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This commit is contained in:
Rasmus Munk Larsen 2018-08-23 11:36:49 -07:00
commit d35880ed91
19 changed files with 236 additions and 228 deletions
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6
Eigen/src/Core/MapBase.h
View File

@ -43,6 +43,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
SizeAtCompileTime = Base::SizeAtCompileTime
};
@ -187,8 +188,11 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
{
#if EIGEN_MAX_ALIGN_BYTES>0
// innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
eigen_assert(( ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
|| (cols() * rows() * innerStride() * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
|| (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
#endif
}

4
Eigen/src/SuperLUSupport/SuperLUSupport.h
View File

@ -297,8 +297,8 @@ SluMatrix asSluMatrix(MatrixType& mat)
template<typename Scalar, int Flags, typename Index>
MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat)
{
eigen_assert((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR
|| (Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC);
eigen_assert(((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR)
|| ((Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC));
Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;

76
unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
View File

@ -91,7 +91,7 @@ EIGEN_STRONG_INLINE void MergeResourceRequirements(
*block_total_size = resources[0].block_total_size;
for (std::vector<TensorOpResourceRequirements>::size_type i = 1; i < resources.size(); ++i) {
if (resources[i].block_shape == kSkewedInnerDims &&
*block_shape ! kSkewedInnerDims) {
*block_shape != kSkewedInnerDims) {
*block_shape = kSkewedInnerDims;
}
*block_total_size =
@ -152,11 +152,11 @@ struct TensorBlockCopyOp {
const Scalar* src_base = &src_data[src_index];
Scalar* dst_base = &dst_data[dst_index];
typedef const Eigen::Array<Scalar, Dynamic, 1> Src;
typedef Eigen::Array<Scalar, Dynamic, 1> Dst;
typedef const Array<Scalar, Dynamic, 1> Src;
typedef Array<Scalar, Dynamic, 1> Dst;
typedef Eigen::Map<Src, 0, InnerStride<> > SrcMap;
typedef Eigen::Map<Dst, 0, InnerStride<> > DstMap;
typedef Map<Src, 0, InnerStride<> > SrcMap;
typedef Map<Dst, 0, InnerStride<> > DstMap;
const SrcMap src(src_base, num_coeff_to_copy, InnerStride<>(src_stride));
DstMap dst(dst_base, num_coeff_to_copy, InnerStride<>(dst_stride));
@ -178,10 +178,8 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout,
bool BlockRead>
class TensorBlockIO {
public:
typedef typename TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef typename TensorBlockCopyOp<Scalar, StorageIndex>
TensorBlockCopyOp;
typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
typedef TensorBlockCopyOp<Scalar, StorageIndex> BlockCopyOp;
protected:
struct BlockIteratorState {
@ -194,7 +192,7 @@ class TensorBlockIO {
};
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Copy(
const TensorBlock& block, StorageIndex first_coeff_index,
const Block& block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data,
Scalar* dst_data) {
@ -290,8 +288,8 @@ class TensorBlockIO {
const StorageIndex block_total_size =
NumDims == 0 ? 1 : block.block_sizes().TotalSize();
for (StorageIndex i = 0; i < block_total_size; i += block_inner_dim_size) {
TensorBlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
dst_data, inputIndex, input_stride, src_data);
BlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
dst_data, inputIndex, input_stride, src_data);
// Update index.
for (int j = 0; j < num_squeezed_dims; ++j) {
if (++block_iter_state[j].count < block_iter_state[j].size) {
@ -320,13 +318,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
Layout, /*BlockRead=*/true> {
public:
typedef typename TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true>
Base;
typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true> Base;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
TensorBlock* block, const Scalar* src_data) {
Block* block, const Scalar* src_data) {
array<StorageIndex, NumDims> tensor_to_block_dim_map;
for (int i = 0; i < NumDims; ++i) {
tensor_to_block_dim_map[i] = i;
@ -336,7 +332,7 @@ class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
TensorBlock* block, StorageIndex first_coeff_index,
Block* block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data) {
Base::Copy(*block, first_coeff_index, tensor_to_block_dim_map,
@ -357,13 +353,11 @@ template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
Layout, /*BlockRead=*/false> {
public:
typedef typename TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false>
Base;
typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false> Base;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const TensorBlock& block, Scalar* dst_data) {
const Block& block, Scalar* dst_data) {
array<StorageIndex, NumDims> tensor_to_block_dim_map;
for (int i = 0; i < NumDims; ++i) {
tensor_to_block_dim_map[i] = i;
@ -373,7 +367,7 @@ class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const TensorBlock& block, StorageIndex first_coeff_index,
const Block& block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, Scalar* dst_data) {
Base::Copy(block, first_coeff_index, tensor_to_block_dim_map,
@ -401,13 +395,13 @@ struct TensorBlockCwiseBinaryOp {
const StorageIndex left_stride, const LeftScalar* left_data,
const StorageIndex right_index, const StorageIndex right_stride,
const RightScalar* right_data) {
typedef const Eigen::Array<LeftScalar, Dynamic, 1> Lhs;
typedef const Eigen::Array<RightScalar, Dynamic, 1> Rhs;
typedef Eigen::Array<OutputScalar, Dynamic, 1> Out;
typedef const Array<LeftScalar, Dynamic, 1> Lhs;
typedef const Array<RightScalar, Dynamic, 1> Rhs;
typedef Array<OutputScalar, Dynamic, 1> Out;
typedef Eigen::Map<Lhs, 0, InnerStride<> > LhsMap;
typedef Eigen::Map<Rhs, 0, InnerStride<> > RhsMap;
typedef Eigen::Map<Out, 0, InnerStride<> > OutMap;
typedef Map<Lhs, 0, InnerStride<> > LhsMap;
typedef Map<Rhs, 0, InnerStride<> > RhsMap;
typedef Map<Out, 0, InnerStride<> > OutMap;
const LeftScalar* lhs_base = &left_data[left_index];
const RightScalar* rhs_base = &right_data[right_index];
@ -417,8 +411,7 @@ struct TensorBlockCwiseBinaryOp {
const RhsMap rhs(rhs_base, num_coeff, InnerStride<>(right_stride));
OutMap out(out_base, num_coeff, InnerStride<>(output_stride));
out =
Eigen::CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
out = CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
}
};
@ -434,8 +427,7 @@ struct TensorBlockCwiseBinaryOp {
template <typename BinaryFunctor, typename StorageIndex, typename OutputScalar,
int NumDims, int Layout>
struct TensorBlockCwiseBinaryIO {
typedef typename TensorBlock<OutputScalar, StorageIndex, NumDims,
Layout>::Dimensions Dimensions;
typedef typename TensorBlock<OutputScalar, StorageIndex, NumDims, Layout>::Dimensions Dimensions;
struct BlockIteratorState {
StorageIndex output_stride, output_span;
@ -627,8 +619,7 @@ struct TensorBlockView {
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlockMapper {
public:
typedef typename TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
typedef DSizes<StorageIndex, NumDims> Dimensions;
TensorBlockMapper(const Dimensions& dims,
@ -663,7 +654,7 @@ class TensorBlockMapper {
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block
GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
StorageIndex first_coeff_index = 0;
DSizes<StorageIndex, NumDims> coords;
@ -711,8 +702,7 @@ class TensorBlockMapper {
}
}
return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
data);
return Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
@ -818,8 +808,7 @@ class TensorBlockMapper {
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorSliceBlockMapper {
public:
typedef typename TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
typedef DSizes<StorageIndex, NumDims> Dimensions;
TensorSliceBlockMapper(const Dimensions& tensor_dims,
@ -860,7 +849,7 @@ class TensorSliceBlockMapper {
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block
GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
StorageIndex first_coeff_index = 0;
DSizes<StorageIndex, NumDims> coords;
@ -917,8 +906,7 @@ class TensorSliceBlockMapper {
}
}
return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
data);
return Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {

