lightning/benchmarks/test_sharded_parity.py

335 lines
13 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 os
import platform
import time
from typing import Type, Union
import pytest
import torch
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
from pytorch_lightning.plugins.sharded_plugin import DDPShardedPlugin
from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, _NATIVE_AMP_AVAILABLE
from tests.backends import DDPLauncher
from tests.base.boring_model import BoringModel, RandomDataset
@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_sharded_plugin_correctness_one_gpu():
plugin_parity_test(
gpus=1,
accelerator='ddp_spawn',
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
)
@pytest.mark.skipif(not _NATIVE_AMP_AVAILABLE, reason="Requires native AMP")
@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_sharded_plugin_correctness_amp_one_gpu():
plugin_parity_test(
gpus=1,
precision=16,
accelerator='ddp_spawn',
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
)
@pytest.mark.skip(reason="Not a critical test, skip till drone CI performance improves.")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_sharded_plugin_correctness_multi_gpu():
plugin_parity_test(
gpus=2,
accelerator='ddp_spawn',
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
max_percent_speed_diff=0.25, # todo: Increase speed diff since only 2 GPUs sharding 2 optimizers
)
@pytest.mark.skipif(not _NATIVE_AMP_AVAILABLE, reason="Requires native AMP")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_sharded_plugin_correctness_amp_multi_gpu():
plugin_parity_test(
gpus=2,
precision=16,
accelerator='ddp_spawn',
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
max_percent_speed_diff=0.25, # todo: Increase speed diff since only 2 GPUs sharding 2 optimizers
)
@pytest.mark.skipif(not _NATIVE_AMP_AVAILABLE, reason="Requires native AMP")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_string_sharded_plugin_correctness_amp_multi_gpu():
plugin_parity_test(
gpus=2,
precision=16,
accelerator='ddp_spawn',
plugin='ddp_sharded',
model_cls=SeedTrainLoaderModel,
max_percent_speed_diff=0.25, # todo: Increase speed diff since only 2 GPUs sharding 2 optimizers
)
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
reason="test should be run outside of pytest")
@DDPLauncher.run("--accelerator ddp --gpus 2 --precision 32")
def test_ddp_sharded_plugin_correctness_multi_gpu_ddp(tmpdir, args=None):
plugin_parity_test(
gpus=args.gpus,
precision=args.precision,
accelerator=args.accelerator,
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
)
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
reason="test should be run outside of pytest")
@DDPLauncher.run("--accelerator ddp --gpus 2 --precision 16")
def test_ddp_sharded_plugin_correctness_amp_multi_gpu_ddp(tmpdir, args=None):
plugin_parity_test(
gpus=args.gpus,
precision=args.precision,
accelerator=args.accelerator,
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
)
@pytest.mark.skip(reason="Current issue with multiple optimizers and FairScale.")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_sharded_plugin_correctness_multi_gpu_multi_optim():
"""
Ensures same results using multiple optimizers across multiple GPUs
"""
plugin_parity_test(
plugin=DDPShardedPlugin(),
gpus=2,
accelerator='ddp_spawn',
model_cls=SeedTrainLoaderMultipleOptimizersModel,
max_percent_speed_diff=0.25, # todo: Increase speed diff since only 2 GPUs sharding 2 optimizers
)
@pytest.mark.skip(reason="Current issue with multiple optimizers and FairScale.")
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
@pytest.mark.skipif(not _FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
def test_ddp_sharded_plugin_correctness_multi_gpu_multi_optim_manual(tmpdir):
"""
Ensures using multiple optimizers across multiple GPUs with manual optimization
"""
plugin_parity_test(
plugin=DDPShardedPlugin(),
gpus=2,
accelerator='ddp_spawn',
model_cls=SeedTrainLoaderManualModel,
max_percent_speed_diff=0.25, # todo: Increase speed diff since only 2 GPUs sharding 2 optimizers
)
class SeedTrainLoaderModel(BoringModel):
"""
Overrides training loader to ensure we enforce the same seed for all DDP processes.
