Snap for 8512216 from bc0f5df265caa21a2120c22453655a7fcc941991 to tm-frc-media-swcodec-releaset_frc_swc_330443040t_frc_swc_330443010android13-frc-media-swcodec-release

Change-Id: I9157ace1e25b57a243718a724a3d7a0d7805125a

1 files changed, 86 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_random_gpu.cu b/unsupported/test/cxx11_tensor_random_gpu.cu
new file mode 100644
index 000000000..090986ebc
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_random_gpu.cu
@@ -0,0 +1,86 @@
+// 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_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <Eigen/CXX11/Tensor>
+
+#include <Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
+
+void test_gpu_random_uniform()
+{
+  Tensor<float, 2> out(72,97);
+  out.setZero();
+
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_out;
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97);
+
+  gpu_out.device(gpu_device) = gpu_out.random();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  // For now we just check this code doesn't crash.
+  // TODO: come up with a valid test of randomness
+}
+
+
+void test_gpu_random_normal()
+{
+  Tensor<float, 2> out(72,97);
+  out.setZero();
+
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_out;
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97);
+
+  Eigen::internal::NormalRandomGenerator<float> gen(true);
+  gpu_out.device(gpu_device) = gpu_out.random(gen);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+}
+
+static void test_complex()
+{
+  Tensor<std::complex<float>, 1> vec(6);
+  vec.setRandom();
+
+  // Fixme: we should check that the generated numbers follow a uniform
+  // distribution instead.
+  for (int i = 1; i < 6; ++i) {
+    VERIFY_IS_NOT_EQUAL(vec(i), vec(i-1));
+  }
+}
+
+
+EIGEN_DECLARE_TEST(cxx11_tensor_random_gpu)
+{
+  CALL_SUBTEST(test_gpu_random_uniform());
+  CALL_SUBTEST(test_gpu_random_normal());
+  CALL_SUBTEST(test_complex());
+}