1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
5// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
6// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
7//
8// This Source Code Form is subject to the terms of the Mozilla
9// Public License v. 2.0. If a copy of the MPL was not distributed
10// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
11
12
13#ifndef EIGEN_PRODUCTEVALUATORS_H
14#define EIGEN_PRODUCTEVALUATORS_H
15
16namespace Eigen {
17
18namespace internal {
19
20/** \internal
21 * Evaluator of a product expression.
22 * Since products require special treatments to handle all possible cases,
23 * we simply deffer the evaluation logic to a product_evaluator class
24 * which offers more partial specialization possibilities.
25 *
26 * \sa class product_evaluator
27 */
28template<typename Lhs, typename Rhs, int Options>
29struct evaluator<Product<Lhs, Rhs, Options> >
30 : public product_evaluator<Product<Lhs, Rhs, Options> >
31{
32 typedef Product<Lhs, Rhs, Options> XprType;
33 typedef product_evaluator<XprType> Base;
34
35 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
36};
37
38// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
39// TODO we should apply that rule only if that's really helpful
40template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
41struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
42 const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
43 const Product<Lhs, Rhs, DefaultProduct> > >
44{
45 static const bool value = true;
46};
47template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
48struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
49 const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
50 const Product<Lhs, Rhs, DefaultProduct> > >
51 : public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
52{
53 typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
54 const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
55 const Product<Lhs, Rhs, DefaultProduct> > XprType;
56 typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
57
58 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
59 : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
60 {}
61};
62
63
64template<typename Lhs, typename Rhs, int DiagIndex>
65struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> >
66 : public evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> >
67{
68 typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
69 typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> > Base;
70
71 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
72 : Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
73 Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()),
74 xpr.index() ))
75 {}
76};
77
78
79// Helper class to perform a matrix product with the destination at hand.
80// Depending on the sizes of the factors, there are different evaluation strategies
81// as controlled by internal::product_type.
82template< typename Lhs, typename Rhs,
83 typename LhsShape = typename evaluator_traits<Lhs>::Shape,
84 typename RhsShape = typename evaluator_traits<Rhs>::Shape,
85 int ProductType = internal::product_type<Lhs,Rhs>::value>
86struct generic_product_impl;
87
88template<typename Lhs, typename Rhs>
89struct evaluator_assume_aliasing<Product<Lhs, Rhs, DefaultProduct> > {
90 static const bool value = true;
91};
92
93// This is the default evaluator implementation for products:
94// It creates a temporary and call generic_product_impl
95template<typename Lhs, typename Rhs, int Options, int ProductTag, typename LhsShape, typename RhsShape>
96struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsShape>
97 : public evaluator<typename Product<Lhs, Rhs, Options>::PlainObject>
98{
99 typedef Product<Lhs, Rhs, Options> XprType;
100 typedef typename XprType::PlainObject PlainObject;
101 typedef evaluator<PlainObject> Base;
102 enum {
103 Flags = Base::Flags | EvalBeforeNestingBit
104 };
105
106 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
107 explicit product_evaluator(const XprType& xpr)
108 : m_result(xpr.rows(), xpr.cols())
109 {
110 ::new (static_cast<Base*>(this)) Base(m_result);
111
112// FIXME shall we handle nested_eval here?,
113// if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.)
114// typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
115// typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
116// typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
117// typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
118//
119// const LhsNested lhs(xpr.lhs());
120// const RhsNested rhs(xpr.rhs());
121//
122// generic_product_impl<LhsNestedCleaned, RhsNestedCleaned>::evalTo(m_result, lhs, rhs);
123
124 generic_product_impl<Lhs, Rhs, LhsShape, RhsShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
125 }
126
127protected:
128 PlainObject m_result;
129};
130
131// The following three shortcuts are enabled only if the scalar types match excatly.
132// TODO: we could enable them for different scalar types when the product is not vectorized.
