aboutsummaryrefslogtreecommitdiff
path: root/unsupported/test/cxx11_tensor_argmax_gpu.cu
diff options
context:
space:
mode:
Diffstat (limited to 'unsupported/test/cxx11_tensor_argmax_gpu.cu')
-rw-r--r--unsupported/test/cxx11_tensor_argmax_gpu.cu253
1 files changed, 253 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_argmax_gpu.cu b/unsupported/test/cxx11_tensor_argmax_gpu.cu
new file mode 100644
index 000000000..79f4066e9
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_argmax_gpu.cu
@@ -0,0 +1,253 @@
+// 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_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+#include <unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
+
+using Eigen::Tensor;
+
+template <int Layout>
+void test_gpu_simple_argmax()
+{
+ Tensor<double, 3, Layout> in(Eigen::array<DenseIndex, 3>(72,53,97));
+ Tensor<DenseIndex, 1, Layout> out_max(Eigen::array<DenseIndex, 1>(1));
+ Tensor<DenseIndex, 1, Layout> out_min(Eigen::array<DenseIndex, 1>(1));
+ in.setRandom();
+ in *= in.constant(100.0);
+ in(0, 0, 0) = -1000.0;
+ in(71, 52, 96) = 1000.0;
+
+ std::size_t in_bytes = in.size() * sizeof(double);
+ std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
+
+ double* d_in;
+ DenseIndex* d_out_max;
+ DenseIndex* d_out_min;
+ gpuMalloc((void**)(&d_in), in_bytes);
+ gpuMalloc((void**)(&d_out_max), out_bytes);
+ gpuMalloc((void**)(&d_out_min), out_bytes);
+
+ gpuMemcpy(d_in, in.data(), in_bytes, gpuMemcpyHostToDevice);
+
+ Eigen::GpuStreamDevice stream;
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<double, 3, Layout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 3>(72,53,97));
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 1, Layout>, Aligned > gpu_out_max(d_out_max, Eigen::array<DenseIndex, 1>(1));
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 1, Layout>, Aligned > gpu_out_min(d_out_min, Eigen::array<DenseIndex, 1>(1));
+
+ gpu_out_max.device(gpu_device) = gpu_in.argmax();
+ gpu_out_min.device(gpu_device) = gpu_in.argmin();
+
+ assert(gpuMemcpyAsync(out_max.data(), d_out_max, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+ assert(gpuMemcpyAsync(out_min.data(), d_out_min, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+ assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+ VERIFY_IS_EQUAL(out_max(Eigen::array<DenseIndex, 1>(0)), 72*53*97 - 1);
+ VERIFY_IS_EQUAL(out_min(Eigen::array<DenseIndex, 1>(0)), 0);
+
+ gpuFree(d_in);
+ gpuFree(d_out_max);
+ gpuFree(d_out_min);
+}
+
+template <int DataLayout>
+void test_gpu_argmax_dim()
+{
+ Tensor<float, 4, DataLayout> tensor(2,3,5,7);
+ std::vector<int> dims;
+ dims.push_back(2); dims.push_back(3); dims.push_back(5); dims.push_back(7);
+
+ for (int dim = 0; dim < 4; ++dim) {
+ tensor.setRandom();
+ tensor = (tensor + tensor.constant(0.5)).log();
+
+ array<DenseIndex, 3> out_shape;
+ for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
+
+ Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape);
+
+ array<DenseIndex, 4> ix;
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
+ if (ix[dim] != 0) continue;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
+ tensor(ix) = 10.0;
+ }
+ }
+ }
+ }
+
+ std::size_t in_bytes = tensor.size() * sizeof(float);
+ std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+ float* d_in;
+ DenseIndex* d_out;
+ gpuMalloc((void**)(&d_in), in_bytes);
+ gpuMalloc((void**)(&d_out), out_bytes);
+
+ gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+ Eigen::GpuStreamDevice stream;
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 4>(2, 3, 5, 7));
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned > gpu_out(d_out, out_shape);
+
+ gpu_out.device(gpu_device) = gpu_in.argmax(dim);
+
+ assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+ assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+ VERIFY_IS_EQUAL(tensor_arg.size(),
+ size_t(2*3*5*7 / tensor.dimension(dim)));
+
+ for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+ // Expect max to be in the first index of the reduced dimension
+ VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
+ }
+
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
+ if (ix[dim] != tensor.dimension(dim) - 1) continue;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+ tensor(ix) = 20.0;
+ }
+ }
+ }
+ }
+
+ gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+ gpu_out.device(gpu_device) = gpu_in.argmax(dim);
+
+ assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+ assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+ for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+ // Expect max to be in the last index of the reduced dimension
+ VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
+ }
+
+ gpuFree(d_in);
+ gpuFree(d_out);
+ }
+}
+
+template <int DataLayout>
+void test_gpu_argmin_dim()
+{
+ Tensor<float, 4, DataLayout> tensor(2,3,5,7);
+ std::vector<int> dims;
+ dims.push_back(2); dims.push_back(3); dims.push_back(5); dims.push_back(7);
+
+ for (int dim = 0; dim < 4; ++dim) {
+ tensor.setRandom();
+ tensor = (tensor + tensor.constant(0.5)).log();
+
+ array<DenseIndex, 3> out_shape;
+ for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
+
+ Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape);
+
+ array<DenseIndex, 4> ix;
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
+ if (ix[dim] != 0) continue;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
+ tensor(ix) = -10.0;
+ }
+ }
+ }
+ }
+
+ std::size_t in_bytes = tensor.size() * sizeof(float);
+ std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+ float* d_in;
+ DenseIndex* d_out;
+ gpuMalloc((void**)(&d_in), in_bytes);
+ gpuMalloc((void**)(&d_out), out_bytes);
+
+ gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+ Eigen::GpuStreamDevice stream;
+ Eigen::GpuDevice gpu_device(&stream);
+
+ Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 4>(2, 3, 5, 7));
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned > gpu_out(d_out, out_shape);
+
+ gpu_out.device(gpu_device) = gpu_in.argmin(dim);
+
+ assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+ assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+ VERIFY_IS_EQUAL(tensor_arg.size(),
+ 2*3*5*7 / tensor.dimension(dim));
+
+ for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+ // Expect min to be in the first index of the reduced dimension
+ VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
+ }
+
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
+ if (ix[dim] != tensor.dimension(dim) - 1) continue;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+ tensor(ix) = -20.0;
+ }
+ }
+ }
+ }
+
+ gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+ gpu_out.device(gpu_device) = gpu_in.argmin(dim);
+
+ assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+ assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+ for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+ // Expect max to be in the last index of the reduced dimension
+ VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
+ }
+
+ gpuFree(d_in);
+ gpuFree(d_out);
+ }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_argmax_gpu)
+{
+ CALL_SUBTEST_1(test_gpu_simple_argmax<RowMajor>());
+ CALL_SUBTEST_1(test_gpu_simple_argmax<ColMajor>());
+ CALL_SUBTEST_2(test_gpu_argmax_dim<RowMajor>());
+ CALL_SUBTEST_2(test_gpu_argmax_dim<ColMajor>());
+ CALL_SUBTEST_3(test_gpu_argmin_dim<RowMajor>());
+ CALL_SUBTEST_3(test_gpu_argmin_dim<ColMajor>());
+}
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-18 03:19:31 +0000