[ShryukGrandhi]

Summary

For masked mixed_type batches whose frames have different numbers of real atoms, the global rmse_* displays of DOSLoss (dos and cdf) and TensorLoss took the RMSE of the whole batch and divided it by mean(real_natoms). That doesn't match normalizing each frame first. With real atom counts [3, 5] and extensive errors [3, 5] (a per-atom error of 1 in each frame), the batch reported sqrt((3² + 5²) / 2) / 4 = 1.0308, while each frame on its own reports 1.

This is a follow-up to #5738. Only the displayed metrics change. The loss and its gradients stay the same.

Changes

• Add masked_global_rmse(diff, mask, natoms) to deepmd/dpmodel/loss/reduction.py:
  • without a mask it returns sqrt(mean(diff**2)) / natoms, the same as before;
  • with a mask it returns sqrt(mean_f(mean(diff_f**2) / n_f**2)), so each frame is divided by its own real atom count before frames are averaged.
• Use it for rmse_global_dos, rmse_global_cdf and rmse_global_<tensor> in deepmd/dpmodel/loss/{dos,tensor}.py and in the PyTorch versions deepmd/pt/loss/{dos,tensor}.py. pt_expt reuses the dpmodel classes, so it gets the fix too.
• In deepmd/pt/loss/dos.py, the cdf RMSE was stored in a variable called rmse_global_dos; it is now rmse_global_cdf.
• Update the TensorLoss docstring.

With an all-ones mask the new value equals the old one up to floating-point rounding. masked_atom_num is no longer used by these losses but is left in place.

Testing

New tests in source/tests/common/dpmodel/test_loss_padding.py (TestGlobalRMSEDisplayPerFrame) and source/tests/pt/test_loss_padding.py (TestPTGlobalRMSEDisplayPerFrame):

• a padded [3, 5] batch with per-atom error 1 reports rmse_global_dos == 1, rmse_global_dipole == 1, and the expected per-frame-normalized rmse_global_cdf;
• for random data, the batch's squared global RMSE equals the mean of the squared values from calling each frame separately (dos, cdf, tensor);
• with no mask, the dpmodel tensor global RMSE is bit-identical to sqrt(mean(diff**2)) / natoms.

The new tests fail on master (they report 1.0308 instead of 1) and pass with this change. Also ran the existing padding, reduction, consistent dos/tensor and pt_expt dos/tensor loss tests; all pass. source/tests/pt/test_loss.py and source/tests/pt/test_loss_tensor.py were not run because they need the compiled TensorFlow backend.

Fixes #5767

Summary by CodeRabbit

• Bug Fixes
  • Corrected global DOS, CDF, and tensor RMSE reporting for batches with padded atoms. Metrics now normalize each frame by its real atom count, making batched results consistent with per-frame values.

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Reviewing files that changed from the base of the PR and between f875b3f and 4ac4967.

📒 Files selected for processing: 2

• deepmd/dpmodel/loss/reduction.py
• source/tests/common/dpmodel/test_loss_reduction.py

🚧 Files skipped from review as they are similar to previous changes: 1

• deepmd/dpmodel/loss/reduction.py

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📝 Walkthrough

Global DOS, CDF, and tensor RMSE display values now normalize masked errors by each frame’s real atom count before aggregation. Global loss calculations remain unchanged. Tests cover padded batches in dpmodel and PyTorch, plus unmasked tensor behavior.

