Optimize where_op and abs_grad_op by the elementwise interface #39609
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AnnaTrainingG
previously approved these changes
Feb 23, 2022
| #include "paddle/phi/kernels/abs_grad_kernel.h" | ||
| #include "paddle/phi/kernels/funcs/complex_functors.h" | ||
| #if defined(__NVCC__) || defined(__HIPCC__) | ||
| #include "paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h" |
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phi下不能include fluid路径下的文件，参考cast 修改一下
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PR描述里面介绍清楚一点做的工作，比如：添加哪些functor，调用哪个Kernel等
| auto functor = CondFunctor<T>(); | ||
| std::vector<const framework::Tensor*> ins = {condition, X, Y}; | ||
| std::vector<framework::Tensor*> outs = {out}; | ||
| paddle::operators::LaunchSameDimsElementwiseCudaKernel<T>(dev_ctx, ins, |
AnnaTrainingG
approved these changes
Feb 24, 2022
| T><<<config.block_per_grid.x, config.thread_per_block.x, 0, stream>>>( | ||
| numel, cond_data, x_data, y_data, out_data); | ||
| auto functor = CondFunctor<T>(); | ||
| std::vector<const framework::Tensor*> ins = {condition, X, Y}; |
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相关的framework Tensor后续可以改成DensorTensor
Liu-xiandong
approved these changes
Feb 24, 2022
xingfeng01
approved these changes
Feb 24, 2022
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| namespace paddle { | ||
| namespace operators { | ||
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Xreki
reviewed
Feb 24, 2022
| }; | ||
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| template <typename T> | ||
| __global__ void WhereCUDAKernel(const int N, const bool* cond, const T* x, |
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| template <typename T> | ||
| struct CondFunctor { | ||
| HOSTDEVICE inline CondFunctor() {} |
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| template <typename T> | ||
| struct AbsGradCUDAFunctor { | ||
| HOSTDEVICE inline AbsGradCUDAFunctor() {} |
| }; | ||
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| template <> | ||
| struct AbsGradCUDAFunctor<phi::dtype::complex<float>> { |
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Functor定义可以简化下，参考：
Paddle/paddle/phi/kernels/gpu/abs_kernel.cu
Lines 29 to 34 in bbe441f
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PR types
Performance optimization
PR changes
OPs
Describe
通过elementwise 接口优化了wehere_op和abs_grad_op。 elementwise 接口打包了一系列性能优化技巧，对于有elementwise行为的op有通用的性能提升。通过重写functor的形式，将代码里的循环遍历元素改写为通过elementwise接口调用functor实现。