add fuse_bn_act op #27230
add fuse_bn_act op #27230
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zhangting2020
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| 'matmul', | ||
| 'mul', | ||
| } | ||
| white_list = {'conv2d', 'matmul', 'mul', 'fused_bn_add_activation'} |
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I think fused_bn_add_activation should be added in the gray_list .
| check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'], | ||
| 'fused_bn_add_act') | ||
| check_variable_and_dtype(y, 'input', ['float16', 'float32', 'float64'], |
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BTW, you have only registered the float16 kernel. So, 'float32' and 'float64' is not needed here.
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The dtype check is performed during the compilation time, and the limit to float16 will cause the check to fail.
| if in_name != 'X' or in_name != 'Z': | ||
| continue |
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I think the condition test here maybe wrong. Maybe the logic below is more understandable.
if in_name not in {'X', 'Z'}:
continueMaybe the condition test about batch_norm can be simplified as below:
if src_dtype == core.VarDesc.VarType.FP32 and op.type in {'batch_norm', 'fused_bn_add_activation'}:
if in_name not in {'X', 'Z'}:
continue
| if op.type == 'batch_norm' and out_name != 'Y': | ||
| continue | ||
| if op.type == 'fused_bn_add_activation' and out_name != 'Y': | ||
| continue |
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if op.type in {'batch_norm', 'fused_bn_add_activation'} and out_name != 'Y':
continue
| saved_mean->template data<BatchNormParamType<float>>(); | ||
| const auto *saved_var_data = | ||
| saved_var->template data<BatchNormParamType<float>>(); | ||
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Please use T instead of float.
zhangting2020
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PR types
Function optimizationPR changes
OPsDescribe
This Op performs batch norm on input x, and adds the result to input y. Then it performs activation on the sum. We use cuDNN API to implements this function, the following points need to be noted:
cudnnBatchNormalizationForwardTrainingEx requires inputs x and z must be float16.
The data format of inputs must be NHWC
[batch, in_height, in_width, in_channels].This Op will be used in automatic mixed precision training of the resnet model. The following image is part of the model. The red parts represent the inputs of this Op. The green parts represent the computation performed by the Op.
Performance of ResNet50 AMP Training
Test on V100, CUDA 10.1, cuDNN 7.6, single card, BS=128
loss and accuracy
set fuse_bn_add_act=true and train 63 epochs
