feat(jax/array-api): energy fitting #4204
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Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
📝 WalkthroughWalkthroughThe pull request introduces several modifications across multiple files to enhance compatibility with array APIs. Key changes include the integration of Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant GeneralFitting
participant AtomExcludeMask
participant EnergyFittingNet
User->>GeneralFitting: call serialize()
GeneralFitting->>GeneralFitting: use to_numpy_array()
GeneralFitting->>User: return serialized data
User->>GeneralFitting: call _call_common(inputs)
GeneralFitting->>GeneralFitting: handle inputs with array_api_compat
GeneralFitting->>User: return processed output
User->>EnergyFittingNet: create instance
EnergyFittingNet->>EnergyFittingNet: call __setattr__()
EnergyFittingNet->>User: return instance
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Actionable comments posted: 10
🧹 Outside diff range and nitpick comments (3)
source/tests/array_api_strict/utils/exclude_mask.py (2)
14-19: LGTM:AtomExcludeMaskclass implementation is correct.The
AtomExcludeMaskclass correctly inherits fromAtomExcludeMaskDPand overrides the__setattr__method to handle thetype_maskattribute. The implementation ensures that thetype_maskis converted to the correct array format usingto_array_api_strict_array.Consider using a set for slightly improved readability:
- if name in {"type_mask"}: + if name in {"type_mask"}:This change doesn't affect functionality but might be slightly more idiomatic for a single-element check.
Line range hint
20-24: LGTM:PairExcludeMaskclass implementation is correct. Consider reducing code duplication.The
PairExcludeMaskclass correctly inherits fromPairExcludeMaskDPand overrides the__setattr__method to handle thetype_maskattribute. The implementation is consistent with theAtomExcludeMaskclass, which is good for maintainability.To reduce code duplication, consider extracting the common
__setattr__logic into a mixin class or a utility function. This would make the code more DRY (Don't Repeat Yourself) and easier to maintain. Here's an example of how you could refactor this:class TypeMaskMixin: def __setattr__(self, name: str, value: Any) -> None: if name in {"type_mask"}: value = to_array_api_strict_array(value) return super().__setattr__(name, value) class AtomExcludeMask(TypeMaskMixin, AtomExcludeMaskDP): pass class PairExcludeMask(TypeMaskMixin, PairExcludeMaskDP): passThis refactoring would centralize the
__setattr__logic and make it easier to update or extend in the future.source/tests/array_api_strict/fitting/fitting.py (1)
19-32: LGTM with suggestions: Utility function for attribute handling.The
setattr_for_general_fittingfunction provides a centralized point for attribute handling, which is good for maintainability. However, consider the following suggestions:
- Add error handling for the NetworkCollection deserialization to gracefully handle potential issues.
- Consider using a more flexible approach for the 'emask' attribute to reduce tight coupling with the AtomExcludeMask class.
Here's a suggested improvement for error handling:
elif name == "nets": try: value = NetworkCollection.deserialize(value.serialize()) except Exception as e: raise ValueError(f"Failed to deserialize NetworkCollection: {str(e)}")
📜 Review details
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📒 Files selected for processing (9)
- deepmd/dpmodel/fitting/general_fitting.py (7 hunks)
- deepmd/dpmodel/utils/exclude_mask.py (2 hunks)
- deepmd/jax/fitting/init.py (1 hunks)
- deepmd/jax/fitting/fitting.py (1 hunks)
- deepmd/jax/utils/exclude_mask.py (1 hunks)
- source/tests/array_api_strict/fitting/init.py (1 hunks)
- source/tests/array_api_strict/fitting/fitting.py (1 hunks)
- source/tests/array_api_strict/utils/exclude_mask.py (1 hunks)
- source/tests/consistent/fitting/test_ener.py (4 hunks)
✅ Files skipped from review due to trivial changes (2)
- deepmd/jax/fitting/init.py
- source/tests/array_api_strict/fitting/init.py
🧰 Additional context used
🔇 Additional comments (17)
source/tests/array_api_strict/utils/exclude_mask.py (1)
6-7: LGTM: Import statements are correct and necessary.The import statements for
AtomExcludeMaskDPandPairExcludeMaskDPare correctly added and are essential for the new classes defined in this file.deepmd/jax/utils/exclude_mask.py (4)
6-9: LGTM: Import statements are appropriate.The new import statements are correctly added to support the implementation of the
AtomExcludeMaskandPairExcludeMaskclasses. The imports fromdeepmd.jax.commonprovide necessary functionality for Flax integration and JAX array conversion.
