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78 lines
3.0 KiB
78 lines
3.0 KiB
import torch
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from typing import List, Optional
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class ProcessGroup:
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"""
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Process Group contains group partition for Tensor Parallel and Data Parallel.
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NOTE, the ProcessGroup must be used after torch.distributed.initialize()
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args:
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rank: the global rank of the current process.
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ranks: List[int], a list of rank id belongings to this process group.
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backend: str, the backend of the process group.
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tp_degree: Optional[int], tensor parallelism degree, default None means 1
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dp_degree: Optional[int], data parallelism degree, default None means len(ranks)
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"""
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def __init__(self,
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rank: Optional[int] = None,
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ranks: Optional[List[int]] = None,
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backend: str = 'nccl',
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tp_degree: Optional[int] = None,
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dp_degree: Optional[int] = None) -> None:
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assert torch.distributed.is_initialized(), f"ProcessGroup must be used after distributed initialized"
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if rank is None:
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self._rank = torch.distributed.get_rank()
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else:
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self._rank = rank
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if ranks is None:
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self._rank_list = list(range(torch.distributed.get_world_size()))
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else:
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self._rank_list = ranks
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self._backend = backend
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self._world_size = len(self._rank_list)
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if dp_degree is None and tp_degree is None:
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self._dp_degree = self._world_size
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self._tp_degree = 1
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if dp_degree and not tp_degree:
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self._dp_degree = dp_degree
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assert self._world_size % self._dp_degree == 0, f"DP degree {dp_degree} should be divisible by {self._world_size} hen DP degree is None"
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self._tp_degree = self._world_size / dp_degree
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if not dp_degree and tp_degree:
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self._tp_degree = tp_degree
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assert self._world_size % self._tp_degree == 0, f"TP degree {tp_degree} should be divisible by {self._world_size} when DP degree is None"
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self._dp_degree = self._world_size / tp_degree
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self._tp_rank_list = []
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self._dp_rank_list = []
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for rank_id in range(self._world_size):
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# rank_id and self._rank in the same tp group
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if rank_id % self._tp_degree == self._rank % self._tp_degree:
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self._dp_rank_list.append(rank_id)
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if rank_id // self._tp_degree == self._rank // self._tp_degree:
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self._tp_rank_list.append(rank_id)
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self._tp_process_group = torch.distributed.new_group(ranks=self._tp_rank_list, backend=backend)
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self._dp_process_group = torch.distributed.new_group(ranks=self._dp_rank_list, backend=backend)
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def world_size(self):
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return self._world_size
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def dp_world_size(self):
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return len(self._dp_rank_list)
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def tp_world_size(self):
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return len(self._tp_rank_list)
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def dp_process_group(self):
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return self._dp_process_group
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def tp_process_group(self):
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return self._tp_process_group
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