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feat(jax): energy, dos, dipole, polar, property atomic model & model #4384

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merged 3 commits into from
Nov 21, 2024

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@njzjz njzjz commented Nov 20, 2024

Summary by CodeRabbit

Release Notes

  • New Features

    • Introduced several new atomic model classes: DPAtomicModelDipole, DPAtomicModelDOS, DPAtomicModelEnergy, DPAtomicModelPolar, and DPAtomicModelProperty.
    • Added new model classes: DipoleModel, DOSModel, PolarModel, and PropertyModel for enhanced functionalities.
    • Implemented a new function to create JAX-compatible models from existing DP models, improving integration with JAX.
  • Bug Fixes

    • Enhanced test suite to support JAX backend, ensuring compatibility and flexibility in testing.
  • Documentation

    • Updated public API to include new models and functionalities.

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>

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Copilot reviewed 5 out of 16 changed files in this pull request and generated no suggestions.

Files not reviewed (11)
  • deepmd/jax/atomic_model/dipole_atomic_model.py: Evaluated as low risk
  • deepmd/jax/atomic_model/dos_atomic_model.py: Evaluated as low risk
  • deepmd/dpmodel/atomic_model/polar_atomic_model.py: Evaluated as low risk
  • deepmd/jax/model/ener_model.py: Evaluated as low risk
  • source/tests/consistent/model/test_dos.py: Evaluated as low risk
  • deepmd/jax/model/model.py: Evaluated as low risk
  • deepmd/jax/atomic_model/dp_atomic_model.py: Evaluated as low risk
  • deepmd/jax/model/init.py: Evaluated as low risk
  • deepmd/jax/model/dp_model.py: Evaluated as low risk
  • deepmd/jax/atomic_model/energy_atomic_model.py: Evaluated as low risk
  • deepmd/jax/atomic_model/property_atomic_model.py: Evaluated as low risk
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coderabbitai bot commented Nov 20, 2024

📝 Walkthrough

Walkthrough

This pull request introduces several modifications across multiple files in the deepmd repository. Key changes include the addition of new classes for JAX-compatible atomic models, the implementation of a new function to facilitate the creation of JAX models from existing DP models, and updates to existing classes to enhance compatibility with the array_api_compat library. The testing framework is also updated to support JAX backends, ensuring that the new features are adequately tested.

Changes

File Path Change Summary
deepmd/dpmodel/atomic_model/polar_atomic_model.py Updated DPPolarAtomicModel to include compatibility with array_api_compat; modified apply_out_stat method to replace NumPy functions with array_api_compat equivalents.
deepmd/jax/atomic_model/dipole_atomic_model.py Added DPAtomicModelDipole class as a subclass of make_jax_dp_atomic_model_from_dpmodel.
deepmd/jax/atomic_model/dos_atomic_model.py Added DPAtomicModelDOS class as a subclass of DPAtomicModelDOSDP.
deepmd/jax/atomic_model/dp_atomic_model.py Introduced make_jax_dp_atomic_model_from_dpmodel function; redefined DPAtomicModel to inherit from a new JAX model class.
deepmd/jax/atomic_model/energy_atomic_model.py Added DPAtomicModelEnergy class as a subclass of a dynamically created class from make_jax_dp_atomic_model_from_dpmodel.
deepmd/jax/atomic_model/polar_atomic_model.py Added DPAtomicModelPolar class as a subclass of make_jax_dp_atomic_model_from_dpmodel.
deepmd/jax/atomic_model/property_atomic_model.py Added DPAtomicModelProperty class as a subclass of make_jax_dp_atomic_model_from_dpmodel.
deepmd/jax/model/__init__.py Updated imports and __all__ list to include new models: DipoleModel, DOSModel, PolarModel, and PropertyModel.
deepmd/jax/model/dipole_model.py Added DipoleModel class registered with BaseModel.
deepmd/jax/model/dos_model.py Added DOSModel class registered with BaseModel.
deepmd/jax/model/dp_model.py Introduced make_jax_dp_model_from_dpmodel function to create a JAX backend DP model.
deepmd/jax/model/ener_model.py Updated EnergyModel to inherit from a new class created by make_jax_dp_model_from_dpmodel.
deepmd/jax/model/model.py Modified get_standard_model to set embedding_width based on descriptor dimensionality for specific fitting types.
deepmd/jax/model/polar_model.py Added PolarModel class registered with BaseModel.
deepmd/jax/model/property_model.py Added PropertyModel class registered with BaseModel.
source/tests/consistent/model/test_dos.py Enhanced test support for JAX backend; updated methods to accommodate JAX model handling.
source/tests/consistent/model/test_dipole.py Introduced a test suite for the DipoleModel across different backends.
source/tests/consistent/model/test_polar.py Introduced a test suite for the PolarModel across different backends.
source/tests/consistent/model/test_property.py Introduced a test suite for property models in the DeepMD framework.

Possibly related PRs

  • feat pt : Support property fitting #3867: The changes in the main PR regarding the DPPolarAtomicModel class and its methods are related to the handling of fitting parameters, which is also a focus in this PR that introduces support for property fitting, including type checks and enhancements in the fitting process.
  • feat(jax): support neural networks #4156: This PR introduces support for neural networks, which may involve similar structural changes in model classes as seen in the main PR's modifications to the DPPolarAtomicModel, particularly in how models are defined and utilized.
  • feat(jax/array-api): DOS fitting #4218: The introduction of the DOSFittingNet class and its modifications in this PR relate to the changes in the main PR, as both involve enhancements to fitting models and their compatibility with array-like structures.
  • feat(jax/array-api): dipole/polarizability fitting #4278: The changes in this PR regarding dipole and polarizability fitting are directly related to the main PR's focus on the DPPolarAtomicModel, as both involve enhancements to fitting mechanisms and model definitions.
  • fix(jax): fix several serialization and jit issues for DPA-2 #4315: This PR addresses serialization and JIT issues, which may overlap with the main PR's focus on ensuring that the DPPolarAtomicModel class functions correctly with various array backends, including serialization aspects.
  • feat(jax): build nlist in the SavedModel & fix nopbc for StableHLO and SavedModel #4318: The modifications in this PR to handle neighbor lists and ghost atoms are relevant to the main PR's changes in the DPPolarAtomicModel, as both involve enhancements to model handling in the context of array operations.
  • fix: consistent DPA-1 model #4320: The focus on consistent handling of DPA-1 models in this PR aligns with the main PR's changes to the DPPolarAtomicModel, as both aim to improve model consistency and functionality across different frameworks.
  • fix(pt): fix precision #4344: The precision fixes in this PR relate to the main PR's focus on ensuring that the DPPolarAtomicModel class operates correctly with various precision settings, enhancing the overall robustness of the model.
  • fix(jax): handle DPA-2 pbc/nopbc without mapping #4363: The handling of PBC and nopbc scenarios in this PR is relevant to the main PR's changes, as both involve ensuring that models can operate correctly under different boundary conditions and configurations.

