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author | Yi Kong <yikong@google.com> | 2022-02-25 15:53:09 +0000 |
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committer | Automerger Merge Worker <android-build-automerger-merge-worker@system.gserviceaccount.com> | 2022-02-25 15:53:09 +0000 |
commit | 10f298fc4175c1b8537c674f654a070c871960e5 (patch) | |
tree | fb979fb4cf4f8052c8cc66b1ec9516d91fcd859b /unsupported/test/cxx11_tensor_argmax_sycl.cpp | |
parent | 892aea0d75825c43d5b630e2060622cbba23694c (diff) | |
parent | 79df15ea886a5fc1b85de433f9b3518c68934bae (diff) | |
download | eigen-10f298fc4175c1b8537c674f654a070c871960e5.tar.gz |
Merge changes Iee153445,Iee274471 am: 79df15ea88android-games-sdk-games-performance-tuner-releaseandroid-games-sdk-games-memory-advice-releaseandroid-games-sdk-games-frame-pacing-releaseandroid-games-sdk-games-controller-releaseandroid-games-sdk-game-text-input-releaseandroid-games-sdk-game-activity-release
Original change: https://android-review.googlesource.com/c/platform/external/eigen/+/1999079
Change-Id: I0c5108390c595f0d39af8797875f2b88accb7b56
Diffstat (limited to 'unsupported/test/cxx11_tensor_argmax_sycl.cpp')
-rw-r--r-- | unsupported/test/cxx11_tensor_argmax_sycl.cpp | 258 |
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 new file mode 100644 index 000000000..7ac71286e --- /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)); + } +} |