Add multi_scale_deform_attn op adapter for NPU.

docs/en/understand_mmcv/ops.md

@@ -33,7 +33,7 @@ We implement common ops used in detection, segmentation, etc.
 | MergeCells                   |     | √    |     |     |        |
 | MinAreaPolygon               |     | √    |     |     |        |
 | ModulatedDeformConv2d        | √   | √    | √   |     | √      |
-| MultiScaleDeformableAttn     |     | √    | √   |     |        |
+| MultiScaleDeformableAttn     |     | √    | √   |     | √      |
 | NMS                          | √   | √    | √   |     | √      |
 | NMSRotated                   | √   | √    | √   |     | √      |
 | NMSQuadri                    | √   | √    |     |     |        |

docs/zh_cn/understand_mmcv/ops.md

@@ -33,7 +33,7 @@ MMCV 提供了检测、分割等任务中常用的算子
 | MergeCells                   |     | √    |     |     |        |
 | MinAreaPolygon               |     | √    |     |     |        |
 | ModulatedDeformConv2d        | √   | √    | √   |     | √      |
-| MultiScaleDeformableAttn     |     | √    | √   |     |        |
+| MultiScaleDeformableAttn     |     | √    | √   |     | √      |
 | NMS                          | √   | √    | √   |     | √      |
 | NMSRotated                   | √   | √    | √   |     | √      |
 | NMSQuadri                    | √   | √    |     |     |        |

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

@@ -0,0 +1,77 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+Tensor ms_deform_attn_impl_forward(const Tensor &value,
+                                   const Tensor &value_spatial_shapes,
+                                   const Tensor &value_level_start_index,
+                                   const Tensor &sampling_locations,
+                                   const Tensor &attention_weights,
+                                   const int im2col_step);
+
+void check_support(const Tensor &value, const Tensor &attention_weights) {
+  TORCH_CHECK(
+      (value.scalar_type() == at::kFloat || value.scalar_type() == at::kHalf),
+      "Dtype of value should be float32 or float16.");
+  int64_t num_heads = value.size(2);
+  int64_t embed_dims = value.size(3);
+  int64_t num_points = attention_weights.size(4);
+  TORCH_CHECK((num_heads >= 4 && num_heads <= 8),
+              "num_heads should be in the range of [4, 8]");
+  TORCH_CHECK((embed_dims >= 32 && embed_dims <= 256),
+              "embed_dims should be in the range of [32, 256]");
+  TORCH_CHECK((num_points >= 4 && num_points <= 8),
+              "num_points should be in the range of [4, 8]");
+}
+
+Tensor ms_deform_attn_forward_npu(const Tensor &value,
+                                  const Tensor &value_spatial_shapes,
+                                  const Tensor &value_level_start_index,
+                                  const Tensor &sampling_locations,
+                                  const Tensor &attention_weights,
+                                  const int im2col_step) {
+  check_support(value, attention_weights);
+  at::Tensor value_fp32 = value;
+  at::Tensor value_spatial_shapes_int32 = value_spatial_shapes;
+  at::Tensor value_level_start_index_int32 = value_level_start_index;
+  at::Tensor sampling_locations_fp32 = sampling_locations;
+  at::Tensor attention_weights_fp32 = attention_weights;
+  if (value.scalar_type() != at::kFloat) {
+    value_fp32 = value.to(at::kFloat);
+  }
+  if (value_spatial_shapes.scalar_type() != at::kInt) {
+    value_spatial_shapes_int32 = value_spatial_shapes.to(at::kInt);
+  }
+  if (value_level_start_index.scalar_type() != at::kInt) {
+    value_level_start_index_int32 = value_level_start_index.to(at::kInt);
+  }
+  if (sampling_locations.scalar_type() != at::kFloat) {
+    sampling_locations_fp32 = sampling_locations.to(at::kFloat);
+  }
+  if (attention_weights.scalar_type() != at::kFloat) {
+    attention_weights_fp32 = attention_weights.to(at::kFloat);
+  }
+
+  c10::SmallVector<int64_t, 3> output_size = {
+      value.size(0), sampling_locations.size(1), value.size(2) * value.size(3)};
+  at::Tensor output = at::empty(output_size, value_fp32.options());
+
+  OpCommand cmd;
+  cmd.Name("MultiScaleDeformableAttnFunction")
+      .Input(value_fp32)
+      .Input(value_spatial_shapes_int32)
+      .Input(value_level_start_index_int32)
+      .Input(sampling_locations_fp32)
+      .Input(attention_weights_fp32)
+      .Output(output)
+      .Run();
+
+  at::Tensor real_output = output;
+  if (value.scalar_type() != at::kFloat) {
+    real_output = output.to(value.scalar_type());
+  }
+  return real_output;
+}
+
+REGISTER_NPU_IMPL(ms_deform_attn_impl_forward, ms_deform_attn_forward_npu);