133
134// Dense = Product
135template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
136struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
137 typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
138{
139 typedef Product<Lhs,Rhs,Options> SrcXprType;
140 static EIGEN_STRONG_INLINE
141 void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
142 {
143 Index dstRows = src.rows();
144 Index dstCols = src.cols();
145 if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
146 dst.resize(dstRows, dstCols);
147 // FIXME shall we handle nested_eval here?
148 generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
149 }
150};
151
152// Dense += Product
153template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
154struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
155 typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
156{
157 typedef Product<Lhs,Rhs,Options> SrcXprType;
158 static EIGEN_STRONG_INLINE
159 void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
160 {
161 eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
162 // FIXME shall we handle nested_eval here?
163 generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
164 }
165};
166
167// Dense -= Product
168template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
169struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
170 typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
171{
172 typedef Product<Lhs,Rhs,Options> SrcXprType;
173 static EIGEN_STRONG_INLINE
174 void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
175 {
176 eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
177 // FIXME shall we handle nested_eval here?
178 generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
179 }
180};
181
182
183// Dense ?= scalar * Product
184// TODO we should apply that rule if that's really helpful
185// for instance, this is not good for inner products
186template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
187struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
188 const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense>
189{
190 typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
191 const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
192 const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
193 static EIGEN_STRONG_INLINE
194 void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
195 {
196 call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
197 }
198};
199
200//----------------------------------------
201// Catch "Dense ?= xpr + Product<>" expression to save one temporary
202// FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct
203
204template<typename OtherXpr, typename Lhs, typename Rhs>
205struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
206 const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
207 static const bool value = true;
208};
209
210template<typename OtherXpr, typename Lhs, typename Rhs>
211struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_difference_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
212 const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
213 static const bool value = true;
214};
215
216template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
217struct assignment_from_xpr_op_product
218{
219 template<typename SrcXprType, typename InitialFunc>
220 static EIGEN_STRONG_INLINE
221 void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
222 {
223 call_assignment_no_alias(dst, src.lhs(), Func1());
224 call_assignment_no_alias(dst, src.rhs(), Func2());
225 }
226};
227
228#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \
229 template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \
230 struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<OtherScalar,ProdScalar>, const OtherXpr, \
231 const Product<Lhs,Rhs,DefaultProduct> >, internal::ASSIGN_OP<DstScalar,SrcScalar>, Dense2Dense> \
232 : assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::ASSIGN_OP<DstScalar,OtherScalar>, internal::ASSIGN_OP2<DstScalar,ProdScalar> > \
233 {}
234
235EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_sum_op,add_assign_op);
236EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op);
237EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op);
238
239EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_difference_op,sub_assign_op);
240EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op);
241EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op);
242
243//----------------------------------------
244
245template<typename Lhs, typename Rhs>
246struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
247{
248 template<typename Dst>
249 static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
250 {
251 dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
252 }
253
254 template<typename Dst>
255 static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
256 {
257 dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum();
258 }
259
260 template<typename Dst>
261 static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
262 { dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); }
263};
264
265
266/***********************************************************************
267* Implementation of outer dense * dense vector product
268***********************************************************************/
269
270// Column major result
271template<typename Dst, typename Lhs, typename Rhs, typename Func>
272void outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
273{
274 evaluator<Rhs> rhsEval(rhs);
275 typename nested_eval<Lhs,Rhs::SizeAtCompileTime>::type actual_lhs(lhs);
276 // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored
277 // FIXME not very good if rhs is real and lhs complex while alpha is real too
278 const Index cols = dst.cols();
279 for (Index j=0; j<cols; ++j)
280 func(dst.col(j), rhsEval.coeff(Index(0),j) * actual_lhs);
281}
282
283// Row major result
284template<typename Dst, typename Lhs, typename Rhs, typename Func>
285void outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
286{
287 evaluator<Lhs> lhsEval(lhs);
288 typename nested_eval<Rhs,Lhs::SizeAtCompileTime>::type actual_rhs(rhs);
289 // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored
290 // FIXME not very good if lhs is real and rhs complex while alpha is real too
291 const Index rows = dst.rows();
292 for (Index i=0; i<rows; ++i)
293 func(dst.row(i), lhsEval.coeff(i,Index(0)) * actual_rhs);
294}
295
296template<typename Lhs, typename Rhs>
297struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
298{
299 template<typename T> struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
300 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
301
302 // TODO it would be nice to be able to exploit our *_assign_op functors for that purpose
303 struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } };
304 struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
305 struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
306 struct adds {
307 Scalar m_scale;
308 explicit adds(const Scalar& s) : m_scale(s) {}
309 template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const {
310 dst.const_cast_derived() += m_scale * src;
311 }
312 };
313
314 template<typename Dst>
315 static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
316 {
317 internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
318 }
319
320 template<typename Dst>
321 static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
322 {
323 internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
324 }
325
326 template<typename Dst>
327 static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
328 {
329 internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
330 }
331
332 template<typename Dst>
333 static EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
334 {
335 internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
336 }
337
338};
339
340
341// This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo
342template<typename Lhs, typename Rhs, typename Derived>
343struct generic_product_impl_base
344{
345 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
346
347 template<typename Dst>
348 static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
349 { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }
350
351 template<typename Dst>
352 static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
353 { scaleAndAddTo(dst,lhs, rhs, Scalar(1)); }
354
355 template<typename Dst>
356 static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
357 { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }
358
359 template<typename Dst>
360 static EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
361 { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }
362
363};
364
365template<typename Lhs, typename Rhs>
366struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
367 : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct> >
368{
369 typedef typename nested_eval<Lhs,1>::type LhsNested;
370 typedef typename nested_eval<Rhs,1>::type RhsNested;
371 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
372 enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
373 typedef typename internal::remove_all<typename internal::conditional<int(Side)==OnTheRight,LhsNested,RhsNested>::type>::type MatrixType;
374
375 template<typename Dest>
376 static EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
377 {
378 LhsNested actual_lhs(lhs);
379 RhsNested actual_rhs(rhs);
380 internal::gemv_dense_selector<Side,
381 (int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
382 bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)
383 >::run(actual_lhs, actual_rhs, dst, alpha);
384 }
385};
386
387template<typename Lhs, typename Rhs>
388struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
389{
390 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
391
392 template<typename Dst>
393 static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
394 {
395 // Same as: dst.noalias() = lhs.lazyProduct(rhs);
396 // but easier on the compiler side
397 call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
398 }
399
400 template<typename Dst>
401 static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
402 {
403 // dst.noalias() += lhs.lazyProduct(rhs);
404 call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
405 }
406
407 template<typename Dst>
408 static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
409 {
410 // dst.noalias() -= lhs.lazyProduct(rhs);
411 call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
412 }
413
414// template<typename Dst>
415// static inline void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
416// { dst.noalias() += alpha * lhs.lazyProduct(rhs); }
417};
418
419// This specialization enforces the use of a coefficient-based evaluation strategy
420template<typename Lhs, typename Rhs>
421struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,LazyCoeffBasedProductMode>
422 : generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> {};
423
424// Case 2: Evaluate coeff by coeff
425//
426// This is mostly taken from CoeffBasedProduct.h
427// The main difference is that we add an extra argument to the etor_product_*_impl::run() function
428// for the inner dimension of the product, because evaluator object do not know their size.
