// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_DNN_CuBLAS_H_
#define DLIB_DNN_CuBLAS_H_
#ifdef DLIB_USE_CUDA
#include "tensor.h"
#include "cuda_errors.h"
namespace dlib
{
namespace cuda
{
// -----------------------------------------------------------------------------------
void gemm (
float beta,
tensor& dest,
float alpha,
const tensor& lhs,
bool trans_lhs,
const tensor& rhs,
bool trans_rhs,
operation_mode mode = operation_mode::CHANNEL_WISE
);
/*!
requires
- The dimensions of lhs and rhs must be compatible for matrix multiplication.
The specific requirements depend on the mode:
For CHANNEL_WISE mode (default):
- Let L == trans_lhs ? trans(mat(lhs)) : mat(lhs)
- Let R == trans_rhs ? trans(mat(rhs)) : mat(rhs)
- Let D == mat(dest)
- D.nr() == L.nr() && D.nc() == R.nc()
(i.e. dest must be preallocated and have the correct output dimensions)
- L.nc() == R.nr()
For PLANE_WISE mode:
- lhs.num_samples() == rhs.num_samples() && lhs.k() == rhs.k()
- If !trans_lhs && !trans_rhs:
lhs.nc() == rhs.nr()
dest.nr() == lhs.nr() && dest.nc() == rhs.nc()
- If trans_lhs && !trans_rhs:
lhs.nr() == rhs.nr()
dest.nr() == lhs.nc() && dest.nc() == rhs.nc()
- If !trans_lhs && trans_rhs:
lhs.nc() == rhs.nc()
dest.nr() == lhs.nr() && dest.nc() == rhs.nr()
- If trans_lhs && trans_rhs:
lhs.nr() == rhs.nc()
dest.nr() == lhs.nc() && dest.nc() == rhs.nr()
ensures
- Performs matrix multiplication based on the specified mode:
For CHANNEL_WISE mode:
- performs: dest = alpha*L*R + beta*mat(dest)
where L, R, and D are as defined above.
For PLANE_WISE mode:
- Performs matrix multiplication for each corresponding 2D plane (nr x nc)
in lhs and rhs across all samples and channels.
- The operation is equivalent to performing the following for each sample
and channel:
dest[s][k] = alpha * (lhs[s][k] * rhs[s][k]) + beta * dest[s][k]
where [s][k] represents the 2D plane for sample s and channel k.
!*/
// ------------------------------------------------------------------------------------
}
}
#endif // DLIB_USE_CUDA
#endif // DLIB_DNN_CuBLAS_H_