Support different data type between input and output #32823
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Thanks for your contribution! |
Xreki
approved these changes
May 10, 2021
| int tid = blockIdx.x * blockDim.x + threadIdx.x; | ||
| int remain = tid < size ? 1 : 0; | ||
| ScalarKernelImpl(data, func, tid, remain); | ||
| } | ||
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| template <ElementwiseType ET, typename T, typename Functor> | ||
| template <ElementwiseType ET, typename InT, typename OutT, typename Functor> |
| int GetVectorizedSize(const std::vector<const framework::Tensor *> &ins, | ||
| const std::vector<framework::Tensor *> &outs) { | ||
| int vec_size = 4; | ||
| for (auto iter = ins.begin(); iter != ins.end(); ++iter) { | ||
| vec_size = | ||
| std::min<int>(vec_size, GetVectorizedSizeImpl((*iter)->data<T>())); | ||
| std::min<int>(vec_size, GetVectorizedSizeImpl((*iter)->data<InT>())); |
There was a problem hiding this comment.
这个循环的写法其实可以简写成如下：
for (auto in : ins) {
vec_size =
std::min<int>(vec_size, GetVectorizedSizeImpl(in->data<InT>()));
}
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PR types
Performance optimization
PR changes
OPs
Describe
elementwise 实现模版支持输入和输出的数据类型不同，并适配 abs 验证，性能数据如下：