feat(jax): energy model (no grad support) #4226
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Signed-off-by: Jinzhe Zeng <[email protected]>
📝 Walkthrough📝 WalkthroughWalkthroughThe pull request introduces several enhancements across multiple files in the Changes
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🪛 Ruff
🔇 Additional comments (6)
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Actionable comments posted: 8
🧹 Outside diff range and nitpick comments (14)
deepmd/jax/fitting/__init__.py (1)
7-10: LGTM: all list correctly updated, with a minor suggestion.The
__all__list has been correctly updated to include the newly importedEnergyFittingNetandDOSFittingNetclasses, making them part of the public API of the module. This is consistent with the changes made in the import statement.For better readability and consistency, consider matching the order of items in the
__all__list with the order of imports:__all__ = [ "DOSFittingNet", "EnergyFittingNet", ]deepmd/jax/fitting/base_fitting.py (1)
1-9: Consider adding documentation and error handling.The file successfully creates a JAX-specific
BaseFitting, which aligns with the PR objectives. To further improve the code:
- Consider adding a docstring or comments to explain the purpose and usage of
BaseFitting.- You might want to add error handling or type checking to ensure
make_base_fittingreceives the expected input type.These additions would enhance the maintainability and robustness of the code.
deepmd/jax/descriptor/__init__.py (1)
9-12: LGTM: Correct all declaration with a minor suggestionThe
__all__declaration correctly includes both imported classes, explicitly defining the public API of the module. This is good practice for controlling what gets exported when usingfrom module import *.Consider aligning the order of items in
__all__with the order of imports for consistency:__all__ = [ "DescrptDPA1", "DescrptSeA", ]deepmd/jax/model/ener_model.py (1)
1-20: Overall implementation looks good, consider adding documentation.The
EnergyModelclass is well-structured and aligns with the PR objectives. It successfully extends theEnergyModelDPclass and is properly registered withBaseModel. The custom handling of the "atomic_model" attribute in__setattr__is a nice touch, although it could be optimized as suggested earlier.To improve maintainability and ease of use, consider adding:
- A class-level docstring explaining the purpose and usage of
EnergyModel.- A comment or docstring for the
__setattr__method, explaining why the custom handling for "atomic_model" is necessary.These additions would make it easier for other developers to understand and work with this class in the future.
deepmd/jax/atomic_model/base_atomic_model.py (1)
11-11: Consider adding type hints to improve code clarity.The function signature is clear, but adding type hints for the parameters and return value would enhance readability and maintainability. Consider updating the signature as follows:
def base_atomic_model_set_attr(name: str, value: Any) -> Any:Don't forget to import
Anyfrom thetypingmodule if you implement this suggestion.deepmd/jax/atomic_model/dp_atomic_model.py (2)
18-22: LGTM: Class definition is well-structured. Consider enhancing docstrings.The
DPAtomicModelclass is correctly defined, inheriting fromDPAtomicModelDP. The class-level attributesbase_descriptor_clsandbase_fitting_clsprovide good flexibility for the model.Consider enhancing the docstrings for the class-level attributes to provide more context on their purpose and usage. For example:
base_descriptor_cls = BaseDescriptor """The base descriptor class used for feature extraction in the atomic model.""" base_fitting_cls = BaseFitting """The base fitting class used for energy and force predictions in the atomic model."""
24-26: LGTM:__setattr__implementation is correct. Consider adding a comment for clarity.The
__setattr__method is well-implemented, usingbase_atomic_model_set_attrfor custom processing before setting the attribute. The use of type hints is commendable.Consider adding a brief comment explaining the purpose of
base_atomic_model_set_attrfor better code readability:def __setattr__(self, name: str, value: Any) -> None: # Process the value using custom logic for atomic model attributes value = base_atomic_model_set_attr(name, value) return super().__setattr__(name, value)deepmd/jax/descriptor/se_e2_a.py (2)
22-23: LGTM: Class registrations for DescrptSeA.The class registrations for "se_e2_a" and "se_a" are correctly implemented. This allows the
DescrptSeAclass to be recognized as a descriptor for both types.Consider adding a brief comment explaining why this class is registered for both "se_e2_a" and "se_a" types. This would improve code readability and maintainability.
