update eigen

1 files changed, 0 insertions, 622 deletions

diff --git a/eigen/unsupported/test/cxx11_tensor_chipping_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_chipping_sycl.cpp
deleted file mode 100644
index 39e4f0a..0000000
--- a/eigen/unsupported/test/cxx11_tensor_chipping_sycl.cpp
+++ /dev/null
@@ -1,622 +0,0 @@
-// 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>
-// 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_TEST_NO_COMPLEX
-#define EIGEN_TEST_FUNC cxx11_tensor_chipping_sycl
-#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
-#define EIGEN_USE_SYCL
-
-#include "main.h"
-
-#include <Eigen/CXX11/Tensor>
-
-using Eigen::Tensor;
-
-template <typename DataType, int DataLayout, typename IndexType>
-static void test_static_chip_sycl(const Eigen::SyclDevice& sycl_device)
-{
-  IndexType sizeDim1 = 2;
-  IndexType sizeDim2 = 3;
-  IndexType sizeDim3 = 5;
-  IndexType sizeDim4 = 7;
-  IndexType sizeDim5 = 11;
-
-  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-
-  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
-  Tensor<DataType, 4, DataLayout,IndexType> chip1(chip1TensorRange);
-
-  tensor.setRandom();
-
-  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
-  const size_t chip1TensorBuffSize =chip1.size()*sizeof(DataType);
-  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
-  DataType* gpu_data_chip1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
-
-  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
-  gpu_chip1.device(sycl_device)=gpu_tensor.template chip<0l>(1l);
-  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip1.dimension(0), sizeDim2);
-  VERIFY_IS_EQUAL(chip1.dimension(1), sizeDim3);
-  VERIFY_IS_EQUAL(chip1.dimension(2), sizeDim4);
-  VERIFY_IS_EQUAL(chip1.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim2; ++i) {
-    for (IndexType j = 0; j < sizeDim3; ++j) {
-      for (IndexType k = 0; k < sizeDim4; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1l,i,j,k,l));
-        }
-      }
-    }
-  }
-
-  array<IndexType, 4> chip2TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip2(chip2TensorRange);
-  const size_t chip2TensorBuffSize =chip2.size()*sizeof(DataType);
-  DataType* gpu_data_chip2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);
-
-  gpu_chip2.device(sycl_device)=gpu_tensor.template chip<1l>(1l);
-  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip2.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip2.dimension(1), sizeDim3);
-  VERIFY_IS_EQUAL(chip2.dimension(2), sizeDim4);
-  VERIFY_IS_EQUAL(chip2.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim3; ++j) {
-      for (IndexType k = 0; k < sizeDim4; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1l,j,k,l));
-        }
-      }
-    }
-  }
-
-  array<IndexType, 4> chip3TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip3(chip3TensorRange);
-  const size_t chip3TensorBuffSize =chip3.size()*sizeof(DataType);
-  DataType* gpu_data_chip3  = static_cast<DataType*>(sycl_device.allocate(chip3TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip3(gpu_data_chip3, chip3TensorRange);
-
-  gpu_chip3.device(sycl_device)=gpu_tensor.template chip<2l>(2l);
-  sycl_device.memcpyDeviceToHost(chip3.data(), gpu_data_chip3, chip3TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip3.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip3.dimension(1), sizeDim2);
-  VERIFY_IS_EQUAL(chip3.dimension(2), sizeDim4);
-  VERIFY_IS_EQUAL(chip3.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim2; ++j) {
-      for (IndexType k = 0; k < sizeDim4; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2l,k,l));
-        }
-      }
-    }
-  }
-
-  array<IndexType, 4> chip4TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip4(chip4TensorRange);
-  const size_t chip4TensorBuffSize =chip4.size()*sizeof(DataType);
