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Use scalar_sum_op and scalar_quotient_op instead of operator+ and operator/ in MeanReducer.

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Improves support for std::complex types when compiling for CUDA.

Expands on e2e9cdd16970914cf0a892fea5e7c4402b3ede41
 and 2bda1b0d93fb627d0c500ec48b20302d44c32cb7
.
This commit is contained in:
RJ Ryan 2017-04-14 13:23:35 -07:00
parent d9084ac8e1
commit 949a2da38c
2 changed files with 42 additions and 2 deletions
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8
unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
View File

@ -166,7 +166,8 @@ template <typename T> struct MeanReducer
return pset1<Packet>(initialize()); return pset1<Packet>(initialize());
} }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
return accum / scalarCount_; internal::scalar_quotient_op<T> quotient_op;
return quotient_op(accum, T(scalarCount_));
} }
template <typename Packet> template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
@ -175,7 +176,10 @@ 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 {
internal::scalar_sum_op<T> sum_op; internal::scalar_sum_op<T> sum_op;
return sum_op(saccum, predux(vaccum)) / (scalarCount_ + packetCount_ * unpacket_traits<Packet>::size); internal::scalar_quotient_op<T> quotient_op;
return quotient_op(
sum_op(saccum, predux(vaccum)),
T(scalarCount_ + packetCount_ * unpacket_traits<Packet>::size));
} }
protected: protected:

36
unsupported/test/cxx11_tensor_complex_cuda.cu
View File

@ -107,6 +107,41 @@ static void test_cuda_sum_reductions() {
gpu_device.deallocate(gpu_out_ptr); gpu_device.deallocate(gpu_out_ptr);
} }
static void test_cuda_mean_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.mean();
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.mean();
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);
}
static void test_cuda_product_reductions() { static void test_cuda_product_reductions() {
@ -149,5 +184,6 @@ 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_mean_reductions());
CALL_SUBTEST(test_cuda_product_reductions()); CALL_SUBTEST(test_cuda_product_reductions());
} }