Suggested labels

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Suggested reviewers

  • wanghan-iapcm

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Actionable comments posted: 3

🧹 Outside diff range and nitpick comments (14)
deepmd/jax/atomic_model/polar_atomic_model.py (1)

10-11: LGTM: Clean implementation of JAX-compatible polar atomic model

The implementation follows a consistent pattern used across other atomic models in the codebase, utilizing the factory function pattern to create JAX-compatible versions of existing DP models. This approach promotes code reuse and maintainability.

This implementation demonstrates good architectural choices:

  1. Using inheritance through a factory function allows for consistent JAX compatibility across different atomic models
  2. The pattern makes it easy to add new JAX-compatible models while maintaining the core functionality of the original DP models
deepmd/jax/atomic_model/dipole_atomic_model.py (1)

10-11: Add docstring to document the JAX compatibility wrapper

Consider adding a docstring to explain that this is a JAX-compatible version of the dipole atomic model and how it differs from the base DP implementation.

Example:

 class DPAtomicModelDipole(make_jax_dp_atomic_model_from_dpmodel(DPAtomicModelDipoleDP)):
+    """JAX-compatible implementation of the dipole atomic model.
+
+    This class wraps the original DPDipoleAtomicModel to provide JAX compatibility
+    through the factory pattern, enabling automatic differentiation and JIT compilation
+    capabilities.
+    """
     pass
deepmd/jax/atomic_model/property_atomic_model.py (1)

10-13: Add docstring to improve code documentation

While the implementation is correct and follows the JAX model conversion pattern, adding a docstring would help users understand:

  • The purpose of this JAX-compatible property atomic model
  • How it relates to the base DP model
  • Any JAX-specific considerations or usage patterns

Consider adding documentation like:

 class DPAtomicModelProperty(
     make_jax_dp_atomic_model_from_dpmodel(DPAtomicModelPropertyDP)
 ):
+    """JAX-compatible implementation of the property atomic model.
+
+    This class provides a JAX-compatible version of DPPropertyAtomicModel,
+    enabling automatic differentiation and JIT compilation through JAX.
+    All functionality is inherited from the base DP model through the JAX
+    conversion process.
+    """
     pass
deepmd/jax/model/ener_model.py (1)

14-16: Consider documenting this pattern for other model implementations

The approach of using make_jax_dp_model_from_dpmodel to create JAX-compatible models is elegant and reusable. Consider:

  1. Adding documentation about this pattern in the developer guide
  2. Creating a migration guide for converting other models to use this pattern
  3. Adding type hints to make the relationship between DP and JAX models more explicit
deepmd/jax/model/dos_model.py (1)

14-16: Consider adding docstring documentation

While the implementation is clean and follows the JAX model creation pattern, adding a docstring would help users understand:

  • The purpose of the DOS model
  • Expected inputs and outputs
  • Any specific behavior inherited from DOSModelDP

Example docstring:

 @BaseModel.register("dos")
 class DOSModel(make_jax_dp_model_from_dpmodel(DOSModelDP, DPAtomicModelDOS)):
+    """JAX-compatible Density of States (DOS) model.
+
+    This model extends the base DOSModelDP to provide JAX compatibility,
+    enabling automatic differentiation and JIT compilation capabilities.
+    """
     pass
deepmd/jax/model/polar_model.py (1)

15-17: Add docstring to document the class purpose and behavior.

While the implementation is correct, adding a docstring would improve code documentation by explaining:

  • The purpose of the PolarModel class
  • Its relationship with PolarModelDP
  • Any specific behavior or requirements when used with JAX

Consider adding documentation like this:

 @BaseModel.register("polar")
 class PolarModel(make_jax_dp_model_from_dpmodel(PolarModelDP, DPAtomicModelPolar)):
+    """JAX-compatible implementation of the Polar Model.
+
+    This class provides a JAX-compatible version of the PolarModelDP, created using
+    the model factory pattern. It inherits all functionality from PolarModelDP while
+    ensuring compatibility with JAX's transformation and compilation features.
+    """
     pass
deepmd/jax/model/property_model.py (1)

15-19: Add docstring to explain the class purpose and implementation.

While the empty implementation is valid since it inherits all functionality from the parent class, adding a docstring would help developers understand:

  • The purpose of this JAX-compatible property model
  • Why no additional implementation is needed
  • How it differs from the original DP model

Consider adding a docstring like this:

 @BaseModel.register("property")
 class PropertyModel(
     make_jax_dp_model_from_dpmodel(PropertyModelDP, DPAtomicModelProperty)
 ):
+    """JAX-compatible property model that inherits all functionality from the DP property model.
+    
+    This class provides a JAX implementation of the property model by converting the original
+    DeePMD-kit property model (PropertyModelDP) using the JAX atomic model (DPAtomicModelProperty).
+    No additional implementation is needed as all functionality is inherited from the parent class.
+    """
     pass
deepmd/dpmodel/atomic_model/polar_atomic_model.py (1)

46-47: Remove unnecessary temp initialization

The initial zeros initialization of temp is immediately overwritten by the mean operation, making it redundant.

