// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #define EIGEN_TEST_NO_LONGDOUBLE #define EIGEN_TEST_NO_COMPLEX #define EIGEN_TEST_FUNC cxx11_tensor_cuda #define EIGEN_USE_GPU #if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500 #include <cuda_fp16.h> #endif #include "main.h" #include <unsupported/Eigen/CXX11/Tensor> using Eigen::Tensor; void test_cuda_nullary() { Tensor<float, 1, 0, int> in1(2); Tensor<float, 1, 0, int> in2(2); in1.setRandom(); in2.setRandom(); std::size_t tensor_bytes = in1.size() * sizeof(float); float* d_in1; float* d_in2; cudaMalloc((void**)(&d_in1), tensor_bytes); cudaMalloc((void**)(&d_in2), tensor_bytes); cudaMemcpy(d_in1, in1.data(), tensor_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in2, in2.data(), tensor_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in1( d_in1, 2); Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in2( d_in2, 2); gpu_in1.device(gpu_device) = gpu_in1.constant(3.14f); gpu_in2.device(gpu_device) = gpu_in2.random(); Tensor<float, 1, 0, int> new1(2); Tensor<float, 1, 0, int> new2(2); assert(cudaMemcpyAsync(new1.data(), d_in1, tensor_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaMemcpyAsync(new2.data(), d_in2, tensor_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 2; ++i) { VERIFY_IS_APPROX(new1(i), 3.14f); VERIFY_IS_NOT_EQUAL(new2(i), in2(i)); } cudaFree(d_in1); cudaFree(d_in2); } void test_cuda_elementwise_small() { Tensor<float, 1> in1(Eigen::array<Eigen::DenseIndex, 1>(2)); Tensor<float, 1> in2(Eigen::array<Eigen::DenseIndex, 1>(2)); Tensor<float, 1> out(Eigen::array<Eigen::DenseIndex, 1>(2)); in1.setRandom(); in2.setRandom(); std::size_t in1_bytes = in1.size() * sizeof(float); std::size_t in2_bytes = in2.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_in1; float* d_in2; float* d_out; cudaMalloc((void**)(&d_in1), in1_bytes); cudaMalloc((void**)(&d_in2), in2_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1( d_in1, Eigen::array<Eigen::DenseIndex, 1>(2)); Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in2( d_in2, Eigen::array<Eigen::DenseIndex, 1>(2)); Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_out( d_out, Eigen::array<Eigen::DenseIndex, 1>(2)); gpu_out.device(gpu_device) = gpu_in1 + gpu_in2; assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 2; ++i) { VERIFY_IS_APPROX( out(Eigen::array<Eigen::DenseIndex, 1>(i)), in1(Eigen::array<Eigen::DenseIndex, 1>(i)) + in2(Eigen::array<Eigen::DenseIndex, 1>(i))); } cudaFree(d_in1); cudaFree(d_in2); cudaFree(d_out); } void test_cuda_elementwise() { Tensor<float, 3> in1(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); Tensor<float, 3> in2(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); Tensor<float, 3> in3(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); Tensor<float, 3> out(Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); in1.setRandom(); in2.setRandom(); in3.setRandom(); std::size_t in1_bytes = in1.size() * sizeof(float); std::size_t in2_bytes = in2.size() * sizeof(float); std::size_t in3_bytes = in3.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_in1; float* d_in2; float* d_in3; float* d_out; cudaMalloc((void**)(&d_in1), in1_bytes); cudaMalloc((void**)(&d_in2), in2_bytes); cudaMalloc((void**)(&d_in3), in3_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in3, in3.data(), in3_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in3(d_in3, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<Eigen::DenseIndex, 3>(72,53,97)); gpu_out.device(gpu_device) = gpu_in1 + gpu_in2 * gpu_in3; assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 53; ++j) { for (int k = 0; k < 97; ++k) { VERIFY_IS_APPROX(out(Eigen::array<Eigen::DenseIndex, 3>(i,j,k)), in1(Eigen::array<Eigen::DenseIndex, 3>(i,j,k)) + in2(Eigen::array<Eigen::DenseIndex, 3>(i,j,k)) * in3(Eigen::array<Eigen::DenseIndex, 3>(i,j,k))); } } } cudaFree(d_in1); cudaFree(d_in2); cudaFree(d_in3); cudaFree(d_out); } void test_cuda_props() { Tensor<float, 1> in1(200); Tensor<bool, 1> out(200); in1.setRandom(); std::size_t in1_bytes = in1.