-  DataType* gpu_data_chip4  = static_cast<DataType*>(sycl_device.allocate(chip4TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip4(gpu_data_chip4, chip4TensorRange);
-
-  gpu_chip4.device(sycl_device)=gpu_tensor.template chip<3l>(5l);
-  sycl_device.memcpyDeviceToHost(chip4.data(), gpu_data_chip4, chip4TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip4.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip4.dimension(1), sizeDim2);
-  VERIFY_IS_EQUAL(chip4.dimension(2), sizeDim3);
-  VERIFY_IS_EQUAL(chip4.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim2; ++j) {
-      for (IndexType k = 0; k < sizeDim3; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5l,l));
-        }
-      }
-    }
-  }
-
-
-  array<IndexType, 4> chip5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip5(chip5TensorRange);
-  const size_t chip5TensorBuffSize =chip5.size()*sizeof(DataType);
-  DataType* gpu_data_chip5  = static_cast<DataType*>(sycl_device.allocate(chip5TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip5(gpu_data_chip5, chip5TensorRange);
-
-  gpu_chip5.device(sycl_device)=gpu_tensor.template chip<4l>(7l);
-  sycl_device.memcpyDeviceToHost(chip5.data(), gpu_data_chip5, chip5TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip5.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip5.dimension(1), sizeDim2);
-  VERIFY_IS_EQUAL(chip5.dimension(2), sizeDim3);
-  VERIFY_IS_EQUAL(chip5.dimension(3), sizeDim4);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim2; ++j) {
-      for (IndexType k = 0; k < sizeDim3; ++k) {
-        for (IndexType l = 0; l < sizeDim4; ++l) {
-          VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7l));
-        }
-      }
-    }
-  }
-
-  sycl_device.deallocate(gpu_data_tensor);
-  sycl_device.deallocate(gpu_data_chip1);
-  sycl_device.deallocate(gpu_data_chip2);
-  sycl_device.deallocate(gpu_data_chip3);
-  sycl_device.deallocate(gpu_data_chip4);
-  sycl_device.deallocate(gpu_data_chip5);
-}
-
-template <typename DataType, int DataLayout, typename IndexType>
-static void test_dynamic_chip_sycl(const Eigen::SyclDevice& sycl_device)
-{
-  IndexType sizeDim1 = 2;
-  IndexType sizeDim2 = 3;
-  IndexType sizeDim3 = 5;
-  IndexType sizeDim4 = 7;
-  IndexType sizeDim5 = 11;
-
-  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-
-  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
-  Tensor<DataType, 4, DataLayout,IndexType> chip1(chip1TensorRange);
-
-  tensor.setRandom();
-
-  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
-  const size_t chip1TensorBuffSize =chip1.size()*sizeof(DataType);
-  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
-  DataType* gpu_data_chip1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
-
-  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
-  gpu_chip1.device(sycl_device)=gpu_tensor.chip(1l,0l);
-  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip1.dimension(0), sizeDim2);
-  VERIFY_IS_EQUAL(chip1.dimension(1), sizeDim3);
-  VERIFY_IS_EQUAL(chip1.dimension(2), sizeDim4);
-  VERIFY_IS_EQUAL(chip1.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim2; ++i) {
-    for (IndexType j = 0; j < sizeDim3; ++j) {
-      for (IndexType k = 0; k < sizeDim4; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1l,i,j,k,l));
-        }
-      }
-    }
-  }
-
-  array<IndexType, 4> chip2TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip2(chip2TensorRange);
-  const size_t chip2TensorBuffSize =chip2.size()*sizeof(DataType);
-  DataType* gpu_data_chip2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);
-
-  gpu_chip2.device(sycl_device)=gpu_tensor.chip(1l,1l);
-  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip2.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip2.dimension(1), sizeDim3);
-  VERIFY_IS_EQUAL(chip2.dimension(2), sizeDim4);
