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      1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
     17 
     18 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
     19 #include "tensorflow/compiler/xla/service/cpu/runtime_matvec.h"
     20 #include "tensorflow/core/platform/dynamic_annotations.h"
     21 #include "tensorflow/core/platform/types.h"
     22 
     23 #if defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL)
     24 #include "tensorflow/core/kernels/eigen_contraction_kernel.h"
     25 #endif
     26 
     27 using tensorflow::int32;
     28 using tensorflow::int64;
     29 
     30 namespace {
     31 
     32 bool Is16BytesAligned(void* ptr) {
     33   return reinterpret_cast<uintptr_t>(ptr) % 16 == 0;
     34 }
     35 
     36 template <typename T, Eigen::AlignmentType Alignment>
     37 void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m,
     38             int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) {
     39   int64 lhs_rows = m;
     40   int64 lhs_cols = k;
     41   if (transpose_lhs) {
     42     std::swap(lhs_rows, lhs_cols);
     43   }
     44 
     45   int64 rhs_rows = k;
     46   int64 rhs_cols = n;
     47   if (transpose_rhs) {
     48     std::swap(rhs_rows, rhs_cols);
     49   }
     50 
     51   const Eigen::TensorMap<Eigen::Tensor<const T, 2>, Alignment> A(lhs, lhs_rows,
     52                                                                  lhs_cols);
     53   const Eigen::TensorMap<Eigen::Tensor<const T, 2>, Alignment> B(rhs, rhs_rows,
     54                                                                  rhs_cols);
     55   Eigen::TensorMap<Eigen::Tensor<T, 2>, Alignment> C(out, m, n);
     56 
     57   typedef typename Eigen::Tensor<T, 2>::DimensionPair DimPair;
     58   int lhs_contract_dim = transpose_lhs ? 0 : 1;
     59   int rhs_contract_dim = transpose_rhs ? 1 : 0;
     60   const Eigen::array<DimPair, 1> dims(
     61       {DimPair(lhs_contract_dim, rhs_contract_dim)});
     62 
     63   // Matrix multiply is a special case of the "contract" operation where
     64   // the contraction is performed along dimension 1 of the lhs and dimension
     65   // 0 of the rhs.
     66   C = A.contract(B, dims);
     67 }
     68 
     69 template <typename T>
     70 void SingleThreadedMatMulDispatch(const void* run_options_ptr, T* out, T* lhs,
     71                                   T* rhs, int64 m, int64 n, int64 k,
     72                                   int32 transpose_lhs, int32 transpose_rhs) {
     73   bool all_buffers_16b_aligned =
     74       Is16BytesAligned(out) && Is16BytesAligned(lhs) && Is16BytesAligned(rhs);
     75 
     76   if (!all_buffers_16b_aligned) {
     77     MatMul<T, Eigen::Unaligned>(run_options_ptr, out, lhs, rhs, m, n, k,
     78                                 transpose_lhs, transpose_rhs);
     79   }
     80 
     81   if (m == 1 || n == 1) {
     82     xla::EigenMatVec<T>(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
     83   } else {
     84     MatMul<T, Eigen::Aligned16>(run_options_ptr, out, lhs, rhs, m, n, k,
     85                                 transpose_lhs, transpose_rhs);
     86   }
     87 }
     88 
     89 }  // namespace
     90 
     91 TF_ATTRIBUTE_NO_SANITIZE_MEMORY void
     92 __xla_cpu_runtime_EigenSingleThreadedMatMulF16(
     93     const void* run_options_ptr, Eigen::half* out, Eigen::half* lhs,
     94     Eigen::half* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs,
     95     int32 transpose_rhs) {
     96   SingleThreadedMatMulDispatch<Eigen::half>(run_options_ptr, out, lhs, rhs, m,
     97                                             n, k, transpose_lhs, transpose_rhs);
     98 }
     99 
    100 TF_ATTRIBUTE_NO_SANITIZE_MEMORY void
    101 __xla_cpu_runtime_EigenSingleThreadedMatMulF32(const void* run_options_ptr,
    102                                                float* out, float* lhs,
    103                                                float* rhs, int64 m, int64 n,
    104                                                int64 k, int32 transpose_lhs,
    105                                                int32 transpose_rhs) {
    106   SingleThreadedMatMulDispatch<float>(run_options_ptr, out, lhs, rhs, m, n, k,
    107                                       transpose_lhs, transpose_rhs);
    108 }
    109 
    110 TF_ATTRIBUTE_NO_SANITIZE_MEMORY void
    111 __xla_cpu_runtime_EigenSingleThreadedMatMulF64(const void* run_options_ptr,
    112                                                double* out, double* lhs,
    113                                                double* rhs, int64 m, int64 n,
    114                                                int64 k, int32 transpose_lhs,
    115                                                int32 transpose_rhs) {
    116   SingleThreadedMatMulDispatch<double>(run_options_ptr, out, lhs, rhs, m, n, k,
    117                                        transpose_lhs, transpose_rhs);
    118 }
    119