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Merge with upstream

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This commit is contained in:
Mehdi Goli 2018-08-14 09:43:45 +01:00
commit 8ba799805b
11 changed files with 66 additions and 54 deletions
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11
Eigen/src/Core/GlobalFunctions.h
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@ -66,6 +66,7 @@ namespace Eigen
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic,scalar_logistic_op,logistic function,\sa ArrayBase::logistic)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
@ -89,7 +90,7 @@ namespace Eigen
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
*
* \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
@ -124,21 +125,21 @@ namespace Eigen
*
* Example: \include Cwise_array_power_array.cpp
* Output: \verbinclude Cwise_array_power_array.out
*
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
x.derived(),
exponents.derived()
);
}
/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power between a scalar and an array of exponents.
@ -147,7 +148,7 @@ namespace Eigen
*
* Example: \include Cwise_scalar_power_array.cpp
* Output: \verbinclude Cwise_scalar_power_array.out
*
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase

32
Eigen/src/Core/functors/UnaryFunctors.h
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@ -701,7 +701,7 @@ template<typename Scalar> struct scalar_isnan_op {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
#if defined(__SYCL_DEVICE_ONLY__)
return numext::isnan(a);
#else
#else
return (numext::isnan)(a);
#endif
}
@ -815,7 +815,7 @@ struct scalar_sign_op<Scalar,true> {
template<typename Scalar>
struct functor_traits<scalar_sign_op<Scalar> >
{ enum {
Cost =
Cost =
NumTraits<Scalar>::IsComplex
? ( 8*NumTraits<Scalar>::MulCost ) // roughly
: ( 3*NumTraits<Scalar>::AddCost),
@ -823,6 +823,34 @@ struct functor_traits<scalar_sign_op<Scalar> >
};
};
/** \internal
* \brief Template functor to compute the logistic function of a scalar
* \sa class CwiseUnaryOp, ArrayBase::logistic()
*/
template <typename T>
struct scalar_logistic_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
const T one = T(1);
return one / (one + numext::exp(-x));
}
template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Packet packetOp(const Packet& x) const {
const Packet one = pset1<Packet>(T(1));
return pdiv(one, padd(one, pexp(pnegate(x))));
}
};
template <typename T>
struct functor_traits<scalar_logistic_op<T> > {
enum {
Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 6,
PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
packet_traits<T>::HasNegate && packet_traits<T>::HasExp
};
};
} // end namespace internal
} // end namespace Eigen

10
Eigen/src/plugins/ArrayCwiseUnaryOps.h
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@ -21,6 +21,7 @@ typedef CwiseUnaryOp<internal::scalar_acos_op<Scalar>, const Derived> AcosReturn
typedef CwiseUnaryOp<internal::scalar_asin_op<Scalar>, const Derived> AsinReturnType;
typedef CwiseUnaryOp<internal::scalar_atan_op<Scalar>, const Derived> AtanReturnType;
typedef CwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> TanhReturnType;
typedef CwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived> LogisticReturnType;
typedef CwiseUnaryOp<internal::scalar_sinh_op<Scalar>, const Derived> SinhReturnType;
typedef CwiseUnaryOp<internal::scalar_cosh_op<Scalar>, const Derived> CoshReturnType;
typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> SquareReturnType;
@ -335,6 +336,15 @@ cosh() const
return CoshReturnType(derived());
}
/** \returns an expression of the coefficient-wise logistic of *this.
*/
EIGEN_DEVICE_FUNC
inline const LogisticReturnType
logistic() const
{
return LogisticReturnType(derived());
}
/** \returns an expression of the coefficient-wise inverse of *this.
*
* Example: \include Cwise_inverse.cpp