Verify the impact of
DescrptSeAregistrations on all test cases.The
DescrptSeAclass is extensively used across numerous test files within thesource/tests/directory. The new registrations for"se_e2_a"and"se_a"may affect how this class is instantiated and utilized in these tests. It's important to ensure that all test cases are updated accordingly to prevent potential failures or unintended behaviors.🔗 Analysis chain
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22-38: Verify the impact of DescrptSeA changes on the codebase.The new registrations for
DescrptSeAmight affect how this class is instantiated or used in other parts of the codebase. While the core functionality seems unchanged, it's important to ensure that these modifications don't introduce any unintended side effects.Please run the following script to check for any potential impacts:
This script will help identify any areas of the codebase that might be affected by these changes, allowing for targeted testing and verification.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for usage of DescrptSeA and potential impacts of the new registrations. # Test 1: Search for direct instantiations of DescrptSeA echo "Searching for direct instantiations of DescrptSeA:" rg --type python "DescrptSeA\s*\(" # Test 2: Search for uses of "se_e2_a" or "se_a" as descriptor types echo "Searching for uses of 'se_e2_a' or 'se_a' as descriptor types:" rg --type python -e "descriptor.*['\"]se_e2_a['\"]" -e "descriptor.*['\"]se_a['\"]" # Test 3: Check for any tests related to DescrptSeA echo "Checking for tests related to DescrptSeA:" rg --type python "test.*DescrptSeA" test/Length of output: 618
Script:
#!/bin/bash # Description: Check for usage of DescrptSeA and potential impacts of the new registrations. # Test 1: Search for direct instantiations of DescrptSeA echo "Searching for direct instantiations of DescrptSeA:" find . -type f -name "*.py" -exec rg "DescrptSeA\s*\(" {} + # Test 2: Search for uses of "se_e2_a" or "se_a" as descriptor types echo "Searching for uses of 'se_e2_a' or 'se_a' as descriptor types:" find . -type f -name "*.py" -exec rg "descriptor.*['\"]se_e2_a['\"]" {} + -o -exec rg "descriptor.*['\"]se_a['\"]" {} + # Test 3: Check for any tests related to DescrptSeA echo "Checking for tests related to DescrptSeA:" find test/ -type f -name "*test*.py" -exec rg "DescrptSeA" {} +Length of output: 10096
Script:
#!/bin/bash # Description: Enhanced checks for usage of DescrptSeA and potential impacts of the new registrations. # Define an array of possible test directories TEST_DIRS=("test" "tests" "src/tests" "source/tests") # Test 1: Search for direct instantiations of DescrptSeA echo "Searching for direct instantiations of DescrptSeA:" find . -type f -name "*.py" -exec rg "DescrptSeA\s*\(" {} + # Test 2: Search for uses of "se_e2_a" or "se_a" as descriptor types echo "Searching for uses of 'se_e2_a' or 'se_a' as descriptor types:" find . -type f -name "*.py" -exec rg "descriptor.*['\"]se_e2_a['\"]" {} + -o -exec rg "descriptor.*['\"]se_a['\"]" {} + # Test 3: Check for any tests related to DescrptSeA in various test directories for dir in "${TEST_DIRS[@]}"; do if [ -d "$dir" ]; then echo "Checking for tests related to DescrptSeA in $dir:" find "$dir" -type f -name "*test*.py" -exec rg "DescrptSeA" {} + else echo "Directory $dir does not exist. Skipping..." fi doneLength of output: 32135
deepmd/dpmodel/model/transform_output.py (1)
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27-38: LGTM: Array operations compatibility improvementThe changes to use
array_api_compatandxp.suminstead ofnp.sumimprove compatibility with different array backends, which is good for supporting JAX as mentioned in the PR objectives.However, consider the performance impact of casting to
GLOBAL_ENER_FLOAT_PRECISIONbefore reduction. Depending on the size of the array, it might be more efficient to perform the reduction first and then cast the result.Consider benchmarking the current implementation against an alternative where reduction is performed before casting:
model_ret[kk_redu] = xp.sum(vv, axis=atom_axis).astype(GLOBAL_ENER_FLOAT_PRECISION)This could potentially improve performance, especially for large arrays.
deepmd/dpmodel/atomic_model/dp_atomic_model.py (1)
172-176: LGTM! Consider enhancing docstrings for clarity.The addition of
base_descriptor_clsandbase_fitting_clsclass attributes improves the extensibility of theDPAtomicModelclass. This change allows subclasses to override the base classes for descriptors and fittings, which is a good design decision.Consider enhancing the docstrings to provide more context:
base_descriptor_cls = BaseDescriptor """The base descriptor class. Can be overridden by subclasses to use custom descriptors.""" base_fitting_cls = BaseFitting """The base fitting class. Can be overridden by subclasses to use custom fitting methods."""deepmd/jax/fitting/fitting.py (1)
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47-51: Remove unnecessaryreturnstatement in__setattr__method.Similarly, the
__setattr__method inDOSFittingNetshould not include areturnstatement sincesuper().__setattr__(name, value)returnsNone.Apply this change to remove the unnecessary
return:def __setattr__(self, name: str, value: Any) -> None: value = setattr_for_general_fitting(name, value) - return super().__setattr__(name, value) + super().__setattr__(name, value)source/tests/consistent/model/common.py (1)
77-77: Add informative message to the assertion inassert_jax_arrayIncluding an error message in the assertion will aid in debugging by clearly indicating the type of the unexpected object if the assertion fails.
