feat(jax/array-api): DPA-2 #4294
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Signed-off-by: Jinzhe Zeng <[email protected]>
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| data.pop("type") | ||
| variables = data.pop("@variables") | ||
| embeddings = data.pop("embeddings") | ||
| env_mat = data.pop("env_mat") |
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📝 WalkthroughWalkthroughThe changes in this pull request primarily enhance the functionality of several descriptor classes within the deepmd library by integrating the Changes
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Actionable comments posted: 14
🧹 Outside diff range and nitpick comments (6)
deepmd/dpmodel/utils/nlist.py (1)
218-241: Consider adding backend-specific optimizationsWhile the current implementation using
array_api_compatprovides good backend compatibility, consider adding backend-specific optimizations (e.g., JAX'svmaporjit) through optional imports. This could improve performance while maintaining the current clean abstraction.🧰 Tools
🪛 Ruff
228-228: Local variable
nallis assigned to but never usedRemove assignment to unused variable
nall(F841)
deepmd/jax/descriptor/se_t_tebd.py (2)
29-46: Consider adding docstrings toDescrptBlockSeTTebdand its methodsAdding docstrings to the
DescrptBlockSeTTebdclass and its methods enhances code readability and maintainability by providing context and explanations for future developers and users of the class.
50-56: Consider adding docstrings toDescrptSeTTebdand its methodsFor better clarity and documentation, consider adding docstrings to the
DescrptSeTTebdclass and its methods. This practice improves understanding and ease of use for other developers interacting with your code.deepmd/dpmodel/descriptor/se_t_tebd.py (2)
381-381: Consistency in variable naming: Replaceto_numpy_arraywithto_np_arrayIn line with the project's coding conventions, consider using
to_np_arrayfor consistency, assuming that other parts of the codebase use this naming.Apply this diff if applicable:
- "davg": to_numpy_array(obj["davg"]), + "davg": to_np_array(obj["davg"]),
Line range hint
12-46: Add docstrings for class methodsSeveral methods in the
DescrptBlockSeTTebdclass lack docstrings. Adding docstrings enhances code readability and maintainability.For example, add a docstring to the
__init__method:def __init__(...): """Initialize the DescrptBlockSeTTebd with the given parameters.""" ...Also applies to: 49-87
deepmd/dpmodel/descriptor/repformers.py (1)
399-399: Rename unused loop variableidxto_The loop control variable
idxinfor idx, ll in enumerate(self.layers):is not used within the loop body. To indicate that it is intentionally unused, consider renaming it to_or_idx.Apply this diff to rename
idxto_:- for idx, ll in enumerate(self.layers): + for _, ll in enumerate(self.layers):🧰 Tools
🪛 Ruff
399-399: Loop control variable
idxnot used within loop bodyRename unused
idxto_idx(B007)
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📒 Files selected for processing (8)
deepmd/dpmodel/descriptor/dpa2.py(7 hunks)deepmd/dpmodel/descriptor/repformers.py(31 hunks)deepmd/dpmodel/descriptor/se_t_tebd.py(7 hunks)deepmd/dpmodel/utils/nlist.py(1 hunks)deepmd/jax/descriptor/dpa2.py(1 hunks)deepmd/jax/descriptor/repformers.py(1 hunks)deepmd/jax/descriptor/se_t_tebd.py(1 hunks)source/tests/consistent/descriptor/test_dpa2.py(4 hunks)
🧰 Additional context used
🪛 Ruff
deepmd/dpmodel/descriptor/repformers.py
399-399: Loop control variable idx not used within loop body
Rename unused idx to _idx
(B007)
deepmd/dpmodel/descriptor/se_t_tebd.py
712-712: Local variable ng is assigned to but never used
Remove assignment to unused variable ng
(F841)
803-803: Local variable env_mat is assigned to but never used
Remove assignment to unused variable env_mat
(F841)
deepmd/dpmodel/utils/nlist.py
228-228: Local variable nall is assigned to but never used
Remove assignment to unused variable nall
(F841)
deepmd/jax/descriptor/repformers.py
97-98: Use a single if statement instead of nested if statements
Combine if statements using and
(SIM102)
🪛 GitHub Check: CodeQL
deepmd/dpmodel/descriptor/se_t_tebd.py
[notice] 803-803: Unused local variable
Variable env_mat is not used.
