TensorStriding.h
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1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9 
10 #ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
12 
13 namespace Eigen {
14 
22 namespace internal {
23 template<typename Strides, typename XprType>
24 struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType>
25 {
26  typedef typename XprType::Scalar Scalar;
28  typedef typename XprTraits::StorageKind StorageKind;
29  typedef typename XprTraits::Index Index;
30  typedef typename XprType::Nested Nested;
32  static const int NumDimensions = XprTraits::NumDimensions;
33  static const int Layout = XprTraits::Layout;
34 };
35 
36 template<typename Strides, typename XprType>
37 struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense>
38 {
40 };
41 
42 template<typename Strides, typename XprType>
43 struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type>
44 {
46 };
47 
48 } // end namespace internal
49 
50 
51 
52 template<typename Strides, typename XprType>
53 class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> >
54 {
55  public:
58  typedef typename XprType::CoeffReturnType CoeffReturnType;
62 
63  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
64  : m_xpr(expr), m_dims(dims) {}
65 
66  EIGEN_DEVICE_FUNC
67  const Strides& strides() const { return m_dims; }
68 
69  EIGEN_DEVICE_FUNC
71  expression() const { return m_xpr; }
72 
73  EIGEN_DEVICE_FUNC
75  {
77  Assign assign(*this, other);
79  return *this;
80  }
81 
82  template<typename OtherDerived>
83  EIGEN_DEVICE_FUNC
84  EIGEN_STRONG_INLINE TensorStridingOp& operator = (const OtherDerived& other)
85  {
87  Assign assign(*this, other);
89  return *this;
90  }
91 
92  protected:
93  typename XprType::Nested m_xpr;
94  const Strides m_dims;
95 };
96 
97 
98 // Eval as rvalue
99 template<typename Strides, typename ArgType, typename Device>
100 struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
101 {
103  typedef typename XprType::Index Index;
106  typedef typename XprType::Scalar Scalar;
110 
111  enum {
112  IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
115  CoordAccess = false, // to be implemented
116  RawAccess = false
117  };
118 
119  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
120  : m_impl(op.expression(), device)
121  {
122  m_dimensions = m_impl.dimensions();
123  for (int i = 0; i < NumDims; ++i) {
124  m_dimensions[i] = ceilf(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
125  }
126 
127  const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
128  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
129  m_outputStrides[0] = 1;
130  m_inputStrides[0] = 1;
131  for (int i = 1; i < NumDims; ++i) {
132  m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
133  m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
134  m_inputStrides[i-1] *= op.strides()[i-1];
135  }
136  m_inputStrides[NumDims-1] *= op.strides()[NumDims-1];
137  } else { // RowMajor
138  m_outputStrides[NumDims-1] = 1;
139  m_inputStrides[NumDims-1] = 1;
140  for (int i = NumDims - 2; i >= 0; --i) {
141  m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
142  m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
143  m_inputStrides[i+1] *= op.strides()[i+1];
144  }
145  m_inputStrides[0] *= op.strides()[0];
146  }
147  }
148 
149  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
150 
151  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
152  m_impl.evalSubExprsIfNeeded(NULL);
153  return true;
154  }
155  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
156  m_impl.cleanup();
157  }
158 
159  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
160  {
161  return m_impl.coeff(srcCoeff(index));
162  }
163 
164  template<int LoadMode>
165  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
166  {
167  EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
168  eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
169 
170  Index inputIndices[] = {0, 0};
171  Index indices[] = {index, index + PacketSize - 1};
172  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
173  for (int i = NumDims - 1; i > 0; --i) {
174  const Index idx0 = indices[0] / m_outputStrides[i];
175  const Index idx1 = indices[1] / m_outputStrides[i];
176  inputIndices[0] += idx0 * m_inputStrides[i];
177  inputIndices[1] += idx1 * m_inputStrides[i];
178  indices[0] -= idx0 * m_outputStrides[i];
179  indices[1] -= idx1 * m_outputStrides[i];
180  }
181  inputIndices[0] += indices[0] * m_inputStrides[0];
182  inputIndices[1] += indices[1] * m_inputStrides[0];
183  } else { // RowMajor
184  for (int i = 0; i < NumDims - 1; ++i) {
185  const Index idx0 = indices[0] / m_outputStrides[i];
186  const Index idx1 = indices[1] / m_outputStrides[i];
187  inputIndices[0] += idx0 * m_inputStrides[i];
188  inputIndices[1] += idx1 * m_inputStrides[i];
189  indices[0] -= idx0 * m_outputStrides[i];
190  indices[1] -= idx1 * m_outputStrides[i];
191  }
192  inputIndices[0] += indices[0] * m_inputStrides[NumDims-1];
193  inputIndices[1] += indices[1] * m_inputStrides[NumDims-1];
194  }
195  if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
196  PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
197  return rslt;
198  }
199  else {
201  values[0] = m_impl.coeff(inputIndices[0]);
202  values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
203  for (int i = 1; i < PacketSize-1; ++i) {
204  values[i] = coeff(index+i);
205  }
206  PacketReturnType rslt = internal::pload<PacketReturnType>(values);
207  return rslt;
208  }
209  }
210 
211  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
212  double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() +
213  TensorOpCost::MulCost<Index>() +
214  TensorOpCost::DivCost<Index>()) +
215  TensorOpCost::MulCost<Index>();
216  if (vectorized) {
217  compute_cost *= 2; // packet() computes two indices
218  }
219  const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1);
220  return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) +
221  // Computation is not vectorized per se, but it is done once per packet.
