290 lines
10 KiB
C++
290 lines
10 KiB
C++
// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
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// Copyright 2008-2016 National ICT Australia (NICTA)
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ------------------------------------------------------------------------
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//! \addtogroup gemm_mixed
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//! @{
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//! \brief
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//! Matrix multplication where the matrices have differing element types.
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//! Uses caching for speedup.
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//! Matrix 'C' is assumed to have been set to the correct size (i.e. taking into account transposes)
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template<const bool do_trans_A=false, const bool do_trans_B=false, const bool use_alpha=false, const bool use_beta=false>
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class gemm_mixed_large
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{
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public:
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template<typename out_eT, typename in_eT1, typename in_eT2>
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arma_hot
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inline
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static
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void
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apply
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(
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Mat<out_eT>& C,
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const Mat<in_eT1>& A,
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const Mat<in_eT2>& B,
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const out_eT alpha = out_eT(1),
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const out_eT beta = out_eT(0)
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)
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{
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arma_extra_debug_sigprint();
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const uword A_n_rows = A.n_rows;
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const uword A_n_cols = A.n_cols;
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const uword B_n_rows = B.n_rows;
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const uword B_n_cols = B.n_cols;
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if( (do_trans_A == false) && (do_trans_B == false) )
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{
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podarray<in_eT1> tmp(A_n_cols);
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in_eT1* A_rowdata = tmp.memptr();
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#if defined(ARMA_USE_OPENMP)
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const bool use_mp = (B_n_cols >= 2) && (B.n_elem >= 8192) && (mp_thread_limit::in_parallel() == false);
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#else
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const bool use_mp = false;
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#endif
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if(use_mp)
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{
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#if defined(ARMA_USE_OPENMP)
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{
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const int n_threads = int( (std::min)( uword(mp_thread_limit::get()), uword(B_n_cols) ) );
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for(uword row_A=0; row_A < A_n_rows; ++row_A)
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{
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tmp.copy_row(A, row_A);
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#pragma omp parallel for schedule(static) num_threads(n_threads)
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for(uword col_B=0; col_B < B_n_cols; ++col_B)
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{
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const in_eT2* B_coldata = B.colptr(col_B);
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out_eT acc = out_eT(0);
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for(uword i=0; i < B_n_rows; ++i)
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{
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acc += upgrade_val<in_eT1,in_eT2>::apply(A_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
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}
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if( (use_alpha == false) && (use_beta == false) ) { C.at(row_A,col_B) = acc; }
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else if( (use_alpha == true ) && (use_beta == false) ) { C.at(row_A,col_B) = alpha*acc; }
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else if( (use_alpha == false) && (use_beta == true ) ) { C.at(row_A,col_B) = acc + beta*C.at(row_A,col_B); }
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else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(row_A,col_B) = alpha*acc + beta*C.at(row_A,col_B); }
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}
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}
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}
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#endif
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}
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else
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{
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for(uword row_A=0; row_A < A_n_rows; ++row_A)
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{
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tmp.copy_row(A, row_A);
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for(uword col_B=0; col_B < B_n_cols; ++col_B)
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{
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const in_eT2* B_coldata = B.colptr(col_B);
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out_eT acc = out_eT(0);
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for(uword i=0; i < B_n_rows; ++i)
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{
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acc += upgrade_val<in_eT1,in_eT2>::apply(A_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
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}
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if( (use_alpha == false) && (use_beta == false) ) { C.at(row_A,col_B) = acc; }
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else if( (use_alpha == true ) && (use_beta == false) ) { C.at(row_A,col_B) = alpha*acc; }
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else if( (use_alpha == false) && (use_beta == true ) ) { C.at(row_A,col_B) = acc + beta*C.at(row_A,col_B); }
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else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(row_A,col_B) = alpha*acc + beta*C.at(row_A,col_B); }
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}
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}
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}
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}
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else
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if( (do_trans_A == true) && (do_trans_B == false) )
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{
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#if defined(ARMA_USE_OPENMP)
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const bool use_mp = (B_n_cols >= 2) && (B.n_elem >= 8192) && (mp_thread_limit::in_parallel() == false);
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#else
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const bool use_mp = false;
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#endif
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if(use_mp)
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{
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#if defined(ARMA_USE_OPENMP)
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{
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const int n_threads = int( (std::min)( uword(mp_thread_limit::get()), uword(B_n_cols) ) );
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for(uword col_A=0; col_A < A_n_cols; ++col_A)
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{
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// col_A is interpreted as row_A when storing the results in matrix C
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const in_eT1* A_coldata = A.colptr(col_A);
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#pragma omp parallel for schedule(static) num_threads(n_threads)
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for(uword col_B=0; col_B < B_n_cols; ++col_B)
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{
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const in_eT2* B_coldata = B.colptr(col_B);
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out_eT acc = out_eT(0);
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for(uword i=0; i < B_n_rows; ++i)
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{
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acc += upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
