mirror of https://github.com/hpcaitech/ColossalAI
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
from enum import Enum
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from typing import List
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__all__ = ["ReplicaSpec", "ShardSpec"]
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class DistPlacementPattern(Enum):
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REPLICATE = "r"
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SHARD = "s"
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class _DistSpec:
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"""_DistSpec
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A class indicates Distributed Specification.
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The DistSpec is only works for the tensor parallel process groups.
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Because the dist spec of data parallel process group can be automatically deduced.
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This is an internal data structure.
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The API for users should be `ShardSpec` and `ReplicaSpec`.
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Args:
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dist_placement_pattern (DistPlacementPattern): the pattern describing how tensors are distributed among processes.
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The dist_placement_pattern is picked from a limited set, now including two patterns: replicate and shard.
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process_group (Optional[ProcessGroup], optional): the process group contains processes. Defaults to None.
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"""
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def __init__(self, dist_placement_pattern: DistPlacementPattern, **meta_info):
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self.placement = dist_placement_pattern
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for k, v in meta_info.items():
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setattr(self, k, v)
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def __eq__(self, other: "_DistSpec") -> bool:
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if dir(self) != dir(other):
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return False
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for attr in dir(self):
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if not attr.startswith("__") and getattr(self, attr) != getattr(other, attr):
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return False
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return True
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def __repr__(self) -> str:
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attr_list = []
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for attr in dir(self):
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if not attr.startswith("__"):
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attr_list.append(f"{attr}={str(getattr(self, attr))}")
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attr_str = ", ".join(attr_list)
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return "DistSpec(" + attr_str + ")"
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def ReplicaSpec() -> _DistSpec:
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"""ReplicaSpec
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A distributed specification represents the tensor is replicated among the tensor parallel process group.
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Returns:
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_DistSpec: an replicated dist spec instance.
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"""
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return _DistSpec(DistPlacementPattern.REPLICATE)
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def ShardSpec(dims: List[int], num_partitions: List[int]) -> _DistSpec:
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"""ShardSpec
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A distributed specification represents the tensor is sharded among the tensor parallel process group.
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Note:
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Currently, only shard on one dimension is valid. In another word, dims should be of size 1.
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Args:
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dims (List[int]): a list of dimensions
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num_partitions (List[int]): a list of partition number of each dimensions.
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Returns:
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_DistSpec: an shard dist spec instance.
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"""
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assert isinstance(dims, list) and isinstance(num_partitions, list)
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assert len(dims) == len(num_partitions)
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return _DistSpec(DistPlacementPattern.SHARD, dims=tuple(dims), num_partitions=tuple(num_partitions))
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