[NFC] polish colossalai/auto_parallel/tensor_shard/deprecated/op_handler/embedding_handler.py code style (#2368)

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Shawn-Kong 2023-01-05 23:47:10 -08:00 committed by GitHub
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@ -5,9 +5,9 @@ from functools import reduce
from typing import Dict, List
import torch
from colossalai.auto_parallel.tensor_shard.deprecated._utils import \
ignore_sharding_exception
from colossalai.auto_parallel.tensor_shard.deprecated.sharding_strategy import (ShardingStrategy, StrategiesVector)
from colossalai.auto_parallel.tensor_shard.deprecated._utils import ignore_sharding_exception
from colossalai.auto_parallel.tensor_shard.deprecated.sharding_strategy import ShardingStrategy, StrategiesVector
from colossalai.tensor.shape_consistency import ShapeConsistencyManager
from colossalai.tensor.sharding_spec import ShardingSpec
@ -42,19 +42,19 @@ class EmbeddingHandler(OperatorHandler):
Argument:
sharding_size_forward(int): The forward activation will be divided
into sharding_size_forward number partions.
sharding_size_backward_activation(int): The backward activation will
sharding_size_backward_activation(int): The backward activation will
be divided into sharding_size_backward_activation number partions.
sharding_size_weight(int): The backward weight will be divided
into sharding_size_weight number partions.
Return:
memory_cost(Tuple[float]): Memory cost per device with this
memory_cost(Tuple[float]): Memory cost per device with this
specific strategy, the first element of this tuple is forward
memory cost, and the second element of this tuple is backward
memory cost.
memory_cost_forward(float): Memory cost of forward activation per
memory_cost_forward(float): Memory cost of forward activation per
device with this specific strategy.
memory_cost_backward_activation(float): Memory cost of backward activation
memory_cost_backward_activation(float): Memory cost of backward activation
per device with this specific strategy.
'''
# compute the memory cost of this strategy