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Add packet generic ops predux_fmin, predux_fmin_nan, predux_fmax, and predux_fmax_nan that implement reductions with PropagateNaN, and PropagateNumbers semantics. Add (slow) generic implementations for most reductions.

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This commit is contained in:
Rasmus Munk Larsen
2020-10-13 21:48:31 +00:00
parent 807e51528d
commit c6953f799b
6 changed files with 395 additions and 294 deletions
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22
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
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@@ -682,28 +682,30 @@ class TensorBase<Derived, ReadOnlyAccessors>
return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::ProdReducer<CoeffReturnType>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const Dims, const Derived>
template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
maximum(const Dims& dims) const {
return TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType>());
return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
}
const TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
template <int NanPropagation=PropagateFast>
const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
maximum() const {
DimensionList<Index, NumDimensions> in_dims;
return TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType>());
return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MinReducer<CoeffReturnType>, const Dims, const Derived>
template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
minimum(const Dims& dims) const {
return TensorReductionOp<internal::MinReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType>());
return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
}
const TensorReductionOp<internal::MinReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
template <int NanPropagation=PropagateFast>
const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
minimum() const {
DimensionList<Index, NumDimensions> in_dims;
return TensorReductionOp<internal::MinReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType>());
return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE

41
unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
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@@ -192,17 +192,19 @@ struct MinMaxBottomValue<T, false, false> {
};
template <typename T> struct MaxReducer
template <typename T, int NaNPropagation=PropagateFast> struct MaxReducer
{
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
if (t > *accum) { *accum = t; }
scalar_max_op<T, T, NaNPropagation> op;
*accum = op(t, *accum);
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = pmax<Packet>(*accum, p);
scalar_max_op<T, T, NaNPropagation> op;
(*accum) = op.packetOp(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return MinMaxBottomValue<T, true, Eigen::NumTraits<T>::IsInteger>::bottom_value();
return MinMaxBottomValue<T, /*IsMax=*/true, Eigen::NumTraits<T>::IsInteger>::bottom_value();
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
@@ -217,32 +219,34 @@ template <typename T> struct MaxReducer
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return numext::maxi(saccum, predux_max(vaccum));
scalar_max_op<T, T, NaNPropagation> op;
return op(saccum, op.predux(vaccum));
}
};
template <typename T, typename Device>
struct reducer_traits<MaxReducer<T>, Device> {
template <typename T, typename Device, int NaNPropagation>
struct reducer_traits<MaxReducer<T, NaNPropagation>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = PacketType<T, Device>::HasMax,
IsStateful = false,
IsExactlyAssociative = true
IsExactlyAssociative = (NaNPropagation!=PropagateFast)
};
};
template <typename T> struct MinReducer
template <typename T, int NaNPropagation=PropagateFast> struct MinReducer
{
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
if (t < *accum) { *accum = t; }
scalar_min_op<T, T, NaNPropagation> op;
