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[autoparallel] fix forward memory calculation (#2062)

pull/2071/head
Boyuan Yao 2022-12-04 15:00:16 +08:00 committed by GitHub
parent 44ea461890
commit 4b40fbd743
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 29 additions and 24 deletions
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10
colossalai/auto_parallel/meta_profiler/meta_registry/activation.py
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@ -49,10 +49,12 @@ def relu_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, Lis
# calculate memory cost
# NOTE: the inplace ReLU don't have forward memory cost
fwd_memory_cost = MemoryCost(activation=0 if inplace else activation_size(output_tensor),
parameter=0,
temp=0,
buffer=0)
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(
activation=activation_size(input_tensor) if inplace else activation_size([output_tensor, input_tensor]),
parameter=0,
temp=0,
buffer=0)
bwd_memory_cost = MemoryCost(activation=activation_size(input_tensor), parameter=0, temp=0, buffer=0)

24
colossalai/auto_parallel/meta_profiler/meta_registry/conv.py
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@ -96,19 +96,19 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
# calculate memory cost
# TODO: use profiler to check conv temp memory
fwd_memory_cost = MemoryCost(activation=activation_size(output_tensor),
parameter=activation_size(weight_tensor) +
activation_size(bias_tensor) if has_bias else activation_size(weight_tensor),
temp=0,
buffer=0)
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(
activation=activation_size([input_tensor, output_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]) if has_bias else activation_size(weight_tensor),
temp=0,
buffer=0)
bwd_memory_cost = MemoryCost(activation=activation_size(input_tensor) + activation_size(weight_tensor) +
activation_size(bias_tensor) if has_bias else activation_size(input_tensor) +
activation_size(weight_tensor),
parameter=activation_size(weight_tensor) +
activation_size(bias_tensor) if has_bias else activation_size(weight_tensor),
temp=0,
buffer=0)
bwd_memory_cost = MemoryCost(
activation=activation_size([input_tensor, weight_tensor, bias_tensor])
if has_bias else activation_size([input_tensor, weight_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]) if has_bias else activation_size(weight_tensor),
temp=0,
buffer=0)
# total cost is the sum of forward and backward cost
total_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation,

13
colossalai/auto_parallel/meta_profiler/meta_registry/linear.py
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@ -106,15 +106,15 @@ def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
# calculate memory cost
# NOTE: Linear don't have buffer and temp in forward and backward phase
# the forward activation cost is the size of output_tensor, parameter cost is the size of weight_tensor and bias_tensor
fwd_memory_cost = MemoryCost(activation=activation_size(output_tensor),
parameter=activation_size(weight_tensor) + activation_size(bias_tensor),
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]),
temp=0,
buffer=0)
# the backward activation cost is the size of input_tensor, weight_tensor and bias_tensor, parameter cost is 0
bwd_memory_cost = MemoryCost(activation=activation_size(input_tensor) + activation_size(weight_tensor) +
activation_size(bias_tensor),
parameter=activation_size(weight_tensor) + activation_size(bias_tensor),
bwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, weight_tensor, bias_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]),
temp=0,
buffer=0)
@ -142,13 +142,14 @@ def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
# calculate memory cost
# NOTE: Linear don't have buffer and temp in forward and backward phase
# the forward activation cost is the size of output_tensor, parameter cost is the size of weight_tensor
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(activation=activation_size(output_tensor),
parameter=activation_size(weight_tensor),
temp=0,
buffer=0)
# the backward activation cost is the size of input_tensor and weight_tensor, parameter cost is 0
bwd_memory_cost = MemoryCost(activation=activation_size(input_tensor) + activation_size(weight_tensor),
bwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, weight_tensor]),
parameter=activation_size(weight_tensor),
temp=0,
buffer=0)

3
colossalai/auto_parallel/meta_profiler/meta_registry/norm.py
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@ -76,7 +76,8 @@ def batchnormnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleIt
# calculate memory cost
# the fwd activation cost is output plus saved mean and saved inv std
fwd_memory_cost = MemoryCost(activation=activation_size([output_tensor, mean_tensor, var_tensor]),
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor, mean_tensor, var_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]),
temp=0,
buffer=activation_size([mean_tensor, var_tensor]))

3
colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py
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@ -110,7 +110,8 @@ def maxpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem,
# calculate memory cost
# NOTE: the index matrix will be discarded in backward phase
fwd_mem_cost = MemoryCost(activation=activation_size(output_tensor) + activation_size(index_matrix))
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_mem_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor, index_matrix]))
# temp memory for backward is the index matrix to be discarded
bwd_mem_cost = MemoryCost(activation=activation_size(input_tensor) - activation_size(index_matrix),