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[Horovod] Fix Reduce for Horovod #6585

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amogkam wants to merge 12 commits into from
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[Horovod] Fix Reduce for Horovod #6585

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2505ccc
bug fix
amogkam Mar 18, 2021
6ccfd68
Update pytorch_lightning/plugins/training_type/horovod.py
amogkam Mar 19, 2021
e9d9c08
try add test
amogkam Mar 19, 2021
cf988b9
Merge branch 'horovod-reduce' of github.com:amogkam/pytorch-lightning…
amogkam Mar 19, 2021
d1790cd
Apply suggestions from code review
Borda Mar 25, 2021
6b9c62f
Reuse code
carmocca Mar 25, 2021
4e28fd2
Merge branch 'master' into horovod-reduce
carmocca Mar 25, 2021
1b05074
Avoid name conflict
carmocca Mar 25, 2021
29fc2e8
Forgot argument
carmocca Mar 25, 2021
cae1419
Minor changes
carmocca Mar 25, 2021
3b293a7
Merge branch 'master' into horovod-reduce
carmocca Apr 19, 2021
d22297d
Bad merge
carmocca Apr 19, 2021
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20 changes: 11 additions & 9 deletions tests/checkpointing/test_checkpoint_callback_frequency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,8 @@
import pytest
import torch

from pytorch_lightning import callbacks, seed_everything, Trainer
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from tests.helpers import BoringModel
from tests.helpers.runif import RunIf

Expand Down Expand Up @@ -93,7 +94,7 @@ def training_step(self, batch, batch_idx):

model = TestModel()
trainer = Trainer(
callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss', save_top_k=k)],
callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='my_loss', save_top_k=k)],
default_root_dir=tmpdir,
max_epochs=epochs,
weights_summary=None,
Expand All @@ -107,8 +108,9 @@ def training_step(self, batch, batch_idx):

@mock.patch('torch.save')
@RunIf(special=True, min_gpus=2)
@pytest.mark.parametrize("accelerator", ["ddp", pytest.param("horovod", marks=RunIf(horovod=True, skip_windows=True))])
@pytest.mark.parametrize(['k', 'epochs', 'val_check_interval', 'expected'], [(1, 1, 1.0, 1), (2, 2, 0.3, 5)])
def test_top_k_ddp(save_mock, tmpdir, k, epochs, val_check_interval, expected):
def test_top_k_distributed(save_mock, tmpdir, accelerator, k, epochs, val_check_interval, expected):
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@awaelchli awaelchli May 4, 2021

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I would not include horovod in the parameterization here yet, otherwise we risk getting another flaky test. I believe we need to improve our testing integration with horovod first. #6935

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@awaelchli awaelchli May 4, 2021

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since the main fix of this PR was merged, I suggest to close this one


class TestModel(BoringModel):

Expand All @@ -117,21 +119,21 @@ def training_step(self, batch, batch_idx):
self.log('my_loss', batch_idx * (1 + local_rank), on_epoch=True)
return super().training_step(batch, batch_idx)

def training_epoch_end(self, outputs) -> None:
def training_epoch_end(self, outputs):
data = str(self.global_rank)
obj = [[data], (data, ), set(data)]
obj = [[data], (data, ), {data}]
out = self.trainer.training_type_plugin.broadcast(obj)
assert obj == [[str(self.global_rank)], (str(self.global_rank), ), set(str(self.global_rank))]
assert out == [['0'], ('0', ), set('0')]
assert obj == [[str(self.global_rank)], (str(self.global_rank), ), {str(self.global_rank)}]
assert out == [['0'], ('0', ), {'0'}]

model = TestModel()
trainer = Trainer(
callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss_step', save_top_k=k, mode="max")],
callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='my_loss_step', save_top_k=k, mode="max")],
default_root_dir=tmpdir,
max_epochs=epochs,
weights_summary=None,
val_check_interval=val_check_interval,
accelerator="ddp",
accelerator=accelerator,
gpus=2,
limit_train_batches=64,
limit_val_batches=32,
Expand Down