// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Benoit Steiner // // 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_USE_GPU #include "main.h" #include #include #define EIGEN_GPU_TEST_C99_MATH EIGEN_HAS_CXX11 using Eigen::Tensor; void test_gpu_nullary() { Tensor in1(2); Tensor in2(2); in1.setRandom(); in2.setRandom(); std::size_t tensor_bytes = in1.size() * sizeof(float); float* d_in1; float* d_in2; gpuMalloc((void**)(&d_in1), tensor_bytes); gpuMalloc((void**)(&d_in2), tensor_bytes); gpuMemcpy(d_in1, in1.data(), tensor_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in2, in2.data(), tensor_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap, Eigen::Aligned> gpu_in1( d_in1, 2); Eigen::TensorMap, 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 new1(2); Tensor new2(2); assert(gpuMemcpyAsync(new1.data(), d_in1, tensor_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuMemcpyAsync(new2.data(), d_in2, tensor_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 2; ++i) { VERIFY_IS_APPROX(new1(i), 3.14f); VERIFY_IS_NOT_EQUAL(new2(i), in2(i)); } gpuFree(d_in1); gpuFree(d_in2); } void test_gpu_elementwise_small() { Tensor in1(Eigen::array(2)); Tensor in2(Eigen::array(2)); Tensor out(Eigen::array(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; gpuMalloc((void**)(&d_in1), in1_bytes); gpuMalloc((void**)(&d_in2), in2_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap, Eigen::Aligned> gpu_in1( d_in1, Eigen::array(2)); Eigen::TensorMap, Eigen::Aligned> gpu_in2( d_in2, Eigen::array(2)); Eigen::TensorMap, Eigen::Aligned> gpu_out( d_out, Eigen::array(2)); gpu_out.device(gpu_device) = gpu_in1 + gpu_in2; assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 2; ++i) { VERIFY_IS_APPROX( out(Eigen::array(i)), in1(Eigen::array(i)) + in2(Eigen::array(i))); } gpuFree(d_in1); gpuFree(d_in2); gpuFree(d_out); } void test_gpu_elementwise() { Tensor in1(Eigen::array(72,53,97)); Tensor in2(Eigen::array(72,53,97)); Tensor in3(Eigen::array(72,53,97)); Tensor out(Eigen::array(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; gpuMalloc((void**)(&d_in1), in1_bytes); gpuMalloc((void**)(&d_in2), in2_bytes); gpuMalloc((void**)(&d_in3), in3_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in3, in3.data(), in3_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in1(d_in1, Eigen::array(72,53,97)); Eigen::TensorMap > gpu_in2(d_in2, Eigen::array(72,53,97)); Eigen::TensorMap > gpu_in3(d_in3, Eigen::array(72,53,97)); Eigen::TensorMap > gpu_out(d_out, Eigen::array(72,53,97)); gpu_out.device(gpu_device) = gpu_in1 + gpu_in2 * gpu_in3; assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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(i,j,k)), in1(Eigen::array(i,j,k)) + in2(Eigen::array(i,j,k)) * in3(Eigen::array(i,j,k))); } } } gpuFree(d_in1); gpuFree(d_in2); gpuFree(d_in3); gpuFree(d_out); } void test_gpu_props() { Tensor in1(200); Tensor 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; gpuMalloc((void**)(&d_in1), in1_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap, Eigen::Aligned> gpu_in1( d_in1, 200); Eigen::TensorMap, Eigen::Aligned> gpu_out( d_out, 200); gpu_out.device(gpu_device) = (gpu_in1.isnan)(); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 200; ++i) { VERIFY_IS_EQUAL(out(i), (std::isnan)(in1(i))); } gpuFree(d_in1); gpuFree(d_out); } void test_gpu_reduction() { Tensor in1(72,53,97,113); Tensor 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; gpuMalloc((void**)(&d_in1), in1_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in1(d_in1, 72,53,97,113); Eigen::TensorMap > gpu_out(d_out, 72,97); array