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Consolidate mesh backward kernels
Summary: This diff creates the generic MeshBackwardKernel which can handle distance calculations between point, edge and faces in either direction. Replaces only point_mesh_face code for now. Reviewed By: gkioxari Differential Revision: D24549374 fbshipit-source-id: 2853c1da1c2a6b6de8d0e40007ba0735b8959044
This commit is contained in:
Dave Schnizlein
Facebook GitHub Bot
parent
c41aff23f0
commit
8dcfe30f66
@ -212,86 +212,135 @@ std::tuple<at::Tensor, at::Tensor> PointFaceDistanceForwardCuda(
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points, 1, points_first_idx, tris, 3, tris_first_idx, max_points);
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}
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__global__ void PointFaceBackwardKernel(
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const float* __restrict__ points, // (P, 3)
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const float* __restrict__ tris, // (T, 3, 3)
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const int64_t* __restrict__ idx_points, // (P,)
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const float* __restrict__ grad_dists, // (P,)
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float* __restrict__ grad_points, // (P, 3)
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float* __restrict__ grad_tris, // (T, 3, 3)
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const size_t P) {
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float3* points_f3 = (float3*)points;
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float3* tris_f3 = (float3*)tris;
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__global__ void DistanceBackwardKernel(
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const float* __restrict__ objects, // (O * oD * 3)
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const size_t objects_size, // O
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const size_t objects_dim, // oD
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const float* __restrict__ targets, // (T * tD * 3)
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const size_t targets_dim, // tD
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const int64_t* __restrict__ idx_objects, // (O,)
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const float* __restrict__ grad_dists, // (O,)
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float* __restrict__ grad_points, // ((O or T) * 3)
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float* __restrict__ grad_face) { // ((O or T) * max(oD, tD) * 3)
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// This kernel is used interchangeably to compute bi-directional backward
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// distances between points and triangles/lines. The direction of the distance
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// computed, i.e. point to triangle/line or triangle/line to point, depends on
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// the order of the input arguments and is inferred based on their shape. The
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// shape is used to distinguish between triangles and lines. Note that
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// grad_points will always be used for the point data and grad_face for the
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// edge/triangle
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// Set references to points/face based on whether objects/targets are which
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float3* points_f3 = objects_dim == 1 ? (float3*)objects : (float3*)targets;
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float3* face_f3 = objects_dim == 1 ? (float3*)targets : (float3*)objects;
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const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
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const size_t stride = gridDim.x * blockDim.x;
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for (size_t p = tid; p < P; p += stride) {
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const float3 p_f3 = points_f3[p];
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for (size_t o = tid; o < objects_size; o += stride) {
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const int64_t tidx = idx_objects[o];
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const int64_t tidx = idx_points[p];
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const float3 v0 = tris_f3[tidx * 3 + 0];
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const float3 v1 = tris_f3[tidx * 3 + 1];
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const float3 v2 = tris_f3[tidx * 3 + 2];
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size_t point_index = objects_dim == 1 ? o : tidx;
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size_t face_index = objects_dim == 1 ? tidx * targets_dim : o * objects_dim;
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bool isTriangle = objects_dim == 3 || targets_dim == 3;
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const float grad_dist = grad_dists[p];
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float3 grad_point, grad_v0, grad_v1, grad_v2;
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if (isTriangle) {
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const auto grads = PointTriangle3DistanceBackward(
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points_f3[point_index],
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face_f3[face_index],
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face_f3[face_index + 1],
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face_f3[face_index + 2],
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grad_dists[o]);
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grad_point = thrust::get<0>(grads);
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grad_v0 = thrust::get<1>(grads);
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grad_v1 = thrust::get<2>(grads);
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grad_v2 = thrust::get<3>(grads);
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} else {
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const auto grads = PointLine3DistanceBackward(
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points_f3[point_index],
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face_f3[face_index],
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face_f3[face_index + 1],
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grad_dists[o]);
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grad_point = thrust::get<0>(grads);
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grad_v0 = thrust::get<1>(grads);
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grad_v1 = thrust::get<2>(grads);
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}
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const auto grads =
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PointTriangle3DistanceBackward(p_f3, v0, v1, v2, grad_dist);
