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-rw-r--r--unsupported/test/cxx11_tensor_cuda.cpp474
1 files changed, 474 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_cuda.cpp b/unsupported/test/cxx11_tensor_cuda.cpp
new file mode 100644
index 000000000..059d23de1
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_cuda.cpp
@@ -0,0 +1,474 @@
+// 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/.
+
+// TODO(mdevin): Free the cuda memory.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+#define EIGEN_TEST_FUNC cxx11_tensor_cuda
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+void test_cuda_elementwise_small() {
+ Tensor<float, 1> in1(Eigen::array<int, 1>(2));
+ Tensor<float, 1> in2(Eigen::array<int, 1>(2));
+ Tensor<float, 1> out(Eigen::array<int, 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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1(
+ d_in1, Eigen::array<int, 1>(2));
+ Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in2(
+ d_in2, Eigen::array<int, 1>(2));
+ Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_out(
+ d_out, Eigen::array<int, 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<int, 1>(i)),
+ in1(Eigen::array<int, 1>(i)) + in2(Eigen::array<int, 1>(i)));
+ }
+}
+
+void test_cuda_elementwise()
+{
+ Tensor<float, 3> in1(Eigen::array<int, 3>(72,53,97));
+ Tensor<float, 3> in2(Eigen::array<int, 3>(72,53,97));
+ Tensor<float, 3> in3(Eigen::array<int, 3>(72,53,97));
+ Tensor<float, 3> out(Eigen::array<int, 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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<int, 3>(72,53,97));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<int, 3>(72,53,97));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in3(d_in3, Eigen::array<int, 3>(72,53,97));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int, 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<int, 3>(i,j,k)), in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k)) * in3(Eigen::array<int, 3>(i,j,k)));
+ }
+ }
+ }
+}
+
+
+void test_cuda_reduction()
+{
+ Tensor<float, 4> in1(Eigen::array<int, 4>(72,53,97,113));
+ Tensor<float, 2> out(Eigen::array<int, 2>(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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_in1(d_in1, Eigen::array<int, 4>(72,53,97,113));
+ Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, Eigen::array<int, 2>(72,97));
+
+ array<int, 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(Eigen::array<int, 4>(i, k, j, l)));
+ }
+ }
+ VERIFY_IS_APPROX(out(Eigen::array<int, 2>(i,j)), expected);
+ }
+ }
+}
+
+template<int DataLayout>
+static 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(Eigen::array<int, 4>(6, 50, 3, 31));
+ Tensor<float, 5, DataLayout> t_right(Eigen::array<int, 5>(3, 31, 7, 20, 1));
+ Tensor<float, 5, DataLayout> t_result(Eigen::array<int, 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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> >
+ gpu_t_left(d_t_left, Eigen::array<int, 4>(6, 50, 3, 31));
+ Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> >
+ gpu_t_right(d_t_right, Eigen::array<int, 5>(3, 31, 7, 20, 1));
+ Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> >
+ gpu_t_result(d_t_result, Eigen::array<int, 5>(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 (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+ if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4) {
+ cout << "mismatch detected at index " << i << ": " << t_result.data()[i] << " vs " << m_result.data()[i] << endl;
+ assert(false);
+ }
+ }
+}
+
+static void test_cuda_convolution_1d()
+{
+ Tensor<float, 4> input(Eigen::array<int, 4>(74,37,11,137));