12
unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
View File

@ -152,13 +152,7 @@ struct TensorContractionParams {
// 1. Elementwise Relu transformation following Conv2D.
// 2. AddBias to the Conv2D output channels dimension.
//
// See expected implementation in NoOpOutputKernel.
struct OutputKernel {
template <typename Index, typename Scalar>
typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
};
// Output kernel that does absolutely nothing.
// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
struct NoOpOutputKernel {
/**
* Tensor contraction evaluator calls this kernel after finishing each block
@ -177,7 +171,7 @@ struct NoOpOutputKernel {
*/
template <typename Index, typename Scalar>
EIGEN_ALWAYS_INLINE void operator()(
const OutputKernel::OutputMapper<Index, Scalar>& /*output_mapper*/,
const internal::blas_data_mapper<Scalar, Index, ColMajor>& /*output_mapper*/,
const TensorContractionParams& /*params*/, Index /*i*/,
Index /*j*/, Index /*num_rows*/, Index /*num_cols*/) const {}
};
@ -666,7 +660,7 @@ struct TensorContractionEvaluatorBase
// call gebp (matrix kernel)
// The parameters here are copied from Eigen's GEMM implementation
const auto output_mapper = output.getSubMapper(i2, j2);
const OutputMapper output_mapper = output.getSubMapper(i2, j2);
gebp(output_mapper, blockA, blockB, actual_mc, actual_kc, actual_nc,
Scalar(1), -1, -1, 0, 0);

27
unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
View File

@ -88,6 +88,7 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Devi
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename PointerType<CoeffReturnType, Device>::Type PointerT;
enum {
IsAligned = false,
@ -106,12 +107,12 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Devi
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) {
if (data) {
evalTo(data);
return false;
} else {
m_result = static_cast<CoeffReturnType*>(
m_result = static_cast<PointerT>(
m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
evalTo(m_result);
return true;
@ -139,23 +140,22 @@ struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Devi
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC typename Eigen::internal::traits<XprType>::PointerType data() const { return m_result; }
EIGEN_DEVICE_FUNC PointerT data() const { return m_result; }
#ifdef EIGEN_USE_SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; }
#endif
protected:
EIGEN_DEVICE_FUNC void evalTo(Scalar* data) {
TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(
data, m_dimensions);
EIGEN_DEVICE_FUNC void evalTo(PointerT data) {
TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(data, m_dimensions);
m_op.func().eval(m_op.expression(), result, m_device);
}
Dimensions m_dimensions;
const ArgType m_op;
const Device& m_device;
CoeffReturnType* m_result;
PointerT m_result;
};
@ -250,6 +250,7 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename PointerType<CoeffReturnType, Device>::Type PointerT;
enum {
IsAligned = false,
@ -268,12 +269,12 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(PointerT data) {
if (data) {
evalTo(data);
return false;
} else {
m_result = static_cast<Scalar *>(m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
m_result = static_cast<PointerT>(m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType)));
evalTo(m_result);
return true;
}
@ -300,22 +301,22 @@ struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType,
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC typename internal::traits<XprType>::PointerType data() const { return m_result; }
EIGEN_DEVICE_FUNC PointerT data() const { return m_result; }
#ifdef EIGEN_USE_SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; }
#endif
protected:
EIGEN_DEVICE_FUNC void evalTo(Scalar* data) {
TensorMap<Tensor<Scalar, NumDims, Layout> > result(data, m_dimensions);
EIGEN_DEVICE_FUNC void evalTo(PointerT data) {
TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(data, m_dimensions);
m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
}
Dimensions m_dimensions;
const XprType m_op;
const Device& m_device;
CoeffReturnType* m_result;
PointerT m_result;
};