Apply this diff to enhance the assertion:
def assert_jax_array(arr): - assert isinstance(arr, jnp.ndarray) or arr is None + assert isinstance(arr, jnp.ndarray) or arr is None, f"Expected jnp.ndarray or None, but got {type(arr)}" return arrdeepmd/dpmodel/atomic_model/base_atomic_model.py (1)
Line range hint
67-70: Avoid Mutable Default ArgumentsUsing mutable default arguments like
exclude_types: list[int] = []can lead to unexpected behavior because the default list is shared across all function calls. It's better to useNoneas the default value and initialize an empty list inside the method if needed.Apply this fix to both
reinit_atom_excludeandreinit_pair_excludemethods:def reinit_atom_exclude( self, - exclude_types: list[int] = [], + exclude_types: Optional[list[int]] = None, ): + if exclude_types is None: + exclude_types = [] self.atom_exclude_types = exclude_types # Rest of the method... def reinit_pair_exclude( self, - exclude_types: list[tuple[int, int]] = [], + exclude_types: Optional[list[tuple[int, int]]] = None, ): + if exclude_types is None: + exclude_types = [] self.pair_exclude_types = exclude_types # Rest of the method...Also applies to: 74-77
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📒 Files selected for processing (20)
- deepmd/dpmodel/atomic_model/base_atomic_model.py (4 hunks)
- deepmd/dpmodel/atomic_model/dp_atomic_model.py (1 hunks)
- deepmd/dpmodel/model/make_model.py (3 hunks)
- deepmd/dpmodel/model/transform_output.py (3 hunks)
- deepmd/jax/atomic_model/init.py (1 hunks)
- deepmd/jax/atomic_model/base_atomic_model.py (1 hunks)
- deepmd/jax/atomic_model/dp_atomic_model.py (1 hunks)
- deepmd/jax/descriptor/init.py (1 hunks)
- deepmd/jax/descriptor/base_descriptor.py (1 hunks)
- deepmd/jax/descriptor/dpa1.py (2 hunks)
- deepmd/jax/descriptor/se_e2_a.py (2 hunks)
- deepmd/jax/fitting/init.py (1 hunks)
- deepmd/jax/fitting/base_fitting.py (1 hunks)
- deepmd/jax/fitting/fitting.py (2 hunks)
- deepmd/jax/model/init.py (1 hunks)
- deepmd/jax/model/base_model.py (1 hunks)
- deepmd/jax/model/ener_model.py (1 hunks)
- deepmd/jax/model/model.py (1 hunks)
- source/tests/consistent/model/common.py (3 hunks)
- source/tests/consistent/model/test_ener.py (5 hunks)
✅ Files skipped from review due to trivial changes (3)
- deepmd/jax/atomic_model/init.py
- deepmd/jax/descriptor/base_descriptor.py
- deepmd/jax/model/init.py
🧰 Additional context used
🪛 Ruff
deepmd/dpmodel/atomic_model/base_atomic_model.py
202-202: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
deepmd/dpmodel/model/make_model.py
373-373: Local variable
nallis assigned to but never usedRemove assignment to unused variable
nall(F841)
deepmd/jax/atomic_model/base_atomic_model.py
17-18: Use a single
ifstatement instead of nestedifstatementsCombine
ifstatements usingand(SIM102)
🔇 Additional comments (38)
deepmd/jax/model/base_model.py (3)
1-1: LGTM: Appropriate license identifier.The inclusion of the LGPL-3.0-or-later license identifier is correct and follows good open-source practices.
2-4: LGTM: Clean and specific import.The import statement is well-structured and imports only the necessary function. This approach promotes clean and maintainable code.
