feat(jax/array-api): dpa1 #4160
Conversation
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
📝 Walkthrough<details>
<summary>📝 Walkthrough</summary>
## Walkthrough
The changes involve updates to several files in the DeepMD project, focusing on improving array handling and network management. Key modifications include the replacement of `np.asarray` with `np.from_dlpack` in the `to_numpy_array` function, enhancements to the `deserialize` method in the `NativeLayer` class, and the introduction of new classes and methods in the JAX network module. These updates aim to streamline operations with weights, biases, and identity variables, ensuring better compatibility across different array backends.
## Changes
| File Path | Change Summary |
|------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `deepmd/dpmodel/common.py` | Updated `to_numpy_array` function to use `np.from_dlpack` instead of `np.asarray`, retaining the `None` check. |
| `deepmd/dpmodel/descriptor/dpa1.py` | Refactored `np_softmax` and `np_normalize` to use `array_api_compat`; modified `DescrptDPA1` class methods to utilize array API; updated `NeighborGatedAttention` and `NeighborGatedAttentionLayer` classes for array API compatibility. |
| `deepmd/dpmodel/utils/exclude_mask.py` | Enhanced `AtomExcludeMask` and `PairExcludeMask` classes for array API compatibility; replaced NumPy functions with `xp` equivalents. |
| `deepmd/dpmodel/utils/network.py` | Modified `NativeLayer` and `LayerNorm` classes to enhance array API compatibility; updated serialization methods and normalization processes. |
| `deepmd/dpmodel/utils/nlist.py` | Updated `build_neighbor_list`, `nlist_distinguish_types`, and `extend_coord_with_ghosts` functions to use `array_api_compat` for array operations. |
| `deepmd/dpmodel/utils/type_embed.py` | Renamed `concatenate` method to `concat` in `TypeEmbedNet` class. |
| `deepmd/jax/utils/network.py` | Introduced new classes (`ArrayAPIParam`, `NetworkCollection`) and restructured existing network classes to enhance compatibility with array API. |
| `pyproject.toml` | Added dependency `flax>=0.8.0;python_version>="3.10"` to `jax` optional dependencies. |
| `source/tests/array_api_strict/utils/network.py` | Added new classes and functionality for neural network layers and collections with array API compliance. |
| `source/tests/consistent/common.py` | Enhanced `CommonTest` class to support `array_api_strict` backend; added methods and properties for evaluation and serialization. |
| `source/tests/consistent/descriptor/test_dpa1.py` | Updated `TestDPA1` class to include support for JAX and Array API Strict backends; added properties and methods for conditional testing. |
| `source/tests/consistent/test_type_embedding.py` | Added support for `array_api_strict` in `TestTypeEmbedding` class; introduced evaluation method and conditional imports. |
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Actionable comments posted: 28
Outside diff range and nitpick comments (19)
deepmd/jax/utils/exclude_mask.py (1)
12-16: LGTM: Well-implemented class with custom attribute handling.The
PairExcludeMaskclass effectively extendsPairExcludeMaskDPand provides custom handling for thetype_maskattribute. The implementation ensures thattype_maskis always stored as a JAX array, which is crucial for compatibility with JAX-based operations.Consider using a set for faster lookup of attribute names:
class PairExcludeMask(PairExcludeMaskDP): + _jax_attributes = {"type_mask"} def __setattr__(self, name: str, value: Any) -> None: - if name in {"type_mask"}: + if name in self._jax_attributes: value = to_jax_array(value) return super().__setattr__(name, value)This change allows for easier extension if more attributes need similar handling in the future.
source/tests/array_api_strict/utils/exclude_mask.py (1)
14-17: LGTM with suggestions:__setattr__implementation is correct but could be more explicit.The
__setattr__method correctly overrides the parent class to provide custom behavior for thetype_maskattribute. However, consider the following suggestions:
- Add a docstring to explain the purpose of this override and what
to_array_api_strict_arraydoes.- Consider using a more explicit condition, such as
if name == "type_mask"instead ofif name in {"type_mask"}, unless you plan to add more attributes to this set in the future.Here's a suggested improvement:
def __setattr__(self, name: str, value: Any) -> None: """ Override __setattr__ to ensure 'type_mask' is converted to a strict array API compliant array. This method intercepts assignments to 'type_mask' and applies the to_array_api_strict_array conversion before setting the attribute. All other attributes are set normally. Args: name (str): The name of the attribute being set. value (Any): The value to assign to the attribute. """ if name == "type_mask": value = to_array_api_strict_array(value) return super().__setattr__(name, value)source/tests/array_api_strict/common.py (3)
10-10: Consider adding the return type to the function signature.While the docstring specifies the return type, it would be beneficial to add it to the function signature as well for better type hinting.
Consider updating the function signature as follows:
def to_array_api_strict_array(array: Optional[np.ndarray]) -> Optional[array_api_strict.Array]:This change will provide more explicit type information and improve code readability.
11-22: Improve docstring for consistency and accuracy.The docstring is well-structured, but there are a few inconsistencies that should be addressed:
- The parameter type should be
Optional[np.ndarray]to match the function signature.- The return type should be
Optional[array_api_strict.Array]to accurately reflect the function's behavior and the use ofarray_api_strict.- The docstring should mention that the function returns
Noneif the input isNone.Consider updating the docstring as follows:
""" Convert a numpy array to a JAX array. Parameters ---------- array : Optional[np.ndarray] The numpy array to convert, or None. Returns ------- Optional[array_api_strict.Array] The JAX array, or None if the input is None. """These changes will improve the accuracy and consistency of the documentation.
23-25: LGTM: Implementation is correct and concise.The function correctly handles the case where the input is None and uses the appropriate method to convert the array.
Consider adding explicit error handling for invalid input types. For example:
def to_array_api_strict_array(array: Optional[np.ndarray]) -> Optional[array_api_strict.Array]: if array is None: return None if not isinstance(array, np.ndarray): raise TypeError(f"Expected np.ndarray or None, got {type(array)}") return array_api_strict.asarray(array)This addition would make the function more robust against potential misuse.
deepmd/jax/common.py (1)
Line range hint
23-33: Update the docstring to reflect optional input and output.The function signature has been updated to handle optional input and output, but the docstring doesn't reflect this change. Please update the docstring to accurately describe the new behavior.
Here's a suggested update for the docstring:
def to_jax_array(array: Optional[np.ndarray]) -> Optional[jnp.ndarray]: """Convert a numpy array to a JAX array or handle None input. Parameters ---------- array : Optional[np.ndarray] The numpy array to convert, or None. Returns ------- Optional[jnp.ndarray] The JAX tensor, or None if the input is None. """source/tests/array_api_strict/utils/type_embed.py (1)
17-22: LGTM with suggestions: Custom attribute setting looks good.The
__setattr__method implementation effectively customizes attribute setting for "econf_tebd" and "embedding_net". This approach aligns well with the PR objective of implementing JAX or Array API compatibility.Suggestions for improvement:
- Consider using a more specific type hint for the
valueparameter instead ofAnyto improve type safety.- The serialization and deserialization of "embedding_net" might benefit from a comment explaining its purpose (e.g., creating a deep copy or ensuring a specific format).
Here's a suggested improvement for the method signature:
from typing import Union from numpy import ndarray from jax import Array def __setattr__(self, name: str, value: Union[ndarray, Array, EmbeddingNet]) -> None:This change would provide more specific type hinting for the
valueparameter, improving type safety and code readability.deepmd/jax/utils/network.py (1)
44-45: LGTM: LayerNorm class is correctly implemented.The LayerNorm class effectively combines functionalities from LayerNormDP and NativeLayer through multiple inheritance. The empty class body is appropriate as no additional methods or attributes are needed.
Consider adding a docstring to explain the purpose of this class and its inheritance structure. For example:
class LayerNorm(LayerNormDP, NativeLayer): """ A layer normalization class that combines functionality from LayerNormDP and NativeLayer. This class inherits methods and properties from both parent classes without modification. """ passsource/tests/array_api_strict/utils/network.py (1)
29-29: Nitpick: Redundantreturnstatement in__setattr__method.In the
__setattr__method, thereturnstatement is unnecessary becausesuper().__setattr__(name, value)does not return a meaningful value (it returnsNone). Omitting thereturnstatement can improve readability.Apply this diff to remove the redundant
returnstatement:- return super().__setattr__(name, value) + super().__setattr__(name, value)deepmd/jax/descriptor/dpa1.py (1)
65-67: Clarify the handling of theenv_matattribute.The attribute
env_mathas a comment indicating it doesn't store any value, followed by apassstatement. To enhance code clarity, consider explicitly settingvaluetoNoneforenv_mat.Apply this diff for explicit assignment:
elif name == "env_mat": # env_mat doesn't store any value + value = None passdeepmd/dpmodel/utils/exclude_mask.py (2)
118-130: Ensure consistent behavior of complex array operations across backendsThe sequence of array operations involving
xp.concat,xp.reshape,xp.where,xp.take, and advanced indexing should be verified for consistency across all supported array backends. Differences in backend implementations could lead to subtle bugs or unexpected behavior.
126-126: Remove commented-out code to improve code cleanlinessThe line
# type_j = xp.take_along_axis(ae, index, axis=1).reshape(nf, nloc, nnei)appears to be obsolete. Removing commented-out code enhances readability and maintainability.deepmd/dpmodel/utils/nlist.py (5)
101-103: Simplify the conditional assignment ofxmaxusing a ternary operatorTo make the code more concise, consider using a ternary operator for assigning
xmax.Apply this diff to simplify the code:
-if coord.size > 0: - xmax = xp.max(coord) + 2.0 * rcut -else: - xmax = 2.0 * rcut +xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutTools
Ruff
100-103: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
276-276: Includecellin array namespace initializationWhen initializing the array namespace with
array_api_compat, include all arrays (coord,atype,cell) to ensure they are compatible within the function.Apply this diff to include
cell:-xp = array_api_compat.array_namespace(coord, atype) +xp = array_api_compat.array_namespace(coord, atype, cell)
308-309: Usexp.transposeinstead ofxp.permute_dimsfor better compatibilityThe function
xp.transposeis commonly used across different array libraries and enhances readability.Apply this diff to use
xp.transpose:-shift_vec = xp.permute_dims(shift_vec, (1, 0, 2)) +shift_vec = xp.transpose(shift_vec, (1, 0, 2))
92-93: Correct the typo in the comment: 'implemantation' to 'implementation'There's a typo in the comment; 'implemantation' should be 'implementation'.
Apply this diff to correct the typo:
-## translated from torch implemantation by chatgpt +## Translated from Torch implementation by ChatGPT
97-98: Fix spelling errors in docstringsThere are several typos in the docstrings, such as 'neightbor' instead of 'neighbor' and 'exptended' instead of 'extended'.