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const float3 grad_point = thrust::get<0>(grads);
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const float3 grad_v0 = thrust::get<1>(grads);
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const float3 grad_v1 = thrust::get<2>(grads);
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const float3 grad_v2 = thrust::get<3>(grads);
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atomicAdd(grad_points + point_index * 3 + 0, grad_point.x);
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atomicAdd(grad_points + point_index * 3 + 1, grad_point.y);
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atomicAdd(grad_points + point_index * 3 + 2, grad_point.z);
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atomicAdd(grad_points + p * 3 + 0, grad_point.x);
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atomicAdd(grad_points + p * 3 + 1, grad_point.y);
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atomicAdd(grad_points + p * 3 + 2, grad_point.z);
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atomicAdd(grad_face + face_index * 3 + 0 * 3 + 0, grad_v0.x);
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atomicAdd(grad_face + face_index * 3 + 0 * 3 + 1, grad_v0.y);
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atomicAdd(grad_face + face_index * 3 + 0 * 3 + 2, grad_v0.z);
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atomicAdd(grad_tris + tidx * 3 * 3 + 0 * 3 + 0, grad_v0.x);
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atomicAdd(grad_tris + tidx * 3 * 3 + 0 * 3 + 1, grad_v0.y);
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atomicAdd(grad_tris + tidx * 3 * 3 + 0 * 3 + 2, grad_v0.z);
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atomicAdd(grad_face + face_index * 3 + 1 * 3 + 0, grad_v1.x);
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atomicAdd(grad_face + face_index * 3 + 1 * 3 + 1, grad_v1.y);
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atomicAdd(grad_face + face_index * 3 + 1 * 3 + 2, grad_v1.z);
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atomicAdd(grad_tris + tidx * 3 * 3 + 1 * 3 + 0, grad_v1.x);
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atomicAdd(grad_tris + tidx * 3 * 3 + 1 * 3 + 1, grad_v1.y);
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atomicAdd(grad_tris + tidx * 3 * 3 + 1 * 3 + 2, grad_v1.z);
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atomicAdd(grad_tris + tidx * 3 * 3 + 2 * 3 + 0, grad_v2.x);
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atomicAdd(grad_tris + tidx * 3 * 3 + 2 * 3 + 1, grad_v2.y);
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atomicAdd(grad_tris + tidx * 3 * 3 + 2 * 3 + 2, grad_v2.z);
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if (isTriangle) {
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atomicAdd(grad_face + face_index * 3 + 2 * 3 + 0, grad_v2.x);
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atomicAdd(grad_face + face_index * 3 + 2 * 3 + 1, grad_v2.y);
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atomicAdd(grad_face + face_index * 3 + 2 * 3 + 2, grad_v2.z);
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}
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}
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}
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std::tuple<at::Tensor, at::Tensor> PointFaceDistanceBackwardCuda(
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const at::Tensor& points,
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const at::Tensor& tris,
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const at::Tensor& idx_points,
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std::tuple<at::Tensor, at::Tensor> DistanceBackwardCuda(
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const at::Tensor& objects,
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const size_t objects_dim,
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const at::Tensor& targets,
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const size_t targets_dim,
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const at::Tensor& idx_objects,
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const at::Tensor& grad_dists) {
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// Check inputs are on the same device
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at::TensorArg points_t{points, "points", 1},
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idx_points_t{idx_points, "idx_points", 2}, tris_t{tris, "tris", 3},
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at::TensorArg objects_t{objects, "objects", 1},
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targets_t{targets, "targets", 2},
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idx_objects_t{idx_objects, "idx_objects", 3},
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grad_dists_t{grad_dists, "grad_dists", 4};
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at::CheckedFrom c = "PointFaceDistanceBackwardCuda";
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at::checkAllSameGPU(c, {points_t, idx_points_t, tris_t, grad_dists_t});
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at::checkAllSameType(c, {points_t, tris_t, grad_dists_t});
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at::CheckedFrom c = "DistanceBackwardCuda";
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at::checkAllSameGPU(c, {objects_t, targets_t, idx_objects_t, grad_dists_t});
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at::checkAllSameType(c, {objects_t, targets_t, grad_dists_t});
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// Set the device for the kernel launch based on the device of the input
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at::cuda::CUDAGuard device_guard(points.device());
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at::cuda::CUDAGuard device_guard(objects.device());
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cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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const int64_t P = points.size(0);
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const int64_t T = tris.size(0);
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const int64_t objects_size = objects.size(0);
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const int64_t targets_size = targets.size(0);
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TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
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TORCH_CHECK(
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(tris.size(1) == 3) && (tris.size(2) == 3),
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"tris must be of shape Tx3x3");
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TORCH_CHECK(idx_points.size(0) == P);
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TORCH_CHECK(grad_dists.size(0) == P);
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at::Tensor grad_points;
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at::Tensor grad_tris;
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// clang-format off
