a model on TPU
|
|
model = BoringModel()
|
|
trainer = Trainer(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True)
|
|
trainer.fit(model)
|
|
assert len(trainer.test(model)) == 1
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_accelerator_tpu():
|
|
|
|
trainer = Trainer(accelerator="tpu", tpu_cores=8)
|
|
|
|
assert trainer._device_type == "tpu"
|
|
assert isinstance(trainer.accelerator, TPUAccelerator)
|
|
|
|
with pytest.raises(
|
|
MisconfigurationException, match="You passed `accelerator='tpu'`, but you didn't pass `tpu_cores` to `Trainer`"
|
|
):
|
|
trainer = Trainer(accelerator="tpu")
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_accelerator_cpu_with_tpu_cores_flag():
|
|
|
|
trainer = Trainer(accelerator="cpu", tpu_cores=8)
|
|
|
|
assert trainer._device_type == "cpu"
|
|
assert isinstance(trainer.accelerator, CPUAccelerator)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_accelerator_tpu_with_auto():
|
|
|
|
trainer = Trainer(accelerator="auto", tpu_cores=8)
|
|
|
|
assert trainer._device_type == "tpu"
|
|
assert isinstance(trainer.accelerator, TPUAccelerator)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_accelerator_tpu_with_devices():
|
|
|
|
trainer = Trainer(accelerator="tpu", devices=8)
|
|
|
|
assert trainer.tpu_cores == 8
|
|
assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
|
|
assert isinstance(trainer.accelerator, TPUAccelerator)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_accelerator_auto_with_devices_tpu():
|
|
|
|
trainer = Trainer(accelerator="auto", devices=8)
|
|
|
|
assert trainer._device_type == "tpu"
|
|
assert trainer.tpu_cores == 8
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_accelerator_tpu_with_tpu_cores_priority():
|
|
"""Test for checking `tpu_cores` flag takes priority over `devices`."""
|
|
|
|
tpu_cores = 8
|
|
with pytest.warns(UserWarning, match="The flag `devices=1` will be ignored,"):
|
|
trainer = Trainer(accelerator="tpu", devices=1, tpu_cores=tpu_cores)
|
|
|
|
assert trainer.tpu_cores == tpu_cores
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_set_devices_if_none_tpu():
|
|
|
|
trainer = Trainer(accelerator="tpu", tpu_cores=8)
|
|
assert trainer.devices == 8
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_manual_optimization_tpus(tmpdir):
|
|
class ManualOptimizationModel(BoringModel):
|
|
|
|
count = 0
|
|
called = collections.defaultdict(int)
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.automatic_optimization = False
|
|
|
|
@property
|
|
def should_update(self):
|
|
return self.count % 2 == 0
|
|
|
|
def on_train_batch_start(self, batch, batch_idx):
|
|
self.called["on_train_batch_start"] += 1
|
|
self.weight_before = self.layer.weight.clone()
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
self.called["training_step"] += 1
|
|
opt = self.optimizers()
|
|
output = self.layer(batch)
|
|
loss = self.loss(batch, output)
|
|
|
|
if self.should_update:
|
|
self.manual_backward(loss)
|
|
opt.step()
|
|
opt.zero_grad()
|
|
return loss
|
|
|
|
def on_train_batch_end(self, outputs, batch, batch_idx):
|
|
self.called["on_train_batch_end"] += 1
|
|
after_before = self.layer.weight.clone()
|
|
if self.should_update:
|
|
assert not torch.equal(self.weight_before, after_before), self.count
|
|
else:
|
|
assert torch.equal(self.weight_before, after_before)
|
|
assert torch.all(self.layer.weight.grad == 0)
|
|
self.count += 1
|
|
|
|
def on_train_start(self):
|
|
opt = self.optimizers()
|
|
self.opt_step_patch = patch.object(opt, "step", wraps=opt.step)
|
|
self.opt_step_mock = self.opt_step_patch.start()
|
|
|
|
def on_train_end(self):
|
|
assert self.called["training_step"] == 5
|
|
assert self.called["on_train_batch_start"] == 5
|
|
assert self.called["on_train_batch_end"] == 5
|
|
|
|
self.opt_step_patch.stop()
|
|
assert self.opt_step_mock.call_count == 3
|
|
|
|
model = ManualOptimizationModel()
|
|
model_copy = deepcopy(model)
|
|
model.training_step_end = None
|
|
model.training_epoch_end = None
|
|
|
|
trainer = Trainer(
|
|
