EVOLUTION-MANAGER

Edit File: gemm_functors.h

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==============================================================================*/

// This is a set of different implementations for the basic matrix by matrix
// multiply function, commonly known as GEMM after the BLAS library's naming.
// Having a standard interface enables us to swap out implementations on
// different platforms, to make sure we're using the optimal version. They are
// implemented as C++ template functors, so they're easy to swap into all of the
// different kernels that use them.

#if !defined(EIGEN_USE_THREADS)
#error "EIGEN_USE_THREADS must be enabled by all .cc files including this."
#endif  // EIGEN_USE_THREADS

#ifndef TENSORFLOW_CORE_KERNELS_GEMM_FUNCTORS_H_
#define TENSORFLOW_CORE_KERNELS_GEMM_FUNCTORS_H_

#include <string.h>
#include <map>
#include <vector>

#include "tensorflow/core/common_runtime/threadpool_device.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_types.h"

#if defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL)
#include "tensorflow/core/kernels/eigen_contraction_kernel.h"
#endif

// Apple provides an optimized BLAS library that is better than Eigen for their
// devices, so use that if possible.
#if defined(__APPLE__) && defined(USE_GEMM_FOR_CONV)
#include <Accelerate/Accelerate.h>
#define USE_CBLAS_GEMM
#endif  // __APPLE__

// Older Raspberry Pi systems don't have NEON SIMD acceleration, so Eigen falls
// back to scalar code, but OpenBLAS has much faster support so prefer that.
#if defined(RASPBERRY_PI) && defined(USE_GEMM_FOR_CONV) && defined(USE_OPENBLAS)
#include <cblas.h>
#define USE_CBLAS_GEMM
#endif

// A readable but slow implementation of matrix multiplication, useful for
// debugging and understanding the algorithm. Use instead of FastGemmFunctor in
// the Im2ColConvFunctor template definition inside the op registration to
// enable. Assumes row-major ordering of the values in memory.
template <class T1, class T2, class T3>
class ReferenceGemmFunctor {
 public:
  void operator()(tensorflow::OpKernelContext* ctx, size_t m, size_t n,
                  size_t k, const T1* a, size_t lda, const T2* b, size_t ldb,
                  T3* c, size_t ldc) {
    const size_t a_i_stride = lda;
    const size_t a_l_stride = 1;
    const size_t b_j_stride = 1;
    const size_t b_l_stride = ldb;
    const size_t c_i_stride = ldc;
    const size_t c_j_stride = 1;
    size_t i, j, l;
    for (j = 0; j < n; j++) {
      for (i = 0; i < m; i++) {
        T3 total(0);
        for (l = 0; l < k; l++) {
          const size_t a_index = ((i * a_i_stride) + (l * a_l_stride));
          const T1 a_value = a[a_index];
          const size_t b_index = ((j * b_j_stride) + (l * b_l_stride));
          const T2 b_value = b[b_index];
          total += (a_value * b_value);
        }
        const size_t c_index = ((i * c_i_stride) + (j * c_j_stride));
        c[c_index] = total;
      }
    }
  }
};

// Uses the optimized EigenTensor library to implement the matrix multiplication
// required by the Im2ColConvFunctor class. We supply the two input and one
// output types so that the accumulator can potentially be higher-precision than
// the inputs, even though we don't currently take advantage of this.
template <class T1, class T2, class T3>
class FastGemmFunctor {
 public:
  void operator()(tensorflow::OpKernelContext* ctx, size_t m, size_t n,
                  size_t k, const T1* a, size_t lda, const T2* b, size_t ldb,
                  T3* c, size_t ldc) {
    typename tensorflow::TTypes<const T1>::Matrix a_matrix(a, m, k);
    typename tensorflow::TTypes<const T2>::Matrix b_matrix(b, k, n);
    typename tensorflow::TTypes<T3>::Matrix c_matrix(c, m, n);

Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair;
    dim_pair[0].first = 1;
    dim_pair[0].second = 0;
    c_matrix.device(ctx->eigen_device<Eigen::ThreadPoolDevice>()) =
        a_matrix.contract(b_matrix, dim_pair);
  }
};

// If we have a fast CBLAS library, use its implementation through a wrapper.
#if defined(USE_CBLAS_GEMM)
template <>
class FastGemmFunctor<float, float, float> {
 public:
  void operator()(tensorflow::OpKernelContext* ctx, size_t m, size_t n,
                  size_t k, const float* a, size_t lda, const float* b,
                  size_t ldb, float* c, size_t ldc) {
    cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, 1.0f, a,
                lda, b, ldb, 0.0f, c, ldc);
  }
};
#endif  // USE_CBLAS_GEMM

#endif  // TENSORFLOW_CORE_KERNELS_GEMM_FUNCTORS_H_