reduction_axis; reduction_axis[0] = 1; reduction_axis[1] = 3; gpu_out.device(gpu_device) = gpu_in1.maximum(reduction_axis); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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(expected, in1(i, k, j, l)); } } VERIFY_IS_APPROX(out(i,j), expected); } } gpuFree(d_in1); gpuFree(d_out); } template void test_gpu_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 t_left(6, 50, 3, 31); Tensor t_right(Eigen::array(3, 31, 7, 20, 1)); Tensor t_result(Eigen::array(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; gpuMalloc((void**)(&d_t_left), t_left_bytes); gpuMalloc((void**)(&d_t_right), t_right_bytes); gpuMalloc((void**)(&d_t_result), t_result_bytes); gpuMemcpy(d_t_left, t_left.data(), t_left_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_t_right, t_right.data(), t_right_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_t_left(d_t_left, 6, 50, 3, 31); Eigen::TensorMap > gpu_t_right(d_t_right, 3, 31, 7, 20, 1); Eigen::TensorMap > gpu_t_result(d_t_result, 6, 50, 7, 20, 1); typedef Eigen::Map > MapXf; MapXf m_left(t_left.data(), 300, 93); MapXf m_right(t_right.data(), 93, 140); Eigen::Matrix m_result(300, 140); typedef Tensor::DimensionPair DimPair; Eigen::array 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); gpuMemcpy(t_result.data(), d_t_result, t_result_bytes, gpuMemcpyDeviceToHost); 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); } } gpuFree(d_t_left); gpuFree(d_t_right); gpuFree(d_t_result); } template void test_gpu_convolution_1d() { Tensor input(74,37,11,137); Tensor kernel(4); Tensor 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; gpuMalloc((void**)(&d_input), input_bytes); gpuMalloc((void**)(&d_kernel), kernel_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_input(d_input, 74,37,11,137); Eigen::TensorMap > gpu_kernel(d_kernel, 4); Eigen::TensorMap > gpu_out(d_out, 74,34,11,137); Eigen::array dims(1); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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); } } } } gpuFree(d_input); gpuFree(d_kernel); gpuFree(d_out); } void test_gpu_convolution_inner_dim_col_major_1d() { Tensor input(74,9,11,7); Tensor kernel(4); Tensor 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; gpuMalloc((void**)(&d_input), input_bytes); gpuMalloc((void**)(&d_kernel), kernel_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_input(d_input,74,9,11,7); Eigen::TensorMap > gpu_kernel(d_kernel,4); Eigen::TensorMap > gpu_out(d_out,71,9,11,7); Eigen::array dims(0); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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); } } } } gpuFree(d_input); gpuFree(d_kernel); gpuFree(d_out); } void test_gpu_convolution_inner_dim_row_major_1d() { Tensor input(7,9,11,74); Tensor kernel(4); Tensor 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; gpuMalloc((void**)(&d_input), input_bytes); gpuMalloc((void**)(&d_kernel), kernel_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_input(d_input, 7,9,11,74); Eigen::TensorMap > gpu_kernel(d_kernel, 4); Eigen::TensorMap > gpu_out(d_out, 7,9,11,71); Eigen::array dims(3); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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); } } } } gpuFree(d_input); gpuFree(d_kernel); gpuFree(d_out); } template void test_gpu_convolution_2d() { Tensor input(74,37,11,137); Tensor kernel(3,4); Tensor 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; gpuMalloc((void**)(&d_input), input_bytes); gpuMalloc((void**)(&d_kernel), kernel_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_input(d_input,74,37,11,137); Eigen::TensorMap > gpu_kernel(d_kernel,3,4); Eigen::TensorMap > gpu_out(d_out,74,35,8,137); Eigen::array dims(1,2); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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); } } } } gpuFree(d_input); gpuFree(d_kernel); gpuFree(d_out); } template void test_gpu_convolution_3d() { Tensor input(Eigen::array(74,37,11,137,17)); Tensor kernel(3,4,2); Tensor out(Eigen::array(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; gpuMalloc((void**)(&d_input), input_bytes); gpuMalloc((void**)(&d_kernel), kernel_bytes); gpuMalloc((void**)(&d_out), out_bytes); gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_input(d_input,74,37,11,137,17); Eigen::TensorMap > gpu_kernel(d_kernel,3,4,2); Eigen::TensorMap > gpu_out(d_out,74,35,8,136,17); Eigen::array dims(1,2,3); gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims); assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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); } } } } } gpuFree(d_input); gpuFree(d_kernel); gpuFree(d_out); } #if EIGEN_GPU_TEST_C99_MATH template void test_gpu_lgamma(const Scalar stddev) { Tensor in(72,97); in.setRandom(); in *= in.constant(stddev); Tensor out(72,97); out.setZero(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; gpuMalloc((void**)(&d_in), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in(d_in, 72, 97); Eigen::TensorMap > gpu_out(d_out, 72, 97); gpu_out.device(gpu_device) = gpu_in.lgamma(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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))); } } gpuFree(d_in); gpuFree(d_out); } #endif template void test_gpu_digamma() { Tensor in(7); Tensor out(7); Tensor 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::infinity(); expected_out(6) = std::numeric_limits::infinity(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; gpuMalloc((void**)(&d_in), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in(d_in, 7); Eigen::TensorMap > gpu_out(d_out, 7); gpu_out.device(gpu_device) = gpu_in.digamma(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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)); } gpuFree(d_in); gpuFree(d_out); } template void test_gpu_zeta() { Tensor in_x(6); Tensor in_q(6); Tensor out(6); Tensor 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::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; gpuMalloc((void**)(&d_in_x), bytes); gpuMalloc((void**)(&d_in_q), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in_q, in_q.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in_x(d_in_x, 6); Eigen::TensorMap > gpu_in_q(d_in_q, 6); Eigen::TensorMap > gpu_out(d_out, 6); gpu_out.device(gpu_device) = gpu_in_x.zeta(gpu_in_q); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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)); } } gpuFree(d_in_x); gpuFree(d_in_q); gpuFree(d_out); } template void test_gpu_polygamma() { Tensor in_x(7); Tensor in_n(7); Tensor out(7); Tensor 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; gpuMalloc((void**)(&d_in_x), bytes); gpuMalloc((void**)(&d_in_n), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in_n, in_n.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in_x(d_in_x, 7); Eigen::TensorMap > gpu_in_n(d_in_n, 7); Eigen::TensorMap > gpu_out(d_out, 7); gpu_out.device(gpu_device) = gpu_in_n.polygamma(gpu_in_x); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 7; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } gpuFree(d_in_x); gpuFree(d_in_n); gpuFree(d_out); } template void test_gpu_igamma() { Tensor a(6, 6); Tensor x(6, 6); Tensor 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::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(gpuMalloc((void**)(&d_a), bytes) == gpuSuccess); assert(gpuMalloc((void**)(&d_x), bytes) == gpuSuccess); assert(gpuMalloc((void**)(&d_out), bytes) == gpuSuccess); gpuMemcpy(d_a, a.