mirror of https://github.com/hpcaitech/ColossalAI
325 lines
10 KiB
Python
325 lines
10 KiB
Python
from typing import List, Optional
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import torch
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from colossalai.context.singleton_meta import SingletonMeta
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from colossalai.logging import get_dist_logger
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class PyTorchProcessGroupDict(metaclass=SingletonMeta):
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def __init__(self):
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# distributed settings
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# use this dict to record all Pytorch ProcessGroups
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self.dict = {}
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# set a distributed logger
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self.logger = get_dist_logger("ProcessGroup")
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def log_pg_init(self, rank_list: List[int], backend: str):
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str_list = ["Pytorch ProcessGroup Init:"]
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str_list.append(f"backend: {backend}")
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str_list.append(f"ranks: {rank_list}")
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self.logger.info("\n\t".join(str_list), ranks=[0])
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def get(self, rank_list: List[int], backend: str = "nccl"):
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"""Reuse Pytorch ProcessGroup when such a group is initialized"""
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# we need to convert the passed list to a tuple
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# since List is unhashable
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processgroup_key = (backend, tuple(rank_list))
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if processgroup_key not in self.dict:
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self.log_pg_init(rank_list=rank_list, backend=backend)
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self.dict[processgroup_key] = torch.distributed.new_group(ranks=rank_list, backend=backend)
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return self.dict[processgroup_key]
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PYTORCHPGDICT_ = PyTorchProcessGroupDict()
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class ProcessGroup:
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"""ProcessGroup
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Process Group indicates how processes are organized in groups for parallel execution using Tensor Parallelism and Data Parallelism.
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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. How many processes are inside a tp process group. default None means 1.
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dp_degree: Optional[int], data parallelism degree. How many processes are inside a dp process group. . default None means len(ranks).
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"""
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def __init__(
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self,
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rank: Optional[int] = None,
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ranks: Optional[List[int]] = None,
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tp_degree: Optional[int] = None,
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dp_degree: Optional[int] = None,
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) -> None:
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if not torch.distributed.is_initialized():
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self.is_init = False
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return
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assert torch.distributed.is_initialized(), f"ProcessGroup must be used after distributed initialized"
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self._rank = torch.distributed.get_rank()
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if rank is not None:
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assert self._rank == rank # make sure that the global rank is correct
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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._rank_list.sort() # ensure that the list is in order
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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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elif dp_degree and not tp_degree:
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self._dp_degree = dp_degree
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assert (
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self._world_size % self._dp_degree == 0
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), 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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elif not dp_degree and tp_degree:
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self._tp_degree = tp_degree
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assert (
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self._world_size % self._tp_degree == 0
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), 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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else:
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self._dp_degree = dp_degree
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self._tp_degree = tp_degree
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assert self._dp_degree * self._tp_degree == self._world_size, (
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f"the world size {self._world_size} should equals to the product of DP degree {self._dp_degree}"
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f"and TP degree {self._tp_degree}"
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)
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self._tp_rank_list = None
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self._dp_rank_list = None
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for i in range(self._dp_degree):
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i_tp_list = [self._rank_list[i * self._tp_degree + j] for j in range(self._tp_degree)]
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PYTORCHPGDICT_.get(i_tp_list, "nccl")
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if self._rank in i_tp_list:
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self._tp_rank_list = i_tp_list
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for j in range(self._tp_degree):
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j_dp_list = [self._rank_list[i * self._tp_degree + j] for i in range(self._dp_degree)]
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PYTORCHPGDICT_.get(j_dp_list, "nccl")
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if self._rank in j_dp_list:
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self._dp_rank_list = j_dp_list
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self._has_cpu_groups = False
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self.is_init = True
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def set_cpu_groups(self):
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"""set_cpu_groups
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Initialize Pytorch process groups for cpu communications.
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"""
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if self.has_cpu_groups:
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return
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for i in range(self._dp_degree):
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i_tp_list = [self._rank_list[i * self._tp_degree + j] for j in range(self._tp_degree)]
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PYTORCHPGDICT_.get(i_tp_list, "gloo")
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for j in range(self._tp_degree):
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j_dp_list = [self._rank_list[i * self._tp_degree + j] for i in range(self._dp_degree)]
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PYTORCHPGDICT_.get(j_dp_list, "gloo")
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self._has_cpu_groups = True
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@property
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def has_cpu_groups(self) -> bool:
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"""has_cpu_groups
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If cpu groups have been initialized.