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(bool); float* d_in1; bool* d_out; cudaMalloc((void**)(&d_in1), in1_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1( d_in1, 200); Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_out( d_out, 200); gpu_out.device(gpu_device) = (gpu_in1.isnan)(); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 200; ++i) { VERIFY_IS_EQUAL(out(i), (std::isnan)(in1(i))); } cudaFree(d_in1); cudaFree(d_out); } void test_cuda_reduction() { Tensor<float, 4> in1(72,53,97,113); Tensor<float, 2> out(72,97); in1.setRandom(); std::size_t in1_bytes = in1.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_in1; float* d_out; cudaMalloc((void**)(&d_in1), in1_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_in1(d_in1, 72,53,97,113); Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97); array<Eigen::DenseIndex, 2> reduction_axis; reduction_axis[0] = 1; reduction_axis[1] = 3; gpu_out.device(gpu_device) = gpu_in1.maximum(reduction_axis); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 97; ++j) { float expected = 0; for (int k = 0; k < 53; ++k) { for (int l = 0; l < 113; ++l) { expected = std::max<float>(expected, in1(i, k, j, l)); } } VERIFY_IS_APPROX(out(i,j), expected); } } cudaFree(d_in1); cudaFree(d_out); } template<int DataLayout> void test_cuda_contraction() { // with these dimensions, the output has 300 * 140 elements, which is // more than 30 * 1024, which is the number of threads in blocks on // a 15 SM GK110 GPU Tensor<float, 4, DataLayout> t_left(6, 50, 3, 31); Tensor<float, 5, DataLayout> t_right(Eigen::array<Eigen::DenseIndex, 5>(3, 31, 7, 20, 1)); Tensor<float, 5, DataLayout> t_result(Eigen::array<Eigen::DenseIndex, 5>(6, 50, 7, 20, 1)); t_left.setRandom(); t_right.setRandom(); std::size_t t_left_bytes = t_left.size() * sizeof(float); std::size_t t_right_bytes = t_right.size() * sizeof(float); std::size_t t_result_bytes = t_result.size() * sizeof(float); float* d_t_left; float* d_t_right; float* d_t_result; cudaMalloc((void**)(&d_t_left), t_left_bytes); cudaMalloc((void**)(&d_t_right), t_right_bytes); cudaMalloc((void**)(&d_t_result), t_result_bytes); cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_t_left(d_t_left, 6, 50, 3, 31); Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_t_right(d_t_right, 3, 31, 7, 20, 1); Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_t_result(d_t_result, 6, 50, 7, 20, 1); typedef Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> > MapXf; MapXf m_left(t_left.data(), 300, 93); MapXf m_right(t_right.data(), 93, 140); Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(300, 140); typedef Tensor<float, 1>::DimensionPair DimPair; Eigen::array<DimPair, 2> dims; dims[0] = DimPair(2, 0); dims[1] = DimPair(3, 1); m_result = m_left * m_right; gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims); cudaMemcpy(t_result.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost); for (DenseIndex i = 0; i < t_result.size(); i++) { if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) { std::cout << "mismatch detected at index " << i << ": " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl; assert(false); } } cudaFree(d_t_left); cudaFree(d_t_right); cudaFree(d_t_result); } template<int DataLayout> void test_cuda_convolution_1d() { Tensor<float, 4, DataLayout> input(74,37,11,137); Tensor<float, 1, DataLayout> kernel(4); Tensor<float, 4, DataLayout> out(74,34,11,137); input = input.constant(10.0f) + input.random(); kernel = kernel.constant(7.0f) + kernel.random(); std::size_t input_bytes = input.size() * sizeof(float); std::size_t kernel_bytes = kernel.