429
430template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
431struct etor_product_coeff_impl;
432
433template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
434struct etor_product_packet_impl;
435
436template<typename Lhs, typename Rhs, int ProductTag>
437struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, DenseShape>
438 : evaluator_base<Product<Lhs, Rhs, LazyProduct> >
439{
440 typedef Product<Lhs, Rhs, LazyProduct> XprType;
441 typedef typename XprType::Scalar Scalar;
442 typedef typename XprType::CoeffReturnType CoeffReturnType;
443
444 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
445 explicit product_evaluator(const XprType& xpr)
446 : m_lhs(xpr.lhs()),
447 m_rhs(xpr.rhs()),
448 m_lhsImpl(m_lhs), // FIXME the creation of the evaluator objects should result in a no-op, but check that!
449 m_rhsImpl(m_rhs), // Moreover, they are only useful for the packet path, so we could completely disable them when not needed,
450 // or perhaps declare them on the fly on the packet method... We have experiment to check what's best.
451 m_innerDim(xpr.lhs().cols())
452 {
453 EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
454 EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
455 EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
456#if 0
457 std::cerr << "LhsOuterStrideBytes= " << LhsOuterStrideBytes << "\n";
458 std::cerr << "RhsOuterStrideBytes= " << RhsOuterStrideBytes << "\n";
459 std::cerr << "LhsAlignment= " << LhsAlignment << "\n";
460 std::cerr << "RhsAlignment= " << RhsAlignment << "\n";
461 std::cerr << "CanVectorizeLhs= " << CanVectorizeLhs << "\n";
462 std::cerr << "CanVectorizeRhs= " << CanVectorizeRhs << "\n";
463 std::cerr << "CanVectorizeInner= " << CanVectorizeInner << "\n";
464 std::cerr << "EvalToRowMajor= " << EvalToRowMajor << "\n";
465 std::cerr << "Alignment= " << Alignment << "\n";
466 std::cerr << "Flags= " << Flags << "\n";
467#endif
468 }
469
470 // Everything below here is taken from CoeffBasedProduct.h
471
472 typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
473 typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
474
475 typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
476 typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
477
478 typedef evaluator<LhsNestedCleaned> LhsEtorType;
479 typedef evaluator<RhsNestedCleaned> RhsEtorType;
480
481 enum {
482 RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
483 ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
484 InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
485 MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
486 MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
487 };
488
489 typedef typename find_best_packet<Scalar,RowsAtCompileTime>::type LhsVecPacketType;
490 typedef typename find_best_packet<Scalar,ColsAtCompileTime>::type RhsVecPacketType;
491
492 enum {
493
494 LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
495 RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
496 CoeffReadCost = InnerSize==0 ? NumTraits<Scalar>::ReadCost
497 : InnerSize == Dynamic ? HugeCost
498 : InnerSize * (NumTraits<Scalar>::MulCost + LhsCoeffReadCost + RhsCoeffReadCost)
499 + (InnerSize - 1) * NumTraits<Scalar>::AddCost,
500
501 Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
502
503 LhsFlags = LhsEtorType::Flags,
504 RhsFlags = RhsEtorType::Flags,
505
506 LhsRowMajor = LhsFlags & RowMajorBit,
507 RhsRowMajor = RhsFlags & RowMajorBit,
508
509 LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
510 RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
511
512 // Here, we don't care about alignment larger than the usable packet size.
513 LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
514 RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
515
516 SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
517
518 CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1),
519 CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1),
520
521 EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
522 : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
523 : (bool(RhsRowMajor) && !CanVectorizeLhs),
524
525 Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & ~RowMajorBit)
526 | (EvalToRowMajor ? RowMajorBit : 0)
527 // TODO enable vectorization for mixed types
528 | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0)
529 | (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0),
530
531 LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
532 RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
533
534 Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
535 : bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
536 : 0,
537
538 /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
539 * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
540 * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
541 * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
542 */
543 CanVectorizeInner = SameType
544 && LhsRowMajor
545 && (!RhsRowMajor)
546 && (LhsFlags & RhsFlags & ActualPacketAccessBit)
547 && (InnerSize % packet_traits<Scalar>::size == 0)
548 };
549
550 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
551 {
552 return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
553 }
554
555 /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
556 * which is why we don't set the LinearAccessBit.