Apply this diff to correct the typos:
-"""Build neightbor list for a single frame. keeps nsel neighbors. Parameters ---------- coord : np.ndarray exptended coordinates of shape [batch_size, nall x 3] ... +"""Build neighbor list for a single frame. Keeps nsel neighbors. Parameters ---------- coord : np.ndarray extended coordinates of shape [batch_size, nall x 3] ...This improves the readability and professionalism of the documentation.
source/tests/consistent/common.py (2)
83-83: Add docstring forarray_api_strict_classTo maintain consistency with other class variables, please add a docstring for
array_api_strict_class.Apply this diff to add the docstring:
array_api_strict_class: ClassVar[Optional[type]] +"""Array API Strict model class."""
Line range hint
267-273: Update docstring to reflect the new order of reference backendsThe docstring for
get_reference_backendlists the order of checking as "Order of checking for ref: DP, TF, PT." Since you've added JAX andARRAY_API_STRICT, please update the docstring to reflect the current order.Apply this diff to update the docstring:
def get_reference_backend(self): """Get the reference backend. - Order of checking for ref: DP, TF, PT. + Order of checking for ref: DP, TF, PT, JAX, ARRAY_API_STRICT. """
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (25)
- deepmd/dpmodel/descriptor/dpa1.py (13 hunks)
- deepmd/dpmodel/utils/env_mat.py (2 hunks)
- deepmd/dpmodel/utils/exclude_mask.py (5 hunks)
- deepmd/dpmodel/utils/network.py (6 hunks)
- deepmd/dpmodel/utils/nlist.py (4 hunks)
- deepmd/dpmodel/utils/region.py (5 hunks)
- deepmd/dpmodel/utils/type_embed.py (1 hunks)
- deepmd/jax/common.py (2 hunks)
- deepmd/jax/descriptor/init.py (1 hunks)
- deepmd/jax/descriptor/dpa1.py (1 hunks)
- deepmd/jax/utils/exclude_mask.py (1 hunks)
- deepmd/jax/utils/network.py (2 hunks)
- source/tests/array_api_strict/init.py (1 hunks)
- source/tests/array_api_strict/common.py (1 hunks)
- source/tests/array_api_strict/descriptor/init.py (1 hunks)
- source/tests/array_api_strict/descriptor/dpa1.py (1 hunks)
- source/tests/array_api_strict/utils/init.py (1 hunks)
- source/tests/array_api_strict/utils/exclude_mask.py (1 hunks)
- source/tests/array_api_strict/utils/network.py (1 hunks)
- source/tests/array_api_strict/utils/type_embed.py (1 hunks)
- source/tests/common/dpmodel/test_descriptor_dpa1.py (1 hunks)
- source/tests/consistent/common.py (10 hunks)
- source/tests/consistent/descriptor/common.py (4 hunks)
- source/tests/consistent/descriptor/test_dpa1.py (5 hunks)
- source/tests/consistent/test_type_embedding.py (4 hunks)
Files skipped from review due to trivial changes (4)
- deepmd/jax/descriptor/init.py
- source/tests/array_api_strict/init.py
- source/tests/array_api_strict/descriptor/init.py
- source/tests/array_api_strict/utils/init.py
Additional context used
Ruff
deepmd/dpmodel/descriptor/dpa1.py
1046-1046: Local variable
env_matis assigned to but never usedRemove assignment to unused variable
env_mat(F841)
deepmd/dpmodel/utils/env_mat.py
79-82: Use ternary operator
env_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weightinstead ofif-else-blockReplace
if-else-block withenv_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weight(SIM108)
deepmd/dpmodel/utils/nlist.py
100-103: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
GitHub Check: CodeQL
deepmd/dpmodel/descriptor/dpa1.py
[notice] 1046-1046: Unused local variable
Variable env_mat is not used.source/tests/consistent/descriptor/test_dpa1.py
[notice] 42-42: Unused global variable
The global variable 'DescrptDPA1JAX' is not used.
Additional comments not posted (49)
deepmd/jax/utils/exclude_mask.py (2)
1-1: LGTM: Appropriate license header.The file includes the correct LGPL-3.0-or-later license header, which is good practice for open-source projects.
2-9: LGTM: Appropriate imports.The imports are well-organized and include all necessary components for the class implementation. The use of type hinting (
Anyfromtyping) is a good practice for improving code readability and maintainability.source/tests/array_api_strict/utils/exclude_mask.py (2)
1-11: LGTM: File structure and imports are well-organized.The file structure follows good practices:
- SPDX license identifier is correctly placed at the top.
- Imports are properly organized, separating standard library imports from local imports.
13-13: LGTM: Class definition and inheritance are appropriate.The
PairExcludeMaskclass is well-defined:
- It clearly extends the functionality of
PairExcludeMaskDP.- The class name follows the convention of the parent class.
source/tests/array_api_strict/common.py (2)
1-8: LGTM: Imports are correct and necessary.The imports are appropriate for the function implementation. The order follows the convention of importing from the standard library first, followed by third-party libraries.
1-25: Overall assessment: Good implementation with minor improvements suggested.The
to_array_api_strict_arrayfunction is well-implemented and serves its purpose effectively. The suggestions made in the review will further enhance its robustness and documentation:
- Add return type to the function signature.
- Update the docstring for consistency and accuracy.
- Consider adding explicit error handling for invalid input types.
These changes will improve type hinting, documentation clarity, and error handling, making the function more maintainable and user-friendly.
deepmd/jax/common.py (4)
3-3: LGTM: Import statement updated correctly.The addition of
Optionalto the import statement is consistent with the changes made to the function signature and is necessary for proper type hinting.
22-22: LGTM: Function signature updated correctly.The change to use
Optional[np.ndarray]for both input and output types allows the function to handleNoneinput, which is a valid use case. The overloaded signatures provide clear type hints for different input types.
Line range hint
34-36: LGTM: Function implementation handles optional input correctly.The function implementation correctly handles the new optional input case by checking for
Noneand returningNonein that case. For non-None input, it properly converts the numpy array to a JAX array. The behavior is consistent with the updated signature and overloaded definitions.
Line range hint
1-36: Summary: Improved flexibility ofto_jax_arrayfunctionThe changes in this PR successfully implement the
feat(jax/array-api): dpa1objective by modifying theto_jax_arrayfunction to handle optional input. This improvement allows the function to work withNonevalues, increasing its flexibility and usability in various scenarios.Key points:
- The function signature and implementation have been updated correctly.
- Proper type hinting has been added, including overloaded function signatures.
- The function behavior is consistent with the new type hints.
The only suggestion for improvement is to update the function's docstring to reflect the new optional nature of the input and output.
Overall, this is a well-implemented feature that enhances the functionality of the
deepmd/jax/common.pymodule.source/tests/array_api_strict/utils/type_embed.py (2)
1-13: LGTM: File structure and imports are well-organized.The file structure follows good practices with a license identifier at the top. The imports are appropriate for the implemented functionality, and the use of relative imports suggests a well-structured project.
16-16: LGTM: Class definition aligns with PR objective.The
TypeEmbedNetclass, inheriting fromTypeEmbedNetDP, appears to be a wrapper or extension designed to provide custom attribute setting behavior. This aligns well with the PR objective of implementing JAX or Array API compatibility.deepmd/jax/utils/network.py (3)
4-5: LGTM: Import statements are correctly updated.The new imports (ClassVar and Dict) are necessary for type hinting in the NetworkCollection class. The imported classes (LayerNormDP and NetworkCollectionDP) are used as base classes for the new classes defined in this file. The imports are well-organized and follow Python's import style guidelines.
Also applies to: 11-11, 13-13
36-41: LGTM: NetworkCollection class is well-implemented.The NetworkCollection class is correctly defined, inheriting from NetworkCollectionDP. The NETWORK_TYPE_MAP class variable is appropriately type-hinted using ClassVar and provides a clear mapping between string identifiers and network types. This implementation follows good practices and can be useful for dynamic network creation or configuration.
Line range hint
1-45: Summary: JAX-specific network implementations added successfully.The changes in this file introduce JAX-specific implementations of NetworkCollection and LayerNorm classes, extending the existing DeepMD functionality. These additions are consistent with the PR objective (feat(jax/array-api): dpa1) and follow good coding practices. The new classes leverage multiple inheritance and type hinting to create a clear and maintainable structure.
Key points:
- NetworkCollection provides a mapping between string identifiers and network types, which can facilitate dynamic network creation or configuration.
- LayerNorm combines functionality from LayerNormDP and NativeLayer, potentially allowing for JAX-specific optimizations.
These changes appear to be a solid foundation for integrating JAX capabilities into the DeepMD framework. As the feature develops, ensure that any JAX-specific optimizations or behaviors are well-documented for users transitioning from the standard DeepMD implementation.
source/tests/common/dpmodel/test_descriptor_dpa1.py (1)
40-57: LGTM! Well-structured test for multi-frame consistency.The new test method
test_multiple_framesis well-structured and effectively tests the consistency ofDescrptDPA1across multiple frames. It properly initializes the descriptor, prepares multi-frame input data, and asserts the consistency of the output.Enhance error messages for easier debugging.
Consider improving the error messages in the assertions to provide more context about the failing comparisons.
You could modify the error messages as follows:
- np.testing.assert_allclose(mm0[ii][0], mm0[ii][2], err_msg=f"{ii} 0~2") - np.testing.assert_allclose(mm0[ii][1], mm0[ii][3], err_msg=f"{ii} 1~3") + np.testing.assert_allclose(mm0[ii][0], mm0[ii][2], err_msg=f"Mismatch in output {ii} between frame 0 and 2") + np.testing.assert_allclose(mm0[ii][1], mm0[ii][3], err_msg=f"Mismatch in output {ii} between frame 1 and 3")Add a comment explaining the test's purpose.
It would be helpful to add a brief comment explaining the purpose of this test method at the beginning.
Consider adding a docstring like this:
def test_multiple_frames(self): """ Test that DescrptDPA1 produces consistent output across multiple frames. This test duplicates the input data to create a two-frame scenario and verifies that the output for both frames is identical. """Verify the correctness of frame duplication.
The test assumes that duplicating the input data (coord_ext, atype_ext, nlist) correctly represents multiple frames. It's important to ensure this approach accurately simulates multi-frame behavior in the actual use case.
To verify this, we can check if there are any frame-specific operations in the
DescrptDPA1class:If this search returns results, it might indicate that the current test method doesn't fully capture the multi-frame behavior, and we may need to adjust the test accordingly.
Verification successful
Frame duplication approach is valid and effective for multi-frame testing.