-                temp = xp.zeros(ntypes, dtype=dtype)
-                temp = xp.mean(
+                temp = xp.mean(
🧰 Tools
🪛 GitHub Check: CodeQL

[warning] 46-46: Variable defined multiple times
This assignment to 'temp' is unnecessary as it is redefined before this value is used.

deepmd/jax/model/model.py (1)

50-51: Consider adding validation and documentation for dipole/polar fitting types

While the implementation is correct, consider:

  1. Adding docstring updates to document the special handling of dipole/polar fitting types
  2. Adding validation to ensure get_dim_emb() returns a valid value

Here's a suggested enhancement:

    if fitting_type in {"dipole", "polar"}:
+       # Get embedding dimension from descriptor for dipole/polar models
+       emb_dim = descriptor.get_dim_emb()
+       if emb_dim <= 0:
+           raise ValueError(f"Invalid embedding dimension {emb_dim} for {fitting_type} fitting")
-       data["fitting_net"]["embedding_width"] = descriptor.get_dim_emb()
+       data["fitting_net"]["embedding_width"] = emb_dim
source/tests/consistent/model/test_dos.py (1)

75-75: Add docstring to skip_jax property

While the implementation is correct, consider adding a docstring explaining the purpose of this property, similar to how skip_tf is documented.

    @property
    def skip_jax(self) -> bool:
+        """Determine if JAX tests should be skipped based on installation status."""
        return not INSTALLED_JAX

Also applies to: 97-97

deepmd/jax/atomic_model/dp_atomic_model.py (1)

42-71: Consider moving class definition outside the function for efficiency

Defining the jax_atomic_model class inside the make_jax_dp_atomic_model_from_dpmodel function causes the class to be redefined every time the function is called. This could have performance implications due to the overhead of class creation.

Consider moving the jax_atomic_model class definition outside the function and parameterizing it as needed. This approach can improve efficiency by avoiding repetitive class creation.

deepmd/jax/model/dp_model.py (3)

45-48: Consider adding type checking in __setattr__ for robustness

To ensure robustness, consider adding type checking to verify that value has the serialize method before calling value.serialize(). This can prevent potential AttributeError exceptions if value does not have the expected attribute.

Apply this diff to implement the change:

def __setattr__(self, name: str, value: Any) -> None:
    if name == "atomic_model":
+       if not hasattr(value, "serialize"):
+           raise TypeError(f"The value assigned to 'atomic_model' must have a 'serialize' method.")
        value = jax_atomicmodel.deserialize(value.serialize())
    return super().__setattr__(name, value)

78-84: Use super() to call the parent format_nlist method

Using super().format_nlist instead of dpmodel_model.format_nlist enhances code maintainability and readability by directly referencing the parent class method.

Apply this diff to implement the change:

- return dpmodel_model.format_nlist(
+ return super().format_nlist(

50-70: Alias the imported forward_common_atomic function to avoid confusion

Since the method forward_common_atomic has the same name as the imported function forward_common_atomic, consider aliasing the imported function to improve code clarity and avoid potential confusion.

Apply these changes:

Modify the import statement at line 21~:

- from deepmd.jax.model.base_model import (
-     forward_common_atomic,
- )
+ from deepmd.jax.model.base_model import (
+     forward_common_atomic as base_forward_common_atomic,
+ )

Update the function call in the method:

- return forward_common_atomic(
+ return base_forward_common_atomic(
📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Reviewing files that changed from the base of the PR and between 6039e0b and 5f41c02.

📒 Files selected for processing (16)
  • deepmd/dpmodel/atomic_model/polar_atomic_model.py (2 hunks)
  • deepmd/jax/atomic_model/dipole_atomic_model.py (1 hunks)
  • deepmd/jax/atomic_model/dos_atomic_model.py (1 hunks)
  • deepmd/jax/atomic_model/dp_atomic_model.py (1 hunks)
  • deepmd/jax/atomic_model/energy_atomic_model.py (1 hunks)
  • deepmd/jax/atomic_model/polar_atomic_model.py (1 hunks)
  • deepmd/jax/atomic_model/property_atomic_model.py (1 hunks)
  • deepmd/jax/model/__init__.py (1 hunks)
  • deepmd/jax/model/dipole_model.py (1 hunks)
  • deepmd/jax/model/dos_model.py (1 hunks)
  • deepmd/jax/model/dp_model.py (1 hunks)
  • deepmd/jax/model/ener_model.py (1 hunks)
  • deepmd/jax/model/model.py (1 hunks)
  • deepmd/jax/model/polar_model.py (1 hunks)
  • deepmd/jax/model/property_model.py (1 hunks)
  • source/tests/consistent/model/test_dos.py (7 hunks)
🧰 Additional context used
🪛 GitHub Check: CodeQL
deepmd/dpmodel/atomic_model/polar_atomic_model.py

[warning] 46-46: Variable defined multiple times
This assignment to 'temp' is unnecessary as it is redefined before this value is used.

🔇 Additional comments (33)
deepmd/jax/atomic_model/dos_atomic_model.py (1)

1-7: LGTM! Clean imports with clear separation of concerns.

The imports are well-structured, with clear aliasing that distinguishes between the base DP model and the JAX conversion utility.

deepmd/jax/atomic_model/polar_atomic_model.py (2)

1-7: LGTM: Clean import structure and proper licensing

The imports are well-organized, with clear aliasing to prevent naming conflicts. The SPDX license header is correctly included.


10-11: Verify consistency with other atomic model implementations

Let's verify that this implementation follows the same pattern as other atomic models in the JAX integration.

✅ Verification successful

Implementation follows consistent pattern across atomic models

The verification shows that all atomic model implementations in the JAX integration follow exactly the same pattern:

  • All use make_jax_dp_atomic_model_from_dpmodel
  • All have an empty class body with just pass
  • Found in files: polar, energy, dos, dp, and dipole atomic models
🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check for consistent implementation patterns across atomic models
# Expected: Similar structure using make_jax_dp_atomic_model_from_dpmodel

# Find all atomic model implementations
echo "Checking atomic model implementations..."
rg -l "make_jax_dp_atomic_model_from_dpmodel" "deepmd/jax/atomic_model/"

# Show the class definitions for comparison
echo "\nComparing class definitions..."
rg -A 2 "^class.*make_jax_dp_atomic_model_from_dpmodel" "deepmd/jax/atomic_model/"

Length of output: 1531

deepmd/jax/atomic_model/dipole_atomic_model.py (2)

1-7: LGTM! Clean and well-organized imports

The imports are properly structured with clear aliasing that maintains consistency with the project's naming conventions.


10-11: Verify the factory pattern implementation

Let's verify that the factory pattern is properly implemented and that the class inherits all necessary methods.

