mirror of https://github.com/hpcaitech/ColossalAI
246 lines
9.1 KiB
Python
246 lines
9.1 KiB
Python
from copy import deepcopy
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from typing import Dict, List
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from ..utils import merge_same_dim_mesh_list
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from .misc import ShardingOutOfIndexError
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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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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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self.build_difference_2d_dict()
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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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def _convert_str_to_shard_list(self, 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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def build_difference_2d_dict(self):
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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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spec_pair = (deepcopy(source_spec), deepcopy(target_spec))
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source_shard_list = self._convert_str_to_shard_list(source_spec)
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target_shard_list = self._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[spec_pair] = difference
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self.difference_dict = difference_dict
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def dim_diff(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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class ShardingSpec:
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"""
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Sharding spec describes how to shard a tensor with dim_size dimensions. The sharding sequence looks like
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[R, R, S0, S1], which means
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Argument:
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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, dim_size: int, dim_partition_dict: Dict[int, List[int]] = None, sharding_sequence: List[DimSpec] = None
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):
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self.dims = dim_size
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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=self.dims, dim_partition_dict=self.dim_partition_dict
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)
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self.sharding_sequence = 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.dim_partition_dict = self.convert_shard_sequence_to_dict()
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self._sanity_check()
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def _sanity_check(self):
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if len(self.sharding_sequence) > self.dims:
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raise ShardingOutOfIndexError(
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f"sharding_sequence should have {self.dims} elements, but got index {len(self.sharding_sequence)}."
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)
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if list(self.dim_partition_dict.keys()) and max(list(self.dim_partition_dict.keys())) >= self.dims:
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raise ShardingOutOfIndexError(
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f"the key of dim_partition_dict should be less than {self.dims}, but got {max(list(self.dim_partition_dict.keys()))}."
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)
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def __repr__(self):
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res_list = ["ShardingSpec:"]
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res_list.append(f"\n\tshard_sequence: " + ",".join(str(dimspec) for dimspec in self.sharding_sequence))
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return " ".join(res_list)
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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([])] * self.dims
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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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return 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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return new_dim_partition_dict
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def spec_diff(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.dim_diff(other_dim_spec)
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return difference
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