111 lines
4.3 KiB
Python
111 lines
4.3 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 torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.plugins import DDPPlugin, DDPSpawnPlugin
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from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_8
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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if torch.distributed.is_available() and _TORCH_GREATER_EQUAL_1_8:
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from torch.distributed.algorithms.ddp_comm_hooks import default_hooks as default
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from torch.distributed.algorithms.ddp_comm_hooks import powerSGD_hook as powerSGD
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@RunIf(skip_windows=True, min_torch="1.9.0", min_gpus=2, special=True)
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def test_ddp_fp16_compress_comm_hook(tmpdir):
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"""Test for DDP FP16 compress hook."""
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model = BoringModel()
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training_type_plugin = DDPPlugin(ddp_comm_hook=default.fp16_compress_hook, sync_batchnorm=True)
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trainer = Trainer(
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max_epochs=1,
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gpus=2,
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plugins=[training_type_plugin],
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default_root_dir=tmpdir,
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sync_batchnorm=True,
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fast_dev_run=True,
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)
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trainer.fit(model)
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trainer_comm_hook = trainer.accelerator.training_type_plugin._model.get_ddp_logging_data().comm_hook
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expected_comm_hook = default.fp16_compress_hook.__qualname__
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assert trainer_comm_hook == expected_comm_hook
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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@RunIf(skip_windows=True, min_torch="1.9.0", min_gpus=2, special=True)
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def test_ddp_sgd_comm_hook(tmpdir):
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"""Test for DDP FP16 compress hook."""
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model = BoringModel()
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training_type_plugin = DDPPlugin(
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ddp_comm_state=powerSGD.PowerSGDState(process_group=None),
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ddp_comm_hook=powerSGD.powerSGD_hook,
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sync_batchnorm=True,
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)
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trainer = Trainer(
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max_epochs=1,
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gpus=2,
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plugins=[training_type_plugin],
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default_root_dir=tmpdir,
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sync_batchnorm=True,
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fast_dev_run=True,
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)
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trainer.fit(model)
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trainer_comm_hook = trainer.accelerator.training_type_plugin._model.get_ddp_logging_data().comm_hook
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expected_comm_hook = powerSGD.powerSGD_hook.__qualname__
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assert trainer_comm_hook == expected_comm_hook
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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@RunIf(skip_windows=True, min_torch="1.9.0", min_gpus=2, special=True)
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def test_ddp_fp16_compress_wrap_sgd_comm_hook(tmpdir):
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"""Test for DDP FP16 compress wrapper for SGD hook."""
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model = BoringModel()
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training_type_plugin = DDPPlugin(
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ddp_comm_state=powerSGD.PowerSGDState(process_group=None),
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ddp_comm_hook=powerSGD.powerSGD_hook,
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ddp_comm_wrapper=default.fp16_compress_wrapper,
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sync_batchnorm=True,
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)
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trainer = Trainer(
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max_epochs=1,
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gpus=2,
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plugins=[training_type_plugin],
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default_root_dir=tmpdir,
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sync_batchnorm=True,
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fast_dev_run=True,
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)
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trainer.fit(model)
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trainer_comm_hook = trainer.accelerator.training_type_plugin._model.get_ddp_logging_data().comm_hook
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expected_comm_hook = default.fp16_compress_wrapper(powerSGD.powerSGD_hook).__qualname__
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assert trainer_comm_hook == expected_comm_hook
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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@RunIf(skip_windows=True, min_torch="1.9.0", min_gpus=2, special=True)
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def test_ddp_spawn_fp16_compress_comm_hook(tmpdir):
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"""Test for DDP Spawn FP16 compress hook."""
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model = BoringModel()
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training_type_plugin = DDPSpawnPlugin(ddp_comm_hook=default.fp16_compress_hook, sync_batchnorm=True)
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trainer = Trainer(
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max_epochs=1,
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gpus=2,
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plugins=[training_type_plugin],
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default_root_dir=tmpdir,
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sync_batchnorm=True,
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fast_dev_run=True,
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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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