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Add npu implementation of assign_score_withk_backward #3225

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Add npu implementation of assign_score_withk_backward #3225

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b358f33
add npu implemention for assign_score_withk_backward
Hua-yuxiu Jan 8, 2025
20da542
add npu implemention for assign_score_withk_backward
Hua-yuxiu Jan 8, 2025
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34 changes: 27 additions & 7 deletions mmcv/ops/csrc/pytorch/npu/assign_score_withk_npu.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,13 @@ using namespace std;

void assign_score_withk_forward_npu(int B, int N0, int N1, int M, int K, int O,
int aggregate, const Tensor& points,
const Tensor& centers, const Tensor& scores,
const Tensor& centers,
const Tensor& scores,
const Tensor& knn_idx, Tensor& output) {
at::Tensor points_trans = points.permute({0, 3, 1, 2});
at::Tensor centers_trans = centers.permute({0, 3, 1, 2});
EXEC_NPU_CMD(aclnnAssignScoreWithk, points_trans, centers_trans, scores,
knn_idx, B, N0, N1, M, K, O, aggregate, output);
at::Tensor points_trans = points.permute({0, 3, 1, 2});
at::Tensor centers_trans = centers.permute({0, 3, 1, 2});

EXEC_NPU_CMD(aclnnAssignScoreWithk, points_trans, centers_trans, scores, knn_idx, B, N0, N1, M, K, O, aggregate, output);
}

void assign_score_withk_forward_impl(int B, int N0, int N1, int M, int K, int O,
Expand All @@ -19,5 +20,24 @@ void assign_score_withk_forward_impl(int B, int N0, int N1, int M, int K, int O,
const Tensor& scores,
const Tensor& knn_idx, Tensor& output);

REGISTER_NPU_IMPL(assign_score_withk_forward_impl,
assign_score_withk_forward_npu);
REGISTER_NPU_IMPL(assign_score_withk_forward_impl, assign_score_withk_forward_npu);


void assign_score_withk_backward_npu(
int B, int N0, int N1, int M, int K, int O, int aggregate,
const Tensor& grad_out, const Tensor& points, const Tensor& centers,
const Tensor& scores, const Tensor& knn_idx, Tensor& grad_points,
Tensor& grad_centers, Tensor& grad_scores) {

at::Tensor grad_out_trans = grad_out.permute({0, 2, 3, 1});

EXEC_NPU_CMD(aclnnAssignScoreWithkGrad, grad_out_trans, points, centers, scores, knn_idx, B, N0, N1, M, K, O, aggregate, grad_scores, grad_points, grad_centers);
}

void assign_score_withk_backward_impl(
int B, int N0, int N1, int M, int K, int O, int aggregate,
const Tensor& grad_out, const Tensor& points, const Tensor& centers,
const Tensor& scores, const Tensor& knn_idx, Tensor& grad_points,
Tensor& grad_centers, Tensor& grad_scores);

REGISTER_NPU_IMPL(assign_score_withk_backward_impl, assign_score_withk_backward_npu);
2 changes: 1 addition & 1 deletion mmcv/ops/csrc/pytorch/npu/pixel_group_npu.cpp
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Expand Up @@ -52,4 +52,4 @@ vector<vector<float>> pixel_group_impl(Tensor score, Tensor mask, Tensor embeddi
Tensor kernel_label, Tensor kernel_contour,
int kernel_region_num, float distance_threshold);

REGISTER_NPU_IMPL(pixel_group_impl, pixel_group_npu);
REGISTER_NPU_IMPL(pixel_group_impl, pixel_group_npu);
146 changes: 72 additions & 74 deletions tests/test_ops/test_assign_score_withk.py
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Expand Up @@ -126,77 +126,75 @@ def test_paconv_assign_scores(device):
assert torch.allclose(output.detach().cpu(), expected_output, atol=1e-6)

