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Added a test for nullary expressions on CUDA

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Also check that we can mix 64 and 32 bit indices in the same compilation unit
This commit is contained in:
Benoit Steiner 2016-09-01 13:28:12 -07:00
parent c53f783705
commit 039e225f7f
1 changed files with 73 additions and 28 deletions
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101
unsupported/test/cxx11_tensor_cuda.cu
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@ -10,7 +10,6 @@
#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 <cuda_fp16.h>
@ -19,10 +18,55 @@
using Eigen::Tensor;
void test_cuda_nullary() {
Tensor<float, 1, 0, int> in1(2);
Tensor<float, 1, 0, int> in2(2);
in1.setRandom();
in2.setRandom();
std::size_t tensor_bytes = in1.size() * sizeof(float);
float* d_in1;
float* d_in2;
cudaMalloc((void**)(&d_in1), tensor_bytes);
cudaMalloc((void**)(&d_in2), tensor_bytes);
cudaMemcpy(d_in1, in1.data(), tensor_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2.data(), tensor_bytes, cudaMemcpyHostToDevice);
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in1(
d_in1, 2);
Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in2(
d_in2, 2);
gpu_in1.device(gpu_device) = gpu_in1.constant(3.14f);
gpu_in2.device(gpu_device) = gpu_in2.random();
Tensor<float, 1, 0, int> new1(2);
Tensor<float, 1, 0, int> new2(2);
assert(cudaMemcpyAsync(new1.data(), d_in1, tensor_bytes, cudaMemcpyDeviceToHost,
gpu_device.stream()) == cudaSuccess);
assert(cudaMemcpyAsync(new2.data(), d_in2, tensor_bytes, cudaMemcpyDeviceToHost,
gpu_device.stream()) == cudaSuccess);
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
for (int i = 0; i < 2; ++i) {
VERIFY_IS_APPROX(new1(i), 3.14f);
VERIFY_IS_NOT_EQUAL(new2(i), in2(i));
}
cudaFree(d_in1);
cudaFree(d_in2);
}
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));
Tensor<float, 1> in1(Eigen::array<int64_t, 1>(2));
Tensor<float, 1> in2(Eigen::array<int64_t, 1>(2));
Tensor<float, 1> out(Eigen::array<int64_t, 1>(2));
in1.setRandom();
in2.setRandom();
@ -44,11 +88,11 @@ void test_cuda_elementwise_small() {
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1(
d_in1, Eigen::array<int, 1>(2));
d_in1, Eigen::array<int64_t, 1>(2));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in2(
d_in2, Eigen::array<int, 1>(2));
d_in2, Eigen::array<int64_t, 1>(2));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_out(
d_out, Eigen::array<int, 1>(2));
d_out, Eigen::array<int64_t, 1>(2));
gpu_out.device(gpu_device) = gpu_in1 + gpu_in2;
@ -58,8 +102,8 @@ void test_cuda_elementwise_small() {
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)));
out(Eigen::array<int64_t, 1>(i)),
in1(Eigen::array<int64_t, 1>(i)) + in2(Eigen::array<int64_t, 1>(i)));
}
cudaFree(d_in1);
@ -69,10 +113,10 @@ void test_cuda_elementwise_small() {
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));
Tensor<float, 3> in1(Eigen::array<int64_t, 3>(72,53,97));
Tensor<float, 3> in2(Eigen::array<int64_t, 3>(72,53,97));
Tensor<float, 3> in3(Eigen::array<int64_t, 3>(72,53,97));
Tensor<float, 3> out(Eigen::array<int64_t, 3>(72,53,97));
in1.setRandom();
in2.setRandom();
in3.setRandom();
@ -98,10 +142,10 @@ void test_cuda_elementwise()
Eigen::CudaStreamDevice stream;
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));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<int64_t, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<int64_t, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in3(d_in3, Eigen::array<int64_t, 3>(72,53,97));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int64_t, 3>(72,53,97));
