fix(pt_expt): dispatch property models to DeepProperty inference

[wanghan-iapcm]

Problem

Fixes #5671. A pt_expt property checkpoint could be constructed and trained by the backend stack but not evaluated. pt_expt DeepEval.model_type dispatched only energy, DOS, dipole, polar, and WFC outputs and then raised RuntimeError("Unknown model type") for a property model, and the evaluator did not expose the get_intensive / get_var_name / get_task_dim getters that DeepProperty needs. Underneath, the pt_expt PropertyModel itself only implemented get_var_name, not get_task_dim or get_intensive, so simply mirroring the dispatch would not have been enough.

Fix

• pt_expt/model/property_model.py: add get_task_dim (fitting output dimension) and get_intensive (from the output def), mirroring the PyTorch property model.
• pt_expt/infer/deep_eval.py: import DeepProperty, dispatch to it when the property variable name appears in the model output, and expose get_var_name / get_task_dim / get_intensive that delegate to the reconstructed model.

The dispatch branch and the getters are guarded by hasattr (and by self._dpmodel is not None), so:

• energy/DOS/dipole/polar/WFC models keep matching their own branches first;
• genuinely unknown model types still fall through to "Unknown model type" rather than raising an AttributeError;
• metadata-only mode (no reconstructed dpmodel, used by the C++ AOTI path) raises a clear NotImplementedError for the property getters instead of mis-dispatching.

Test

Adds source/tests/pt_expt/infer/test_deep_eval_property.py, a full serialize -> .pte -> DeepEval round trip asserting model_type is DeepProperty, the three getters, and the eval output shape. Without the fix, constructing the DeepEval raises RuntimeError("Unknown model type").

Verification for the reviewer's peace of mind: pt-backend property inference works end to end via the same mechanism (dispatch + getters + eval), and the full pt_expt energy inference suite (source/tests/pt_expt/infer/test_deep_eval.py, 92 passed / 2 skipped) is unaffected by the added branch.

Summary by CodeRabbit

• New Features

  • Added support for property-based models in inference, with automatic detection and handling.
  • Exposed property metadata through new getters for variable name, task dimension, and intensity.

• Bug Fixes

  • Improved inference behavior so property model outputs are recognized and shaped correctly.

• Tests

  • Added end-to-end coverage for property model loading, metadata access, and output shape validation.

[coderabbitai]

📝 Walkthrough

Walkthrough

This PR adds property-model support to the experimental PyTorch (pt_expt) backend's DeepEval inference wrapper, mirroring existing stable PyTorch/Paddle support. It adds a DeepProperty import, model_type dispatch logic, three new metadata accessor methods, corresponding PropertyModel methods, and a new end-to-end test.

Changes

Property model support in pt_expt DeepEval

Layer / File(s)	Summary
PropertyModel metadata methods deepmd/pt_expt/model/property_model.py	Adds get_task_dim() returning fitting network output dimension and get_intensive() returning the intensive flag from model_output_def().
DeepEval dispatch and accessors deepmd/pt_expt/infer/deep_eval.py	Imports DeepProperty, extends model_type to dispatch to DeepProperty when the dpmodel exposes a matching get_var_name(), and adds get_var_name, get_task_dim, get_intensive accessor methods that raise NotImplementedError in metadata-only mode.
End-to-end property inference test source/tests/pt_expt/infer/test_deep_eval_property.py	New TestDeepEvalProperty unittest builds a PropertyModel, serializes to .pte, reloads via DeepEval, and verifies dispatch, metadata getter parity, and output shape.

Estimated code review effort: 2 (Simple) | ~10 minutes

Sequence Diagram(s)

sequenceDiagram
  participant Test as TestDeepEvalProperty
  participant DeepEval
  participant PropertyModel

  Test->>DeepEval: load .pte checkpoint
  DeepEval->>PropertyModel: get_var_name()
  PropertyModel-->>DeepEval: var_name
  DeepEval->>DeepEval: model_type() checks var_name in output types
  DeepEval-->>Test: returns DeepProperty
  Test->>DeepEval: get_task_dim() / get_intensive()
  DeepEval->>PropertyModel: get_task_dim() / get_intensive()
  PropertyModel-->>DeepEval: task_dim / intensive flag
  Test->>DeepEval: eval(...)
  DeepEval-->>Test: property output

Loading

Possibly related PRs

• deepmodeling/deepmd-kit#5416: Both PRs modify DeepEval.model_type logic in the same pt_expt deep_eval.py code path.
• deepmodeling/deepmd-kit#5587: Both PRs rely on the same property-model metadata API (get_var_name, task dimension, intensive flag).