+ Tensor<float, 1> kernel(Eigen::array<int, 1>(4));
+ Tensor<float, 4> out(Eigen::array<int, 4>(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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_input(d_input, Eigen::array<int, 4>(74,37,11,137));
+ Eigen::TensorMap<Eigen::Tensor<float, 1> > gpu_kernel(d_kernel, Eigen::array<int, 1>(4));
+ Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_out(d_out, Eigen::array<int, 4>(74,34,11,137));
+
+ Eigen::array<int, 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(Eigen::array<int, 4>(i,j,k,l));
+ const float expected = input(Eigen::array<int, 4>(i,j+0,k,l)) * kernel(Eigen::array<int, 1>(0)) +
+ input(Eigen::array<int, 4>(i,j+1,k,l)) * kernel(Eigen::array<int, 1>(1)) +
+ input(Eigen::array<int, 4>(i,j+2,k,l)) * kernel(Eigen::array<int, 1>(2)) +
+ input(Eigen::array<int, 4>(i,j+3,k,l)) * kernel(Eigen::array<int, 1>(3));
+ VERIFY_IS_APPROX(result, expected);
+ }
+ }
+ }
+ }
+}
+
+
+static void test_cuda_convolution_2d()
+{
+ Tensor<float, 4> input(Eigen::array<int, 4>(74,37,11,137));
+ Tensor<float, 2> kernel(Eigen::array<int, 2>(3,4));
+ Tensor<float, 4> out(Eigen::array<int, 4>(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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_input(d_input, Eigen::array<int, 4>(74,37,11,137));
+ Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_kernel(d_kernel, Eigen::array<int, 2>(3,4));
+ Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_out(d_out, Eigen::array<int, 4>(74,35,8,137));
+
+ Eigen::array<int, 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(Eigen::array<int, 4>(i,j,k,l));
+ const float expected = input(Eigen::array<int, 4>(i,j+0,k+0,l)) * kernel(Eigen::array<int, 2>(0,0)) +
+ input(Eigen::array<int, 4>(i,j+1,k+0,l)) * kernel(Eigen::array<int, 2>(1,0)) +
+ input(Eigen::array<int, 4>(i,j+2,k+0,l)) * kernel(Eigen::array<int, 2>(2,0)) +
+ input(Eigen::array<int, 4>(i,j+0,k+1,l)) * kernel(Eigen::array<int, 2>(0,1)) +
+ input(Eigen::array<int, 4>(i,j+1,k+1,l)) * kernel(Eigen::array<int, 2>(1,1)) +
+ input(Eigen::array<int, 4>(i,j+2,k+1,l)) * kernel(Eigen::array<int, 2>(2,1)) +
+ input(Eigen::array<int, 4>(i,j+0,k+2,l)) * kernel(Eigen::array<int, 2>(0,2)) +
+ input(Eigen::array<int, 4>(i,j+1,k+2,l)) * kernel(Eigen::array<int, 2>(1,2)) +
+ input(Eigen::array<int, 4>(i,j+2,k+2,l)) * kernel(Eigen::array<int, 2>(2,2)) +
+ input(Eigen::array<int, 4>(i,j+0,k+3,l)) * kernel(Eigen::array<int, 2>(0,3)) +
+ input(Eigen::array<int, 4>(i,j+1,k+3,l)) * kernel(Eigen::array<int, 2>(1,3)) +
+ input(Eigen::array<int, 4>(i,j+2,k+3,l)) * kernel(Eigen::array<int, 2>(2,3));
+ VERIFY_IS_APPROX(result, expected);
+ }
+ }
+ }
+ }
+}
+
+
+static void test_cuda_convolution_3d()
+{
+ Tensor<float, 5> input(Eigen::array<int, 5>(74,37,11,137,17));
+ Tensor<float, 3> kernel(Eigen::array<int, 3>(3,4,2));
+ Tensor<float, 5> out(Eigen::array<int, 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);
+
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 5> > gpu_input(d_input, Eigen::array<int, 5>(74,37,11,137,17));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_kernel(d_kernel, Eigen::array<int, 3>(3,4,2));
+ Eigen::TensorMap<Eigen::Tensor<float, 5> > gpu_out(d_out, Eigen::array<int, 5>(74,35,8,136,17));
+
+ Eigen::array<int, 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(Eigen::array<int, 5>(i,j,k,l,m));
+ const float expected = input(Eigen::array<int, 5>(i,j+0,k+0,l+0,m)) * kernel(Eigen::array<int, 3>(0,0,0)) +
+ input(Eigen::array<int, 5>(i,j+1,k+0,l+0,m)) * kernel(Eigen::array<int, 3>(1,0,0)) +
+ input(Eigen::array<int, 5>(i,j+2,k+0,l+0,m)) * kernel(Eigen::array<int, 3>(2,0,0)) +
