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authorGravatar Benoit Steiner <benoit.steiner.goog@gmail.com>2016-02-19 06:23:28 +0000
committerGravatar Benoit Steiner <benoit.steiner.goog@gmail.com>2016-02-19 06:23:28 +0000
commitf36c0c2c65a78959f6ccbbc29c6e80f86b062bc8 (patch)
treeb17478350333d3257bc6c93ad7d38136061c3a3f /unsupported/test
parent7151bd876845c15cb6b8abc0886d7917ece635ed (diff)
Added regression test for float16
Diffstat (limited to 'unsupported/test')
-rw-r--r--unsupported/test/cxx11_tensor_of_float16_cuda.cu60
1 files changed, 60 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_of_float16_cuda.cu b/unsupported/test/cxx11_tensor_of_float16_cuda.cu
new file mode 100644
index 000000000..e9f5dd968
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_of_float16_cuda.cu
@@ -0,0 +1,60 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 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_TEST_FUNC cxx11_tensor_of_float16_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_conversion() {
+ Eigen::CudaStreamDevice stream;
+ Eigen::GpuDevice gpu_device(&stream);
+ int num_elem = 101;
+
+ float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+ half* d_half = (half*)gpu_device.allocate(num_elem * sizeof(half));
+ float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+ Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
+ d_float, num_elem);
+ Eigen::TensorMap<Eigen::Tensor<half, 1>, Eigen::Aligned> gpu_half(
+ d_half, num_elem);
+ Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(
+ d_conv, num_elem);
+
+ gpu_float.device(gpu_device) = gpu_float.random();
+ gpu_half.device(gpu_device) = gpu_float.cast<half>();
+ gpu_conv.device(gpu_device) = gpu_half.cast<float>();
+
+ Tensor<float, 1> initial(num_elem);
+ Tensor<float, 1> final(num_elem);
+ gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float));
+ gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem*sizeof(float));
+
+ for (int i = 0; i < num_elem; ++i) {
+ VERIFY_IS_APPROX(initial(i), final(i));
+ }
+
+ gpu_device.deallocate(d_float);
+ gpu_device.deallocate(d_half);
+ gpu_device.deallocate(d_conv);
+}
+
+
+void test_cxx11_tensor_of_float16_cuda()
+{
+ CALL_SUBTEST_1(test_cuda_conversion());
+}