557 * TODO: this seems possible when the result is a vector
558 */
559 EIGEN_DEVICE_FUNC const CoeffReturnType coeff(Index index) const
560 {
561 const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
562 const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
563 return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
564 }
565
566 template<int LoadMode, typename PacketType>
567 const PacketType packet(Index row, Index col) const
568 {
569 PacketType res;
570 typedef etor_product_packet_impl<bool(int(Flags)&RowMajorBit) ? RowMajor : ColMajor,
571 Unroll ? int(InnerSize) : Dynamic,
572 LhsEtorType, RhsEtorType, PacketType, LoadMode> PacketImpl;
573 PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res);
574 return res;
575 }
576
577 template<int LoadMode, typename PacketType>
578 const PacketType packet(Index index) const
579 {
580 const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
581 const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
582 return packet<LoadMode,PacketType>(row,col);
583 }
584
585protected:
586 typename internal::add_const_on_value_type<LhsNested>::type m_lhs;
587 typename internal::add_const_on_value_type<RhsNested>::type m_rhs;
588
589 LhsEtorType m_lhsImpl;
590 RhsEtorType m_rhsImpl;
591
592 // TODO: Get rid of m_innerDim if known at compile time
593 Index m_innerDim;
594};
595
596template<typename Lhs, typename Rhs>
597struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, LazyCoeffBasedProductMode, DenseShape, DenseShape>
598 : product_evaluator<Product<Lhs, Rhs, LazyProduct>, CoeffBasedProductMode, DenseShape, DenseShape>
599{
600 typedef Product<Lhs, Rhs, DefaultProduct> XprType;
601 typedef Product<Lhs, Rhs, LazyProduct> BaseProduct;
602 typedef product_evaluator<BaseProduct, CoeffBasedProductMode, DenseShape, DenseShape> Base;
603 enum {
604 Flags = Base::Flags | EvalBeforeNestingBit
605 };
606 EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
607 : Base(BaseProduct(xpr.lhs(),xpr.rhs()))
608 {}
609};
610
611/****************************************
612*** Coeff based product, Packet path ***
613****************************************/
614
615template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
616struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
617{
618 static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
619 {
620 etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
621 res = pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet<LoadMode,Packet>(Index(UnrollingIndex-1), col), res);
622 }
623};
624
625template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
626struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
627{
628 static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
629 {
630 etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
631 res = pmadd(lhs.template packet<LoadMode,Packet>(row, Index(UnrollingIndex-1)), pset1<Packet>(rhs.coeff(Index(UnrollingIndex-1), col)), res);
632 }
633};
634
635template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
636struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode>
637{
638 static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
639 {
640 res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),rhs.template packet<LoadMode,Packet>(Index(0), col));
641 }
642};
643
644template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
645struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode>
646{
647 static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
648 {
649 res = pmul(lhs.template packet<LoadMode,Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
650 }
651};
652
653template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
654struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
655{
656 static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
657 {
658 res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
659 }
660};
661
662template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
663struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
664{
665 static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
666 {
667 res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
668 }
669};
670
671template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
672struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
673{
674 static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
675 {
676 res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
677 for(Index i = 0; i < innerDim; ++i)
678 res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode,Packet>(i, col), res);
679 }
680};
681
682template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
683struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
684{
685 static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
686 {
687 res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
688 for(Index i = 0; i < innerDim; ++i)
689 res = pmadd(lhs.template packet<LoadMode,Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
690 }
691};
692
693
694/***************************************************************************
695* Triangular products
696***************************************************************************/
697template<int Mode, bool LhsIsTriangular,
698 typename Lhs, bool LhsIsVector,
699 typename Rhs, bool RhsIsVector>
700struct triangular_product_impl;
701
702template<typename Lhs, typename Rhs, int ProductTag>
703struct generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag>
704 : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag> >
705{
706 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
707
708 template<typename Dest>
709 static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
710 {
711 triangular_product_impl<Lhs::Mode,true,typename Lhs::MatrixType,false,Rhs, Rhs::ColsAtCompileTime==1>
712 ::run(dst, lhs.nestedExpression(), rhs, alpha);
713 }