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_input; float* d_kernel; float* d_out; cudaMalloc((void**)(&d_input), input_bytes); cudaMalloc((void**)(&d_kernel), kernel_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_input(d_input, 74,37,11,137); Eigen::TensorMap<Eigen::Tensor<float, 1, DataLayout> > gpu_kernel(d_kernel, 4); Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out, 74,34,11,137); Eigen::array<Eigen::DenseIndex, 1> dims(1); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 74; ++i) { for (int j = 0; j < 34; ++j) { for (int k = 0; k < 11; ++k) { for (int l = 0; l < 137; ++l) { const float result = out(i,j,k,l); const float expected = input(i,j+0,k,l) * kernel(0) + input(i,j+1,k,l) * kernel(1) + input(i,j+2,k,l) * kernel(2) + input(i,j+3,k,l) * kernel(3); VERIFY_IS_APPROX(result, expected); } } } } cudaFree(d_input); cudaFree(d_kernel); cudaFree(d_out); } void test_cuda_convolution_inner_dim_col_major_1d() { Tensor<float, 4, ColMajor> input(74,9,11,7); Tensor<float, 1, ColMajor> kernel(4); Tensor<float, 4, ColMajor> out(71,9,11,7); input = input.constant(10.0f) + input.random(); kernel = kernel.constant(7.0f) + kernel.random(); std::size_t input_bytes = input.size() * sizeof(float); std::size_t kernel_bytes = kernel.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_input; float* d_kernel; float* d_out; cudaMalloc((void**)(&d_input), input_bytes); cudaMalloc((void**)(&d_kernel), kernel_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_input(d_input,74,9,11,7); Eigen::TensorMap<Eigen::Tensor<float, 1, ColMajor> > gpu_kernel(d_kernel,4); Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_out(d_out,71,9,11,7); Eigen::array<Eigen::DenseIndex, 1> dims(0); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 71; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 11; ++k) { for (int l = 0; l < 7; ++l) { const float result = out(i,j,k,l); const float expected = input(i+0,j,k,l) * kernel(0) + input(i+1,j,k,l) * kernel(1) + input(i+2,j,k,l) * kernel(2) + input(i+3,j,k,l) * kernel(3); VERIFY_IS_APPROX(result, expected); } } } } cudaFree(d_input); cudaFree(d_kernel); cudaFree(d_out); } void test_cuda_convolution_inner_dim_row_major_1d() { Tensor<float, 4, RowMajor> input(7,9,11,74); Tensor<float, 1, RowMajor> kernel(4); Tensor<float, 4, RowMajor> out(7,9,11,71); input = input.constant(10.0f) + input.random(); kernel = kernel.constant(7.0f) + kernel.random(); std::size_t input_bytes = input.size() * sizeof(float); std::size_t kernel_bytes = kernel.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_input; float* d_kernel; float* d_out; cudaMalloc((void**)(&d_input), input_bytes); cudaMalloc((void**)(&d_kernel), kernel_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_input(d_input, 7,9,11,74); Eigen::TensorMap<Eigen::Tensor<float, 1, RowMajor> > gpu_kernel(d_kernel, 4); Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_out(d_out, 7,9,11,71); Eigen::array<Eigen::DenseIndex, 1> dims(3); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 7; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 11; ++k) { for (int l = 0; l < 71; ++l) { const float result = out(i,j,k,l); const float expected = input(i,j,k,l+0) * kernel(0) + input(i,j,k,l+1) * kernel(1) + input(i,j,k,l+2) * kernel(2) + input(i,j,k,l+3) * kernel(3); VERIFY_IS_APPROX(result, expected); } } } } cudaFree(d_input); cudaFree(d_kernel); cudaFree(d_out); } template<int DataLayout> void test_cuda_convolution_2d() { Tensor<float, 4, DataLayout> input(74,37,11,137); Tensor<float, 2, DataLayout> kernel(3,4); Tensor<float, 4, DataLayout> out(74,35,8,137); input = input.constant(10.0f) + input.random(); kernel = kernel.constant(7.0f) + kernel.random(); std::size_t input_bytes = input.size() * sizeof(float); std::size_t kernel_bytes = kernel.