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[DTensor] refactor sharding spec (#2987)

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* [autoparallel] refactor sharding spec

* rename function name
This commit is contained in:
YuliangLiu0306
2023-03-07 11:08:11 +08:00
committed by GitHub
parent 400f63012e
commit cd2b0eaa8d
6 changed files with 341 additions and 7 deletions
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237
colossalai/tensor/d_tensor/sharding_spec.py Normal file
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from copy import deepcopy
from typing import Dict, List
from ..utils import merge_same_dim_mesh_list
from .misc import ShardingOutOfIndexError
__all__ = ['DimSpec', 'ShardingException', 'ShardingSpec']
ALLGATHER_COST = 20
SHARD_COST = 5
STEP_PENALTY = 6
NAN = 'nan'
class DimSpec:
'''
Sharding spec for single dimension of the sharded tensor decribe the sharding dimension of
logical device mesh and give a method to compute the difference between them.
This class is used internally in ShardingSpec.
Argument:
shard_list(List[int]): if shard_list is None, the dim spec will be 'R' type.
Otherwise, the element in shard_list means the data will be sharded in that dimension.
'''
def __init__(self, shard_list):
self.is_replica = len(shard_list) == 0
self.shard_list = shard_list
self.build_difference_2d_dict()
def __eq__(self, other):
return str(self) == str(other)
def __repr__(self):
if self.is_replica:
return 'R'
target = 'S'
for dim in self.shard_list:
target += str(dim)
return target
def _convert_str_to_shard_list(self, str_spec):
'''
Conver str_spec into shard_list.
Argument:
str_spec(str): dim spec in str type.
'''
if str_spec == 'R':
return []
if str_spec == 'S0':
return [0]
if str_spec == 'S1':
return [1]
if str_spec == 'S01':
return [0, 1]
def build_difference_2d_dict(self):
'''
Build a difference maping for 2D device mesh case. It will be used to
compute the difference between DimSpec pairs.
'''
source_spec_list = ['R', 'S0', 'S1', 'S01']
target_spec_list = ['R', 'S0', 'S1', 'S01']
difference_dict = {}
for source_spec in source_spec_list:
for target_spec in target_spec_list:
legal_sharding_dims = []
spec_pair = (deepcopy(source_spec), deepcopy(target_spec))
source_shard_list = self._convert_str_to_shard_list(source_spec)
target_shard_list = self._convert_str_to_shard_list(target_spec)
# source same as target
if source_shard_list == target_shard_list:
difference = 0
# all_gather(source) -> target
elif len(source_shard_list
) == len(target_shard_list) + 1 and source_shard_list[:-1] == target_shard_list:
difference = ALLGATHER_COST
# shard(source) -> target
elif len(source_shard_list) == len(
target_shard_list) - 1 and source_shard_list == target_shard_list[:-1] and target_shard_list[
-1] not in source_shard_list:
difference = SHARD_COST
# S1 -> S0 or S0 -> S1
elif len(source_shard_list) == len(target_shard_list):
# source -> R -> target
difference = ALLGATHER_COST + STEP_PENALTY + SHARD_COST
# R -> S01
elif len(source_shard_list) == len(target_shard_list) - 2:
difference = SHARD_COST + STEP_PENALTY + SHARD_COST
# S01 -> R
elif len(source_shard_list) == len(target_shard_list) + 2:
difference = ALLGATHER_COST + STEP_PENALTY + ALLGATHER_COST
# S1 -> S01
elif len(source_shard_list) == len(target_shard_list) - 1:
difference = ALLGATHER_COST + STEP_PENALTY + SHARD_COST + STEP_PENALTY + SHARD_COST
# S01 -> S1
elif len(source_shard_list) == len(target_shard_list) + 1:
difference = ALLGATHER_COST + STEP_PENALTY + ALLGATHER_COST + STEP_PENALTY + SHARD_COST
else:
difference = NAN
difference_dict[spec_pair] = difference
self.difference_dict = difference_dict
def dim_diff(self, other):
'''
The difference between two _DimSpec.
Argument:
other(_DimSpec): the dim spec to compare with.
Return:
difference(int): the difference between two _DimSpec.
