add multi npu op

docs/zh_cn/understand_mmcv/ops.md

@@ -22,8 +22,8 @@ MMCV 提供了检测、分割等任务中常用的算子
 | Deformable RoIPool           |     | √    | √   |     | √      |
 | DiffIoURotated               |     | √    | √   |     |        |
 | DynamicScatter               |     | √    | √   |     |        |
-| FurthestPointSample          |     | √    |     |     |        |
-| FurthestPointSampleWithDist  |     | √    |     |     |        |
+| FurthestPointSample          |     | √    |     |     | √      |
+| FurthestPointSampleWithDist  |     | √    |     |     | √      |
 | FusedBiasLeakyrelu           |     | √    |     |     | √      |
 | GatherPoints                 |     | √    |     |     | √      |
 | GroupPoints                  |     | √    |     |     |        |

mmcv/ops/csrc/pytorch/npu/furthest_point_sample_npu.cpp

@@ -0,0 +1,19 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void furthest_point_sampling_forward_npu(Tensor points_tensor, Tensor temp_tensor, Tensor idx_tensor,
+    int b, int n, int m) {
+  TORCH_CHECK(
+      (points_tensor.sizes()[1] >= m),
+      "the num of sampled points needs to be smaller than total num of points.");
+  at::Tensor points_xyz = points_tensor.transpose(1, 2).contiguous();
+  at::Tensor nearest_dist = temp_tensor.contiguous();
+  EXEC_NPU_CMD(aclnnFurthestPointSampling, points_xyz, nearest_dist, m, idx_tensor);
+}
+
+void furthest_point_sampling_forward_impl(Tensor points_tensor, Tensor temp_tensor, Tensor idx_tensor,
+    int b, int n, int m);
+
+REGISTER_NPU_IMPL(furthest_point_sampling_forward_impl, furthest_point_sampling_forward_npu);

mmcv/ops/csrc/pytorch/npu/furthest_point_sampling_with_dist_npu.cpp

@@ -0,0 +1,20 @@
+#include "pytorch_npu_helper.hpp"
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void furthest_point_sampling_with_dist_npu(Tensor points_tensor,
+                                        Tensor temp_tensor,
+                                        Tensor idx_tensor, int b, int n,
+                                        int m) {
+    auto output_size = {b, m};
+    at::Tensor result = at::empty(output_size, points_tensor.options().dtype(at::kInt));
+    EXEC_NPU_CMD(aclnnFurthestPointSamplingWithDist, points_tensor, temp_tensor, m, result);
+}
+
+void  furthest_point_sampling_with_dist_forward_impl(Tensor points_tensor,
+                                                    Tensor temp_tensor,
+                                                    Tensor idx_tensor, int b, int n,
+                                                    int m);
+
+REGISTER_NPU_IMPL(furthest_point_sampling_with_dist_forward_impl,
+                  furthest_point_sampling_with_dist_npu);

mmcv/ops/csrc/pytorch/npu/nms3d_normal_npu.cpp

@@ -0,0 +1,21 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+
+void iou3d_nms3d_normal_forward_npu(const Tensor boxes, Tensor &keep,
+                             Tensor &keep_num, float nms_overlap_thresh) {
+  int32_t box_num = boxes.size(0);
+  int32_t data_align = 16;
+  int32_t mask_num = ((box_num - 1) / data_align + 1) * data_align;
+  at::Tensor mask = at::empty({ box_num, mask_num }, boxes.options().dtype(at::kShort));
+  EXEC_NPU_CMD(aclnnNms3dNormal, boxes, nms_overlap_thresh, mask);
+
+  keep = at::zeros({ box_num }, mask.options());
+  keep_num = at::zeros(1, mask.options());
+  EXEC_NPU_CMD(aclnnGatherNms3dMask, mask, keep, keep_num);
+}
+
+void iou3d_nms3d_normal_forward_impl(const Tensor boxes, Tensor &keep,
+                              Tensor &keep_num, float nms_overlap_thresh);
+
+REGISTER_NPU_IMPL(iou3d_nms3d_normal_forward_impl, iou3d_nms3d_normal_forward_npu);