The extensive code search results provide strong evidence that the
DescrptDPA1class and related components are designed to handle multiple frames. Thetest_multiple_framesmethod intest_descriptor_dpa1.pyappropriately leverages this capability by duplicating input data to create a two-frame scenario. This approach is consistent with how multi-frame data is processed throughout the codebase, as evidenced by numerousnframes-related operations and reshaping. The test method effectively verifies the descriptor's behavior across multiple frames, which is crucial for ensuring the model's performance in real-world scenarios with varying numbers of atoms and frames.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for frame-specific operations in DescrptDPA1 class # Test: Search for frame-related operations in DescrptDPA1 ast-grep --lang python --pattern $'class DescrptDPA1($_): $$$ def $_($$$): $$$ for $_ in range($_): # Possible frame iteration $$$ 'Length of output: 152
Script:
#!/bin/bash # Description: Search for methods and conditionals related to frame handling in DescrptDPA1 class # Search for methods that might handle multiple frames ast-grep --lang python --pattern $'class DescrptDPA1($_): $$$ def $_($$$): $$$ if "frame" in $_: $$$ ' # Additionally, search for any attributes that might relate to frames rg --ignore-case "frame" $(fd --type f --extension py)Length of output: 306399
deepmd/dpmodel/utils/type_embed.py (1)
110-110: Verify API compatibility and update other occurrencesThe change from
xp.concatenatetoxp.concataligns with newer array API standards. While this maintains the core functionality, consider the following:
- Ensure this change is consistent across the entire codebase.
- Verify that this doesn't break compatibility with older versions of the array library being used.
- Update the documentation if necessary to reflect this API change.
To ensure consistency and identify potential issues, run the following script:
This script will help identify if there are other occurrences of
concatenatethat might need updating, verify the correct import ofarray_api_compat, and check for any explicit imports ofconcatenatethat might conflict with this change.source/tests/array_api_strict/utils/network.py (4)
25-30: LGTM!The
NativeLayerclass correctly overrides__setattr__to ensure that attributesw,b, andidtare converted to strict array API arrays usingto_array_api_strict_array. This maintains compatibility with the strict array API.
32-34: LGTM!The network classes
NativeNet,EmbeddingNet, andFittingNetare properly constructed using the provided factory functions and correctly utilizeNativeLayer.
37-42: LGTM!The
NetworkCollectionclass definesNETWORK_TYPE_MAPappropriately, mapping network type strings to their corresponding classes.
45-46: Verify the method resolution order (MRO) inLayerNormclass.The
LayerNormclass inherits from bothLayerNormDPandNativeLayer. Multiple inheritance can introduce complexity due to the method resolution order. Please verify that the MRO aligns with your expectations and that there are no conflicts between methods or attributes inherited fromLayerNormDPandNativeLayer.deepmd/dpmodel/utils/region.py (1)
72-72: Ensurephys2interhandles edge cases before usageIn
normalize_coord, the functionphys2interis called, which relies on inverting thecellmatrix. Ensure thatcellis always invertible in this context or add appropriate error handling inphys2interto prevent potential exceptions.Also applies to: 74-74
deepmd/jax/descriptor/dpa1.py (4)
58-63: Consistent handling ofNonevalues for embeddings.In
DescrptBlockSeAtten, whennameis"embeddings"or"embeddings_strip", you check ifvalueis notNonebefore deserializing. This is good practice. Ensure that all assignments consider the possibility ofNonevalues to prevent unexpected errors.
76-80: Consistency in deserialization methods.In
DescrptDPA1, when settingse_attenandtype_embedding, you deserializevalueusing their respective classes. This maintains consistency across the deserialization process and ensures that attributes are properly initialized.
31-35: Verify the compatibility ofNativeLayerdeserialization.In the
GatedAttentionLayerclass, when setting the attributesin_projandout_proj, the code deserializesvalueusingNativeLayer.deserialize(value.serialize()). Ensure thatvalueindeed hasserializeanddeserializemethods and that this operation is appropriate for all expected types ofvalue.
69-69: Ensurevaluehas required attributes foremask.When assigning to
emask, the code assumes thatvaluehasntypesandexclude_typesattributes. Ifvaluemay not always have these attributes, add error handling to prevent attribute access errors.Verification successful
valueconsistently has thentypesandexclude_typesattributes when assigning toemask.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that `value` always has `ntypes` and `exclude_types` when setting `emask`. # Test: Search for all instances where `emask` is set and check the type of `value`. # Expect: `value` should be an instance with `ntypes` and `exclude_types`. rg --type python 'emask\s*=' # Further inspection may be needed based on the search results.Length of output: 61
Script:
#!/bin/bash # Description: Verify that `value` always has `ntypes` and `exclude_types` when setting `emask`. # Search for all instances where `emask` is set in Python files rg 'emask\s*=' -- '*.py'Length of output: 71
Script:
#!/bin/bash # Description: Verify that `value` always has `ntypes` and `exclude_types` when setting `emask`. # Search for all instances where `emask` is set in Python files using the correct glob pattern rg 'emask\s*=' -g '*.py'Length of output: 1622
deepmd/dpmodel/utils/exclude_mask.py (4)
7-7: Appropriate import ofarray_api_compatfor backend compatibilityImporting
array_api_compatensures that the code can operate with different array backends, enhancing portability and flexibility.
53-55: Utilization ofarray_namespaceenhances array backend supportInitializing
xpusingarray_api_compat.array_namespace(atype)and replacing NumPy functions with theirxpcounterparts ensures compatibility across various array backends, which is beneficial for broader support.
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73-84: Local definition and reshaping oftype_maskimproves encapsulationDefining
type_masklocally and then reshaping it before assigning toself.type_maskenhances code clarity and reduces the potential for unintended side effects from external modifications.
111-114: Efficient handling of emptyexclude_typeswith early returnBy returning
xp.ones_like(nlist, dtype=xp.int32)whenexclude_typesis empty, the function avoids unnecessary computations, thus optimizing performance.source/tests/consistent/test_type_embedding.py (3)
16-16: LGTMThe addition of
INSTALLED_ARRAY_API_STRICTto the imports ensures that the availability ofarray_api_strictis correctly checked.
132-138: Implementation ofeval_array_api_strictis consistentThe
eval_array_api_strictmethod is correctly implemented and follows the pattern established by othereval_*methods.
42-47: Verify the correctness of the relative import pathPlease ensure that the relative import on line 45 is correct:
from ..array_api_strict.utils.type_embed import TypeEmbedNet as TypeEmbedNetStrictIncorrect import paths can lead to
ImportErrorat runtime. Verify that theTypeEmbedNetclass exists in the specified module.Run the following script to confirm the presence of the
TypeEmbedNetclass in the codebase:Verification successful
Import path is correct.
The
TypeEmbedNetclass is properly defined insource/tests/array_api_strict/utils/type_embed.py, ensuring that the relative import path is valid and should not result in anImportErrorat runtime.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that TypeEmbedNet is defined in array_api_strict/utils/type_embed.py # Test: Search for the definition of TypeEmbedNet in the codebase. rg --type python --files-with-matches "class TypeEmbedNet" | grep "array_api_strict/utils/type_embed.py"Length of output: 135
Script:
#!/bin/bash # Description: Verify that TypeEmbedNet is defined in array_api_strict/utils/type_embed.py # Test: Search for the definition of TypeEmbedNet in the specified file. rg "class TypeEmbedNet" array_api_strict/utils/type_embed.pyLength of output: 139
Script:
#!/bin/bash # Description: Search for the TypeEmbedNet class definition in the entire codebase. # Test: Use rg to find the definition of TypeEmbedNet in all Python files. rg "class TypeEmbedNet" --glob "*.py"Length of output: 449
source/tests/consistent/descriptor/common.py (3)
6-6: Import statement fornumpyadded correctly.The import of
numpyasnpis appropriate and used correctly in the code.
17-18: Conditional installation flags updated.The addition of
INSTALLED_ARRAY_API_STRICTandINSTALLED_JAXensures that the imports and method definitions for these libraries are conditionally included based on their availability.
36-41: Conditional imports for JAX and Array API Strict are properly implemented.The imports under
if INSTALLED_JAXandif INSTALLED_ARRAY_API_STRICTcorrectly handle the inclusion ofjnpfrom JAX andarray_api_strictwhen these libraries are installed.deepmd/dpmodel/utils/nlist.py (4)
9-9: Importarray_api_compatseems appropriateThe addition of
array_api_compatensures compatibility with different array-like structures, which is beneficial for extending support across various backends.
94-96: Initialize array namespacexpfor array compatibilityThe introduction of
xpusingarray_api_compat.array_namespaceand replacingnpfunctions withxpfunctions enhances compatibility with multiple array libraries.
161-161: Initialize array namespacexpinnlist_distinguish_typesfunctionEnsure that the array namespace
xpis correctly initialized in thenlist_distinguish_typesfunction for consistent array operations.
168-168: Confirm compatibility ofxp.take_along_axiswith Array APIEnsure that
xp.take_along_axisis available and behaves as expected in the Array API compatibility layer, as not all array libraries may support it fully.Run the following script to check the availability and usage of
take_along_axis:deepmd/dpmodel/utils/network.py (4)
151-162: Refactored deserialization improves clarityThe changes in the
deserializemethod simplify the unpacking and assignment of variables, enhancing code readability and maintainability.
371-375: Correct initialization of weights and biases inLayerNormInitializing weights to ones and biases to zeros is appropriate for
LayerNorm. The use ofxp.ones_likeandxp.zeros_likeensures compatibility with different array backends.
483-488: Validation of axis calculations in normalizationThe calculation of axes using
tuple(range(-len(shape), 0))correctly targets the lastlen(shape)dimensions, ensuring proper computation of mean and variance for layer normalization.
260-260: Ensure compatibility ofxp.concatacross backendsWhile replacing
xp.concatenatewithxp.concataligns with the array API standards, please verify that all target backends supportxp.concat, as some may only implementxp.concatenate.Run the following script to check for the availability of
concatin the array backends:deepmd/dpmodel/descriptor/dpa1.py (5)
11-11: Addedarray_api_compatimport for array backend compatibilityThe import statement of
array_api_compatis correct and necessary for ensuring compatibility with various array backends.
62-66: Handling NaN values withoutnan_to_numThe original line using
xp.nan_to_num(x)is commented out, and replaced withx = xp.where(xp.isnan(x), xp.zeros_like(x), x). Sincexp.nan_to_nummay not be available in somearray_api_compatbackends, this substitution effectively handles NaN values.
70-71: Normalized vectors using array API compatible functionsThe use of
xp.linalg.vector_normensures that vector normalization is compatible across different array backends.
480-480: Initializing array namespace incallmethodThe addition of
xp = array_api_compat.array_namespace(coord_ext, atype_ext, nlist)ensures that all subsequent array operations within thecallmethod use the appropriate namespace for compatibility.
484-487: Reshaping and indexing with array API functionsThe use of
xp.reshapeandxp.takefunctions aligns with array API standards, ensuring compatibility when manipulating arrays.
