mirror of
https://gitlab.com/libeigen/eigen.git
synced 2025-06-04 18:54:00 +08:00
Merged in rryan/eigen/tensorfunctors (pull request PR-233)
Fully support complex types in SumReducer and MeanReducer when building for CUDA by using scalar_sum_op and scalar_product_op instead of operator+ and operator*.
This commit is contained in:
commit
33fba3f08d
@ -124,7 +124,8 @@ template <typename T> struct SumReducer
|
|||||||
}
|
}
|
||||||
template <typename Packet>
|
template <typename Packet>
|
||||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
|
||||||
return saccum + predux(vaccum);
|
internal::scalar_sum_op<T> sum_op;
|
||||||
|
return sum_op(saccum, predux(vaccum));
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
@ -173,7 +174,8 @@ template <typename T> struct MeanReducer
|
|||||||
}
|
}
|
||||||
template <typename Packet>
|
template <typename Packet>
|
||||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
|
||||||
return (saccum + predux(vaccum)) / (scalarCount_ + packetCount_ * unpacket_traits<Packet>::size);
|
internal::scalar_sum_op<T> sum_op;
|
||||||
|
return sum_op(saccum, predux(vaccum)) / (scalarCount_ + packetCount_ * unpacket_traits<Packet>::size);
|
||||||
}
|
}
|
||||||
|
|
||||||
protected:
|
protected:
|
||||||
@ -304,7 +306,8 @@ template <typename T> struct ProdReducer
|
|||||||
static const bool IsStateful = false;
|
static const bool IsStateful = false;
|
||||||
|
|
||||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
|
||||||
(*accum) *= t;
|
internal::scalar_product_op<T> prod_op;
|
||||||
|
(*accum) = prod_op(*accum, t);
|
||||||
}
|
}
|
||||||
template <typename Packet>
|
template <typename Packet>
|
||||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
|
||||||
@ -328,7 +331,8 @@ template <typename T> struct ProdReducer
|
|||||||
}
|
}
|
||||||
template <typename Packet>
|
template <typename Packet>
|
||||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
|
||||||
return saccum * predux_mul(vaccum);
|
internal::scalar_product_op<T> prod_op;
|
||||||
|
return prod_op(saccum, predux_mul(vaccum));
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
|
@ -108,8 +108,46 @@ static void test_cuda_sum_reductions() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
static void test_cuda_product_reductions() {
|
||||||
|
|
||||||
|
Eigen::CudaStreamDevice stream;
|
||||||
|
Eigen::GpuDevice gpu_device(&stream);
|
||||||
|
|
||||||
|
const int num_rows = internal::random<int>(1024, 5*1024);
|
||||||
|
const int num_cols = internal::random<int>(1024, 5*1024);
|
||||||
|
|
||||||
|
Tensor<std::complex<float>, 2> in(num_rows, num_cols);
|
||||||
|
in.setRandom();
|
||||||
|
|
||||||
|
Tensor<std::complex<float>, 0> full_redux;
|
||||||
|
full_redux = in.prod();
|
||||||
|
|
||||||
|
std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
|
||||||
|
std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
|
||||||
|
std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
|
||||||
|
std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
|
||||||
|
gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
|
||||||
|
|
||||||
|
TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
|
||||||
|
TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
|
||||||
|
|
||||||
|
out_gpu.device(gpu_device) = in_gpu.prod();
|
||||||
|
|
||||||
|
Tensor<std::complex<float>, 0> full_redux_gpu;
|
||||||
|
gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
|
||||||
|
gpu_device.synchronize();
|
||||||
|
|
||||||
|
// Check that the CPU and GPU reductions return the same result.
|
||||||
|
VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
|
||||||
|
|
||||||
|
gpu_device.deallocate(gpu_in_ptr);
|
||||||
|
gpu_device.deallocate(gpu_out_ptr);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
void test_cxx11_tensor_complex()
|
void test_cxx11_tensor_complex()
|
||||||
{
|
{
|
||||||
CALL_SUBTEST(test_cuda_nullary());
|
CALL_SUBTEST(test_cuda_nullary());
|
||||||
CALL_SUBTEST(test_cuda_sum_reductions());
|
CALL_SUBTEST(test_cuda_sum_reductions());
|
||||||
|
CALL_SUBTEST(test_cuda_product_reductions());
|
||||||
}
|
}
|
||||||
|
Loading…
x
Reference in New Issue
Block a user