Suggested labels: new feature, Python

Suggested reviewers: njzjz, iProzd

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly matches the main change: routing pt_expt property models to DeepProperty inference.
Linked Issues check	✅ Passed	The PR adds the missing property metadata APIs and dispatches pt_expt property models to DeepProperty as requested.
Out of Scope Changes check	✅ Passed	The changes stay focused on property-model inference support and the accompanying test coverage.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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[coderabbitai]

🧹 Nitpick comments (1)

deepmd/pt_expt/infer/deep_eval.py (1)

711-718: 🎯 Functional Correctness | 🔵 Trivial | 💤 Low value

Property dispatch requires the reconstructed dpmodel; metadata-only .pte/.pt2 property archives still hit RuntimeError("Unknown model type").

The new elif branch only fires when self._dpmodel is not None. In metadata-only mode (_init_from_metadata, used when model.json is absent), a property model's output type would still be present in model_output_type, but there is no get_var_name() to check against, so it falls through to raise RuntimeError("Unknown model type") — the exact error this PR intends to fix. This is consistent with the existing pattern that get_var_name/get_task_dim/get_intensive are also unavailable in metadata-only mode, so it's a known limitation rather than a regression, but the resulting generic error message will confuse users who hit it via a metadata-only archive.

💡 Optional: give metadata-only property models a clearer error

         elif (
             self._dpmodel is not None
             and hasattr(self._dpmodel, "get_var_name")
             and self._dpmodel.get_var_name() in model_output_type
         ):
             return DeepProperty
+        elif self._dpmodel is None and "task_dim" in self.metadata:
+            raise NotImplementedError(
+                "Property model dispatch requires the reconstructed dpmodel "
+                "(model.json); this archive was loaded in metadata-only mode."
+            )
         else:
             raise RuntimeError("Unknown model type")

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@deepmd/pt_expt/infer/deep_eval.py` around lines 711 - 718, The property-type
dispatch in the model-type selection logic is too strict because it only checks
self._dpmodel.get_var_name() when self._dpmodel is available, so metadata-only
.pte/.pt2 archives fall through to the generic RuntimeError("Unknown model
type"). Update the dispatch around the self._dpmodel branch to handle
metadata-only property archives explicitly, either by recognizing the property
model from the available metadata/model_output_type path or by raising a
clearer, targeted error from this selector in DeepEval. Keep the fix localized
to the model-type resolution logic and preserve the existing behavior for
non-property models.

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Nitpick comments:
In `@deepmd/pt_expt/infer/deep_eval.py`:
- Around line 711-718: The property-type dispatch in the model-type selection
logic is too strict because it only checks self._dpmodel.get_var_name() when
self._dpmodel is available, so metadata-only .pte/.pt2 archives fall through to
the generic RuntimeError("Unknown model type"). Update the dispatch around the
self._dpmodel branch to handle metadata-only property archives explicitly,
either by recognizing the property model from the available
metadata/model_output_type path or by raising a clearer, targeted error from
this selector in DeepEval. Keep the fix localized to the model-type resolution
logic and preserve the existing behavior for non-property models.

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: Repository UI

Review profile: CHILL

Plan: Pro

Run ID: 5da33ea0-3592-4528-83f9-9761860d1752

📥 Commits

Reviewing files that changed from the base of the PR and between bbc3908 and d477393.

📒 Files selected for processing (3)

• deepmd/pt_expt/infer/deep_eval.py
• deepmd/pt_expt/model/property_model.py
• source/tests/pt_expt/infer/test_deep_eval_property.py

[codecov]

Codecov Report

❌ Patch coverage is 84.21053% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 79.19%. Comparing base (dd38b35) to head (d477393).
⚠️ Report is 1 commits behind head on master.

Files with missing lines	Patch %	Lines
deepmd/pt_expt/infer/deep_eval.py	80.00%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #5724      +/-   ##
==========================================
- Coverage   81.26%   79.19%   -2.07%     
==========================================
  Files         988      988              
  Lines      110877   110902      +25     
  Branches     4234     4234              
==========================================
- Hits        90103    87830    -2273     
- Misses      19249    21552    +2303     
+ Partials     1525     1520       -5     

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[njzjz-bot]

Thanks for the fix. I think this should be handled consistently across the DeepEval backends before merging.

This PR adds the DeepProperty dispatch and the property metadata getters for pt_expt, but the same gap still exists in the other backend DeepEval.model_type implementations I checked:

• deepmd/dpmodel/infer/deep_eval.py
• deepmd/jax/infer/deep_eval.py
• deepmd/tf2/infer/deep_eval.py

Those backends still only dispatch energy / dos / dipole / polar / wfc, so a property model reaching the generic DeepEval(...) path would still raise Unknown model type. They also do not currently expose the get_var_name() / get_task_dim() / get_intensive() hooks needed by DeepProperty.change_output_def().

Please extend the same DeepProperty dispatch logic and metadata getter support to dpmodel, jax, and tf2 as well, with tests where the backend supports property inference. If any of these backends intentionally cannot support property models, please make that explicit in the code/tests/docs rather than leaving the same Unknown model type failure mode.

Authored by OpenClaw (model: custom-chat-jinzhezeng-group/gpt-5.5)