Cuda function for points2vols

Summary: Added CUDA implementation to match the new, still unused, C++ function for the core of points2vols.

Reviewed By: nikhilaravi

Differential Revision: D29548608

fbshipit-source-id: 16ebb61787fcb4c70461f9215a86ad5f97aecb4e

pytorch3d/csrc/points_to_volumes/points_to_volumes.cu

@@ -0,0 +1,347 @@
+/*
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <torch/extension.h>
+
+using torch::PackedTensorAccessor64;
+using torch::RestrictPtrTraits;
+
+// A chunk of work is blocksize-many points.
+// There are N clouds in the batch, and P points in each cloud.
+// The number of potential chunks to do per cloud is (1+(P-1)/blocksize),
+// which we call chunks_per_cloud.
+// These (N*chunks_per_cloud) chunks are divided among the gridSize-many blocks.
+// In block b, we work on chunks b, b+gridSize, b+2*gridSize etc .
+// In chunk i, we work on cloud (i/chunks_per_cloud) on points starting from
+// blocksize*(i%chunks_per_cloud).
+
+// Explanation of the calculation is in the cpp file.
+
+// EightDirections(t) runs t(a,b,c) for every combination of boolean a, b, c.
+template <class T>
+static __device__ void EightDirections(T&& t) {
+  t(false, false, false);
+  t(false, false, true);
+  t(false, true, false);
+  t(false, true, true);
+  t(true, false, false);
+  t(true, false, true);
+  t(true, true, false);
+  t(true, true, true);
+}
+
+__global__ void PointsToVolumesForwardKernel(
+    const PackedTensorAccessor64<float, 3, RestrictPtrTraits> points_3d,
+    const PackedTensorAccessor64<float, 3, RestrictPtrTraits> points_features,
+    PackedTensorAccessor64<float, 5, RestrictPtrTraits> volume_densities,
+    PackedTensorAccessor64<float, 5, RestrictPtrTraits> volume_features,
+    PackedTensorAccessor64<int64_t, 2, RestrictPtrTraits> grid_sizes,
+    PackedTensorAccessor64<float, 2, RestrictPtrTraits> mask,
+    const float point_weight,
+    const bool align_corners,
+    const bool splat,
+    const int64_t batch_size,
+    const int64_t P,
+    const int64_t n_features) {
+  const int64_t chunks_per_cloud = (1 + (P - 1) / blockDim.x);
+  const int64_t chunks_to_do = batch_size * chunks_per_cloud;
+  const int scale_offset = align_corners ? 1 : 0;
+  const float offset = align_corners ? 0 : 0.5;
+  for (int64_t chunk = blockIdx.x; chunk < chunks_to_do; chunk += gridDim.x) {
+    const int64_t batch_index = chunk / chunks_per_cloud;
+    const int64_t start_point = blockDim.x * (chunk % chunks_per_cloud);
+    int64_t point_idx = start_point + threadIdx.x;
+    if (point_idx >= P) {
+      continue;
+    }
+    if (mask[batch_index][point_idx] == 0) {
+      continue;
+    }
+    auto volume_densities_aa = volume_densities[batch_index][0];
+    auto volume_features_aa = volume_features[batch_index];
+    auto point = points_3d[batch_index][point_idx];
+    auto point_features = points_features[batch_index][point_idx];
+    const int64_t grid_size_x = grid_sizes[batch_index][2];
+    const int64_t grid_size_y = grid_sizes[batch_index][1];
+    const int64_t grid_size_z = grid_sizes[batch_index][0];
+    auto increment_location =
+        [&](int64_t x, int64_t y, int64_t z, float weight) {
+          if (x >= grid_size_x || y >= grid_size_y || z >= grid_size_z) {
+            return;
+          }
+          if (x < 0 || y < 0 || z < 0) {
+            return;
+          }
+
+          atomicAdd(&volume_densities_aa[z][y][x], weight * point_weight);
+
+          for (int64_t feature_idx = 0; feature_idx < n_features;
+               ++feature_idx) {
+            atomicAdd(
+                &volume_features_aa[feature_idx][z][y][x],
