mirror of
https://github.com/ROCm/composable_kernel.git
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212 lines
6.6 KiB
Plaintext
212 lines
6.6 KiB
Plaintext
#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "nvToolsExt.h"
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#include "tensor.hpp"
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#include "constant_tensor_descriptor.cuh"
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#include "device_direct_convolution_3.cuh"
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template <class T>
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struct GeneratorConstant
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{
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T value = 0;
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template <class... Is>
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T operator()(Is... is)
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{
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return value;
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}
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};
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template <class T>
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struct GeneratorTensor
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{
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template <class... Is>
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T operator()(Is... is)
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{
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#if 1
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return T(std::rand()) / T(RAND_MAX);
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#elif 1
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return 1;
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#elif 0
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std::initializer_list<std::size_t> ls = {static_cast<std::size_t>(is)...};
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return std::accumulate(ls.begin(), ls.end(), std::size_t(0));
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#else
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assert(sizeof...(Is) > 0);
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std::initializer_list<std::size_t> ids = {static_cast<std::size_t>(is)...};
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std::vector<std::size_t> lens(sizeof...(Is), 100);
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std::vector<std::size_t> strides(sizeof...(Is), 1);
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std::partial_sum(lens.rbegin(), lens.rbegin() + (sizeof...(Is) - 1), strides.rbegin() + 1);
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return std::inner_product(ids.begin(), ids.end(), strides.begin(), std::size_t(0)) + 1;
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#endif
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}
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};
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// this is ugly, only for 4d
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template <class TConstTensorDesc>
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void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout)
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{
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static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
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constexpr auto I0 = Index<0>{};
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constexpr auto I1 = Index<1>{};
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constexpr auto I2 = Index<2>{};
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constexpr auto I3 = Index<3>{};
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constexpr auto desc = TConstTensorDesc{};
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os << "Lengths: {" << desc.GetLength(I0) << ", " << desc.GetLength(I1) << ", "
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<< desc.GetLength(I2) << ", " << desc.GetLength(I3) << "}, "
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<< "Strides: {" << desc.GetStride(I0) << ", " << desc.GetStride(I1) << ", "
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<< desc.GetStride(I2) << ", " << desc.GetStride(I3) << "}" << std::endl;
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}
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// this is ugly, only for 4d
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template <class TConstTensorDesc>
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auto make_TensorDescriptor(TConstTensorDesc)
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{
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static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
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constexpr auto I0 = Index<0>{};
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constexpr auto I1 = Index<1>{};
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constexpr auto I2 = Index<2>{};
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constexpr auto I3 = Index<3>{};
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constexpr auto desc = TConstTensorDesc{};
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std::initializer_list<unsigned> lengths = {
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desc.GetLength(I0), desc.GetLength(I1), desc.GetLength(I2), desc.GetLength(I3)};
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std::initializer_list<unsigned> strides = {
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desc.GetStride(I0), desc.GetStride(I1), desc.GetStride(I2), desc.GetStride(I3)};
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return TensorDescriptor(lengths, strides);
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}
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template <class T>
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void host_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out)
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{
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auto f = [&](auto n, auto k, auto ho, auto wo) {
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double v = 0;
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for(int c = 0; c < wei.mDesc.GetLengths()[1]; ++c)
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{
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for(int y = 0; y < wei.mDesc.GetLengths()[2]; ++y)
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{
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int hi = ho + y;
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for(int x = 0; x < wei.mDesc.GetLengths()[3]; ++x)
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{
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int wi = wo + x;
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v += in(n, c, hi, wi) * wei(k, c, y, x);
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}
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}
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}
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out(n, k, ho, wo) = v;
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};
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auto f_par = make_ParallelTensorFunctor(f,
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out.mDesc.GetLengths()[0],
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out.mDesc.GetLengths()[1],
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out.mDesc.GetLengths()[2],
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out.mDesc.GetLengths()[3]);
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f_par(std::thread::hardware_concurrency());
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}
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int main()
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{
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#if 0
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constexpr unsigned N = 1;
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constexpr unsigned C = 1;
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constexpr unsigned HI = 34;
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constexpr unsigned WI = 34;
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constexpr unsigned K = 1;
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constexpr unsigned S = 3;
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constexpr unsigned R = 3;
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#elif 1
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constexpr unsigned N = 64;
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constexpr unsigned C = 256;
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constexpr unsigned HI = 34;
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constexpr unsigned WI = 34;
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constexpr unsigned K = 64;
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constexpr unsigned S = 3;
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constexpr unsigned R = 3;
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#elif 0
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constexpr unsigned N = 1;
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constexpr unsigned C = 1;
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constexpr unsigned HI = 18;
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constexpr unsigned WI = 18;
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constexpr unsigned K = 1;
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constexpr unsigned S = 3;
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constexpr unsigned R = 3;
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#elif 0
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constexpr unsigned N = 2;
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constexpr unsigned C = 3;
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constexpr unsigned HI = 130;
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constexpr unsigned WI = 130;
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constexpr unsigned K = 5;
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constexpr unsigned S = 3;
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constexpr unsigned R = 3;
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#elif 0
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constexpr unsigned N = 3;
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constexpr unsigned C = 16;
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constexpr unsigned HI = 130;
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constexpr unsigned WI = 130;
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constexpr unsigned K = 4;
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constexpr unsigned S = 3;
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constexpr unsigned R = 3;
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#endif
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auto in_desc = make_ConstantTensorDescriptor(Sequence<N, C, HI, WI>{});
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auto wei_desc = make_ConstantTensorDescriptor(Sequence<K, C, S, R>{});
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auto out_desc = get_output_4d_tensor_descriptor(in_desc, wei_desc);
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ostream_ConstantTensorDescriptor(in_desc, std::cout << "in_desc: ");
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ostream_ConstantTensorDescriptor(wei_desc, std::cout << "wei_desc: ");
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ostream_ConstantTensorDescriptor(out_desc, std::cout << "out_desc: ");
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Tensor<float> in(make_TensorDescriptor(in_desc));
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Tensor<float> wei(make_TensorDescriptor(wei_desc));
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Tensor<float> out_host(make_TensorDescriptor(out_desc));
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Tensor<float> out_device(make_TensorDescriptor(out_desc));
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int num_thread = std::thread::hardware_concurrency();
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#if 0
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in.GenerateTensorValue(GeneratorTensor<float>{}, num_thread);
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wei.GenerateTensorValue(GeneratorTensor<float>{}, num_thread);
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#endif
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for(int i = 0; i < 20; ++i)
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{
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device_convolution(in_desc, in, wei_desc, wei, out_desc, out_device);
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}
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#if 0
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host_convolution(in, wei, out_host);
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float error = 0;
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float max_diff = 0;
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float host_value = 0, device_value = 0;
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for(int i = 0; i < out_host.mData.size(); ++i)
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{
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error += std::abs(out_host.mData[i] - out_device.mData[i]);
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float diff = std::abs(out_host.mData[i] - out_device.mData[i]);
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if(max_diff < diff)
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{
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max_diff = diff;
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host_value = out_host.mData[i];
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device_value = out_device.mData[i];
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}
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}
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std::cout << "error: " << error << std::endl;
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std::cout << "max_diff: " << max_diff << ", " << host_value << ", " << device_value
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<< std::endl;
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#endif
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#if 0
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LogRange(std::cout << "in : ", in.mData, ",") << std::endl;
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LogRange(std::cout << "wei: ", wei.mData, ",") << std::endl;
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LogRange(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
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LogRange(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
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#endif
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}
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