408 lines
15 KiB
Python
408 lines
15 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.
|
|
from unittest.mock import Mock
|
|
|
|
import pytest
|
|
import torch
|
|
from torch import nn
|
|
from torch.optim import Adam, SGD
|
|
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.loggers import TensorBoardLogger
|
|
from pytorch_lightning.utilities.exceptions import MisconfigurationException
|
|
from tests.helpers import BoringModel
|
|
from tests.helpers.runif import RunIf
|
|
|
|
|
|
def test_property_current_epoch():
|
|
"""Test that the current_epoch in LightningModule is accessible via the Trainer."""
|
|
model = BoringModel()
|
|
assert model.current_epoch == 0
|
|
|
|
trainer = Mock(current_epoch=123)
|
|
model.trainer = trainer
|
|
assert model.current_epoch == 123
|
|
|
|
|
|
def test_property_global_step():
|
|
"""Test that the global_step in LightningModule is accessible via the Trainer."""
|
|
model = BoringModel()
|
|
assert model.global_step == 0
|
|
|
|
trainer = Mock(global_step=123)
|
|
model.trainer = trainer
|
|
assert model.global_step == 123
|
|
|
|
|
|
def test_property_global_rank():
|
|
"""Test that the global rank in LightningModule is accessible via the Trainer."""
|
|
model = BoringModel()
|
|
assert model.global_rank == 0
|
|
|
|
trainer = Mock(global_rank=123)
|
|
model.trainer = trainer
|
|
assert model.global_rank == 123
|
|
|
|
|
|
def test_property_local_rank():
|
|
"""Test that the local rank in LightningModule is accessible via the Trainer."""
|
|
model = BoringModel()
|
|
assert model.local_rank == 0
|
|
|
|
trainer = Mock(local_rank=123)
|
|
model.trainer = trainer
|
|
assert model.local_rank == 123
|
|
|
|
|
|
def test_property_logger(tmpdir):
|
|
"""Test that the logger in LightningModule is accessible via the Trainer."""
|
|
model = BoringModel()
|
|
assert model.logger is None
|
|
|
|
logger = TensorBoardLogger(tmpdir)
|
|
trainer = Mock(logger=logger)
|
|
model.trainer = trainer
|
|
assert model.logger == logger
|
|
|
|
|
|
def test_params_groups_and_state_are_accessible(tmpdir):
|
|
class TestModel(BoringModel):
|
|
def training_step(self, batch, batch_idx, optimizer_idx):
|
|
output = self.layer(batch)
|
|
loss = self.loss(batch, output)
|
|
return {"loss": loss}
|
|
|
|
def configure_optimizers(self):
|
|
optimizer = SGD(self.layer.parameters(), lr=0.1)
|
|
optimizer_2 = Adam(self.layer.parameters(), lr=0.1)
|
|
return [optimizer, optimizer_2]
|
|
|
|
def optimizer_step(
|
|
self,
|
|
epoch,
|
|
batch_idx,
|
|
optimizer,
|
|
optimizer_idx,
|
|
optimizer_closure,
|
|
on_tpu=False,
|
|
using_native_amp=False,
|
|
using_lbfgs=False,
|
|
):
|
|
# warm up lr
|
|
if self.trainer.global_step < 500:
|
|
lr_scale = min(1.0, float(self.trainer.global_step + 1) / 500.0)
|
|
for pg in optimizer.param_groups:
|
|
pg["lr"] = lr_scale * 0.01
|
|
|
|
optimizer.step(closure=optimizer_closure)
|
|
|
|
model = TestModel()
|
|
model.training_epoch_end = None
|
|
|
|
trainer = Trainer(
|
|
max_epochs=1, default_root_dir=tmpdir, limit_train_batches=8, limit_val_batches=1, accumulate_grad_batches=1
|
|
)
|
|
|
|
trainer.fit(model)
|
|
|
|
|
|
def test_toggle_untoggle_2_optimizers_no_shared_parameters(tmpdir):
|
|
class TestModel(BoringModel):
|
|
def training_step(self, batch, batch_idx, optimizer_idx=None):
|
|
return super().training_step(batch, batch_idx)
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer_1 = nn.Sequential(nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32))
|
|
|
|
self.layer_2 = nn.Sequential(
|
|
nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 2)
|
|
)
