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DDP sampler #1513

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merged 6 commits into from
Apr 19, 2020
Merged

DDP sampler #1513

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465eecc
Add explicit flag for ddp sampler replacement
justusschock Apr 17, 2020
b4a51bb
Add flag for sampler replacement in ddp
justusschock Apr 17, 2020
0bb6519
Update data_loading.py
justusschock Apr 17, 2020
03bcca1
Update CHANGELOG.md
justusschock Apr 17, 2020
d0c0380
pep8 fixes
justusschock Apr 17, 2020
8ef962f
pep8
justusschock Apr 17, 2020
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Added

- Added flag `replace_sampler_ddp` to manually disaple sampler replacement in ddp ([#1513](https://github.com/PyTorchLightning/pytorch-lightning/pull/1513))
- Added `auto_select_gpus` flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
- Added learning rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))

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7 changes: 3 additions & 4 deletions pytorch_lightning/trainer/data_loading.py
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Expand Up @@ -61,6 +61,7 @@ class TrainerDataLoadingMixin(ABC):
train_percent_check: float
val_percent_check: float
test_percent_check: float
replace_sampler_ddp: bool

@abstractmethod
def is_overriden(self, *args):
Expand Down Expand Up @@ -88,10 +89,8 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
# don't do anything if it's not a dataloader
if not isinstance(dataloader, DataLoader):
return dataloader

need_dist_sampler = self.use_ddp or self.use_ddp2 or self.use_tpu

if need_dist_sampler:
need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_tpu)
if self.replace_sampler_ddp and need_dist_sampler:
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Rather than adding a new flag for this, is it possible to just check for the default samplers:
(isinstance(dataloader.sampler, RandomSampler) or isinstance(dataloader.sampler, SequentialSampler) or instance(dataloader.sampler, _InfiniteConstantSampler)) and need_dist_sampler?

I had a hard to find regression that happened because my custom sampler was overridden after #1425. Considering replace_sampler_ddp is set to True by default I think a lot of users will run into similar issues both in terms of regressions and new projects. E.g. users are going to expect that when they write a new sampler and pass it in the dataloader it'll be used without having to change a setting somewhere.

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I understand that, but I don't think, that this is a good idea. If I explicitly set a standard sampler and don't want it to be replaced, I can't do anything this way.

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Fair enough. I'd prefer the trainer flag default to False at least, but let's leave it up to the opinion of others @PyTorchLightning/core-contributors


skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']

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5 changes: 5 additions & 0 deletions pytorch_lightning/trainer/trainer.py
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Expand Up @@ -127,6 +127,7 @@ def __init__(
benchmark: bool = False,
reload_dataloaders_every_epoch: bool = False,
auto_lr_find: Union[bool, str] = False,
replace_sampler_ddp: bool = True,
default_save_path=None, # backward compatible, todo: remove in v0.8.0
gradient_clip=None, # backward compatible, todo: remove in v0.8.0
nb_gpu_nodes=None, # backward compatible, todo: remove in v0.8.0
Expand Down Expand Up @@ -282,6 +283,9 @@ def __init__(
rate in self.hparams.lr | self.hparams.learning_rate in the lightning module.
To use a different key, set a string instead of True with the key name.

replace_sampler_ddp: Explicitly enables or disables sampler replacement.
If not specified this will toggled automatically ddp is used

benchmark: If true enables cudnn.benchmark.

terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the
Expand Down Expand Up @@ -362,6 +366,7 @@ def __init__(
self.reload_dataloaders_every_epoch = reload_dataloaders_every_epoch

self.auto_lr_find = auto_lr_find
self.replace_sampler_ddp = replace_sampler_ddp

self.truncated_bptt_steps = truncated_bptt_steps
self.resume_from_checkpoint = resume_from_checkpoint
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