mmcv/ops/multi_scale_deform_attn.py

@@ -13,7 +13,7 @@
 from mmcv.cnn import constant_init, xavier_init
 from mmcv.cnn.bricks.registry import ATTENTION
 from mmcv.runner import BaseModule
-from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
 from ..utils import ext_loader
 
 ext_module = ext_loader.load_ext(
@@ -85,7 +85,7 @@ def backward(ctx, grad_output: torch.Tensor) -> tuple:
         Returns:
             tuple[Tensor]: Gradient of input tensors in forward.
         """
-        value, value_spatial_shapes, value_level_start_index,\
+        value, value_spatial_shapes, value_level_start_index, \
             sampling_locations, attention_weights = ctx.saved_tensors
         grad_value = torch.zeros_like(value)
         grad_sampling_loc = torch.zeros_like(sampling_locations)
@@ -361,7 +361,8 @@ def forward(self,
                 f'Last dim of reference_points must be'
                 f' 2 or 4, but get {reference_points.shape[-1]} instead.')
         if ((IS_CUDA_AVAILABLE and value.is_cuda)
-                or (IS_MLU_AVAILABLE and value.is_mlu)):
+                or (IS_MLU_AVAILABLE and value.is_mlu)
+                or (IS_NPU_AVAILABLE and value.device.type == 'npu')):
             output = MultiScaleDeformableAttnFunction.apply(
                 value, spatial_shapes, level_start_index, sampling_locations,
                 attention_weights, self.im2col_step)

tests/test_ops/test_ms_deformable_attn.py

@@ -5,7 +5,7 @@
 from mmcv.ops.multi_scale_deform_attn import (
     MultiScaleDeformableAttention, MultiScaleDeformableAttnFunction,
     multi_scale_deformable_attn_pytorch)
-from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
 
 _USING_PARROTS = True
 _IS_AUTOCAST_AVAILABLE = True
@@ -116,6 +116,40 @@ def test_forward_equal_with_pytorch_double():
     assert max_rel_err < 1e-15
 
 
+@pytest.mark.skipif(not IS_NPU_AVAILABLE, reason='requires NPU support')
+def test_forward_equal_with_pytorch_npu():
+    N, M, D = 6, 4, 8
+    Lq, L, P = 10000, 4, 8
+    shapes = torch.as_tensor([(60, 40), (30, 20), (16, 24), (53, 32)],
+                             dtype=torch.int32)
+    level_start_index = torch.cat((shapes.new_zeros(
+        (1, )), shapes.prod(1).cumsum(0)[:-1]))
+    S = sum((H * W).item() for H, W in shapes)
+
+    torch.manual_seed(3)
+    value = torch.rand(N, S, M, D) * 0.01
+    sampling_locations = torch.rand(N, Lq, M, L, P, 2)
+    attention_weights = torch.rand(N, Lq, M, L, P) + 1e-5
+    attention_weights /= attention_weights.sum(
+        -1, keepdim=True).sum(
+            -2, keepdim=True)
+    im2col_step = 2
+    output_pytorch = multi_scale_deformable_attn_pytorch(
+        value.float(), shapes, sampling_locations.float(),
+        attention_weights.float()).detach().cpu()
+
+    output_npu = MultiScaleDeformableAttnFunction.apply(
+        value.npu().float(), shapes.npu(), level_start_index.npu(),
+        sampling_locations.npu().float(),
+        attention_weights.npu().float(), im2col_step).detach().cpu()
+    assert torch.allclose(output_npu, output_pytorch)
+    max_abs_err = (output_npu - output_pytorch).abs().max()
+    max_rel_err = ((output_npu - output_pytorch).abs() /
+                   output_pytorch.abs()).max()
+    assert max_abs_err < 1e-18
+    assert max_rel_err < 1e-15
+
+
 @pytest.mark.parametrize('device', [
     pytest.param(
         'cuda',