8
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
View File

@ -132,7 +132,7 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable,
if (needs_assign) {
// Size tensor blocks to fit in cache (or requested target block size).
Index block_total_size = numext::mini(cache_size, total_size);
TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
TensorBlockShapeType block_shape = kSkewedInnerDims;
// Query expression tree for desired block size/shape.
std::vector<TensorOpResourceRequirements> resources;
evaluator.getResourceRequirements(&resources);
@ -229,10 +229,6 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
Evaluator evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
const StorageIndex PacketSize =
Vectorizable
? unpacket_traits<typename Evaluator::PacketReturnType>::size
: 1;
const StorageIndex size = array_prod(evaluator.dimensions());
device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
EvalRange::alignBlockSize,
@ -272,7 +268,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
TensorBlockShapeType block_shape = kSkewedInnerDims;
Index block_total_size = 0;
// Query expression tree for desired block size/shape.
std::vector<internal::TensorOpResourceRequirements> resources;

8
unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
View File

@ -24,6 +24,14 @@ template<typename T> struct MakePointer {
typedef T ScalarType;
};
// The PointerType class is a container of the device specefic pointer
// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression
// is a device-agnostic type and need MakePointer class for type conversion,
// the TensorEvaluator calss can be specialized for a device, hence it is possible
// to construct different types of temproray storage memory in TensorEvaluator
// for different devices by specializing the following PointerType class.
template<typename T, typename Device> struct PointerType : MakePointer<T>{};
namespace internal{
template<typename A, typename B> struct Pointer_type_promotion {
static const bool val=false;

36
unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h
View File

@ -57,6 +57,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
coprimes_.push_back(i);
}
}
queues_.resize(num_threads_);
for (int i = 0; i < num_threads_; i++) {
queues_.push_back(new Queue());
}
@ -64,7 +65,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
init_barrier_.reset(new Barrier(num_threads_));
#endif
for (int i = 0; i < num_threads_; i++) {
threads_.push_back(env_.CreateThread([this, i]() { WorkerLoop(i); }));
threads_.emplace_back(env_.CreateThread([this, i]() { WorkerLoop(i); }));
}
#ifndef EIGEN_THREAD_LOCAL
// Wait for workers to initialize per_thread_map_. Otherwise we might race
@ -85,13 +86,13 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
// Since we were cancelled, there might be entries in the queues.
// Empty them to prevent their destructor from asserting.
for (size_t i = 0; i < queues_.size(); i++) {
queues_[i]->Flush();
queues_[i].Flush();
}
}
// Join threads explicitly to avoid destruction order issues.
for (int i = 0; i < num_threads_; i++) delete threads_[i];
for (int i = 0; i < num_threads_; i++) delete queues_[i];
threads_.resize(0);
queues_.resize(0);
#ifndef EIGEN_THREAD_LOCAL
for (auto it : per_thread_map_) delete it.second;
#endif
@ -102,13 +103,13 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
PerThread* pt = GetPerThread();
if (pt->pool == this) {
// Worker thread of this pool, push onto the thread's queue.
Queue* q = queues_[pt->thread_id];
t = q->PushFront(std::move(t));
Queue& q = queues_[pt->thread_id];
t = q.PushFront(std::move(t));
} else {
// A free-standing thread (or worker of another pool), push onto a random
// queue.
Queue* q = queues_[Rand(&pt->rand) % queues_.size()];
t = q->PushBack(std::move(t));
Queue& q = queues_[Rand(&pt->rand) % queues_.size()];
t = q.PushBack(std::move(t));
}
// Note: below we touch this after making w available to worker threads.
// Strictly speaking, this can lead to a racy-use-after-free. Consider that
@ -163,8 +164,8 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
Environment env_;
const int num_threads_;
const bool allow_spinning_;
MaxSizeVector<Thread*> threads_;
MaxSizeVector<Queue*> queues_;
MaxSizeVector<std::unique_ptr<Thread> > threads_;
MaxSizeVector<Queue> queues_;
MaxSizeVector<unsigned> coprimes_;
MaxSizeVector<EventCount::Waiter> waiters_;
std::atomic<unsigned> blocked_;
@ -193,7 +194,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
pt->pool = this;
pt->rand = GlobalThreadIdHash();
pt->thread_id = thread_id;
Queue* q = queues_[thread_id];
Queue& q = queues_[thread_id];
EventCount::Waiter* waiter = &waiters_[thread_id];
// TODO(dvyukov,rmlarsen): The time spent in Steal() is proportional
// to num_threads_ and we assume that new work is scheduled at a
@ -209,10 +210,10 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
// counter-productive for the types of I/O workloads the single thread
// pools tend to be used for.
while (!cancelled_) {
Task t = q->PopFront();
Task t = q.PopFront();
for (int i = 0; i < spin_count && !t.f; i++) {
if (!cancelled_.load(std::memory_order_relaxed)) {
t = q->PopFront();
t = q.PopFront();
}
}
if (!t.f) {
@ -226,7 +227,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
}
} else {
while (!cancelled_) {
Task t = q->PopFront();
Task t = q.PopFront();
if (!t.f) {
t = Steal();
if (!t.f) {
@ -263,7 +264,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
unsigned inc = coprimes_[r % coprimes_.size()];
unsigned victim = r % size;
for (unsigned i = 0; i < size; i++) {
Task t = queues_[victim]->PopBack();
Task t = queues_[victim].PopBack();
if (t.f) {
return t;
}
@ -290,7 +291,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
if (cancelled_) {
return false;
} else {
*t = queues_[victim]->PopBack();
*t = queues_[victim].PopBack();
return true;
}
}
@ -298,6 +299,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
// If we are shutting down and all worker threads blocked without work,
// that's we are done.
blocked_++;
// TODO is blocked_ required to be unsigned?
if (done_ && blocked_ == static_cast<unsigned>(num_threads_)) {
ec_.CancelWait(waiter);
// Almost done, but need to re-check queues.
@ -331,7 +333,7 @@ class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
unsigned inc = coprimes_[r % coprimes_.size()];
unsigned victim = r % size;
for (unsigned i = 0; i < size; i++) {
if (!queues_[victim]->Empty()) {
if (!queues_[victim].Empty()) {
return victim;
}
victim += inc;

