1 files changed, 0 insertions, 134 deletions
diff --git a/unsupported/test/cxx11_tensor_contraction_mkldnn.cpp b/unsupported/test/cxx11_tensor_contraction_mkldnn.cpp
deleted file mode 100644
index 8ba0f986d..000000000
--- a/unsupported/test/cxx11_tensor_contraction_mkldnn.cpp
+++ /dev/null
@@ -1,134 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.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/.
-
-#include "main.h"
-
-#include <Eigen/CXX11/Tensor>
-
-using Eigen::internal::blas_data_mapper;
-using Eigen::internal::mkldnn_gemm_kernel;
-using Eigen::internal::mkldnn_gemm_pack;
-
-template <int NumDims>
-static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) {
-  array<Index, NumDims> dims;
-  for (int i = 0; i < NumDims; ++i) {
-    dims[i] = internal::random<int>(min_dim, max_dim);
-  }
-  return dims;
-}
-
-// Packing with mkldnn_gemm_pack is the same as taking a slice of 2 dimensional
-// Tensor.
-template <typename Scalar>
-static void test_mkldnn_gemm_pack() {
-  static const int Options = 0 | ColMajor;
-
-  typedef blas_data_mapper<Scalar, Index, ColMajor> DataMapper;
-  typedef mkldnn_gemm_pack<Scalar, Index, DataMapper, ColMajor> MkldnnGemmPack;
-  typedef Tensor<Scalar, 2, Options, Index> Tensor2d;
-
-  array<Index, 2> dims = RandomDims<2>(1, 500);
-
-  // Create a tensor initialized with random data.
-  Tensor2d src(dims);
-  src.setRandom();
-
-  // Pick a random slice of src tensor.
-  array<Index, 2> slice_start = RandomDims<2>(0, 250);
-  array<Index, 2> slice_size = RandomDims<2>(100, 500);
-  // Make sure that slice start + size do not overflow tensor dims.
-  for (int i = 0; i < 2; ++i) {
-    slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
-    slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
-  }
-
-  // Prepare tensors for packing and slicing results.
-  Tensor2d pack_dst(slice_size[0], slice_size[1]);
-  Tensor2d slice_dst(slice_size[0], slice_size[1]);
-
-  // Pack memory using mkldnn_gemm_pack.
-  DataMapper data_mapper(src.data(), dims[0]);
-  MkldnnGemmPack gemm_pack;
-  gemm_pack(pack_dst.data(),
-            data_mapper.getSubMapper(slice_start[0], slice_start[1]),
-            slice_size[0], slice_size[1]);
-  // Slice the source tensor.
-  slice_dst = src.slice(slice_start, slice_size);
-
-  // Verify that dst tensors are equal.
-  VERIFY_IS_EQUAL(pack_dst.dimensions().TotalSize(),
-                  slice_dst.dimensions().TotalSize());
-  for (Index i = 0; i < pack_dst.dimensions().TotalSize(); ++i) {
-    Scalar packed = pack_dst.coeff(i);
-    Scalar sliced = slice_dst.coeff(i);
-    VERIFY_IS_EQUAL(packed, sliced);
-  }
-}
-template <typename Scalar>
-static void test_mkldnn_gemm_kernel() {
-  static const int Options = 0 | ColMajor;
-
-  typedef Tensor<Scalar, 2, Options, Index> Tensor2d;
-
-  int m = internal::random<int>(1, 100);
-  int n = internal::random<int>(1, 100);
-  int k = internal::random<int>(1, 100);
-
-  Tensor2d lhs(m, k);
-  lhs.setRandom();
-
-  Tensor2d rhs(k, n);
-  rhs.setRandom();
-
-  // Compute matmul with mkldnn gemm kernel.
-  typedef blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-  typedef mkldnn_gemm_kernel<Scalar, Index, OutputMapper, ColMajor>
-      MkldnnGemmKernel;
-
-  Tensor2d mkldnn_result(m, n);
-  mkldnn_result.setZero();
-
-  OutputMapper output_mapper(mkldnn_result.data(), m);
-  MkldnnGemmKernel gemm_kernel;
-  gemm_kernel(output_mapper, lhs.data(), rhs.data(), m, k, n, /*alpha*/ 1.0);
-
-  // Compute matmul with Eigen::Matrix.
-  typedef Eigen::Matrix<Scalar, Dynamic, Dynamic, ColMajor> Matrix;
-  typedef Map<Eigen::Matrix<Scalar, Dynamic, Dynamic, ColMajor> > MatrixMap;
-
-  MatrixMap lhs_mat(lhs.data(), m, k);
-  MatrixMap rhs_mat(rhs.data(), k, n);
-  Matrix matmul_result(m, n);
-  matmul_result.setZero();
-
-  matmul_result = lhs_mat * rhs_mat;
-
-  static const float error_threshold = 1e-4f;
-
-  // Verify that results are equal.
-  for (Index i = 0; i < m * n; ++i) {
-    Scalar gemm = mkldnn_result(i);
-    Scalar matmul = matmul_result(i % m, i / m);
-    if ((std::abs)(gemm) > error_threshold &&
-        (std::abs)(matmul) > error_threshold) {
-      if (!Eigen::internal::isApprox(gemm, matmul, error_threshold))
-        std::cout << "gemm=" << gemm << " matmul=" << matmul << std::endl;
-      VERIFY(Eigen::internal::isApprox(gemm, matmul, error_threshold));
-    }
-  }
-}
-
-EIGEN_DECLARE_TEST(cxx11_tensor_contraction_mkldnn) {
-  CALL_SUBTEST(test_mkldnn_gemm_pack<float>());
-  CALL_SUBTEST(test_mkldnn_gemm_pack<double>());
-
-  // mkldnn has only sgemm (aka gemm for floats).
-  CALL_SUBTEST(test_mkldnn_gemm_kernel<float>());
-}