6-6: Approve BaseModel creation with suggestions for improvement.The creation of
BaseModelusingmake_base_model()looks correct. However, consider the following suggestions to enhance clarity and maintainability:
- Add a comment or docstring explaining the purpose and intended usage of
BaseModel.- Evaluate if
make_base_model()requires any configuration parameters for this specific use case.- Ensure that
BaseModelis properly utilized in other parts of the codebase.To verify the usage of
BaseModelin the codebase, you can run the following script:This will help ensure that
BaseModelis being properly imported and used throughout the project.✅ Verification successful
Approve BaseModel creation with suggestions for improvement.
The creation of
BaseModelusingmake_base_model()is correctly implemented and is actively utilized throughout the codebase. To enhance clarity and maintainability, consider the following suggestions:
Add Documentation:
- Include a comment or docstring explaining the purpose and intended usage of
BaseModel.- This will aid future developers in understanding its role and how to extend or interact with it.
Evaluate Configuration Parameters:
- Assess whether
make_base_model()requires any configuration parameters for specific use cases.- Custom configurations can increase flexibility and adaptability across different modules.
Ensure Comprehensive Usage:
- Verify that
BaseModelis consistently utilized across all relevant parts of the codebase.- This ensures that any changes to
BaseModelwill have the intended effect throughout the project.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for imports and usage of BaseModel in the codebase # Search for imports of BaseModel echo "Checking imports of BaseModel:" rg --type python "from deepmd.jax.model.base_model import BaseModel" # Search for usage of BaseModel echo "\nChecking usage of BaseModel:" rg --type python "BaseModel"Length of output: 303
Script:
#!/bin/bash # Description: Check for imports and usage of BaseModel in the codebase # Search for imports of BaseModel echo "Checking imports of BaseModel:" rg -g "*.py" "from deepmd\.jax\.model\.base_model import BaseModel" # Search for usage of BaseModel echo "\nChecking usage of BaseModel:" rg -g "*.py" "\bBaseModel\b"Length of output: 4441
Script:
#!/bin/bash # Description: Comprehensive search for import and definition patterns of BaseModel # Search for all import statements that include BaseModel echo "Searching for any import statements of BaseModel:" rg -g "*.py" "import .*BaseModel" --glob "*.py" echo "\nSearching for from ... import ... statements of BaseModel:" rg -g "*.py" "from .* import .*BaseModel" --glob "*.py" # Search for any assignments or definitions to BaseModel echo "\nSearching for definitions or assignments to BaseModel:" rg -g "*.py" "^BaseModel\s*=" --glob "*.py" # Search for usages of make_base_model to understand BaseModel creation echo "\nSearching for make_base_model invocations:" rg -g "*.py" "make_base_model\(" --glob "*.py"Length of output: 1235
deepmd/jax/fitting/__init__.py (1)
2-5: LGTM: Import statement is clear and follows best practices.The import statement explicitly imports
DOSFittingNetandEnergyFittingNetfrom thedeepmd.jax.fitting.fittingmodule. This approach is in line with Python best practices and provides clear visibility of the imported components.deepmd/jax/fitting/base_fitting.py (2)
1-7: LGTM: Imports and license look good.The file starts with an appropriate SPDX license identifier, which is a good practice. The imports are clear and specific, bringing in the necessary components for creating a JAX-specific base fitting. This aligns well with the PR objective of introducing a JAX energy model.
9-9: Verify the usage ofjnp.ndarrayinmake_base_fitting.The creation of
BaseFittingusingmake_base_fitting(jnp.ndarray)appears to be setting up a JAX-specific version of the base fitting, which aligns with the PR objectives. However, it's important to ensure thatmake_base_fittingis designed to handle JAX'sndarraytype correctly.To verify this, please run the following script:
This script will help us verify that
make_base_fittingis indeed designed to work with different array types, including JAX'sndarray.deepmd/jax/descriptor/__init__.py (3)
2-4: LGTM: Clean import of DescrptDPA1The import statement for
DescrptDPA1is well-structured and follows Python conventions. Using parentheses for multi-line imports enhances readability.
5-7: LGTM: Clean import of DescrptSeAThe import statement for
DescrptSeAis well-structured, consistent with the previous import, and follows Python conventions. The use of parentheses for multi-line imports maintains good readability.
1-12: Summary: Well-structured module initialization for JAX descriptorsThis
__init__.pyfile is well-organized and correctly sets up the public API for the JAX descriptors. The imports and__all__declaration are properly implemented, contributing to the PR's objective of introducing JAX support in the energy model.The changes align with good Python practices and module structuring conventions. Great job on maintaining clean and readable code!
deepmd/jax/model/ener_model.py (2)
1-12: LGTM: Imports are appropriate and well-structured.The import statements are clear and concise, importing only the necessary components. The use of the alias
EnergyModelDPfor the importedEnergyModelis a good practice to avoid naming conflicts with the newEnergyModelclass being defined in this file.