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_input; float* d_kernel; float* d_out; cudaMalloc((void**)(&d_input), input_bytes); cudaMalloc((void**)(&d_kernel), kernel_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_input(d_input,74,37,11,137); Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_kernel(d_kernel,3,4); Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out,74,35,8,137); Eigen::array<Eigen::DenseIndex, 2> dims(1,2); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 74; ++i) { for (int j = 0; j < 35; ++j) { for (int k = 0; k < 8; ++k) { for (int l = 0; l < 137; ++l) { const float result = out(i,j,k,l); const float expected = input(i,j+0,k+0,l) * kernel(0,0) + input(i,j+1,k+0,l) * kernel(1,0) + input(i,j+2,k+0,l) * kernel(2,0) + input(i,j+0,k+1,l) * kernel(0,1) + input(i,j+1,k+1,l) * kernel(1,1) + input(i,j+2,k+1,l) * kernel(2,1) + input(i,j+0,k+2,l) * kernel(0,2) + input(i,j+1,k+2,l) * kernel(1,2) + input(i,j+2,k+2,l) * kernel(2,2) + input(i,j+0,k+3,l) * kernel(0,3) + input(i,j+1,k+3,l) * kernel(1,3) + input(i,j+2,k+3,l) * kernel(2,3); VERIFY_IS_APPROX(result, expected); } } } } cudaFree(d_input); cudaFree(d_kernel); cudaFree(d_out); } template<int DataLayout> void test_cuda_convolution_3d() { Tensor<float, 5, DataLayout> input(Eigen::array<Eigen::DenseIndex, 5>(74,37,11,137,17)); Tensor<float, 3, DataLayout> kernel(3,4,2); Tensor<float, 5, DataLayout> out(Eigen::array<Eigen::DenseIndex, 5>(74,35,8,136,17)); input = input.constant(10.0f) + input.random(); kernel = kernel.constant(7.0f) + kernel.random(); std::size_t input_bytes = input.size() * sizeof(float); std::size_t kernel_bytes = kernel.size() * sizeof(float); std::size_t out_bytes = out.size() * sizeof(float); float* d_input; float* d_kernel; float* d_out; cudaMalloc((void**)(&d_input), input_bytes); cudaMalloc((void**)(&d_kernel), kernel_bytes); cudaMalloc((void**)(&d_out), out_bytes); cudaMemcpy(d_input, input.data(), input_bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_kernel, kernel.data(), kernel_bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_input(d_input,74,37,11,137,17); Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > gpu_kernel(d_kernel,3,4,2); Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_out(d_out,74,35,8,136,17); Eigen::array<Eigen::DenseIndex, 3> dims(1,2,3); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 74; ++i) { for (int j = 0; j < 35; ++j) { for (int k = 0; k < 8; ++k) { for (int l = 0; l < 136; ++l) { for (int m = 0; m < 17; ++m) { const float result = out(i,j,k,l,m); const float expected = input(i,j+0,k+0,l+0,m) * kernel(0,0,0) + input(i,j+1,k+0,l+0,m) * kernel(1,0,0) + input(i,j+2,k+0,l+0,m) * kernel(2,0,0) + input(i,j+0,k+1,l+0,m) * kernel(0,1,0) + input(i,j+1,k+1,l+0,m) * kernel(1,1,0) + input(i,j+2,k+1,l+0,m) * kernel(2,1,0) + input(i,j+0,k+2,l+0,m) * kernel(0,2,0) + input(i,j+1,k+2,l+0,m) * kernel(1,2,0) + input(i,j+2,k+2,l+0,m) * kernel(2,2,0) + input(i,j+0,k+3,l+0,m) * kernel(0,3,0) + input(i,j+1,k+3,l+0,m) * kernel(1,3,0) + input(i,j+2,k+3,l+0,m) * kernel(2,3,0) + input(i,j+0,k+0,l+1,m) * kernel(0,0,1) + input(i,j+1,k+0,l+1,m) * kernel(1,0,1) + input(i,j+2,k+0,l+1,m) * kernel(2,0,1) + input(i,j+0,k+1,l+1,m) * kernel(0,1,1) + input(i,j+1,k+1,l+1,m) * kernel(1,1,1) + input(i,j+2,k+1,l+1,m) * kernel(2,1,1) + input(i,j+0,k+2,l+1,m) * kernel(0,2,1) + input(i,j+1,k+2,l+1,m) * kernel(1,2,1) + input(i,j+2,k+2,l+1,m) * kernel(2,2,1) + input(i,j+0,k+3,l+1,m) * kernel(0,3,1) + input(i,j+1,k+3,l+1,m) * kernel(1,3,1) + input(i,j+2,k+3,l+1,m) * kernel(2,3,1); VERIFY_IS_APPROX(result, expected); } } } } } cudaFree(d_input); cudaFree(d_kernel); cudaFree(d_out); } template <typename Scalar> void test_cuda_lgamma(const Scalar stddev) { Tensor<Scalar, 2> in(72,97); in.setRandom(); in *= in.constant(stddev); Tensor<Scalar, 2> out(72,97); out.setZero(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; cudaMalloc((void**)(&d_in), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97); gpu_out.device(gpu_device) = gpu_in.lgamma(); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 97; ++j) { VERIFY_IS_APPROX(out(i,j), (std::lgamma)(in(i,j))); } } cudaFree(d_in); cudaFree(d_out); } template <typename Scalar> void test_cuda_digamma() { Tensor<Scalar, 1> in(7); Tensor<Scalar, 1> out(7); Tensor<Scalar, 1> expected_out(7); out.setZero(); in(0) = Scalar(1); in(1) = Scalar(1.5); in(2) = Scalar(4); in(3) = Scalar(-10.5); in(4) = Scalar(10000.5); in(5) = Scalar(0); in(6) = Scalar(-1); expected_out(0) = Scalar(-0.5772156649015329); expected_out(1) = Scalar(0.03648997397857645); expected_out(2) = Scalar(1.2561176684318); expected_out(3) = Scalar(2.398239129535781); expected_out(4) = Scalar(9.210340372392849); expected_out(5) = std::numeric_limits<Scalar>::infinity(); expected_out(6) = std::numeric_limits<Scalar>::infinity(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; cudaMalloc((void**)(&d_in), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in(d_in, 7); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 7); gpu_out.device(gpu_device) = gpu_in.digamma(); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 5; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } for (int i = 5; i < 7; ++i) { VERIFY_IS_EQUAL(out(i), expected_out(i)); } cudaFree(d_in); cudaFree(d_out); } template <typename Scalar> void test_cuda_zeta() { Tensor<Scalar, 1> in_x(6); Tensor<Scalar, 1> in_q(6); Tensor<Scalar, 1> out(6); Tensor<Scalar, 1> expected_out(6); out.setZero(); in_x(0) = Scalar(1); in_x(1) = Scalar(1.5); in_x(2) = Scalar(4); in_x(3) = Scalar(-10.5); in_x(4) = Scalar(10000.5); in_x(5) = Scalar(3); in_q(0) = Scalar(1.2345); in_q(1) = Scalar(2); in_q(2) = Scalar(1.5); in_q(3) = Scalar(3); in_q(4) = Scalar(1.0001); in_q(5) = Scalar(-2.5); expected_out(0) = std::numeric_limits<Scalar>::infinity(); expected_out(1) = Scalar(1.61237534869); expected_out(2) = Scalar(0.234848505667); expected_out(3) = Scalar(1.03086757337e-5); expected_out(4) = Scalar(0.367879440865); expected_out(5) = Scalar(0.054102025820864097); std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_in_x; Scalar* d_in_q; Scalar* d_out; cudaMalloc((void**)(&d_in_x), bytes); cudaMalloc((void**)(&d_in_q), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in_q, in_q.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 6); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_q(d_in_q, 6); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 6); gpu_out.device(gpu_device) = gpu_in_x.zeta(gpu_in_q); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); VERIFY_IS_EQUAL(out(0), expected_out(0)); VERIFY((std::isnan)(out(3))); for (int i = 1; i < 6; ++i) { if (i != 3) { VERIFY_IS_APPROX(out(i), expected_out(i)); } } cudaFree(d_in_x); cudaFree(d_in_q); cudaFree(d_out); } template <typename Scalar> void test_cuda_polygamma() { Tensor<Scalar, 1> in_x(7); Tensor<Scalar, 1> in_n(7); Tensor<Scalar, 1> out(7); Tensor<Scalar, 1> expected_out(7); out.setZero(); in_n(0) = Scalar(1); in_n(1) = Scalar(1); in_n(2) = Scalar(1); in_n(3) = Scalar(17); in_n(4) = Scalar(31); in_n(5) = Scalar(28); in_n(6) = Scalar(8); in_x(0) = Scalar(2); in_x(1) = Scalar(3); in_x(2) = Scalar(25.5); in_x(3) = Scalar(4.7); in_x(4) = Scalar(11.8); in_x(5) = Scalar(17.7); in_x(6) = Scalar(30.2); expected_out(0) = Scalar(0.644934066848); expected_out(1) = Scalar(0.394934066848); expected_out(2) = Scalar(0.0399946696496); expected_out(3) = Scalar(293.334565435); expected_out(4) = Scalar(0.445487887616); expected_out(5) = Scalar(-2.47810300902e-07); expected_out(6) = Scalar(-8.29668781082e-09); std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_in_x; Scalar* d_in_n; Scalar* d_out; cudaMalloc((void**)(&d_in_x), bytes); cudaMalloc((void**)(&d_in_n), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in_n, in_n.