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-rw-r--r--unsupported/test/cxx11_tensor_argmax_sycl.cpp258
1 files changed, 258 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_argmax_sycl.cpp b/unsupported/test/cxx11_tensor_argmax_sycl.cpp
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--- /dev/null
+++ b/unsupported/test/cxx11_tensor_argmax_sycl.cpp
@@ -0,0 +1,258 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli Codeplay Software Ltd.
+// Ralph Potter Codeplay Software Ltd.
+// Luke Iwanski Codeplay Software Ltd.
+// Contact: <eigen@codeplay.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 int64_t
+#define EIGEN_USE_SYCL
+#define EIGEN_HAS_CONSTEXPR 1
+
+#include "main.h"
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int Layout, typename DenseIndex>
+static void test_sycl_simple_argmax(const Eigen::SyclDevice& sycl_device) {
+ Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2, 2, 2}});
+ Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
+ Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
+ in.setRandom();
+ in *= in.constant(100.0);
+ in(0, 0, 0) = -1000.0;
+ in(1, 1, 1) = 1000.0;
+
+ std::size_t in_bytes = in.size() * sizeof(DataType);
+ std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
+
+ DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
+ DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+ DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+
+ Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in,
+ Eigen::array<DenseIndex, 3>{{2, 2, 2}});
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
+ sycl_device.memcpyHostToDevice(d_in, in.data(), in_bytes);
+
+ gpu_out_max.device(sycl_device) = gpu_in.argmax();
+ gpu_out_min.device(sycl_device) = gpu_in.argmin();
+
+ sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
+ sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
+
+ VERIFY_IS_EQUAL(out_max(), 2 * 2 * 2 - 1);
+ VERIFY_IS_EQUAL(out_min(), 0);
+
+ sycl_device.deallocate(d_in);
+ sycl_device.deallocate(d_out_max);
+ sycl_device.deallocate(d_out_min);
+}
+
+template <typename DataType, int DataLayout, typename DenseIndex>
+static void test_sycl_argmax_dim(const Eigen::SyclDevice& sycl_device) {
+ DenseIndex sizeDim0 = 9;
+ DenseIndex sizeDim1 = 3;
+ DenseIndex sizeDim2 = 5;
+ DenseIndex sizeDim3 = 7;
+ Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
+
+ std::vector<DenseIndex> dims;
+ dims.push_back(sizeDim0);
+ dims.push_back(sizeDim1);
+ dims.push_back(sizeDim2);
+ dims.push_back(sizeDim3);
+ for (DenseIndex dim = 0; dim < 4; ++dim) {
+ array<DenseIndex, 3> out_shape;
+ for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
+
+ Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
+
+ array<DenseIndex, 4> ix;
+ for (DenseIndex i = 0; i < sizeDim0; ++i) {
+ for (DenseIndex j = 0; j < sizeDim1; ++j) {
+ for (DenseIndex k = 0; k < sizeDim2; ++k) {
+ for (DenseIndex l = 0; l < sizeDim3; ++l) {
+ ix[0] = i;
+ ix[1] = j;
+ ix[2] = k;
+ ix[3] = l;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l)
+ // = 10.0
+ tensor(ix) = (ix[dim] != 0) ? -1.0 : 10.0;
+ }
+ }
+ }
+ }
+
+ std::size_t in_bytes = tensor.size() * sizeof(DataType);
+ std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+ DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
+ DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+
+ Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
+ d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
+
+ sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+ gpu_out.device(sycl_device) = gpu_in.argmax(dim);
+ sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+ VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
+ size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / 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);
+ }
+
+ sycl_device.synchronize();
+
+ for (DenseIndex i = 0; i < sizeDim0; ++i) {
+ for (DenseIndex j = 0; j < sizeDim1; ++j) {
+ for (DenseIndex k = 0; k < sizeDim2; ++k) {
+ for (DenseIndex l = 0; l < sizeDim3; ++l) {
+ ix[0] = i;
+ ix[1] = j;
+ ix[2] = k;
+ ix[3] = l;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+ tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? -1.0 : 20.0;
+ }
+ }
+ }
+ }
+
+ sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+ gpu_out.device(sycl_device) = gpu_in.argmax(dim);
+ sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+ 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);
+ }
+ sycl_device.deallocate(d_in);
+ sycl_device.deallocate(d_out);
+ }
+}
+
+template <typename DataType, int DataLayout, typename DenseIndex>
+static void test_sycl_argmin_dim(const Eigen::SyclDevice& sycl_device) {
+ DenseIndex sizeDim0 = 9;
+ DenseIndex sizeDim1 = 3;
+ DenseIndex sizeDim2 = 5;
+ DenseIndex sizeDim3 = 7;
+ Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
+
+ std::vector<DenseIndex> dims;
+ dims.push_back(sizeDim0);
+ dims.push_back(sizeDim1);
+ dims.push_back(sizeDim2);
+ dims.push_back(sizeDim3);
+ for (DenseIndex dim = 0; dim < 4; ++dim) {
+ array<DenseIndex, 3> out_shape;
+ for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
+
+ Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
+
+ array<DenseIndex, 4> ix;
+ for (DenseIndex i = 0; i < sizeDim0; ++i) {
+ for (DenseIndex j = 0; j < sizeDim1; ++j) {
+ for (DenseIndex k = 0; k < sizeDim2; ++k) {
+ for (DenseIndex l = 0; l < sizeDim3; ++l) {
+ ix[0] = i;
+ ix[1] = j;
+ ix[2] = k;
+ ix[3] = l;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
+ tensor(ix) = (ix[dim] != 0) ? 1.0 : -10.0;
+ }
+ }
+ }
+ }
+
+ std::size_t in_bytes = tensor.size() * sizeof(DataType);
+ std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+ DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
+ DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+
+ Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
+ d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
+ Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
+
+ sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+ gpu_out.device(sycl_device) = gpu_in.argmin(dim);
+ sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+ VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
+ size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / 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);
+ }
+
+ sycl_device.synchronize();
+
+ for (DenseIndex i = 0; i < sizeDim0; ++i) {
+ for (DenseIndex j = 0; j < sizeDim1; ++j) {
+ for (DenseIndex k = 0; k < sizeDim2; ++k) {
+ for (DenseIndex l = 0; l < sizeDim3; ++l) {
+ ix[0] = i;
+ ix[1] = j;
+ ix[2] = k;
+ ix[3] = l;
+ // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
+ tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? 1.0 : -20.0;
+ }
+ }
+ }
+ }
+
+ sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+ gpu_out.device(sycl_device) = gpu_in.argmin(dim);
+ sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+ 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);
+ }
+ sycl_device.deallocate(d_in);
+ sycl_device.deallocate(d_out);
+ }
+}
+
+template <typename DataType, typename Device_Selector>
+void sycl_argmax_test_per_device(const Device_Selector& d) {
+ QueueInterface queueInterface(d);
+ auto sycl_device = Eigen::SyclDevice(&queueInterface);
+ test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
+ test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
+ test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
+ test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
+ test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
+ test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_argmax_sycl) {
+ for (const auto& device : Eigen::get_sycl_supported_devices()) {
+ CALL_SUBTEST(sycl_argmax_test_per_device<float>(device));
+ }
+}
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