5
unsupported/Eigen/CXX11/src/util/EmulateArray.h
View File

@ -25,6 +25,11 @@ template <typename T, size_t n> class array {
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { return values[index]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& at(size_t index) { eigen_assert(index < size()); return values[index]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& at(size_t index) const { eigen_assert(index < size()); return values[index]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& front() { return values[0]; }
EIGEN_DEVICE_FUNC

44
unsupported/Eigen/CXX11/src/util/MaxSizeVector.h
View File

@ -35,7 +35,6 @@ class MaxSizeVector {
explicit MaxSizeVector(size_t n)
: reserve_(n), size_(0),
data_(static_cast<T*>(internal::aligned_malloc(n * sizeof(T)))) {
for (size_t i = 0; i < n; ++i) { new (&data_[i]) T; }
}
// Construct a new MaxSizeVector, reserve and resize to n.
@ -44,35 +43,55 @@ class MaxSizeVector {
MaxSizeVector(size_t n, const T& init)
: reserve_(n), size_(n),
data_(static_cast<T*>(internal::aligned_malloc(n * sizeof(T)))) {
for (size_t i = 0; i < n; ++i) { new (&data_[i]) T(init); }
size_t i = 0;
EIGEN_TRY
{
for(; i < size_; ++i) { new (&data_[i]) T(init); }
}
EIGEN_CATCH(...)
{
// Construction failed, destruct in reverse order:
for(; (i+1) > 0; --i) { data_[i-1].~T(); }
internal::aligned_free(data_);
EIGEN_THROW;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
~MaxSizeVector() {
for (size_t i = 0; i < size_; ++i) {
data_[i].~T();
for (size_t i = size_; i > 0; --i) {
data_[i-1].~T();
}
internal::aligned_free(data_);
}
void resize(size_t n) {
eigen_assert(n <= reserve_);
for (size_t i = size_; i < n; ++i) {
new (&data_[i]) T;
for (; size_ < n; ++size_) {
new (&data_[size_]) T;
}
for (size_t i = n; i < size_; ++i) {
data_[i].~T();
for (; size_ > n; --size_) {
data_[size_-1].~T();
}
size_ = n;
eigen_assert(size_ == n);
}
// Append new elements (up to reserved size).
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void push_back(const T& t) {
eigen_assert(size_ < reserve_);
data_[size_++] = t;
new (&data_[size_++]) T(t);
}
// For C++03 compatibility this only takes one argument
template<class X>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void emplace_back(const X& x) {
eigen_assert(size_ < reserve_);
new (&data_[size_++]) T(x);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const T& operator[] (size_t i) const {
eigen_assert(i < size_);
@ -99,11 +118,8 @@ class MaxSizeVector {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void pop_back() {
// NOTE: This does not destroy the value at the end the way
// std::vector's version of pop_back() does. That happens when
// the Vector is destroyed.
eigen_assert(size_ > 0);
size_--;
data_[--size_].~T();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE

5
unsupported/Eigen/FFT
View File

@ -289,6 +289,7 @@ class FFT
void inv( MatrixBase<OutputDerived> & dst, const MatrixBase<ComplexDerived> & src, Index nfft=-1)
{
typedef typename ComplexDerived::Scalar src_type;
typedef typename ComplexDerived::RealScalar real_type;
typedef typename OutputDerived::Scalar dst_type;
const bool realfft= (NumTraits<dst_type>::IsComplex == 0);
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OutputDerived)
@ -329,9 +330,9 @@ class FFT
tmp.head(nhead) = src.head(nhead);
tmp.tail(ntail) = src.tail(ntail);
if (resize_input<0) { //shrinking -- create the Nyquist bin as the average of the two bins that fold into it
tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*src_type(.5);
tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*real_type(.5);
}else{ // expanding -- split the old Nyquist bin into two halves
tmp(nhead) = src(nhead) * src_type(.5);
tmp(nhead) = src(nhead) * real_type(.5);
tmp(tmp.size()-nhead) = tmp(nhead);
}
}

