mirror of https://github.com/hpcaitech/ColossalAI
[NFC] polish colossalai/auto_parallel/tensor_shard/deprecated/op_handler/embedding_handler.py code style (#2368)
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@ -5,9 +5,9 @@ from functools import reduce
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from typing import Dict, List
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import torch
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from colossalai.auto_parallel.tensor_shard.deprecated._utils import \
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ignore_sharding_exception
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from colossalai.auto_parallel.tensor_shard.deprecated.sharding_strategy import (ShardingStrategy, StrategiesVector)
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from colossalai.auto_parallel.tensor_shard.deprecated._utils import ignore_sharding_exception
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from colossalai.auto_parallel.tensor_shard.deprecated.sharding_strategy import ShardingStrategy, StrategiesVector
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from colossalai.tensor.shape_consistency import ShapeConsistencyManager
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from colossalai.tensor.sharding_spec import ShardingSpec
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@ -42,19 +42,19 @@ class EmbeddingHandler(OperatorHandler):
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Argument:
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sharding_size_forward(int): The forward activation will be divided
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into sharding_size_forward number partions.
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sharding_size_backward_activation(int): The backward activation will
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sharding_size_backward_activation(int): The backward activation will
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be divided into sharding_size_backward_activation number partions.
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sharding_size_weight(int): The backward weight will be divided
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into sharding_size_weight number partions.
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Return:
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memory_cost(Tuple[float]): Memory cost per device with this
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memory_cost(Tuple[float]): Memory cost per device with this
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specific strategy, the first element of this tuple is forward
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memory cost, and the second element of this tuple is backward
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memory cost.
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memory_cost_forward(float): Memory cost of forward activation per
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memory_cost_forward(float): Memory cost of forward activation per
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device with this specific strategy.
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memory_cost_backward_activation(float): Memory cost of backward activation
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memory_cost_backward_activation(float): Memory cost of backward activation
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per device with this specific strategy.
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'''
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# compute the memory cost of this strategy
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