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at::Tensor grad_points = at::zeros({P, 3}, points.options());
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at::Tensor grad_tris = at::zeros({T, 3, 3}, tris.options());
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// clang-format on
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TORCH_CHECK(idx_objects.size(0) == objects_size);
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TORCH_CHECK(grad_dists.size(0) == objects_size);
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if (objects_dim == 1) {
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TORCH_CHECK(
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targets_dim >= 2 && targets_dim <= 3,
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"either object or target must be edge or face");
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TORCH_CHECK(objects.size(1) == 3, "points must be of shape Px3");
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TORCH_CHECK(
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targets.size(2) == 3,
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"face must be of shape Tx3x3, lines must be of shape Tx2x3");
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// clang-format off
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grad_points = at::zeros({objects_size, 3}, objects.options());
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grad_tris = at::zeros({targets_size, int64_t(targets_dim), 3}, targets.options());
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// clang-format on
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} else {
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TORCH_CHECK(targets_dim == 1, "either object or target must be point");
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TORCH_CHECK(
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objects_dim >= 2 && objects_dim <= 3,
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"either object or target must be edge or face");
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TORCH_CHECK(targets.size(1) == 3, "points must be of shape Px3");
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TORCH_CHECK(
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objects.size(2) == 3,
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"face must be of shape Tx3x3, lines must be of shape Tx2x3");
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// clang-format off
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grad_points = at::zeros({targets_size, 3}, targets.options());
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grad_tris = at::zeros({objects_size, int64_t(objects_dim), 3}, objects.options());
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// clang-format on
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}
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if (grad_points.numel() == 0 || grad_tris.numel() == 0) {
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AT_CUDA_CHECK(cudaGetLastError());
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@ -301,19 +350,29 @@ std::tuple<at::Tensor, at::Tensor> PointFaceDistanceBackwardCuda(
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const int blocks = 64;
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const int threads = 512;
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PointFaceBackwardKernel<<<blocks, threads, 0, stream>>>(
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points.contiguous().data_ptr<float>(),
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tris.contiguous().data_ptr<float>(),
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idx_points.contiguous().data_ptr<int64_t>(),
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DistanceBackwardKernel<<<blocks, threads, 0, stream>>>(
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objects.contiguous().data_ptr<float>(),
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objects_size,
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objects_dim,
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targets.contiguous().data_ptr<float>(),
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targets_dim,
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idx_objects.contiguous().data_ptr<int64_t>(),
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grad_dists.contiguous().data_ptr<float>(),
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grad_points.data_ptr<float>(),
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grad_tris.data_ptr<float>(),
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P);
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grad_tris.data_ptr<float>());
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AT_CUDA_CHECK(cudaGetLastError());
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return std::make_tuple(grad_points, grad_tris);
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}
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std::tuple<at::Tensor, at::Tensor> PointFaceDistanceBackwardCuda(
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const at::Tensor& points,
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const at::Tensor& tris,
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const at::Tensor& idx_points,
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const at::Tensor& grad_dists) {
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return DistanceBackwardCuda(points, 1, tris, 3, idx_points, grad_dists);
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}
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// ****************************************************************************
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// * FacePointDistance *
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// ****************************************************************************
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@ -328,107 +387,12 @@ std::tuple<at::Tensor, at::Tensor> FacePointDistanceForwardCuda(
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tris, 3, tris_first_idx, points, 1, points_first_idx, max_tris);
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}
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__global__ void FacePointBackwardKernel(
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const float* __restrict__ points, // (P, 3)
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const float* __restrict__ tris, // (T, 3, 3)
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const int64_t* __restrict__ idx_tris, // (T,)
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const float* __restrict__ grad_dists, // (T,)
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float* __restrict__ grad_points, // (P, 3)
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float* __restrict__ grad_tris, // (T, 3, 3)
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const size_t T) {
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float3* points_f3 = (float3*)points;
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float3* tris_f3 = (float3*)tris;
