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Improved the functors defined for standard reductions

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Added a functor to encapsulate the generation of random numbers on cpu and gpu.
This commit is contained in:
Benoit Steiner 2014-10-09 15:36:23 -07:00
parent 44beee9d68
commit 767424af18
2 changed files with 101 additions and 4 deletions
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72
unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
View File

@ -25,12 +25,12 @@ template <typename T> struct SumReducer
}
private:
T m_sum;
typename internal::remove_all<T>::type m_sum;
};
template <typename T> struct MaxReducer
{
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MaxReducer() : m_max((std::numeric_limits<T>::min)()) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MaxReducer() : m_max(-(std::numeric_limits<T>::max)()) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t) {
if (t > m_max) { m_max = t; }
}
@ -39,7 +39,7 @@ template <typename T> struct MaxReducer
}
private:
T m_max;
typename internal::remove_all<T>::type m_max;
};
template <typename T> struct MinReducer
@ -53,9 +53,73 @@ template <typename T> struct MinReducer
}
private:
T m_min;
typename internal::remove_all<T>::type m_min;
};
#if !defined (EIGEN_USE_GPU) || !defined(__CUDACC__) || !defined(__CUDA_ARCH__)
// We're not compiling a cuda kernel
template <typename T> struct UniformRandomGenerator {
template<typename Index>
T operator()(Index, Index = 0) const {
return random<T>();
}
template<typename Index>
typename internal::packet_traits<T>::type packetOp(Index, Index = 0) const {
const int packetSize = internal::packet_traits<T>::size;
EIGEN_ALIGN_DEFAULT T values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = random<T>();
}
return internal::pload<typename internal::packet_traits<T>::type>(values);
}
};
#else
// We're compiling a cuda kernel
template <typename T> struct UniformRandomGenerator;
template <> struct UniformRandomGenerator<float> {
UniformRandomGenerator() {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(0, tid, 0, &m_state);
}
template<typename Index>
float operator()(Index, Index = 0) const {
return curand_uniform(&m_state);
}
template<typename Index>
float4 packetOp(Index, Index = 0) const {
return curand_uniform4(&m_state);
}
private:
mutable curandStatePhilox4_32_10_t m_state;
};
template <> struct UniformRandomGenerator<double> {
UniformRandomGenerator() {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(0, tid, 0, &m_state);
}
template<typename Index>
double operator()(Index, Index = 0) const {
return curand_uniform_double(&m_state);
}
template<typename Index>
double2 packetOp(Index, Index = 0) const {
return curand_uniform2_double(&m_state);
}
private:
mutable curandStatePhilox4_32_10_t m_state;
};
#endif
} // end namespace internal
} // end namespace Eigen

33
unsupported/test/cxx11_tensor_reduction.cpp
View File

@ -139,9 +139,42 @@ static void test_user_defined_reductions()
}
static void test_tensor_maps()
{
int inputs[2*3*5*7];
TensorMap<Tensor<int, 4> > tensor_map(inputs, 2,3,5,7);
TensorMap<Tensor<const int, 4> > tensor_map_const(inputs, 2,3,5,7);
const TensorMap<Tensor<const int, 4> > tensor_map_const_const(inputs, 2,3,5,7);
tensor_map.setRandom();
array<ptrdiff_t, 2> reduction_axis;
reduction_axis[0] = 1;
reduction_axis[1] = 3;
Tensor<int, 2> result = tensor_map.sum(reduction_axis);
Tensor<int, 2> result2 = tensor_map_const.sum(reduction_axis);
Tensor<int, 2> result3 = tensor_map_const_const.sum(reduction_axis);
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 5; ++j) {
int sum = 0;
for (int k = 0; k < 3; ++k) {
for (int l = 0; l < 7; ++l) {
sum += tensor_map(i, k, j, l);
}
}
VERIFY_IS_EQUAL(result(i, j), sum);
VERIFY_IS_EQUAL(result2(i, j), sum);
VERIFY_IS_EQUAL(result3(i, j), sum);
}
}
}
void test_cxx11_tensor_reduction()
{
CALL_SUBTEST(test_simple_reductions());
CALL_SUBTEST(test_full_reductions());
CALL_SUBTEST(test_user_defined_reductions());
CALL_SUBTEST(test_tensor_maps());
}