lightning/pytorch_lightning/overrides/distributed.py

# 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 itertools
from typing import Any, Iterator, List, Optional

import torch
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import BatchSampler, DistributedSampler, Sampler

import pytorch_lightning as pl
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase


class LightningDistributedModule(_LightningModuleWrapperBase):

    def __init__(self, pl_module: 'pl.LightningModule') -> None:
        """
        Wraps the user's LightningModule and redirects the forward call to the appropriate
        method, either ``training_step``, ``validation_step``, ``test_step`` or ``predict``.
        This class is used in combination with :class:`~torch.nn.parallel.DistributedDataParallel` as
        shown in the example.

        Example:

            ddp_model = torch.nn.parallel.DistributedDataParallel(
                module=LightningDistributedModule(lightning_module),
                device_ids=[local_rank],
                ...
            )

        Args:
            pl_module: the model to wrap

        """
        super().__init__(pl_module)


def _find_tensors(obj):  # pragma: no-cover
    r"""
    Recursively find all tensors contained in the specified object.
    """
    if isinstance(obj, torch.Tensor):
        return [obj]
    if isinstance(obj, (list, tuple)):
        return itertools.chain(*map(_find_tensors, obj))
    if isinstance(obj, dict):
        return itertools.chain(*map(_find_tensors, obj.values()))
    return []


# In manual_optimization, we need to call reducer prepare_for_backward.
# Note: Keep track of Pytorch DDP and update if there is a change
# https://github.com/pytorch/pytorch/blob/v1.7.1/torch/nn/parallel/distributed.py#L626-L638
def prepare_for_backward(model: DistributedDataParallel, output: Any):
    if torch.is_grad_enabled() and model.require_backward_grad_sync:
        model.require_forward_param_sync = True
        # We'll return the output object verbatim since it is a freeform
        # object. We need to find any tensors in this object, though,
        # because we need to figure out which parameters were used during
        # this forward pass, to ensure we short circuit reduction for any
        # unused parameters. Only if `find_unused_parameters` is set.
        if model.find_unused_parameters:
            model.reducer.prepare_for_backward(list(_find_tensors(output)))
        else:
            model.reducer.prepare_for_backward([])
    else:
        model.require_forward_param_sync = False


class UnrepeatedDistributedSampler(DistributedSampler):
    """
    A fork of the pytorch DistributedSampler that doesn't repeat data, instead
    allowing the number of batches per process to be off-by-one from each other.
    This makes this sampler usable for predictions (it's deterministic and
    doesn't require shuffling). It is potentially unsafe to use this sampler for
    training, because during training the DistributedDataParallel syncs buffers
    on each forward pass, so it could freeze if one of the processes runs one
    fewer batch. During prediction, buffers are only synced on the first batch,
    so this is safe to use as long as each process runs at least one batch. We
    verify this in an assert.

    Taken from https://github.com/jpuigcerver/PyLaia/blob/v1.0.0/laia/data/unpadded_distributed_sampler.py
    and https://github.com/pytorch/pytorch/issues/25162#issuecomment-634146002
    """

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        super().__init__(*args, **kwargs)
        self.num_samples = len(range(self.rank, len(self.dataset), self.num_replicas))
        self.total_size = len(self.dataset)
        # If any process has at least one batch, every other process needs to
        # have at least one batch, or the DistributedDataParallel could lock up.
        assert self.num_samples >= 1 or self.total_size == 0

    def __iter__(self) -> Iterator[List[int]]:
        if self.shuffle:
            # deterministically shuffle based on epoch
            g = torch.Generator()
            g.manual_seed(self.epoch)
            indices = torch.randperm(len(self.dataset), generator=g).tolist()
        else:
            indices = list(range(len(self.dataset)))

        assert len(indices) == self.total_size

        # subsample
        indices = indices[self.rank:self.total_size:self.num_replicas]
        assert len(indices) == self.num_samples

        return iter(indices)


class IndexBatchSamplerWrapper:
    """This class is used to wrap a :class:`torch.utils.data.BatchSampler` and capture its indices."""

    def __init__(self, sampler: BatchSampler) -> None:
        self._sampler = sampler
        self.batch_indices: Optional[List[int]] = None

    def __iter__(self) -> Iterator[List[int]]:
        for batch in self._sampler:
            self.batch_indices = batch
            yield batch

    def __len__(self) -> int:
        return len(self._sampler)

    @property
    def drop_last(self) -> bool:
        return self._sampler.drop_last

    @property
    def batch_size(self) -> int:
        return self._sampler.batch_size

    @property
    def sampler(self) -> Sampler:
        return self._sampler.sampler