+                point_features[feature_idx] * weight * point_weight);
+          }
+        };
+    if (!splat) {
+      long x = std::lround(
+          (point[0] + 1) * 0.5 * (grid_size_x - scale_offset) - offset);
+      long y = std::lround(
+          (point[1] + 1) * 0.5 * (grid_size_y - scale_offset) - offset);
+      long z = std::lround(
+          (point[2] + 1) * 0.5 * (grid_size_z - scale_offset) - offset);
+      increment_location(x, y, z, 1);
+    } else {
+      float x = 0, y = 0, z = 0;
+      float rx = std::modf(
+          (point[0] + 1) * 0.5 * (grid_size_x - scale_offset) - offset, &x);
+      float ry = std::modf(
+          (point[1] + 1) * 0.5 * (grid_size_y - scale_offset) - offset, &y);
+      float rz = std::modf(
+          (point[2] + 1) * 0.5 * (grid_size_z - scale_offset) - offset, &z);
+      auto handle_point = [&](bool up_x, bool up_y, bool up_z) {
+        float weight =
+            (up_x ? rx : 1 - rx) * (up_y ? ry : 1 - ry) * (up_z ? rz : 1 - rz);
+        increment_location(x + up_x, y + up_y, z + up_z, weight);
+      };
+      EightDirections(handle_point);
+    }
+  }
+}
+
+void PointsToVolumesForwardCuda(
+    const torch::Tensor& points_3d,
+    const torch::Tensor& points_features,
+    const torch::Tensor& volume_densities,
+    const torch::Tensor& volume_features,
+    const torch::Tensor& grid_sizes,
+    const torch::Tensor& mask,
+    const float point_weight,
+    const bool align_corners,
+    const bool splat) {
+  // Check inputs are on the same device
+  at::TensorArg points_3d_t{points_3d, "points_3d", 1},
+      points_features_t{points_features, "points_features", 2},
+      volume_densities_t{volume_densities, "volume_densities", 3},
+      volume_features_t{volume_features, "volume_features", 4},
+      grid_sizes_t{grid_sizes, "grid_sizes", 5}, mask_t{mask, "mask", 6};
+  at::CheckedFrom c = "PointsToVolumesForwardCuda";
+  at::checkAllSameGPU(
+      c,
+      {points_3d_t,
+       points_features_t,
+       volume_densities_t,
+       volume_features_t,
+       grid_sizes_t,
+       mask_t});
+
+  // Set the device for the kernel launch based on the device of the input
+  at::cuda::CUDAGuard device_guard(points_3d.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  const int blocks = 1024;
+  const int threads = 32;
+
+  const int64_t batch_size = points_3d.size(0);
+  const int64_t P = points_3d.size(1);
+  const int64_t n_features = points_features.size(2);
+
+  PointsToVolumesForwardKernel<<<blocks, threads, 0, stream>>>(
+      points_3d.packed_accessor64<float, 3, RestrictPtrTraits>(),
+      points_features.packed_accessor64<float, 3, RestrictPtrTraits>(),
+      volume_densities.packed_accessor64<float, 5, RestrictPtrTraits>(),
+      volume_features.packed_accessor64<float, 5, RestrictPtrTraits>(),
+      grid_sizes.packed_accessor64<int64_t, 2, RestrictPtrTraits>(),
+      mask.packed_accessor64<float, 2, RestrictPtrTraits>(),
+      point_weight,
+      align_corners,
+      splat,
+      batch_size,
+      P,
+      n_features);
+}
+
+__global__ void PointsToVolumesBackwardKernel(
+    const PackedTensorAccessor64<float, 3, RestrictPtrTraits> points_3d,
+    const PackedTensorAccessor64<float, 3, RestrictPtrTraits> points_features,
+    const PackedTensorAccessor64<int64_t, 2, RestrictPtrTraits> grid_sizes,
+    const PackedTensorAccessor64<float, 2, RestrictPtrTraits> mask,
+    PackedTensorAccessor64<float, 5, RestrictPtrTraits> grad_volume_densities,
+    PackedTensorAccessor64<float, 5, RestrictPtrTraits> grad_volume_features,
+    PackedTensorAccessor64<float, 3, RestrictPtrTraits> grad_points_3d,
+    PackedTensorAccessor64<float, 3, RestrictPtrTraits> grad_points_features,