-  VERIFY_IS_EQUAL(chip2.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim3; ++j) {
-      for (IndexType k = 0; k < sizeDim4; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1l,j,k,l));
-        }
-      }
-    }
-  }
-
-  array<IndexType, 4> chip3TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip3(chip3TensorRange);
-  const size_t chip3TensorBuffSize =chip3.size()*sizeof(DataType);
-  DataType* gpu_data_chip3  = static_cast<DataType*>(sycl_device.allocate(chip3TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip3(gpu_data_chip3, chip3TensorRange);
-
-  gpu_chip3.device(sycl_device)=gpu_tensor.chip(2l,2l);
-  sycl_device.memcpyDeviceToHost(chip3.data(), gpu_data_chip3, chip3TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip3.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip3.dimension(1), sizeDim2);
-  VERIFY_IS_EQUAL(chip3.dimension(2), sizeDim4);
-  VERIFY_IS_EQUAL(chip3.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim2; ++j) {
-      for (IndexType k = 0; k < sizeDim4; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2l,k,l));
-        }
-      }
-    }
-  }
-
-  array<IndexType, 4> chip4TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip4(chip4TensorRange);
-  const size_t chip4TensorBuffSize =chip4.size()*sizeof(DataType);
-  DataType* gpu_data_chip4  = static_cast<DataType*>(sycl_device.allocate(chip4TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip4(gpu_data_chip4, chip4TensorRange);
-
-  gpu_chip4.device(sycl_device)=gpu_tensor.chip(5l,3l);
-  sycl_device.memcpyDeviceToHost(chip4.data(), gpu_data_chip4, chip4TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip4.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip4.dimension(1), sizeDim2);
-  VERIFY_IS_EQUAL(chip4.dimension(2), sizeDim3);
-  VERIFY_IS_EQUAL(chip4.dimension(3), sizeDim5);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim2; ++j) {
-      for (IndexType k = 0; k < sizeDim3; ++k) {
-        for (IndexType l = 0; l < sizeDim5; ++l) {
-          VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5l,l));
-        }
-      }
-    }
-  }
-
-
-  array<IndexType, 4> chip5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
-  Tensor<DataType, 4, DataLayout,IndexType> chip5(chip5TensorRange);
-  const size_t chip5TensorBuffSize =chip5.size()*sizeof(DataType);
-  DataType* gpu_data_chip5  = static_cast<DataType*>(sycl_device.allocate(chip5TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip5(gpu_data_chip5, chip5TensorRange);
-
-  gpu_chip5.device(sycl_device)=gpu_tensor.chip(7l,4l);
-  sycl_device.memcpyDeviceToHost(chip5.data(), gpu_data_chip5, chip5TensorBuffSize);
-
-  VERIFY_IS_EQUAL(chip5.dimension(0), sizeDim1);
-  VERIFY_IS_EQUAL(chip5.dimension(1), sizeDim2);
-  VERIFY_IS_EQUAL(chip5.dimension(2), sizeDim3);
-  VERIFY_IS_EQUAL(chip5.dimension(3), sizeDim4);
-
-  for (IndexType i = 0; i < sizeDim1; ++i) {
-    for (IndexType j = 0; j < sizeDim2; ++j) {
-      for (IndexType k = 0; k < sizeDim3; ++k) {
-        for (IndexType l = 0; l < sizeDim4; ++l) {
-          VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7l));
-        }
-      }
-    }
-  }
-  sycl_device.deallocate(gpu_data_tensor);
-  sycl_device.deallocate(gpu_data_chip1);
-  sycl_device.deallocate(gpu_data_chip2);
-  sycl_device.deallocate(gpu_data_chip3);
-  sycl_device.deallocate(gpu_data_chip4);
-  sycl_device.deallocate(gpu_data_chip5);
-}
-
-template <typename DataType, int DataLayout, typename IndexType>
-static void test_chip_in_expr(const Eigen::SyclDevice& sycl_device) {
-
-  IndexType sizeDim1 = 2;
-  IndexType sizeDim2 = 3;
-  IndexType sizeDim3 = 5;
-  IndexType sizeDim4 = 7;
-  IndexType sizeDim5 = 11;
-
-  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-
-  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
-
-  Tensor<DataType, 4, DataLayout,IndexType> chip1(chip1TensorRange);