🔇 Additional comments (16)
deepmd/dpmodel/utils/nlist.py (3)
218-225: LGTM: Array namespace initialization and padding implementation
The array namespace initialization and padding logic is well-implemented, ensuring compatibility across different array backends while maintaining correct neighbor list dimensions.
231-236: LGTM: Robust coordinate manipulation and distance calculation
The implementation correctly handles coordinate transformations and distance calculations while maintaining backend compatibility. The use of float("inf") for masked values is an elegant solution.
240-241: LGTM: Efficient neighbor list filtering
The neighbor list filtering implementation is well-vectorized and correctly handles the cutoff distance check while maintaining backend compatibility.
source/tests/consistent/descriptor/test_dpa2.py (3)
18-18: LGTM! Clean implementation of conditional JAX support.
The conditional import pattern is consistent with other backends and ensures graceful handling when JAX is not installed.
Also applies to: 32-35
278-279: LGTM! Properties follow established patterns.
The skip_jax and jax_class properties are well-integrated with the existing backend properties.
Also applies to: 283-283
379-388: LGTM! Consistent implementation of JAX evaluation method.
The eval_jax method follows the same pattern as other backend evaluation methods and correctly handles all required parameters.
deepmd/jax/descriptor/se_t_tebd.py (1)
48-49: Verify the order of decorators for DescrptSeTTebd
The class DescrptSeTTebd is decorated with @BaseDescriptor.register("se_e3_tebd") followed by @flax_module. The order of decorators affects how the class is registered and initialized. Ensure that this order is intentional and that DescrptSeTTebd is correctly registered and behaves as expected.
deepmd/dpmodel/descriptor/se_t_tebd.py (1)
329-329: Potential division by zero in calculation of nall
When computing nall, there's a division operation that could potentially result in a division by zero if coord_ext is improperly shaped.
Please ensure that coord_ext is correctly shaped and that nf and nall are properly computed in all scenarios.
deepmd/dpmodel/descriptor/dpa2.py (7)
Line range hint 794-798: Proper initialization of array namespace for backend compatibility
The use of xp = array_api_compat.array_namespace(coord_ext, atype_ext, nlist) correctly obtains a backend-agnostic array namespace, ensuring compatibility across different array backends for subsequent array operations.
831-831: Ensure shapes are compatible for concatenation
At line 831, g1 = xp.concatenate([g1, g1_three_body], axis=-1), please verify that g1 and g1_three_body have compatible shapes along the last axis. This ensures that concatenation proceeds without errors when use_three_body is True.
839-840: Confirm correct usage of xp.tile and xp.take_along_axis
The operations using xp.tile and xp.take_along_axis are crucial for aligning g1_ext with the extended mapping. Double-check that mapping.reshape(nframes, nall, 1) and the subsequent tiling align correctly with g1's dimensions to prevent indexing errors.
854-854: Validate concatenation of g1 and g1_inp
At line 854, the concatenation g1 = xp.concatenate([g1, g1_inp], axis=-1) combines the updated representations with the original input features. Ensure that this operation aligns with the intended architecture and that the dimensions of g1 and g1_inp match appropriately along the concatenation axis.
891-892: Convert davg and dstd to NumPy arrays for serialization
Converting repinit["davg"] and repinit["dstd"] to NumPy arrays using to_numpy_array ensures that these statistics are properly serialized. This change enhances compatibility with different array backends during the serialization process.
904-905: Ensure proper serialization of repformers statistics
Similarly, converting repformers["davg"] and repformers["dstd"] to NumPy arrays is necessary for consistent serialization of the repformers component. This ensures that the statistics are correctly stored and retrievable.
