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PyTorchEMD/cuda/emd_kernel.cu
Laurent FAINSIN 7e145fd312 🗑️ replace deprecated cuda instructions
2023-04-24 11:39:02 +02:00

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/**********************************
* Original Author: Haoqiang Fan
* Modified by: Kaichun Mo
*********************************/
#ifndef _EMD_KERNEL
#define _EMD_KERNEL
#include <cmath>
#include <vector>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAApplyUtils.cuh> // at::cuda::getApplyGrid
#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
/********************************
* Forward kernel for approxmatch
*********************************/
template<typename scalar_t>
__global__ void approxmatch(int b,int n,int m,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,scalar_t * __restrict__ match,scalar_t * temp){
scalar_t * remainL=temp+blockIdx.x*(n+m)*2, * remainR=temp+blockIdx.x*(n+m)*2+n,*ratioL=temp+blockIdx.x*(n+m)*2+n+m,*ratioR=temp+blockIdx.x*(n+m)*2+n+m+n;
scalar_t multiL,multiR;
if (n>=m){
multiL=1;
multiR=n/m;
}else{
multiL=m/n;
multiR=1;
}
const int Block=1024;
__shared__ scalar_t buf[Block*4];
for (int i=blockIdx.x;i<b;i+=gridDim.x){
for (int j=threadIdx.x;j<n*m;j+=blockDim.x)
match[i*n*m+j]=0;
for (int j=threadIdx.x;j<n;j+=blockDim.x)
remainL[j]=multiL;
for (int j=threadIdx.x;j<m;j+=blockDim.x)
remainR[j]=multiR;
__syncthreads();
for (int j=7;j>=-2;j--){
scalar_t level=-powf(4.0f,j);
if (j==-2){
level=0;
}
for (int k0=0;k0<n;k0+=blockDim.x){
int k=k0+threadIdx.x;
scalar_t x1=0,y1=0,z1=0;
if (k<n){
x1=xyz1[i*n*3+k*3+0];
y1=xyz1[i*n*3+k*3+1];
z1=xyz1[i*n*3+k*3+2];
}
scalar_t suml=1e-9f;
for (int l0=0;l0<m;l0+=Block){
int lend=min(m,l0+Block)-l0;
for (int l=threadIdx.x;l<lend;l+=blockDim.x){
scalar_t x2=xyz2[i*m*3+l0*3+l*3+0];
scalar_t y2=xyz2[i*m*3+l0*3+l*3+1];
scalar_t z2=xyz2[i*m*3+l0*3+l*3+2];
buf[l*4+0]=x2;
buf[l*4+1]=y2;
buf[l*4+2]=z2;
buf[l*4+3]=remainR[l0+l];
}
__syncthreads();
for (int l=0;l<lend;l++){
scalar_t x2=buf[l*4+0];
scalar_t y2=buf[l*4+1];
scalar_t z2=buf[l*4+2];
scalar_t d=level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1));
scalar_t w=__expf(d)*buf[l*4+3];
suml+=w;
}
__syncthreads();
}
if (k<n)
ratioL[k]=remainL[k]/suml;
}
__syncthreads();
for (int l0=0;l0<m;l0+=blockDim.x){
int l=l0+threadIdx.x;
scalar_t x2=0,y2=0,z2=0;
if (l<m){
x2=xyz2[i*m*3+l*3+0];
y2=xyz2[i*m*3+l*3+1];
z2=xyz2[i*m*3+l*3+2];
}
scalar_t sumr=0;
for (int k0=0;k0<n;k0+=Block){
int kend=min(n,k0+Block)-k0;
for (int k=threadIdx.x;k<kend;k+=blockDim.x){
buf[k*4+0]=xyz1[i*n*3+k0*3+k*3+0];
buf[k*4+1]=xyz1[i*n*3+k0*3+k*3+1];
buf[k*4+2]=xyz1[i*n*3+k0*3+k*3+2];
buf[k*4+3]=ratioL[k0+k];
}
__syncthreads();
for (int k=0;k<kend;k++){
scalar_t x1=buf[k*4+0];
scalar_t y1=buf[k*4+1];
scalar_t z1=buf[k*4+2];
scalar_t w=__expf(level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)))*buf[k*4+3];
sumr+=w;
}
__syncthreads();
}
if (l<m){
sumr*=remainR[l];
scalar_t consumption=fminf(remainR[l]/(sumr+1e-9f),1.0f);
ratioR[l]=consumption*remainR[l];
remainR[l]=fmaxf(0.0f,remainR[l]-sumr);
}
}
__syncthreads();
for (int k0=0;k0<n;k0+=blockDim.x){
int k=k0+threadIdx.x;
scalar_t x1=0,y1=0,z1=0;
if (k<n){
x1=xyz1[i*n*3+k*3+0];
y1=xyz1[i*n*3+k*3+1];
z1=xyz1[i*n*3+k*3+2];
}
scalar_t suml=0;
for (int l0=0;l0<m;l0+=Block){
int lend=min(m,l0+Block)-l0;
for (int l=threadIdx.x;l<lend;l+=blockDim.x){
buf[l*4+0]=xyz2[i*m*3+l0*3+l*3+0];
buf[l*4+1]=xyz2[i*m*3+l0*3+l*3+1];
