mirror of https://github.com/hpcaitech/ColossalAI
319 lines
12 KiB
Python
319 lines
12 KiB
Python
import operator
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from functools import reduce
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import torch
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from colossalai.device.device_mesh import DeviceMesh
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from .utils import merge_same_dim_mesh_list
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__all__ = ["_DimSpec", "ShardingException", "ShardingSpec"]
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ALLGATHER_COST = 20
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SHARD_COST = 5
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STEP_PENALTY = 6
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NAN = "nan"
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class _DimSpec:
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"""
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Sharding spec for single dimension of the sharded tensor describe the sharding dimension of
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logical device mesh and give a method to compute the difference between them.
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This class is used internally in ShardingSpec.
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Argument:
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shard_list(List[int]): if shard_list is None, the dim spec will be 'R' type.
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Otherwise, the element in shard_list means the data will be sharded in that dimension.
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"""
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_DIFFERENCE_DICT = None
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def __init__(self, shard_list):
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self.is_replica = len(shard_list) == 0
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self.shard_list = shard_list
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def __eq__(self, other):
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return str(self) == str(other)
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def __repr__(self):
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if self.is_replica:
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return "R"
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target = "S"
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for dim in self.shard_list:
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target += str(dim)
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return target
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@property
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def difference_dict(self):
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"""
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Returns the difference dict, and lazily initializes it when needed
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Return:
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difference_dict(Dict[Tuple[int, int], Union[int, float, str]]):
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difference dict
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"""
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if self._DIFFERENCE_DICT is None:
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self._DIFFERENCE_DICT = self._build_difference_2d_dict()
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return self._DIFFERENCE_DICT
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def difference(self, other):
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"""
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The difference between two _DimSpec.
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Argument:
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other(_DimSpec): the dim spec to compare with.
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Return:
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difference(int): the difference between two _DimSpec.
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Example:
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dim_spec = _DimSpec([0])
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other_dim_spec = _DimSpec([0, 1])
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print(dim_spec.difference(other_dim_spec))
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Output:
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5
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"""
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difference = self.difference_dict[(str(self), str(other))]
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return difference
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@classmethod
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def _build_difference_2d_dict(cls):
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"""
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Build a difference mapping for 2D device mesh case. It will be used to
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compute the difference between _DimSpec pairs.
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"""
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source_spec_list = ["R", "S0", "S1", "S01"]
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target_spec_list = ["R", "S0", "S1", "S01"]
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difference_dict = {}
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for source_spec in source_spec_list:
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for target_spec in target_spec_list:
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source_shard_list = cls._convert_str_to_shard_list(source_spec)
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target_shard_list = cls._convert_str_to_shard_list(target_spec)
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# source same as target
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if source_shard_list == target_shard_list:
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difference = 0
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# all_gather(source) -> target
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elif (
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len(source_shard_list) == len(target_shard_list) + 1 and source_shard_list[:-1] == target_shard_list
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):
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difference = ALLGATHER_COST
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# shard(source) -> target
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elif (
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len(source_shard_list) == len(target_shard_list) - 1
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and source_shard_list == target_shard_list[:-1]
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and target_shard_list[-1] not in source_shard_list
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):
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difference = SHARD_COST
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# S1 -> S0 or S0 -> S1
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elif len(source_shard_list) == len(target_shard_list):
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# source -> R -> target
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difference = ALLGATHER_COST + STEP_PENALTY + SHARD_COST
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# R -> S01
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elif len(source_shard_list) == len(target_shard_list) - 2:
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difference = SHARD_COST + STEP_PENALTY + SHARD_COST
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# S01 -> R
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elif len(source_shard_list) == len(target_shard_list) + 2:
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difference = ALLGATHER_COST + STEP_PENALTY + ALLGATHER_COST
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# S1 -> S01
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elif len(source_shard_list) == len(target_shard_list) - 1:
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difference = ALLGATHER_COST + STEP_PENALTY + SHARD_COST + STEP_PENALTY + SHARD_COST
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# S01 -> S1
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elif len(source_shard_list) == len(target_shard_list) + 1:
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difference = ALLGATHER_COST + STEP_PENALTY + ALLGATHER_COST + STEP_PENALTY + SHARD_COST
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else:
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difference = NAN
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difference_dict[(source_spec, target_spec)] = difference
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return difference_dict
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@staticmethod
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def _convert_str_to_shard_list(str_spec):
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"""
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Convert str_spec into shard_list.
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Argument:
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str_spec(str): dim spec in str type.
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"""
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if str_spec == "R":
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return []
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if str_spec == "S0":
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return [0]
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if str_spec == "S1":
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return [1]
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if str_spec == "S01":
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return [0, 1]
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class ShardingSpecException(Exception):
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pass
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class ShardingOutOfIndexError(ShardingSpecException):
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pass
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class DuplicatedShardingDimensionError(ShardingSpecException):
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pass
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class ShardingNotDivisibleError(ShardingSpecException):
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pass
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class ShardingSpec:
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"""
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Sharding spec for a tensor, it contains info of the logical device mesh this tensor belong
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to, the entire shape of the tensor before sharded, and the sharding sequence looks like
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[R, R, S0, S1].
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Argument:
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device_mesh(DeviceMesh): A logical view of a physical mesh.
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entire_shape(torch.Size): The entire shape of tensor before sharded.
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dim_partition_dict(Dict[int, List[int]]， optional): The key is the dimension of tensor to be sharded,
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and the value of the key describe which logical axis will be sharded in that dimension.