"""
def train_dataloader(self):
seed_everything(42)
return torch.utils.data.DataLoader(RandomDataset(32, 64))
class SeedTrainLoaderManualModel(SeedTrainLoaderModel):
def training_step(self, batch, batch_idx, optimizer_idx):
# manual
# access your optimizers with use_pl_optimizer=False. Default is True
(opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)
loss_1 = self.step(batch)
self.manual_backward(loss_1, opt_a)
opt_a.step()
# fake discriminator
loss_2 = self.step(batch[0])
# ensure we forward the correct params to the optimizer
# without retain_graph we can't do multiple backward passes
self.manual_backward(loss_2, opt_b)
# todo: understand why synchronization breaks there.
# self.manual_backward(loss_2, opt_a, retain_graph=True)
opt_b.step()
assert self.layer.weight.grad is None or torch.all(self.layer.weight.grad == 0)
def training_epoch_end(self, outputs) -> None:
# outputs should be an array with an entry per optimizer
assert len(outputs) == 2
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
return optimizer, optimizer_2
@property
def automatic_optimization(self) -> bool:
return False
class SeedTrainLoaderMultipleOptimizersModel(SeedTrainLoaderModel):
def training_step(self, batch, batch_idx, optimizer_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def training_epoch_end(self, outputs) -> None:
# outputs should be an array with an entry per optimizer
assert len(outputs) == 2
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
return optimizer, optimizer_2
def record_ddp_fit_model_stats(trainer, model, use_cuda):
"""
Helper to calculate wall clock time for fit + max allocated memory.
Args:
trainer: The trainer object.
model: The model to fit.
use_cuda: Whether to sync CUDA kernels.
Returns:
Max Memory if using GPUs, and total wall clock time.
"""
max_memory = None
time_start = time.perf_counter()
if use_cuda:
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
trainer.fit(model)
if use_cuda:
torch.cuda.synchronize()
max_memory = torch.cuda.max_memory_allocated() / 2 ** 20
total_time = time.perf_counter() - time_start
return max_memory, total_time
def plugin_parity_test(
model_cls: Type[SeedTrainLoaderModel],
plugin: Union[str, DDPPlugin],
seed: int = 42,
accelerator: str = 'ddp_spawn',
gpus: int = 0,
precision: int = 32,
max_percent_speed_diff: float = 0.1,
):
"""
Ensures that the trained model is identical to the standard DDP implementation.
Also checks for speed/memory regressions, we should expect always less memory but performance to fluctuate.
Args:
model_cls: Model class to use for test.
plugin: Plugin to parity test.
seed: Seed for generators. Note that this does not handle the seed for data-loading on multi-process.
accelerator: Accelerator type for test.
gpus: Number of GPUS to enable.
precision: Whether to use AMP or normal FP32 training.
max_percent_speed_diff: The maximum speed difference compared to normal DDP training.
This is more a safety net for variability in CI which can vary in speed, not for benchmarking.
"""
# Train normal DDP
seed_everything(seed)
ddp_model = model_cls()
use_cuda = gpus > 0
trainer = Trainer(
fast_dev_run=True,
max_epochs=1,
gpus=gpus,
precision=precision,
accelerator=accelerator,
)
max_memory_ddp, ddp_time = record_ddp_fit_model_stats(
trainer=trainer,
model=ddp_model,
use_cuda=use_cuda
)
# Reset and train Custom DDP
seed_everything(seed)
custom_plugin_model = model_cls()
trainer = Trainer(
fast_dev_run=True,
max_epochs=1,
gpus=gpus,
precision=precision,
accelerator=accelerator,
plugins=[plugin],
)
max_memory_custom, custom_model_time = record_ddp_fit_model_stats(
trainer=trainer,
model=custom_plugin_model,
use_cuda=use_cuda
)
# Assert model parameters are identical after fit
for ddp_param, custom_param in zip(ddp_model.parameters(), custom_plugin_model.parameters()):
assert torch.equal(ddp_param, custom_param), 'Model parameters are different between DDP and Custom plugin'
# Assert speed parity by ensuring percentage difference between custom/ddp is below threshold
percent_diff = (custom_model_time - ddp_time) / custom_model_time
assert percent_diff <= max_percent_speed_diff, \
f'Custom DDP plugin was too slow compared to DDP, Custom Plugin Time: {custom_model_time}, DDP Time: {ddp_time}'
if use_cuda:
# Assert CUDA memory parity
assert max_memory_custom <= max_memory_ddp, \
f'Custom plugin used too much memory compared to DDP,' \
f'Custom Mem: {max_memory_custom}, DDP Mem: {max_memory_ddp}'