14-20: LGTM: AtomExcludeMask class implementation is correct and consistent.The
AtomExcludeMaskclass is well-implemented:
- It correctly uses the
@flax_moduledecorator for Flax integration.- It inherits from
AtomExcludeMaskDP, extending its functionality.- The
__setattr__method ensures that thetype_maskattribute is always stored as a JAX array, which is consistent with JAX-based implementations.- The implementation is similar to the existing
PairExcludeMaskclass, maintaining consistency in the codebase.
Line range hint
23-27: Consistency between AtomExcludeMask and PairExcludeMask is maintained.The implementation of
PairExcludeMaskremains unchanged and is consistent with the newly addedAtomExcludeMaskclass. This consistency in design and implementation across similar classes is a good practice and enhances code maintainability.
Line range hint
1-27: Summary: Changes align well with PR objectives and maintain code quality.The modifications in this file contribute to the PR's objective of enhancing compatibility with array APIs:
- The new
AtomExcludeMaskclass and the existingPairExcludeMaskclass both use the@flax_moduledecorator and converttype_maskto JAX arrays.- These changes are consistent with the integration of
array_api_compatmentioned in the PR objectives.- The implementation maintains good code quality through consistency between classes and proper use of inheritance.
The SPDX license identifier is correctly included at the top of the file.
source/tests/array_api_strict/fitting/fitting.py (3)
1-16: LGTM: Imports and license are correctly specified.The SPDX license identifier is present, and the imports are appropriate for the functionality being implemented. Good practice in renaming the imported EnergyFittingNet to avoid naming conflicts.
35-38: LGTM: Well-implemented class extension.The
EnergyFittingNetclass effectively extendsEnergyFittingNetDPwith custom attribute setting. The implementation is concise and makes good use of the utility functionsetattr_for_general_fitting. The use ofsuper()in__setattr__ensures proper inheritance behavior.
1-38: Overall: Well-implemented functionality with minor suggestions for improvement.This new file introduces functionality for handling general fitting attributes in energy fitting networks. The implementation is well-structured, with a utility function for centralized attribute handling and a class that extends existing functionality.
Key points:
- Good use of type hints and imports.
- The utility function
setattr_for_general_fittingprovides a centralized point for attribute handling.- The
EnergyFittingNetclass effectively extendsEnergyFittingNetDPwith custom attribute setting.Consider implementing the suggested improvements for error handling in the NetworkCollection deserialization and exploring ways to reduce coupling with the AtomExcludeMask class.
deepmd/jax/fitting/fitting.py (3)
1-17: Imports are correctly structured and completeThe import statements successfully include all necessary modules and classes required for the functionality of the file. They are organized following standard Python conventions.
19-33:setattr_for_general_fittingfunction is well-implementedThe function
setattr_for_general_fittingcorrectly handles attribute assignment based on the attribute name. It applies the necessary transformations tovaluefor specific attribute names, ensuring that attributes are correctly processed before assignment.
35-39:EnergyFittingNetclass override of__setattr__is appropriateThe
EnergyFittingNetclass appropriately overrides the__setattr__method to utilizesetattr_for_general_fitting, ensuring that any attributes set are processed according to the defined logic before being assigned. This maintains consistency and control over attribute assignments.deepmd/dpmodel/utils/exclude_mask.py (1)
21-26: Improved Variable Assignment Enhances ClarityUsing a local variable
type_maskbefore assigning it toself.type_maskimproves code readability and maintainability. It allows for intermediate operations without directly modifying the instance attribute.source/tests/consistent/fitting/test_ener.py (5)
15-16: LGTM!Importing
INSTALLED_ARRAY_API_STRICTandINSTALLED_JAXto handle conditional imports is appropriate.
41-47: Conditional import of JAX componentsThe conditional import of JAX modules and setting
EnerFittingJAXtoobjectwhen JAX is not installed is properly handled. Ensure that any usage ofEnerFittingJAXin the tests accounts for this scenario to prevent runtime errors.
48-55: Conditional import of Array API Strict componentsSimilarly, the conditional import of
array_api_strictand settingEnerFittingStricttoNonewhen not installed is correctly implemented. Make sure to handle cases whereEnerFittingStrictisNoneto avoid attribute errors during testing.
97-107: LGTM!The
skip_array_api_strictproperty correctly handles cases wherearray_api_strictis not installed or when the precision is"bfloat16", which is unsupported.
112-113: LGTM!Assigning
jax_classandarray_api_strict_classto the appropriate classes ensures that the tests utilize the correct backend implementations.
Summary by CodeRabbit
Release Notes
New Features
AtomExcludeMaskclass for improved attribute handling in exclusion masks.Improvements
Documentation