2
unsupported/Eigen/OpenGLSupport
View File

@ -184,7 +184,7 @@ inline void glRotate(const Rotation2D<float>& rot)
}
inline void glRotate(const Rotation2D<double>& rot)
{
glRotated(rot.angle()*180.0/EIGEN_PI, 0.0, 0.0, 1.0);
glRotated(rot.angle()*180.0/double(EIGEN_PI), 0.0, 0.0, 1.0);
}
template<typename Derived> void glRotate(const RotationBase<Derived,3>& rot)

1
unsupported/Eigen/src/BVH/KdBVH.h
View File

@ -35,6 +35,7 @@ struct get_boxes_helper {
{
outBoxes.insert(outBoxes.end(), boxBegin, boxEnd);
eigen_assert(outBoxes.size() == objects.size());
EIGEN_ONLY_USED_FOR_DEBUG(objects);
}
};

195
unsupported/test/cxx11_tensor_block_access.cpp
View File

@ -10,6 +10,7 @@
#include "main.h"
#include <algorithm>
#include <set>
#include <Eigen/CXX11/Tensor>
@ -19,17 +20,16 @@ using Eigen::Index;
using Eigen::RowMajor;
using Eigen::ColMajor;
using internal::TensorBlockShapeType;
template<typename T>
static const T& choose(int layout, const T& col, const T& row) {
return layout == ColMajor ? col : row;
}
static const TensorBlockShapeType RandomShape() {
static internal::TensorBlockShapeType RandomShape() {
return internal::random<bool>()
? internal::TensorBlockShapeType::kUniformAllDims
: internal::TensorBlockShapeType::kSkewedInnerDims;
? internal::kUniformAllDims
: internal::kSkewedInnerDims;
}
template <int NumDims>
@ -44,12 +44,12 @@ static DSizes<Index, NumDims> RandomDims() {
dims[i] = internal::random<int>(1, 20);
}
return DSizes<Index, NumDims>(dims);
};
}
/** Dummy data type to test TensorBlock copy ops. */
struct Data {
Data() : Data(0) {}
explicit Data(int v) { value = v; }
Data() : value(0) {}
explicit Data(int v) : value(v) { }
int value;
};
@ -91,21 +91,19 @@ static void Debug(DSizes<Index, NumDims> dims) {
template <int Layout>
static void test_block_mapper_sanity()
{
using T = int;
using TensorBlock = internal::TensorBlock<T, Index, 2, Layout>;
using TensorBlockMapper = internal::TensorBlockMapper<T, Index, 2, Layout>;
typedef internal::TensorBlockMapper<int, Index, 2, Layout> TensorBlockMapper;
DSizes<Index, 2> tensor_dims(100, 100);
// Test uniform blocks.
TensorBlockMapper uniform_block_mapper(
tensor_dims, internal::TensorBlockShapeType::kUniformAllDims, 100);
tensor_dims, internal::kUniformAllDims, 100);
VERIFY_IS_EQUAL(uniform_block_mapper.total_block_count(), 100);
VERIFY_IS_EQUAL(uniform_block_mapper.block_dims_total_size(), 100);
// 10x10 blocks
auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, nullptr);
typename TensorBlockMapper::Block uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(0), 10);
VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(1), 10);
// Depending on a layout we stride by cols rows.
@ -117,13 +115,13 @@ static void test_block_mapper_sanity()
// Test skewed to inner dims blocks.
TensorBlockMapper skewed_block_mapper(
tensor_dims, internal::TensorBlockShapeType::kSkewedInnerDims, 100);
tensor_dims, internal::kSkewedInnerDims, 100);
VERIFY_IS_EQUAL(skewed_block_mapper.total_block_count(), 100);
VERIFY_IS_EQUAL(skewed_block_mapper.block_dims_total_size(), 100);
// 1x100 (100x1) rows/cols depending on a tensor layout.
auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, nullptr);
typename TensorBlockMapper::Block skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(0), choose(Layout, 100, 1));
VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(1), choose(Layout, 1, 100));
// Depending on a layout we stride by cols rows.
@ -145,7 +143,8 @@ static void UpdateCoeffSet(
for (int i = 0; i < block_sizes[dim_index]; ++i) {
if (tensor_strides[dim_index] == 1) {
auto inserted = visited_coeffs->insert(first_coeff_index + i);
typedef std::pair<std::set<Index>::iterator, bool> ReturnType;
ReturnType inserted = visited_coeffs->insert(first_coeff_index + i);
VERIFY_IS_EQUAL(inserted.second, true);
} else {
int next_dim_index = dim_index + choose(Layout, -1, 1);
@ -158,9 +157,8 @@ static void UpdateCoeffSet(
template <typename T, int NumDims, int Layout>
static void test_block_mapper_maps_every_element() {
using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>;
using TensorBlockMapper =
internal::TensorBlockMapper<T, Index, NumDims, Layout>;
typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, NumDims, Layout> TensorBlockMapper;
DSizes<Index, NumDims> dims = RandomDims<NumDims>();
@ -171,7 +169,7 @@ static void test_block_mapper_maps_every_element() {
TensorBlockMapper block_mapper(dims, RandomShape(), RandomTargetSize(dims));
for (int i = 0; i < block_mapper.total_block_count(); ++i) {
TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(i, NULL);
UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
choose(Layout, NumDims - 1, 0),
&coeff_set);
@ -187,9 +185,8 @@ static void test_block_mapper_maps_every_element() {
template <typename T, int NumDims, int Layout>
static void test_slice_block_mapper_maps_every_element() {
using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>;
using TensorSliceBlockMapper =
internal::TensorSliceBlockMapper<T, Index, NumDims, Layout>;
typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
typedef internal::TensorSliceBlockMapper<T, Index, NumDims, Layout> TensorSliceBlockMapper;
DSizes<Index, NumDims> tensor_dims = RandomDims<NumDims>();
DSizes<Index, NumDims> tensor_slice_offsets = RandomDims<NumDims>();
@ -206,7 +203,7 @@ static void test_slice_block_mapper_maps_every_element() {
// Keep track of elements indices available via block access.
std::set<Index> coeff_set;
auto total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
int total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
// Pick a random dimension sizes for the tensor blocks.
DSizes<Index, NumDims> block_sizes;
@ -219,7 +216,7 @@ static void test_slice_block_mapper_maps_every_element() {
DimensionList<Index, NumDims>());
for (int i = 0; i < block_mapper.total_block_count(); ++i) {
TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(i, NULL);
UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
choose(Layout, NumDims - 1, 0),
&coeff_set);
@ -240,7 +237,7 @@ static void test_block_io_copy_data_from_source_to_target() {
TensorBlockWriter;
DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
const Index input_tensor_size = input_tensor_dims.TotalSize();
T* input_data = GenerateRandomData<T>(input_tensor_size);
T* output_data = new T[input_tensor_size];
@ -319,7 +316,7 @@ static void test_block_io_copy_using_reordered_dimensions() {
TensorBlockWriter;
DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
const Index input_tensor_size = input_tensor_dims.TotalSize();
// Create a random input tensor.
T* input_data = GenerateRandomData<T>(input_tensor_size);
@ -327,7 +324,7 @@ static void test_block_io_copy_using_reordered_dimensions() {
// Create a random dimension re-ordering/shuffle.
std::vector<Index> shuffle;
for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937());
std::random_shuffle(shuffle.begin(), shuffle.end());
DSizes<Index, NumDims> output_tensor_dims;
array<Index, NumDims> input_to_output_dim_map;
@ -342,8 +339,8 @@ static void test_block_io_copy_using_reordered_dimensions() {
TensorBlockMapper block_mapper(output_tensor_dims, RandomShape(),
RandomTargetSize(input_tensor_dims));
auto* block_data = new T[block_mapper.block_dims_total_size()];
auto* output_data = new T[input_tensor_size];
T* block_data = new T[block_mapper.block_dims_total_size()];
T* output_data = new T[input_tensor_size];
array<Index, NumDims> input_tensor_strides =
ComputeStrides<Layout, NumDims>(input_tensor_dims);
@ -385,8 +382,8 @@ static void test_block_io_zero_stride()
input_tensor_dims[0] = 1;
input_tensor_dims[2] = 1;
input_tensor_dims[4] = 1;
const auto input_tensor_size = input_tensor_dims.TotalSize();
auto* input_data = GenerateRandomData<float>(input_tensor_size);
const Index input_tensor_size = input_tensor_dims.TotalSize();
float* input_data = GenerateRandomData<float>(input_tensor_size);
DSizes<Index, 5> output_tensor_dims = rnd_dims;
@ -427,7 +424,7 @@ static void test_block_io_zero_stride()
};
{
auto* output_data = new float[output_tensor_dims.TotalSize()];
float* output_data = new float[output_tensor_dims.TotalSize()];
TensorBlock read_block(0, output_tensor_dims, output_tensor_strides,
input_tensor_strides_with_zeros, output_data);
TensorBlockReader::Run(&read_block, input_data);
@ -436,7 +433,7 @@ static void test_block_io_zero_stride()
}
{
auto* output_data = new float[output_tensor_dims.TotalSize()];
float* output_data = new float[output_tensor_dims.TotalSize()];
TensorBlock write_block(0, output_tensor_dims,
input_tensor_strides_with_zeros,
output_tensor_strides, input_data);
@ -459,14 +456,14 @@ static void test_block_io_squeeze_ones() {
// Total size > 1.
{
DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensor.
auto* input_data = GenerateRandomData<float>(total_size);
float* input_data = GenerateRandomData<float>(total_size);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock read_block(0, block_sizes, strides, strides, output_data);
TensorBlockReader::Run(&read_block, input_data);
for (int i = 0; i < total_size; ++i) {
@ -476,7 +473,7 @@ static void test_block_io_squeeze_ones() {
}
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock write_block(0, block_sizes, strides, strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
for (int i = 0; i < total_size; ++i) {
@ -489,14 +486,14 @@ static void test_block_io_squeeze_ones() {
// Total size == 1.
{
DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensor.
auto* input_data = GenerateRandomData<float>(total_size);
float* input_data = GenerateRandomData<float>(total_size);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock read_block(0, block_sizes, strides, strides, output_data);
TensorBlockReader::Run(&read_block, input_data);
for (int i = 0; i < total_size; ++i) {
@ -506,7 +503,7 @@ static void test_block_io_squeeze_ones() {
}
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock write_block(0, block_sizes, strides, strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
for (int i = 0; i < total_size; ++i) {
@ -527,7 +524,7 @@ static void test_block_cwise_binary_io_basic() {
DSizes<Index, NumDims> block_sizes = RandomDims<NumDims>();
DSizes<Index, NumDims> strides(ComputeStrides<Layout, NumDims>(block_sizes));
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensors.
T* left_data = GenerateRandomData<T>(total_size);
@ -556,13 +553,13 @@ static void test_block_cwise_binary_io_squeeze_ones() {
DSizes<Index, 5> block_sizes(1, 2, 1, 3, 1);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensors.
auto* left_data = GenerateRandomData<float>(total_size);
auto* right_data = GenerateRandomData<float>(total_size);
float* left_data = GenerateRandomData<float>(total_size);
float* right_data = GenerateRandomData<float>(total_size);
auto* output_data = new float[total_size];
float* output_data = new float[total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, block_sizes, strides, output_data,
strides, left_data, strides, right_data);
@ -603,14 +600,14 @@ static void test_block_cwise_binary_io_zero_strides() {
right_strides[3] = 0;
// Generate random data.
auto* left_data = GenerateRandomData<float>(left_sizes.TotalSize());
auto* right_data = GenerateRandomData<float>(right_sizes.TotalSize());
float* left_data = GenerateRandomData<float>(left_sizes.TotalSize());
float* right_data = GenerateRandomData<float>(right_sizes.TotalSize());
DSizes<Index, 5> output_sizes = rnd_dims;
DSizes<Index, 5> output_strides(ComputeStrides<Layout, 5>(output_sizes));
const auto output_total_size = output_sizes.TotalSize();
auto* output_data = new float[output_total_size];
const Index output_total_size = output_sizes.TotalSize();