gpu_out.device(gpu_device) = gpu_in1 + gpu_in2 * gpu_in3;
@ -111,7 +155,7 @@ void test_cuda_elementwise()
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)));
VERIFY_IS_APPROX(out(Eigen::array<int64_t, 3>(i,j,k)), in1(Eigen::array<int64_t, 3>(i,j,k)) + in2(Eigen::array<int64_t, 3>(i,j,k)) * in3(Eigen::array<int64_t, 3>(i,j,k)));
}
}
}
@ -181,7 +225,7 @@ void test_cuda_reduction()
Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_in1(d_in1, 72,53,97,113);
Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97);
array<int, 2> reduction_axis;
array<int64_t, 2> reduction_axis;
reduction_axis[0] = 1;
reduction_axis[1] = 3;
@ -214,8 +258,8 @@ void test_cuda_contraction()
// more than 30 * 1024, which is the number of threads in blocks on
// a 15 SM GK110 GPU
Tensor<float, 4, DataLayout> t_left(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));
Tensor<float, 5, DataLayout> t_right(Eigen::array<int64_t, 5>(3, 31, 7, 20, 1));
Tensor<float, 5, DataLayout> t_result(Eigen::array<int64_t, 5>(6, 50, 7, 20, 1));
t_left.setRandom();
t_right.setRandom();
@ -299,7 +343,7 @@ void test_cuda_convolution_1d()
Eigen::TensorMap<Eigen::Tensor<float, 1, DataLayout> > gpu_kernel(d_kernel, 4);
Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out, 74,34,11,137);
Eigen::array<int, 1> dims(1);
Eigen::array<int64_t, 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);
@ -352,7 +396,7 @@ void test_cuda_convolution_inner_dim_col_major_1d()
Eigen::TensorMap<Eigen::Tensor<float, 1, ColMajor> > gpu_kernel(d_kernel,4);
Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_out(d_out,71,9,11,7);
Eigen::array<int, 1> dims(0);
Eigen::array<int64_t, 1> dims(0);
gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
@ -405,7 +449,7 @@ void test_cuda_convolution_inner_dim_row_major_1d()
Eigen::TensorMap<Eigen::Tensor<float, 1, RowMajor> > gpu_kernel(d_kernel, 4);
Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_out(d_out, 7,9,11,71);
Eigen::array<int, 1> dims(3);
Eigen::array<int64_t, 1> dims(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);
@ -459,7 +503,7 @@ void test_cuda_convolution_2d()
Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_kernel(d_kernel,3,4);
Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out,74,35,8,137);
Eigen::array<int, 2> dims(1,2);
Eigen::array<int64_t, 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);
@ -496,9 +540,9 @@ void test_cuda_convolution_2d()
template<int DataLayout>
void test_cuda_convolution_3d()
{
Tensor<float, 5, DataLayout> input(Eigen::array<int, 5>(74,37,11,137,17));
Tensor<float, 5, DataLayout> input(Eigen::array<int64_t, 5>(74,37,11,137,17));
Tensor<float, 3, DataLayout> kernel(3,4,2);
Tensor<float, 5, DataLayout> out(Eigen::array<int, 5>(74,35,8,136,17));
Tensor<float, 5, DataLayout> out(Eigen::array<int64_t, 5>(74,35,8,136,17));
input = input.constant(10.0f) + input.random();
kernel = kernel.constant(7.0f) + kernel.random();
@ -523,7 +567,7 @@ void test_cuda_convolution_3d()
Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > gpu_kernel(d_kernel,3,4,2);
Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_out(d_out,74,35,8,136,17);
Eigen::array<int, 3> dims(1,2,3);
Eigen::array<int64_t, 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);
@ -1168,6 +1212,7 @@ void test_cuda_betainc()
void test_cxx11_tensor_cuda()
{
CALL_SUBTEST_1(test_cuda_nullary());
CALL_SUBTEST_1(test_cuda_elementwise_small());
CALL_SUBTEST_1(test_cuda_elementwise());
CALL_SUBTEST_1(test_cuda_props());