+    const float point_weight,
+    const bool align_corners,
+    const bool splat,
+    const int64_t batch_size,
+    const int64_t P,
+    const int64_t n_features) {
+  const int64_t chunks_per_cloud = (1 + (P - 1) / blockDim.x);
+  const int64_t chunks_to_do = batch_size * chunks_per_cloud;
+  const int scale_offset = align_corners ? 1 : 0;
+  const float offset = align_corners ? 0 : 0.5;
+  // Note that the gradients belonging to each point are only touched by
+  // a single thread in one of our "chunks", which is in a single block.
+  // So unlike in the forward pass, there's no need for atomics here.
+  for (int64_t chunk = blockIdx.x; chunk < chunks_to_do; chunk += gridDim.x) {
+    const int64_t batch_index = chunk / chunks_per_cloud;
+    const int64_t start_point = blockDim.x * (chunk % chunks_per_cloud);
+    int64_t point_idx = start_point + threadIdx.x;
+    if (point_idx >= P) {
+      continue;
+    }
+    if (mask[batch_index][point_idx] == 0) {
+      continue;
+    }
+    auto point = points_3d[batch_index][point_idx];
+    auto point_features = points_features[batch_index][point_idx];
+    auto grad_point = grad_points_3d[batch_index][point_idx];
+    auto grad_point_features = grad_points_features[batch_index][point_idx];
+    auto grad_volume_densities_a = grad_volume_densities[batch_index][0];
+    auto grad_volume_features_a = grad_volume_features[batch_index];
+    const int64_t grid_size_x = grid_sizes[batch_index][2];
+    const int64_t grid_size_y = grid_sizes[batch_index][1];
+    const int64_t grid_size_z = grid_sizes[batch_index][0];
+
+    auto increment_location =
+        [&](int64_t x, int64_t y, int64_t z, float weight) {
+          if (x >= grid_size_x || y >= grid_size_y || z >= grid_size_z) {
+            return false;
+          }
+          if (x < 0 || y < 0 || z < 0) {
+            return false;
+          }
+
+          // This is a forward line, for comparison
+          // volume_densities_aa[z][y][x] += weight * point_weight;
+
+          for (int64_t feature_idx = 0; feature_idx < n_features;
+               ++feature_idx) {
+            // This is a forward line, for comparison
+            // volume_features_aa[feature_idx][z][y][x] +=
+            //    point_features[feature_idx] * weight * point_weight;
+            grad_point_features[feature_idx] +=
+                grad_volume_features_a[feature_idx][z][y][x] * weight *
+                point_weight;
+          }
+          return true;
+        };
+
+    if (!splat) {
+      long x = std::lround(
+          (point[0] + 1) * 0.5 * (grid_size_x - scale_offset) - offset);
+      long y = std::lround(
+          (point[1] + 1) * 0.5 * (grid_size_y - scale_offset) - offset);
+      long z = std::lround(
+          (point[2] + 1) * 0.5 * (grid_size_z - scale_offset) - offset);
+      increment_location(x, y, z, 1);
+    } else {
+      float x = 0, y = 0, z = 0;
+      float rx = std::modf(
+          (point[0] + 1) * 0.5 * (grid_size_x - scale_offset) - offset, &x);
+      float ry = std::modf(
+          (point[1] + 1) * 0.5 * (grid_size_y - scale_offset) - offset, &y);
+      float rz = std::modf(
+          (point[2] + 1) * 0.5 * (grid_size_z - scale_offset) - offset, &z);
+      auto handle_point = [&](bool up_x, bool up_y, bool up_z) {
+        float weight_x = (up_x ? rx : 1 - rx);
+        float weight_y = (up_y ? ry : 1 - ry);
+        float weight_z = (up_z ? rz : 1 - rz);
+        float weight = weight_x * weight_y * weight_z;
+        if (increment_location(x + up_x, y + up_y, z + up_z, weight)) {
+          // weight * point_weight has been added to