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_x, x.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_a(d_a, 6, 6); Eigen::TensorMap > gpu_x(d_x, 6, 6); Eigen::TensorMap > gpu_out(d_out, 6, 6); gpu_out.device(gpu_device) = gpu_a.igamma(gpu_x); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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]); } } } gpuFree(d_a); gpuFree(d_x); gpuFree(d_out); } template void test_gpu_igammac() { Tensor a(6, 6); Tensor x(6, 6); Tensor 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::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; gpuMalloc((void**)(&d_a), bytes); gpuMalloc((void**)(&d_x), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_a, a.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_x, x.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_a(d_a, 6, 6); Eigen::TensorMap > gpu_x(d_x, 6, 6); Eigen::TensorMap > gpu_out(d_out, 6, 6); gpu_out.device(gpu_device) = gpu_a.igammac(gpu_x); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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]); } } } gpuFree(d_a); gpuFree(d_x); gpuFree(d_out); } #if EIGEN_GPU_TEST_C99_MATH template void test_gpu_erf(const Scalar stddev) { Tensor in(72,97); in.setRandom(); in *= in.constant(stddev); Tensor out(72,97); out.setZero(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; assert(gpuMalloc((void**)(&d_in), bytes) == gpuSuccess); assert(gpuMalloc((void**)(&d_out), bytes) == gpuSuccess); gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in(d_in, 72, 97); Eigen::TensorMap > gpu_out(d_out, 72, 97); gpu_out.device(gpu_device) = gpu_in.erf(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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))); } } gpuFree(d_in); gpuFree(d_out); } template void test_gpu_erfc(const Scalar stddev) { Tensor in(72,97); in.setRandom(); in *= in.constant(stddev); Tensor out(72,97); out.setZero(); std::size_t bytes = in.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; gpuMalloc((void**)(&d_in), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in(d_in, 72, 97); Eigen::TensorMap > gpu_out(d_out, 72, 97); gpu_out.device(gpu_device) = gpu_in.erfc(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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))); } } gpuFree(d_in); gpuFree(d_out); } #endif template void test_gpu_ndtri() { Tensor in_x(8); Tensor out(8); Tensor expected_out(8); out.setZero(); in_x(0) = Scalar(1); in_x(1) = Scalar(0.); in_x(2) = Scalar(0.5); in_x(3) = Scalar(0.2); in_x(4) = Scalar(0.8); in_x(5) = Scalar(0.9); in_x(6) = Scalar(0.1); in_x(7) = Scalar(0.99); in_x(8) = Scalar(0.01); expected_out(0) = std::numeric_limits::infinity(); expected_out(1) = -std::numeric_limits::infinity(); expected_out(2) = Scalar(0.0); expected_out(3) = Scalar(-0.8416212335729142); expected_out(4) = Scalar(0.8416212335729142); expected_out(5) = Scalar(1.2815515655446004); expected_out(6) = Scalar(-1.2815515655446004); expected_out(7) = Scalar(2.3263478740408408); expected_out(8) = Scalar(-2.3263478740408408); std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_in_x; Scalar* d_out; gpuMalloc((void**)(&d_in_x), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in_x(d_in_x, 6); Eigen::TensorMap > gpu_out(d_out, 6); gpu_out.device(gpu_device) = gpu_in_x.ndtri(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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)); } } gpuFree(d_in_x); gpuFree(d_out); } template void test_gpu_betainc() { Tensor in_x(125); Tensor in_a(125); Tensor in_b(125); Tensor out(125); Tensor expected_out(125); out.setZero(); Scalar nan = std::numeric_limits::quiet_NaN(); Array x(125); Array a(125); Array b(125); Array 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; gpuMalloc((void**)(&d_in_x), bytes); gpuMalloc((void**)(&d_in_a), bytes); gpuMalloc((void**)(&d_in_b), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in_a, in_a.