+ input(Eigen::array<int, 5>(i,j+0,k+1,l+0,m)) * kernel(Eigen::array<int, 3>(0,1,0)) +
+ input(Eigen::array<int, 5>(i,j+1,k+1,l+0,m)) * kernel(Eigen::array<int, 3>(1,1,0)) +
+ input(Eigen::array<int, 5>(i,j+2,k+1,l+0,m)) * kernel(Eigen::array<int, 3>(2,1,0)) +
+ input(Eigen::array<int, 5>(i,j+0,k+2,l+0,m)) * kernel(Eigen::array<int, 3>(0,2,0)) +
+ input(Eigen::array<int, 5>(i,j+1,k+2,l+0,m)) * kernel(Eigen::array<int, 3>(1,2,0)) +
+ input(Eigen::array<int, 5>(i,j+2,k+2,l+0,m)) * kernel(Eigen::array<int, 3>(2,2,0)) +
+ input(Eigen::array<int, 5>(i,j+0,k+3,l+0,m)) * kernel(Eigen::array<int, 3>(0,3,0)) +
+ input(Eigen::array<int, 5>(i,j+1,k+3,l+0,m)) * kernel(Eigen::array<int, 3>(1,3,0)) +
+ input(Eigen::array<int, 5>(i,j+2,k+3,l+0,m)) * kernel(Eigen::array<int, 3>(2,3,0)) +
+ input(Eigen::array<int, 5>(i,j+0,k+0,l+1,m)) * kernel(Eigen::array<int, 3>(0,0,1)) +
+ input(Eigen::array<int, 5>(i,j+1,k+0,l+1,m)) * kernel(Eigen::array<int, 3>(1,0,1)) +
+ input(Eigen::array<int, 5>(i,j+2,k+0,l+1,m)) * kernel(Eigen::array<int, 3>(2,0,1)) +
+ input(Eigen::array<int, 5>(i,j+0,k+1,l+1,m)) * kernel(Eigen::array<int, 3>(0,1,1)) +
+ input(Eigen::array<int, 5>(i,j+1,k+1,l+1,m)) * kernel(Eigen::array<int, 3>(1,1,1)) +
+ input(Eigen::array<int, 5>(i,j+2,k+1,l+1,m)) * kernel(Eigen::array<int, 3>(2,1,1)) +
+ input(Eigen::array<int, 5>(i,j+0,k+2,l+1,m)) * kernel(Eigen::array<int, 3>(0,2,1)) +
+ input(Eigen::array<int, 5>(i,j+1,k+2,l+1,m)) * kernel(Eigen::array<int, 3>(1,2,1)) +
+ input(Eigen::array<int, 5>(i,j+2,k+2,l+1,m)) * kernel(Eigen::array<int, 3>(2,2,1)) +
+ input(Eigen::array<int, 5>(i,j+0,k+3,l+1,m)) * kernel(Eigen::array<int, 3>(0,3,1)) +
+ input(Eigen::array<int, 5>(i,j+1,k+3,l+1,m)) * kernel(Eigen::array<int, 3>(1,3,1)) +
+ input(Eigen::array<int, 5>(i,j+2,k+3,l+1,m)) * kernel(Eigen::array<int, 3>(2,3,1));
+ VERIFY_IS_APPROX(result, expected);
+ }
+ }
+ }
+ }
+ }
+}
+
+static float* CudaCopyFloat(float* data, int size) {
+ const int nbytes = size * sizeof(float);
+ float* result = NULL;
+ if (cudaMalloc((void**)(&result), nbytes) != cudaSuccess) {
+ return NULL;
+ } else {
+ if (data != NULL) {
+ cudaMemcpy(result, data, nbytes, cudaMemcpyHostToDevice);
+ }
+ return result;
+ }
+}
+
+static void test_cuda_constant_broadcast()
+{
+ cudaStream_t stream;
+ assert(cudaStreamCreate(&stream) == cudaSuccess);
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Tensor<float, 1> t1(10);
+ for (int i = 0; i < 10; ++i) {
+ t1(i) = 10.0f * i;
+ }
+ float* t1_cuda = CudaCopyFloat(t1.data(), t1.size());
+ Eigen::TensorMap<Eigen::Tensor<float, 1> > t1_gpu(t1_cuda, 10);
+
+ Tensor<float, 1> t2(1);
+ t2 = t2.constant(20.0f);
+ float* t2_cuda = CudaCopyFloat(t2.data(), t2.size());
+ Eigen::TensorMap<Eigen::TensorFixedSize<float, Sizes<1> > > t2_gpu(t2_cuda, 1);
+
+ float* t3_cuda = CudaCopyFloat(NULL, 10);
+ Eigen::TensorMap<Eigen::Tensor<float, 1> > t3_gpu(t3_cuda, 10);
+
+ t3_gpu.device(gpu_device) =
+ t1_gpu + t2_gpu.broadcast(Eigen::array<int, 1>(10));
+
+ Eigen::Tensor<float, 1> t3(10);
+ cudaMemcpy(t3.data(), t3_gpu.data(), 10 * sizeof(float),
+ cudaMemcpyDeviceToHost);
+
+ for (int i = 0; i < 10; ++i) {
+ VERIFY_IS_APPROX(t3(i), t1(i) + t2(0));
+ }
+}
+
+void test_cxx11_tensor_cuda()
+{
+ CALL_SUBTEST(test_cuda_elementwise_small());
+ CALL_SUBTEST(test_cuda_elementwise());
+ CALL_SUBTEST(test_cuda_reduction());
+ CALL_SUBTEST(test_cuda_contraction<ColMajor>());
+ CALL_SUBTEST(test_cuda_contraction<RowMajor>());
+ CALL_SUBTEST(test_cuda_convolution_1d());
+ CALL_SUBTEST(test_cuda_convolution_2d());
+ CALL_SUBTEST(test_cuda_convolution_3d());
+ CALL_SUBTEST(test_cuda_constant_broadcast());
+}
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-06 09:51:33 +0000