1 files changed, 121 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_contract_cuda.cpp b/unsupported/test/cxx11_tensor_contract_cuda.cpp
new file mode 100644
index 000000000..9599607c6
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_contract_cuda.cpp
@@ -0,0 +1,121 @@
+// 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
+
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template<int DataLayout>
+static void test_cuda_contraction(int m_size, int k_size, int n_size)
+{
+  cout<<"Calling with ("<<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(Eigen::array<int, 2>(m_size, k_size));
+  Tensor<float, 2, DataLayout> t_right(Eigen::array<int, 2>(k_size, n_size));
+  Tensor<float, 2, DataLayout> t_result(Eigen::array<int, 2>(m_size, n_size));
+  Tensor<float, 2, DataLayout> t_result_gpu(Eigen::array<int, 2>(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);
+
+  cudaStream_t stream;
+  assert(cudaStreamCreate(&stream) == cudaSuccess);
+  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 (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    if (fabs(t_result.data()[i] - t_result_gpu.data()[i]) >= 1e-4) {
+      cout << "mismatch detected at index " << i << ": " << t_result.data()[i]
+           << " vs " <<  t_result_gpu.data()[i] << endl;
+      assert(false);
+    }
+  }
+
+  cudaFree((void*)d_t_left);
+  cudaFree((void*)d_t_right);
+  cudaFree((void*)d_t_result);
+}
+
+
+void test_cxx11_tensor_cuda()
+{
+  cout<<"Calling contraction tests"<<std::endl;
+  CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, 128, 128));
+  CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, 128, 128));
+  for (int k = 32; k < 256; k++) {
+    CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, k, 128));
+    CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, k, 128));
+  }
+  for (int k = 32; k < 256; k++) {
+    CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, 128, k));
+    CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, 128, k));
+  }
+  for (int k = 32; k < 256; k++) {
+    CALL_SUBTEST(test_cuda_contraction<ColMajor>(k, 128, 128));
+    CALL_SUBTEST(test_cuda_contraction<RowMajor>(k, 128, 128));
+  }
+
+  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++) {
+        CALL_SUBTEST(test_cuda_contraction<ColMajor>(m_sizes[i], n_sizes[j], k_sizes[k]));
+        CALL_SUBTEST(test_cuda_contraction<RowMajor>(m_sizes[i], n_sizes[j], k_sizes[k]));
+      }
+}