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
for more information, see https://pre-commit.ci
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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Actionable comments posted: 0
Outside diff range and nitpick comments (5)
deepmd/dpmodel/utils/nlist.py (1)
101-109: LGTM: Improved virtual atom handling with a minor optimization suggestionThe changes effectively adapt the virtual atom handling to use the new array API. The use of
xp.wherefor conditional value setting is appropriate and maintains the original logic.Consider using a ternary operator for a more concise expression:
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutThis change would slightly improve readability without altering the functionality.
deepmd/dpmodel/descriptor/dpa1.py (4)
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351-368: Note unimplemented method and approve implemented onesThe
compute_input_statsmethod is not implemented and raises a NotImplementedError. This suggests that this functionality is not yet available or needs to be implemented in the future.The
set_stat_mean_and_stddevandget_stat_mean_and_stddevmethods are correctly implemented, delegating to the se_atten attribute, which is consistent with the class design.Would you like assistance in implementing the
compute_input_statsmethod or creating a GitHub issue to track this task?
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407-501: Replacexp.concatwithxp.concatenatefor array API complianceThe
callmethod is well-implemented and correctly computes the descriptor based on the input data. The use ofarray_api_compatenhances compatibility across different array libraries, which is a good practice.However, there are several instances where
xp.concatis used. For better compliance with the array API standard, these should be replaced withxp.concatenate.Please apply the following changes:
- grrg = xp.concat( + grrg = xp.concatenate(- ss = xp.concat([ss, atype_embd_nlist, atype_embd_nnei], axis=-1) + ss = xp.concatenate([ss, atype_embd_nlist, atype_embd_nnei], axis=-1)- ss = xp.concat([ss, atype_embd_nlist], axis=-1) + ss = xp.concatenate([ss, atype_embd_nlist], axis=-1)- tt = xp.concat([atype_embd_nlist, atype_embd_nnei], axis=-1) + tt = xp.concatenate([atype_embd_nlist, atype_embd_nnei], axis=-1)These changes will ensure consistency with the array API standard across different backend implementations.
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563-605: Remove unused variable and approve deserialization implementationThe
deserializeclass method is well-implemented, correctly reconstructing a DescrptDPA1 object from a serialized dictionary. The version compatibility check is a good practice, and the reconstruction of nested objects is handled properly.However, there's an unused variable that should be removed:
- env_mat = data.pop("env_mat")This line assigns a value to
env_mat, but the variable is never used afterwards. Removing it will clean up the code and eliminate unnecessary assignments.Overall, the deserialization process is comprehensive and correct.
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633-1445: Replace remainingxp.concatwithxp.concatenateand approve class implementationsThe implementations of
DescrptBlockSeAtten,NeighborGatedAttention,NeighborGatedAttentionLayer, andGatedAttentionLayerclasses are correct and consistent with the overall descriptor design. They properly implement the necessary methods for initialization, computation, serialization, and deserialization.However, there are a few remaining instances of
xp.concatthat should be replaced withxp.concatenatefor consistency with the array API standard:In the
DescrptBlockSeAttenclass:- ss = xp.concat([ss, atype_embd_nlist, atype_embd_nnei], axis=-1) + ss = xp.concatenate([ss, atype_embd_nlist, atype_embd_nnei], axis=-1)- ss = xp.concat([ss, atype_embd_nlist], axis=-1) + ss = xp.concatenate([ss, atype_embd_nlist], axis=-1)- tt = xp.concat([atype_embd_nlist, atype_embd_nnei], axis=-1) + tt = xp.concatenate([atype_embd_nlist, atype_embd_nnei], axis=-1)These changes will ensure consistency across the entire implementation.
Overall, the classes are well-implemented, with proper use of array operations and attention mechanisms. The serialization and deserialization methods for each class ensure that the model state can be correctly saved and loaded.
Tools
Ruff
1047-1047: Local variable
env_matis assigned to but never usedRemove assignment to unused variable
env_mat(F841)
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Files selected for processing (2)
- deepmd/dpmodel/descriptor/dpa1.py (13 hunks)
- deepmd/dpmodel/utils/nlist.py (4 hunks)
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Ruff
deepmd/dpmodel/descriptor/dpa1.py
1047-1047: Local variable
env_matis assigned to but never usedRemove assignment to unused variable
env_mat(F841)
deepmd/dpmodel/utils/nlist.py
100-103: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
Additional comments not posted (17)
deepmd/dpmodel/utils/nlist.py (11)
9-10: LGTM: Enhanced array compatibilityThe addition of
array_api_compatimport is a good step towards improving compatibility with different array libraries, which aligns well with the overall changes in this file.
94-96: LGTM: Flexible array operationsThe introduction of
xpusingarray_api_compat.array_namespaceenhances flexibility in array operations. The reshaping ofcoordmaintains consistency with the original implementation while leveraging the new array API.
115-116: Optimize distance calculation using broadcastingThe current calculation of
diffis correct but may consume significant memory for large arrays.As suggested in a previous review, consider optimizing the operation to improve performance:
-diff = ( - xp.reshape(coord1, [batch_size, -1, 3])[:, None, :, :] - - xp.reshape(coord0, [batch_size, -1, 3])[:, :, None, :] -) +coord1_reshaped = xp.reshape(coord1, (batch_size, -1, 3)) +coord0_reshaped = xp.reshape(coord0, (batch_size, -1, 3)) +diff = coord0_reshaped[:, :, xp.newaxis, :] - coord1_reshaped[:, xp.newaxis, :, :]This optimization reduces the need for large intermediate arrays and takes advantage of broadcasting for better efficiency.
131-144: LGTM: Consistent use of array API for padding and maskingThe changes in this segment effectively adapt the padding and masking operations to use the new array API. The logic remains consistent with the original implementation, while leveraging
xpmethods likexp.logical_orandxp.where. This ensures compatibility and maintains the intended functionality.
161-178: LGTM: Effective adaptation of type distinction logic to array APIThe changes in the
nlist_distinguish_typesfunction successfully adapt the atom type distinction logic to use the new array API. The use ofxpmethods likexp.tile,xp.take_along_axis, andxp.argsortis appropriate and maintains the original functionality while ensuring compatibility with different array libraries.
276-283: LGTM: Consistent adaptation to array API in ghost coordinate extensionThe changes in this segment of
extend_coord_with_ghostsfunction effectively adapt the initial setup and the handling of thecell is Nonecase to use the new array API. The use ofxpmethods maintains the original logic while ensuring compatibility with different array libraries.
285-292: LGTM: Effective adaptation of array operations in coordinate extensionThe changes in this segment successfully adapt the array reshaping and
nbuffcalculations to use the new array API. The use ofxpmethods for operations likereshape,astype, andarangeis appropriate and maintains the original functionality while ensuring compatibility with different array libraries.
302-317: LGTM: Effective adaptation of complex array operationsThe changes in this final segment successfully adapt complex array operations to use the new array API. Notable improvements include:
- Appropriate use of
xpmethods for array manipulations.- Replacement of
einsumwithtensordot, which maintains the original functionality while being compatible with the array API.These changes ensure compatibility with different array libraries while preserving the intended behavior of the
extend_coord_with_ghostsfunction.
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1-317: Summary: Successful adaptation to array API with minor improvements neededThe changes in this file effectively adapt the neighbor list calculations to use the
array_api_compatlibrary, improving compatibility with different array libraries. The majority of the changes are well-implemented and maintain the original functionality.Key points:
- Consistent use of the
xpnamespace for array operations.- Appropriate adaptation of complex calculations to the new API.
- Maintained logic and functionality throughout the changes.
However, a few areas require attention:
- Potential optimization of the
diffcalculation for memory efficiency.- Addressing the use of
xp.eyefor non-square matrices.- Correcting the use of
xp.linalg.outertoxp.outer.Addressing these minor issues will further improve the code's robustness and efficiency. Overall, the transition to the array API is a positive change that enhances the flexibility and compatibility of the codebase.
Tools
Ruff
100-103: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
119-123:⚠️ Potential issuePotential issue with
xp.eyefor non-square matricesThe changes to use
xp.linalg.vector_normandxpsorting methods are appropriate adaptations to the new array API. However, there's a potential issue with the use ofxp.eye.As noted in a previous review, the use of
xp.eye(nloc, nall, dtype=diff.dtype)may raise an error ifnlocandnallare not equal, as theeyefunction in the Array API standard typically supports only square matrices.Consider replacing this line with an alternative approach:
-rr -= xp.eye(nloc, nall, dtype=diff.dtype)[xp.newaxis, :, :] +# Create a mask to set self-distances to a large value +self_mask = xp.arange(nloc)[:, xp.newaxis] == xp.arange(nall) +rr += xp.where(self_mask, xp.inf, 0)[xp.newaxis, :, :]This ensures that self-distances are effectively ignored during sorting without relying on
xp.eyefor non-square matrices.
293-301:⚠️ Potential issueIncorrect use of
xp.linalg.outerinstead ofxp.outerAs noted in a previous review, the
outerfunction should be called from the top-level namespacexp, not fromxp.linalg. Usingxp.linalg.outermay result in anAttributeError.Please apply the following changes to fix the function calls:
-xyz = xp.linalg.outer(xi, xp.asarray([1, 0, 0]))[:, xp.newaxis, xp.newaxis, :] +xyz = xp.outer(xi, xp.asarray([1, 0, 0]))[:, xp.newaxis, xp.newaxis, :] ... -xyz = xyz + xp.linalg.outer(yi, xp.asarray([0, 1, 0]))[xp.newaxis, :, xp.newaxis, :] +xyz = xyz + xp.outer(yi, xp.asarray([0, 1, 0]))[xp.newaxis, :, xp.newaxis, :] ... -xyz = xyz + xp.linalg.outer(zi, xp.asarray([0, 0, 1]))[xp.newaxis, xp.newaxis, :, :] +xyz = xyz + xp.outer(zi, xp.asarray([0, 0, 1]))[xp.newaxis, xp.newaxis, :, :]This ensures compatibility with the Array API, as
outeris typically in the main namespace rather than thelinalgsubmodule.deepmd/dpmodel/descriptor/dpa1.py (6)
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1-161: LGTM: Well-structured imports and class definitionThe import statements are organized correctly, and the DescrptDPA1 class is well-defined with appropriate inheritance. The detailed docstring provides comprehensive information about the descriptor, its parameters, and mathematical formulation, which is excellent for code maintainability and understanding.
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162-292: LGTM: Comprehensive initialization with proper error handlingThe init method is well-structured and initializes all necessary attributes. It handles backward compatibility with the deprecated 'stripped_type_embedding' parameter and properly raises NotImplementedError for unsupported features. The initialization of se_atten and type_embedding objects is correct and consistent with the class design.
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294-349: LGTM: Well-implemented getter methodsThe getter methods are correctly implemented, providing access to various descriptor properties. The delegation to the se_atten attribute maintains consistency and reduces code duplication. The additional logic in get_dim_out for concatenating output_tebd is appropriate for the descriptor's functionality.