Changes: Global RMSE display normalization

Layer / File(s)	Summary
Per-frame normalization and loss integration deepmd/dpmodel/loss/reduction.py, deepmd/dpmodel/loss/dos.py, deepmd/dpmodel/loss/tensor.py, deepmd/pt/loss/dos.py, deepmd/pt/loss/tensor.py	A shared helper computes masked global RMSE by normalizing each frame’s squared error by its real atom count squared, then averaging and taking the square root. DOS, CDF, and tensor loss displays use the helper. Global loss calculations remain unchanged.
Padded-batch display tests source/tests/common/dpmodel/test_loss_padding.py, source/tests/common/dpmodel/test_loss_reduction.py, source/tests/pt/test_loss_padding.py	Tests check DOS, CDF, and tensor display values against per-frame results for padded batches. They also check known values, all-padding behavior, and unmasked tensor behavior.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Bug fix · Severity of issue fixed: Medium

Suggested reviewers: outisli

Merge Risk: ⚪ Minimal · up to 4ac49

RMSE displays now normalize each frame separately while training losses remain unchanged. No concrete blocking issue is identified, so the change appears ready for normal checks.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 30.77% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 26 functions across 8 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely describes the main change: per-frame normalization of global RMSE displays for mixed-size batches.
Linked Issues check		The changes satisfy the coding requirements in issue #5767. masked_global_rmse keeps the unmasked formula and computes masked RMSE from per-frame squared errors divided by each frame's real atom cou…
Out of Scope Changes check		The changes stay within issue #5767. The reduction helper, display-metric call-site updates, CDF variable correction, documentation update, and focused tests support the requested display-only normali…

Full details: Linked Issues check

Explanation

The changes satisfy the coding requirements in issue #5767. masked_global_rmse keeps the unmasked formula and computes masked RMSE from per-frame squared errors divided by each frame's real atom count. The helper is used for global DOS, CDF, and tensor display metrics in dpmodel and PyTorch paths. The global loss calculations are unchanged. The added tests cover unequal-size [3, 5] batches, DOS, CDF, tensor metrics, PyTorch paths, all-padding handling, and unmasked behavior.

Full details: Out of Scope Changes check

Explanation

The changes stay within issue #5767. The reduction helper, display-metric call-site updates, CDF variable correction, documentation update, and focused tests support the requested display-only normalization. No unrelated behavior or training-objective change is shown.

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Inline comments:
Review comments at @deepmd/dpmodel/loss/reduction.py:
- Around line 237-239: Update the RMSE calculation using real_natoms and
frame_mse to exclude frames where real_natoms is zero, and return zero when no
valid frames remain; preserve the existing calculation for valid frames.

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Reviewing files that changed from the base of the PR and between a78bdfe and f875b3f.

📒 Files selected for processing: 7

• deepmd/dpmodel/loss/dos.py
• deepmd/dpmodel/loss/reduction.py
• deepmd/dpmodel/loss/tensor.py
• deepmd/pt/loss/dos.py
• deepmd/pt/loss/tensor.py
• source/tests/common/dpmodel/test_loss_padding.py
• source/tests/pt/test_loss_padding.py

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[njzjz-bot]

Agent: dot

Source LGTM for 4ac4967. Reviewed all eight changed files and the current resolution of the zero-real-atom review finding.

The helper now normalizes each frame's component MSE by its own atom-count squared before reducing across valid frames. Zero-count frames are excluded with safe denominators; an entirely padded batch yields zero. All six DOS/CDF/tensor display call sites use the helper, while the existing extensive training MSE, prefactors, and loss accumulation remain unchanged. The PyTorch display values remain detached. The unmasked expression is preserved.

Independent bounded verification: 97 assertions against the exact helper and dpmodel DOS/tensor call functions passed using NumPy with an array-namespace stand-in. Coverage includes float32/float64, varied frame/component counts, unequal atom counts, unmasked and all-ones equivalence, exclusion of zero-count frames, all-padding batches, the [3,5] unit per-atom error example, CDF normalization, and unchanged training-loss values. This is not a full installed-package/PyTorch/autograd run; Torch and array_api_compat are unavailable in this executor. I inspected the added native backend tests but did not execute them.

Current head's seven native/build/security workflows report action_required (fork workflow approval), not passed; CUDA execution and performance are unverified. No source defect found that warrants blocking this display-only fix, independently of those pending checks.