4
test/array.cpp
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@ -231,6 +231,7 @@ template<typename ArrayType> void array_real(const ArrayType& m)
VERIFY_IS_APPROX(m1.sinh(), sinh(m1));
VERIFY_IS_APPROX(m1.cosh(), cosh(m1));
VERIFY_IS_APPROX(m1.tanh(), tanh(m1));
VERIFY_IS_APPROX(m1.logistic(), logistic(m1));
VERIFY_IS_APPROX(m1.arg(), arg(m1));
VERIFY_IS_APPROX(m1.round(), round(m1));
@ -266,6 +267,7 @@ template<typename ArrayType> void array_real(const ArrayType& m)
VERIFY_IS_APPROX(sinh(m1), 0.5*(exp(m1)-exp(-m1)));
VERIFY_IS_APPROX(cosh(m1), 0.5*(exp(m1)+exp(-m1)));
VERIFY_IS_APPROX(tanh(m1), (0.5*(exp(m1)-exp(-m1)))/(0.5*(exp(m1)+exp(-m1))));
VERIFY_IS_APPROX(logistic(m1), (1.0/(1.0+exp(-m1))));
VERIFY_IS_APPROX(arg(m1), ((m1<0).template cast<Scalar>())*std::acos(-1.0));
VERIFY((round(m1) <= ceil(m1) && round(m1) >= floor(m1)).all());
VERIFY((Eigen::isnan)((m1*0.0)/0.0).all());
@ -345,6 +347,7 @@ template<typename ArrayType> void array_complex(const ArrayType& m)
VERIFY_IS_APPROX(m1.sinh(), sinh(m1));
VERIFY_IS_APPROX(m1.cosh(), cosh(m1));
VERIFY_IS_APPROX(m1.tanh(), tanh(m1));
VERIFY_IS_APPROX(m1.logistic(), logistic(m1));
VERIFY_IS_APPROX(m1.arg(), arg(m1));
VERIFY((m1.isNaN() == (Eigen::isnan)(m1)).all());
VERIFY((m1.isInf() == (Eigen::isinf)(m1)).all());
@ -368,6 +371,7 @@ template<typename ArrayType> void array_complex(const ArrayType& m)
VERIFY_IS_APPROX(sinh(m1), 0.5*(exp(m1)-exp(-m1)));
VERIFY_IS_APPROX(cosh(m1), 0.5*(exp(m1)+exp(-m1)));
VERIFY_IS_APPROX(tanh(m1), (0.5*(exp(m1)-exp(-m1)))/(0.5*(exp(m1)+exp(-m1))));
VERIFY_IS_APPROX(logistic(m1), (1.0/(1.0 + exp(-m1))));
for (Index i = 0; i < m.rows(); ++i)
for (Index j = 0; j < m.cols(); ++j)

2
unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
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@ -187,7 +187,7 @@ struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(TensorBlock* block) {
if (TensorEvaluator<LeftArgType, Device>::RawAccess &&
m_leftImpl.data() != nullptr) {
m_leftImpl.data() != NULL) {
TensorBlock left_block(block->first_coeff_index(), block->block_sizes(),
block->tensor_strides(), block->tensor_strides(),
m_leftImpl.data() + block->first_coeff_index());

4
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
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@ -200,9 +200,9 @@ class TensorBase<Derived, ReadOnlyAccessors>
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sigmoid_op<Scalar>, const Derived>
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived>
sigmoid() const {
return unaryExpr(internal::scalar_sigmoid_op<Scalar>());
return unaryExpr(internal::scalar_logistic_op<Scalar>());
}
EIGEN_DEVICE_FUNC

14
unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
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@ -155,8 +155,8 @@ struct TensorBlockCopyOp {
typedef const Eigen::Array<Scalar, Dynamic, 1> Src;
typedef Eigen::Array<Scalar, Dynamic, 1> Dst;
typedef Eigen::Map<Src, 0, InnerStride<>> SrcMap;
typedef Eigen::Map<Dst, 0, InnerStride<>> DstMap;
typedef Eigen::Map<Src, 0, InnerStride<> > SrcMap;
typedef Eigen::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));
@ -405,9 +405,9 @@ struct TensorBlockCwiseBinaryOp {
typedef const Eigen::Array<RightScalar, Dynamic, 1> Rhs;
typedef Eigen::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 Eigen::Map<Lhs, 0, InnerStride<> > LhsMap;
typedef Eigen::Map<Rhs, 0, InnerStride<> > RhsMap;
typedef Eigen::Map<Out, 0, InnerStride<> > OutMap;
const LeftScalar* lhs_base = &left_data[left_index];
const RightScalar* rhs_base = &right_data[right_index];
@ -501,7 +501,7 @@ struct TensorBlockCwiseBinaryIO {
if (size == 1) {
continue;
}
auto& state = block_iter_state[num_squeezed_dims];
BlockIteratorState& state = block_iter_state[num_squeezed_dims];
state.output_stride = block_strides[dim];
state.left_stride = left_strides[dim];
state.right_stride = right_strides[dim];
@ -523,7 +523,7 @@ struct TensorBlockCwiseBinaryIO {
right_stride, right_data);
// Update index.
for (int j = 0; j < num_squeezed_dims; ++j) {
auto& state = block_iter_state[j];
BlockIteratorState& state = block_iter_state[j];
if (++state.count < state.size) {
output_index += state.output_stride;
left_index += state.left_stride;