✅ Verification successful

Let me try a different approach to verify the factory pattern implementation.


Let me check one more detail about the DPAtomicModelDipoleDP class since it's the base class being used in the factory pattern.


Based on the gathered information, I can now provide a final response:

Factory pattern implementation is correctly structured

The implementation follows the proper factory pattern design:

  • The factory function make_jax_dp_atomic_model_from_dpmodel is properly defined in dp_atomic_model.py
  • DPAtomicModelDipole correctly uses the factory pattern to inherit from DPAtomicModelDipoleDP
  • The imports show proper dependency chain from the base DPModel implementation
🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Verify the factory implementation and inherited methods

# Check the factory function implementation
echo "Checking factory function implementation..."
ast-grep --pattern 'def make_jax_dp_atomic_model_from_dpmodel($_) {
  $$$
}'

# Check the base dipole model methods that should be inherited
echo "Checking base dipole model methods..."
ast-grep --pattern 'class DPDipoleAtomicModel($_) {
  $$$
}'

Length of output: 323


Script:

#!/bin/bash
# Let's check the actual implementation details

# First, let's find the factory function
echo "Looking for make_jax_dp_atomic_model_from_dpmodel implementation..."
rg "def make_jax_dp_atomic_model_from_dpmodel" -A 10

# Check the DPAtomicModelDipoleDP class implementation
echo -e "\nChecking DPAtomicModelDipoleDP class..."
rg "class DPAtomicModelDipoleDP" -A 10

# Look for any related base classes
echo -e "\nChecking for related base classes..."
rg "class.*Dipole.*Model" -A 10

Length of output: 14362


Script:

#!/bin/bash
# Find DPAtomicModelDipoleDP definition and inheritance
echo "Looking for DPAtomicModelDipoleDP definition..."
rg "class DPAtomicModelDipoleDP" -B 5 -A 15 --no-filename

# Check imports in the dipole_atomic_model.py
echo -e "\nChecking imports in dipole_atomic_model.py..."
rg "^from|^import" deepmd/jax/atomic_model/dipole_atomic_model.py

Length of output: 455

deepmd/jax/atomic_model/energy_atomic_model.py (1)

1-7: LGTM! Well-structured imports with clear aliasing.

The imports are well-organized, and the aliasing of DPEnergyAtomicModel to DPAtomicModelEnergyDP improves code readability.

deepmd/jax/atomic_model/property_atomic_model.py (2)

1-7: LGTM! Well-organized imports with clear aliasing

The imports are properly structured with explicit aliasing that improves code readability.


10-13: Verify consistent implementation across atomic models

Let's ensure this implementation follows the same pattern as other JAX atomic models in the PR.

✅ Verification successful

Implementation follows consistent pattern across JAX atomic models

The verification shows that all JAX atomic models follow the exact same pattern:

  • Each model inherits from make_jax_dp_atomic_model_from_dpmodel() with its corresponding DP model
  • All implementations use an empty class body with pass

The implementation in property_atomic_model.py perfectly aligns with this consistent pattern.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check if all JAX atomic models follow the same pattern
# Expected: Similar structure with make_jax_dp_atomic_model_from_dpmodel

# Find and display all JAX atomic model implementations
fd -e py . deepmd/jax/atomic_model/ --exec grep -l "make_jax_dp_atomic_model_from_dpmodel" {} \; | \
while read -r file; do
    echo "=== $file ==="
    grep -A 5 "class.*make_jax_dp_atomic_model_from_dpmodel" "$file"
done

Length of output: 2006

deepmd/jax/model/ener_model.py (2)

3-10: LGTM! Clean and well-organized imports

The imports are logically grouped and properly reflect the new JAX-compatible architecture.


14-16: Clean architectural improvement using composition!

The shift to using make_jax_dp_model_from_dpmodel is a good architectural choice that promotes code reuse and maintainability.

Let's verify that the removed methods' functionality is preserved in the new implementation:

✅ Verification successful

Based on the evidence gathered from our shell script executions, I can now provide the final response:

Implementation is properly preserved through composition pattern

The new implementation preserves functionality through a well-structured composition pattern where:

  • EnergyModel uses make_jax_dp_model_from_dpmodel to inherit from both EnergyModelDP and DPAtomicModelEnergy
  • DPAtomicModelEnergy itself uses make_jax_dp_atomic_model_from_dpmodel to inherit from DPAtomicModelEnergyDP
  • The functionality is maintained through these transformation functions that properly map the DPModel backend implementations to JAX compatible ones

This architectural change successfully preserves the behavior while improving code organization through composition.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check for the presence of previously existing methods in the new base classes

# Look for forward_common_atomic implementation
ast-grep --pattern 'def forward_common_atomic($$$)'

# Look for format_nlist implementation
ast-grep --pattern 'def format_nlist($$$)'

# Check for atomic_model deserialization handling
rg -A 5 "atomic_model.*deserialize"

Length of output: 1098


Script:

#!/bin/bash
# Let's check the base classes to understand the implementation details

# Check EnergyModelDP implementation
ast-grep --pattern 'class EnergyModelDP($$$)'

# Check DPAtomicModelEnergy implementation
ast-grep --pattern 'class DPAtomicModelEnergy($$$)'

# Look for any forward method implementations in these classes
rg -A 10 "def forward" deepmd/jax/model/ener_model.py

Length of output: 157


Script:

#!/bin/bash
# Let's try to locate the base classes in the codebase first

# Find files containing EnergyModelDP
rg -l "class EnergyModelDP"

# Find files containing DPAtomicModelEnergy
rg -l "class DPAtomicModelEnergy"

# Look for make_jax_dp_model_from_dpmodel implementation
rg -A 10 "def make_jax_dp_model_from_dpmodel"

Length of output: 878


Script:

#!/bin/bash
# Let's examine the implementation details of the classes

# Check DPAtomicModelEnergy implementation
rg -A 20 "class DPAtomicModelEnergy" deepmd/jax/atomic_model/energy_atomic_model.py

# Check the base class DPModelCommon for inherited functionality
rg -A 20 "class DPModelCommon"

# Look for EnergyModelDP in dpmodel directory since it's a dpmodel backend class
fd -e py -x rg -l "class EnergyModelDP" {} \; -x cat {} \; deepmd/dpmodel/

Length of output: 3315


Script:

#!/bin/bash
# Let's fix the fd command and check for EnergyModelDP implementation

# Look for EnergyModelDP in the entire codebase
rg -A 20 "class EnergyModelDP"

# Check the implementation of make_jax_dp_atomic_model_from_dpmodel since DPAtomicModelEnergy uses it
rg -A 10 "def make_jax_dp_atomic_model_from_dpmodel"

# Look for DPAtomicModelEnergyDP implementation
rg -A 20 "class DPAtomicModelEnergyDP"

Length of output: 941

deepmd/jax/model/dos_model.py (2)

1-11: LGTM: Proper license and well-organized imports

The file includes the LGPL-3.0-or-later license header and has clear, organized imports that separate the base DP model, atomic model, and JAX-specific components.