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370-405: LGTM: Comprehensive type map update handlingThe
change_type_mapmethod is well-implemented, handling type map changes comprehensively. It correctly updates various attributes, remaps indices, and handles new types appropriately. The use ofextend_descrpt_statfor new types ensures that the descriptor statistics are properly updated.
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503-561: LGTM: Comprehensive serialization implementationThe
serializemethod is well-implemented, providing a comprehensive serialization of the descriptor's state. It correctly includes all necessary attributes and handles nested objects (embeddings, attention_layers, etc.) appropriately. The special case for 'strip' tebd_input_mode is properly addressed, ensuring all relevant data is captured in the serialized output.
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607-631: LGTM: Correct implementation of selection updateThe
update_selclass method is well-implemented. It correctly updates the selection parameters and performs neighbor statistics using theUpdateSel().update_one_selmethod. The method returns the updated local data and the minimum neighbor distance, which provides necessary information for further processing in the descriptor setup.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4160 +/- ##
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+ Coverage 83.45% 83.51% +0.05%
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Files 537 539 +2
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- Do we change back to
take_along_axiswhen it is supported? - for modules implemented by jax, you may want to take a look at flax
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
However, in d65206f, I add a Python implementation of
Is there any advantage to using flax? |
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Actionable comments posted: 8
🧹 Outside diff range and nitpick comments (8)
deepmd/dpmodel/utils/env_mat.py (1)
70-74: Approve changes and suggest improvementsThe use of
xp.astypeandxp.expand_dimsfor weight calculation is appropriate and consistent with the array API compatibility layer. However, there are two suggestions for improvement:
- The
if-elseblock can be simplified using a ternary operator for better readability.- The function
xp.concatshould bexp.concatenateto align with the array API standard.Consider applying the following changes:
- if radial_only: - env_mat = t0 * weight - else: - env_mat = xp.concat([t0, t1], axis=-1) * weight + env_mat = t0 * weight if radial_only else xp.concatenate([t0, t1], axis=-1) * weightThis change enhances code readability and ensures compatibility with the array API standard.
🧰 Tools
Ruff
71-74: Use ternary operator
env_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weightinstead ofif-else-blockReplace
if-else-block withenv_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weight(SIM108)
deepmd/dpmodel/utils/nlist.py (1)
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1-321: Summary of changes and recommendationsThe changes in this file successfully integrate the
array_api_compatlibrary, enhancing compatibility with different array-like structures. Most of the changes are well-implemented and maintain the original functionality. However, there are a few areas that require attention:
- The distance calculation in
build_neighbor_listcould be optimized for better memory efficiency.- The use of
xp.eyeinbuild_neighbor_listmay cause issues with non-square matrices.- The
xp.linalg.outercalls inextend_coord_with_ghostsshould be changed toxp.outer.- A minor optimization using a ternary operator can be applied in
build_neighbor_list.Addressing these points will further improve the code's efficiency and correctness while maintaining the enhanced array compatibility.
🧰 Tools
Ruff
104-107: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
deepmd/dpmodel/descriptor/dpa1.py (6)
65-69: Improvednp_softmaxfunction with better compatibility and NaN handlingThe changes to the
np_softmaxfunction enhance its compatibility across different array libraries and improve its robustness by explicitly handling NaN values. These are positive improvements.However, there's a minor optimization opportunity:
Consider combining the NaN handling and the exponential calculation to reduce the number of operations:
- x = xp.where(xp.isnan(x), xp.zeros_like(x), x) - e_x = xp.exp(x - xp.max(x, axis=axis, keepdims=True)) + max_x = xp.max(xp.where(xp.isnan(x), -xp.inf, x), axis=axis, keepdims=True) + e_x = xp.exp(xp.where(xp.isnan(x), 0, x - max_x))This change would handle NaN values and compute the exponential in a single pass, potentially improving performance.
986-1029: Comprehensive serialization method for DescrptDPA1The new
serializemethod provides a comprehensive way to convert theDescrptDPA1object into a dictionary format. This is crucial for saving and loading models, and the method covers all relevant attributes, including special handling for the "strip" mode.To improve maintainability, consider using a constant for the version number:
+ VERSION = 1 ... - "@version": 1, + "@version": self.VERSION,This would make it easier to update the version number in the future if needed.
1031-1055: Robust deserialization method for DescrptDPA1The new
deserializeclass method provides a robust way to reconstruct aDescrptDPA1object from a serialized dictionary. The method includes version compatibility checks and correctly handles special cases like the "strip" mode.Consider adding error handling for missing keys in the input dictionary:
+ required_keys = ["embeddings", "attention_layers", "env_mat", "tebd_input_mode"] + for key in required_keys: + if key not in data: + raise ValueError(f"Missing required key '{key}' in serialized data")This would make the deserialization process more robust against incomplete or corrupted input data.
🧰 Tools
Ruff
1041-1041: Local variable
env_matis assigned to but never usedRemove assignment to unused variable
env_mat(F841)
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1161-1180: Comprehensive serialization method for NeighborGatedAttentionThe new
serializemethod provides a thorough way to convert theNeighborGatedAttentionobject into a dictionary format. This is essential for saving and loading models, and the method covers all relevant attributes, including the serialized attention layers.For consistency with the
DescrptDPA1class, consider adding aVERSIONclass attribute:+ VERSION = 1 ... - "@version": 1, + "@version": self.VERSION,This would maintain a consistent approach to versioning across the codebase.
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1182-1197: Robust deserialization method for NeighborGatedAttentionThe new
deserializeclass method provides a solid way to reconstruct aNeighborGatedAttentionobject from a serialized dictionary. The method includes version compatibility checks and correctly reconstructs the attention layers.Consider adding error handling for missing or invalid data:
+ if "attention_layers" not in data: + raise ValueError("Missing required key 'attention_layers' in serialized data") + if not isinstance(data["attention_layers"], list): + raise TypeError("'attention_layers' must be a list")This would make the deserialization process more robust against incomplete or incorrectly formatted input data.
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1349-1405: ImprovedGatedAttentionLayer.callmethod with better compatibility and potential performance enhancementsThe changes to the
callmethod enhance its compatibility across different array libraries by usingarray_api_compat. The manual linear projection and explicit reshaping operations are likely to be more efficient. These changes improve the overall quality and potential performance of the function.Consider using
xp.einsumfor the matrix multiplications, which might be more readable and potentially more efficient:- attn_weights = q @ xp.permute_dims(k, (0, 1, 3, 2)) + attn_weights = xp.einsum('bhid,bhjd->bhij', q, k) - o = attn_weights @ v + o = xp.einsum('bhij,bhjd->bhid', attn_weights, v)This change would make the operations more explicit and might allow for better optimizations by the underlying array library.
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📒 Files selected for processing (5)
- deepmd/dpmodel/array_api.py (2 hunks)
- deepmd/dpmodel/descriptor/dpa1.py (13 hunks)
- deepmd/dpmodel/utils/env_mat.py (3 hunks)
- deepmd/dpmodel/utils/exclude_mask.py (5 hunks)
- deepmd/dpmodel/utils/nlist.py (4 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/dpmodel/utils/exclude_mask.py
🧰 Additional context used
Ruff
deepmd/dpmodel/descriptor/dpa1.py
1041-1041: Local variable
env_matis assigned to but never usedRemove assignment to unused variable
env_mat(F841)
deepmd/dpmodel/utils/env_mat.py
71-74: Use ternary operator
env_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weightinstead ofif-else-blockReplace
if-else-block withenv_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weight(SIM108)
deepmd/dpmodel/utils/nlist.py
104-107: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
🔇 Additional comments not posted (22)
deepmd/dpmodel/utils/env_mat.py (11)
15-15: LGTM: New import for array API compatibilityThe addition of
xp_take_along_axisimport is consistent with the integration of the array API compatibility layer. This custom function likely provides a unified interface for different array backends.
48-51: LGTM: Array API compatibility integrationThe introduction of the
xpvariable and the use ofxp.reshapeinstead ofnp.reshapeare good changes that enhance the flexibility of the code. This allows the function to work with different array backends, improving its compatibility and reusability.
53-53: LGTM: Consistent type handling in masking operationThe use of
xp.astypein the masking operation ensures type consistency across different array backends. This change aligns well with the array API compatibility integration.
55-56: LGTM: Improved indexing with array API compatibilityThe use of
xp.tile,xp.reshape, andxp_take_along_axisdemonstrates a good adaptation to the array API compatibility layer. These changes allow for consistent indexing operations across different array backends, enhancing the code's flexibility.
58-60: LGTM: Consistent use of array API in reshaping operationsThe changes to use
xp.reshapeinstead ofnp.reshapeare appropriate and maintain consistency with the array API compatibility layer. This ensures that reshaping operations work across different array backends.
64-64: LGTM: Updated norm calculation for array API compatibilityThe change from
np.linalg.normtoxp.linalg.vector_normis appropriate for array API compatibility. Note that the function name is slightly different (vector_norminstead ofnorm), which aligns with the array API standard. This change ensures consistent norm calculations across different array backends.
66-66: LGTM: Consistent mask handling with array APIThe use of
xp.astypeandxp.expand_dimsfor mask handling is appropriate and maintains consistency with the array API compatibility layer. This ensures that mask operations work correctly across different array backends.
75-75: LGTM: Consistent type handling in return statementThe use of
xp.astypeandxp.expand_dimsin the return statement is appropriate and maintains consistency with the array API compatibility layer. This ensures that the returned values have the correct type and shape across different array backends.
127-127: LGTM: Array API compatibility in class methodThe introduction of the
xpvariable usingarray_api_compat.array_namespacein the class method is consistent with the array API compatibility changes throughout the file. This allows the method to work seamlessly with different array backends, improving its flexibility and reusability.
132-134: LGTM: Consistent array operations in data normalizationThe use of
xp.reshapeandxp.takein the data normalization operations is appropriate and maintains consistency with the array API compatibility layer. These changes ensure that the normalization process works correctly across different array backends, enhancing the method's flexibility.
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1-164: Summary: Successful integration of array API compatibilityThe changes in this file consistently implement array API compatibility, allowing the code to work with different array backends. This enhances the flexibility and reusability of the
EnvMatclass and its associated functions. The core logic of the code remains intact, with only the necessary changes to use the abstractxpnamespace instead of direct NumPy operations.One minor suggestion for improvement was made regarding the use of a ternary operator and the correct function name for concatenation. Overall, the changes are well-implemented and improve the code's compatibility with different array libraries.
🧰 Tools
Ruff
71-74: Use ternary operator
env_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weightinstead ofif-else-blockReplace
if-else-block withenv_mat = t0 * weight if radial_only else xp.concat([t0, t1], axis=-1) * weight(SIM108)
deepmd/dpmodel/utils/nlist.py (8)
9-15: LGTM: Enhanced array compatibilityThe addition of
array_api_compatandxp_take_along_axisimports improves the flexibility of array operations in the code, allowing for better compatibility with different array-like structures.
