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diff --git a/unsupported/test/cxx11_tensor_contract_cuda.cu b/unsupported/test/cxx11_tensor_contract_cuda.cu
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+++ b/unsupported/test/cxx11_tensor_contract_cuda.cu
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.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_DEFAULT_DENSE_INDEX_TYPE int
+#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;
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template<int DataLayout>
+void test_cuda_contraction(int m_size, int k_size, int n_size)
+{
+ std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
+ // 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, 2, DataLayout> t_left(m_size, k_size);
+ Tensor<float, 2, DataLayout> t_right(k_size, n_size);
+ Tensor<float, 2, DataLayout> t_result(m_size, n_size);
+ Tensor<float, 2, DataLayout> t_result_gpu(m_size, n_size);
+ Eigen::array<DimPair, 1> dims(DimPair(1, 0));
+
+ 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, 2, DataLayout> >
+ gpu_t_left(d_t_left, Eigen::array<int, 2>(m_size, k_size));
+ Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
+ gpu_t_right(d_t_right, Eigen::array<int, 2>(k_size, n_size));
+ Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
+ gpu_t_result(d_t_result, Eigen::array<int, 2>(m_size, n_size));
+
+
+ gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
+ t_result = t_left.contract(t_right, dims);
+
+ cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
+ for (DenseIndex i = 0; i < t_result.size(); i++) {
+ if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
+ continue;
+ }
+ if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
+ continue;
+ }
+ std::cout << "mismatch detected at index " << i << ": " << t_result(i)
+ << " vs " << t_result_gpu(i) << std::endl;
+ assert(false);
+ }
+
+ cudaFree((void*)d_t_left);
+ cudaFree((void*)d_t_right);
+ cudaFree((void*)d_t_result);
+}
+
+
+template<int DataLayout>
+void test_scalar(int m_size, int k_size, int n_size)
+{
+ std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
+ // 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, 2, DataLayout> t_left(m_size, k_size);
+ Tensor<float, 2, DataLayout> t_right(k_size, n_size);
+ Tensor<float, 0, DataLayout> t_result;
+ Tensor<float, 0, DataLayout> t_result_gpu;
+ Eigen::array<DimPair, 2> dims(DimPair(0, 0), DimPair(1, 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 = 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, 2, DataLayout> >
+ gpu_t_left(d_t_left, m_size, k_size);
+ Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
+ gpu_t_right(d_t_right, k_size, n_size);
+ Eigen::TensorMap<Eigen::Tensor<float, 0, DataLayout> >
+ gpu_t_result(d_t_result);
+
+ gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
+ t_result = t_left.contract(t_right, dims);
+
+ cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
+ if (fabs(t_result() - t_result_gpu()) > 1e-4f &&
+ !Eigen::internal::isApprox(t_result(), t_result_gpu(), 1e-4f)) {
+ std::cout << "mismatch detected: " << t_result()
+ << " vs " << t_result_gpu() << std::endl;
+ assert(false);
+ }
+
+ cudaFree((void*)d_t_left);
+ cudaFree((void*)d_t_right);
+ cudaFree((void*)d_t_result);
+}
+
+
+template<int DataLayout>
+void test_cuda_contraction_m() {
+ for (int k = 32; k < 256; k++) {
+ test_cuda_contraction<ColMajor>(k, 128, 128);
+ test_cuda_contraction<RowMajor>(k, 128, 128);
+ }
+}
+
+template<int DataLayout>
+void test_cuda_contraction_k() {
+ for (int k = 32; k < 256; k++) {
+ test_cuda_contraction<ColMajor>(128, k, 128);
+ test_cuda_contraction<RowMajor>(128, k, 128);
+ }
+}
+
+template<int DataLayout>
+void test_cuda_contraction_n() {
+ for (int k = 32; k < 256; k++) {
+ test_cuda_contraction<ColMajor>(128, 128, k);
+ test_cuda_contraction<RowMajor>(128, 128, k);
+ }
+}
+
+
+template<int DataLayout>
+void test_cuda_contraction_sizes() {
+ int m_sizes[] = { 31, 39, 63, 64, 65,
+ 127, 129, 255, 257 , 511,
+ 512, 513, 1023, 1024, 1025};
+
+ int n_sizes[] = { 31, 39, 63, 64, 65,
+ 127, 129, 255, 257, 511,
+ 512, 513, 1023, 1024, 1025};
+
+ int k_sizes[] = { 31, 39, 63, 64, 65,
+ 95, 96, 127, 129, 255,
+ 257, 511, 512, 513, 1023,
+ 1024, 1025};
+
+ for (int i = 0; i < 15; i++) {
+ for (int j = 0; j < 15; j++) {
+ for (int k = 0; k < 17; k++) {
+ test_cuda_contraction<DataLayout>(m_sizes[i], n_sizes[j], k_sizes[k]);
+ }
+ }
+ }
+}
+
+void test_cxx11_tensor_cuda()
+{
+ CALL_SUBTEST_1(test_cuda_contraction<ColMajor>(128, 128, 128));
+ CALL_SUBTEST_1(test_cuda_contraction<RowMajor>(128, 128, 128));
+
+ CALL_SUBTEST_1(test_scalar<ColMajor>(128, 128, 128));
+ CALL_SUBTEST_1(test_scalar<RowMajor>(128, 128, 128));
+
+ CALL_SUBTEST_2(test_cuda_contraction_m<ColMajor>());
+ CALL_SUBTEST_3(test_cuda_contraction_m<RowMajor>());
+
+ CALL_SUBTEST_4(test_cuda_contraction_k<ColMajor>());
+ CALL_SUBTEST_5(test_cuda_contraction_k<RowMajor>());
+
+ CALL_SUBTEST_6(test_cuda_contraction_n<ColMajor>());
+ CALL_SUBTEST_7(test_cuda_contraction_n<RowMajor>());
+
+ CALL_SUBTEST_8(test_cuda_contraction_sizes<ColMajor>());
+ CALL_SUBTEST_9(test_cuda_contraction_sizes<RowMajor>());
+}
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