buf[l*4+2]=xyz2[i*m*3+l0*3+l*3+2];
buf[l*4+3]=ratioR[l0+l];
}
__syncthreads();
scalar_t rl=ratioL[k];
if (k<n){
for (int l=0;l<lend;l++){
scalar_t x2=buf[l*4+0];
scalar_t y2=buf[l*4+1];
scalar_t z2=buf[l*4+2];
scalar_t w=__expf(level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)))*rl*buf[l*4+3];
match[i*n*m+(l0+l)*n+k]+=w;
suml+=w;
}
}
__syncthreads();
}
if (k<n)
remainL[k]=fmaxf(0.0f,remainL[k]-suml);
}
__syncthreads();
}
}
}
//void approxmatchLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,scalar_t * match,scalar_t * temp){
// approxmatch<<<32,512>>>(b,n,m,xyz1,xyz2,match,temp);
//}
/* ApproxMatch forward interface
Input:
xyz1: (B, N1, 3) # dataset_points
xyz2: (B, N2, 3) # query_points
Output:
match: (B, N2, N1)
*/
at::Tensor ApproxMatchForward(
const at::Tensor xyz1,
const at::Tensor xyz2){
const auto b = xyz1.size(0);
const auto n = xyz1.size(1);
const auto m = xyz2.size(1);
TORCH_CHECK_EQ (xyz2.size(0), b);
TORCH_CHECK_EQ (xyz1.size(2), 3);
TORCH_CHECK_EQ (xyz2.size(2), 3);
CHECK_INPUT(xyz1);
CHECK_INPUT(xyz2);
auto match = at::zeros({b, m, n}, xyz1.type());
auto temp = at::zeros({b, (n+m)*2}, xyz1.type());
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "ApproxMatchForward", ([&] {
approxmatch<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), temp.data<scalar_t>());
}));
C10_CUDA_CHECK(cudaGetLastError());
return match;
}
/********************************
* Forward kernel for matchcost
*********************************/
template<typename scalar_t>
__global__ void matchcost(int b,int n,int m,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ out){
__shared__ scalar_t allsum[512];
const int Block=1024;
__shared__ scalar_t buf[Block*3];
for (int i=blockIdx.x;i<b;i+=gridDim.x){
scalar_t subsum=0;
for (int k0=0;k0<n;k0+=blockDim.x){
int k=k0+threadIdx.x;
scalar_t x1=0,y1=0,z1=0;
if (k<n){
x1=xyz1[i*n*3+k*3+0];
y1=xyz1[i*n*3+k*3+1];
z1=xyz1[i*n*3+k*3+2];
}
for (int l0=0;l0<m;l0+=Block){
int lend=min(m,l0+Block)-l0;
for (int l=threadIdx.x;l<lend*3;l+=blockDim.x)
buf[l]=xyz2[i*m*3+l0*3+l];
__syncthreads();
if (k<n){
for (int l=0;l<lend;l++){
scalar_t x2=buf[l*3+0];
scalar_t y2=buf[l*3+1];
scalar_t z2=buf[l*3+2];
scalar_t d=(x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1);
subsum+=d*match[i*n*m+(l0+l)*n+k];
}
}
__syncthreads();
}
}
allsum[threadIdx.x]=subsum;
for (int j=1;j<blockDim.x;j<<=1){
__syncthreads();
if ((threadIdx.x&j)==0 && threadIdx.x+j<blockDim.x){
allsum[threadIdx.x]+=allsum[threadIdx.x+j];
}
}
if (threadIdx.x==0)
out[i]=allsum[0];
__syncthreads();
}
}
//void matchcostLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * out){
// matchcost<<<32,512>>>(b,n,m,xyz1,xyz2,match,out);
//}
/* MatchCost forward interface
Input:
xyz1: (B, N1, 3) # dataset_points
xyz2: (B, N2, 3) # query_points
match: (B, N2, N1)
Output:
cost: (B)
*/
at::Tensor MatchCostForward(
const at::Tensor xyz1,
const at::Tensor xyz2,
const at::Tensor match){
const auto b = xyz1.size(0);
const auto n = xyz1.size(1);
const auto m = xyz2.size(1);
TORCH_CHECK_EQ (xyz2.size(0), b);
TORCH_CHECK_EQ (xyz1.size(2), 3);
TORCH_CHECK_EQ (xyz2.size(2), 3);
CHECK_INPUT(xyz1);
CHECK_INPUT(xyz2);
auto cost = at::zeros({b}, xyz1.type());
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&] {
matchcost<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), cost.data<scalar_t>());
}));
C10_CUDA_CHECK(cudaGetLastError());
return cost;
}
/********************************