|
|
|
|
# set some weights to False to check untoggle works as expected.
|
|
self.layer_1[2].weight.requires_grad = False
|
|
self.layer_1[4].weight.requires_grad = False
|
|
|
|
self.layer_2[1].weight.requires_grad = False
|
|
self.layer_2[3].weight.requires_grad = False
|
|
|
|
def configure_optimizers(self):
|
|
optimizer = SGD(self.layer_1.parameters(), lr=0.1)
|
|
optimizer_2 = Adam(self.layer_2.parameters(), lr=0.1)
|
|
return [optimizer, optimizer_2]
|
|
|
|
def optimizer_step(
|
|
self,
|
|
current_epoch,
|
|
batch_nb,
|
|
optimizer,
|
|
optimizer_idx,
|
|
closure,
|
|
on_tpu=False,
|
|
using_native_amp=False,
|
|
using_lbfgs=False,
|
|
):
|
|
if optimizer_idx == 0:
|
|
assert self.layer_1[0].weight.requires_grad is True
|
|
assert self.layer_1[2].weight.requires_grad is False
|
|
assert self.layer_1[4].weight.requires_grad is False
|
|
|
|
assert self.layer_2[1].weight.requires_grad is False
|
|
assert self.layer_2[3].weight.requires_grad is False
|
|
assert self.layer_2[5].weight.requires_grad is False
|
|
|
|
if optimizer_idx == 1:
|
|
assert self.layer_1[0].weight.requires_grad is False
|
|
assert self.layer_1[2].weight.requires_grad is False
|
|
assert self.layer_1[4].weight.requires_grad is False
|
|
|
|
assert self.layer_2[1].weight.requires_grad is False
|
|
assert self.layer_2[3].weight.requires_grad is False
|
|
assert self.layer_2[5].weight.requires_grad is True
|
|
|
|
optimizer.step(closure=closure)
|
|
|
|
model = TestModel()
|
|
model.training_epoch_end = None
|
|
|
|
trainer = Trainer(
|
|
max_epochs=1, default_root_dir=tmpdir, limit_train_batches=8, accumulate_grad_batches=2, limit_val_batches=0
|
|
)
|
|
trainer.fit(model)
|
|
|
|
|
|
def test_toggle_untoggle_3_optimizers_shared_parameters(tmpdir):
|
|
class TestModel(BoringModel):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer_1 = nn.Sequential(nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32))
|
|
|
|
self.layer_2 = nn.Sequential(
|
|
nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 2)
|
|
)
|
|
|
|
self.layer_3 = nn.Sequential(
|
|
nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 32), nn.ReLU(), nn.Linear(32, 2)
|
|
)