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}
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if( (use_alpha == false) && (use_beta == false) ) { C.at(col_A,col_B) = acc; }
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else if( (use_alpha == true ) && (use_beta == false) ) { C.at(col_A,col_B) = alpha*acc; }
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else if( (use_alpha == false) && (use_beta == true ) ) { C.at(col_A,col_B) = acc + beta*C.at(col_A,col_B); }
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else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(col_A,col_B) = alpha*acc + beta*C.at(col_A,col_B); }
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}
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}
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}
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#endif
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}
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else
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{
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for(uword col_A=0; col_A < A_n_cols; ++col_A)
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{
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// col_A is interpreted as row_A when storing the results in matrix C
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const in_eT1* A_coldata = A.colptr(col_A);
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for(uword col_B=0; col_B < B_n_cols; ++col_B)
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{
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const in_eT2* B_coldata = B.colptr(col_B);
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out_eT acc = out_eT(0);
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for(uword i=0; i < B_n_rows; ++i)
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{
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acc += upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
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}
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if( (use_alpha == false) && (use_beta == false) ) { C.at(col_A,col_B) = acc; }
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else if( (use_alpha == true ) && (use_beta == false) ) { C.at(col_A,col_B) = alpha*acc; }
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else if( (use_alpha == false) && (use_beta == true ) ) { C.at(col_A,col_B) = acc + beta*C.at(col_A,col_B); }
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else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(col_A,col_B) = alpha*acc + beta*C.at(col_A,col_B); }
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}
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}
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}
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}
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else
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if( (do_trans_A == false) && (do_trans_B == true) )
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{
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Mat<in_eT2> B_tmp;
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op_strans::apply_mat_noalias(B_tmp, B);
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gemm_mixed_large<false, false, use_alpha, use_beta>::apply(C, A, B_tmp, alpha, beta);
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}
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else
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if( (do_trans_A == true) && (do_trans_B == true) )
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{
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// mat B_tmp = trans(B);
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// dgemm_arma<true, false, use_alpha, use_beta>::apply(C, A, B_tmp, alpha, beta);
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// By using the trans(A)*trans(B) = trans(B*A) equivalency,
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// transpose operations are not needed
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podarray<in_eT2> tmp(B_n_cols);
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in_eT2* B_rowdata = tmp.memptr();
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for(uword row_B=0; row_B < B_n_rows; ++row_B)
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{
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tmp.copy_row(B, row_B);
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for(uword col_A=0; col_A < A_n_cols; ++col_A)
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{
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const in_eT1* A_coldata = A.colptr(col_A);
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out_eT acc = out_eT(0);
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for(uword i=0; i < A_n_rows; ++i)
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{
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acc += upgrade_val<in_eT1,in_eT2>::apply(B_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]);
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}
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if( (use_alpha == false) && (use_beta == false) ) { C.at(col_A,row_B) = acc; }
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else if( (use_alpha == true ) && (use_beta == false) ) { C.at(col_A,row_B) = alpha*acc; }
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else if( (use_alpha == false) && (use_beta == true ) ) { C.at(col_A,row_B) = acc + beta*C.at(col_A,row_B); }
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else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(col_A,row_B) = alpha*acc + beta*C.at(col_A,row_B); }
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}
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}
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}
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}
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};
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//! \brief
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//! Matrix multplication where the matrices have differing element types.
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template<const bool do_trans_A=false, const bool do_trans_B=false, const bool use_alpha=false, const bool use_beta=false>
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class gemm_mixed
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{
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public:
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//! immediate multiplication of matrices A and B, storing the result in C
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template<typename out_eT, typename in_eT1, typename in_eT2>
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inline
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static
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void
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apply
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(
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Mat<out_eT>& C,
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const Mat<in_eT1>& A,
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const Mat<in_eT2>& B,
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const out_eT alpha = out_eT(1),
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const out_eT beta = out_eT(0)
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)
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{
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arma_extra_debug_sigprint();
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if((is_cx<in_eT1>::yes && do_trans_A) || (is_cx<in_eT2>::yes && do_trans_B))
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{
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// better-than-nothing handling of hermitian transpose
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Mat<in_eT1> tmp_A;
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Mat<in_eT2> tmp_B;
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const bool predo_trans_A = ( (do_trans_A == true) && (is_cx<in_eT1>::yes) );
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const bool predo_trans_B = ( (do_trans_B == true) && (is_cx<in_eT2>::yes) );
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if(predo_trans_A) { op_htrans::apply_mat_noalias(tmp_A, A); }
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if(predo_trans_B) { op_htrans::apply_mat_noalias(tmp_B, B); }
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const Mat<in_eT1>& AA = (predo_trans_A == false) ? A : tmp_A;
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const Mat<in_eT2>& BB = (predo_trans_B == false) ? B : tmp_B;
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gemm_mixed_large<((predo_trans_A) ? false : do_trans_A), ((predo_trans_B) ? false : do_trans_B), use_alpha, use_beta>::apply(C, AA, BB, alpha, beta);
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}
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else
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{
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gemm_mixed_large<do_trans_A, do_trans_B, use_alpha, use_beta>::apply(C, A, B, alpha, beta);
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}
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}
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};
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//! @}
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