15-16: LGTM: Class definition and registration are well-implemented.The
EnergyModelclass is correctly defined, inheriting fromEnergyModelDP. The registration withBaseModelusing the "ener" identifier is appropriate and aligns with the PR objectives of introducing a JAX energy model.deepmd/jax/atomic_model/base_atomic_model.py (3)
1-8: LGTM: Imports are clean and relevant.The imports are well-organized and directly related to the functionality implemented in this file. Good job on keeping the imports concise and relevant.
12-20: LGTM: Function logic is clear and concise.The function effectively handles different attribute types, applying appropriate transformations for each case. The use of
to_jax_arrayfor specific attributes and the conditional creation ofPairExcludeMaskandAtomExcludeMaskinstances are well-implemented.🧰 Tools
🪛 Ruff
17-18: Use a single
ifstatement instead of nestedifstatementsCombine
ifstatements usingand(SIM102)
1-20: Overall, good implementation of the JAX energy model attribute setter.This new file successfully introduces the
base_atomic_model_set_attrfunction, which is a key component for the JAX energy model mentioned in the PR objectives. The function handles various attribute types appropriately, including the conversion to JAX arrays and the creation of exclude masks.A few minor suggestions have been made to further improve the code:
- Adding type hints to enhance code clarity.
- Combining nested if statements for better readability.
These changes will contribute to making the code more maintainable as the JAX implementation progresses.
Regarding the PR objectives:
- This file supports the introduction of the JAX energy model.
- While gradient support is not implemented here (as mentioned in the PR summary), this function provides a solid foundation for future enhancements.
- The function appropriately uses JAX-specific functionality (e.g.,
to_jax_array), aligning with the goal of JAX integration.Great job on this implementation!
🧰 Tools
🪛 Ruff
17-18: Use a single
ifstatement instead of nestedifstatementsCombine
ifstatements usingand(SIM102)
deepmd/jax/atomic_model/dp_atomic_model.py (2)
1-15: LGTM: Imports are appropriate and well-organized.The imports are correctly structured and provide the necessary components for the
DPAtomicModelclass. The renaming ofDPAtomicModeltoDPAtomicModelDPis a good practice to avoid naming conflicts with the new class being defined.
1-26: Overall assessment: Well-implemented JAX atomic model foundation.This new file successfully introduces the
DPAtomicModelclass for JAX support, aligning well with the PR objectives. The implementation is concise, follows good practices, and provides a solid foundation for further development of JAX support in the DeePMD-kit.Key points:
- Appropriate imports and class inheritance.
- Well-defined class-level attributes for flexibility.
- Custom
__setattr__implementation for attribute processing.The suggested minor improvements in documentation will further enhance the code's readability and maintainability.
deepmd/jax/descriptor/se_e2_a.py (2)
11-13: LGTM: New import for BaseDescriptor.The new import for
BaseDescriptoris correctly placed and necessary for the class registrations.
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1-38: Summary: DescrptSeA changes align with JAX implementation goals.The modifications to
DescrptSeAare well-implemented and consistent with the PR objectives of introducing a JAX energy model. The new class registrations extend the functionality without altering the core behavior.Key points:
- New import and class registrations are correctly implemented.
- The class maintains its existing structure and attribute handling.
- The changes support the transition to JAX as intended.
To ensure a smooth integration, please:
- Add documentation explaining the dual registration.
- Verify the impact of these changes on the rest of the codebase using the provided script.
- Update any relevant tests to cover the new registration behavior.
deepmd/dpmodel/model/transform_output.py (2)
3-3: LGTM: Import of array_api_compatThe addition of
array_api_compatimport is a good step towards improving array operations compatibility, which aligns with the PR objectives.
Line range hint
1-85: Summary: Improved array operations compatibilityThe changes in this file successfully introduce
array_api_compatand modify thefit_output_to_model_outputfunction to use a flexible array namespace. These changes align with the PR objectives of introducing JAX support and addressing array operation compatibility.Key points:
- The addition of
array_api_compatimport enhances flexibility for different array backends.- The use of
xp = array_api_compat.get_namespace(coord_ext)and subsequentxp.sumallows for compatibility with various array libraries, including JAX.- Type casting to
GLOBAL_ENER_FLOAT_PRECISIONbefore reduction ensures consistent precision but may have performance implications.Overall, these changes are a positive step towards the goals outlined in the PR objectives.
deepmd/dpmodel/atomic_model/dp_atomic_model.py (2)
172-185: Summary: Excellent changes enhancing model flexibilityThe modifications to the
DPAtomicModelclass, including the addition ofbase_descriptor_clsandbase_fitting_clsattributes and the corresponding changes in thedeserializemethod, significantly improve the flexibility and extensibility of the model. These changes align well with the PR objectives, particularly in preparing the groundwork for future JAX support.The implementation is clean and maintains backwards compatibility. The suggested minor improvements to the docstrings and the verification script will further enhance the code quality and ensure smooth integration with existing codebase.
