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#include "caffe2/operators/relu_op.h"
#include <algorithm>
#include <functional>
#include "caffe2/core/context_gpu.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
namespace {
#ifdef __HIPCC__
using half2 = __half2;
#endif // __HIPCC__
template <typename T>
__global__ void ReluCUDAKernel(const int N, const T* X, T* Y);
#define DELEGATE_RELU_CUDA_KERNEL(T, MaxFunc) \
template <> \
__global__ void ReluCUDAKernel<T>(const int N, const T* X, T* Y) { \
const int i = blockIdx.x * CAFFE_CUDA_NUM_THREADS + threadIdx.x; \
if (i < N) { \
Y[i] = MaxFunc(X[i], T(0)); \
} \
}
DELEGATE_RELU_CUDA_KERNEL(float, fmaxf)
#undef DELEGATE_RELU_CUDA_KERNEL
template <>
__global__ void ReluCUDAKernel<half>(const int N, const half* X, half* Y) {
const int i = blockIdx.x * CAFFE_CUDA_NUM_THREADS + threadIdx.x;
if (i < N) {
const half kZero = __float2half(0.0f);
#if __CUDA_ARCH__ >= 530 || TORCH_HIP_VERSION >= 300
Y[i] = __hgt(__ldg(X + i), kZero) ? __ldg(X + i) : kZero;
#else
Y[i] = (__half2float(X[i]) > 0) ? X[i] : kZero;
#endif
}
}
template <>
__global__ void ReluCUDAKernel<half2>(const int N, const half2* X, half2* Y) {
const int i = blockIdx.x * CAFFE_CUDA_NUM_THREADS + threadIdx.x;
if (i < N) {
const half2 kZero = __float2half2_rn(0.0f);
#if __CUDA_ARCH__ >= 530 || TORCH_HIP_VERSION >= 300
Y[i] = __hmul2(__hgt2(__ldg(X + i), kZero), __ldg(X + i));
#else
const float2 xx = __half22float2(X[i]);
// There are explicit cast to float here, because it may otherwise cause ambiguity on ROCm and can be triggered
// sometimes:
//
// error: conditional expression is ambiguous; 'const hip_impl::Scalar_accessor<float, Native_vec_, 0>' can be
// converted to 'float' and vice versa
Y[i] = __floats2half2_rn(xx.x > 0.0f ? static_cast<float>(xx.x) : 0.0f,
xx.y > 0.0f ? static_cast<float>(xx.y) : 0.0f);
#endif
}
}
template <typename T>
__global__ void
ReluGradientCUDAKernel(const int N, const T* dY, const T* Y, T* dX) {
const int i = blockIdx.x * CAFFE_CUDA_NUM_THREADS + threadIdx.x;
if (i < N) {
#if __CUDA_ARCH__ >= 350 || TORCH_HIP_VERSION >= 300
dX[i] = __ldg(Y + i) > T(0) ? __ldg(dY + i) : T(0);
#else
dX[i] = Y[i] > T(0) ? dY[i] : T(0);
#endif
}
}
template <>
__global__ void ReluGradientCUDAKernel<half>(
const int N,
const half* dY,
const half* Y,
half* dX) {
const int i = blockIdx.x * CAFFE_CUDA_NUM_THREADS + threadIdx.x;
if (i < N) {
const half kZero = __float2half(0.0f);
#if __CUDA_ARCH__ >= 530 || TORCH_HIP_VERSION >= 300
dX[i] = __hgt(__ldg(Y + i), kZero) ? __ldg(dY + i) : kZero;
#else
dX[i] = (__half2float(Y[i]) > 0) ? dY[i] : kZero;
#endif
}
}
template <>
__global__ void ReluGradientCUDAKernel<half2>(
const int N,
const half2* dY,
const half2* Y,
half2* dX) {
const int i = blockIdx.x * CAFFE_CUDA_NUM_THREADS + threadIdx.x;
if (i < N) {
const half2 kZero = __float2half2_rn(0.0f);
#if __CUDA_ARCH__ >= 530 || TORCH_HIP_VERSION >= 300
dX[i] = __hmul2(__hgt2(__ldg(Y + i), kZero), __ldg(dY + i));
#else
const float2 dy = __half22float2(dY[i]);
const float2 yy = __half22float2(Y[i]);
// There are explicit cast to float here, because it may otherwise cause ambiguity on ROCm and can be triggered
// sometimes:
//
// error: conditional expression is ambiguous; 'const hip_impl::Scalar_accessor<float, Native_vec_, 1>' can be
// converted to 'float' and vice versa
dX[i] = __floats2half2_rn(yy.x > 0.0f ? static_cast<float>(dy.x) : 0.0f,
yy.y > 0.0f ? static_cast<float>(dy.y) : 0.0f);
#endif
}
}
} // namespace
template <>
template <typename T>
bool ReluFunctor<CUDAContext>::
operator()(const int N, const T* X, T* Y, CUDAContext* context) const {
if (N > 0) {
const int M = math::DivUp(N, CAFFE_CUDA_NUM_THREADS);
ReluCUDAKernel<T>
<<<M, CAFFE_CUDA_NUM_THREADS, 0, context->cuda_stream()>>>(N, X, Y);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
template <>
template <>
bool ReluFunctor<CUDAContext>::operator()<at::Half>(
const int N,
const at::Half* X,
at::Half* Y,
CUDAContext* context) const {
if (N == 0) {
return true;
}
if (N % 2 == 0) {
const int M = math::DivUp(N / 2, CAFFE_CUDA_NUM_THREADS);
ReluCUDAKernel<half2>
<<<M, CAFFE_CUDA_NUM_THREADS, 0, context->cuda_stream()>>>(
N / 2,
reinterpret_cast<const half2*>(X),
reinterpret_cast<half2*>(Y));
C10_CUDA_KERNEL_LAUNCH_CHECK();
} else {
const int M = math::DivUp(N, CAFFE_CUDA_NUM_THREADS);
ReluCUDAKernel<half>
<<<M, CAFFE_CUDA_NUM_THREADS, 0, context->cuda_stream()>>>(
N, reinterpret_cast<const half*>(X), reinterpret_cast<half*>(Y));
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
template <>
template <typename T>
bool ReluGradientFunctor<CUDAContext>::Forward(
const std::vector<int>& Y_dims,
const std::vector<int>& /* dY_dims */,
const T* Y,
const T* dY,
T* dX,
CUDAContext* context) const {
const int N = std::accumulate(
Y_dims.cbegin(), Y_dims.cend(), 1, std::multiplies<int>());
if (N > 0) {
const int M = math::DivUp(N, CAFFE_CUDA_NUM_THREADS);
ReluGradientCUDAKernel<T>
<<<M, CAFFE_CUDA_NUM_THREADS, 0, context->cuda_stream()>>>(
N, dY, Y, dX);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
template <>
template <>
bool ReluGradientFunctor<CUDAContext>::Forward<at::Half>(
const std::vector<int>& Y_dims,
const std::vector<int>& /* dY_dims */,
const at::Half* Y,
const at::Half* dY,
at::Half* dX,
CUDAContext* context) const {
const int N = std::accumulate(
Y_dims.cbegin(), Y_dims.cend(), 1, std::multiplies<int>());
if (N == 0) {
return true;
}
if (N % 2 == 0) {
const int M = math::DivUp(N / 2, CAFFE_CUDA_NUM_THREADS);
ReluGradientCUDAKernel<half2>
<<<M, CAFFE_CUDA_NUM_THREADS, 0, context->cuda_stream()>>>(
N / 2,
reinterpret_cast<const half2*>(dY),
reinterpret_cast<const half2*>(Y),
reinterpret_cast<half2*>(dX));
C10_CUDA_KERNEL_LAUNCH_CHECK();
} else {
const int M = math::DivUp(N, CAFFE_CUDA_NUM_THREADS);
ReluGradientCUDAKernel<half>
<<<M, CAFFE_CUDA_NUM_THREADS, 0, context->cuda_stream()>>>(
N,
reinterpret_cast<const half*>(dY),
reinterpret_cast<const half*>(Y),
reinterpret_cast<half*>(dX));
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
REGISTER_CUDA_OPERATOR(
Relu,
UnaryElementwiseOp<
TensorTypes<float, at::Half>,
CUDAContext,
ReluFunctor<CUDAContext>>);
REGISTER_CUDA_OPERATOR(
ReluGradient,
BinaryElementwiseOp<
TensorTypes<float, at::Half>,
CUDAContext,
ReluGradientFunctor<CUDAContext>>);
} // namespace caffe2
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