98-100: LGTM: Consistent use of array namespaceThe introduction of
xpas the array namespace and its use in reshapingcoordis consistent with the new imports and enhances array compatibility while maintaining functional equivalence.
105-113: LGTM: Consistent array operationsThe changes in this segment maintain the original logic for handling virtual atoms while consistently using the
xpnamespace for array operations. This enhances compatibility without altering the functionality.
135-148: LGTM: Consistent use of array operationsThe changes in this segment correctly use
xp.concatenate,xp.logical_or, andxp.wherefor array operations. This maintains the original functionality while enhancing compatibility with different array-like structures.
165-173: LGTM: Consistent array operations innlist_distinguish_typesThe changes in this segment correctly use the
xpnamespace for array operations andxp_take_along_axisfor indexing. This enhances compatibility while maintaining the original functionality of thenlist_distinguish_typesfunction.
176-182: LGTM: Consistent use of array operationsThe changes in this segment correctly use the
xpnamespace for all array operations, including sorting, masking, and concatenation. This enhances compatibility with different array-like structures while maintaining the original functionality.
280-282: LGTM: Consistent array operations inextend_coord_with_ghostsThe introduction of
xpas the array namespace and its use inxp.tileandxp.arangeoperations is consistent with the new array compatibility approach. This enhances flexibility while maintaining the original functionality.
285-290: LGTM: Consistent use of array operationsThe changes in this segment correctly use the
xpnamespace for reshaping operations oncoordandcell. This enhances compatibility with different array-like structures while maintaining the original functionality.deepmd/dpmodel/descriptor/dpa1.py (3)
11-21: Improved array compatibility and custom array operationsThe addition of
array_api_compatandxp_take_along_axisenhances the code's compatibility with different array libraries and introduces custom array operations. This change is likely to improve the flexibility and portability of the code across different array backends.
73-74: Improvednp_normalizefunction with better compatibility and efficiencyThe changes to the
np_normalizefunction enhance its compatibility across different array libraries by usingarray_api_compat. The use ofxp.linalg.vector_normfor normalization is likely to be more efficient and numerically stable than a manual implementation. These changes are well-considered and improve the overall quality of the function.
483-483: Consistent improvements in array compatibility throughout the fileThe numerous small changes throughout the file, particularly the use of
array_api_compatand thexpnamespace, significantly enhance the code's compatibility with different array libraries. These modifications make the code more flexible and portable, allowing it to work seamlessly with various array backends.Key improvements include:
- Consistent use of
xpnamespace for array operations.- Replacing NumPy-specific functions with more generic alternatives.
- Using
xp_take_along_axisfor indexed array operations.- Adjusting reshaping and dimension permutation operations to use the
xpnamespace.These changes collectively contribute to a more robust and adaptable implementation of the DPA-1 descriptor.
Also applies to: 485-485, 487-490, 502-503, 549-550, 696-696, 701-701, 709-709, 711-711, 716-716, 724-724, 852-852, 854-855, 866-866, 868-869, 882-882, 890-890, 892-893, 895-895, 897-897, 899-899, 901-901, 905-907, 909-911, 915-916, 922-922, 925-925, 935-935, 942-942, 948-953, 959-960, 964-965, 967-968, 971-975
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Here, I make some additional notes for Array API:
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Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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deepmd/dpmodel/array_api.py
59-62: Use ternary operator
indices = xp.reshape(indices, (-1, n)) if n != 0 else xp.reshape(indices, (0, 0))instead ofif-else-blockReplace
if-else-block withindices = xp.reshape(indices, (-1, n)) if n != 0 else xp.reshape(indices, (0, 0))(SIM108)
🔇 Additional comments not posted (1)
deepmd/dpmodel/array_api.py (1)
4-4: LGTM: Import and comments provide necessary contextThe added import statement for
array_api_compatis essential for the new functions. The comments explaining the implementation oftake_along_axisprovide valuable context about the origin and purpose of the code.Also applies to: 34-37
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
jax does not natively support any equivalent to |
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (10)
deepmd/jax/env.py (1)
8-17: Consider documenting the rationale for introducing Flax'snnx.The addition of
nnxfrom Flax suggests a shift towards using Flax for JAX-based modules. To ensure clarity for all team members and future contributors, it would be beneficial to document:
- The rationale behind introducing Flax's
nnx.- The expected benefits and use cases within the project.
- Any architectural or development practice changes this introduction might entail.
This documentation could be added as a comment in this file or in a separate document (e.g., README or ARCHITECTURE.md).
deepmd/jax/utils/type_embed.py (2)
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18-24: LGTM:__setattr__implementation, with a suggestion for improvement.The
__setattr__method is well-implemented, handling specific attributes appropriately:
econf_tebdis converted to a JAX array.embedding_netis serialized and deserialized, likely to ensure compatibility with Flax.The use of type annotations and
super().__setattr__is commendable.Consider adding a brief comment explaining the rationale behind the special handling of
econf_tebdandembedding_net. This would improve code maintainability. For example:def __setattr__(self, name: str, value: Any) -> None: # Convert econf_tebd to JAX array for compatibility if name == "econf_tebd": value = to_jax_array(value) # Ensure embedding_net is properly serialized for Flax compatibility elif name == "embedding_net": value = EmbeddingNet.deserialize(value.serialize()) return super().__setattr__(name, value)
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1-24: Overall assessment: Changes align with PR objectives and improve JAX/Flax integration.The modifications to
TypeEmbedNetsuccessfully integrate Flax functionality:
- The
@flax_moduledecorator likely enhances support for parameter initialization and backward propagation.- The
__setattr__method ensures proper handling of JAX arrays and Flax-compatible serialization.These changes align well with the PR objectives of using Flax for JAX-implemented modules. The implementation is correct and consistent with the stated goals.
To further improve this implementation:
- Consider adding documentation explaining the benefits of using the
@flax_moduledecorator for this specific class.- Add comments in the
__setattr__method to clarify the rationale behind the special handling ofeconf_tebdandembedding_net.- If not already present, consider adding unit tests to verify the correct behavior of the Flax integration, especially focusing on the serialization and deserialization of the
embedding_netattribute.deepmd/jax/utils/network.py (6)
25-31: LGTM: NativeLayer implementation looks good.The
NativeLayerclass is correctly decorated with@flax_moduleand inherits fromNativeLayerDP. The custom__setattr__method appropriately converts specific attributes to JAX arrays usingto_jax_array.Consider using a set for faster lookup of attribute names:
CONVERT_TO_JAX = {"w", "b", "idt"} def __setattr__(self, name: str, value: Any) -> None: if name in CONVERT_TO_JAX: value = to_jax_array(value) return super().__setattr__(name, value)This change would slightly improve performance, especially if the method is called frequently.
33-35: LGTM: NativeNet class is correctly defined.The
NativeNetclass is appropriately decorated with@flax_moduleand inherits from the result ofmake_multilayer_network(NativeLayer, NativeOP). This structure aligns with the PR objectives of implementing JAX-specific versions of existing classes.Consider adding a docstring to explain the purpose of this class and its relationship to the parent class:
@flax_module class NativeNet(make_multilayer_network(NativeLayer, NativeOP)): """ A JAX-compatible implementation of a multi-layer network. This class inherits all functionality from the parent class created by make_multilayer_network, using NativeLayer and NativeOP as building blocks. """ pass
38-40: LGTM: EmbeddingNet class is correctly defined.The
EmbeddingNetclass is appropriately decorated with@flax_moduleand inherits from the result ofmake_embedding_network(NativeNet, NativeLayer). This structure is consistent with the implementation of JAX-specific versions of existing classes.Consider adding a docstring to explain the purpose of this class and its relationship to the parent class:
@flax_module class EmbeddingNet(make_embedding_network(NativeNet, NativeLayer)): """ A JAX-compatible implementation of an embedding network. This class inherits all functionality from the parent class created by make_embedding_network, using NativeNet and NativeLayer as building blocks. """ pass
43-45: LGTM: FittingNet class is correctly defined.The
FittingNetclass is appropriately decorated with@flax_moduleand inherits from the result ofmake_fitting_network(EmbeddingNet, NativeNet, NativeLayer). This structure is consistent with the implementation of JAX-specific versions of existing classes.Consider adding a docstring to explain the purpose of this class and its relationship to the parent class:
@flax_module class FittingNet(make_fitting_network(EmbeddingNet, NativeNet, NativeLayer)): """ A JAX-compatible implementation of a fitting network. This class inherits all functionality from the parent class created by make_fitting_network, using EmbeddingNet, NativeNet, and NativeLayer as building blocks. """ pass
48-54: LGTM: NetworkCollection class is well-structured.The
NetworkCollectionclass is appropriately decorated with@flax_moduleand inherits fromNetworkCollectionDP. TheNETWORK_TYPE_MAPclass variable provides a centralized way to map network types to their JAX-specific implementations, which is a good design choice.Consider adding a docstring to explain the purpose of this class and the
NETWORK_TYPE_MAP:@flax_module class NetworkCollection(NetworkCollectionDP): """ A collection of JAX-compatible network implementations. This class provides a mapping between network type identifiers and their corresponding JAX-specific implementations. """ NETWORK_TYPE_MAP: ClassVar[Dict[str, type]] = { "network": NativeNet, "embedding_network": EmbeddingNet, "fitting_network": FittingNet, }Additionally, you might want to consider using an Enum for the network type keys to prevent typos and improve type checking:
from enum import Enum, auto class NetworkType(Enum): NETWORK = auto() EMBEDDING_NETWORK = auto() FITTING_NETWORK = auto() NETWORK_TYPE_MAP: ClassVar[Dict[NetworkType, type]] = { NetworkType.NETWORK: NativeNet, NetworkType.EMBEDDING_NETWORK: EmbeddingNet, NetworkType.FITTING_NETWORK: FittingNet, }
57-59: LGTM: LayerNorm class is correctly defined.The
LayerNormclass is appropriately decorated with@flax_moduleand inherits from bothLayerNormDPandNativeLayer. This multiple inheritance structure effectively combines the functionality ofLayerNormDPwith the JAX-specificNativeLayer.Consider adding a docstring to explain the purpose of this class and its relationship to the parent classes:
@flax_module class LayerNorm(LayerNormDP, NativeLayer): """ A JAX-compatible implementation of Layer Normalization. This class combines the functionality of LayerNormDP with the JAX-specific features of NativeLayer to provide a layer normalization implementation compatible with the JAX ecosystem. """ passdeepmd/jax/common.py (1)
44-77: Good implementation, but consider improving class initialization.The
flax_modulefunction is well-implemented and documented. The use of a dynamic metaclass to handle multiple inheritance is a good approach. However, there's a potential issue with the class creation.Consider modifying the
FlaxModuleclass to properly initialize both parent classes. Here's a suggested improvement:class FlaxModule(module, nnx.Module, metaclass=MixedMetaClass): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) nnx.Module.__init__(self)This ensures that both
moduleandnnx.Moduleare properly initialized.
