import operator
from functools import reduce
import torch
from colossalai.device.device_mesh import DeviceMesh
from .utils import merge_same_dim_mesh_list
__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 describe 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.
"""
_DIFFERENCE_DICT = None
def __init__(self, shard_list):
self.is_replica = len(shard_list) == 0
self.shard_list = shard_list
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
@property
def difference_dict(self):
"""
Returns the difference dict, and lazily initializes it when needed
Return:
difference_dict(Dict[Tuple[int, int], Union[int, float, str]]):
difference dict
"""
if self._DIFFERENCE_DICT is None:
self._DIFFERENCE_DICT = self._build_difference_2d_dict()
return self._DIFFERENCE_DICT
def difference(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
@classmethod
def _build_difference_2d_dict(cls):
"""
Build a difference mapping 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:
source_shard_list = cls._convert_str_to_shard_list(source_spec)
target_shard_list = cls._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[(source_spec, target_spec)] = difference
return difference_dict
@staticmethod
def _convert_str_to_shard_list(str_spec):
"""
Convert 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]
class ShardingSpecException(Exception):
pass
class ShardingOutOfIndexError(ShardingSpecException):
pass
class DuplicatedShardingDimensionError(ShardingSpecException):
pass
class ShardingNotDivisibleError(ShardingSpecException):
pass
class ShardingSpec:
"""
Sharding spec for a tensor, it contains info of the logical device mesh this tensor belong
to, the entire shape of the tensor before sharded, and the sharding sequence looks like
[R, R, S0, S1].
Argument:
device_mesh(DeviceMesh): A logical view of a physical mesh.
entire_shape(torch.Size): The entire shape of tensor before sharded.
dim_partition_dict(Dict[int, List[int]]， optional): The key is the dimension of tensor to be sharded,
and the value of the key describe 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, device_mesh: DeviceMesh, entire_shape: torch.Size, dim_partition_dict=None, sharding_sequence=None
):
self.device_mesh = device_mesh
if isinstance(entire_shape, (list, tuple)):
entire_shape = torch.Size(entire_shape)
self.entire_shape = entire_shape
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=len(entire_shape), dim_partition_dict=self.dim_partition_dict
)
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.convert_shard_sequence_to_dict()
self._sanity_check()
def __repr__(self):
res_list = ["DistSpec:"]
res_list.append(f"\n\tshard_sequence: " + ",".join(str(dimspec) for dimspec in self.sharding_sequence))
res_list.append(f"\n\tdevice_mesh_shape: {self.device_mesh.shape}")
return " ".join(res_list)
def _sanity_check(self):
# make sure all axes in logical device mesh only be used once
dim_check_list = list(range(self.device_mesh.logical_mesh_id.dim()))
for dim, shard_list in self.dim_partition_dict.items():
for element in shard_list:
if element in dim_check_list:
dim_check_list.remove(element)
else:
raise DuplicatedShardingDimensionError(
f"find an invalid sharding axis {element} in dim_partition_dict in tensor dimension {dim}."
)
# make sure that the dimension is not out of index
for dim in self.dim_partition_dict.keys():
if dim >= len(self.entire_shape):
raise ShardingOutOfIndexError(
f"The dim_partition_dict specifies to shard dimension {dim} but the entire_shape only has {len(self.entire_shape)} dimensions"
)
# make sure that the sharding for a dimension is divisible by the number of devices
for dim, shard_list in self.dim_partition_dict.items():
tensor_dim_size = self.entire_shape[dim]
num_devices = 1
for element in shard_list:
num_devices *= self.device_mesh.shape[element]
if tensor_dim_size % num_devices != 0:
raise ShardingNotDivisibleError(
f"The size of dimension at index {dim} is {tensor_dim_size}, it cannot be sharded over {num_devices} devices."
)
def convert_dict_to_shard_sequence(self):
"""
Convert dim_partition_dict into list of _DimSpec, and assign it to sharding_sequence.
"""
sharding_sequence = [_DimSpec([])] * len(self.entire_shape)
for dim, shard_list in self.dim_partition_dict.items():
sharding_sequence[dim] = _DimSpec(shard_list)
self.sharding_sequence = 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)
self.dim_partition_dict = new_dim_partition_dict
def sharding_sequence_difference(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.difference(other_dim_spec)
return difference
def get_sharded_shape_per_device(self):
sharded_shape = list(self.entire_shape)
for dim, shard_list in self.dim_partition_dict.items():
mesh_list = [self.device_mesh.shape[mesh_dim] for mesh_dim in shard_list]
shard_partitions = reduce(operator.mul, mesh_list, 1)
assert (
sharded_shape[dim] % shard_partitions == 0
), f"Cannot shard dimension {dim} into {shard_partitions} partitions."
sharded_shape[dim] //= shard_partitions
return torch.Size(sharded_shape)