【Paddle Tensor 规范化第二期】pow support complex #71230
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| funcs::CudaPowFunctor<T> functor; | ||
| auto attrs = functor.GetAttrs(); | ||
| *(attrs[0].second) = factor.to<float>(); | ||
| ActivationGPUImpl<T, Context, funcs::CudaPowFunctor<T>>( | ||
| dev_ctx, x, out, functor); |
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这里可以特判一下factor为0和1情况，0的话直接构造 实部全1，虚部全0 的张量作为结果，1的话直接拷贝x即可
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| template <typename T, typename Context> | ||
| void PowGradKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const DenseTensor& dout, | ||
| const Scalar& factor, | ||
| DenseTensor* dx) { | ||
| PADDLE_ENFORCE_NOT_NULL( | ||
| dx, errors::NotFound("The output DenseTensor dX can not be nullptr")); | ||
| dev_ctx.template Alloc<T>(dx); | ||
| auto dout_flatten = EigenVector<T>::Flatten( | ||
| GET_DATA_SAFELY(&dout, "Input", "Out@GRAD", "PowGrad")); | ||
| auto dx_flatten = EigenVector<T>::Flatten( | ||
| GET_DATA_SAFELY(dx, "Output", "X@GRAD", "PowGrad")); | ||
| auto x_flatten = | ||
| EigenVector<T>::Flatten(GET_DATA_SAFELY(&x, "Input", "X", "PowGrad")); | ||
| auto* place = dev_ctx.eigen_device(); | ||
| phi::funcs::PowGradFunctor<T> functor; | ||
| auto attrs = functor.GetAttrs(); | ||
| *(attrs[0].second) = factor.to<float>(); | ||
| functor(*place, x_flatten, nullptr, dout_flatten, dx_flatten); | ||
| } |
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同理，特判一下factor=0的情况，1反向稍微麻烦点，可以正常调functor计算
| + 1j * np.random.uniform(0.1, 1, [1, 3, 6]) | ||
| ).astype(np.complex64), | ||
| } | ||
| self.attrs = {"factor": 2.0} |
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factor建议多测几组，比如：[-3.4, -3, -2, -1, -0.1, 0, 0.7, 1, 1.3, 2, 3, 5]
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PR Category
User Experience
PR Types
Bug fixes
Description
Pcard-75624
为pd_op.pow增加了复数支持与cuda实现，将pd_op.elementwise_powGPU上的实现由std改为cuda实现
通过nsys分析，powkernel执行时间从平均7.364μs降到6.986μs，elementwise_powkernel从平均9.631μs降到6.744μs