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#include <ATen/ATen.h>
#include <cuda_fp16.h>
#include <vector>
#include "utils/checks.h"
#include "utils/cuda.cuh"
#include "inplace_abn.h"
#include <ATen/cuda/CUDAContext.h>
// Operations for reduce
struct SumOpH {
__device__ SumOpH(const half *t, int c, int s)
: tensor(t), chn(c), sp(s) {}
__device__ __forceinline__ float operator()(int batch, int plane, int n) {
return __half2float(tensor[(batch * chn + plane) * sp + n]);
}
const half *tensor;
const int chn;
const int sp;
};
struct VarOpH {
__device__ VarOpH(float m, const half *t, int c, int s)
: mean(m), tensor(t), chn(c), sp(s) {}
__device__ __forceinline__ float operator()(int batch, int plane, int n) {
const auto t = __half2float(tensor[(batch * chn + plane) * sp + n]);
return (t - mean) * (t - mean);
}
const float mean;
const half *tensor;
const int chn;
const int sp;
};
struct GradOpH {
__device__ GradOpH(float _weight, float _bias, const half *_z, const half *_dz, int c, int s)
: weight(_weight), bias(_bias), z(_z), dz(_dz), chn(c), sp(s) {}
__device__ __forceinline__ Pair<float> operator()(int batch, int plane, int n) {
float _y = (__half2float(z[(batch * chn + plane) * sp + n]) - bias) / weight;
float _dz = __half2float(dz[(batch * chn + plane) * sp + n]);
return Pair<float>(_dz, _y * _dz);
}
const float weight;
const float bias;
const half *z;
const half *dz;
const int chn;
const int sp;
};
/***********
* mean_var
***********/
__global__ void mean_var_kernel_h(const half *x, float *mean, float *var, int num, int chn, int sp) {
int plane = blockIdx.x;
float norm = 1.f / static_cast<float>(num * sp);
float _mean = reduce<float, SumOpH>(SumOpH(x, chn, sp), plane, num, sp) * norm;
__syncthreads();
float _var = reduce<float, VarOpH>(VarOpH(_mean, x, chn, sp), plane, num, sp) * norm;
if (threadIdx.x == 0) {
mean[plane] = _mean;
var[plane] = _var;
}
}
std::vector<at::Tensor> mean_var_cuda_h(at::Tensor x) {
CHECK_CUDA_INPUT(x);
// Extract dimensions
int64_t num, chn, sp;
get_dims(x, num, chn, sp);
// Prepare output tensors
auto mean = at::empty({chn},x.options().dtype(at::kFloat));
auto var = at::empty({chn},x.options().dtype(at::kFloat));
// Run kernel
dim3 blocks(chn);
dim3 threads(getNumThreads(sp));
auto stream = at::cuda::getCurrentCUDAStream();
mean_var_kernel_h<<<blocks, threads, 0, stream>>>(
reinterpret_cast<half*>(x.data<at::Half>()),
mean.data<float>(),
var.data<float>(),
num, chn, sp);
return {mean, var};
}
/**********
* forward
**********/
__global__ void forward_kernel_h(half *x, const float *mean, const float *var, const float *weight, const float *bias,
bool affine, float eps, int num, int chn, int sp) {
int plane = blockIdx.x;
const float _mean = mean[plane];
const float _var = var[plane];
const float _weight = affine ? abs(weight[plane]) + eps : 1.f;
const float _bias = affine ? bias[plane] : 0.f;
const float mul = rsqrt(_var + eps) * _weight;
for (int batch = 0; batch < num; ++batch) {
for (int n = threadIdx.x; n < sp; n += blockDim.x) {
half *x_ptr = x + (batch * chn + plane) * sp + n;
float _x = __half2float(*x_ptr);
float _y = (_x - _mean) * mul + _bias;
*x_ptr = __float2half(_y);
}
}
}
at::Tensor forward_cuda_h(at::Tensor x, at::Tensor mean, at::Tensor var, at::Tensor weight, at::Tensor bias,
bool affine, float eps) {
CHECK_CUDA_INPUT(x);
CHECK_CUDA_INPUT(mean);
CHECK_CUDA_INPUT(var);
CHECK_CUDA_INPUT(weight);
CHECK_CUDA_INPUT(bias);
// Extract dimensions
int64_t num, chn, sp;
get_dims(x, num, chn, sp);
// Run kernel
dim3 blocks(chn);
dim3 threads(getNumThreads(sp));
auto stream = at::cuda::getCurrentCUDAStream();
forward_kernel_h<<<blocks, threads, 0, stream>>>(
reinterpret_cast<half*>(x.data<at::Half>()),
mean.data<float>(),
var.data<float>(),
weight.data<float>(),
bias.data<float>(),
affine, eps, num, chn, sp);
return x;
}
