update eigen

1 files changed, 0 insertions, 221 deletions

diff --git a/eigen/unsupported/test/cxx11_tensor_reverse_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_reverse_sycl.cpp
deleted file mode 100644
index 2f54844..0000000
--- a/eigen/unsupported/test/cxx11_tensor_reverse_sycl.cpp
+++ /dev/null
@@ -1,221 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015
-// 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_TEST_FUNC cxx11_tensor_reverse_sycl
-#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
-#define EIGEN_USE_SYCL
-
-#include "main.h"
-#include <unsupported/Eigen/CXX11/Tensor>
-
-
-template <typename DataType, int DataLayout, typename IndexType>
-static void test_simple_reverse(const Eigen::SyclDevice&  sycl_device) {
-
-  IndexType dim1 = 2;
-  IndexType dim2 = 3;
-  IndexType dim3 = 5;
-  IndexType dim4 = 7;
-
-  array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
-  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
-  Tensor<DataType, 4, DataLayout, IndexType> reversed_tensor(tensorRange);
-  tensor.setRandom();
-
-  array<bool, 4> dim_rev;
-  dim_rev[0] = false;
-  dim_rev[1] = true;
-  dim_rev[2] = true;
-  dim_rev[3] = false;
-
-  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
-  DataType* gpu_out_data =static_cast<DataType*>(sycl_device.allocate(reversed_tensor.dimensions().TotalSize()*sizeof(DataType)));
-
-  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  in_gpu(gpu_in_data, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  out_gpu(gpu_out_data, tensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
-  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
-  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
-  // Check that the CPU and GPU reductions return the same result.
-  for (IndexType i = 0; i < 2; ++i) {
-    for (IndexType j = 0; j < 3; ++j) {
-      for (IndexType k = 0; k < 5; ++k) {
-        for (IndexType l = 0; l < 7; ++l) {
-          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(i,2-j,4-k,l));
-        }
-      }
-    }
-  }
-  dim_rev[0] = true;
-  dim_rev[1] = false;
-  dim_rev[2] = false;
-  dim_rev[3] = false;
-
-  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
-  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
-
-  for (IndexType i = 0; i < 2; ++i) {
-    for (IndexType j = 0; j < 3; ++j) {
-      for (IndexType k = 0; k < 5; ++k) {
-        for (IndexType l = 0; l < 7; ++l) {
-          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,l));
-        }
-      }
-    }
-  }
-
-  dim_rev[0] = true;
-  dim_rev[1] = false;
-  dim_rev[2] = false;
-  dim_rev[3] = true;
-  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
-  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
-
-  for (IndexType i = 0; i < 2; ++i) {
-    for (IndexType j = 0; j < 3; ++j) {
-      for (IndexType k = 0; k < 5; ++k) {
-        for (IndexType l = 0; l < 7; ++l) {
-          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,6-l));
-        }
-      }
-    }
-  }
-
-  sycl_device.deallocate(gpu_in_data);
-  sycl_device.deallocate(gpu_out_data);
-}
-
-
-
-template <typename DataType, int DataLayout, typename IndexType>
-static void test_expr_reverse(const Eigen::SyclDevice&  sycl_device, bool LValue)
-{
-  IndexType dim1 = 2;
-  IndexType dim2 = 3;
-  IndexType dim3 = 5;
-  IndexType dim4 = 7;
-
-  array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
-  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
-  Tensor<DataType, 4, DataLayout, IndexType> expected(tensorRange);
-  Tensor<DataType, 4, DataLayout, IndexType> result(tensorRange);
-  tensor.setRandom();
-
-  array<bool, 4> dim_rev;
-  dim_rev[0] = false;
-  dim_rev[1] = true;
-  dim_rev[2] = false;
-  dim_rev[3] = true;
-
-  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
-  DataType* gpu_out_data_expected =static_cast<DataType*>(sycl_device.allocate(expected.dimensions().TotalSize()*sizeof(DataType)));
-  DataType* gpu_out_data_result =static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));
-
-  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  in_gpu(gpu_in_data, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  out_gpu_expected(gpu_out_data_expected, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  out_gpu_result(gpu_out_data_result, tensorRange);
-
-
-  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
-
-  if (LValue) {
-    out_gpu_expected.reverse(dim_rev).device(sycl_device) = in_gpu;
-  } else {
-    out_gpu_expected.device(sycl_device) = in_gpu.reverse(dim_rev);
-  }
-  sycl_device.memcpyDeviceToHost(expected.data(), gpu_out_data_expected, expected.dimensions().TotalSize()*sizeof(DataType));
-
-
-  array<IndexType, 4> src_slice_dim;
-  src_slice_dim[0] = 2;
-  src_slice_dim[1] = 3;
-  src_slice_dim[2] = 1;
-  src_slice_dim[3] = 7;
-  array<IndexType, 4> src_slice_start;
-  src_slice_start[0] = 0;
-  src_slice_start[1] = 0;
-  src_slice_start[2] = 0;
-  src_slice_start[3] = 0;
-  array<IndexType, 4> dst_slice_dim = src_slice_dim;
-  array<IndexType, 4> dst_slice_start = src_slice_start;
-
-  for (IndexType i = 0; i < 5; ++i) {
-    if (LValue) {
-        out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
-          in_gpu.slice(src_slice_start, src_slice_dim);
-    } else {
-      out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
-          in_gpu.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
-    }
-    src_slice_start[2] += 1;
-    dst_slice_start[2] += 1;
-  }
-  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));
-
-  for (IndexType i = 0; i < expected.dimension(0); ++i) {
-    for (IndexType j = 0; j < expected.dimension(1); ++j) {
-      for (IndexType k = 0; k < expected.dimension(2); ++k) {
-        for (IndexType l = 0; l < expected.dimension(3); ++l) {
-          VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
-        }
-      }
-    }
-  }
-
-  dst_slice_start[2] = 0;
-  result.setRandom();
-  sycl_device.memcpyHostToDevice(gpu_out_data_result, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
-  for (IndexType i = 0; i < 5; ++i) {
-     if (LValue) {
-       out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
-           in_gpu.slice(dst_slice_start, dst_slice_dim);
-     } else {
-       out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
-           in_gpu.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
-     }
-    dst_slice_start[2] += 1;
-  }
-  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));
-
-  for (IndexType i = 0; i < expected.dimension(0); ++i) {
-    for (IndexType j = 0; j < expected.dimension(1); ++j) {
-      for (IndexType k = 0; k < expected.dimension(2); ++k) {
-        for (IndexType l = 0; l < expected.dimension(3); ++l) {
-          VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
-        }
-      }
-    }
-  }
-}
-
-
-
-template<typename DataType> void sycl_reverse_test_per_device(const cl::sycl::device& d){
-  std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
-  QueueInterface queueInterface(d);
-  auto sycl_device = Eigen::SyclDevice(&queueInterface);
-  test_simple_reverse<DataType, RowMajor, int64_t>(sycl_device);
-  test_simple_reverse<DataType, ColMajor, int64_t>(sycl_device);
-  test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, false);
-  test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, false);
-  test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, true);
-  test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, true);
-}
-void test_cxx11_tensor_reverse_sycl() {
-  for (const auto& device :Eigen::get_sycl_supported_devices()) {
-    CALL_SUBTEST(sycl_reverse_test_per_device<float>(device));
-  }
-}