14-16: Verify the model registration and inheritance chain

The implementation looks correct, but let's verify the model registration and inheritance chain to ensure proper integration.

✅ Verification successful

Model registration and inheritance chain verified successfully

The verification shows:

  • The model is correctly registered with @BaseModel.register("dos") in JAX implementation
  • Proper inheritance chain is maintained through make_jax_dp_model_from_dpmodel
  • The registration pattern is consistent across different implementations (JAX, PyTorch, DPModel)
  • Imports and model hierarchy are correctly structured
🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Verify model registration and inheritance chain

# Check if the base model is properly defined
echo "Checking base model definition..."
rg -A 5 "class DOSModelDP" 

# Verify atomic model implementation
echo "Checking atomic model implementation..."
rg -A 5 "class DPAtomicModelDOS"

# Check for other DOS model registrations to avoid conflicts
echo "Checking for registration conflicts..."
rg "@BaseModel\.register\(\"dos\"\)"

Length of output: 713


Script:

#!/bin/bash
# Let's check the DOSModelDP definition and inheritance
echo "Checking DOSModelDP definition..."
rg -A 10 "class DOSModelDP"

# Check the BaseModel definition and its registration mechanism
echo "Checking BaseModel definition..."
rg -A 5 "class BaseModel"

# Check if there are any imports of these models in other files
echo "Checking model imports..."
rg "from.*dos_model import"

Length of output: 1945

deepmd/jax/model/polar_model.py (2)

1-12: LGTM! Well-organized imports and proper licensing.

The imports are logically structured and include all necessary components for the JAX-compatible polar model implementation.


15-15: Verify model registration uniqueness.

Let's ensure the "polar" identifier is unique across all model registrations and that the model is properly exposed in the module's init.py.

deepmd/jax/model/dipole_model.py (2)

1-12: LGTM! Well-organized imports with clear dependencies.

The imports clearly show the relationship between the original DP model and its JAX counterpart, with proper organization and necessary components for the conversion.


15-17: LGTM! Clean implementation using factory pattern.

The implementation follows a clean pattern of converting DP models to JAX models using registration and inheritance.

Let's verify this pattern is consistently used across other JAX models:

✅ Verification successful

Let's gather more specific information about the registration patterns in these files.


Let me check the two files that showed different patterns (dp_zbl_model.py and property_model.py) to understand their full implementation.


Pattern consistency verified across JAX models

The implementation pattern is consistently used across all JAX models with only one justified exception:

  • Most models (dipole, ener, polar, dos, property) follow the exact same pattern using @BaseModel.register and make_jax_dp_model_from_dpmodel
  • Only dp_zbl_model.py differs slightly due to its specialized ZBL model requirements, but still maintains the core registration pattern
  • All implementations are minimal and clean, deriving functionality from their parent classes

The original review comment's approval was correct, and the pattern is indeed consistently used throughout the codebase.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check if other JAX models follow the same pattern
# Expected: Similar structure with BaseModel.register and make_jax_dp_model_from_dpmodel

# Find all JAX model files
echo "Checking JAX model implementations..."
fd --type f --full-path "model.*\.py$" "deepmd/jax/model" --exec rg -l "BaseModel.register|make_jax_dp_model_from_dpmodel" {}

# Check registration patterns
echo "\nChecking model registration patterns..."
ast-grep --pattern 'class $NAME(make_jax_dp_model_from_dpmodel($_, $_))'

Length of output: 603


Script:

#!/bin/bash
# Check each model file for their registration and inheritance patterns
echo "=== Checking dipole_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/dipole_model.py -A 1

echo -e "\n=== Checking ener_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/ener_model.py -A 1

echo -e "\n=== Checking polar_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/polar_model.py -A 1

echo -e "\n=== Checking dp_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/dp_model.py -A 1

echo -e "\n=== Checking dos_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/dos_model.py -A 1

echo -e "\n=== Checking dp_zbl_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/dp_zbl_model.py -A 1

echo -e "\n=== Checking property_model.py ==="
rg "@BaseModel.register|class.*make_jax_dp_model_from_dpmodel" deepmd/jax/model/property_model.py -A 1

Length of output: 1836


Script:

#!/bin/bash
# Check the full class definitions for the two files that showed different patterns
echo "=== Checking dp_zbl_model.py full class definition ==="
rg -A 10 "^@BaseModel.register" deepmd/jax/model/dp_zbl_model.py

echo -e "\n=== Checking property_model.py full class definition ==="
rg -A 10 "^@BaseModel.register" deepmd/jax/model/property_model.py

Length of output: 899

deepmd/jax/model/__init__.py (2)

2-19: LGTM! Clean and well-organized imports.

The import statements are well-structured, following a consistent style and logical grouping.


24-27: LGTM! Proper public API exposure.

The new models are correctly exposed in the public API, maintaining consistency with the PR objectives for JAX model support.

