distconv_matmul.hpp

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// Copyright (c) 2014-2023, Lawrence Livermore National Security, LLC.
// Produced at the Lawrence Livermore National Laboratory.
// Written by the LBANN Research Team (B. Van Essen, et al.) listed in
// the CONTRIBUTORS file. <lbann-dev@llnl.gov>
//
// LLNL-CODE-697807.
// All rights reserved.
//
// This file is part of LBANN: Livermore Big Artificial Neural Network
// Toolkit. For details, see http://software.llnl.gov/LBANN or
// https://github.com/LLNL/LBANN.
//
// Licensed under the Apache License, Version 2.0 (the "Licensee"); you
// may not use this file except in compliance with the License.  You may
// obtain a copy of the License at:
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
// implied. See the License for the specific language governing
// permissions and limitations under the license.
#ifndef LBANN_LAYERS_MATH_DISTCONV_MATMUL
#define LBANN_LAYERS_MATH_DISTCONV_MATMUL
#include "distconv/base.hpp"
#include "distconv/tensor/tensor.hpp"
#include "distconv/tensor/tensor_mpi.hpp"
#include "lbann/utils/distconv.hpp"

#ifdef LBANN_HAS_DISTCONV
namespace distconv {
template <typename Backend, typename DataType>
class MatMul
{
  using LocalMPI = tensor::LocaleMPI;

public:
  MatMul(Backend& backend) : m_be(backend){};

  template <typename Allocator>
  int forward(
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_0,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_1,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output,
    const bool transpose_0,
    const bool transpose_1);

  template <typename Allocator>
  int backward(
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_0,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_1,
    const tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& output_grad,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_grad_0,
    tensor::Tensor<DataType, tensor::LocaleMPI, Allocator>& input_grad_1,
    const bool transpose_0,
    const bool transpose_1);

protected:
  Backend& m_be;
};

template <typename DataType, typename locale, typename Allocator>
tensor::Shape get_matmul_local_tensor_shape(
  const tensor::Tensor<DataType, locale, Allocator>& input_0,
  const tensor::Tensor<DataType, locale, Allocator>& input_1,
  bool transpose_1,
  bool transpose_2)
{
  // Use input dims to fill channel and mini-batch dimensions
  auto output_local_shape = input_0.get_local_shape();

  auto inp_0_dims = input_0.get_local_shape();
  auto inp_1_dims = input_1.get_local_shape();

  // Update the matrix dimensions according to transpose and input matrix shapes
  output_local_shape[0] = transpose_2 ? inp_1_dims[1] : inp_1_dims[0];
  output_local_shape[1] = transpose_1 ? inp_0_dims[0] : inp_0_dims[1];

  return output_local_shape;
}

extern template class MatMul<::distconv::BackendDNNLib, float>;
extern template class MatMul<::distconv::BackendDNNLib, double>;
} // namespace distconv

#endif // LBANN_HAS_DISTCONV
#endif // LBANN_LAYERS_MATH_DISTCONV_MATMUL