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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from torch import nn | |
| from fairseq.distributed import utils | |
| class TPUDistributedDataParallel(nn.Module): | |
| def __init__(self, module, process_group): | |
| super().__init__() | |
| self.module = module | |
| self.process_group = process_group | |
| self.world_size = utils.get_world_size(self.process_group) | |
| def forward(self, *inputs, **kwargs): | |
| return self.module(*inputs, **kwargs) | |
| def all_reduce_grads(self): | |
| gradients = [] | |
| for p in self.parameters(): | |
| if not p.requires_grad: | |
| continue | |
| if p.grad is None: | |
| p.grad = torch.zeros_like(p) | |
| if p.grad.requires_grad: | |
| raise RuntimeError( | |
| "TPUDistributedDataParallel only works with gradients that don't " | |
| "require grad" | |
| ) | |
| gradients.append(p.grad) | |
| import torch_xla.core.xla_model as xm | |
| xm.all_reduce( | |
| 'sum', | |
| gradients, | |
| scale=1. / self.world_size, | |
| groups=self.process_group[1], | |
| ) | |