|
|
|
|
# set some weights to False to check untoggle works as expected.
|
|
self.layer_1[2].weight.requires_grad = False
|
|
self.layer_1[4].weight.requires_grad = False
|
|
|
|
self.layer_2[1].weight.requires_grad = False
|
|
self.layer_2[3].weight.requires_grad = False
|
|
|
|
self.layer_3[1].weight.requires_grad = False
|
|
self.layer_3[5].weight.requires_grad = False
|
|
|
|
def optimizer_step(
|
|
self,
|
|
current_epoch,
|
|
batch_nb,
|
|
optimizer,
|
|
optimizer_idx,
|
|
closure,
|
|
on_tpu=False,
|
|
using_native_amp=False,
|
|
using_lbfgs=False,
|
|
):
|
|
if optimizer_idx == 0:
|
|
assert self.layer_1[0].weight.requires_grad is True
|
|
assert self.layer_1[2].weight.requires_grad is False
|
|
assert self.layer_1[4].weight.requires_grad is False
|
|
|
|
assert self.layer_2[1].weight.requires_grad is False
|
|
assert self.layer_2[3].weight.requires_grad is False
|
|
assert self.layer_2[5].weight.requires_grad is True
|
|
|
|
assert self.layer_3[1].weight.requires_grad is False
|
|
assert self.layer_3[3].weight.requires_grad is False
|
|
assert self.layer_3[5].weight.requires_grad is False
|
|
|
|
if optimizer_idx == 1:
|
|
assert self.layer_1[0].weight.requires_grad is False
|
|
assert self.layer_1[2].weight.requires_grad is False
|
|
assert self.layer_1[4].weight.requires_grad is False
|
|
|
|
assert self.layer_2[1].weight.requires_grad is False
|
|
assert self.layer_2[3].weight.requires_grad is False
|
|
assert self.layer_2[5].weight.requires_grad is True
|
|
|
|
assert self.layer_3[1].weight.requires_grad is False
|
|
assert self.layer_3[3].weight.requires_grad is True
|
|
assert self.layer_3[5].weight.requires_grad is False
|
|
|
|
if optimizer_idx == 2:
|
|
assert self.layer_1[0].weight.requires_grad is True
|
|
assert self.layer_1[2].weight.requires_grad is False
|
|
assert self.layer_1[4].weight.requires_grad is False
|
|
|
|
assert self.layer_2[1].weight.requires_grad is False
|
|
assert self.layer_2[3].weight.requires_grad is False
|
|
assert self.layer_2[5].weight.requires_grad is False
|
|
|
|
assert self.layer_3[1].weight.requires_grad is False
|
|
assert self.layer_3[3].weight.requires_grad is True
|
|
assert self.layer_3[5].weight.requires_grad is False
|
|
|
|
optimizer.step(closure=closure)
|
|
|
|
def training_step(self, batch, batch_idx, optimizer_idx=None):
|
|
loss = super().training_step(batch, batch_idx)
|
|
# make sure the model is untoggle when returning None
|
|
return loss if batch_idx % 2 == 0 else None
|
|
|
|
@staticmethod
|
|
def combine_generators(gen_1, gen_2):
|
|
yield from gen_1
|
|
yield from gen_2
|
|
|
|
def configure_optimizers(self):
|
|
optimizer_1 = SGD(self.combine_generators(self.layer_1.parameters(), self.layer_2.parameters()), lr=0.1)
|
|
optimizer_2 = Adam(self.combine_generators(self.layer_2.parameters(), self.layer_3.parameters()), lr=0.1)
|
|
optimizer_3 = SGD(self.combine_generators(self.layer_3.parameters(), self.layer_1.parameters()), lr=0.1)
|
|
return [optimizer_1, optimizer_2, optimizer_3]
|
|
|
|
model = TestModel()
|
|
model.training_epoch_end = None
|
|
|
|
trainer = Trainer(max_epochs=1, default_root_dir=tmpdir, limit_train_batches=8, accumulate_grad_batches=2)
|
|
|
|
trainer.fit(model)
|
|
|
|
|
|
@RunIf(min_gpus=1)
|
|
def test_device_placement(tmpdir):
|
|
|
|
model = BoringModel()
|
|
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="gpu", devices=1)
|
|
trainer.fit(model)
|
|
|
|
def assert_device(device: torch.device) -> None:
|
|
assert model.device == device
|
|
for p in model.parameters():
|
|
assert p.device == device
|
|
|
|
assert_device(torch.device("cpu"))
|
|
model.to(torch.device("cuda:0"))
|
|
assert_device(torch.device("cuda:0"))
|
|
trainer.test(model)
|
|
assert_device(torch.device("cpu"))
|
|
trainer.predict(model, dataloaders=model.train_dataloader())
