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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 | |