knn/tnn npu added

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

@@ -0,0 +1,21 @@
+#include "pytorch_npu_helper.hpp"
+#include "torch_npu/csrc/aten/NPUNativeFunctions.h"
+#include "torch_npu/csrc/framework/utils/OpAdapter.h"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void knn_forward_npu(int b, int n, int m, int nsample, const Tensor xyz,
+                     const Tensor new_xyz, Tensor idx, Tensor dist2) {
+  // transpose known from [B, N, 3] to [B, 3, N]
+  at::Tensor source = xyz.transpose(1, 2).contiguous();
+  at::Tensor target = new_xyz.contiguous();
+
+  bool is_from_knn = true;
+  EXEC_NPU_CMD(aclnnKnn, source, target, nsample, is_from_knn, idx, dist2);
+}
+
+void knn_forward_impl(int b, int n, int m, int nsample, const Tensor xyz,
+                      const Tensor new_xyz, Tensor idx, Tensor dist2);
+
+REGISTER_NPU_IMPL(knn_forward_impl, knn_forward_npu);

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

@@ -0,0 +1,30 @@
+#include "pytorch_npu_helper.hpp"
+#include "torch_npu/csrc/aten/NPUNativeFunctions.h"
+#include "torch_npu/csrc/framework/utils/OpAdapter.h"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void three_nn_forward_npu(int b, int n, int m, const Tensor unknown,
+                          const Tensor known, Tensor dist2, Tensor idx) {
+  // transpose known  [B, N, 3] -> [B, 3, N]
+  at::Tensor source = known.transpose(1, 2).contiguous();
+  at::Tensor target = unknown.contiguous();
+  auto originDtype = source.scalar_type();
+  if (originDtype == at::kHalf) {
+    source = source.to(at::kFloat);
+    target = target.to(at::kFloat);
+  }
+
+  bool is_from_knn = false;
+  uint32_t nsample = 3;
+  EXEC_NPU_CMD(aclnnKnn, source, target, nsample, is_from_knn, idx, dist2);
+  if (originDtype == at::kHalf) {
+    dist2 = dist2.to(at::kHalf);
+  }
+}
+
+void three_nn_forward_impl(int b, int n, int m, const Tensor unknown,
+                           const Tensor known, Tensor dist2, Tensor idx);
+
+REGISTER_NPU_IMPL(three_nn_forward_impl, three_nn_forward_npu);

mmcv/ops/knn.py

@@ -55,8 +55,9 @@ def forward(ctx,
         center_xyz_device = center_xyz.get_device()
         assert center_xyz_device == xyz.get_device(), \
             'center_xyz and xyz should be put on the same device'
-        if torch.cuda.current_device() != center_xyz_device:
-            torch.cuda.set_device(center_xyz_device)
+        if xyz.device.type != 'npu':
+            if torch.cuda.current_device() != center_xyz_device:
+                torch.cuda.set_device(center_xyz_device)
 
         B, npoint, _ = center_xyz.shape
         N = xyz.shape[1]