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_in_b, in_b.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in_x(d_in_x, 125); Eigen::TensorMap > gpu_in_a(d_in_a, 125); Eigen::TensorMap > gpu_in_b(d_in_b, 125); Eigen::TensorMap > gpu_out(d_out, 125); gpu_out.device(gpu_device) = betainc(gpu_in_a, gpu_in_b, gpu_in_x); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); 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)); } } gpuFree(d_in_x); gpuFree(d_in_a); gpuFree(d_in_b); gpuFree(d_out); } template void test_gpu_i0e() { Tensor in_x(21); Tensor out(21); Tensor expected_out(21); out.setZero(); Array in_x_array(21); Array expected_out_array(21); in_x_array << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0; expected_out_array << 0.0897803118848, 0.0947062952128, 0.100544127361, 0.107615251671, 0.116426221213, 0.127833337163, 0.143431781857, 0.16665743264, 0.207001921224, 0.308508322554, 1.0, 0.308508322554, 0.207001921224, 0.16665743264, 0.143431781857, 0.127833337163, 0.116426221213, 0.107615251671, 0.100544127361, 0.0947062952128, 0.0897803118848; for (int i = 0; i < 21; ++i) { in_x(i) = in_x_array(i); expected_out(i) = expected_out_array(i); } std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; gpuMalloc((void**)(&d_in), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in, in_x.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in(d_in, 21); Eigen::TensorMap > gpu_out(d_out, 21); gpu_out.device(gpu_device) = gpu_in.bessel_i0e(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 21; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } gpuFree(d_in); gpuFree(d_out); } template void test_gpu_i1e() { Tensor in_x(21); Tensor out(21); Tensor expected_out(21); out.setZero(); Array in_x_array(21); Array expected_out_array(21); in_x_array << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0; expected_out_array << -0.0875062221833, -0.092036796872, -0.0973496147565, -0.103697667463, -0.11146429929, -0.121262681384, -0.134142493293, -0.152051459309, -0.178750839502, -0.215269289249, 0.0, 0.215269289249, 0.178750839502, 0.152051459309, 0.134142493293, 0.121262681384, 0.11146429929, 0.103697667463, 0.0973496147565, 0.092036796872, 0.0875062221833; for (int i = 0; i < 21; ++i) { in_x(i) = in_x_array(i); expected_out(i) = expected_out_array(i); } std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_in; Scalar* d_out; gpuMalloc((void**)(&d_in), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_in, in_x.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_in(d_in, 21); Eigen::TensorMap > gpu_out(d_out, 21); gpu_out.device(gpu_device) = gpu_in.bessel_i1e(); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 21; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } gpuFree(d_in); gpuFree(d_out); } template void test_gpu_igamma_der_a() { Tensor in_x(30); Tensor in_a(30); Tensor out(30); Tensor expected_out(30); out.setZero(); Array in_a_array(30); Array in_x_array(30); Array expected_out_array(30); // See special_functions.cpp for the Python code that generates the test data. in_a_array << 