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Removing dynamic shape ops and boolean indexing in matrix_to_quaternion

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Summary:
The current implementation of `matrix_to_quaternion` and `_sqrt_positive_part` uses boolean indexing, which can slow down performance and cause incompatibility with `torch.compile` unless `torch._dynamo.config.capture_dynamic_output_shape_ops` is set to `True`.

To enhance performance and compatibility, I recommend using  `torch.gather` to select the best-conditioned quaternions and `F.relu` instead of `x>0` (bottler's suggestion)

For a detailed comparison of the implementation differences when using `torch.compile`, please refer to my Bento notebook
N7438339.

Reviewed By: bottler

Differential Revision: D77176230

fbshipit-source-id: 9a6a2e0015b5865056297d5f45badc3c425b93ce
This commit is contained in:
Srivathsan Govindarajan 2025-06-25 01:18:46 -07:00 committed by Facebook GitHub Bot
parent 6020323d94
commit 71db7a0ea2
1 changed files with 5 additions and 10 deletions
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15
pytorch3d/transforms/rotation_conversions.py
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@ -95,13 +95,7 @@ def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
Returns torch.sqrt(torch.max(0, x)) Returns torch.sqrt(torch.max(0, x))
but with a zero subgradient where x is 0. but with a zero subgradient where x is 0.
""" """
ret = torch.zeros_like(x) return torch.sqrt(F.relu(x))
positive_mask = x > 0
if torch.is_grad_enabled():
ret[positive_mask] = torch.sqrt(x[positive_mask])
else:
ret = torch.where(positive_mask, torch.sqrt(x), ret)
return ret
def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor: def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
@ -160,9 +154,10 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
# if not for numerical problems, quat_candidates[i] should be same (up to a sign), # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
# forall i; we pick the best-conditioned one (with the largest denominator) # forall i; we pick the best-conditioned one (with the largest denominator)
out = quat_candidates[ indices = q_abs.argmax(dim=-1, keepdim=True)
F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : expand_dims = list(batch_dim) + [1, 4]
].reshape(batch_dim + (4,)) gather_indices = indices.unsqueeze(-1).expand(expand_dims)
out = torch.gather(quat_candidates, -2, gather_indices).squeeze(-2)
return standardize_quaternion(out) return standardize_quaternion(out)