[Feature] Support Voxelization with cambricon MLU device (#2500)

* [Feature] Support hard_voxelize with cambricon MLU backend

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

docs/en/understand_mmcv/ops.md

@@ -58,5 +58,5 @@ We implement common ops used in detection, segmentation, etc.
 | ThreeNN                      |     | √    | √   |     |        |
 | TINShift                     |     | √    | √   |     |        |
 | UpFirDn2d                    |     | √    |     |     |        |
-| Voxelization                 | √   | √    |     |     |        |
+| Voxelization                 | √   | √    | √   |     |        |
 | PrRoIPool                    |     | √    |     |     |        |

docs/zh_cn/understand_mmcv/ops.md

@@ -58,5 +58,5 @@ MMCV 提供了检测、分割等任务中常用的算子
 | ThreeNN                      |     | √    | √   |     |        |
 | TINShift                     |     | √    | √   |     |        |
 | UpFirDn2d                    |     | √    |     |     |        |
-| Voxelization                 | √   | √    |     |     |        |
+| Voxelization                 | √   | √    | √   |     |        |
 | PrRoIPool                    |     | √    |     |     |        |

mmcv/ops/csrc/pytorch/mlu/voxelization_mlu.cpp

+/*************************************************************************
+ * Copyright (C) 2022 by Cambricon.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ *************************************************************************/
+#include "pytorch_device_registry.hpp"
+#include "pytorch_mlu_helper.hpp"
+
+#define MIN(a, b) (((a) < (b)) ? (a) : (b))
+
+void KernelDynamicVoxelize(
+    cnrtDim3_t k_dim, cnrtFunctionType_t k_type, cnrtQueue_t queue,
+    const void *points, void *coors, const float voxel_x, const float voxel_y,
+    const float voxel_z, const float coors_x_min, const float coors_y_min,
+    const float coors_z_min, const float coors_x_max, const float coors_y_max,
+    const float coors_z_max, const int32_t grid_x, const int32_t grid_y,
+    const int32_t grid_z, const int32_t num_points, const int32_t num_features);
+
+void KernelPoint2Voxel(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                       cnrtQueue_t queue, void *coors, void *point_to_pointidx,
+                       void *point_to_voxelidx, const int32_t num_points,
+                       const int32_t max_points);
+
+void KernelCalcPointsPerVoxel(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                              cnrtQueue_t queue, void *point_to_pointidx,
+                              void *point_to_voxelidx, void *coor_to_voxelidx,
+                              void *num_points_per_voxel, void *voxel_num,
+                              const int32_t max_voxels,
+                              const int32_t num_points);
+
+void KernelAssignVoxelsCoors(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                             cnrtQueue_t queue, const void *points,
+                             void *temp_coors, void *point_to_voxelidx,
+                             void *coor_to_voxelidx, void *voxels, void *coors,
+                             const int32_t max_points, const int32_t num_points,
+                             const int32_t num_features);
+
+// policy function
+static void policyFuncDefault(cnrtDim3_t *k_dim, cnrtFunctionType_t *k_type,
+                              const int num_points) {
+  k_dim->x = torch_mlu::getDeviceAttr(cnrtAttrMcorePerCluster);
+  k_dim->y = MIN((num_points + k_dim->x - 1) / k_dim->x,
+                 torch_mlu::getDeviceAttr(cnrtAttrClusterCount));
+  k_dim->z = 1;
+  *k_type = CNRT_FUNC_TYPE_UNION1;
+}
+
+// policy function
+static void policyFuncCalcPointsPerVoxel(cnrtDim3_t *k_dim,
+                                         cnrtFunctionType_t *k_type,
+                                         const int num_points) {
+  k_dim->x = 1;
+  k_dim->y = 1;
+  k_dim->z = 1;
+  *k_type = CNRT_FUNC_TYPE_BLOCK;
+}
+
+int HardVoxelizeForwardMLUKernelLauncher(
+    const at::Tensor &points, at::Tensor &voxels, at::Tensor &coors,
+    at::Tensor &num_points_per_voxel, const std::vector<float> voxel_size,
+    const std::vector<float> coors_range, const int max_points,
+    const int max_voxels, const int NDim = 3) {
+  // check datatype
+  TORCH_CHECK(points.scalar_type() == at::kFloat,
+              "points type should be Float, got ", points.scalar_type(), ".");
+  TORCH_CHECK(voxels.scalar_type() == at::kFloat,