*accum = op(t, *accum);
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = pmin<Packet>(*accum, p);
scalar_min_op<T, T, NaNPropagation> op;
(*accum) = op.packetOp(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return MinMaxBottomValue<T, false, Eigen::NumTraits<T>::IsInteger>::bottom_value();
return MinMaxBottomValue<T, /*IsMax=*/false, Eigen::NumTraits<T>::IsInteger>::bottom_value();
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
@@ -257,21 +261,21 @@ template <typename T> struct MinReducer
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return numext::mini(saccum, predux_min(vaccum));
scalar_min_op<T, T, NaNPropagation> op;
return op(saccum, op.predux(vaccum));
}
};
template <typename T, typename Device>
struct reducer_traits<MinReducer<T>, Device> {
template <typename T, typename Device, int NaNPropagation>
struct reducer_traits<MinReducer<T, NaNPropagation>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = PacketType<T, Device>::HasMin,
IsStateful = false,
IsExactlyAssociative = true
IsExactlyAssociative = (NaNPropagation!=PropagateFast)
};
};
template <typename T> struct ProdReducer
{
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
@@ -282,7 +286,6 @@ template <typename T> struct ProdReducer
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = pmul<Packet>(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
internal::scalar_cast_op<int, T> conv;
return conv(1);

94
unsupported/test/cxx11_tensor_expr.cpp
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@@ -302,12 +302,17 @@ static void test_select()
template <typename Scalar>
void test_minmax_nan_propagation_templ() {
for (int size = 1; size < 17; ++size) {
const Scalar kNan = std::numeric_limits<Scalar>::quiet_NaN();
std::cout << "size = " << size << std::endl;
const Scalar kNaN = std::numeric_limits<Scalar>::quiet_NaN();
const Scalar kInf = std::numeric_limits<Scalar>::infinity();
const Scalar kZero(0);
Tensor<Scalar, 1> vec_nan(size);
Tensor<Scalar, 1> vec_all_nan(size);
Tensor<Scalar, 1> vec_one_nan(size);
Tensor<Scalar, 1> vec_zero(size);
vec_nan.setConstant(kNan);
vec_all_nan.setConstant(kNaN);
vec_zero.setZero();
vec_one_nan.setZero();
vec_one_nan(size/2) = kNaN;
auto verify_all_nan = [&](const Tensor<Scalar, 1>& v) {
for (int i = 0; i < size; ++i) {
@@ -326,12 +331,12 @@ void test_minmax_nan_propagation_templ() {
// max(nan, 0) = nan
// max(0, nan) = nan
// max(0, 0) = 0
verify_all_nan(vec_nan.template cwiseMax<PropagateNaN>(kNan));
verify_all_nan(vec_nan.template cwiseMax<PropagateNaN>(vec_nan));
verify_all_nan(vec_nan.template cwiseMax<PropagateNaN>(kZero));
verify_all_nan(vec_nan.template cwiseMax<PropagateNaN>(vec_zero));
verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(kNan));
verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(vec_nan));
verify_all_nan(vec_all_nan.template cwiseMax<PropagateNaN>(kNaN));
verify_all_nan(vec_all_nan.template cwiseMax<PropagateNaN>(vec_all_nan));
verify_all_nan(vec_all_nan.template cwiseMax<PropagateNaN>(kZero));
verify_all_nan(vec_all_nan.template cwiseMax<PropagateNaN>(vec_zero));
verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(kNaN));
verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(vec_all_nan));
verify_all_zero(vec_zero.template cwiseMax<PropagateNaN>(kZero));
verify_all_zero(vec_zero.template cwiseMax<PropagateNaN>(vec_zero));
@@ -340,12 +345,12 @@ void test_minmax_nan_propagation_templ() {
// max(nan, 0) = 0
// max(0, nan) = 0
// max(0, 0) = 0
verify_all_nan(vec_nan.template cwiseMax<PropagateNumbers>(kNan));
verify_all_nan(vec_nan.template cwiseMax<PropagateNumbers>(vec_nan));
verify_all_zero(vec_nan.template cwiseMax<PropagateNumbers>(kZero));
verify_all_zero(vec_nan.template cwiseMax<PropagateNumbers>(vec_zero));
verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kNan));
verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_nan));
verify_all_nan(vec_all_nan.template cwiseMax<PropagateNumbers>(kNaN));