Let's verify that the imported models exist and are properly implemented:

✅ Verification successful

All models are properly implemented and exposed in the public API

The verification confirms:

  • All model files (dos_model.py, dipole_model.py, polar_model.py, property_model.py) exist in the JAX implementation
  • Each model is correctly implemented using the make_jax_dp_model_from_dpmodel pattern, following the same structure as other models
  • The models inherit from their respective DP model counterparts and atomic models
  • The implementation is consistent across all new models
🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Verify that all model files exist and contain the expected classes

# Check for the existence of model files
echo "Checking model files..."
for model in dipole dos polar property; do
    fd "${model}_model.py$" --type f
done

# Verify class definitions
echo "Checking class definitions..."
for model in DOSModel DipoleModel PolarModel PropertyModel; do
    ast-grep --pattern "class $model"
done

# Verify make_jax_dp_model_from_dpmodel usage
echo "Checking model creation pattern..."
rg "make_jax_dp_model_from_dpmodel" -A 2

Length of output: 3121

deepmd/dpmodel/atomic_model/polar_atomic_model.py (4)

3-3: LGTM: Clean implementation of array compatibility and type safety

The addition of array_api_compat and type checking for the fitting parameter are well-implemented changes that enhance both compatibility and safety.

Also applies to: 16-20


38-38: LGTM: Proper array namespace extraction

Correctly extracts the array namespace from the input atype, enabling backend-agnostic operations.


47-61: LGTM: Clean array operations implementation

The array operations are correctly implemented using array_api_compat:

  • Proper use of xp.diagonal for extracting diagonal elements
  • Correct broadcasting with xp.eye and xp.tile
  • Appropriate shape transformations for the bias modifications

38-61: Verify consistent array_api_compat usage across related files

Let's ensure this pattern of array_api_compat usage is consistent across related atomic models.

✅ Verification successful

Let me analyze the implementation details of array operations in these files.


Array operations are consistently using array_api_compat across atomic models

The verification shows that array operations in related atomic model files are consistently using the xp namespace obtained from array_api_compat. The remaining numpy imports (np.) are only used for type hints and a few error state context managers, which is acceptable.

Key findings:

  • All array operations (zeros, ones, eye, newaxis, etc.) use the xp namespace
  • Base model sets the pattern that other models follow
  • Type hints appropriately use numpy types for static analysis
  • The only direct numpy usage is for initialization in base model and error handling in linear model
🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for other atomic model files and check their array operations
rg -l "array_api_compat" "deepmd/dpmodel/atomic_model/"

# Check for any remaining direct numpy usage in atomic models
rg "np\." "deepmd/dpmodel/atomic_model/" --type py

Length of output: 7412


Script:

#!/bin/bash
# Check array operations in the related atomic model files
for file in deepmd/dpmodel/atomic_model/pairtab_atomic_model.py deepmd/dpmodel/atomic_model/linear_atomic_model.py deepmd/dpmodel/atomic_model/base_atomic_model.py; do
    echo "=== $file ==="
    rg "zeros|ones|eye|diagonal|tile|newaxis|mean" "$file" -B1 -A1
done

Length of output: 4108

🧰 Tools
🪛 GitHub Check: CodeQL

[warning] 46-46: Variable defined multiple times
This assignment to 'temp' is unnecessary as it is redefined before this value is used.

deepmd/jax/model/model.py (2)

50-51: Verify integration with model creation flow

The changes look good, but let's ensure the end-to-end model creation flow works correctly.

Let's check the model creation patterns:


50-51: Verify descriptor dimension compatibility with dipole and polar models

The code correctly sets the embedding width for dipole and polar fitting types. However, we should verify that the descriptor's embedding dimension is compatible with these models' requirements.

Let's check the descriptor implementations to ensure they provide appropriate dimensions:

✅ Verification successful

Let me gather more information about the fitting networks to understand how they use the embedding width.


Descriptor dimension compatibility is correctly handled

The verification shows that:

  1. The descriptors (se_t.py and se_e2_a.py) properly implement get_dim_emb() which returns the last neuron dimension.
  2. Both dipole and polar fitting networks correctly use the embedding width:
    • DipoleNet uses it for a 1D output tensor that's transformed into a 3D vector
    • PolarNet uses it for either diagonal or full matrix output (embedding_width or embedding_width^2)
  3. The dimension passing is consistent across all implementations (JAX, PyTorch, TensorFlow)

The code change is safe and properly integrates with the rest of the codebase.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for get_dim_emb implementations in descriptors
ast-grep --pattern 'def get_dim_emb(self):
  $$$
'

# Search for dipole/polar model usage of embedding_width
rg -A 5 'embedding_width.*dipole|embedding_width.*polar'

Length of output: 1737


Script:

#!/bin/bash
# Search for dipole and polar fitting network implementations
ast-grep --pattern 'class $_(DipoleNet|PolarNet|DipoleFitting|PolarFitting)$$$:
  $$$'

# Search for embedding_width usage in fitting networks
rg -B 2 -A 5 'embedding_width.*=|self\.embedding_width' deepmd/

Length of output: 14487

source/tests/consistent/model/test_dos.py (4)

16-16: LGTM: Clean JAX import integration

The JAX import structure follows the established pattern used for other backends, with proper conditional imports and null fallbacks.

Also applies to: 34-38


Line range hint 185-189: LGTM: Clean backend handling in extract_ret

The JAX backend handling is elegantly combined with the DP backend using a set comparison, and the return structure maintains consistency with other backends.


108-109: Consider parameter initialization consistency

The PT backend initializes model parameters (e.g., model.atomic_model.out_bias.uniform_()), but there's no equivalent initialization for the JAX backend. Verify if this is intentional or if similar initialization is needed for consistency.


58-58: Verify the impact of type_one_side parameter

The addition of "type_one_side": True to the descriptor configuration might affect the model behavior.

✅ Verification successful

Let me analyze the search results. The type_one_side parameter appears extensively in test files and is used in various descriptor implementations (se_a, se_r, se_atten, dpa1, dpa2, etc.). From the test files, particularly test_type_one_side.py, we can see that:

  1. When type_one_side is true, the descriptor should be the same for different types when their environments are the same
  2. The parameter is well-tested across different implementations and frameworks (TensorFlow, PyTorch, JAX)
  3. The parameter is used in both training and inference configurations

Let me search for the actual implementation to understand its impact:


Let me try a different search pattern to find the implementation:


Based on the extensive test files and usage patterns found, I can now provide a conclusive response about the type_one_side parameter:

The type_one_side parameter is safe to enable in this context

The parameter is a well-tested feature across multiple descriptor implementations (se_a, se_r, se_atten, dpa1, dpa2) that controls how atomic environments are treated with respect to atom types:

  • When type_one_side=True: The descriptor will generate the same output for different atom types when their environments are identical, making it more focused on geometric features
  • When type_one_side=False: The descriptor will generate different outputs for different atom types even if their environments are identical

The test files show this is a standard configuration parameter that has been thoroughly tested for correctness and compatibility across different frameworks (TensorFlow, PyTorch, JAX).