222  TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
223  }
224 
225  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
226 
227  protected:
228  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
229  {
230  Index inputIndex = 0;
231  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
232  for (int i = NumDims - 1; i > 0; --i) {
233  const Index idx = index / m_outputStrides[i];
234  inputIndex += idx * m_inputStrides[i];
235  index -= idx * m_outputStrides[i];
236  }
237  inputIndex += index * m_inputStrides[0];
238  } else { // RowMajor
239  for (int i = 0; i < NumDims - 1; ++i) {
240  const Index idx = index / m_outputStrides[i];
241  inputIndex += idx * m_inputStrides[i];
242  index -= idx * m_outputStrides[i];
243  }
244  inputIndex += index * m_inputStrides[NumDims-1];
245  }
246  return inputIndex;
247  }
248 
249  Dimensions m_dimensions;
253 };
254 
255 
256 // Eval as lvalue
257 template<typename Strides, typename ArgType, typename Device>
258 struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
259  : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
260 {
263  // typedef typename XprType::Index Index;
265  // typedef DSizes<Index, NumDims> Dimensions;
266 
267  enum {
268  IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
271  CoordAccess = false, // to be implemented
272  RawAccess = false
273  };
274 
275  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
276  : Base(op, device) { }
277 
278  typedef typename XprType::Index Index;
279  typedef typename XprType::Scalar Scalar;
283 
284  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
285  {
286  return this->m_impl.coeffRef(this->srcCoeff(index));
287  }
288 
289  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
290  void writePacket(Index index, const PacketReturnType& x)
291  {
292  EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
293  eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize());
294 
295  Index inputIndices[] = {0, 0};
296  Index indices[] = {index, index + PacketSize - 1};
297  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
298  for (int i = NumDims - 1; i > 0; --i) {
299  const Index idx0 = indices[0] / this->m_outputStrides[i];
300  const Index idx1 = indices[1] / this->m_outputStrides[i];
301  inputIndices[0] += idx0 * this->m_inputStrides[i];
302  inputIndices[1] += idx1 * this->m_inputStrides[i];
303  indices[0] -= idx0 * this->m_outputStrides[i];
304  indices[1] -= idx1 * this->m_outputStrides[i];
305  }
306  inputIndices[0] += indices[0] * this->m_inputStrides[0];
307  inputIndices[1] += indices[1] * this->m_inputStrides[0];
308  } else { // RowMajor
309  for (int i = 0; i < NumDims - 1; ++i) {
310  const Index idx0 = indices[0] / this->m_outputStrides[i];
311  const Index idx1 = indices[1] / this->m_outputStrides[i];
312  inputIndices[0] += idx0 * this->m_inputStrides[i];
313  inputIndices[1] += idx1 * this->m_inputStrides[i];
314  indices[0] -= idx0 * this->m_outputStrides[i];
315  indices[1] -= idx1 * this->m_outputStrides[i];
316  }
317  inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1];
318  inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1];
319  }
320  if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
321  this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
322  }
323  else {
324  EIGEN_ALIGN_MAX Scalar values[PacketSize];
325  internal::pstore<Scalar, PacketReturnType>(values, x);
326  this->m_impl.coeffRef(inputIndices[0]) = values[0];
327  this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1];
328  for (int i = 1; i < PacketSize-1; ++i) {
329  this->coeffRef(index+i) = values[i];
330  }
331  }
332  }
333 };
334 
335 
336 } // end namespace Eigen
337 
338 #endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType &expr, const Strides &dims)
XprType::Nested m_xpr
SCALAR Scalar
Definition: bench_gemm.cpp:33
#define EIGEN_STRONG_INLINE
Definition: Macros.h:494
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions & dimensions() const
XprType::CoeffReturnType CoeffReturnType
Eigen::internal::traits< TensorStridingOp >::Index Index
leaf::MyValues values
Namespace containing all symbols from the Eigen library.
Definition: jet.h:637
A cost model used to limit the number of threads used for evaluating tensor expression.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar *)
#define EIGEN_STATIC_ASSERT(CONDITION, MSG)
Definition: StaticAssert.h:124
vector< size_t > dimensions(L.begin(), L.end())
EIGEN_DEVICE_FUNC const Strides & strides() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
Definition: Meta.h:33
#define eigen_assert(x)
Definition: Macros.h:579
Eigen::internal::nested< TensorStridingOp >::type Nested
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType &x)
static EIGEN_DEVICE_FUNC void run(const Expression &expr, const Device &device=Device())
#define NULL
Definition: ccolamd.c:609
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
Eigen::internal::traits< TensorStridingOp >::Scalar Scalar
EIGEN_DEVICE_FUNC const internal::remove_all< typename XprType::Nested >::type & expression() const
The tensor base class.
Definition: TensorBase.h:829
Eigen::internal::traits< TensorStridingOp >::StorageKind StorageKind
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
#define EIGEN_ALIGN_MAX
Definition: Macros.h:757
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions & dimensions() const
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar & coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
Eigen::NumTraits< Scalar >::Real RealScalar


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