-  Tensor<DataType, 4, DataLayout,IndexType> tensor1(chip1TensorRange);
-  tensor.setRandom();
-  tensor1.setRandom();
-
-  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
-  const size_t chip1TensorBuffSize =chip1.size()*sizeof(DataType);
-  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
-  DataType* gpu_data_chip1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
-  DataType* gpu_data_tensor1  = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
-
-  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor1(gpu_data_tensor1, chip1TensorRange);
-
-
-  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
-  sycl_device.memcpyHostToDevice(gpu_data_tensor1, tensor1.data(), chip1TensorBuffSize);
-  gpu_chip1.device(sycl_device)=gpu_tensor.template chip<0l>(0l) + gpu_tensor1;
-  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);
-
-  for (int i = 0; i < sizeDim2; ++i) {
-    for (int j = 0; j < sizeDim3; ++j) {
-      for (int k = 0; k < sizeDim4; ++k) {
-        for (int l = 0; l < sizeDim5; ++l) {
-          float expected = tensor(0l,i,j,k,l) + tensor1(i,j,k,l);
-          VERIFY_IS_EQUAL(chip1(i,j,k,l), expected);
-        }
-      }
-    }
-  }
-
-  array<IndexType, 3> chip2TensorRange = {{sizeDim2, sizeDim4, sizeDim5}};
-  Tensor<DataType, 3, DataLayout,IndexType> tensor2(chip2TensorRange);
-  Tensor<DataType, 3, DataLayout,IndexType> chip2(chip2TensorRange);
-  tensor2.setRandom();
-  const size_t chip2TensorBuffSize =tensor2.size()*sizeof(DataType);
-  DataType* gpu_data_tensor2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
-  DataType* gpu_data_chip2  = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
-  TensorMap<Tensor<DataType, 3, DataLayout,IndexType>> gpu_tensor2(gpu_data_tensor2, chip2TensorRange);
-  TensorMap<Tensor<DataType, 3, DataLayout,IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_tensor2, tensor2.data(), chip2TensorBuffSize);
-  gpu_chip2.device(sycl_device)=gpu_tensor.template chip<0l>(0l).template chip<1l>(2l) + gpu_tensor2;
-  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);
-
-  for (int i = 0; i < sizeDim2; ++i) {
-    for (int j = 0; j < sizeDim4; ++j) {
-      for (int k = 0; k < sizeDim5; ++k) {
-        float expected = tensor(0l,i,2l,j,k) + tensor2(i,j,k);
-        VERIFY_IS_EQUAL(chip2(i,j,k), expected);
-      }
-    }
-  }
-  sycl_device.deallocate(gpu_data_tensor);
-  sycl_device.deallocate(gpu_data_tensor1);
-  sycl_device.deallocate(gpu_data_chip1);
-  sycl_device.deallocate(gpu_data_tensor2);
-  sycl_device.deallocate(gpu_data_chip2);
-}
-
-template <typename DataType, int DataLayout, typename IndexType>
-static void test_chip_as_lvalue_sycl(const Eigen::SyclDevice& sycl_device)
-{
-
-  IndexType sizeDim1 = 2;
-  IndexType sizeDim2 = 3;
-  IndexType sizeDim3 = 5;
-  IndexType sizeDim4 = 7;
-  IndexType sizeDim5 = 11;
-
-  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-  array<IndexType, 4> input2TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
-
-  Tensor<DataType, 5, DataLayout,IndexType> tensor(tensorRange);
-  Tensor<DataType, 5, DataLayout,IndexType> input1(tensorRange);
-  Tensor<DataType, 4, DataLayout,IndexType> input2(input2TensorRange);
-  input1.setRandom();
-  input2.setRandom();
-
-
-  const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
-  const size_t input2TensorBuffSize =input2.size()*sizeof(DataType);
-  DataType* gpu_data_tensor  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
-  DataType* gpu_data_input1  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
-  DataType* gpu_data_input2  = static_cast<DataType*>(sycl_device.allocate(input2TensorBuffSize));
-
-  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
-  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_input1(gpu_data_input1, tensorRange);