float* output_data = new float[output_total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, output_sizes, output_strides,
@ -647,17 +644,16 @@ static void test_block_cwise_binary_io_zero_strides() {
template <int Layout>
static void test_uniform_block_shape()
{
using T = int;
typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
typedef internal::TensorBlock<int, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<int, Index, 5, Layout> TensorBlockMapper;
{
// Test shape 'UniformAllDims' with uniform 'max_coeff count'.
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 5 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
for (int i = 0; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
}
@ -669,9 +665,9 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 7 * 5 * 5 * 5 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@ -680,9 +676,9 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 5 * 6;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(6, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@ -695,9 +691,9 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 5 * 5 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@ -706,9 +702,9 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 5 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
@ -721,9 +717,9 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(7, 5, 6, 17, 7);
const size_t max_coeff_count = 7 * 5 * 6 * 7 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@ -733,9 +729,9 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(7, 5, 6, 9, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 6 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(6, block.block_sizes()[3]);
VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
@ -748,9 +744,9 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(7, 5, 6, 17, 7);
const size_t max_coeff_count = 7 * 5 * 6 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@ -760,9 +756,9 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(7, 5, 6, 9, 7);
const size_t max_coeff_count = 7 * 5 * 6 * 9 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
TensorBlockMapper block_mapper(dims, internal::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(9, block.block_sizes()[3]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@ -775,17 +771,16 @@ static void test_uniform_block_shape()
template <int Layout>
static void test_skewed_inner_dim_block_shape()
{
using T = int;
typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
typedef internal::TensorBlock<int, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<int, Index, 5, Layout> TensorBlockMapper;
// Test shape 'SkewedInnerDims' with partial allocation to inner-most dim.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 10 * 1 * 1 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(10, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@ -794,9 +789,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 1 * 1 * 6;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(6, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@ -808,9 +803,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 1 * 1 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@ -819,9 +814,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 1 * 1 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
@ -834,9 +829,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 3 * 1 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY_IS_EQUAL(3, block.block_sizes()[1]);
for (int i = 2; i < 5; ++i) {
@ -846,9 +841,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 1 * 15 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(15, block.block_sizes()[3]);
for (int i = 2; i >= 0; --i) {
@ -862,9 +857,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 5 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
@ -875,9 +870,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 5 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
@ -891,9 +886,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@ -903,9 +898,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
TensorBlockMapper block_mapper(dims, internal::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
TensorBlock block = block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
@ -918,15 +913,13 @@ static void test_skewed_inner_dim_block_shape()
template <int Layout>
static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
{
using T = int;
// Test blocking of tensors with zero dimensions:
// - we must not crash on asserts and divisions by zero
// - we must not return block with zero dimensions
// (recipe for overflows/underflows, divisions by zero and NaNs later)
// - total block count must be zero
{
typedef internal::TensorBlockMapper<T, Index, 1, Layout> TensorBlockMapper;
typedef internal::TensorBlockMapper<int, Index, 1, Layout> TensorBlockMapper;
DSizes<Index, 1> dims(0);
for (int max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
TensorBlockMapper block_mapper(dims, block_shape, max_coeff_count);
@ -936,7 +929,7 @@ static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
}
{
typedef internal::TensorBlockMapper<T, Index, 2, Layout> TensorBlockMapper;
typedef internal::TensorBlockMapper<int, Index, 2, Layout> TensorBlockMapper;
for (int dim1 = 0; dim1 < 3; ++dim1) {
for (int dim2 = 0; dim2 < 3; ++dim2) {
DSizes<Index, 2> dims(dim1, dim2);
@ -987,9 +980,9 @@ EIGEN_DECLARE_TEST(cxx11_tensor_block_access) {
TEST_LAYOUTS(test_block_cwise_binary_io_zero_strides);
TEST_LAYOUTS(test_uniform_block_shape);
TEST_LAYOUTS(test_skewed_inner_dim_block_shape);
TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims);
TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims);
TEST_LAYOUTS_WITH_ARG(test_empty_dims, internal::kUniformAllDims);
TEST_LAYOUTS_WITH_ARG(test_empty_dims, internal::kSkewedInnerDims);
}
#undef TEST_LAYOUTS
#undef TEST_LAYOUTS_WITH_ARG
#undef TEST_LAYOUTS_WITH_ARG