+          // volume_densities_aa[z+up_z][y+up_y][x+up_x]
+          // Also for each feature_idx,
+          //   point_features[feature_idx] * weight * point_weight
+          // has been added to
+          // volume_features_aa[feature_idx][z+up_z][y+up_y][x+up_x]
+
+          double source_gradient =
+              grad_volume_densities_a[z + up_z][y + up_y][x + up_x];
+          for (int64_t feature_idx = 0; feature_idx < n_features;
+               ++feature_idx) {
+            source_gradient += point_features[feature_idx] *
+                grad_volume_features_a[feature_idx][z + up_z][y + up_y]
+                                      [x + up_x];
+          }
+          grad_point[0] += source_gradient * (up_x ? 1 : -1) * weight_y *
+              weight_z * 0.5 * (grid_size_x - scale_offset) * point_weight;
+          grad_point[1] += source_gradient * (up_y ? 1 : -1) * weight_x *
+              weight_z * 0.5 * (grid_size_y - scale_offset) * point_weight;
+          grad_point[2] += source_gradient * (up_z ? 1 : -1) * weight_x *
+              weight_y * 0.5 * (grid_size_z - scale_offset) * point_weight;
+        }
+      };
+      EightDirections(handle_point);
+    }
+  }
+}
+
+void PointsToVolumesBackwardCuda(
+    const torch::Tensor& points_3d,
+    const torch::Tensor& points_features,
+    const torch::Tensor& grid_sizes,
+    const torch::Tensor& mask,
+    const float point_weight,
+    const bool align_corners,
+    const bool splat,
+    const torch::Tensor& grad_volume_densities,
+    const torch::Tensor& grad_volume_features,
+    const torch::Tensor& grad_points_3d,
+    const torch::Tensor& grad_points_features) {
+  // Check inputs are on the same device
+  at::TensorArg points_3d_t{points_3d, "points_3d", 1},
+      points_features_t{points_features, "points_features", 2},
+      grid_sizes_t{grid_sizes, "grid_sizes", 3}, mask_t{mask, "mask", 4},
+      grad_volume_densities_t{
+          grad_volume_densities, "grad_volume_densities", 8},
+      grad_volume_features_t{grad_volume_features, "grad_volume_features", 9},
+      grad_points_3d_t{grad_points_3d, "grad_points_3d", 10},
+      grad_points_features_t{grad_points_features, "grad_points_features", 11};
+
+  at::CheckedFrom c = "PointsToVolumesBackwardCuda";
+  at::checkAllSameGPU(
+      c,
+      {points_3d_t,
+       points_features_t,
+       grid_sizes_t,
+       mask_t,
+       grad_volume_densities_t,
+       grad_volume_features_t,
+       grad_points_3d_t,
+       grad_points_features_t});
+
+  // Set the device for the kernel launch based on the device of the input
+  at::cuda::CUDAGuard device_guard(points_3d.device());
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  const int blocks = 1024;
+  const int threads = 32;
+
+  const int64_t batch_size = points_3d.size(0);
+  const int64_t P = points_3d.size(1);
+  const int64_t n_features = points_features.size(2);
+
+  PointsToVolumesBackwardKernel<<<blocks, threads, 0, stream>>>(
+      points_3d.packed_accessor64<float, 3, RestrictPtrTraits>(),
+      points_features.packed_accessor64<float, 3, RestrictPtrTraits>(),
+      grid_sizes.packed_accessor64<int64_t, 2, RestrictPtrTraits>(),
+      mask.packed_accessor64<float, 2, RestrictPtrTraits>(),
+      grad_volume_densities.packed_accessor64<float, 5, RestrictPtrTraits>(),
+      grad_volume_features.packed_accessor64<float, 5, RestrictPtrTraits>(),
+      grad_points_3d.packed_accessor64<float, 3, RestrictPtrTraits>(),
+      grad_points_features.packed_accessor64<float, 3, RestrictPtrTraits>(),
+      point_weight,
+      align_corners,
+      splat,
+      batch_size,
+      P,
+      n_features);
+}