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input2(gpu_data_input2, input2TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_input1, input1.data(), tensorBuffSize);
-  gpu_tensor.device(sycl_device)=gpu_input1;
-  sycl_device.memcpyHostToDevice(gpu_data_input2, input2.data(), input2TensorBuffSize);
-  gpu_tensor.template chip<0l>(1l).device(sycl_device)=gpu_input2;
-  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
-
-  for (int i = 0; i < sizeDim1; ++i) {
-    for (int j = 0; j < sizeDim2; ++j) {
-      for (int k = 0; k < sizeDim3; ++k) {
-        for (int l = 0; l < sizeDim4; ++l) {
-          for (int m = 0; m < sizeDim5; ++m) {
-            if (i != 1) {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
-            } else {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input2(j,k,l,m));
-            }
-          }
-        }
-      }
-    }
-  }
-
-  gpu_tensor.device(sycl_device)=gpu_input1;
-  array<IndexType, 4> input3TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> input3(input3TensorRange);
-  input3.setRandom();
-
-  const size_t input3TensorBuffSize =input3.size()*sizeof(DataType);
-  DataType* gpu_data_input3  = static_cast<DataType*>(sycl_device.allocate(input3TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input3(gpu_data_input3, input3TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_input3, input3.data(), input3TensorBuffSize);
-  gpu_tensor.template chip<1l>(1l).device(sycl_device)=gpu_input3;
-  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
-
-  for (int i = 0; i < sizeDim1; ++i) {
-    for (int j = 0; j < sizeDim2; ++j) {
-      for (int k = 0; k <sizeDim3; ++k) {
-        for (int l = 0; l < sizeDim4; ++l) {
-          for (int m = 0; m < sizeDim5; ++m) {
-            if (j != 1) {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
-            } else {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input3(i,k,l,m));
-            }
-          }
-        }
-      }
-    }
-  }
-
-  gpu_tensor.device(sycl_device)=gpu_input1;
-  array<IndexType, 4> input4TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> input4(input4TensorRange);
-  input4.setRandom();
-
-  const size_t input4TensorBuffSize =input4.size()*sizeof(DataType);
-  DataType* gpu_data_input4  = static_cast<DataType*>(sycl_device.allocate(input4TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input4(gpu_data_input4, input4TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_input4, input4.data(), input4TensorBuffSize);
-  gpu_tensor.template chip<2l>(3l).device(sycl_device)=gpu_input4;
-  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
-
-  for (int i = 0; i < sizeDim1; ++i) {
-    for (int j = 0; j < sizeDim2; ++j) {
-      for (int k = 0; k <sizeDim3; ++k) {
-        for (int l = 0; l < sizeDim4; ++l) {
-          for (int m = 0; m < sizeDim5; ++m) {
-            if (k != 3) {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
-            } else {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input4(i,j,l,m));
-            }
-          }
-        }
-      }
-    }
-  }
-
-  gpu_tensor.device(sycl_device)=gpu_input1;
-  array<IndexType, 4> input5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
-  Tensor<DataType, 4, DataLayout,IndexType> input5(input5TensorRange);
-  input5.setRandom();
-
-  const size_t input5TensorBuffSize =input5.size()*sizeof(DataType);
-  DataType* gpu_data_input5  = static_cast<DataType*>(sycl_device.allocate(input5TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input5(gpu_data_input5, input5TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_input5, input5.data(), input5TensorBuffSize);
-  gpu_tensor.template chip<3l>(4l).device(sycl_device)=gpu_input5;
-  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
-
-  for (int i = 0; i < sizeDim1; ++i) {
-    for (int j = 0; j < sizeDim2; ++j) {