+              "voxels type should be Float, got ", voxels.scalar_type(), ".");
+  TORCH_CHECK(coors.scalar_type() == at::kInt,
+              "coors type should be Float, got ", coors.scalar_type(), ".");
+  TORCH_CHECK(num_points_per_voxel.scalar_type() == at::kInt,
+              "num_points_per_voxel type should be Float, got ",
+              num_points_per_voxel.scalar_type(), ".");
+
+  // check shape
+  TORCH_CHECK(points.dim() == 2, "points should be a 2d tensor, got ",
+              points.dim(), "D.");
+  TORCH_CHECK(voxels.dim() == 3, "voxels should be a 3d tensor, got ",
+              voxels.dim(), "D.");
+  TORCH_CHECK(coors.dim() == 2, "coors should be a 2d tensor, got ",
+              coors.dim(), "D.");
+  TORCH_CHECK(num_points_per_voxel.dim() == 1,
+              "num_points_per_voxel should be a 1d tensor, got ",
+              num_points_per_voxel.dim(), "D.");
+
+  const int num_points = points.size(0);
+  const int num_features = points.size(1);
+
+  TORCH_CHECK(points.size(0) == num_points,
+              "the 1st dimensions of points should be num_points, got ",
+              points.size(0), ".");
+  TORCH_CHECK(points.size(1) == num_features,
+              "the 2nd dimensions of points should be num_features, got ",
+              points.size(1), ".");
+  TORCH_CHECK(voxels.size(0) == max_voxels,
+              "the 1st dimensions of voxels should be max_voxels, got ",
+              voxels.size(0), ".");
+  TORCH_CHECK(voxels.size(1) == max_points,
+              "the 2nd dimensions of voxels should be max_points, got ",
+              voxels.size(1), ".");
+  TORCH_CHECK(voxels.size(2) == num_features,
+              "the 3rd dimensions of voxels should be num_features, got ",
+              voxels.size(2), ".");
+  TORCH_CHECK(coors.size(0) == max_voxels,
+              "the 1st dimensions of coors should be max_voxels, got ",
+              coors.size(0), ".");
+  TORCH_CHECK(coors.size(1) == 3,
+              "the 2nd dimensions of coors should be 3, got ", coors.size(1),
+              ".");
+  TORCH_CHECK(num_points_per_voxel.size(0) == max_voxels,
+              "the 1st dimensions of num_points_per_voxel should be 3, got ",
+              num_points_per_voxel.size(0), ".");
+
+  // large tensor check
+  const size_t max_input_size = 2147483648;
+  TORCH_CHECK(points.numel() < max_input_size,
+              "points element num should be less than 2^31, got ",
+              points.numel(), ".");
+  TORCH_CHECK(voxels.numel() < max_input_size,
+              "voxels element num should be less than 2^31, got ",
+              voxels.numel(), ".");
+  TORCH_CHECK(coors.numel() < max_input_size,
+              "coors element num should be less than 2^31, got ", coors.numel(),
+              ".");
+
+  // check zero element
+  if (max_points == 0 || max_voxels == 0) {
+    return 0;
+  }
+
+  // get compute queue
+  auto queue = torch_mlu::getCurQueue();
+
+  // get ptr of tensors
+  auto points_ = points.contiguous();
+  auto points_impl = torch_mlu::getMluTensorImpl(points_);
+  auto points_ptr = points_impl->cnnlMalloc();
+  auto voxels_ = voxels.contiguous();
+  auto voxels_impl = torch_mlu::getMluTensorImpl(voxels_);
+  auto voxels_ptr = voxels_impl->cnnlMalloc();
+  auto coors_ = coors.contiguous();
+  auto coors_impl = torch_mlu::getMluTensorImpl(coors_);
+  auto coors_ptr = coors_impl->cnnlMalloc();
+  auto num_points_per_voxel_ = num_points_per_voxel.contiguous();
+  auto num_points_per_voxel_impl =
+      torch_mlu::getMluTensorImpl(num_points_per_voxel_);
+  auto num_points_per_voxel_ptr = num_points_per_voxel_impl->cnnlMalloc();
+
+  // calculate task dimension
+  cnrtDim3_t k_dim;
+  cnrtFunctionType_t k_type;
+  policyFuncDefault(&k_dim, &k_type, num_points);
+
+  // 1. link point to corresponding voxel coors
+  const float voxel_x = voxel_size[0];
+  const float voxel_y = voxel_size[1];
+  const float voxel_z = voxel_size[2];
+  const float coors_x_min = coors_range[0];
+  const float coors_y_min = coors_range[1];
+  const float coors_z_min = coors_range[2];
+  const float coors_x_max = coors_range[3];
+  const float coors_y_max = coors_range[4];
+  const float coors_z_max = coors_range[5];
+
+  const int grid_x = round((coors_x_max - coors_x_min) / voxel_x);
+  const int grid_y = round((coors_y_max - coors_y_min) / voxel_y);
+  const int grid_z = round((coors_z_max - coors_z_min) / voxel_z);
+
+  auto temp_coors =
+      at::zeros({NDim, num_points}, points.options().dtype(at::kInt))
