fix: nan error when all fparam/aparam have equal values

[Chengqian-Zhang]

For standard deviation of fparam/aparam, $\sigma = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2}=\sqrt{\frac{\sum x_i^2}{N} - \left( \frac{\sum x_i}{N} \right)^2}$.
When all fparam/aparam have equal values in one dimension, $\frac{\sum x_i^2}{N} - \left( \frac{\sum x_i}{N} \right)^2$ equals zero.

However, it sometimes becomes a very small negative number(for example, 1e-18) due to numerical instability, so $\sqrt{\frac{\sum x_i^2}{N} - \left( \frac{\sum x_i}{N} \right)^2}$ becomes nan.

Summary by CodeRabbit

• Bug Fixes
  • Improved numerical stability in variance/std calculations by ensuring intermediate variance values are non-negative before taking the square root. This prevents occasional floating-point underflow from producing invalid results and yields more reliable statistical outputs across edge-case inputs.

[coderabbitai]

📝 Walkthrough

Walkthrough

compute_std now clips the computed variance with np.clip(..., a_min=0) before taking the square root to prevent negative values from floating-point underflow.

Changes

Cohort / File(s)	Summary
Variance clamping deepmd/utils/env_mat_stat.py	Added np.clip to enforce non-negative variance before the np.sqrt in compute_std, preventing negative values from floating-point precision loss. Lines changed: +6/-2.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

Suggested reviewers

• wanghan-iapcm

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The PR title accurately describes the main fix: addressing NaN errors when all fparam/aparam values are equal, which directly matches the code change that clips variance before square root.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.30%. Comparing base (4ce5c73) to head (3e70f35).
⚠️ Report is 4 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master    #5321   +/-   ##
=======================================
  Coverage   82.30%   82.30%           
=======================================
  Files         775      775           
  Lines       77628    77626    -2     
  Branches     3675     3678    +3     
=======================================
+ Hits        63888    63889    +1     
+ Misses      12568    12565    -3     
  Partials     1172     1172           

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[njzjz]

pre-commit.ci autofix

[coderabbitai]

🧹 Nitpick comments (1)

deepmd/utils/env_mat_stat.py (1)

92-97: Align TensorFlow _compute_std implementations with the guard applied in deepmd/utils/env_mat_stat.py.

Both deepmd/tf/fit/ener.py (line 380) and deepmd/tf/descriptor/se_a.py (line 918) take sqrt on unclamped variance without protecting against negative values. Their post-check thresholds do not catch NaN results that can arise from numerical instability (e.g., when floating-point errors produce negative variance estimates). Apply pre-sqrt variance clamping (a_min=0) to both _compute_std methods to match the fix already in place.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@deepmd/utils/env_mat_stat.py` around lines 92 - 97, The two TensorFlow
implementations of _compute_std (in deepmd/tf/fit/ener.py and
deepmd/tf/descriptor/se_a.py) take tf.sqrt on a variance expression without
guarding against small negative values; update each _compute_std to clamp the
variance before sqrt (e.g., use tf.clip_by_value or tf.maximum to enforce
a_min=0) so you compute tf.sqrt(clamped_variance) instead of
tf.sqrt(raw_variance), matching the np.clip fix in deepmd/utils/env_mat_stat.py;
ensure you replace the direct variance -> sqrt usage in both _compute_std
functions with the clamping step while keeping downstream logic unchanged.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Nitpick comments:
In `@deepmd/utils/env_mat_stat.py`:
- Around line 92-97: The two TensorFlow implementations of _compute_std (in
deepmd/tf/fit/ener.py and deepmd/tf/descriptor/se_a.py) take tf.sqrt on a
variance expression without guarding against small negative values; update each
_compute_std to clamp the variance before sqrt (e.g., use tf.clip_by_value or
tf.maximum to enforce a_min=0) so you compute tf.sqrt(clamped_variance) instead
of tf.sqrt(raw_variance), matching the np.clip fix in
deepmd/utils/env_mat_stat.py; ensure you replace the direct variance -> sqrt
usage in both _compute_std functions with the clamping step while keeping
downstream logic unchanged.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Repository UI

Review profile: CHILL

Plan: Pro

Run ID: 90ca07ae-2c9b-4464-8b60-6062f9448bdc

📥 Commits

Reviewing files that changed from the base of the PR and between 4ab1427 and 3e70f35.

📒 Files selected for processing (1)

• deepmd/utils/env_mat_stat.py