-      for (int k = 0; k <sizeDim3; ++k) {
-        for (int l = 0; l < sizeDim4; ++l) {
-          for (int m = 0; m < sizeDim5; ++m) {
-            if (l != 4) {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
-            } else {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input5(i,j,k,m));
-            }
-          }
-        }
-      }
-    }
-  }
-  gpu_tensor.device(sycl_device)=gpu_input1;
-  array<IndexType, 4> input6TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
-  Tensor<DataType, 4, DataLayout,IndexType> input6(input6TensorRange);
-  input6.setRandom();
-
-  const size_t input6TensorBuffSize =input6.size()*sizeof(DataType);
-  DataType* gpu_data_input6  = static_cast<DataType*>(sycl_device.allocate(input6TensorBuffSize));
-  TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_input6(gpu_data_input6, input6TensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_input6, input6.data(), input6TensorBuffSize);
-  gpu_tensor.template chip<4l>(5l).device(sycl_device)=gpu_input6;
-  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
-
-  for (int i = 0; i < sizeDim1; ++i) {
-    for (int j = 0; j < sizeDim2; ++j) {
-      for (int k = 0; k <sizeDim3; ++k) {
-        for (int l = 0; l < sizeDim4; ++l) {
-          for (int m = 0; m < sizeDim5; ++m) {
-            if (m != 5) {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
-            } else {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input6(i,j,k,l));
-            }
-          }
-        }
-      }
-    }
-  }
-
-
-  gpu_tensor.device(sycl_device)=gpu_input1;
-  Tensor<DataType, 5, DataLayout,IndexType> input7(tensorRange);
-  input7.setRandom();
-
-  DataType* gpu_data_input7  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
-  TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_input7(gpu_data_input7, tensorRange);
-
-  sycl_device.memcpyHostToDevice(gpu_data_input7, input7.data(), tensorBuffSize);
-  gpu_tensor.chip(0l,0l).device(sycl_device)=gpu_input7.chip(0l,0l);
-  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
-
-  for (int i = 0; i < sizeDim1; ++i) {
-    for (int j = 0; j < sizeDim2; ++j) {
-      for (int k = 0; k <sizeDim3; ++k) {
-        for (int l = 0; l < sizeDim4; ++l) {
-          for (int m = 0; m < sizeDim5; ++m) {
-            if (i != 0) {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
-            } else {
-              VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input7(i,j,k,l,m));
-            }
-          }
-        }
-      }
-    }
-  }
-  sycl_device.deallocate(gpu_data_tensor);
-  sycl_device.deallocate(gpu_data_input1);
-  sycl_device.deallocate(gpu_data_input2);
-  sycl_device.deallocate(gpu_data_input3);
-  sycl_device.deallocate(gpu_data_input4);
-  sycl_device.deallocate(gpu_data_input5);
-  sycl_device.deallocate(gpu_data_input6);
-  sycl_device.deallocate(gpu_data_input7);
-
-}
-
-template<typename DataType, typename dev_Selector> void sycl_chipping_test_per_device(dev_Selector s){
-  QueueInterface queueInterface(s);
-  auto sycl_device = Eigen::SyclDevice(&queueInterface);
-  test_static_chip_sycl<DataType, RowMajor, int64_t>(sycl_device);
-  test_static_chip_sycl<DataType, ColMajor, int64_t>(sycl_device);
-  test_dynamic_chip_sycl<DataType, RowMajor, int64_t>(sycl_device);
-  test_dynamic_chip_sycl<DataType, ColMajor, int64_t>(sycl_device);
-  test_chip_in_expr<DataType, RowMajor, int64_t>(sycl_device);
-  test_chip_in_expr<DataType, ColMajor, int64_t>(sycl_device);
-  test_chip_as_lvalue_sycl<DataType, RowMajor, int64_t>(sycl_device);
-  test_chip_as_lvalue_sycl<DataType, ColMajor, int64_t>(sycl_device);
-}
-void test_cxx11_tensor_chipping_sycl()
-{
-  for (const auto& device :Eigen::get_sycl_supported_devices()) {
-    CALL_SUBTEST(sycl_chipping_test_per_device<float>(device));
-  }
-}