| |
| import torch |
| import torch.distributed as dist |
|
|
|
|
| def init_distributed_group(): |
| """r initialize sequence parallel group. |
| """ |
| if not dist.is_initialized(): |
| dist.init_process_group(backend='nccl') |
|
|
|
|
| def get_rank(): |
| return dist.get_rank() |
|
|
|
|
| def get_world_size(): |
| return dist.get_world_size() |
|
|
|
|
| def all_to_all(x, scatter_dim, gather_dim, group=None, **kwargs): |
| """ |
| `scatter` along one dimension and `gather` along another. |
| """ |
| world_size = get_world_size() |
| if world_size > 1: |
| inputs = [u.contiguous() for u in x.chunk(world_size, dim=scatter_dim)] |
| outputs = [torch.empty_like(u) for u in inputs] |
| dist.all_to_all(outputs, inputs, group=group, **kwargs) |
| x = torch.cat(outputs, dim=gather_dim).contiguous() |
| return x |
|
|
|
|
| def all_gather(tensor): |
| world_size = dist.get_world_size() |
| if world_size == 1: |
| return [tensor] |
| tensor_list = [torch.empty_like(tensor) for _ in range(world_size)] |
| torch.distributed.all_gather(tensor_list, tensor) |
| return tensor_list |
|
|
|
|
| def gather_forward(input, dim): |
| |
| world_size = dist.get_world_size() |
| if world_size == 1: |
| return input |
|
|
| |
| output = all_gather(input) |
| return torch.cat(output, dim=dim).contiguous() |
|
|
