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Refactor: clean trainer device & distrib setters #5297

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Jan 4, 2021
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Refactor: clean trainer device & distrib setters #5297

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2 changes: 2 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Fixed

- Fixed distributed setting and `ddp_cpu` only with `num_processes>1` ([#5297](https://github.com/PyTorchLightning/pytorch-lightning/pull/5297))


## [1.1.0] - 2020-12-09

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57 changes: 21 additions & 36 deletions benchmarks/test_sharded_parity.py
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@@ -1,7 +1,7 @@
import os
import platform
import time
from typing import Union
from typing import Type, Union

import pytest
import torch
Expand All @@ -14,64 +14,48 @@
from tests.base.boring_model import BoringModel, RandomDataset


@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_device():
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plugin_parity_test(
accelerator='ddp_cpu',
max_percent_speed_diff=0.15, # slower speed due to one CPU doing additional sequential memory saving calls
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel
)


@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(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
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(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
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(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
max_percent_speed_diff=0.25,
)


@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(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():
Expand All @@ -81,13 +65,12 @@ def test_ddp_sharded_plugin_correctness_amp_multi_gpu():
accelerator='ddp_spawn',
plugin=DDPShardedPlugin(),
model_cls=SeedTrainLoaderModel,
max_percent_speed_diff=0.25
max_percent_speed_diff=0.25,
)


@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(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():
Expand All @@ -97,7 +80,7 @@ def test_ddp_string_sharded_plugin_correctness_amp_multi_gpu():
accelerator='ddp_spawn',
plugin='ddp_sharded',
model_cls=SeedTrainLoaderModel,
max_percent_speed_diff=0.25
max_percent_speed_diff=0.25,
)


Expand Down Expand Up @@ -133,8 +116,7 @@ def test_ddp_sharded_plugin_correctness_amp_multi_gpu_ddp(tmpdir, args=None):

@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(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():
"""
Expand All @@ -145,14 +127,13 @@ def test_ddp_sharded_plugin_correctness_multi_gpu_multi_optim():
gpus=2,
accelerator='ddp_spawn',
model_cls=SeedTrainLoaderMultipleOptimizersModel,
max_percent_speed_diff=0.25 # Increase speed diff since only 2 GPUs sharding 2 optimizers
max_percent_speed_diff=0.25, # 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(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):
"""
Expand All @@ -163,7 +144,7 @@ def test_ddp_sharded_plugin_correctness_multi_gpu_multi_optim_manual(tmpdir):
gpus=2,
accelerator='ddp_spawn',
model_cls=SeedTrainLoaderManualModel,
max_percent_speed_diff=0.25 # Increase speed diff since only 2 GPUs sharding 2 optimizers
max_percent_speed_diff=0.25, # Increase speed diff since only 2 GPUs sharding 2 optimizers
)


Expand Down Expand Up @@ -259,13 +240,15 @@ def record_ddp_fit_model_stats(trainer, model, use_cuda):


def plugin_parity_test(
model_cls: SeedTrainLoaderModel,
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):
max_percent_speed_diff: float = 0.1,
**kwargs,
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):
"""
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.
Expand Down Expand Up @@ -293,6 +276,7 @@ def plugin_parity_test(
gpus=gpus,
precision=precision,
accelerator=accelerator,
**kwargs,
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)

max_memory_ddp, ddp_time = record_ddp_fit_model_stats(
Expand All @@ -312,6 +296,7 @@ def plugin_parity_test(
precision=precision,
accelerator=accelerator,
plugins=[plugin],
**kwargs,
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)

max_memory_custom, custom_model_time = record_ddp_fit_model_stats(
Expand Down
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