6
unsupported/test/cxx11_tensor_contraction.cpp
View File

@ -471,7 +471,7 @@ static void test_tensor_product()
mat1.setRandom();
mat2.setRandom();
Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{{}});
Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{});
VERIFY_IS_EQUAL(result.dimension(0), 2);
VERIFY_IS_EQUAL(result.dimension(1), 3);
@ -514,7 +514,7 @@ static void test_const_inputs()
struct SqrtOutputKernel {
template <typename Index, typename Scalar>
EIGEN_ALWAYS_INLINE void operator()(
const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
const TensorContractionParams&, Index, Index, Index num_rows,
Index num_cols) const {
for (int i = 0; i < num_rows; ++i) {
@ -553,7 +553,7 @@ static void test_large_contraction_with_output_kernel() {
m_result = m_left * m_right;
for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
for (std::ptrdiff_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
VERIFY(&t_result.data()[i] != &m_result.data()[i]);
VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
}

2
unsupported/test/cxx11_tensor_convolution.cpp
View File

@ -25,7 +25,7 @@ static void test_evals()
Tensor<float, 2, DataLayout> result(2,3);
result.setZero();
Eigen::array<Tensor<float, 2>::Index, 1> dims3{{0}};
Eigen::array<Tensor<float, 2>::Index, 1> dims3{0};
typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator;
Evaluator eval(input.convolve(kernel, dims3), DefaultDevice());

1
unsupported/test/cxx11_tensor_index_list.cpp
View File

@ -170,7 +170,6 @@ static void test_type2indexpair_list()
typedef Eigen::IndexPairList<Eigen::type2indexpair<0,10>, Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<2,12>> Dims2_b;
typedef Eigen::IndexPairList<Eigen::IndexPair<DenseIndex>, Eigen::type2indexpair<1,11>, Eigen::IndexPair<DenseIndex>> Dims2_c;
Dims0 d0;
Dims2_a d2_a;
Dims2_b d2_b;

2
unsupported/test/cxx11_tensor_thread_pool.cpp
View File

@ -255,7 +255,7 @@ void test_multithread_contraction_agrees_with_singlethread() {
struct SqrtOutputKernel {
template <typename Index, typename Scalar>
EIGEN_ALWAYS_INLINE void operator()(
const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
const TensorContractionParams&, Index, Index, Index num_rows,
Index num_cols) const {
for (int i = 0; i < num_rows; ++i) {

24
unsupported/test/kronecker_product.cpp
View File

@ -9,6 +9,7 @@
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifdef EIGEN_TEST_PART_1
#include "sparse.h"
@ -95,7 +96,7 @@ EIGEN_DECLARE_TEST(kronecker_product)
SM_a.insert(1,0) = DM_a.coeffRef(1,0) = -0.9076572187376921;
SM_a.insert(1,1) = DM_a.coeffRef(1,1) = 0.6469156566545853;
SM_a.insert(1,2) = DM_a.coeffRef(1,2) = -0.3658010398782789;
MatrixXd DM_b(3,2);
SparseMatrix<double> SM_b(3,2);
SM_b.insert(0,0) = DM_b.coeffRef(0,0) = 0.9004440976767099;
@ -165,7 +166,7 @@ EIGEN_DECLARE_TEST(kronecker_product)
SM_a.insert(0,3) = -0.2;
SM_a.insert(2,4) = 0.3;
SM_a.finalize();
SM_b.insert(0,0) = 0.4;
SM_b.insert(2,1) = -0.5;
SM_b.finalize();
@ -183,7 +184,7 @@ EIGEN_DECLARE_TEST(kronecker_product)
DM_b2.resize(4,8);
DM_ab2 = kroneckerProduct(DM_a2,DM_b2);
CALL_SUBTEST(check_dimension(DM_ab2,10*4,9*8));
for(int i = 0; i < g_repeat; i++)
{
double density = Eigen::internal::random<double>(0.01,0.5);
@ -196,35 +197,35 @@ EIGEN_DECLARE_TEST(kronecker_product)
MatrixXf dA(ra,ca), dB(rb,cb), dC;
initSparse(density, dA, sA);
initSparse(density, dB, sB);
sC = kroneckerProduct(sA,sB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC),dC);
sC = kroneckerProduct(sA.transpose(),sB);
dC = kroneckerProduct(dA.transpose(),dB);
VERIFY_IS_APPROX(MatrixXf(sC),dC);
sC = kroneckerProduct(sA.transpose(),sB.transpose());
dC = kroneckerProduct(dA.transpose(),dB.transpose());
VERIFY_IS_APPROX(MatrixXf(sC),dC);
sC = kroneckerProduct(sA,sB.transpose());
dC = kroneckerProduct(dA,dB.transpose());
VERIFY_IS_APPROX(MatrixXf(sC),dC);
sC2 = kroneckerProduct(sA,sB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
sC2 = kroneckerProduct(dA,sB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
sC2 = kroneckerProduct(sA,dB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
sC2 = kroneckerProduct(2*sA,sB);
dC = kroneckerProduct(2*dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
@ -236,7 +237,6 @@ EIGEN_DECLARE_TEST(kronecker_product)
#ifdef EIGEN_TEST_PART_2
// simply check that for a dense kronecker product, sparse module is not needed
#include "main.h"
#include <Eigen/KroneckerProduct>