# test backward
if device == 'cuda':
loss = output.sum()
loss.backward()
expected_scores_grad = torch.tensor([[[[0.04288036, -0.18217683],
[-0.78873926, 0.7485497],
[-0.6866992, 0.05346543],
[0.04288036, -0.18217683]],
[[-1.1407862, 0.13533896],
[-0.06964391, -0.22948086],
[-1.1407862, 0.13533896],
[-0.06964391, -0.22948086]]],
[[[-0.3363995, -2.212181],
[-1.1589496, -2.7724311],
[-0.9387654, -1.3163853],
[-1.4385346, -1.0614843]],
[[-0.5048497, 1.4143617],
[-0.47332114, 0.6017133],
[-0.30974793, 1.1995442],
[-0.5048497,
1.4143617]]]]).float()
expected_points_grad = torch.tensor(
[[[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.15585709, 0.15585709, 0.15585709, 0.15585709],
[1.1893613, 1.1893613, 1.1893613, 1.1893613]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[1.6530733, 1.6530733, 1.6530733, 1.6530733],
[1.8130021, 1.8130021, 1.8130021, 1.8130021]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.58863074, 0.58863074, 0.58863074, 0.58863074],
[1.3727596, 1.3727596, 1.3727596, 1.3727596]],
[[0.28462553, 0.28462553, 0.28462553, 0.28462553],
[0.8378516, 0.8378516, 0.8378516, 0.8378516]]],
[[[0.13817799, 0.13817799, 0.13817799, 0.13817799],
[0.34856772, 0.34856772, 0.34856772, 0.34856772]],
[[0.7405102, 0.7405102, 0.7405102, 0.7405102],
[0.06438422, 0.06438422, 0.06438422, 0.06438422]],
[[0.8491963, 0.8491963, 0.8491963, 0.8491963],
[1.1301711, 1.1301711, 1.1301711, 1.1301711]],
[[0.6887394, 0.6887394, 0.6887394, 0.6887394],
[0.22089851, 0.22089851, 0.22089851, 0.22089851]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.605832, 0.605832, 0.605832, 0.605832],
[0.92364264, 0.92364264, 0.92364264, 0.92364264]],
[[0.23089725, 0.23089725, 0.23089725, 0.23089725],
[0.5568468, 0.5568468, 0.5568468, 0.5568468]]]]).float()
expected_centers_grad = torch.tensor(
[[[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[-1.0493311, -1.0493311, -1.0493311, -1.0493311],
[-2.0301602, -2.0301602, -2.0301602, -2.0301602]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[-1.6328557, -1.6328557, -1.6328557, -1.6328557],
[-3.1828144, -3.1828144, -3.1828144, -3.1828144]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]]],
[[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[-1.5429721, -1.5429721, -1.5429721, -1.5429721],
[-1.6100934, -1.6100934, -1.6100934, -1.6100934]],
[[-1.7103812, -1.7103812, -1.7103812, -1.7103812],
[-1.6344175, -1.6344175, -1.6344175, -1.6344175]]]]).float()
assert torch.allclose(
scores.grad.detach().cpu(), expected_scores_grad, atol=1e-6)
assert torch.allclose(
points.grad.detach().cpu(), expected_points_grad, atol=1e-6)
assert torch.allclose(
centers.grad.detach().cpu(), expected_centers_grad, atol=1e-6)
loss = output.sum()
loss.backward()
expected_scores_grad = torch.tensor([[[[0.04288036, -0.18217683],
[-0.78873926, 0.7485497],
[-0.6866992, 0.05346543],
[0.04288036, -0.18217683]],
[[-1.1407862, 0.13533896],
[-0.06964391, -0.22948086],
[-1.1407862, 0.13533896],
[-0.06964391, -0.22948086]]],
[[[-0.3363995, -2.212181],
[-1.1589496, -2.7724311],
[-0.9387654, -1.3163853],
[-1.4385346, -1.0614843]],
[[-0.5048497, 1.4143617],
[-0.47332114, 0.6017133],
[-0.30974793, 1.1995442],
[-0.5048497, 1.4143617]]]]).float()
expected_points_grad = torch.tensor(
[[[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.15585709, 0.15585709, 0.15585709, 0.15585709],
[1.1893613, 1.1893613, 1.1893613, 1.1893613]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[1.6530733, 1.6530733, 1.6530733, 1.6530733],
[1.8130021, 1.8130021, 1.8130021, 1.8130021]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.58863074, 0.58863074, 0.58863074, 0.58863074],
[1.3727596, 1.3727596, 1.3727596, 1.3727596]],
[[0.28462553, 0.28462553, 0.28462553, 0.28462553],
[0.8378516, 0.8378516, 0.8378516, 0.8378516]]],
[[[0.13817799, 0.13817799, 0.13817799, 0.13817799],
[0.34856772, 0.34856772, 0.34856772, 0.34856772]],
[[0.7405102, 0.7405102, 0.7405102, 0.7405102],
[0.06438422, 0.06438422, 0.06438422, 0.06438422]],
[[0.8491963, 0.8491963, 0.8491963, 0.8491963],
[1.1301711, 1.1301711, 1.1301711, 1.1301711]],
[[0.6887394, 0.6887394, 0.6887394, 0.6887394],
[0.22089851, 0.22089851, 0.22089851, 0.22089851]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0.605832, 0.605832, 0.605832, 0.605832],
[0.92364264, 0.92364264, 0.92364264, 0.92364264]],
[[0.23089725, 0.23089725, 0.23089725, 0.23089725],
[0.5568468, 0.5568468, 0.5568468, 0.5568468]]]]).float()
expected_centers_grad = torch.tensor(
[[[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[-1.0493311, -1.0493311, -1.0493311, -1.0493311],
[-2.0301602, -2.0301602, -2.0301602, -2.0301602]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[-1.6328557, -1.6328557, -1.6328557, -1.6328557],
[-3.1828144, -3.1828144, -3.1828144, -3.1828144]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]]],
[[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[-1.5429721, -1.5429721, -1.5429721, -1.5429721],
[-1.6100934, -1.6100934, -1.6100934, -1.6100934]],
[[-1.7103812, -1.7103812, -1.7103812, -1.7103812],
[-1.6344175, -1.6344175, -1.6344175, -1.6344175]]]]).float()
assert torch.allclose(
scores.grad.detach().cpu(), expected_scores_grad, atol=1e-6)
assert torch.allclose(
points.grad.detach().cpu(), expected_points_grad, atol=1e-6)
assert torch.allclose(
centers.grad.detach().cpu(), expected_centers_grad, atol=1e-6)