Overall, these changes are well-thought-out and contribute positively to the project's goals.
184-185: LGTM! Verify usage of custom descriptor and fitting classes.The changes in the
deserializemethod align well with the newly added class attributes. This modification enhances the flexibility of theDPAtomicModelclass by allowing subclasses to use custom descriptor and fitting classes.To ensure that this change doesn't introduce any unintended side effects, let's verify the usage of custom descriptor and fitting classes in the codebase:
This script will help identify any existing subclasses of
DPAtomicModeland check if they're already overriding the new class attributes. If there are no results, it suggests that the change is safe and doesn't affect existing code. If there are results, we should review those subclasses to ensure they're compatible with this change.✅ Verification successful
Changes Verified Successfully
The
deserializemethod changes in theDPAtomicModelclass have been verified. No subclasses overridebase_descriptor_clsorbase_fitting_cls, ensuring that the modifications do not introduce any unintended side effects.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for subclasses of DPAtomicModel that might override base_descriptor_cls or base_fitting_cls # Search for subclasses of DPAtomicModel echo "Searching for DPAtomicModel subclasses:" rg --type python -e "class.*\(.*DPAtomicModel.*\):" # Search for overrides of base_descriptor_cls or base_fitting_cls echo "\nSearching for overrides of base_descriptor_cls or base_fitting_cls:" rg --type python -e "base_descriptor_cls\s*=|base_fitting_cls\s*="Length of output: 422
Script:
#!/bin/bash # Description: Check for subclasses of DPAtomicModel that might override base_descriptor_cls or base_fitting_cls # Search for subclasses of DPAtomicModel in Python files echo "Searching for DPAtomicModel subclasses:" rg --glob "*.py" "class\s+\w+\(.*DPAtomicModel.*\):" # Search for overrides of base_descriptor_cls or base_fitting_cls in Python files echo "\nSearching for overrides of base_descriptor_cls or base_fitting_cls:" rg --glob "*.py" "base_descriptor_cls\s*=|base_fitting_cls\s*="Length of output: 1334
deepmd/jax/fitting/fitting.py (3)
12-14: Import ofBaseFittingis appropriate.The addition of
BaseFittingimport is necessary for class registration and integrates correctly.
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18-28: Helper functionsetattr_for_general_fittingis well-implemented.The function correctly processes attribute values based on their names, ensuring they are converted appropriately for JAX compatibility.
39-43:⚠️ Potential issueRemove unnecessary
returnstatement in__setattr__method.The
__setattr__method should not return a value. Thesuper().__setattr__(name, value)call returnsNone, so includingreturnis unnecessary and can be removed to adhere to Python conventions.Apply this change to remove the unnecessary
return:def __setattr__(self, name: str, value: Any) -> None: value = setattr_for_general_fitting(name, value) - return super().__setattr__(name, value) + super().__setattr__(name, value)Likely invalid or redundant comment.
source/tests/consistent/model/common.py (1)
75-87: Integration of JAX model evaluation is correct and consistentThe
eval_jax_modelmethod effectively adds support for evaluating JAX models within the testing framework, following the conventions established by the existingeval_dp_modelandeval_pt_modelmethods.deepmd/jax/descriptor/dpa1.py (2)
19-21: LGTM!The import of
BaseDescriptoris correct and necessary for class registration.
82-83: Verify the usage of registered identifiers 'dpa1' and 'se_atten'Registering
DescrptDPA1with identifiers'dpa1'and'se_atten'appears correct. Ensure these identifiers are used consistently throughout the codebase.Run the following script to check the usage of these identifiers:
✅ Verification successful
Verified usage of 'dpa1' and 'se_atten' is consistent throughout the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of 'dpa1' and 'se_atten' in the codebase. # Test: Search for occurrences of 'dpa1' and 'se_atten' in the codebase. rg --type python '"dpa1"' rg --type python '"se_atten"'Length of output: 121
Script:
#!/bin/bash # Description: Verify the usage of 'dpa1' and 'se_atten' in the Python codebase. # Test: Search for occurrences of 'dpa1' and 'se_atten' in Python files. rg --type py '"dpa1"' rg --type py '"se_atten"'Length of output: 4218
Script:
#!/bin/bash # Description: Verify the usage of 'dpa1' and 'se_atten' in all relevant files. # Test: Search for occurrences of 'dpa1' and 'se_atten' without specifying file type. rg '"dpa1"' rg '"se_atten"'Length of output: 6095
source/tests/consistent/model/test_ener.py (5)
16-16: Addition ofINSTALLED_JAXfor JAX supportThe
INSTALLED_JAXvariable is added to the imports, enabling conditional checks for JAX installation. This is consistent with the handling of other backends and is necessary for implementing JAX support.