Example:
dim_spec = _DimSpec([0])
other_dim_spec = _DimSpec([0, 1])
print(dim_spec.difference(other_dim_spec))
Output:
5
'''
difference = self.difference_dict[(str(self), str(other))]
return difference
class ShardingSpec:
'''
Sharding spec describes how to shard a tensor with dim_size dimensions. The sharding sequence looks like
[R, R, S0, S1], which means
Argument:
dim_partition_dict(Dict[int, List[int]], optional): The key is the dimension of tensor to be sharded,
and the value of the key decribe which logical axis will be sharded in that dimension.
sharding_sequence(List[DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1].
'''
def __init__(self,
dim_size: int,
dim_partition_dict: Dict[int, List[int]] = None,
sharding_sequence: List[DimSpec] = None):
self.dims = dim_size
self.dim_partition_dict = dim_partition_dict
self.sharding_sequence = sharding_sequence
if self.sharding_sequence is None:
assert self.dim_partition_dict is not None, f'dim_partition_dict should not be None, if sharding_sequence is NoneType object.'
self.dim_partition_dict = merge_same_dim_mesh_list(dim_size=self.dims,
dim_partition_dict=self.dim_partition_dict)
self.sharding_sequence = self.convert_dict_to_shard_sequence()
elif self.dim_partition_dict is None:
assert self.sharding_sequence is not None, f'sharding_sequence should not be None, if dim_partition_dict is NoneType object.'
self.dim_partition_dict = self.convert_shard_sequence_to_dict()
self._sanity_check()
def _sanity_check(self):
if len(self.sharding_sequence) > self.dims:
raise ShardingOutOfIndexError(
f'sharding_sequence should have {self.dims} elements, but got index {len(self.sharding_sequence)}.')
if max(list(self.dim_partition_dict.keys())) >= self.dims:
raise ShardingOutOfIndexError(
f'the key of dim_partition_dict should be less than {self.dims}, but got {max(list(self.dim_partition_dict.keys()))}.'
)
def __repr__(self):
res_list = ["ShardingSpec:"]
res_list.append(f"\n\tshard_sequence: " + ",".join(str(dimspec) for dimspec in self.sharding_sequence))
return ' '.join(res_list)
def convert_dict_to_shard_sequence(self):
'''
Convert dim_partition_dict into list of DimSpec, and assign it to sharding_sequence.
'''
sharding_sequence = [DimSpec([])] * self.dims
for dim, shard_list in self.dim_partition_dict.items():
sharding_sequence[dim] = DimSpec(shard_list)
return sharding_sequence
def convert_shard_sequence_to_dict(self):
'''
Convert sharding_sequence into dim_partition_dict.
'''
new_dim_partition_dict = {}
for index, dim_spec in enumerate(self.sharding_sequence):
if not dim_spec.is_replica:
if index not in new_dim_partition_dict:
new_dim_partition_dict[index] = []
new_dim_partition_dict[index].extend(dim_spec.shard_list)
return new_dim_partition_dict
def spec_diff(self, other):
'''
This function is a naive version of difference computation. It just simply accumulates difference every dimension between the
pair of sharding sequence.
Example:
dim_partition_dict = {0: [0, 1]}
# DistSpec:
# shard_sequence: S01,R,R
# device_mesh_shape: (4, 4)
sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
dim_partition_dict_to_compare = {0: [0], 1: [1]}
# DistSpec:
# shard_sequence: S0,S1,R
# device_mesh_shape: (4, 4)
sharding_spec_to_compare = ShardingSpec(device_mesh, entire_shape, dim_partition_dict_to_compare)
print(sharding_spec.sharding_sequence_difference(sharding_spec_to_compare))
Output:
25
Argument:
other(ShardingSpec): The ShardingSpec to compared with.
Return:
difference(int): Difference between two ShardingSpec.
'''
assert len(self.sharding_sequence) == len(
other.sharding_sequence), f'Cannot compare difference for two sharding specs with different length.'
difference = 0
for orig_dim_spec, other_dim_spec in zip(self.sharding_sequence, other.sharding_sequence):
difference += orig_dim_spec.dim_diff(other_dim_spec)
return difference