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📒 Files selected for processing (7)
- deepmd/jax/common.py (3 hunks)
- deepmd/jax/descriptor/dpa1.py (1 hunks)
- deepmd/jax/env.py (1 hunks)
- deepmd/jax/utils/exclude_mask.py (1 hunks)
- deepmd/jax/utils/network.py (1 hunks)
- deepmd/jax/utils/type_embed.py (1 hunks)
- pyproject.toml (1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- deepmd/jax/descriptor/dpa1.py
- deepmd/jax/utils/exclude_mask.py
🔇 Additional comments (8)
deepmd/jax/env.py (2)
17-17: LGTM! Consistent addition to__all__.The addition of
nnxto the__all__list is consistent with the import change and follows the existing format. This change correctly exposesnnxwhen using wildcard imports from this module.
8-10: LGTM! Verify the usage ofnnxin the codebase.The import of
nnxfromflaxis correctly added and aligns with the discussion about using Flax for modules implemented by JAX. This change looks good and follows the existing code style.To ensure this addition is utilized effectively, let's verify its usage across the codebase:
✅ Verification successful
nnxUsage VerifiedThe usage of
nnxindeepmd/jax/common.pyhas been confirmed, and no TODO comments related tonnxwere found. This confirms that the addition aligns with the project objectives and is properly implemented.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for usage of `nnx` in the codebase # Search for `nnx` usage in Python files echo "Searching for 'nnx' usage in Python files:" rg --type python 'nnx\.' -C 2 # Check if there are any TODO comments related to 'nnx' echo "Checking for TODO comments related to 'nnx':" rg --type python 'TODO.*nnx'Length of output: 330
Script:
#!/bin/bash # Description: Check for usage of `nnx` in Python files with corrected file type # Search for `nnx` usage in Python files echo "Searching for 'nnx' usage in Python files:" rg --type py 'nnx\.' -C 2 # Check if there are any TODO comments related to 'nnx' echo "Checking for TODO comments related to 'nnx':" rg --type py 'TODO.*nnx'Length of output: 1255
deepmd/jax/utils/type_embed.py (1)
8-8: LGTM: Import offlax_moduleis correct.The import of
flax_modulefromdeepmd.jax.commonis correctly placed and necessary for the@flax_moduledecorator used in the file.deepmd/jax/utils/network.py (1)
4-5: LGTM: New imports are appropriate for the changes.The added imports are necessary for the new class definitions and type annotations. The imports from
deepmd.jax.commonindicate the introduction of JAX-specific functionality, which aligns with the PR objectives.Also applies to: 11-11, 13-13, 20-21
deepmd/jax/common.py (3)
3-3: LGTM: Import changes are appropriate.The added imports (
Optional,NativeOP, andnnx) are necessary for the new function signatures and implementations in this file.Also applies to: 9-11, 14-14
26-26: LGTM: Function signature update improves flexibility.The change from
Union[np.ndarray]toOptional[np.ndarray]in theto_jax_arrayfunction signature is appropriate. It accurately reflects the function's ability to handleNoneinput and improves type hinting. This change is consistent with the overloaded function signatures defined earlier in the file.
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1-77: LGTM: File structure and consistency maintained.The overall file structure is clean and well-organized. The new additions are consistent with the existing code style and align well with the PR objectives, particularly the integration of Flax support. Good job maintaining code quality and consistency throughout the changes.
pyproject.toml (1)
137-137: LGTM! Consider verifying compatibility across Python versions.The addition of Flax as an optional dependency for JAX is appropriate and aligns with the discussion mentioned in the PR objectives. This change enhances support for parameter initialization and backward propagation when using JAX.
To ensure this change doesn't introduce any unexpected issues, please run the following verification script:
This script will help verify that:
- Flax is properly integrated when using Python 3.10 or higher.
- Flax is not imported when using Python 3.9 or lower.
- There are no unexpected warnings or errors related to the Flax integration.
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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Actionable comments posted: 6
🧹 Outside diff range and nitpick comments (7)
source/tests/array_api_strict/utils/network.py (1)
25-28: Consider generalizing attribute handling in__setattr__Currently, the
__setattr__method checks for specific attribute names"w","b", and"idt". To enhance maintainability and accommodate future extensions, consider applyingto_array_api_strict_arraybased on attribute types or other criteria, especially if more attributes may require similar processing.deepmd/dpmodel/utils/exclude_mask.py (2)
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73-84: Consistency: Replacenp.arraywithxp.arrayfor array API compatibilityIn the
__init__method of thePairExcludeMaskclass,type_maskis initialized usingnp.array. Since you're moving towards array API compatibility and usingxpnamespace elsewhere, consider replacingnp.arraywithxp.array. However,xpis not available in the__init__method. One approach is to setself.type_maskduring the first call tobuild_type_exclude_maskwhenxpis available, or convertself.type_maskto anxparray within that method.Consider modifying
build_type_exclude_maskto convertself.type_maskto anxparray:def build_type_exclude_mask( self, nlist: np.ndarray, atype_ext: np.ndarray, ): """Compute type exclusion mask for atom pairs.""" xp = array_api_compat.array_namespace(nlist, atype_ext) + type_mask_xp = xp.asarray(self.type_mask) if len(self.exclude_types) == 0: # safely return 1 if nothing is excluded. return xp.ones_like(nlist, dtype=xp.int32) nf, nloc, nnei = nlist.shape nall = atype_ext.shape[1] # add virtual atom of type ntypes. nf x nall+1 ae = xp.concat( [atype_ext, self.ntypes * xp.ones([nf, 1], dtype=atype_ext.dtype)], axis=-1 ) type_i = xp.reshape(atype_ext[:, :nloc], (nf, nloc)) * (self.ntypes + 1) # nf x nloc x nnei index = xp.reshape( xp.where(nlist == -1, xp.full_like(nlist, nall), nlist), (nf, nloc * nnei) ) type_j = xp_take_along_axis(ae, index, axis=1) type_j = xp.reshape(type_j, (nf, nloc, nnei)) type_ij = type_i[:, :, None] + type_j # nf x (nloc x nnei) type_ij = xp.reshape(type_ij, (nf, nloc * nnei)) mask = xp.reshape( xp.take( - self.type_mask, + type_mask_xp, xp.reshape(type_ij, (-1,)) ), (nf, nloc, nnei) ) return mask
111-114: Ensure Correct Data Types withxp.ones_likeIn the
build_type_exclude_maskmethod, when returning early becauseself.exclude_typesis empty, you usexp.ones_like(nlist, dtype=xp.int32). Ensure thatxp.int32correctly represents the integer data type in the array API namespace. Depending on the backend, you might need to use a standard data type like"int32"or usenlist.dtypeif appropriate.Consider updating the dtype specification:
- return xp.ones_like(nlist, dtype=xp.int32) + return xp.ones_like(nlist, dtype="int32")source/tests/consistent/common.py (3)
80-80: Add missing docstring forarray_api_strict_classThe class variable
array_api_strict_classlacks a docstring. Adding one will enhance code documentation and maintain consistency.Apply this diff to add the docstring:
array_api_strict_class: ClassVar[Optional[type]] + """array_api_strict model class."""
174-183: Markeval_array_api_strictas an abstract method and improve docstringTo maintain consistency with other
eval_*methods likeeval_dpandeval_pt, consider markingeval_array_api_strictwith@abstractmethod. Also, adjust the docstring formatting for clarity.Apply this diff:
+ @abstractmethod def eval_array_api_strict(self, array_api_strict_obj: Any) -> Any: """Evaluate the return value of array_api_strict. - Parameters - - ---------- - array_api_strict_obj : Any - The object of array_api_strict - """ + Parameters + ---------- + array_api_strict_obj : Any + The object of array_api_strict + """ raise NotImplementedError("Not implemented")
275-276: Update docstring to reflect new backend orderThe
get_reference_backendmethod now includesARRAY_API_STRICTin its checks. Update the docstring to match the new order of backends.Apply this diff:
"""Get the reference backend. - Order of checking for ref: DP, TF, PT. + Order of checking for ref: DP, TF, PT, JAX, ARRAY_API_STRICT. """deepmd/dpmodel/utils/network.py (1)
258-258: Clarify the logic behind concatenating input tensorsIn the residual connection, when
self.w.shape[1]is twiceself.w.shape[0], the code concatenatesxwith itself along the last axis. To enhance readability, consider adding a comment explaining the rationale behind this operation.Apply this diff to add a clarifying comment:
elif self.resnet and self.w.shape[1] == 2 * self.w.shape[0]: + # When output dimension is double the input, concatenate x with itself y += xp.concat([x, x], axis=-1)
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- deepmd/dpmodel/descriptor/dpa1.py (13 hunks)
- deepmd/dpmodel/utils/exclude_mask.py (5 hunks)
- deepmd/dpmodel/utils/network.py (6 hunks)
- deepmd/dpmodel/utils/nlist.py (4 hunks)
- deepmd/dpmodel/utils/type_embed.py (1 hunks)
- deepmd/jax/utils/network.py (1 hunks)
- pyproject.toml (1 hunks)
- source/tests/array_api_strict/utils/network.py (1 hunks)
- source/tests/consistent/common.py (10 hunks)
- source/tests/consistent/descriptor/test_dpa1.py (5 hunks)
- source/tests/consistent/test_type_embedding.py (4 hunks)
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📓 Learnings (1)
deepmd/dpmodel/utils/nlist.py (1)
Learnt from: njzjz PR: deepmodeling/deepmd-kit#4160 File: deepmd/dpmodel/utils/nlist.py:292-321 Timestamp: 2024-09-25T06:36:03.578Z Learning: In the Array API, `outer` is only available in `xp.linalg`, not in the main namespace `xp`.
🪛 Ruff
deepmd/dpmodel/descriptor/dpa1.py
1039-1039: Local variable
env_matis assigned to but never usedRemove assignment to unused variable
env_mat(F841)
deepmd/dpmodel/utils/nlist.py
102-105: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
🪛 GitHub Check: CodeQL
deepmd/dpmodel/descriptor/dpa1.py
[notice] 1039-1039: Unused local variable
Variable env_mat is not used.
🔇 Additional comments (32)
source/tests/consistent/test_type_embedding.py (4)
15-15: LGTM: Import of INSTALLED_ARRAY_API_STRICTThe addition of
INSTALLED_ARRAY_API_STRICTto the imports is consistent with the existing pattern and necessary for the conditional import of array_api_strict related code.