714};
715
716template<typename Lhs, typename Rhs, int ProductTag>
717struct generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag>
718: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag> >
719{
720 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
721
722 template<typename Dest>
723 static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
724 {
725 triangular_product_impl<Rhs::Mode,false,Lhs,Lhs::RowsAtCompileTime==1, typename Rhs::MatrixType, false>::run(dst, lhs, rhs.nestedExpression(), alpha);
726 }
727};
728
729
730/***************************************************************************
731* SelfAdjoint products
732***************************************************************************/
733template <typename Lhs, int LhsMode, bool LhsIsVector,
734 typename Rhs, int RhsMode, bool RhsIsVector>
735struct selfadjoint_product_impl;
736
737template<typename Lhs, typename Rhs, int ProductTag>
738struct generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag>
739 : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag> >
740{
741 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
742
743 template<typename Dest>
744 static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
745 {
746 selfadjoint_product_impl<typename Lhs::MatrixType,Lhs::Mode,false,Rhs,0,Rhs::IsVectorAtCompileTime>::run(dst, lhs.nestedExpression(), rhs, alpha);
747 }
748};
749
750template<typename Lhs, typename Rhs, int ProductTag>
751struct generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag>
752: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag> >
753{
754 typedef typename Product<Lhs,Rhs>::Scalar Scalar;
755
756 template<typename Dest>
757 static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
758 {
759 selfadjoint_product_impl<Lhs,0,Lhs::IsVectorAtCompileTime,typename Rhs::MatrixType,Rhs::Mode,false>::run(dst, lhs, rhs.nestedExpression(), alpha);
760 }
761};
762
763
764/***************************************************************************
765* Diagonal products
766***************************************************************************/
767
768template<typename MatrixType, typename DiagonalType, typename Derived, int ProductOrder>
769struct diagonal_product_evaluator_base
770 : evaluator_base<Derived>
771{
772 typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
773public:
774 enum {
775 CoeffReadCost = NumTraits<Scalar>::MulCost + evaluator<MatrixType>::CoeffReadCost + evaluator<DiagonalType>::CoeffReadCost,
776
777 MatrixFlags = evaluator<MatrixType>::Flags,
778 DiagFlags = evaluator<DiagonalType>::Flags,
779 _StorageOrder = MatrixFlags & RowMajorBit ? RowMajor : ColMajor,
780 _ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
781 ||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
782 _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
783 // FIXME currently we need same types, but in the future the next rule should be the one
784 //_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagFlags)&PacketAccessBit))),
785 _Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))),
786 _LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
787 Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0),
788 Alignment = evaluator<MatrixType>::Alignment,
789
790 AsScalarProduct = (DiagonalType::SizeAtCompileTime==1)
791 || (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft)
792 || (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight)
793 };
794
795 diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag)
796 : m_diagImpl(diag), m_matImpl(mat)
797 {
798 EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
799 EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
800 }
801
802 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
803 {
804 if(AsScalarProduct)
805 return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
806 else
807 return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
808 }
809
810protected:
811 template<int LoadMode,typename PacketType>
812 EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const
813 {
814 return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
815 internal::pset1<PacketType>(m_diagImpl.coeff(id)));
816 }
817
818 template<int LoadMode,typename PacketType>
819 EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const
820 {
821 enum {
822 InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
823 DiagonalPacketLoadMode = EIGEN_PLAIN_ENUM_MIN(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
824 };
825 return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
826 m_diagImpl.template packet<DiagonalPacketLoadMode,PacketType>(id));
827 }
828
829 evaluator<DiagonalType> m_diagImpl;
830 evaluator<MatrixType> m_matImpl;
831};
832
833// diagonal * dense
834template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
835struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalShape, DenseShape>
836 : diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft>
837{
838 typedef diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft> Base;
839 using Base::m_diagImpl;
840 using Base::m_matImpl;
841 using Base::coeff;
842 typedef typename Base::Scalar Scalar;
843
844 typedef Product<Lhs, Rhs, ProductKind> XprType;
845 typedef typename XprType::PlainObject PlainObject;
846
847 enum {
848 StorageOrder = int(Rhs::Flags) & RowMajorBit ? RowMajor : ColMajor
849 };
850
851 EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
852 : Base(xpr.rhs(), xpr.lhs().diagonal())
853 {
854 }
855
856 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
857 {
858 return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col);
859 }
860
861#ifndef __CUDACC__
862 template<int LoadMode,typename PacketType>
863 EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
864 {
865 // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
866 // See also similar calls below.
