Chore: remove grad from nlist linear model #4380
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📝 Walkthrough📝 WalkthroughWalkthroughThe changes in this pull request involve modifications to the Changes
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🔇 Additional comments (1)deepmd/pt/model/atomic_model/linear_atomic_model.py (1)
Potential Issue with Detaching The extended_coord.detach(),Detaching Recommendation:
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🧹 Outside diff range and nitpick comments (3)
deepmd/pt/model/atomic_model/linear_atomic_model.py (3)
259-260: LGTM! Optimized gradient trackingThe conditional gradient tracking is a good optimization that ensures gradients are only computed when needed through
do_grad_r()ordo_grad_c().Consider adding a debug log to track when gradients are enabled, which could help with performance analysis:
if self.do_grad_r() or self.do_grad_c(): + logger.debug("Enabling gradients for extended_coord") extended_coord.requires_grad_(True)
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673-674: Consider increasing the clamp threshold for better numerical stabilityThe current clamp threshold of 1e-20 for preventing division by zero might be too small for float64 precision.
-denominator = torch.sum( +denominator = torch.clamp(torch.sum( torch.where( nlist_larger != -1, torch.exp(-pairwise_rr / self.smin_alpha), torch.zeros_like(nlist_larger), ), dim=-1, - ) # handle masked nnei. +), min=1e-16) # increased threshold for better numerical stability
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675-686: Optimize tensor operations for better performanceThe current implementation creates multiple intermediate tensors. Consider combining operations to reduce memory allocations.
-u = (sigma - self.sw_rmin) / (self.sw_rmax - self.sw_rmin) -coef = torch.zeros_like(u) -left_mask = sigma < self.sw_rmin -mid_mask = (self.sw_rmin <= sigma) & (sigma < self.sw_rmax) -right_mask = sigma >= self.sw_rmax -coef[left_mask] = 1 -smooth = -6 * u**5 + 15 * u**4 - 10 * u**3 + 1 -coef[mid_mask] = smooth[mid_mask] -coef[right_mask] = 0 +# Compute normalized distance once +u = torch.clamp((sigma - self.sw_rmin) / (self.sw_rmax - self.sw_rmin), 0.0, 1.0) +# Compute smooth transition directly +coef = torch.where(sigma < self.sw_rmin, + torch.ones_like(sigma), + torch.where(sigma >= self.sw_rmax, + torch.zeros_like(sigma), + -6 * u**5 + 15 * u**4 - 10 * u**3 + 1))
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4380 +/- ##
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Coverage 84.50% 84.50%
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Files 596 604 +8
Lines 56665 56942 +277
Branches 3459 3486 +27
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+ Hits 47884 48120 +236
- Misses 7654 7697 +43
+ Partials 1127 1125 -2 ☔ View full report in Codecov by Sentry. 🚨 Try these New Features:
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