+          .contiguous();
+  auto temp_coors_impl = torch_mlu::getMluTensorImpl(temp_coors);
+  auto temp_coors_ptr = temp_coors_impl->cnnlMalloc();
+
+  KernelDynamicVoxelize(k_dim, k_type, queue, points_ptr, temp_coors_ptr,
+                        voxel_x, voxel_y, voxel_z, coors_x_min, coors_y_min,
+                        coors_z_min, coors_x_max, coors_y_max, coors_z_max,
+                        grid_x, grid_y, grid_z, num_points, num_features);
+
+  // 2. map point to the idx of the corresponding voxel, find duplicate coor
+  auto point_to_pointidx = at::zeros(
+                               {
+                                   num_points,
+                               },
+                               points.options().dtype(at::kInt))
+                               .contiguous();
+  auto point_to_pointidx_impl = torch_mlu::getMluTensorImpl(point_to_pointidx);
+  auto point_to_pointidx_ptr = point_to_pointidx_impl->cnnlMalloc();
+  auto point_to_voxelidx = at::zeros(
+                               {
+                                   num_points,
+                               },
+                               points.options().dtype(at::kInt))
+                               .contiguous();
+  auto point_to_voxelidx_impl = torch_mlu::getMluTensorImpl(point_to_voxelidx);
+  auto point_to_voxelidx_ptr = point_to_voxelidx_impl->cnnlMalloc();
+
+  KernelPoint2Voxel(k_dim, k_type, queue, temp_coors_ptr, point_to_pointidx_ptr,
+                    point_to_voxelidx_ptr, num_points, max_points);
+
+  // calculate task dimension
+  cnrtDim3_t k_dim_calc_points_per_voxel;
+  cnrtFunctionType_t k_type_calc_points_per_voxel;
+  policyFuncCalcPointsPerVoxel(&k_dim_calc_points_per_voxel,
+                               &k_type_calc_points_per_voxel, num_points);
+
+  // 3. determine voxel num and voxel's coor index
+  auto coor_to_voxelidx = at::zeros(
+                              {
+                                  num_points,
+                              },
+                              points.options().dtype(at::kInt))
+                              .contiguous();
+  auto coor_to_voxelidx_impl = torch_mlu::getMluTensorImpl(coor_to_voxelidx);
+  auto coor_to_voxelidx_ptr = coor_to_voxelidx_impl->cnnlMalloc();
+  auto voxel_num = at::zeros(
+                       {
+                           1,
+                       },
+                       points.options().dtype(at::kInt))
+                       .contiguous();
+  auto voxel_num_impl = torch_mlu::getMluTensorImpl(voxel_num);
+  auto voxel_num_ptr = voxel_num_impl->cnnlMalloc();
+
+  KernelCalcPointsPerVoxel(
+      k_dim_calc_points_per_voxel, k_type_calc_points_per_voxel, queue,
+      point_to_pointidx_ptr, point_to_voxelidx_ptr, coor_to_voxelidx_ptr,
+      num_points_per_voxel_ptr, voxel_num_ptr, max_voxels, num_points);
+
+  // 4. copy point features and coors of each voxels to voxels
+  KernelAssignVoxelsCoors(k_dim, k_type, queue, points_ptr, temp_coors_ptr,
+                          point_to_voxelidx_ptr, coor_to_voxelidx_ptr,
+                          voxels_ptr, coors_ptr, max_points, num_points,
+                          num_features);
+
+  auto voxel_num_cpu = voxel_num.to(at::kCPU);
+  int voxel_num_int = voxel_num_cpu.data_ptr<int>()[0];
+
+  return voxel_num_int;
+}
+
+int hard_voxelize_forward_mlu(const at::Tensor &points, at::Tensor &voxels,
+                              at::Tensor &coors,
+                              at::Tensor &num_points_per_voxel,
+                              const std::vector<float> voxel_size,
+                              const std::vector<float> coors_range,
+                              const int max_points, const int max_voxels,
+                              const int NDim) {
+  return HardVoxelizeForwardMLUKernelLauncher(
+      points, voxels, coors, num_points_per_voxel, voxel_size, coors_range,
+      max_points, max_voxels, NDim);
+};
+
+int hard_voxelize_forward_impl(const at::Tensor &points, at::Tensor &voxels,
+                               at::Tensor &coors,
+                               at::Tensor &num_points_per_voxel,
+                               const std::vector<float> voxel_size,
+                               const std::vector<float> coors_range,
+                               const int max_points, const int max_voxels,
+                               const int NDim);
+
+REGISTER_DEVICE_IMPL(hard_voxelize_forward_impl, MLU,
+                     hard_voxelize_forward_mlu);