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 7); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_n(d_in_n, 7); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 7); gpu_out.device(gpu_device) = gpu_in_n.polygamma(gpu_in_x); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 7; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } cudaFree(d_in_x); cudaFree(d_in_n); cudaFree(d_out); } template <typename Scalar> void test_cuda_igamma() { Tensor<Scalar, 2> a(6, 6); Tensor<Scalar, 2> x(6, 6); Tensor<Scalar, 2> out(6, 6); out.setZero(); Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; for (int i = 0; i < 6; ++i) { for (int j = 0; j < 6; ++j) { a(i, j) = a_s[i]; x(i, j) = x_s[j]; } } Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); Scalar igamma_s[][6] = {{0.0, nan, nan, nan, nan, nan}, {0.0, 0.6321205588285578, 0.7768698398515702, 0.9816843611112658, 9.999500016666262e-05, 1.0}, {0.0, 0.4275932955291202, 0.608374823728911, 0.9539882943107686, 7.522076445089201e-07, 1.0}, {0.0, 0.01898815687615381, 0.06564245437845008, 0.5665298796332909, 4.166333347221828e-18, 1.0}, {0.0, 0.9999780593618628, 0.9999899967080838, 0.9999996219837988, 0.9991370418689945, 1.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.5042041932513908}}; std::size_t bytes = a.size() * sizeof(Scalar); Scalar* d_a; Scalar* d_x; Scalar* d_out; assert(cudaMalloc((void**)(&d_a), bytes) == cudaSuccess); assert(cudaMalloc((void**)(&d_x), bytes) == cudaSuccess); assert(cudaMalloc((void**)(&d_out), bytes) == cudaSuccess); cudaMemcpy(d_a, a.data(), bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_x, x.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_a(d_a, 6, 6); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_x(d_x, 6, 6); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 6, 6); gpu_out.device(gpu_device) = gpu_a.igamma(gpu_x); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 6; ++i) { for (int j = 0; j < 6; ++j) { if ((std::isnan)(igamma_s[i][j])) { VERIFY((std::isnan)(out(i, j))); } else { VERIFY_IS_APPROX(out(i, j), igamma_s[i][j]); } } } cudaFree(d_a); cudaFree(d_x); cudaFree(d_out); } template <typename Scalar> void test_cuda_igammac() { Tensor<Scalar, 2> a(6, 6); Tensor<Scalar, 2> x(6, 6); Tensor<Scalar, 2> out(6, 6); out.setZero(); Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)}; for (int i = 0; i < 6; ++i) { for (int j = 0; j < 6; ++j) { a(i, j) = a_s[i]; x(i, j) = x_s[j]; } } Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); Scalar igammac_s[][6] = {{nan, nan, nan, nan, nan, nan}, {1.0, 0.36787944117144233, 0.22313016014842982, 0.018315638888734182, 0.9999000049998333, 0.0}, {1.0, 0.5724067044708798, 0.3916251762710878, 0.04601170568923136, 0.9999992477923555, 0.0}, {1.0, 0.9810118431238462, 0.9343575456215499, 0.4334701203667089, 1.0, 0.0}, {1.0, 2.1940638138146658e-05, 1.0003291916285e-05, 3.7801620118431334e-07, 0.0008629581310054535, 0.0}, {1.0, 1.0, 1.0, 1.0, 1.0, 0.49579580674813944}}; std::size_t bytes = a.size() * sizeof(Scalar); Scalar* d_a; Scalar* d_x; Scalar* d_out; cudaMalloc((void**)(&d_a), bytes); cudaMalloc((void**)(&d_x), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_a, a.data(), bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_x, x.