max_epochs=1,
|
|
default_root_dir=tmpdir,
|
|
limit_train_batches=5,
|
|
limit_test_batches=0,
|
|
limit_val_batches=0,
|
|
tpu_cores=8,
|
|
)
|
|
trainer.fit(model)
|
|
|
|
for param, param_copy in zip(model.parameters(), model_copy.parameters()):
|
|
assert not torch.equal(param.cpu().data, param_copy.data)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_ddp_cpu_not_supported_on_tpus():
|
|
with pytest.raises(MisconfigurationException, match="`accelerator='ddp_cpu'` is not supported on TPU machines"):
|
|
Trainer(accelerator="ddp_cpu")
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@pytest.mark.parametrize("strategy", ["ddp_spawn", "tpu_spawn_debug"])
|
|
def test_strategy_choice_tpu_str(tmpdir, strategy):
|
|
trainer = Trainer(strategy=strategy, accelerator="tpu", devices=8)
|
|
assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_strategy_choice_tpu_plugin(tmpdir):
|
|
trainer = Trainer(strategy=TPUSpawnPlugin(), accelerator="tpu", devices=8)
|
|
assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_auto_parameters_tying_tpus(tmpdir):
|
|
|
|
model = WeightSharingModule()
|
|
shared_params = find_shared_parameters(model)
|
|
|
|
assert shared_params[0] == ["layer_1.weight", "layer_3.weight"]
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, tpu_cores=8, max_epochs=1)
|
|
trainer.fit(model)
|
|
|
|
assert torch.all(torch.eq(model.layer_1.weight, model.layer_3.weight))
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_auto_parameters_tying_tpus_nested_module(tmpdir):
|
|
class SubModule(nn.Module):
|
|
def __init__(self, layer):
|
|
super().__init__()
|
|
self.layer = layer
|
|
|
|
def forward(self, x):
|
|
return self.layer(x)
|
|
|
|
class NestedModule(BoringModel):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer = nn.Linear(32, 10, bias=False)
|
|
self.net_a = SubModule(self.layer)
|
|
self.layer_2 = nn.Linear(10, 32, bias=False)
|
|
self.net_b = SubModule(self.layer)
|
|
|
|
def forward(self, x):
|
|
x = self.net_a(x)
|
|
x = self.layer_2(x)
|
|
x = self.net_b(x)
|
|
return x
|
|
|
|
model = NestedModule()
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, tpu_cores=8, max_epochs=1)
|
|
trainer.fit(model)
|
|
|
|
assert torch.all(torch.eq(model.net_a.layer.weight, model.net_b.layer.weight))
|
|
|
|
|
|
def test_tpu_invalid_raises():
|
|
accelerator = TPUAccelerator(object(), TPUSpawnPlugin())
|
|
with pytest.raises(ValueError, match="TPUAccelerator` can only be used with a `TPUPrecisionPlugin"):
|
|
accelerator.setup(object())
|
|
|
|
accelerator = TPUAccelerator(TPUPrecisionPlugin(), DDPPlugin())
|
|
with pytest.raises(ValueError, match="TPUAccelerator` can only be used with a `SingleTPUPlugin` or `TPUSpawnPlugi"):
|
|
accelerator.setup(object())
|
|
|
|
|
|
def test_tpu_invalid_raises_set_precision_with_strategy():
|
|
accelerator = TPUAccelerator(object(), TPUSpawnPlugin(precision_plugin=object()))
|
|
with pytest.raises(ValueError, match="`TPUAccelerator` can only be used with a `TPUPrecisionPlugin`"):
|
|
accelerator.setup(object())
|
|
|
|
accelerator = TPUAccelerator(None, DDPPlugin(precision_plugin=TPUPrecisionPlugin()))
|
|
with pytest.raises(
|
|
ValueError, match="TPUAccelerator` can only be used with a `SingleTPUPlugin` or `TPUSpawnPlugin"
|
|
):
|
|
accelerator.setup(object())
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_xla_checkpoint_plugin_being_default():
|
|
trainer = Trainer(tpu_cores=8)
|
|
assert isinstance(trainer.training_type_plugin.checkpoint_io, XLACheckpointIO)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@patch("pytorch_lightning.plugins.training_type.tpu_spawn.xm")
|
|
def test_mp_device_dataloader_attribute(_):
|
|
dataset = RandomDataset(32, 64)
|
|
dataloader = TPUSpawnPlugin().process_dataloader(DataLoader(dataset))
|
|
assert dataloader.dataset == dataset
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_devices_auto_choice_tpu():
|
|
trainer = Trainer(accelerator="auto", devices="auto")
|
|
assert trainer.devices == 8
|
|
assert trainer.tpu_cores == 8
|