| from collections import OrderedDict | |
| import torch.distributed as dist | |
| from mmcv.runner import OptimizerHook | |
| from torch._utils import (_flatten_dense_tensors, _take_tensors, | |
| _unflatten_dense_tensors) | |
| def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): | |
| if bucket_size_mb > 0: | |
| bucket_size_bytes = bucket_size_mb * 1024 * 1024 | |
| buckets = _take_tensors(tensors, bucket_size_bytes) | |
| else: | |
| buckets = OrderedDict() | |
| for tensor in tensors: | |
| tp = tensor.type() | |
| if tp not in buckets: | |
| buckets[tp] = [] | |
| buckets[tp].append(tensor) | |
| buckets = buckets.values() | |
| for bucket in buckets: | |
| flat_tensors = _flatten_dense_tensors(bucket) | |
| dist.all_reduce(flat_tensors) | |
| flat_tensors.div_(world_size) | |
| for tensor, synced in zip( | |
| bucket, _unflatten_dense_tensors(flat_tensors, bucket)): | |
| tensor.copy_(synced) | |
| def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): | |
| grads = [ | |
| param.grad.data for param in params | |
| if param.requires_grad and param.grad is not None | |
| ] | |
| world_size = dist.get_world_size() | |
| if coalesce: | |
| _allreduce_coalesced(grads, world_size, bucket_size_mb) | |
| else: | |
| for tensor in grads: | |
| dist.all_reduce(tensor.div_(world_size)) | |
| class DistOptimizerHook(OptimizerHook): | |
| def __init__(self, grad_clip=None, coalesce=True, bucket_size_mb=-1): | |
| self.grad_clip = grad_clip | |
| self.coalesce = coalesce | |
| self.bucket_size_mb = bucket_size_mb | |
| def after_train_iter(self, runner): | |
| runner.optimizer.zero_grad() | |
| runner.outputs['loss'].backward() | |
| if self.grad_clip is not None: | |
| self.clip_grads(runner.model.parameters()) | |
| runner.optimizer.step() | |