867 return this->template packet_impl<LoadMode,PacketType>(row,col, row,
868 typename internal::conditional<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>::type());
869 }
870
871 template<int LoadMode,typename PacketType>
872 EIGEN_STRONG_INLINE PacketType packet(Index idx) const
873 {
874 return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
875 }
876#endif
877};
878
879// dense * diagonal
880template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
881struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape, DiagonalShape>
882 : diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight>
883{
884 typedef diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight> Base;
885 using Base::m_diagImpl;
886 using Base::m_matImpl;
887 using Base::coeff;
888 typedef typename Base::Scalar Scalar;
889
890 typedef Product<Lhs, Rhs, ProductKind> XprType;
891 typedef typename XprType::PlainObject PlainObject;
892
893 enum { StorageOrder = int(Lhs::Flags) & RowMajorBit ? RowMajor : ColMajor };
894
895 EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
896 : Base(xpr.lhs(), xpr.rhs().diagonal())
897 {
898 }
899
900 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
901 {
902 return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col);
903 }
904
905#ifndef __CUDACC__
906 template<int LoadMode,typename PacketType>
907 EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
908 {
909 return this->template packet_impl<LoadMode,PacketType>(row,col, col,
910 typename internal::conditional<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>::type());
911 }
912
913 template<int LoadMode,typename PacketType>
914 EIGEN_STRONG_INLINE PacketType packet(Index idx) const
915 {
916 return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
917 }
918#endif
919};
920
921/***************************************************************************
922* Products with permutation matrices
923***************************************************************************/
924
925/** \internal
926 * \class permutation_matrix_product
927 * Internal helper class implementing the product between a permutation matrix and a matrix.
928 * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h
929 */
930template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
931struct permutation_matrix_product;
932
933template<typename ExpressionType, int Side, bool Transposed>
934struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
935{
936 typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
937 typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
938
939 template<typename Dest, typename PermutationType>
940 static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
941 {
942 MatrixType mat(xpr);
943 const Index n = Side==OnTheLeft ? mat.rows() : mat.cols();
944 // FIXME we need an is_same for expression that is not sensitive to constness. For instance
945 // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
946 //if(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat))
947 if(is_same_dense(dst, mat))
948 {
949 // apply the permutation inplace
950 Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(perm.size());
951 mask.fill(false);
952 Index r = 0;
953 while(r < perm.size())
954 {
955 // search for the next seed
956 while(r<perm.size() && mask[r]) r++;
957 if(r>=perm.size())
958 break;
959 // we got one, let's follow it until we are back to the seed
960 Index k0 = r++;
961 Index kPrev = k0;
962 mask.coeffRef(k0) = true;
963 for(Index k=perm.indices().coeff(k0); k!=k0; k=perm.indices().coeff(k))
964 {
965 Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
966 .swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
967 (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
968
969 mask.coeffRef(k) = true;
970 kPrev = k;
971 }
972 }
973 }
974 else
975 {
976 for(Index i = 0; i < n; ++i)
977 {
978 Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
979 (dst, ((Side==OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i)
980
981 =
982
983 Block<const MatrixTypeCleaned,Side==OnTheLeft ? 1 : MatrixTypeCleaned::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixTypeCleaned::ColsAtCompileTime>
984 (mat, ((Side==OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i);
985 }
986 }
987 }
988};
989