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Returns:
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bool: cpu process groups have been initialized or not.
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"""
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return self._has_cpu_groups
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def __repr__(self):
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if self.is_init:
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ranks_str = f"ProcessGroup(ranks={self._rank_list},\n"
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personal_str = f" rank={self._rank}, dp={self._dp_degree}, tp={self._tp_degree})"
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return ranks_str + personal_str
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else:
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return "ProcessGroup not initialized"
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def __eq__(self, obj: "ProcessGroup") -> bool:
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if not isinstance(obj, ProcessGroup):
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return False
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if self._rank != obj._rank:
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return False
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if self._rank_list != obj._rank_list:
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return False
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if self._tp_rank_list != obj._tp_rank_list:
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return False
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if self._dp_rank_list != obj._dp_rank_list:
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return False
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if self._tp_degree != obj._tp_degree:
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return False
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if self._dp_degree != obj._dp_degree:
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return False
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return True
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def rank(self) -> int:
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"""rank
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The current rank in the global process group.
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Returns:
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int: the rank number
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"""
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return self._rank
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def ranks_in_group(self) -> List[int]:
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"""ranks_in_group
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a list of rank number in in the global process group.
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Returns:
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List[int]: a list of rank number.
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"""
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return self._rank_list
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def world_size(self) -> int:
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"""world_size
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The world size of the global process group.
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Returns:
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int: world size
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"""
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return self._world_size
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def tp_rank_list(self) -> List[int]:
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"""tp_rank_list
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the rank list in the TP process group containing the current rank.
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Returns:
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List[int]: the list of rank number.
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"""
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return self._tp_rank_list
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def dp_rank_list(self) -> List[int]:
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"""dp_rank_list
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the rank list in the DP process group containing the current rank.
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Returns:
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List[int]: the list of rank number.
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"""
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return self._dp_rank_list
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def tp_local_rank(self) -> int:
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"""tp_local_rank
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The local rank number in the current TP process group.
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Returns:
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int: tp rank number.
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"""
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return self._rank % self._tp_degree
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def dp_local_rank(self) -> int:
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"""dp_local_rank
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The local rank number in the current DP process group.
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Returns:
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int: dp rank number.
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"""
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return self._rank // self._tp_degree
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def dp_world_size(self) -> int:
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"""dp_world_size
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The world size of the current DP process group.
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Returns:
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int: dp world size
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"""
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return len(self._dp_rank_list)
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def tp_world_size(self) -> int:
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"""tp_world_size
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The world size of the current TP process group.
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Returns:
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int: tp world size
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"""
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return len(self._tp_rank_list)
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def dp_process_group(self):
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"""dp_process_group
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the pytorch DP process group containing the current rank.
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Returns:
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`torch._C._distributed_c10d.ProcessGroup`: the pytorch DP process group.
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"""
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return PYTORCHPGDICT_.get(self._dp_rank_list, "nccl")
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def tp_process_group(self):
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"""tp_process_group
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the pytorch TP process group containing the current rank.
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Returns:
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`torch._C._distributed_c10d.ProcessGroup`: the pytorch TP process group.
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"""
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return PYTORCHPGDICT_.get(self._tp_rank_list, "nccl")
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def cpu_dp_process_group(self):
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"""cpu_dp_process_group
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the pytorch CPU DP process group containing the current rank.
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assert failed if cpu process group is not initialized.
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Returns:
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`torch._C._distributed_c10d.ProcessGroup`: the pytorch DP process group.
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"""
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assert self._has_cpu_groups
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return PYTORCHPGDICT_.get(self._dp_rank_list, "gloo")
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def cpu_tp_process_group(self):
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"""cpu_tp_process_group
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the pytorch CPU TP process group containing the current rank.
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assert failed if cpu process group is not initialized.
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Returns:
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`torch._C._distributed_c10d.ProcessGroup`: the pytorch TP process group.
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"""
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assert self._has_cpu_groups
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return PYTORCHPGDICT_.get(self._tp_rank_list, "gloo")
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def get_ranks_in_dp(self) -> List[int]:
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"""get_ranks_in_dp
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ranks in current dp process group.
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Returns:
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List[int]: a list of rank number.
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"""
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return self._dp_rank_list
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def get_ranks_in_tp(self):
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"""get_ranks_in_tp
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ranks in current tp process group.
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Returns:
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List[int]: a list of rank number.
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"""
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return self._tp_rank_list
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