tests/test_ops/test_voxelization.py

@@ -4,6 +4,7 @@ import pytest
 import torch
 
 from mmcv.ops import Voxelization
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
 
 
 def _get_voxel_points_indices(points, coors, voxel):
@@ -16,7 +17,7 @@ def _get_voxel_points_indices(points, coors, voxel):
     pytest.param(
         'cuda:0',
         marks=pytest.mark.skipif(
-            not torch.cuda.is_available(), reason='requires CUDA support'))
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
 ])
 def test_voxelization(device_type):
     voxel_size = [0.5, 0.5, 0.5]
@@ -62,8 +63,7 @@ def test_voxelization(device_type):
         assert num_points_current_voxel == expected_num_points_per_voxel[i]
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
+@pytest.mark.skipif(not IS_CUDA_AVAILABLE, reason='requires CUDA support')
 def test_voxelization_nondeterministic():
     voxel_size = [0.5, 0.5, 0.5]
     point_cloud_range = [0, -40, -3, 70.4, 40, 1]
@@ -137,3 +137,38 @@ def test_voxelization_nondeterministic():
     coors_all_set = {tuple(c) for c in coors_all}
 
     assert len(coors_set) == len(coors) == len(coors_all_set)
+
+
+@pytest.mark.parametrize('device_type', [
+    pytest.param(
+        'mlu',
+        marks=pytest.mark.skipif(
+            not IS_MLU_AVAILABLE, reason='requires MLU support'))
+])
+def test_voxelization_mlu(device_type):
+    voxel_size = [0.5, 0.5, 0.5]
+    point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+
+    voxel_dict = np.load(
+        'tests/data/for_3d_ops/test_voxel.npy', allow_pickle=True).item()
+    expected_coors = voxel_dict['coors']
+    expected_voxels = voxel_dict['voxels']
+    expected_num_points_per_voxel = voxel_dict['num_points_per_voxel']
+    points = voxel_dict['points']
+
+    points = torch.tensor(points)
+    max_num_points = 1000
+    hard_voxelization = Voxelization(voxel_size, point_cloud_range,
+                                     max_num_points)
+
+    device = torch.device(device_type)
+
+    # test hard_voxelization on mlu
+    points = points.contiguous().to(device)
+    coors, voxels, num_points_per_voxel = hard_voxelization.forward(points)
+    coors = coors.cpu().detach().numpy()
+    voxels = voxels.cpu().detach().numpy()
+    num_points_per_voxel = num_points_per_voxel.cpu().detach().numpy()
+    assert np.all(coors == expected_coors)
+    assert np.all(voxels == expected_voxels)
+    assert np.all(num_points_per_voxel == expected_num_points_per_voxel)