mmcv/ops/csrc/pytorch/npu/nms3d_npu.cpp

@@ -0,0 +1,26 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+constexpr int32_t BOX_DIM = 7;
+
+void iou3d_nms3d_forward_npu(const Tensor boxes, Tensor &keep,
+                             Tensor &keep_num, float nms_overlap_thresh)
+{
+    TORCH_CHECK((boxes.sizes()[1] == BOX_DIM), "Input boxes shape should be (N, 7)");
+    int32_t box_num = boxes.size(0);
+    int32_t data_align = 16;
+    int32_t mask_num = ((box_num - 1) / data_align + 1) * data_align;
+    at::Tensor mask = at::empty({box_num, mask_num}, boxes.options().dtype(at::kShort));
+    EXEC_NPU_CMD(aclnnNms3d, boxes, nms_overlap_thresh, mask);
+
+    keep = at::zeros({box_num}, mask.options());
+    keep_num = at::zeros(1, mask.options());
+    EXEC_NPU_CMD(aclnnGatherNms3dMask, mask, keep, keep_num);
+}
+
+void iou3d_nms3d_forward_impl(const Tensor boxes, Tensor &keep,
+                              Tensor &keep_num, float nms_overlap_thresh);
+
+REGISTER_NPU_IMPL(iou3d_nms3d_forward_impl, iou3d_nms3d_forward_npu);

mmcv/ops/furthest_point_sample.py

@@ -27,8 +27,12 @@ def forward(ctx, points_xyz: torch.Tensor,
         assert points_xyz.is_contiguous()
 
         B, N = points_xyz.size()[:2]
-        output = torch.cuda.IntTensor(B, num_points)
-        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
+        if points_xyz.device.type == 'npu':
+            output = torch.IntTensor(B, num_points).npu()
+            temp = torch.FloatTensor(B, N).fill_(1e10).npu()
+        else:
+            output = torch.cuda.IntTensor(B, num_points)
+            temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
 
         ext_module.furthest_point_sampling_forward(
             points_xyz,

tests/test_ops/test_furthest_point_sample.py

@@ -3,38 +3,55 @@
 import torch
 
 from mmcv.ops import furthest_point_sample, furthest_point_sample_with_dist
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_NPU_AVAILABLE
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_fps():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
+])
+def test_fps(device):
     xyz = torch.tensor([[[-0.2748, 1.0020, -1.1674], [0.1015, 1.3952, -1.2681],
                          [-0.8070, 2.4137,
                           -0.5845], [-1.0001, 2.1982, -0.5859],
                          [0.3841, 1.8983, -0.7431]],
                         [[-1.0696, 3.0758,
                           -0.1899], [-0.2559, 3.5521, -0.1402],
                          [0.8164, 4.0081, -0.1839], [-1.1000, 3.0213, -0.8205],
-                         [-0.0518, 3.7251, -0.3950]]]).cuda()
+                         [-0.0518, 3.7251, -0.3950]]]).to(device)
 
     idx = furthest_point_sample(xyz, 3)
-    expected_idx = torch.tensor([[0, 2, 4], [0, 2, 1]]).cuda()
+    expected_idx = torch.tensor([[0, 2, 4], [0, 2, 1]]).to(device)
     assert torch.all(idx == expected_idx)
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_fps_with_dist():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
+])
+def test_fps_with_dist(device):
     xyz = torch.tensor([[[-0.2748, 1.0020, -1.1674], [0.1015, 1.3952, -1.2681],
                          [-0.8070, 2.4137,
                           -0.5845], [-1.0001, 2.1982, -0.5859],
                          [0.3841, 1.8983, -0.7431]],
                         [[-1.0696, 3.0758,
                           -0.1899], [-0.2559, 3.5521, -0.1402],
                          [0.8164, 4.0081, -0.1839], [-1.1000, 3.0213, -0.8205],
-                         [-0.0518, 3.7251, -0.3950]]]).cuda()
+                         [-0.0518, 3.7251, -0.3950]]]).to(device)
 