41-44: LGTM: Conditional import of TypeEmbedNetStrictThe conditional import of
TypeEmbedNetStrictis well-implemented, following the established pattern for other backends. Setting it toNonewhen not installed is a good practice to prevent potentialAttributeErrors.
79-79: LGTM: Addition of array_api_strict_class and skip_array_api_strictThe additions of
array_api_strict_classandskip_array_api_strictare consistent with the existing pattern for other backends. Theskip_array_api_strictflag is correctly implemented based on the installation status.Thank you for implementing the suggestion from the previous review to add the
skip_array_api_strictcondition.Also applies to: 82-82
Line range hint
1-174: Overall assessment: Well-implemented array_api_strict supportThe changes to this file successfully integrate support for the array_api_strict backend. The implementation is consistent with existing patterns for other backends and includes:
- Proper imports and conditional logic
- Addition of necessary class attributes
- Implementation of the
eval_array_api_strictmethodThese changes enhance the test suite's capability to handle the new array_api_strict backend while maintaining existing functionality. All previous review comments have been addressed, demonstrating attentiveness to feedback.
Great job on this implementation!
deepmd/dpmodel/utils/type_embed.py (1)
109-109: LGTM! Verify consistency across the codebase.The change from
concatenatetoconcatis correct and aligns with the Array API standard. This modification is consistent with the PR objectives.To ensure consistency, let's check for any remaining uses of
concatenatein the codebase:✅ Verification successful
Consistency verified: 'concatenate' to 'concat' change applied throughout the codebase.
The change from 'concatenate' to 'concat' in the file
deepmd/dpmodel/utils/type_embed.pyis consistent with changes made throughout the codebase. No occurrences of 'concatenate' were found, while numerous instances of 'concat' are present across various files. This indicates that the change has been applied systematically, aligning with the PR objectives to replace 'concatenate' with 'concat'.🏁 Scripts executed
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#!/bin/bash # Description: Check for any remaining uses of 'concatenate' in Python files # Search for 'concatenate' in Python files echo "Searching for 'concatenate' in Python files:" rg --type python 'concatenate' -C 3 # Search for 'concat' in Python files to verify the changes echo "Searching for 'concat' in Python files:" rg --type python 'concat' -C 3Length of output: 328
Script:
#!/bin/bash # Description: Check for any remaining uses of 'concatenate' and verify 'concat' in Python files # Search for 'concatenate' in Python files echo "Searching for 'concatenate' in Python files:" rg --type py 'concatenate' -C 3 # Search for 'concat' in Python files to verify the changes echo "Searching for 'concat' in Python files:" rg --type py 'concat' -C 3Length of output: 369813
pyproject.toml (1)
140-140: LGTM: Addition of Flax dependency aligns with PR objectivesThe addition of Flax (>=0.8.0) as a dependency for Python versions 3.10 and above is a good choice. This aligns with the discussion in the PR comments about using Flax for modules implemented by JAX. Flax provides a structure similar to
torch.nn.Module, which can enhance support for parameter initialization and backward propagation in JAX-based implementations.source/tests/array_api_strict/utils/network.py (3)
36-42: LGTMThe
NetworkCollectionclass and itsNETWORK_TYPE_MAPare well-defined, providing a clear mapping of network types to their corresponding classes.
44-45: Verify method resolution order (MRO) forLayerNormIn the multiple inheritance of
class LayerNorm(LayerNormDP, NativeLayer), the order of base classes affects the method resolution order. Verify that this order ensures the desired behavior, and that methods from bothLayerNormDPandNativeLayerare correctly inherited and invoked.Run the following script to inspect the inheritance hierarchy and confirm the MRO:
#!/bin/bash # Description: Inspect the class definition and inheritance of LayerNorm. # Search for the LayerNorm class definition. rg --type python -A 5 $'class LayerNorm' # Optionally, check other classes that inherit from LayerNormDP or NativeLayer. rg --type python -A 5 $'(LayerNormDP|NativeLayer)'
31-33: Verify compatibility of network constructors withNativeLayerEnsure that
make_multilayer_network,make_embedding_network, andmake_fitting_networkare compatible with the updatedNativeLayerclass. Confirm that these functions accept the new parameters and integrate smoothly with the modified layer definitions.Run the following script to check the definitions and usages of the network constructors:
deepmd/jax/utils/network.py (4)
27-39: Implementation ofArrayAPIParamenhances interoperabilityThe
ArrayAPIParamclass correctly extendsnnx.Paramand implements array interface methods such as__array__,__array_namespace__,__dlpack__, and__dlpack_device__. This ensures that parameters can seamlessly integrate with different array operations and backends, improving the flexibility and compatibility of the code.
41-49: Properly wrapping parameters inNativeLayerwithArrayAPIParamIn the
__setattr__method ofNativeLayer, the attributesw,b, andidtare suitably converted to JAX arrays usingto_jax_array(value). If the value is notNone, it is wrapped withArrayAPIParam. This approach ensures that these parameters support the necessary array interfaces for downstream computations.
64-71:NetworkCollectioncorrectly definesNETWORK_TYPE_MAPfor dynamic network selectionThe
NETWORK_TYPE_MAPinNetworkCollectionappropriately maps string identifiers to their corresponding network classes. The use ofClassVarwith explicit type hints ensures proper type checking. This setup facilitates dynamic selection and instantiation of different network types based on configuration.
73-74: Verify the method resolution order (MRO) in theLayerNormclassThe
LayerNormclass inherits from bothLayerNormDPandNativeLayer. Multiple inheritance can sometimes lead to unexpected behaviors if there are overlapping methods or attributes. Ensure that the MRO is as intended and that the correct methods from parent classes are being invoked.To inspect the MRO and confirm the inheritance hierarchy, run the following script:
This will output the MRO, helping you verify that the inheritance structure behaves as expected.
deepmd/dpmodel/utils/nlist.py (4)
103-105: Consider simplifying theif-elseblock with a ternary operatorThe current
if-elsestatement can be condensed into a single line using a ternary operator for improved readability.🧰 Tools
🪛 Ruff
102-105: Use ternary operator
xmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcutinstead ofif-else-blockReplace
if-else-block withxmax = xp.max(coord) + 2.0 * rcut if coord.size > 0 else 2.0 * rcut(SIM108)
170-171: Ensure correct broadcasting inxp_take_along_axisand subsequent operationsIn the
nlist_distinguish_typesfunction, verify that the use ofxp_take_along_axisand the subsequentsqueeze()operation correctly handle the array dimensions. Improper squeezing might lead to unexpected behavior if dimensions are collapsed incorrectly.Please check the shapes of
tnlistbefore and after squeezing to ensure that they align with the expected dimensions for further processing.
295-303: Confirmed correct usage ofxp.linalg.outerThe use of
xp.linalg.outeris appropriate here, as per the Array API standard,outeris available in thelinalgnamespace and not in the main namespacexp.
123-125:⚠️ Potential issuePotential compatibility issue with
xp.eyefor non-square matricesThe use of
xp.eye(nloc, nall, dtype=diff.dtype)may not be supported across all array backends ifnlocandnallare not equal. Some implementations of the Array API'seyefunction only support creating square matrices.Please verify whether the array backends you intend to support allow non-square matrices with
xp.eye. If not, consider an alternative approach to exclude self-distances, such as creating a mask with broadcasting.source/tests/consistent/descriptor/test_dpa1.py (4)
196-224: Refactor duplicated parameter unpacking inskip_jaxmethodThe unpacking of
self.paramis repeated from lines 199 to 218, similar to other methods. Consider refactoring this into a helper method to improve maintainability.
226-257: Refactor duplicated parameter unpacking inskip_array_api_strictmethodLines 229 to 248 repeat the unpacking of
self.param. Refactoring this into a shared helper method can reduce code duplication and potential errors.
392-401:eval_jaxmethod implementation is correctThe
eval_jaxmethod correctly invokeseval_jax_descriptorwith the appropriate parameters. This ensures consistency with the evaluation methods of other backends.
402-411:eval_array_api_strictmethod implementation looks goodThe
eval_array_api_strictmethod appropriately callseval_array_api_strict_descriptorwith the necessary arguments, aligning with the structure of existing evaluation methods.source/tests/consistent/common.py (6)
13-15: Importfind_specappropriatelyThe import of
find_specfromimportlib.utilis correctly added to check for the presence of thearray_api_strictmodule.
39-39: Detectarray_api_strictinstallation usingfind_specProperly sets
INSTALLED_ARRAY_API_STRICTby checking ifarray_api_strictis installed.
63-63: IncludeINSTALLED_ARRAY_API_STRICTin public APIAdds
INSTALLED_ARRAY_API_STRICTto the__all__list, exposing it as part of the module's public interface.
191-191: AddARRAY_API_STRICTtoRefBackendenumThe addition of
ARRAY_API_STRICTto theRefBackendenum is appropriate and extends backend support.
257-261: Implement serialization method forarray_api_strictThe
get_array_api_strict_ret_serialization_from_clsmethod correctly retrieves return values and serialization data forarray_api_strict.
290-295: SupportARRAY_API_STRICTinget_reference_ret_serializationAdds handling for
ARRAY_API_STRICTinget_reference_ret_serialization, ensuring it can retrieve return values and serialization data.deepmd/dpmodel/descriptor/dpa1.py (1)
1351-1353:⚠️ Potential issueAssign variable
vbefore usageThe variable
vis used later but has not been assigned. Please ensurevis properly initialized to avoidNameError.Apply this diff to assign
v:_query = self.in_proj(query) q = _query[..., 0 : self.head_dim] k = _query[..., self.head_dim : self.head_dim * 2] + v = _query[..., self.head_dim * 2 : self.head_dim * 3]Likely invalid or redundant comment.
deepmd/dpmodel/utils/network.py (4)
149-160: ApprovedThe deserialization logic correctly handles the assignment and reshaping of
w,b, andidt. Usingravel()ensures that these variables are flattened appropriately before being set to the object's attributes. The code is clear and maintains consistency.
369-373: ApprovedThe initialization of
self.wandself.bappropriately utilizes the array API namespace. The use ofxp.squeezeensures that the weight shape is maintained as[num_in]. Settingself.wandself.bwithxp.ones_likeandxp.zeros_likewhenuni_initisTrueis correct and ensures consistency across backends.
386-387: ApprovedThe
serializemethod now correctly usesto_numpy_arrayfor convertingself.wandself.b. This change ensures consistent serialization across different backends and maintains compatibility, addressing previous concerns.
481-486: ApprovedThe
layer_norm_numpymethod correctly employs the array API namespace for calculating the mean and variance. The use oftuple(range(-len(shape), 0))for theaxisparameter ensures that the normalization operates over the correct dimensions, accommodating inputs of varying shapes. The updated calculations enhance compatibility and maintain the functionality of layer normalization.
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Summary by CodeRabbit
New Features
array_api_strictbackend in testing.Bug Fixes
Tests
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