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for other test files using this parameter to understand its usage
rg "type_one_side" -A 5 -B 5 "source/tests"

# Search for parameter documentation
rg -l "type_one_side" "docs/"

Length of output: 132488


Script:

#!/bin/bash
# Search for type_one_side implementation in source code
ast-grep --pattern 'type_one_side = $_' source/
rg -A 5 -B 5 'type_one_side' 'source/op/' 'source/model/'

Length of output: 157


Script:

#!/bin/bash
# Search for type_one_side in Python files
rg -t py "class.*Descriptor" -A 10 source/
rg -t py "def.*type_one_side" -A 10 source/

Length of output: 18828

deepmd/jax/atomic_model/dp_atomic_model.py (4)

26-28: Function definition is properly typed

The function make_jax_dp_atomic_model_from_dpmodel is correctly defined with appropriate type annotations, enhancing code readability and facilitating type checking.


29-40: Docstring follows NumPy documentation style

The docstring provides comprehensive details using NumPy-style formatting, which aids in understanding the function's purpose, parameters, and return value.


65-65: Confirm that stopping the gradient on nlist is intended

Applying jax.lax.stop_gradient to nlist prevents gradients from flowing through nlist. Ensure that this is the desired behavior and that it doesn't unintentionally affect the model's training or inference.


74-75: Class DPAtomicModel correctly redefined with JAX backend

The redefinition of DPAtomicModel using the JAX backend via make_jax_dp_atomic_model_from_dpmodel ensures compatibility and leverages JAX's capabilities effectively.

deepmd/jax/model/dp_model.py (1)

25-86: Implementation of make_jax_dp_model_from_dpmodel is correct

The function make_jax_dp_model_from_dpmodel and the nested jax_model class are correctly implemented, integrating JAX functionalities into the existing DP model framework.

deepmd/jax/atomic_model/dos_atomic_model.py Show resolved Hide resolved
deepmd/jax/model/property_model.py Show resolved Hide resolved
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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Actionable comments posted: 0

🧹 Outside diff range and nitpick comments (2)
deepmd/dpmodel/atomic_model/polar_atomic_model.py (2)

46-49: Consider inlining the temporary variable.

The temp variable is used only once and could be inlined directly into the modified_bias assignment for better code conciseness.

-                temp = xp.mean(
-                    xp.diagonal(out_bias[kk].reshape(ntypes, 3, 3), axis1=1, axis2=2),
-                    axis=1,
-                )
-                modified_bias = temp[atype]
+                modified_bias = xp.mean(
+                    xp.diagonal(out_bias[kk].reshape(ntypes, 3, 3), axis1=1, axis2=2),
+                    axis=1,
+                )[atype]

41-60: Add documentation for tensor operations.

The tensor operations for diagonal modifications and bias application are complex. Consider adding docstring comments explaining:

  1. The mathematical operations being performed
  2. The shape transformations at each step
  3. The purpose of the diagonal shift and eye matrix operations
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📥 Commits

Reviewing files that changed from the base of the PR and between 5f41c02 and c4c98b2.

📒 Files selected for processing (1)
  • deepmd/dpmodel/atomic_model/polar_atomic_model.py (2 hunks)
🔇 Additional comments (1)
deepmd/dpmodel/atomic_model/polar_atomic_model.py (1)

Line range hint 3-21: LGTM! Well-structured class definition with proper type checking.

The addition of array_api_compat import and type checking for the fitting parameter enhances the robustness of the code.

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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Actionable comments posted: 4

🧹 Outside diff range and nitpick comments (9)
source/tests/consistent/model/test_dipole.py (1)

93-93: Consider enabling TensorFlow tests once inconsistencies are resolved

The skip_tf property always returns True, which means TensorFlow tests are being skipped. If possible, address the TensorFlow inconsistencies to enable these tests and ensure comprehensive testing across all supported backends.

source/tests/consistent/model/test_property.py (5)

60-61: Address TODO comment regarding numb_fparam.

The TODO indicates missing functionality in the property fitting implementation.

Would you like me to help implement the numb_fparam argument in the property fitting network?


42-65: Consider parameterizing the model configuration.

The descriptor and fitting net configurations use hardcoded values. Consider parameterizing these values to:

  1. Make tests more flexible for different scenarios
  2. Allow testing edge cases and boundary conditions

87-88: Track TensorFlow consistency fix.

The comment indicates a known consistency issue with the TensorFlow backend that needs to be fixed.

Would you like me to:

  1. Create an issue to track the TensorFlow consistency fix?
  2. Help investigate and resolve the consistency issues?

111-138: Consider extracting test data to fixtures.

The hardcoded coordinates and box values would be better managed as test fixtures or data files, improving maintainability and reusability.


184-196: Standardize return structures across backends.

The different return key structures between backends ('property_redu'/'property' vs 'property'/'atom_property') could lead to confusion and maintenance issues.

Consider:

  1. Standardizing the return structure across all backends
  2. Adding documentation about the expected return format for each backend
  3. Creating a common interface or adapter to normalize the returns
source/tests/consistent/model/test_polar.py (3)

65-66: Address TODO comment regarding numb_fparam argument.

The TODO comment indicates that the numb_fparam argument needs to be added to polar fitting. This should be addressed before merging to ensure complete functionality.

Would you like me to help implement the numb_fparam argument in the polar fitting network?


112-150: Consider improving test data organization.

The current implementation has several maintainability concerns:

  1. Hardcoded test coordinates and values could be moved to test fixtures
  2. The comment about TF requiring sorted atype suggests a backend-specific requirement that should be documented more formally

Consider:

  1. Moving test data to a separate fixture file
  2. Adding docstring explaining the TF sorting requirement
  3. Creating helper methods for data preparation

Example structure:

from .fixtures import test_coordinates, test_atom_types, test_boxes

def setUp(self) -> None:
    """Set up test data.
    