-    expected_idx = torch.tensor([[0, 2, 4], [0, 2, 1]]).cuda()
+    expected_idx = torch.tensor([[0, 2, 4], [0, 2, 1]]).to(device)
     xyz_square_dist = ((xyz.unsqueeze(dim=1) -
                         xyz.unsqueeze(dim=2))**2).sum(-1)
     idx = furthest_point_sample_with_dist(xyz_square_dist, 3)
@@ -44,7 +61,7 @@ def test_fps_with_dist():
     fps_idx = np.load('tests/data/for_3d_ops/fps_idx.npy')
     features_for_fps_distance = np.load(
         'tests/data/for_3d_ops/features_for_fps_distance.npy')
-    expected_idx = torch.from_numpy(fps_idx).cuda()
+    expected_idx = torch.from_numpy(fps_idx).to(device)
     features_for_fps_distance = torch.from_numpy(
         features_for_fps_distance).cuda()
 

tests/test_ops/test_iou3d.py

@@ -4,7 +4,7 @@
 import torch
 
 from mmcv.ops import boxes_iou3d, boxes_overlap_bev, nms3d, nms3d_normal
-from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
 
 
 @pytest.mark.parametrize('device', [
@@ -77,7 +77,11 @@ def test_boxes_iou3d(device):
     pytest.param(
         'mlu',
         marks=pytest.mark.skipif(
-            not IS_MLU_AVAILABLE, reason='requires MLU support'))
+            not IS_MLU_AVAILABLE, reason='requires MLU support')),
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
 ])
 def test_nms3d(device):
     # test for 5 boxes
@@ -116,7 +120,11 @@ def test_nms3d(device):
     pytest.param(
         'cuda',
         marks=pytest.mark.skipif(
-            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
 ])
 def test_nms3d_normal(device):
     # test for 5 boxes

tests/test_ops/test_roiaware_pool3d.py

@@ -5,7 +5,7 @@
 
 from mmcv.ops import (RoIAwarePool3d, points_in_boxes_all, points_in_boxes_cpu,
                       points_in_boxes_part)
-from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
 
 
 @pytest.mark.parametrize('device', [
@@ -56,38 +56,46 @@ def test_RoIAwarePool3d(device, dtype):
                           torch.tensor(49.750, dtype=dtype).to(device), 1e-3)
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_points_in_boxes_part():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
+])
+def test_points_in_boxes_part(device):
     boxes = torch.tensor(
         [[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3]],
          [[-10.0, 23.0, 16.0, 10, 20, 20, 0.5]]],
-        dtype=torch.float32).cuda(
-        )  # boxes (b, t, 7) with bottom center in lidar coordinate
+        dtype=torch.float32).to(
+            device)  # boxes (b, t, 7) with bottom center in lidar coordinate
     pts = torch.tensor(
         [[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6],
           [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3],
           [4.7, 3.5, -12.2]],
          [[3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9], [-21.3, -52, -5],
           [0, 0, 0], [6, 7, 8], [-2, -3, -4], [6, 4, 9]]],
-        dtype=torch.float32).cuda()  # points (b, m, 3) in lidar coordinate
+        dtype=torch.float32).to(device)  # points (b, m, 3) in lidar coordinate
 
     point_indices = points_in_boxes_part(points=pts, boxes=boxes)
     expected_point_indices = torch.tensor(
         [[0, 0, 0, 0, 0, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1]],
-        dtype=torch.int32).cuda()
+        dtype=torch.int32).to(device)
     assert point_indices.shape == torch.Size([2, 8])
     assert (point_indices == expected_point_indices).all()
 
     boxes = torch.tensor([[[0.0, 0.0, 0.0, 1.0, 20.0, 1.0, 0.523598]]],
-                         dtype=torch.float32).cuda()  # 30 degrees
+                         dtype=torch.float32).to(device)  # 30 degrees
     pts = torch.tensor(
         [[[4, 6.928, 0], [6.928, 4, 0], [4, -6.928, 0], [6.928, -4, 0],
           [-4, 6.928, 0], [-6.928, 4, 0], [-4, -6.928, 0], [-6.928, -4, 0]]],
-        dtype=torch.float32).cuda()
+        dtype=torch.float32).to(device)
     point_indices = points_in_boxes_part(points=pts, boxes=boxes)
     expected_point_indices = torch.tensor([[-1, -1, 0, -1, 0, -1, -1, -1]],
-                                          dtype=torch.int32).cuda()
+                                          dtype=torch.int32).to(device)
     assert (point_indices == expected_point_indices).all()