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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.parallel.distributed import (DistributedDataParallel,
_find_tensors)
from annotator.uniformer.mmcv import print_log
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .scatter_gather import scatter_kwargs
class MMDistributedDataParallel(DistributedDataParallel):
"""The DDP module that supports DataContainer.
MMDDP has two main differences with PyTorch DDP:
- It supports a custom type :class:`DataContainer` which allows more
flexible control of input data.
- It implement two APIs ``train_step()`` and ``val_step()``.
"""
def to_kwargs(self, inputs, kwargs, device_id):
# Use `self.to_kwargs` instead of `self.scatter` in pytorch1.8
# to move all tensors to device_id
return scatter_kwargs(inputs, kwargs, [device_id], dim=self.dim)
def scatter(self, inputs, kwargs, device_ids):
return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
def train_step(self, *inputs, **kwargs):
"""train_step() API for module wrapped by DistributedDataParallel.
This method is basically the same as
``DistributedDataParallel.forward()``, while replacing
``self.module.forward()`` with ``self.module.train_step()``.
It is compatible with PyTorch 1.1 - 1.5.
"""
# In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the
# end of backward to the beginning of forward.
if ('parrots' not in TORCH_VERSION
and digit_version(TORCH_VERSION) >= digit_version('1.7')
and self.reducer._rebuild_buckets()):
print_log(
'Reducer buckets have been rebuilt in this iteration.',
logger='mmcv')
if getattr(self, 'require_forward_param_sync', True):
self._sync_params()
if self.device_ids:
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
if len(self.device_ids) == 1:
output = self.module.train_step(*inputs[0], **kwargs[0])
else:
outputs = self.parallel_apply(
self._module_copies[:len(inputs)], inputs, kwargs)
output = self.gather(outputs, self.output_device)
else:
output = self.module.train_step(*inputs, **kwargs)
if torch.is_grad_enabled() and getattr(
self, 'require_backward_grad_sync', True):
if self.find_unused_parameters:
self.reducer.prepare_for_backward(list(_find_tensors(output)))
else:
self.reducer.prepare_for_backward([])
else:
if ('parrots' not in TORCH_VERSION
and digit_version(TORCH_VERSION) > digit_version('1.2')):
self.require_forward_param_sync = False
return output
def val_step(self, *inputs, **kwargs):
"""val_step() API for module wrapped by DistributedDataParallel.
This method is basically the same as
``DistributedDataParallel.forward()``, while replacing
``self.module.forward()`` with ``self.module.val_step()``.
It is compatible with PyTorch 1.1 - 1.5.
"""
# In PyTorch >= 1.7, ``reducer._rebuild_buckets()`` is moved from the
# end of backward to the beginning of forward.
if ('parrots' not in TORCH_VERSION
and digit_version(TORCH_VERSION) >= digit_version('1.7')
and self.reducer._rebuild_buckets()):
print_log(
'Reducer buckets have been rebuilt in this iteration.',
logger='mmcv')
if getattr(self, 'require_forward_param_sync', True):
self._sync_params()
if self.device_ids:
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
if len(self.device_ids) == 1:
output = self.module.val_step(*inputs[0], **kwargs[0])
else:
outputs = self.parallel_apply(
self._module_copies[:len(inputs)], inputs, kwargs)
output = self.gather(outputs, self.output_device)
else:
output = self.module.val_step(*inputs, **kwargs)
if torch.is_grad_enabled() and getattr(
self, 'require_backward_grad_sync', True):
if self.find_unused_parameters:
self.reducer.prepare_for_backward(list(_find_tensors(output)))
else:
self.reducer.prepare_for_backward([])
else:
if ('parrots' not in TORCH_VERSION
and digit_version(TORCH_VERSION) > digit_version('1.2')):
self.require_forward_param_sync = False
return output
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