990template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
991struct generic_product_impl<Lhs, Rhs, PermutationShape, MatrixShape, ProductTag>
992{
993 template<typename Dest>
994 static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
995 {
996 permutation_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
997 }
998};
999
1000template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1001struct generic_product_impl<Lhs, Rhs, MatrixShape, PermutationShape, ProductTag>
1002{
1003 template<typename Dest>
1004 static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
1005 {
1006 permutation_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
1007 }
1008};
1009
1010template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1011struct generic_product_impl<Inverse<Lhs>, Rhs, PermutationShape, MatrixShape, ProductTag>
1012{
1013 template<typename Dest>
1014 static void evalTo(Dest& dst, const Inverse<Lhs>& lhs, const Rhs& rhs)
1015 {
1016 permutation_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
1017 }
1018};
1019
1020template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1021struct generic_product_impl<Lhs, Inverse<Rhs>, MatrixShape, PermutationShape, ProductTag>
1022{
1023 template<typename Dest>
1024 static void evalTo(Dest& dst, const Lhs& lhs, const Inverse<Rhs>& rhs)
1025 {
1026 permutation_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
1027 }
1028};
1029
1030
1031/***************************************************************************
1032* Products with transpositions matrices
1033***************************************************************************/
1034
1035// FIXME could we unify Transpositions and Permutation into a single "shape"??
1036
1037/** \internal
1038 * \class transposition_matrix_product
1039 * Internal helper class implementing the product between a permutation matrix and a matrix.
1040 */
1041template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
1042struct transposition_matrix_product
1043{
1044 typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
1045 typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
1046
1047 template<typename Dest, typename TranspositionType>
1048 static inline void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr)
1049 {
1050 MatrixType mat(xpr);
1051 typedef typename TranspositionType::StorageIndex StorageIndex;
1052 const Index size = tr.size();
1053 StorageIndex j = 0;
1054
1055 if(!is_same_dense(dst,mat))
1056 dst = mat;
1057
1058 for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
1059 if(Index(j=tr.coeff(k))!=k)
1060 {
1061 if(Side==OnTheLeft) dst.row(k).swap(dst.row(j));
1062 else if(Side==OnTheRight) dst.col(k).swap(dst.col(j));
1063 }
1064 }
1065};
1066
1067template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1068struct generic_product_impl<Lhs, Rhs, TranspositionsShape, MatrixShape, ProductTag>
1069{
1070 template<typename Dest>
1071 static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
1072 {
1073 transposition_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
1074 }
1075};
1076
1077template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1078struct generic_product_impl<Lhs, Rhs, MatrixShape, TranspositionsShape, ProductTag>
1079{
1080 template<typename Dest>
1081 static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
1082 {
1083 transposition_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
1084 }
1085};
1086
1087
1088template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1089struct generic_product_impl<Transpose<Lhs>, Rhs, TranspositionsShape, MatrixShape, ProductTag>
1090{
1091 template<typename Dest>
1092 static void evalTo(Dest& dst, const Transpose<Lhs>& lhs, const Rhs& rhs)
1093 {
1094 transposition_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
1095 }
1096};
1097
1098template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
1099struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShape, ProductTag>
1100{
1101 template<typename Dest>
1102 static void evalTo(Dest& dst, const Lhs& lhs, const Transpose<Rhs>& rhs)
1103 {
1104 transposition_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
1105 }
1106};
1107
1108} // end namespace internal
1109
1110} // end namespace Eigen
1111
1112#endif // EIGEN_PRODUCT_EVALUATORS_H
1113