From 892b79f5c5f86a18f445ab743575a61ba8d8a5ae Mon Sep 17 00:00:00 2001 From: Laurent FAINSIN Date: Mon, 24 Apr 2023 11:53:37 +0200 Subject: [PATCH] =?UTF-8?q?=F0=9F=8E=A8=20autoformatting=20cpp/cu=20files?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cuda/emd.cpp | 11 +- cuda/emd_kernel.cu | 690 ++++++++++++++++++++++++--------------------- 2 files changed, 368 insertions(+), 333 deletions(-) diff --git a/cuda/emd.cpp b/cuda/emd.cpp index b94db14..53be069 100755 --- a/cuda/emd.cpp +++ b/cuda/emd.cpp @@ -4,7 +4,7 @@ #include #include -//CUDA declarations +// CUDA declarations at::Tensor ApproxMatchForward( const at::Tensor xyz1, const at::Tensor xyz2); @@ -20,10 +20,11 @@ std::vector MatchCostBackward( const at::Tensor xyz2, const at::Tensor match); -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("approxmatch_forward", &ApproxMatchForward,"ApproxMatch forward (CUDA)"); - m.def("matchcost_forward", &MatchCostForward,"MatchCost forward (CUDA)"); - m.def("matchcost_backward", &MatchCostBackward,"MatchCost backward (CUDA)"); +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("approxmatch_forward", &ApproxMatchForward, "ApproxMatch forward (CUDA)"); + m.def("matchcost_forward", &MatchCostForward, "MatchCost forward (CUDA)"); + m.def("matchcost_backward", &MatchCostBackward, "MatchCost backward (CUDA)"); } #endif diff --git a/cuda/emd_kernel.cu b/cuda/emd_kernel.cu index de4db29..4da67c6 100644 --- a/cuda/emd_kernel.cu +++ b/cuda/emd_kernel.cu @@ -10,390 +10,424 @@ #include #include -#include // at::cuda::getApplyGrid +#include // 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) - +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) /******************************** -* Forward kernel for approxmatch -*********************************/ + * Forward kernel for approxmatch + *********************************/ -template -__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=-2;j--){ - scalar_t level=-powf(4.0f,j); - if (j==-2){ - level=0; - } - for (int k0=0;k0 +__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; + scalar_t *remainR = temp + blockIdx.x * (n + m) * 2 + n; + scalar_t *ratioL = temp + blockIdx.x * (n + m) * 2 + n + m; + scalar_t *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 + xyz1: (B, N1, 3) # dataset_points + xyz2: (B, N2, 3) # query_points Output: - match: (B, N2, N1) + 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); + 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); + 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()); + 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<<<32,512>>>(b, n, m, xyz1.data(), xyz2.data(), match.data(), temp.data()); - })); - C10_CUDA_CHECK(cudaGetLastError()); + AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "ApproxMatchForward", ([&] + { approxmatch<<<32, 512>>>(b, n, m, xyz1.data(), xyz2.data(), match.data(), temp.data()); })); + C10_CUDA_CHECK(cudaGetLastError()); - return match; + return match; } - /******************************** -* Forward kernel for matchcost -*********************************/ + * Forward kernel for matchcost + *********************************/ -template -__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 +__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) + xyz1: (B, N1, 3) # dataset_points + xyz2: (B, N2, 3) # query_points + match: (B, N2, N1) Output: - cost: (B) + 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); + 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); + 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()); + auto cost = at::zeros({b}, xyz1.type()); - AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&] { - matchcost<<<32,512>>>(b, n, m, xyz1.data(), xyz2.data(), match.data(), cost.data()); - })); - C10_CUDA_CHECK(cudaGetLastError()); + AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&] + { matchcost<<<32, 512>>>(b, n, m, xyz1.data(), xyz2.data(), match.data(), cost.data()); })); + C10_CUDA_CHECK(cudaGetLastError()); - return cost; -} - - -/******************************** -* matchcostgrad2 kernel -*********************************/ - -template -__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 -__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 +__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(); + } + } } -//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<<>>(b,n,m,xyz1,xyz2,match,grad2); -//} +/******************************** + * matchcostgrad1 kernel + *********************************/ +template +__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]; + } + } +} /* 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) + 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) + grad1: (B, N1, 3) + grad2: (B, N2, 3) */ std::vector 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); + 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); + 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()); + 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", ([&] { + AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostBackward", ([&] + { matchcostgrad1<<<32,512>>>(b, n, m, grad_cost.data(), xyz1.data(), xyz2.data(), match.data(), grad1.data()); - matchcostgrad2<<>>(b, n, m, grad_cost.data(), xyz1.data(), xyz2.data(), match.data(), grad2.data()); - })); - C10_CUDA_CHECK(cudaGetLastError()); + matchcostgrad2<<>>(b, n, m, grad_cost.data(), xyz1.data(), xyz2.data(), match.data(), grad2.data()); })); + C10_CUDA_CHECK(cudaGetLastError()); - return std::vector({grad1, grad2}); + return std::vector({grad1, grad2}); } #endif