|
|
assert_device(torch.device("cpu"))
|
|
|
|
|
|
@RunIf(min_torch="1.10", skip_windows=True)
|
|
def test_sharded_tensor_state_dict(tmpdir, single_process_pg):
|
|
from torch.distributed._sharded_tensor import empty as sharded_tensor_empty
|
|
from torch.distributed._sharding_spec import ChunkShardingSpec
|
|
|
|
class BoringModelWithShardedTensor(BoringModel):
|
|
def __init__(self, spec):
|
|
super().__init__()
|
|
self.sharded_tensor = sharded_tensor_empty(spec, 10, 20)
|
|
self.sharded_tensor.local_shards()[0].tensor.fill_(0)
|
|
|
|
spec = ChunkShardingSpec(
|
|
dim=0,
|
|
placements=[
|
|
"rank:0/cpu",
|
|
],
|
|
)
|
|
|
|
m_0 = BoringModelWithShardedTensor(spec)
|
|
m_0.sharded_tensor.local_shards()[0].tensor.fill_(1)
|
|
assert "sharded_tensor" in m_0.state_dict(), 'Expect "sharded_tensor" to appear in the state dict'
|
|
|
|
m_1 = BoringModelWithShardedTensor(spec)
|
|
assert not torch.allclose(
|
|
m_1.sharded_tensor.local_shards()[0].tensor, m_0.sharded_tensor.local_shards()[0].tensor
|
|
), "Expect the shards to be different before `m_1` loading `m_0`'s state dict"
|
|
|
|
m_1.load_state_dict(m_0.state_dict(), strict=False)
|
|
assert torch.allclose(
|
|
m_1.sharded_tensor.local_shards()[0].tensor, m_0.sharded_tensor.local_shards()[0].tensor
|
|
), "Expect the shards to be same after `m_1` loading `m_0`'s state dict"
|
|
|
|
|
|
def test_lightning_module_configure_gradient_clipping(tmpdir):
|
|
"""Test custom gradient clipping inside `configure_gradient_clipping` hook."""
|
|
|
|
class TestModel(BoringModel):
|
|
|
|
has_validated_gradients = False
|
|
custom_gradient_clip_val = 1e-2
|
|
|
|
def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm):
|
|
assert gradient_clip_val == self.trainer.gradient_clip_val
|
|
assert gradient_clip_algorithm == self.trainer.gradient_clip_algorithm
|
|
|
|
for pg in optimizer.param_groups:
|
|
for p in pg["params"]:
|
|
p.grad.clamp_(min=0, max=self.custom_gradient_clip_val)
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir, max_epochs=1, limit_train_batches=1, limit_val_batches=0, gradient_clip_val=1e-4
|
|
)
|
|
trainer.fit(model)
|
|
|
|
optimizer = model.optimizers()
|
|
for pg in optimizer.param_groups:
|
|
for p in pg["params"]:
|
|
if p.grad is not None:
|
|
assert p.grad.min() >= 0
|
|
assert p.grad.max() <= model.custom_gradient_clip_val
|
|
|
|
|
|
def test_lightning_module_configure_gradient_clipping_different_argument_values(tmpdir):
|
|
"""Test that setting gradient clipping arguments in `Trainer` and cusotmizing gradient clipping inside
|
|
`configure_gradient_clipping` with different values raises an exception."""
|
|
|
|
class TestModel(BoringModel):
|
|
custom_gradient_clip_val = 1e-2
|
|
|
|
def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm):
|
|
self.clip_gradients(optimizer, gradient_clip_val=self.custom_gradient_clip_val)
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir, max_epochs=1, limit_train_batches=2, limit_val_batches=0, gradient_clip_val=1e-4
|
|
)
|
|
with pytest.raises(
|
|
MisconfigurationException,
|
|
match=r"gradient_clip_val=0.0001\)` and have passed `clip_gradients\(gradient_clip_val=0.01",
|
|
):
|
|
trainer.fit(model)
|
|
|
|
class TestModel(BoringModel):
|
|
custom_gradient_clip_algorithm = "foo"
|
|
|
|
def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm):
|
|
self.clip_gradients(optimizer, gradient_clip_algorithm=self.custom_gradient_clip_algorithm)
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=1,
|
|
limit_train_batches=2,
|
|
limit_val_batches=0,
|
|
gradient_clip_algorithm="norm",
|
|
)
|
|
with pytest.raises(
|
|
MisconfigurationException,
|
|
match=r"gradient_clip_algorithm='norm'\)` and have passed `clip_gradients\(gradient_clip_algorithm='foo'",
|
|
):
|
|
trainer.fit(model)
|