921-922: Handle serialization of three-body repinit statistics
For the three-body repinit, converting repinit_three_body["davg"] and repinit_three_body["dstd"] to NumPy arrays is essential. This modification ensures that all statistical data is serialized uniformly, maintaining consistency across different descriptor components.
deepmd/dpmodel/descriptor/repformers.py (1)
396-396: Verify the replacement of -1 with zeros in nlist
Replacing -1 values in nlist with zeros may cause invalid neighbor indices to reference the first atom (index 0). Ensure that this replacement does not introduce unintended behavior in subsequent computations, especially if the first atom is a valid index.
| value = DescrptBlockRepformers.deserialize(value.serialize()) | ||
| elif name in {"type_embedding"}: | ||
| value = TypeEmbedNet.deserialize(value.serialize()) | ||
| elif name in {"g1_shape_tranform", "tebd_transform"}: |
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Typo in attribute name 'g1_shape_tranform'
It seems there's a typo in the attribute name 'g1_shape_tranform'. It should likely be 'g1_shape_transform'.
Apply this diff to correct the typo:
- elif name in {"g1_shape_tranform", "tebd_transform"}:
+ elif name in {"g1_shape_transform", "tebd_transform"}:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| elif name in {"g1_shape_tranform", "tebd_transform"}: | |
| elif name in {"g1_shape_transform", "tebd_transform"}: |
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@iProzd I found all g1_shape_transform in the code are g1_shape_tranform...
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Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4294 +/- ##
==========================================
+ Coverage 84.33% 84.37% +0.04%
==========================================
Files 555 558 +3
Lines 51878 52140 +262
Branches 3048 3054 +6
==========================================
+ Hits 43751 43995 +244
- Misses 7167 7189 +22
+ Partials 960 956 -4 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 10
🧹 Outside diff range and nitpick comments (3)
source/tests/array_api_strict/descriptor/se_t_tebd.py (1)
25-25: Add docstring to explain attribute transformationsThe class would benefit from documentation explaining the special handling of different attributes.
Add a docstring:
class DescrptBlockSeTTebd(DescrptBlockSeTTebdDP): + """Descriptor block for SE-T-TEBD with array API support. + + This class extends DescrptBlockSeTTebdDP with custom attribute handling: + - mean, stddev: Converted to array API arrays + - embeddings, embeddings_strip: Deserialized as NetworkCollection + - env_mat: No value storage + - emask: Created as PairExcludeMask + """deepmd/dpmodel/descriptor/dpa2.py (1)
844-848: Consider moving the mapping assertion earlier.The assertion
assert mapping is not Noneshould be moved before using the mapping variable to fail fast and provide clearer error messages.def call(self, coord_ext: np.ndarray, atype_ext: np.ndarray, nlist: np.ndarray, mapping: Optional[np.ndarray] = None): + assert mapping is not None, "mapping must be provided" xp = array_api_compat.array_namespace(coord_ext, atype_ext, nlist) use_three_body = self.use_three_body nframes, nloc, nnei = nlist.shape # ... - assert mapping is not None mapping_ext = xp.tile(xp.reshape(mapping, (nframes, nall, 1)), (1, 1, g1.shape[-1]))deepmd/dpmodel/descriptor/repformers.py (1)
448-450: Remove commented codeThe old transpose implementation is left as a comment but is no longer needed since the code has been migrated to use array_api_compat.