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0; in_x_array << 1.25668890405e-26, 1.17549435082e-38, 1.20938905072e-05, 1.17549435082e-38, 1.17549435082e-38, 5.66572070696e-16, 0.0132865061065, 0.0200034203853, 6.29263709118e-17, 1.37160367764e-06, 0.333412038288, 1.18135687766, 0.580629033777, 0.170631439426, 0.786686768458, 7.63873279537, 13.1944344379, 11.896042354, 10.5830172417, 10.5020942233, 92.8918587747, 95.003720371, 86.3715926467, 96.0330217672, 82.6389930677, 968.702906754, 969.463546828, 1001.79726022, 955.047416547, 1044.27458568; expected_out_array << -32.7256441441, -36.4394150514, -9.66467612263, -36.4394150514, -36.4394150514, -1.0891900302, -2.66351229645, -2.48666868596, -0.929700494428, -3.56327722764, -0.455320135314, -0.391437214323, -0.491352055991, -0.350454834292, -0.471773162921, -0.104084440522, -0.0723646747909, -0.0992828975532, -0.121638215446, -0.122619605294, -0.0317670267286, -0.0359974812869, -0.0154359225363, -0.0375775365921, -0.00794899153653, -0.00777303219211, -0.00796085782042, -0.0125850719397, -0.00455500206958, -0.00476436993148; for (int i = 0; i < 30; ++i) { in_x(i) = in_x_array(i); in_a(i) = in_a_array(i); expected_out(i) = expected_out_array(i); } std::size_t bytes = in_x.size() * sizeof(Scalar); Scalar* d_a; Scalar* d_x; Scalar* d_out; gpuMalloc((void**)(&d_a), bytes); gpuMalloc((void**)(&d_x), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_a, in_a.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_x, in_x.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_a(d_a, 30); Eigen::TensorMap > gpu_x(d_x, 30); Eigen::TensorMap > gpu_out(d_out, 30); gpu_out.device(gpu_device) = gpu_a.igamma_der_a(gpu_x); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 30; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } gpuFree(d_a); gpuFree(d_x); gpuFree(d_out); } template void test_gpu_gamma_sample_der_alpha() { Tensor in_alpha(30); Tensor in_sample(30); Tensor out(30); Tensor expected_out(30); out.setZero(); Array in_alpha_array(30); Array in_sample_array(30); Array expected_out_array(30); // See special_functions.cpp for the Python code that generates the test data. in_alpha_array << 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0; in_sample_array << 1.25668890405e-26, 1.17549435082e-38, 1.20938905072e-05, 1.17549435082e-38, 1.17549435082e-38, 5.66572070696e-16, 0.0132865061065, 0.0200034203853, 6.29263709118e-17, 1.37160367764e-06, 0.333412038288, 1.18135687766, 0.580629033777, 0.170631439426, 0.786686768458, 7.63873279537, 13.1944344379, 11.896042354, 10.5830172417, 10.5020942233, 92.8918587747, 95.003720371, 86.3715926467, 96.0330217672, 82.6389930677, 968.702906754, 969.463546828, 1001.79726022, 955.047416547, 1044.27458568; expected_out_array << 7.42424742367e-23, 1.02004297287e-34, 0.0130155240738, 1.02004297287e-34, 1.02004297287e-34, 1.96505168277e-13, 0.525575786243, 0.713903991771, 2.32077561808e-14, 0.000179348049886, 0.635500453302, 1.27561284917, 0.878125852156, 0.41565819538, 1.03606488534, 0.885964824887, 1.16424049334, 1.10764479598, 1.04590810812, 1.04193666963, 0.965193152414, 0.976217589464, 0.93008035061, 0.98153216096, 0.909196397698, 0.98434963993, 0.984738050206, 1.00106492525, 0.97734200649, 1.02198794179; for (int i = 0; i < 30; ++i) { in_alpha(i) = in_alpha_array(i); in_sample(i) = in_sample_array(i); expected_out(i) = expected_out_array(i); } std::size_t bytes = in_alpha.size() * sizeof(Scalar); Scalar* d_alpha; Scalar* d_sample; Scalar* d_out; gpuMalloc((void**)(&d_alpha), bytes); gpuMalloc((void**)(&d_sample), bytes); gpuMalloc((void**)(&d_out), bytes); gpuMemcpy(d_alpha, in_alpha.data(), bytes, gpuMemcpyHostToDevice); gpuMemcpy(d_sample, in_sample.data(), bytes, gpuMemcpyHostToDevice); Eigen::GpuStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); Eigen::TensorMap > gpu_alpha(d_alpha, 30); Eigen::TensorMap > gpu_sample(d_sample, 30); Eigen::TensorMap > gpu_out(d_out, 30); gpu_out.device(gpu_device) = gpu_alpha.gamma_sample_der_alpha(gpu_sample); assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess); assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess); for (int i = 0; i < 30; ++i) { VERIFY_IS_APPROX(out(i), expected_out(i)); } gpuFree(d_alpha); gpuFree(d_sample); gpuFree(d_out); } EIGEN_DECLARE_TEST(cxx11_tensor_gpu) { CALL_SUBTEST_1(test_gpu_nullary()); CALL_SUBTEST_1(test_gpu_elementwise_small()); CALL_SUBTEST_1(test_gpu_elementwise()); CALL_SUBTEST_1(test_gpu_props()); CALL_SUBTEST_1(test_gpu_reduction()); CALL_SUBTEST_2(test_gpu_contraction()); CALL_SUBTEST_2(test_gpu_contraction()); CALL_SUBTEST_3(test_gpu_convolution_1d()); CALL_SUBTEST_3(test_gpu_convolution_1d()); CALL_SUBTEST_3(test_gpu_convolution_inner_dim_col_major_1d()); CALL_SUBTEST_3(test_gpu_convolution_inner_dim_row_major_1d()); CALL_SUBTEST_3(test_gpu_convolution_2d()); CALL_SUBTEST_3(test_gpu_convolution_2d()); #if !defined(EIGEN_USE_HIP) // disable these tests on HIP for now. // they hang..need to investigate and fix CALL_SUBTEST_3(test_gpu_convolution_3d()); CALL_SUBTEST_3(test_gpu_convolution_3d()); #endif #if EIGEN_GPU_TEST_C99_MATH // 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_gpu_lgamma(1.0f)); CALL_SUBTEST_4(test_gpu_lgamma(100.0f)); CALL_SUBTEST_4(test_gpu_lgamma(0.01f)); CALL_SUBTEST_4(test_gpu_lgamma(0.001f)); CALL_SUBTEST_4(test_gpu_lgamma(1.0)); CALL_SUBTEST_4(test_gpu_lgamma(100.0)); CALL_SUBTEST_4(test_gpu_lgamma(0.01)); CALL_SUBTEST_4(test_gpu_lgamma(0.001)); CALL_SUBTEST_4(test_gpu_erf(1.0f)); CALL_SUBTEST_4(test_gpu_erf(100.0f)); CALL_SUBTEST_4(test_gpu_erf(0.01f)); CALL_SUBTEST_4(test_gpu_erf(0.001f)); CALL_SUBTEST_4(test_gpu_erfc(1.0f)); // CALL_SUBTEST(test_gpu_erfc(100.0f)); CALL_SUBTEST_4(test_gpu_erfc(5.0f)); // GPU erfc lacks precision for large inputs CALL_SUBTEST_4(test_gpu_erfc(0.01f)); CALL_SUBTEST_4(test_gpu_erfc(0.001f)); CALL_SUBTEST_4(test_gpu_erf(1.0)); CALL_SUBTEST_4(test_gpu_erf(100.0)); CALL_SUBTEST_4(test_gpu_erf(0.01)); CALL_SUBTEST_4(test_gpu_erf(0.001)); CALL_SUBTEST_4(test_gpu_erfc(1.0)); // CALL_SUBTEST(test_gpu_erfc(100.0)); CALL_SUBTEST_4(test_gpu_erfc(5.0)); // GPU erfc lacks precision for large inputs CALL_SUBTEST_4(test_gpu_erfc(0.01)); CALL_SUBTEST_4(test_gpu_erfc(0.001)); #if !defined(EIGEN_USE_HIP) // disable these tests on HIP for now. CALL_SUBTEST_5(test_gpu_ndtri()); CALL_SUBTEST_5(test_gpu_ndtri()); CALL_SUBTEST_5(test_gpu_digamma()); CALL_SUBTEST_5(test_gpu_digamma()); CALL_SUBTEST_5(test_gpu_polygamma()); CALL_SUBTEST_5(test_gpu_polygamma()); CALL_SUBTEST_5(test_gpu_zeta()); CALL_SUBTEST_5(test_gpu_zeta()); #endif CALL_SUBTEST_5(test_gpu_igamma()); CALL_SUBTEST_5(test_gpu_igammac()); CALL_SUBTEST_5(test_gpu_igamma()); CALL_SUBTEST_5(test_gpu_igammac()); #if !defined(EIGEN_USE_HIP) // disable these tests on HIP for now. CALL_SUBTEST_6(test_gpu_betainc()); CALL_SUBTEST_6(test_gpu_betainc()); CALL_SUBTEST_6(test_gpu_i0e()); CALL_SUBTEST_6(test_gpu_i0e()); CALL_SUBTEST_6(test_gpu_i1e()); CALL_SUBTEST_6(test_gpu_i1e()); CALL_SUBTEST_6(test_gpu_i1e()); CALL_SUBTEST_6(test_gpu_i1e()); CALL_SUBTEST_6(test_gpu_igamma_der_a()); CALL_SUBTEST_6(test_gpu_igamma_der_a()); CALL_SUBTEST_6(test_gpu_gamma_sample_der_alpha()); CALL_SUBTEST_6(test_gpu_gamma_sample_der_alpha()); #endif #endif }