pytorch3d/csrc/points_to_volumes/points_to_volumes.h

@@ -57,6 +57,17 @@ void PointsToVolumesForwardCpu(
     bool align_corners,
     bool splat);
 
+void PointsToVolumesForwardCuda(
+    const torch::Tensor& points_3d,
+    const torch::Tensor& points_features,
+    const torch::Tensor& volume_densities,
+    const torch::Tensor& volume_features,
+    const torch::Tensor& grid_sizes,
+    const torch::Tensor& mask,
+    float point_weight,
+    bool align_corners,
+    bool splat);
+
 inline void PointsToVolumesForward(
     const torch::Tensor& points_3d,
     const torch::Tensor& points_features,
@@ -69,7 +80,23 @@ inline void PointsToVolumesForward(
     bool splat) {
   if (points_3d.is_cuda()) {
 #ifdef WITH_CUDA
-    AT_ERROR("CUDA not implemented yet");
+    CHECK_CUDA(points_3d);
+    CHECK_CUDA(points_features);
+    CHECK_CUDA(volume_densities);
+    CHECK_CUDA(volume_features);
+    CHECK_CUDA(grid_sizes);
+    CHECK_CUDA(mask);
+    PointsToVolumesForwardCuda(
+        points_3d,
+        points_features,
+        volume_densities,
+        volume_features,
+        grid_sizes,
+        mask,
+        point_weight,
+        align_corners,
+        splat);
+    return;
 #else
     AT_ERROR("Not compiled with GPU support.");
 #endif
@@ -101,6 +128,19 @@ void PointsToVolumesBackwardCpu(
     const torch::Tensor& grad_points_3d,
     const torch::Tensor& grad_points_features);
 
+void PointsToVolumesBackwardCuda(
+    const torch::Tensor& points_3d,
+    const torch::Tensor& points_features,
+    const torch::Tensor& grid_sizes,
+    const torch::Tensor& mask,
+    float point_weight,
+    bool align_corners,
+    bool splat,
+    const torch::Tensor& grad_volume_densities,
+    const torch::Tensor& grad_volume_features,
+    const torch::Tensor& grad_points_3d,
+    const torch::Tensor& grad_points_features);
+
 inline void PointsToVolumesBackward(
     const torch::Tensor& points_3d,
     const torch::Tensor& points_features,
@@ -115,7 +155,27 @@ inline void PointsToVolumesBackward(
     const torch::Tensor& grad_points_features) {
   if (points_3d.is_cuda()) {
 #ifdef WITH_CUDA
-    AT_ERROR("CUDA not implemented yet");
+    CHECK_CUDA(points_3d);
+    CHECK_CUDA(points_features);
+    CHECK_CUDA(grid_sizes);
+    CHECK_CUDA(mask);
+    CHECK_CUDA(grad_volume_densities);
+    CHECK_CUDA(grad_volume_features);
+    CHECK_CUDA(grad_points_3d);
+    CHECK_CUDA(grad_points_features);
+    PointsToVolumesBackwardCuda(
+        points_3d,
+        points_features,
+        grid_sizes,
+        mask,
+        point_weight,
+        align_corners,
+        splat,
+        grad_volume_densities,
+        grad_volume_features,
+        grad_points_3d,
+        grad_points_features);
+    return;
 #else
     AT_ERROR("Not compiled with GPU support.");
 #endif

tests/test_points_to_volumes.py

@@ -420,6 +420,18 @@ class TestRawFunction(TestCaseMixin, unittest.TestCase):
     def test_grad_round_cpu(self):
         self.do_gradcheck(torch.device("cpu"), False, False)
 
+    def test_grad_corners_splat_cuda(self):
+        self.do_gradcheck(torch.device("cuda:0"), True, True)
+
+    def test_grad_corners_round_cuda(self):
+        self.do_gradcheck(torch.device("cuda:0"), False, True)
+
+    def test_grad_splat_cuda(self):
+        self.do_gradcheck(torch.device("cuda:0"), True, False)
+
+    def test_grad_round_cuda(self):
+        self.do_gradcheck(torch.device("cuda:0"), False, False)
+
     def do_gradcheck(self, device, splat: bool, align_corners: bool):
         """
         Use gradcheck to verify the gradient of _points_to_volumes
@@ -492,6 +504,18 @@ class TestRawFunction(TestCaseMixin, unittest.TestCase):
     def test_single_splat_cpu(self):
         self.single_point(torch.device("cpu"), True, False)
 
+    def test_single_corners_round_cuda(self):
+        self.single_point(torch.device("cuda:0"), False, True)
+
+    def test_single_corners_splat_cuda(self):
+        self.single_point(torch.device("cuda:0"), True, True)
+
+    def test_single_round_cuda(self):
+        self.single_point(torch.device("cuda:0"), False, False)
+
+    def test_single_splat_cuda(self):
+        self.single_point(torch.device("cuda:0"), True, False)
+
     def single_point(self, device, splat: bool, align_corners: bool):
         """
         Check the outcome of _points_to_volumes where a single point