Apply this diff to clean up the code:
- # rot_mat = xp.transpose(h2g2, (0, 1, 3, 2)) rot_mat = xp.matrix_transpose(h2g2)
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📒 Files selected for processing (9)
deepmd/dpmodel/descriptor/dpa2.py(8 hunks)deepmd/dpmodel/descriptor/repformers.py(31 hunks)deepmd/dpmodel/utils/nlist.py(1 hunks)deepmd/jax/descriptor/dpa2.py(1 hunks)deepmd/jax/descriptor/repformers.py(1 hunks)source/tests/array_api_strict/descriptor/dpa2.py(1 hunks)source/tests/array_api_strict/descriptor/repformers.py(1 hunks)source/tests/array_api_strict/descriptor/se_t_tebd.py(1 hunks)source/tests/consistent/descriptor/test_dpa2.py(4 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/jax/descriptor/dpa2.py
🧰 Additional context used
🪛 Ruff
deepmd/dpmodel/descriptor/repformers.py
424-424: Loop control variable idx not used within loop body
Rename unused idx to _idx
(B007)
deepmd/dpmodel/utils/nlist.py
228-228: Local variable nall is assigned to but never used
Remove assignment to unused variable nall
(F841)
deepmd/jax/descriptor/repformers.py
104-105: Use a single if statement instead of nested if statements
Combine if statements using and
(SIM102)
source/tests/array_api_strict/descriptor/repformers.py
95-96: Use a single if statement instead of nested if statements
Combine if statements using and
(SIM102)
🔇 Additional comments (31)
source/tests/array_api_strict/descriptor/se_t_tebd.py (2)
1-23: LGTM: Well-organized imports with proper type hints
The imports are logically grouped, properly typed, and include all necessary dependencies for the descriptor classes.
11-13: Verify consistent array API usage across the codebase
Let's ensure the array API integration is consistent across related descriptor classes.
✅ Verification successful
Based on the results, I can generate the final response as we have sufficient information about the array API usage across the descriptor classes.
Array API integration is consistently implemented across descriptor classes ✓
The codebase shows uniform implementation of array API strict handling:
- All descriptor classes (
se_e2_r.py,se_t_tebd.py,hybrid.py,se_e2_a.py,repformers.py,dpa1.py,dpa2.py) properly import and useto_array_api_strict_array - Consistent attribute handling pattern where values are converted using
to_array_api_strict_array - Special handling in
hybrid.pyandrepformers.pyfor list values with list comprehension
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for consistent array API usage in descriptor classes
# Test 1: Check for array_api_compat usage in descriptor files
echo "Checking array_api_compat usage in descriptors..."
rg -l "array_api_compat" "source/tests/array_api_strict/descriptor/"
# Test 2: Check for to_array_api_strict_array usage
echo "Checking to_array_api_strict_array usage..."
rg "to_array_api_strict_array" "source/tests/array_api_strict/descriptor/"
# Test 3: Check for similar __setattr__ implementations
echo "Checking for similar attribute handling..."
ast-grep --pattern 'class $_ {
$$$
def __setattr__($_, $_, $_) {
$$$
to_array_api_strict_array($$$)
$$$
}
}'
Length of output: 2019
deepmd/dpmodel/utils/nlist.py (3)
218-227: LGTM: Padding logic is well-implemented
The padding implementation correctly handles variable-sized neighbor lists using -1 as sentinel values, maintaining compatibility with array_api_compat.
230-237: LGTM: Robust coordinate transformation and distance calculation
The implementation correctly:
- Handles coordinate reshaping for vectorized operations
- Properly masks invalid neighbor indices
- Uses array_api_compat's vector_norm for backend-agnostic distance calculations
228-229:
Remove unused variable nall
The variable nall is assigned but never used in this scope.
- coord1 = xp.reshape(coord, (nb, -1, 3))
- nall = coord1.shape[1]
+ coord1 = xp.reshape(coord, (nb, -1, 3))Likely invalid or redundant comment.
🧰 Tools
🪛 Ruff
228-228: Local variable nall is assigned to but never used
Remove assignment to unused variable nall
(F841)
deepmd/dpmodel/descriptor/dpa2.py (3)
7-7: LGTM: Array compatibility imports are well-structured.
The new imports enhance array operations compatibility across different backends and provide necessary conversion utilities.
Also applies to: 13-18
Line range hint 797-812: LGTM: Array operations are properly abstracted.
The code effectively uses the array API compatibility layer for backend-agnostic operations:
- Obtains array namespace from input tensors
- Uses xp.reshape and xp.concat consistently
- Handles type embeddings correctly
Also applies to: 837-837
899-900: LGTM: Consistent array serialization.
The code properly converts array variables to numpy format during serialization using to_numpy_array, ensuring compatibility across different array backends.
Also applies to: 912-913, 929-930
deepmd/dpmodel/descriptor/repformers.py (6)
8-8: Well-structured utility functions for tensor operations!