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sharding_sequence(List[_DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1].
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"""
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def __init__(
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self, device_mesh: DeviceMesh, entire_shape: torch.Size, dim_partition_dict=None, sharding_sequence=None
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):
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self.device_mesh = device_mesh
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if isinstance(entire_shape, (list, tuple)):
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entire_shape = torch.Size(entire_shape)
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self.entire_shape = entire_shape
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self.dim_partition_dict = dim_partition_dict
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self.sharding_sequence = sharding_sequence
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if self.sharding_sequence is None:
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assert (
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self.dim_partition_dict is not None
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), f"dim_partition_dict should not be None, if sharding_sequence is NoneType object."
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self.dim_partition_dict = merge_same_dim_mesh_list(
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dim_size=len(entire_shape), dim_partition_dict=self.dim_partition_dict
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)
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self.convert_dict_to_shard_sequence()
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elif self.dim_partition_dict is None:
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assert (
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self.sharding_sequence is not None
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), f"sharding_sequence should not be None, if dim_partition_dict is NoneType object."
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self.convert_shard_sequence_to_dict()
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self._sanity_check()
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def __repr__(self):
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res_list = ["DistSpec:"]
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res_list.append(f"\n\tshard_sequence: " + ",".join(str(dimspec) for dimspec in self.sharding_sequence))
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res_list.append(f"\n\tdevice_mesh_shape: {self.device_mesh.shape}")
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return " ".join(res_list)
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def _sanity_check(self):
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# make sure all axes in logical device mesh only be used once
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dim_check_list = list(range(self.device_mesh.logical_mesh_id.dim()))
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for dim, shard_list in self.dim_partition_dict.items():
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for element in shard_list:
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if element in dim_check_list:
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dim_check_list.remove(element)
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else:
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raise DuplicatedShardingDimensionError(
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f"find an invalid sharding axis {element} in dim_partition_dict in tensor dimension {dim}."
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)
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# make sure that the dimension is not out of index
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for dim in self.dim_partition_dict.keys():
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if dim >= len(self.entire_shape):
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raise ShardingOutOfIndexError(
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f"The dim_partition_dict specifies to shard dimension {dim} but the entire_shape only has {len(self.entire_shape)} dimensions"
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)
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# make sure that the sharding for a dimension is divisible by the number of devices
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for dim, shard_list in self.dim_partition_dict.items():
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tensor_dim_size = self.entire_shape[dim]
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num_devices = 1
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for element in shard_list:
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num_devices *= self.device_mesh.shape[element]
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if tensor_dim_size % num_devices != 0:
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raise ShardingNotDivisibleError(
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f"The size of dimension at index {dim} is {tensor_dim_size}, it cannot be sharded over {num_devices} devices."
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)
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def convert_dict_to_shard_sequence(self):
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"""
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Convert dim_partition_dict into list of _DimSpec, and assign it to sharding_sequence.
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"""
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sharding_sequence = [_DimSpec([])] * len(self.entire_shape)
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for dim, shard_list in self.dim_partition_dict.items():
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sharding_sequence[dim] = _DimSpec(shard_list)
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self.sharding_sequence = sharding_sequence
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def convert_shard_sequence_to_dict(self):
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"""
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Convert sharding_sequence into dim_partition_dict.
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"""
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new_dim_partition_dict = {}
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for index, dim_spec in enumerate(self.sharding_sequence):
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if not dim_spec.is_replica:
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if index not in new_dim_partition_dict:
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new_dim_partition_dict[index] = []
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new_dim_partition_dict[index].extend(dim_spec.shard_list)
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self.dim_partition_dict = new_dim_partition_dict
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def sharding_sequence_difference(self, other):
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"""
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This function is a naive version of difference computation. It just simply accumulates difference every dimension between the
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pair of sharding sequence.
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Example:
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dim_partition_dict = {0: [0, 1]}
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# DistSpec:
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# shard_sequence: S01,R,R
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# device_mesh_shape: (4, 4)
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sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
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dim_partition_dict_to_compare = {0: [0], 1: [1]}
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# DistSpec:
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# shard_sequence: S0,S1,R
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# device_mesh_shape: (4, 4)
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sharding_spec_to_compare = ShardingSpec(device_mesh, entire_shape, dim_partition_dict_to_compare)
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print(sharding_spec.sharding_sequence_difference(sharding_spec_to_compare))
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Output:
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25
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Argument:
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other(ShardingSpec): The ShardingSpec to compared with.
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Return:
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difference(int): Difference between two ShardingSpec.
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"""
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assert len(self.sharding_sequence) == len(
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other.sharding_sequence
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), f"Cannot compare difference for two sharding specs with different length."
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difference = 0
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for orig_dim_spec, other_dim_spec in zip(self.sharding_sequence, other.sharding_sequence):
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difference += orig_dim_spec.difference(other_dim_spec)
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return difference
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def get_sharded_shape_per_device(self):
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sharded_shape = list(self.entire_shape)
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for dim, shard_list in self.dim_partition_dict.items():
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mesh_list = [self.device_mesh.shape[mesh_dim] for mesh_dim in shard_list]
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shard_partitions = reduce(operator.mul, mesh_list, 1)
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assert (
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sharded_shape[dim] % shard_partitions == 0
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), f"Cannot shard dimension {dim} into {shard_partitions} partitions."
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sharded_shape[dim] //= shard_partitions
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return torch.Size(sharded_shape)
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