40-45: Conditional import of JAX modulesThe code correctly adds conditional imports for
EnergyModelJAXandget_model_jaxwhen JAX is installed, following the existing pattern used for other backends (PTandTF). This ensures that the codebase remains compatible even if JAX is not installed.
94-94: Addition ofjax_classpropertyThe
jax_classproperty is added to theTestEnerclass to support testing with the JAX backend. This aligns with the existing properties for other backends (tf_class,dp_class,pt_class), maintaining consistency across the test suite.
104-107: Simplification ofskip_jaxpropertyThe
skip_jaxproperty skips JAX tests if JAX is not installed, which is straightforward and effective. Unlikeskip_tf, it does not consider data-dependent conditions. Ensure that there are no specific data configurations under which JAX tests should be skipped beyond the installation check.Please confirm whether additional conditions are required for skipping JAX tests, similar to how
skip_tfhandles specific data conditions.
115-116: Adding JAX support inpass_data_to_clsThe
pass_data_to_clsmethod now includes support for the JAX backend by adding a conditional branch forEnergyModelJAXand callingget_model_jax(data). This mirrors the structure used for other backends and ensures seamless integration.deepmd/dpmodel/atomic_model/base_atomic_model.py (2)
178-189: Correct Use ofarray_api_compatThe integration of
array_api_compatand the use of thexpnamespace enhance compatibility with different array libraries like NumPy and JAX. Replacing direct calls to NumPy withxpfunctions likexp.whereandxp.astypeensures that the code is backend-agnostic.
239-240: Consistent Serialization of Output VariablesConverting
out_biasandout_stdto NumPy arrays usingto_numpy_arraybefore serialization ensures that the output variables have a consistent format. This improves compatibility and prevents potential issues during model saving and loading.deepmd/dpmodel/model/make_model.py (3)
6-6: Approve addition ofarray_api_compatfor enhanced array handlingThe import of
array_api_compatenables compatibility with different array backends, improving the flexibility and maintainability of the code.
378-381: Approve use ofxp.concatfor array concatenation with array API compatibilityReplacing direct NumPy calls with
xp.concatenhances compatibility across different array libraries, aligning with the project's move towards array API compatibility.
390-399: Approve updates to neighbor list formatting usingarray_api_compatThe modifications utilizing the
xpnamespace for array operations in neighbor list formatting improve consistency and support for multiple array backends.
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 2
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📒 Files selected for processing (2)
- deepmd/jax/atomic_model/base_atomic_model.py (1 hunks)
- deepmd/jax/model/model.py (1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/jax/atomic_model/base_atomic_model.py
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🔇 Additional comments (3)
deepmd/jax/model/model.py (3)
1-14: LGTM: Import statements are well-organized and appropriate.The import statements are clean, relevant, and follow good practices. They import the necessary components for the functionality of this module.
17-25: LGTM: Input data mutation issue addressed.Great job addressing the previous review comment by using
deepcopy(data). This prevents unintended side effects from modifying the input dictionary.
1-63: Overall, well-implemented model creation functions with room for minor enhancements.The
get_standard_modelandget_modelfunctions are well-structured and provide a flexible way to create models based on input data. The code addresses previous review comments and follows good practices. The suggested improvements focus on enhancing error handling, type hinting, and documentation, which will further improve the robustness and usability of these functions.Great job on implementing the JAX energy model! The code provides a solid foundation for further development, including the potential addition of gradient support in the future.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4226 +/- ##
==========================================
+ Coverage 83.52% 84.57% +1.05%
==========================================
Files 542 547 +5
Lines 52544 51327 -1217
Branches 3047 3047
==========================================
- Hits 43886 43412 -474
+ Misses 7711 6967 -744
- Partials 947 948 +1 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
deepmd/dpmodel/model/make_model.py (1)
Line range hint
258-267: Approve the update to use RESERVED_PRECISON_DICTThe change from
self.reverse_precision_dicttoRESERVED_PRECISON_DICTis appropriate and consistent with the removal of the instance attribute initialization. This modification maintains the existing logic while using a global dictionary.Consider adding a comment explaining why the type conversion is necessary despite the type checking not passing jit. This would improve code readability and maintainability.
deepmd/jax/atomic_model/dp_atomic_model.py (1)
23-26: Use comments instead of docstrings for class attribute documentationThe strings following the class attribute assignments are not recognized as docstrings in Python. Docstrings are intended for modules, classes, and functions. To document class attributes, consider using comments instead.