4
unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
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@ -102,7 +102,7 @@ class Allocator {
// Build a thread pool device on top the an existing pool of threads.
struct ThreadPoolDevice {
// The ownership of the thread pool remains with the caller.
ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores, Allocator* allocator = nullptr)
ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores, Allocator* allocator = NULL)
: pool_(pool), num_threads_(num_cores), allocator_(allocator) { }
EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
@ -282,7 +282,7 @@ struct ThreadPoolDevice {
// Convenience wrapper for parallelFor that does not align blocks.
void parallelFor(Index n, const TensorOpCost& cost,
std::function<void(Index, Index)> f) const {
parallelFor(n, cost, nullptr, std::move(f));
parallelFor(n, cost, NULL, std::move(f));
}
// Thread pool accessor.

5
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
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@ -227,7 +227,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
Evaluator evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
const StorageIndex PacketSize =
Vectorizable
@ -257,7 +257,6 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
static EIGEN_STRONG_INLINE void run(const Expression& expr,
const ThreadPoolDevice& device) {
typedef TensorBlock<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout> TensorBlock;
typedef TensorBlockMapper<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout> TensorBlockMapper;
Evaluator evaluator(expr, device);
@ -271,7 +270,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
return;
}
const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
Index block_total_size = 0;

30
unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
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@ -54,36 +54,6 @@ struct functor_traits<scalar_fmod_op<Scalar> > {
PacketAccess = false };
};
/** \internal
* \brief Template functor to compute the sigmoid of a scalar
* \sa class CwiseUnaryOp, ArrayBase::sigmoid()
*/
template <typename T>
struct scalar_sigmoid_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
const T one = T(1);
return one / (one + numext::exp(-x));
}
template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Packet packetOp(const Packet& x) const {
const Packet one = pset1<Packet>(T(1));
return pdiv(one, padd(one, pexp(pnegate(x))));
}
};
template <typename T>
struct functor_traits<scalar_sigmoid_op<T> > {
enum {
Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 6,
PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
packet_traits<T>::HasNegate && packet_traits<T>::HasExp
};
};
template<typename Reducer, typename Device>
struct reducer_traits {
enum {

4
unsupported/test/cxx11_tensor_thread_pool.cpp
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@ -428,7 +428,7 @@ void test_threadpool_allocate(TestAllocator* allocator)
void* ptr = device.allocate(512);
device.deallocate(ptr);
}
VERIFY(allocator != nullptr);
VERIFY(allocator != NULL);
VERIFY_IS_EQUAL(allocator->alloc_count(), num_allocs);
VERIFY_IS_EQUAL(allocator->dealloc_count(), num_allocs);
}
@ -460,7 +460,7 @@ EIGEN_DECLARE_TEST(cxx11_tensor_thread_pool)
CALL_SUBTEST_6(test_multithread_random());
TestAllocator test_allocator;
CALL_SUBTEST_6(test_multithread_shuffle<ColMajor>(nullptr));
CALL_SUBTEST_6(test_multithread_shuffle<ColMajor>(NULL));
CALL_SUBTEST_6(test_multithread_shuffle<RowMajor>(&test_allocator));
CALL_SUBTEST_6(test_threadpool_allocate(&test_allocator));
}