mirror of https://github.com/hpcaitech/ColossalAI
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
from enum import Enum
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from typing import Tuple, List
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from colossalai.context.parallel_mode import ParallelMode
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class ComputePattern(Enum):
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TP1DRow = 1
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TP1DCol = 2
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ZeRO = 3
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DP = 4
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class ParallelAction(object):
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def __init__(self, priority=0, compute_pattern=ComputePattern.DP, parallel_mode=ParallelMode.DATA) -> None:
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self.priority = priority
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self.compute_pattern = compute_pattern
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self.parallel_mode = parallel_mode
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class TensorSpec(object):
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"""
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It contains two aspects of information:
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First, How are tensors distributed in Heterougenous memory space.
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Second, if the tensor is a model parameter, the Spec contains the
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parallel computation pattern of the Operator (Layer).
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We have to consider the hybrid parallel mode.
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"""
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# a list of parallel actions.
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# For example: On 8 GPUs, a hybrid parallel strategy is applied using
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# using ZeRO with DP-degree = 4 and 1DRowTP with TP-degree = 2.
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# parallel_action_list = [
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# ParallelAction(10, ComputePattern.ZeRO, gpc.get_group(ParallelMode.DATA)),
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# ParallelAction(1, ComputePattern.TP1DRow, gpc.get_group(ParallelMode.PARALLEL_1D))
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# ]
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# When the ColoTensor is initialized,
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# we first splitting tensor according to ParallelAction of ZeRO,
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# then splitting tensor according to ParallelAction of TP1DRow.
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# During Linear computation
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# Before Linear Op, we gather the tensors according to ZeRO.
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# We perform Linear Op according to compute pattern of TP1DRow.
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# After Linear Op, we split the tensors according to ZeRO.
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def __init__(self, parallel_action_list: List[ParallelAction] = []):
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self._parallel_action_list = parallel_action_list
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self.sort()
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@property
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def parallel_action_list(self):
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return self._parallel_action_list
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@property
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def num_action(self):
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return len(self._parallel_action_list)
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@property
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def compute_patterns(self):
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return [parallel_action.compute_pattern for parallel_action in self._parallel_action_list]
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def sort(self):
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if len(self._parallel_action_list) > 0:
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self._parallel_action_list.sort(key=lambda parallel_action : parallel_action.priority)
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def get_action_by_compute_pattern(self, compute_pattern: ComputePattern):
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for parallel_action in self._parallel_action_list:
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if parallel_action.compute_pattern == compute_pattern:
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return parallel_action
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return None
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