Apply this diff to convert the docstrings to comments:
base_descriptor_cls = BaseDescriptor -"""The base descriptor class.""" +# The base descriptor class base_fitting_cls = BaseFitting -"""The base fitting class.""" +# The base fitting class
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- deepmd/dpmodel/model/make_model.py (7 hunks)
- deepmd/jax/atomic_model/dp_atomic_model.py (1 hunks)
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📓 Learnings (1)
deepmd/dpmodel/model/make_model.py (1)
Learnt from: njzjz PR: deepmodeling/deepmd-kit#4226 File: deepmd/dpmodel/model/make_model.py:370-373 Timestamp: 2024-10-16T21:50:10.680Z Learning: In `deepmd/dpmodel/model/make_model.py`, the variable `nall` assigned but not used is intentional and should not be flagged in future reviews.
🪛 Ruff
deepmd/dpmodel/model/make_model.py
368-368: Local variable
nallis assigned to but never usedRemove assignment to unused variable
nall(F841)
🔇 Additional comments (5)
deepmd/dpmodel/model/make_model.py (4)
6-6: Approve the addition of array_api_compat importThe addition of
array_api_compatimport is a positive change. It aligns with the PR objectives of integrating JAX and enhances the library's compatibility with different array operations.
286-286: Approve the update in output_type_cast methodThe modification to use
RESERVED_PRECISON_DICTin thedo_castcondition is consistent with the changes made in theinput_type_castmethod. This change maintains the existing logic while using the global dictionary, ensuring consistency across the class.
Line range hint
365-394: Approve the update to use array_api_compat in format_nlist methodThe modifications to use
array_api_compatin theformat_nlistmethod are well-implemented. These changes improve the compatibility of the code with different array libraries, including JAX, which aligns with the PR objectives. The logic of the method remains intact while benefiting from a more generic array API.Note: The variable
nallon line 368 is assigned but not used. This is intentional as per the retrieved learning and should not be flagged as an issue.🧰 Tools
🪛 Ruff
368-368: Local variable
nallis assigned to but never usedRemove assignment to unused variable
nall(F841)
Line range hint
1-524: Overall approval: Successful integration of array_api_compatThe changes made to this file successfully integrate
array_api_compat, which aligns well with the PR objectives of JAX integration and improved compatibility with different array libraries. The modifications maintain the existing logic while enhancing the flexibility of the code. These changes are consistently implemented throughout the file, particularly in theformat_nlist,input_type_cast, andoutput_type_castmethods.This update significantly improves the interoperability of the code with various array libraries, including JAX, without altering the core functionality. Great job on maintaining consistency and improving the overall flexibility of the codebase.
🧰 Tools
🪛 Ruff
368-368: Local variable
nallis assigned to but never usedRemove assignment to unused variable
nall(F841)
deepmd/jax/atomic_model/dp_atomic_model.py (1)
28-30: 🛠️ Refactor suggestionVerify the necessity of overriding
__setattr__Overriding the
__setattr__method can introduce complexity and potential side effects. Ensure that this override is necessary for your class's functionality. If it is required, consider adding a docstring to explain its purpose and how it modifies attribute assignment.
Signed-off-by: Jinzhe Zeng <[email protected]>
Add JAX energy model without grad support. The grad support needs discussion.
Array API is not supported in this PR as it needs more effort. (JAX has more APIs than Array API)
This PR also fixes a
skip_tfbug introduced in #3357. When no@propertywas added,xx.skip_tfis always cast toTrue.Summary by CodeRabbit
Release Notes
New Features
BaseAtomicModelandDPAtomicModelclasses with improved array compatibility and new output definitions.EnergyFittingNetandDOSFittingNetfor advanced fitting capabilities.get_standard_modelandget_modelfor flexible model creation based on input data.BaseDescriptorandBaseFittingclasses to streamline descriptor and fitting processes.EnergyModelclass for improved atomic model handling.Bug Fixes
Tests