__global__ void edz_eydz_kernel_h(const half *z, const half *dz, const float *weight, const float *bias,
float *edz, float *eydz, bool affine, float eps, int num, int chn, int sp) {
int plane = blockIdx.x;
float _weight = affine ? abs(weight[plane]) + eps : 1.f;
float _bias = affine ? bias[plane] : 0.f;
Pair<float> res = reduce<Pair<float>, GradOpH>(GradOpH(_weight, _bias, z, dz, chn, sp), plane, num, sp);
__syncthreads();
if (threadIdx.x == 0) {
edz[plane] = res.v1;
eydz[plane] = res.v2;
}
}
std::vector<at::Tensor> edz_eydz_cuda_h(at::Tensor z, at::Tensor dz, at::Tensor weight, at::Tensor bias,
bool affine, float eps) {
CHECK_CUDA_INPUT(z);
CHECK_CUDA_INPUT(dz);
CHECK_CUDA_INPUT(weight);
CHECK_CUDA_INPUT(bias);
// Extract dimensions
int64_t num, chn, sp;
get_dims(z, num, chn, sp);
auto edz = at::empty({chn},z.options().dtype(at::kFloat));
auto eydz = at::empty({chn},z.options().dtype(at::kFloat));
// Run kernel
dim3 blocks(chn);
dim3 threads(getNumThreads(sp));
auto stream = at::cuda::getCurrentCUDAStream();
edz_eydz_kernel_h<<<blocks, threads, 0, stream>>>(
reinterpret_cast<half*>(z.data<at::Half>()),
reinterpret_cast<half*>(dz.data<at::Half>()),
weight.data<float>(),
bias.data<float>(),
edz.data<float>(),
eydz.data<float>(),
affine, eps, num, chn, sp);
return {edz, eydz};
}
__global__ void backward_kernel_h(const half *z, const half *dz, const float *var, const float *weight, const float *bias, const float *edz,
const float *eydz, half *dx, bool affine, float eps, int num, int chn, int sp) {
int plane = blockIdx.x;
float _weight = affine ? abs(weight[plane]) + eps : 1.f;
float _bias = affine ? bias[plane] : 0.f;
float _var = var[plane];
float _edz = edz[plane];
float _eydz = eydz[plane];
float _mul = _weight * rsqrt(_var + eps);
float count = float(num * sp);
for (int batch = 0; batch < num; ++batch) {
for (int n = threadIdx.x; n < sp; n += blockDim.x) {
float _dz = __half2float(dz[(batch * chn + plane) * sp + n]);
float _y = (__half2float(z[(batch * chn + plane) * sp + n]) - _bias) / _weight;
dx[(batch * chn + plane) * sp + n] = __float2half((_dz - _edz / count - _y * _eydz / count) * _mul);
}
}
}
at::Tensor backward_cuda_h(at::Tensor z, at::Tensor dz, at::Tensor var, at::Tensor weight, at::Tensor bias,
at::Tensor edz, at::Tensor eydz, bool affine, float eps) {
CHECK_CUDA_INPUT(z);
CHECK_CUDA_INPUT(dz);
CHECK_CUDA_INPUT(var);
CHECK_CUDA_INPUT(weight);
CHECK_CUDA_INPUT(bias);
CHECK_CUDA_INPUT(edz);
CHECK_CUDA_INPUT(eydz);
// Extract dimensions
int64_t num, chn, sp;
get_dims(z, num, chn, sp);
auto dx = at::zeros_like(z);
// Run kernel
dim3 blocks(chn);
dim3 threads(getNumThreads(sp));
auto stream = at::cuda::getCurrentCUDAStream();
backward_kernel_h<<<blocks, threads, 0, stream>>>(
reinterpret_cast<half*>(z.data<at::Half>()),
reinterpret_cast<half*>(dz.data<at::Half>()),
var.data<float>(),
weight.data<float>(),
bias.data<float>(),
edz.data<float>(),
eydz.data<float>(),
reinterpret_cast<half*>(dx.data<at::Half>()),
affine, eps, num, chn, sp);
return dx;
}
__global__ void leaky_relu_backward_impl_h(half *z, half *dz, float slope, int64_t count) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < count; i += blockDim.x * gridDim.x){
float _z = __half2float(z[i]);
if (_z < 0) {
dz[i] = __float2half(__half2float(dz[i]) * slope);
z[i] = __float2half(_z / slope);
}
}
}
void leaky_relu_backward_cuda_h(at::Tensor z, at::Tensor dz, float slope) {
CHECK_CUDA_INPUT(z);
CHECK_CUDA_INPUT(dz);
int64_t count = z.numel();
dim3 threads(getNumThreads(count));
dim3 blocks = (count + threads.x - 1) / threads.x;
auto stream = at::cuda::getCurrentCUDAStream();
leaky_relu_backward_impl_h<<<blocks, threads, 0, stream>>>(
reinterpret_cast<half*>(z.data<at::Half>()),
reinterpret_cast<half*>(dz.data<at::Half>()),
slope, count);
}
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