    Note: TensorFlow backend requires atom types to be sorted.
    """
    CommonTest.setUp(self)
    self.coords = test_coordinates
    self.atype = test_atom_types
    self.box = test_boxes
    self._prepare_tf_compatible_data()

def _prepare_tf_compatible_data(self):
    """Prepare data specifically for TensorFlow compatibility."""
    idx_map = np.argsort(self.atype.ravel())
    self.atype = self.atype[:, idx_map]
    self.coords = self.coords[:, idx_map]

183-200: Improve documentation of backend differences.

The current implementation has several areas that could benefit from better documentation:

  1. The comment "shape not matched" needs more context about why shapes differ
  2. Different key names across backends (polarizability_redu vs global_polar) should be documented
  3. Shape handling could be more explicit

Consider adding a detailed docstring:

def extract_ret(self, ret: Any, backend) -> tuple[np.ndarray, ...]:
    """Extract polarizability values from backend-specific return formats.
    
    Different backends use different key names and shape formats:
    - DP/JAX: Uses 'polarizability_redu' and 'polarizability'
    - PT: Uses 'global_polar' and 'polar'
    - TF: Returns tuple of tensors
    
    Note: Shapes are flattened to enable consistent comparison across backends.
    
    Args:
        ret: Backend-specific return value
        backend: Backend identifier
    
    Returns:
        Tuple of (global_polarizability, atomic_polarizability) as flattened arrays
    """
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Reviewing files that changed from the base of the PR and between c4c98b2 and aae7533.

📒 Files selected for processing (3)
  • source/tests/consistent/model/test_dipole.py (1 hunks)
  • source/tests/consistent/model/test_polar.py (1 hunks)
  • source/tests/consistent/model/test_property.py (1 hunks)
🔇 Additional comments (4)
source/tests/consistent/model/test_dipole.py (2)

105-106: Clarify the initialization of out_bias in the PyTorch model

In the pass_data_to_cls method for the PyTorch backend, the out_bias of model.atomic_model is initialized with uniform_(). This step is specific to PyTorch. Please clarify the necessity of this initialization and consider whether similar initialization is needed for other backends to maintain consistency.

If this initialization is crucial, consider documenting the reason or aligning the initialization process across other backends.


147-150: Verify the necessity of sorting atype and coords

The atom types (atype) and coordinates (coords) are sorted to meet TensorFlow requirements. Since TensorFlow tests are currently skipped (skip_tf returns True), please verify if this sorting is necessary for other backends. If not, consider conditionally applying this sorting only when TensorFlow tests are enabled to avoid unnecessary data manipulation.

Would you like assistance in adjusting the code to conditionally sort the data based on the active backend?

source/tests/consistent/model/test_property.py (2)

1-37: LGTM! Well-structured imports with proper dependency handling.

The imports are well-organized with appropriate conditional handling of optional dependencies (PT and JAX) and good use of type hints.


141-144: Verify impact of atom type sorting.

The requirement to sort atom types for TensorFlow might mask issues in the actual use case where atom types aren't sorted.

✅ Verification successful

Let's examine the actual usage of atype sorting in these files to understand if this is a consistent requirement or if there are cases where unsorted atypes are handled.


Let's check the documentation and implementation details to understand if this is a fundamental requirement or just for testing purposes.


Let's check the actual implementation of the sorting functionality in the data modifier and test files to understand its purpose better.


Based on the gathered evidence, I can now provide a final response:

Atom type sorting is a legitimate TensorFlow requirement

The sorting of atom types is a fundamental requirement for TensorFlow operations in DeePMD-kit, not just a test artifact. This is evidenced by:

  1. Multiple model implementations consistently noting "TF requires the atype to be sort"
  2. The sorting is implemented in the core data modifier (deepmd/tf/infer/data_modifier.py)
  3. Specific requirement for sorted atom types in descriptor implementations (e.g., "TF se_e2_a type_one_side=False requires atype sorted")

The sorting is not masking issues but rather ensuring the data is in the correct format for TensorFlow operations. The current implementation is correct and necessary.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for other instances where atype sorting might be required
rg -l 'sort.*atype' | grep -v 'test_property.py'

Length of output: 606


Script:

#!/bin/bash
# Check the context of atype sorting in relevant files
echo "=== TF Data Modifier Implementation ==="
rg -C 3 'sort.*atype' deepmd/tf/infer/data_modifier.py

echo -e "\n=== Test Implementation Examples ==="
rg -C 3 'sort.*atype' source/tests/tf/common.py

echo -e "\n=== Main Implementation References ==="
rg -C 3 'sort.*atype' source/lib/tests/test_coord.cc

Length of output: 1764


Script:

#!/bin/bash
# Check documentation about atom type sorting
echo "=== Documentation on Type Embedding ==="
cat doc/development/type-embedding.md

echo -e "\n=== Implementation in Data Modifier ==="
ast-grep --pattern 'def sort_input' deepmd/tf/infer/data_modifier.py

echo -e "\n=== Check if sorting is mentioned in README or other docs ==="
rg -i "atom.*sort" README.md docs/

Length of output: 6674


Script:

#!/bin/bash
# Check the data modifier implementation
echo "=== Data Modifier Sort Implementation ==="
rg -C 10 'sort_input' deepmd/tf/infer/data_modifier.py

echo -e "\n=== Test Files Sort Implementation ==="
rg -C 10 'idx_map.*sort' source/tests/consistent/model/test_property.py source/tests/tf/common.py

echo -e "\n=== Check for any comments about sorting requirement ==="
rg -C 3 -i "requires.*sort" source/

Length of output: 14543

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codecov bot commented Nov 20, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 84.58%. Comparing base (6039e0b) to head (aae7533).
Report is 5 commits behind head on devel.

Additional details and impacted files
@@            Coverage Diff             @@
##            devel    #4384      +/-   ##
==========================================
+ Coverage   84.50%   84.58%   +0.08%     
==========================================
  Files         604      614      +10     
  Lines       56945    57010      +65     
  Branches     3487     3488       +1     
==========================================
+ Hits        48121    48224     +103     
+ Misses       7698     7659      -39     
- Partials     1126     1127       +1     

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@wanghan-iapcm wanghan-iapcm added this pull request to the merge queue Nov 21, 2024
Merged via the queue into deepmodeling:devel with commit e7925f3 Nov 21, 2024
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This was referenced Dec 13, 2024
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3 participants