[Feature] Add Ascend support for RoIPoolGrad op (#2569)

* add roipoolgrad adapter

* amend

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

@@ -11,7 +11,6 @@ void roi_pool_forward_npu(Tensor input, Tensor rois, Tensor output,
   int64_t pooled_channel = 1;
   at::Tensor roi_actual_num = at_npu::native::OpPreparation::ApplyTensor(
       {}, rois.options().dtype(at::kInt), rois);
-
   OpCommand cmd;
   cmd.Name("RoiPoolingWithArgMax")
       .Input(input)
@@ -27,8 +26,38 @@ void roi_pool_forward_npu(Tensor input, Tensor rois, Tensor output,
       .Run();
 }
 
+void roi_pool_backward_npu(Tensor grad_output, Tensor rois, Tensor argmax,
+                           Tensor grad_input, int pooled_height,
+                           int pooled_width, float spatial_scale) {
+  int64_t pooled_height_64 = pooled_height;
+  int64_t pooled_width_64 = pooled_width;
+  int64_t pooled_channel = 1;
+  at::Tensor roi_actual_num = at_npu::native::OpPreparation::ApplyTensor(
+      {}, rois.options().dtype(at::kInt), rois);
+  at::Tensor x = at::ones_like(grad_input);
+  OpCommand cmd;
+  cmd.Name("RoiPoolingGradWithArgMax")
+      .Input(grad_output)
+      .Input(x)
+      .Input(rois)
+      .Input(roi_actual_num)
+      .Input(argmax)
+      .Output(grad_input)
+      .Attr("pooled_h", pooled_height_64)
+      .Attr("pooled_w", pooled_width_64)
+      .Attr("spatial_scale_h", spatial_scale)
+      .Attr("spatial_scale_w", spatial_scale)
+      .Attr("pool_channel", pooled_channel)
+      .Run();
+}
+
 void roi_pool_forward_impl(Tensor input, Tensor rois, Tensor output,
                            Tensor argmax, int pooled_height, int pooled_width,
                            float spatial_scale);
 
+void roi_pool_backward_impl(Tensor grad_output, Tensor rois, Tensor argmax,
+                            Tensor grad_input, int pooled_height,
+                            int pooled_width, float spatial_scale);
+
 REGISTER_NPU_IMPL(roi_pool_forward_impl, roi_pool_forward_npu);
+REGISTER_NPU_IMPL(roi_pool_backward_impl, roi_pool_backward_npu);

tests/test_ops/test_roi_pool.py

@@ -69,20 +69,13 @@ class TestRoiPool:
             np_output = np.array(output[0])
             np_grad = np.array(output[1])
 
-            if device == 'npu':
-                import torch_npu  # noqa: F401
-                x = torch.tensor(np_input, dtype=dtype).npu()
-                rois = torch.tensor(np_rois, dtype=dtype).npu()
-                output = roi_pool(x, rois, (pool_h, pool_w), spatial_scale)
-                assert np.allclose(output.data.cpu().numpy(), np_output, 1e-3)
-            else:
-                x = torch.tensor(
-                    np_input, dtype=dtype, device=device, requires_grad=True)
-                rois = torch.tensor(np_rois, dtype=dtype, device=device)
-                output = roi_pool(x, rois, (pool_h, pool_w), spatial_scale)
-                output.backward(torch.ones_like(output))
-                assert np.allclose(output.data.cpu().numpy(), np_output, 1e-3)
-                assert np.allclose(x.grad.data.cpu().numpy(), np_grad, 1e-3)
+            x = torch.tensor(
+                np_input, dtype=dtype, device=device, requires_grad=True)
+            rois = torch.tensor(np_rois, dtype=dtype, device=device)
+            output = roi_pool(x, rois, (pool_h, pool_w), spatial_scale)
+            output.backward(torch.ones_like(output))
+            assert np.allclose(output.data.cpu().numpy(), np_output, 1e-3)
+            assert np.allclose(x.grad.data.cpu().numpy(), np_grad, 1e-3)
 
     @pytest.mark.parametrize('device', [
         pytest.param(
@@ -103,8 +96,8 @@ class TestRoiPool:
         pytest.param(
             torch.double,
             marks=pytest.mark.skipif(
-                IS_MLU_AVAILABLE,
-                reason='MLU does not support for 64-bit floating point')),
+                IS_MLU_AVAILABLE or IS_NPU_AVAILABLE,
+                reason='MLU, NPU does not support for 64-bit floating point')),
         torch.half
     ])
     def test_roipool_allclose(self, device, dtype):