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fix npu bug. #3136

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fix npu bug. #3136

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momo609
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@momo609 momo609 commented Jun 18, 2024

Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.

Motivation

Please describe the motivation of this PR and the goal you want to achieve through this PR.

Modification

Please briefly describe what modification is made in this PR.

BC-breaking (Optional)

Does the modification introduce changes that break the backward-compatibility of the downstream repositories?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

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If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

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  • I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
  • Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
  • Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
  • New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
  • The documentation has been modified accordingly, including docstring or example tutorials.

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if torch.cuda.current_device() != points_device:
torch.cuda.set_device(points_device)
elif points.device.type == 'npu':
boxes[:, :, 2] += boxes[:, :, 5] / 2.0
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Could we move this into npu kernel?

Comment on lines +40 to +58
if ctx.device == 'npu':
import ads_c
voxel_idx = ads_c.point_to_voxel(coors, None, None)
unique_res = ads_c.unique_voxel(voxel_idx)
num_voxels, uniqued_voxel_idx, prefix_sum, \
argsort_coor = unique_res
voxel_coors = ads_c.voxel_to_point(uniqued_voxel_idx, None, None)
voxel_feats, \
compare_mask = ads_c.npu_dynamic_scatter(feats, coors,
prefix_sum,
argsort_coor,
num_voxels,
reduce_type)
ctx.reduce_type = reduce_type
ctx.feats_shape = feats.shape
ctx.save_for_backward(prefix_sum, argsort_coor, compare_mask)
ctx.mark_non_differentiable(voxel_coors)
return voxel_feats, voxel_coors

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same above

@@ -50,6 +69,19 @@ def forward(ctx: Any,
def backward(ctx: Any,
grad_voxel_feats: torch.Tensor,
grad_voxel_coors: Optional[torch.Tensor] = None) -> tuple:
if ctx.device == 'npu':
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same above

@@ -34,6 +34,21 @@ def forward(ctx: Any, target: torch.Tensor,

B, N, _ = target.size()
m = source.size(1)
if source.device.type == 'npu':
# strict to fp32
source = source.transpose(2, 1).contiguous()
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same above

Comment on lines +418 to +419
dets_cw = dets_cw.float()
scores = scores.float()
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Could we move this into npu kernel?

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2 participants