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[legacy] clean up legacy code (#4743)

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* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci
This commit is contained in:
Hongxin Liu
2023-09-18 16:31:06 +08:00
committed by GitHub
parent 32e7f99416
commit b5f9e37c70
342 changed files with 2919 additions and 4182 deletions
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319
colossalai/legacy/tensor/process_group.py Normal file
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from typing import List, Optional
import torch
from colossalai.context.singleton_meta import SingletonMeta
from colossalai.logging import get_dist_logger
class PyTorchProcessGroupDict(metaclass=SingletonMeta):
def __init__(self):
# distributed settings
# use this dict to record all Pytorch ProcessGroups
self.dict = {}
# set a distributed logger
self.logger = get_dist_logger('ProcessGroup')
def log_pg_init(self, rank_list: List[int], backend: str):
str_list = ["Pytorch ProcessGroup Init:"]
str_list.append(f"backend: {backend}")
str_list.append(f"ranks: {rank_list}")
self.logger.info("\n\t".join(str_list), ranks=[0])
def get(self, rank_list: List[int], backend: str = 'nccl'):
"""Reuse Pytorch ProcessGroup when such a group is initialized
"""
# we need to convert the passed list to a tuple
# since List is unhashable
processgroup_key = (backend, tuple(rank_list))
if processgroup_key not in self.dict:
self.log_pg_init(rank_list=rank_list, backend=backend)
self.dict[processgroup_key] = torch.distributed.new_group(ranks=rank_list, backend=backend)
return self.dict[processgroup_key]
PYTORCHPGDICT_ = PyTorchProcessGroupDict()
class ProcessGroup:
"""ProcessGroup
Process Group indicates how processes are organized in groups for parallel execution using Tensor Parallelism and Data Parallelism.
NOTE, the ProcessGroup must be used after `torch.distributed.initialize()`
Args:
rank: the global rank of the current process.
ranks: List[int], a list of rank id belongings to this process group.
backend: str, the backend of the process group.
tp_degree: Optional[int], tensor parallelism degree. How many processes are inside a tp process group. default None means 1.
dp_degree: Optional[int], data parallelism degree. How many processes are inside a dp process group. . default None means len(ranks).
"""
def __init__(self,
rank: Optional[int] = None,
ranks: Optional[List[int]] = None,
tp_degree: Optional[int] = None,
dp_degree: Optional[int] = None) -> None:
if not torch.distributed.is_initialized():
self.is_init = False
return
assert torch.distributed.is_initialized(), f"ProcessGroup must be used after distributed initialized"
self._rank = torch.distributed.get_rank()
if rank is not None:
assert self._rank == rank # make sure that the global rank is correct
if ranks is None:
self._rank_list = list(range(torch.distributed.get_world_size()))
else:
self._rank_list = ranks
self._rank_list.sort() # ensure that the list is in order
self._world_size = len(self._rank_list)
if dp_degree is None and tp_degree is None:
self._dp_degree = self._world_size
self._tp_degree = 1
elif dp_degree and not tp_degree:
self._dp_degree = dp_degree
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"
self._tp_degree = self._world_size // dp_degree
elif not dp_degree and tp_degree:
self._tp_degree = tp_degree
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"
self._dp_degree = self._world_size // tp_degree
else:
self._dp_degree = dp_degree
self._tp_degree = tp_degree
assert self._dp_degree * self._tp_degree == self._world_size, \
f"the world size {self._world_size} should equals to the product of DP degree {self._dp_degree}" \
f"and TP degree {self._tp_degree}"
self._tp_rank_list = None
self._dp_rank_list = None
for i in range(self._dp_degree):
i_tp_list = [self._rank_list[i * self._tp_degree + j] for j in range(self._tp_degree)]
PYTORCHPGDICT_.get(i_tp_list, 'nccl')
if self._rank in i_tp_list:
self._tp_rank_list = i_tp_list
for j in range(self._tp_degree):
j_dp_list = [self._rank_list[i * self._tp_degree + j] for i in range(self._dp_degree)]
PYTORCHPGDICT_.get(j_dp_list, 'nccl')
if self._rank in j_dp_list:
self._dp_rank_list = j_dp_list
self._has_cpu_groups = False
self.is_init = True
def set_cpu_groups(self):
"""set_cpu_groups
Initialize Pytorch process groups for cpu communications.
