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Fixing a CUDA / P100 regression introduced by PR 181

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PR 181 ( https://gitlab.com/libeigen/eigen/-/merge_requests/181 ) adds `__launch_bounds__(1024)` attribute to GPU kernels, that did not have that attribute explicitly specified.

That PR seems to cause regressions on the CUDA platform. This PR/commit makes the changes in PR 181, to be applicable for HIP only
This commit is contained in:
Deven Desai
2020-08-19 20:06:39 +00:00
parent c060114a25
commit 603e213d13
6 changed files with 40 additions and 21 deletions
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18
unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
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@@ -121,7 +121,7 @@ __device__ inline void atomicReduce(float* output, float accum, SumReducer<float
template <typename CoeffType, typename Index>
__global__ __launch_bounds__(1024) void ReductionInitKernel(const CoeffType val, Index num_preserved_coeffs, CoeffType* output) {
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernel(const CoeffType val, Index num_preserved_coeffs, CoeffType* output) {
const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
const Index num_threads = blockDim.x * gridDim.x;
for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
@@ -132,7 +132,7 @@ __global__ __launch_bounds__(1024) void ReductionInitKernel(const CoeffType val,
template <int BlockSize, int NumPerThread, typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs,
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs,
typename Self::CoeffReturnType* output, unsigned int* semaphore) {
#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
// Initialize the output value
@@ -214,7 +214,7 @@ __global__ __launch_bounds__(1024) void FullReductionKernel(Reducer reducer, con
#ifdef EIGEN_HAS_GPU_FP16
template <typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void ReductionInitFullReduxKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
packet_traits<Eigen::half>::type* scratch) {
eigen_assert(blockDim.x == 1);
eigen_assert(gridDim.x == 1);
@@ -239,7 +239,7 @@ __global__ __launch_bounds__(1024) void ReductionInitFullReduxKernelHalfFloat(Re
template <typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void ReductionInitKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, half* output) {
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, half* output) {
const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
const Index num_threads = blockDim.x * gridDim.x;
typedef typename packet_traits<Eigen::half>::type PacketType;
@@ -259,7 +259,7 @@ __global__ __launch_bounds__(1024) void ReductionInitKernelHalfFloat(Reducer red
template <int BlockSize, int NumPerThread, typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void FullReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
half* output, packet_traits<Eigen::half>::type* scratch) {
typedef typename packet_traits<Eigen::half>::type PacketType;
const int packet_width = unpacket_traits<PacketType>::size;
@@ -358,7 +358,7 @@ __global__ __launch_bounds__(1024) void FullReductionKernelHalfFloat(Reducer red
}
template <typename Op>
__global__ __launch_bounds__(1024) void ReductionCleanupKernelHalfFloat(Op reducer, half* output, packet_traits<Eigen::half>::type* scratch) {
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionCleanupKernelHalfFloat(Op reducer, half* output, packet_traits<Eigen::half>::type* scratch) {
eigen_assert(threadIdx.x == 1);
half2* pscratch = reinterpret_cast<half2*>(scratch);
half tmp = __float2half(0.f);
@@ -476,7 +476,7 @@ struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
template <int NumPerThread, typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
typename Self::CoeffReturnType* output) {
#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
typedef typename Self::CoeffReturnType Type;
@@ -561,7 +561,7 @@ __global__ __launch_bounds__(1024) void InnerReductionKernel(Reducer reducer, co
template <int NumPerThread, typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void InnerReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
half* output) {
eigen_assert(blockDim.y == 1);
eigen_assert(blockDim.z == 1);
@@ -868,7 +868,7 @@ struct InnerReducer<Self, Op, GpuDevice> {
template <int NumPerThread, typename Self,
typename Reducer, typename Index>
__global__ __launch_bounds__(1024) void OuterReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
typename Self::CoeffReturnType* output) {
const Index num_threads = blockDim.x * gridDim.x;
const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;