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const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
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const size_t stride = gridDim.x * blockDim.x;
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for (size_t t = tid; t < T; t += stride) {
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const float3 v0 = tris_f3[t * 3 + 0];
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const float3 v1 = tris_f3[t * 3 + 1];
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const float3 v2 = tris_f3[t * 3 + 2];
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const int64_t pidx = idx_tris[t];
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const float3 p_f3 = points_f3[pidx];
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const float grad_dist = grad_dists[t];
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const auto grads =
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PointTriangle3DistanceBackward(p_f3, v0, v1, v2, grad_dist);
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const float3 grad_point = thrust::get<0>(grads);
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const float3 grad_v0 = thrust::get<1>(grads);
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const float3 grad_v1 = thrust::get<2>(grads);
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const float3 grad_v2 = thrust::get<3>(grads);
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atomicAdd(grad_points + pidx * 3 + 0, grad_point.x);
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atomicAdd(grad_points + pidx * 3 + 1, grad_point.y);
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atomicAdd(grad_points + pidx * 3 + 2, grad_point.z);
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atomicAdd(grad_tris + t * 3 * 3 + 0 * 3 + 0, grad_v0.x);
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atomicAdd(grad_tris + t * 3 * 3 + 0 * 3 + 1, grad_v0.y);
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atomicAdd(grad_tris + t * 3 * 3 + 0 * 3 + 2, grad_v0.z);
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atomicAdd(grad_tris + t * 3 * 3 + 1 * 3 + 0, grad_v1.x);
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atomicAdd(grad_tris + t * 3 * 3 + 1 * 3 + 1, grad_v1.y);
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atomicAdd(grad_tris + t * 3 * 3 + 1 * 3 + 2, grad_v1.z);
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atomicAdd(grad_tris + t * 3 * 3 + 2 * 3 + 0, grad_v2.x);
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atomicAdd(grad_tris + t * 3 * 3 + 2 * 3 + 1, grad_v2.y);
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atomicAdd(grad_tris + t * 3 * 3 + 2 * 3 + 2, grad_v2.z);
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}
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}
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std::tuple<at::Tensor, at::Tensor> FacePointDistanceBackwardCuda(
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const at::Tensor& points,
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const at::Tensor& tris,
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const at::Tensor& idx_tris,
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const at::Tensor& grad_dists) {
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// Check inputs are on the same device
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at::TensorArg points_t{points, "points", 1},
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idx_tris_t{idx_tris, "idx_tris", 2}, tris_t{tris, "tris", 3},
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grad_dists_t{grad_dists, "grad_dists", 4};
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at::CheckedFrom c = "FacePointDistanceBackwardCuda";
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at::checkAllSameGPU(c, {points_t, idx_tris_t, tris_t, grad_dists_t});
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at::checkAllSameType(c, {points_t, tris_t, grad_dists_t});
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// Set the device for the kernel launch based on the device of the input
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at::cuda::CUDAGuard device_guard(points.device());
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cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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const int64_t P = points.size(0);
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const int64_t T = tris.size(0);
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TORCH_CHECK(points.size(1) == 3, "points must be of shape Px3");
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TORCH_CHECK(
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(tris.size(1) == 3) && (tris.size(2) == 3),
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"tris must be of shape Tx3x3");
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TORCH_CHECK(idx_tris.size(0) == T);
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TORCH_CHECK(grad_dists.size(0) == T);
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// clang-format off
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at::Tensor grad_points = at::zeros({P, 3}, points.options());
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at::Tensor grad_tris = at::zeros({T, 3, 3}, tris.options());
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// clang-format on
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if (grad_points.numel() == 0 || grad_tris.numel() == 0) {
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AT_CUDA_CHECK(cudaGetLastError());
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return std::make_tuple(grad_points, grad_tris);
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}
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const int blocks = 64;
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const int threads = 512;
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FacePointBackwardKernel<<<blocks, threads, 0, stream>>>(
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points.contiguous().data_ptr<float>(),
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tris.contiguous().data_ptr<float>(),
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idx_tris.contiguous().data_ptr<int64_t>(),
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grad_dists.contiguous().data_ptr<float>(),
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grad_points.data_ptr<float>(),
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grad_tris.data_ptr<float>(),
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T);
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AT_CUDA_CHECK(cudaGetLastError());
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return std::make_tuple(grad_points, grad_tris);
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return DistanceBackwardCuda(tris, 3, points, 1, idx_tris, grad_dists);
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}
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// ****************************************************************************
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