Fix boolean float conversion and product warnings.

This fixes some gcc warnings such as:
```
Eigen/src/Core/GenericPacketMath.h:655:63: warning: implicit conversion turns floating-point number into bool: 'typename __gnu_cxx::__enable_if<__is_integer<bool>::__value, double>::__type' (aka 'double') to 'bool' [-Wimplicit-conversion-floating-point-to-bool]
    Packet psqrt(const Packet& a) { EIGEN_USING_STD(sqrt); return sqrt(a); }
```

Details:

- Added `scalar_sqrt_op<bool>` (`-Wimplicit-conversion-floating-point-to-bool`).

- Added `scalar_square_op<bool>` and `scalar_cube_op<bool>`
specializations (`-Wint-in-bool-context`)

- Deprecated above specialized ops for bool.

- Modified `cxx11_tensor_block_eval` to specialize generator for
booleans (`-Wint-in-bool-context`) and to use `abs` instead of `square` to
avoid deprecated bool ops.
This commit is contained in:
Antonio Sanchez 2020-11-19 10:22:42 -08:00 committed by Rasmus Munk Larsen
parent a3b300f1af
commit 22f67b5958
5 changed files with 78 additions and 12 deletions

View File

@ -1374,6 +1374,11 @@ T sqrt(const T &x)
return sqrt(x);
}
// Boolean specialization, avoids implicit float to bool conversion (-Wimplicit-conversion-floating-point-to-bool).
template<>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
bool sqrt<bool>(const bool &x) { return x; }
#if defined(SYCL_DEVICE_ONLY)
SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sqrt, sqrt)
#endif

View File

@ -99,6 +99,9 @@ Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); }
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet2d psqrt<Packet2d>(const Packet2d& x) { return _mm_sqrt_pd(x); }
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet16b psqrt<Packet16b>(const Packet16b& x) { return x; }
#if EIGEN_FAST_MATH
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED

View File

@ -218,7 +218,8 @@ template<> struct packet_traits<bool> : default_packet_traits
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasConj = 0
HasConj = 0,
HasSqrt = 1
};
};

View File

@ -422,6 +422,18 @@ struct functor_traits<scalar_sqrt_op<Scalar> > {
};
};
// Boolean specialization to eliminate -Wimplicit-conversion-floating-point-to-bool warnings.
template<> struct scalar_sqrt_op<bool> {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
template <typename Packet>
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return a; }
};
template <>
struct functor_traits<scalar_sqrt_op<bool> > {
enum { Cost = 1, PacketAccess = packet_traits<bool>::Vectorizable };
};
/** \internal
* \brief Template functor to compute the reciprocal square root of a scalar
* \sa class CwiseUnaryOp, Cwise::rsqrt()
@ -719,6 +731,19 @@ template<typename Scalar>
struct functor_traits<scalar_square_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
template<>
struct scalar_square_op<bool> {
EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
template<typename Packet>
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return a; }
};
template<>
struct functor_traits<scalar_square_op<bool> >
{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
/** \internal
* \brief Template functor to compute the cube of a scalar
* \sa class CwiseUnaryOp, Cwise::cube()
@ -735,6 +760,19 @@ template<typename Scalar>
struct functor_traits<scalar_cube_op<Scalar> >
{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
template<>
struct scalar_cube_op<bool> {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
template<typename Packet>
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return a; }
};
template<>
struct functor_traits<scalar_cube_op<bool> >
{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
/** \internal
* \brief Template functor to compute the rounded value of a scalar
* \sa class CwiseUnaryOp, ArrayBase::round()

View File

@ -222,7 +222,7 @@ static void test_eval_tensor_unary_expr_block() {
input.setRandom();
VerifyBlockEvaluator<T, NumDims, Layout>(
input.square(), [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
input.abs(), [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
}
template <typename T, int NumDims, int Layout>
@ -274,7 +274,7 @@ static void test_eval_tensor_broadcast() {
// Check that desc.destination() memory is not shared between two broadcast
// materializations.
VerifyBlockEvaluator<T, NumDims, Layout>(
input.broadcast(bcast) * input.square().broadcast(bcast),
input.broadcast(bcast) * input.abs().broadcast(bcast),
[&bcasted_dims]() { return SkewedInnerBlock<Layout>(bcasted_dims); });
}
@ -391,27 +391,46 @@ static void test_eval_tensor_chipping() {
// Block expression assignment.
VerifyBlockEvaluator<T, NumDims - 1, Layout>(
input.square().chip(chip_offset, chip_dim),
input.abs().chip(chip_offset, chip_dim),
[&chipped_dims]() { return FixedSizeBlock(chipped_dims); });
VerifyBlockEvaluator<T, NumDims - 1, Layout>(
input.square().chip(chip_offset, chip_dim),
input.abs().chip(chip_offset, chip_dim),
[&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); });
}
template<typename T, int NumDims>
struct SimpleTensorGenerator {
T operator()(const array<Index, NumDims>& coords) const {
T result = static_cast<T>(0);
for (int i = 0; i < NumDims; ++i) {
result += static_cast<T>((i + 1) * coords[i]);
}
return result;
}
};
// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
template<int NumDims>
struct SimpleTensorGenerator<bool, NumDims> {
bool operator()(const array<Index, NumDims>& coords) const {
bool result = false;
for (int i = 0; i < NumDims; ++i) {
result ^= coords[i];
}
return result;
}
};
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_generator() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
auto generator = [](const array<Index, NumDims>& coords) -> T {
T result = static_cast<T>(0);
for (int i = 0; i < NumDims; ++i) {
result += static_cast<T>((i + 1) * coords[i]);
}
return result;
};
auto generator = SimpleTensorGenerator<T, NumDims>();
VerifyBlockEvaluator<T, NumDims, Layout>(
input.generate(generator), [&dims]() { return FixedSizeBlock(dims); });