* matchcostgrad2 kernel
*********************************/
template<typename scalar_t>
__global__ void matchcostgrad2(int b,int n,int m,const scalar_t * __restrict__ grad_cost,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ grad2){
__shared__ scalar_t sum_grad[256*3];
for (int i=blockIdx.x;i<b;i+=gridDim.x){
int kbeg=m*blockIdx.y/gridDim.y;
int kend=m*(blockIdx.y+1)/gridDim.y;
for (int k=kbeg;k<kend;k++){
scalar_t x2=xyz2[(i*m+k)*3+0];
scalar_t y2=xyz2[(i*m+k)*3+1];
scalar_t z2=xyz2[(i*m+k)*3+2];
scalar_t subsumx=0,subsumy=0,subsumz=0;
for (int j=threadIdx.x;j<n;j+=blockDim.x){
scalar_t x1=x2-xyz1[(i*n+j)*3+0];
scalar_t y1=y2-xyz1[(i*n+j)*3+1];
scalar_t z1=z2-xyz1[(i*n+j)*3+2];
scalar_t d=match[i*n*m+k*n+j]*2;
subsumx+=x1*d;
subsumy+=y1*d;
subsumz+=z1*d;
}
sum_grad[threadIdx.x*3+0]=subsumx;
sum_grad[threadIdx.x*3+1]=subsumy;
sum_grad[threadIdx.x*3+2]=subsumz;
for (int j=1;j<blockDim.x;j<<=1){
__syncthreads();
int j1=threadIdx.x;
int j2=threadIdx.x+j;
if ((j1&j)==0 && j2<blockDim.x){
sum_grad[j1*3+0]+=sum_grad[j2*3+0];
sum_grad[j1*3+1]+=sum_grad[j2*3+1];
sum_grad[j1*3+2]+=sum_grad[j2*3+2];
}
}
if (threadIdx.x==0){
grad2[(i*m+k)*3+0]=sum_grad[0]*grad_cost[i];
grad2[(i*m+k)*3+1]=sum_grad[1]*grad_cost[i];
grad2[(i*m+k)*3+2]=sum_grad[2]*grad_cost[i];
}
__syncthreads();
}
}
}
/********************************
* matchcostgrad1 kernel
*********************************/
template<typename scalar_t>
__global__ void matchcostgrad1(int b,int n,int m,const scalar_t * __restrict__ grad_cost,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ grad1){
for (int i=blockIdx.x;i<b;i+=gridDim.x){
for (int l=threadIdx.x;l<n;l+=blockDim.x){
scalar_t x1=xyz1[i*n*3+l*3+0];
scalar_t y1=xyz1[i*n*3+l*3+1];
scalar_t z1=xyz1[i*n*3+l*3+2];
scalar_t dx=0,dy=0,dz=0;
for (int k=0;k<m;k++){
scalar_t x2=xyz2[i*m*3+k*3+0];
scalar_t y2=xyz2[i*m*3+k*3+1];
scalar_t z2=xyz2[i*m*3+k*3+2];
scalar_t d=match[i*n*m+k*n+l]*2;
dx+=(x1-x2)*d;
dy+=(y1-y2)*d;
dz+=(z1-z2)*d;
}
grad1[i*n*3+l*3+0]=dx*grad_cost[i];
grad1[i*n*3+l*3+1]=dy*grad_cost[i];
grad1[i*n*3+l*3+2]=dz*grad_cost[i];
}
}
}
//void matchcostgradLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * grad1,scalar_t * grad2){
// matchcostgrad1<<<32,512>>>(b,n,m,xyz1,xyz2,match,grad1);
// matchcostgrad2<<<dim3(32,32),256>>>(b,n,m,xyz1,xyz2,match,grad2);
//}
/* MatchCost backward interface
Input:
grad_cost: (B) # gradients on cost
xyz1: (B, N1, 3) # dataset_points
xyz2: (B, N2, 3) # query_points
match: (B, N2, N1)
Output:
grad1: (B, N1, 3)
grad2: (B, N2, 3)
*/
std::vector<at::Tensor> MatchCostBackward(
const at::Tensor grad_cost,
const at::Tensor xyz1,
const at::Tensor xyz2,
const at::Tensor match){
const auto b = xyz1.size(0);
const auto n = xyz1.size(1);
const auto m = xyz2.size(1);
TORCH_CHECK_EQ (xyz2.size(0), b);
TORCH_CHECK_EQ (xyz1.size(2), 3);
TORCH_CHECK_EQ (xyz2.size(2), 3);
CHECK_INPUT(xyz1);
CHECK_INPUT(xyz2);
auto grad1 = at::zeros({b, n, 3}, xyz1.type());
auto grad2 = at::zeros({b, m, 3}, xyz1.type());
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostBackward", ([&] {
matchcostgrad1<scalar_t><<<32,512>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad1.data<scalar_t>());
matchcostgrad2<scalar_t><<<dim3(32,32),256>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad2.data<scalar_t>());
}));
C10_CUDA_CHECK(cudaGetLastError());
return std::vector<at::Tensor>({grad1, grad2});
}
#endif
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