"""
if self.has_cpu_groups:
return
for i in range(self._dp_degree):
i_tp_list = [self._rank_list[i * self._tp_degree + j] for j in range(self._tp_degree)]
PYTORCHPGDICT_.get(i_tp_list, 'gloo')
for j in range(self._tp_degree):
j_dp_list = [self._rank_list[i * self._tp_degree + j] for i in range(self._dp_degree)]
PYTORCHPGDICT_.get(j_dp_list, 'gloo')
self._has_cpu_groups = True
@property
def has_cpu_groups(self) -> bool:
"""has_cpu_groups
If cpu groups have been initialized.
Returns:
bool: cpu process groups have been initialized or not.
"""
return self._has_cpu_groups
def __repr__(self):
if self.is_init:
ranks_str = f"ProcessGroup(ranks={self._rank_list},\n"
personal_str = f" rank={self._rank}, dp={self._dp_degree}, tp={self._tp_degree})"
return ranks_str + personal_str
else:
return "ProcessGroup not initialized"
def __eq__(self, obj: 'ProcessGroup') -> bool:
if not isinstance(obj, ProcessGroup):
return False
if self._rank != obj._rank:
return False
if self._rank_list != obj._rank_list:
return False
if self._tp_rank_list != obj._tp_rank_list:
return False
if self._dp_rank_list != obj._dp_rank_list:
return False
if self._tp_degree != obj._tp_degree:
return False
if self._dp_degree != obj._dp_degree:
return False
return True
def rank(self) -> int:
"""rank
The current rank in the global process group.
Returns:
int: the rank number
"""
return self._rank
def ranks_in_group(self) -> List[int]:
"""ranks_in_group
a list of rank number in in the global process group.
Returns:
List[int]: a list of rank number.
"""
return self._rank_list
def world_size(self) -> int:
"""world_size
The world size of the global process group.
Returns:
int: world size
"""
return self._world_size
def tp_rank_list(self) -> List[int]:
"""tp_rank_list
the rank list in the TP process group containing the current rank.
Returns:
List[int]: the list of rank number.
"""
return self._tp_rank_list
def dp_rank_list(self) -> List[int]:
"""dp_rank_list
the rank list in the DP process group containing the current rank.
Returns:
List[int]: the list of rank number.
"""
return self._dp_rank_list
def tp_local_rank(self) -> int:
"""tp_local_rank
The local rank number in the current TP process group.
Returns:
int: tp rank number.
"""
return self._rank % self._tp_degree
def dp_local_rank(self) -> int:
"""dp_local_rank
The local rank number in the current DP process group.
Returns:
int: dp rank number.
"""
return self._rank // self._tp_degree
def dp_world_size(self) -> int:
"""dp_world_size
The world size of the current DP process group.
Returns:
int: dp world size
"""
return len(self._dp_rank_list)
def tp_world_size(self) -> int:
"""tp_world_size
The world size of the current TP process group.
Returns:
int: tp world size
"""
return len(self._tp_rank_list)
def dp_process_group(self):
"""dp_process_group
the pytorch DP process group containing the current rank.
Returns:
`torch._C._distributed_c10d.ProcessGroup`: the pytorch DP process group.
"""
return PYTORCHPGDICT_.get(self._dp_rank_list, 'nccl')
def tp_process_group(self):
"""tp_process_group
the pytorch TP process group containing the current rank.
Returns:
`torch._C._distributed_c10d.ProcessGroup`: the pytorch TP process group.
"""
return PYTORCHPGDICT_.get(self._tp_rank_list, 'nccl')
def cpu_dp_process_group(self):
"""cpu_dp_process_group
the pytorch CPU DP process group containing the current rank.
assert failed if cpu process group is not initialized.
Returns:
`torch._C._distributed_c10d.ProcessGroup`: the pytorch DP process group.
"""
assert self._has_cpu_groups
return PYTORCHPGDICT_.get(self._dp_rank_list, 'gloo')
def cpu_tp_process_group(self):
"""cpu_tp_process_group
the pytorch CPU TP process group containing the current rank.
assert failed if cpu process group is not initialized.
Returns:
`torch._C._distributed_c10d.ProcessGroup`: the pytorch TP process group.
"""
assert self._has_cpu_groups
return PYTORCHPGDICT_.get(self._tp_rank_list, 'gloo')
def get_ranks_in_dp(self) -> List[int]:
"""get_ranks_in_dp
ranks in current dp process group.
Returns:
List[int]: a list of rank number.
"""
return self._dp_rank_list
def get_ranks_in_tp(self):
"""get_ranks_in_tp
ranks in current tp process group.
Returns:
List[int]: a list of rank number.
"""
return self._tp_rank_list