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_a(d_a, 6, 6); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_x(d_x, 6, 6); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 6, 6); gpu_out.device(gpu_device) = gpu_a.igammac(gpu_x); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 6; ++i) { for (int j = 0; j < 6; ++j) { if ((std::isnan)(igammac_s[i][j])) { VERIFY((std::isnan)(out(i, j))); } else { VERIFY_IS_APPROX(out(i, j), igammac_s[i][j]); } } } cudaFree(d_a); cudaFree(d_x); cudaFree(d_out); } template <typename Scalar> void test_cuda_erf(const Scalar stddev) { Tensor<Scalar, 2> in(72,97); in.setRandom(); in *= in.constant(stddev); Tensor<Scalar, 2> out(72,97); out.setZero(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; assert(cudaMalloc((void**)(&d_in), bytes) == cudaSuccess); assert(cudaMalloc((void**)(&d_out), bytes) == cudaSuccess); cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97); gpu_out.device(gpu_device) = gpu_in.erf(); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 97; ++j) { VERIFY_IS_APPROX(out(i,j), (std::erf)(in(i,j))); } } cudaFree(d_in); cudaFree(d_out); } template <typename Scalar> void test_cuda_erfc(const Scalar stddev) { Tensor<Scalar, 2> in(72,97); in.setRandom(); in *= in.constant(stddev); Tensor<Scalar, 2> out(72,97); out.setZero(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; cudaMalloc((void**)(&d_in), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_in, in.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97); Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97); gpu_out.device(gpu_device) = gpu_in.erfc(); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 0; i < 72; ++i) { for (int j = 0; j < 97; ++j) { VERIFY_IS_APPROX(out(i,j), (std::erfc)(in(i,j))); } } cudaFree(d_in); cudaFree(d_out); } template <typename Scalar> void test_cuda_betainc() { Tensor<Scalar, 1> in_x(125); Tensor<Scalar, 1> in_a(125); Tensor<Scalar, 1> in_b(125); Tensor<Scalar, 1> out(125); Tensor<Scalar, 1> expected_out(125); out.setZero(); Scalar nan = std::numeric_limits<Scalar>::quiet_NaN(); Array<Scalar, 1, Dynamic> x(125); Array<Scalar, 1, Dynamic> a(125); Array<Scalar, 1, Dynamic> b(125); Array<Scalar, 1, Dynamic> v(125); a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999; b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999; x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1; v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan, 0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan, 0.999995949033062, 0.9999999999993698, 0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan, 0.006827081192655869, 0.0210336989586256, 0.04813160422599567, nan, nan, 0.20014344256217678, 0.5000000000000001, 0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403, 0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan, 1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06, nan, nan, 7.864342668429763e-23, 3.015969667594166e-10, 0.0008598571564165444, nan, nan, 6.031987710123844e-08, 0.5000000000000007, 0.9999999396801229, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan, 0.0, 7.029920380986636e-306, 2.2450728208591345e-101, nan, nan, 0.0, 9.275871147869727e-302, 1.2232913026152827e-97, nan, nan, 0.0, 3.0891393081932924e-252, 2.9303043666183996e-60, nan, nan, 2.248913486879199e-196, 0.5000000000004947, 0.9999999999999999, nan; for (int i = 0; i < 125; ++i) { in_x(i) = x(i); in_a(i) = a(i); in_b(i) = b(i); expected_out(i) = v(i); } std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_in_x; Scalar* d_in_a; Scalar* d_in_b; Scalar* d_out; cudaMalloc((void**)(&d_in_x), bytes); cudaMalloc((void**)(&d_in_a), bytes); cudaMalloc((void**)(&d_in_b), bytes); cudaMalloc((void**)(&d_out), bytes); cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in_a, in_a.data(), bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_in_b, in_b.data(), bytes, cudaMemcpyHostToDevice); Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 125); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_a(d_in_a, 125); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_b(d_in_b, 125); Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 125); gpu_out.device(gpu_device) = betainc(gpu_in_a, gpu_in_b, gpu_in_x); assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess); assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess); for (int i = 1; i < 125; ++i) { if ((std::isnan)(expected_out(i))) { VERIFY((std::isnan)(out(i))); } else { VERIFY_IS_APPROX(out(i), expected_out(i)); } } cudaFree(d_in_x); cudaFree(d_in_a); cudaFree(d_in_b); cudaFree(d_out); } void test_cxx11_tensor_cuda() { CALL_SUBTEST_1(test_cuda_nullary()); CALL_SUBTEST_1(test_cuda_elementwise_small()); CALL_SUBTEST_1(test_cuda_elementwise()); CALL_SUBTEST_1(test_cuda_props()); CALL_SUBTEST_1(test_cuda_reduction()); CALL_SUBTEST_2(test_cuda_contraction<ColMajor>()); CALL_SUBTEST_2(test_cuda_contraction<RowMajor>()); CALL_SUBTEST_3(test_cuda_convolution_1d<ColMajor>()); CALL_SUBTEST_3(test_cuda_convolution_1d<RowMajor>()); CALL_SUBTEST_3(test_cuda_convolution_inner_dim_col_major_1d()); CALL_SUBTEST_3(test_cuda_convolution_inner_dim_row_major_1d()); CALL_SUBTEST_3(test_cuda_convolution_2d<ColMajor>()); CALL_SUBTEST_3(test_cuda_convolution_2d<RowMajor>()); CALL_SUBTEST_3(test_cuda_convolution_3d<ColMajor>()); CALL_SUBTEST_3(test_cuda_convolution_3d<RowMajor>()); #if __cplusplus > 199711L // std::erf, std::erfc, and so on where only added in c++11. We use them // as a golden reference to validate the results produced by Eigen. Therefore // we can only run these tests if we use a c++11 compiler. CALL_SUBTEST_4(test_cuda_lgamma<float>(1.0f)); CALL_SUBTEST_4(test_cuda_lgamma<float>(100.0f)); CALL_SUBTEST_4(test_cuda_lgamma<float>(0.01f)); CALL_SUBTEST_4(test_cuda_lgamma<float>(0.001f)); CALL_SUBTEST_4(test_cuda_lgamma<double>(1.0)); CALL_SUBTEST_4(test_cuda_lgamma<double>(100.0)); CALL_SUBTEST_4(test_cuda_lgamma<double>(0.01)); CALL_SUBTEST_4(test_cuda_lgamma<double>(0.001)); CALL_SUBTEST_4(test_cuda_erf<float>(1.0f)); CALL_SUBTEST_4(test_cuda_erf<float>(100.0f)); CALL_SUBTEST_4(test_cuda_erf<float>(0.01f)); CALL_SUBTEST_4(test_cuda_erf<float>(0.001f)); CALL_SUBTEST_4(test_cuda_erfc<float>(1.0f)); // CALL_SUBTEST(test_cuda_erfc<float>(100.0f)); CALL_SUBTEST_4(test_cuda_erfc<float>(5.0f)); // CUDA erfc lacks precision for large inputs CALL_SUBTEST_4(test_cuda_erfc<float>(0.01f)); CALL_SUBTEST_4(test_cuda_erfc<float>(0.001f)); CALL_SUBTEST_4(test_cuda_erf<double>(1.0)); CALL_SUBTEST_4(test_cuda_erf<double>(100.0)); CALL_SUBTEST_4(test_cuda_erf<double>(0.01)); CALL_SUBTEST_4(test_cuda_erf<double>(0.001)); CALL_SUBTEST_4(test_cuda_erfc<double>(1.0)); // CALL_SUBTEST(test_cuda_erfc<double>(100.0)); CALL_SUBTEST_4(test_cuda_erfc<double>(5.0)); // CUDA erfc lacks precision for large inputs CALL_SUBTEST_4(test_cuda_erfc<double>(0.01)); CALL_SUBTEST_4(test_cuda_erfc<double>(0.001)); CALL_SUBTEST_5(test_cuda_digamma<float>()); CALL_SUBTEST_5(test_cuda_digamma<double>()); CALL_SUBTEST_5(test_cuda_polygamma<float>()); CALL_SUBTEST_5(test_cuda_polygamma<double>()); CALL_SUBTEST_5(test_cuda_zeta<float>()); CALL_SUBTEST_5(test_cuda_zeta<double>()); CALL_SUBTEST_5(test_cuda_igamma<float>()); CALL_SUBTEST_5(test_cuda_igammac<float>()); CALL_SUBTEST_5(test_cuda_igamma<double>()); CALL_SUBTEST_5(test_cuda_igammac<double>()); CALL_SUBTEST_6(test_cuda_betainc<float>()); CALL_SUBTEST_6(test_cuda_betainc<double>()); #endif }