verify_all_nan(vec_all_nan.template cwiseMax<PropagateNumbers>(vec_all_nan));
verify_all_zero(vec_all_nan.template cwiseMax<PropagateNumbers>(kZero));
verify_all_zero(vec_all_nan.template cwiseMax<PropagateNumbers>(vec_zero));
verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kNaN));
verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_all_nan));
verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kZero));
verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_zero));
@@ -354,12 +359,12 @@ void test_minmax_nan_propagation_templ() {
// min(nan, 0) = nan
// min(0, nan) = nan
// min(0, 0) = 0
verify_all_nan(vec_nan.template cwiseMin<PropagateNaN>(kNan));
verify_all_nan(vec_nan.template cwiseMin<PropagateNaN>(vec_nan));
verify_all_nan(vec_nan.template cwiseMin<PropagateNaN>(kZero));
verify_all_nan(vec_nan.template cwiseMin<PropagateNaN>(vec_zero));
verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(kNan));
verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(vec_nan));
verify_all_nan(vec_all_nan.template cwiseMin<PropagateNaN>(kNaN));
verify_all_nan(vec_all_nan.template cwiseMin<PropagateNaN>(vec_all_nan));
verify_all_nan(vec_all_nan.template cwiseMin<PropagateNaN>(kZero));
verify_all_nan(vec_all_nan.template cwiseMin<PropagateNaN>(vec_zero));
verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(kNaN));
verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(vec_all_nan));
verify_all_zero(vec_zero.template cwiseMin<PropagateNaN>(kZero));
verify_all_zero(vec_zero.template cwiseMin<PropagateNaN>(vec_zero));
@@ -368,14 +373,49 @@ void test_minmax_nan_propagation_templ() {
// min(nan, 0) = 0
// min(0, nan) = 0
// min(0, 0) = 0
verify_all_nan(vec_nan.template cwiseMin<PropagateNumbers>(kNan));
verify_all_nan(vec_nan.template cwiseMin<PropagateNumbers>(vec_nan));
verify_all_zero(vec_nan.template cwiseMin<PropagateNumbers>(kZero));
verify_all_zero(vec_nan.template cwiseMin<PropagateNumbers>(vec_zero));
verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kNan));
verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_nan));
verify_all_nan(vec_all_nan.template cwiseMin<PropagateNumbers>(kNaN));
verify_all_nan(vec_all_nan.template cwiseMin<PropagateNumbers>(vec_all_nan));
verify_all_zero(vec_all_nan.template cwiseMin<PropagateNumbers>(kZero));
verify_all_zero(vec_all_nan.template cwiseMin<PropagateNumbers>(vec_zero));
verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kNaN));
verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_all_nan));
verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kZero));
verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_zero));
// Test min and max reduction
Tensor<Scalar, 0> val;
val = vec_zero.minimum();
VERIFY_IS_EQUAL(val(), kZero);
val = vec_zero.template minimum<PropagateNaN>();
VERIFY_IS_EQUAL(val(), kZero);
val = vec_zero.template minimum<PropagateNumbers>();
VERIFY_IS_EQUAL(val(), kZero);
val = vec_zero.maximum();
VERIFY_IS_EQUAL(val(), kZero);
val = vec_zero.template maximum<PropagateNaN>();
VERIFY_IS_EQUAL(val(), kZero);
val = vec_zero.template maximum<PropagateNumbers>();
VERIFY_IS_EQUAL(val(), kZero);
// Test NaN propagation for tensor of all NaNs.
val = vec_all_nan.template minimum<PropagateNaN>();
VERIFY((numext::isnan)(val()));
val = vec_all_nan.template minimum<PropagateNumbers>();
VERIFY_IS_EQUAL(val(), kInf);
val = vec_all_nan.template maximum<PropagateNaN>();
VERIFY((numext::isnan)(val()));
val = vec_all_nan.template maximum<PropagateNumbers>();
VERIFY_IS_EQUAL(val(), -kInf);
// Test NaN propagation for tensor with a single NaN.
val = vec_one_nan.template minimum<PropagateNaN>();
VERIFY((numext::isnan)(val()));
val = vec_one_nan.template minimum<PropagateNumbers>();
VERIFY_IS_EQUAL(val(), (size == 1 ? kInf : kZero));
val = vec_one_nan.template maximum<PropagateNaN>();
VERIFY((numext::isnan)(val()));
val = vec_one_nan.template maximum<PropagateNumbers>();
VERIFY_IS_EQUAL(val(), (size == 1 ? -kInf : kZero));
}
}