The new transpose utility functions are well-implemented and properly documented. They handle specific transposition patterns needed for tensor operations in a clean and efficient way.
Also applies to: 15-20, 48-67
Line range hint 392-404: Clean migration to array_api_compat!
The array operations have been properly migrated to use array_api_compat, making the code more backend-agnostic while maintaining the same functionality.
412-421: Proper handling of distance calculations with array API!
The direct distance calculations and array operations have been correctly migrated to use the array API while maintaining the original logic.
1528-1561: Well-implemented array operations in _update_g1_conv!
The array operations are properly implemented using array_api_compat with careful handling of shapes, types, and edge cases. The code is well-documented and maintains good structure.
Line range hint 1294-1474: Robust residual initialization and handling!
The residual lists are properly initialized and consistently handled across different update types. The implementation follows good practices for managing neural network residual connections.
1880-1882: Consistent array serialization!
The residual arrays are properly converted to NumPy arrays during serialization, ensuring consistent data storage and compatibility.
source/tests/array_api_strict/descriptor/dpa2.py (1)
47-47:
Typo in attribute name 'g1_shape_tranform'
The attribute name 'g1_shape_tranform' seems misspelled. Did you mean 'g1_shape_transform'?
Run the following script to verify the usage of 'g1_shape_tranform' and 'g1_shape_transform' in the codebase:
source/tests/array_api_strict/descriptor/repformers.py (5)
31-46: Good implementation of custom __setattr__ in DescrptBlockRepformers
The method effectively handles attribute assignments with appropriate transformations and maintains consistency.
48-53: Proper customization of __setattr__ in Atten2Map
The method correctly handles the mapqk attribute by deserializing it appropriately.
55-60: Correct handling of attributes in Atten2MultiHeadApply
The __setattr__ method processes mapv and head_map attributes accurately.
62-67: Effective use of __setattr__ in Atten2EquiVarApply
The method properly handles the head_map attribute through deserialization.
69-74: Appropriate attribute management in LocalAtten
The __setattr__ method correctly processes mapq, mapkv, and head_map attributes.
deepmd/jax/descriptor/repformers.py (7)
32-49: Implementation of DescrptBlockRepformers is correct
The __setattr__ method correctly handles attribute assignments, deserialization, and type conversions for attributes like mean, stddev, layers, g2_embd, and emask.
53-58: Implementation of Atten2Map is correct
The __setattr__ method properly deserializes the mapqk attribute into a NativeLayer instance.
60-65: Implementation of Atten2MultiHeadApply is correct
The __setattr__ method accurately deserializes the mapv and head_map attributes into NativeLayer instances.
68-73: Implementation of Atten2EquiVarApply is correct
The __setattr__ method correctly handles the deserialization of the head_map attribute into a NativeLayer instance.
76-81: Implementation of LocalAtten is correct
The __setattr__ method effectively deserializes the mapq, mapkv, and head_map attributes into NativeLayer instances.
Line range hint 85-108: Implementation of RepformerLayer is consistent and correct
The __setattr__ method appropriately handles attribute assignments, deserializations, and type conversions for various attributes, ensuring correct processing within the class.
🧰 Tools
🪛 Ruff
104-105: Use a single if statement instead of nested if statements
Combine if statements using and
(SIM102)
105-107: 🛠️ Refactor suggestion
Simplify nested if statements by combining conditions
To enhance code readability, combine the nested if statements into a single condition using and. This reduces indentation and makes the code more concise.
Apply the following change:
- elif name in {"loc_attn"}:
- if value is not None:
- value = LocalAtten.deserialize(value.serialize())
+ elif name in {"loc_attn"} and value is not None:
+ value = LocalAtten.deserialize(value.serialize())Likely invalid or redundant comment.
source/tests/consistent/descriptor/test_dpa2.py (4)
18-19: Import INSTALLED_ARRAY_API_STRICT appropriately
The addition of INSTALLED_ARRAY_API_STRICT to the import list is correct and necessary for conditional feature support.
33-41: Conditional imports for new descriptors are correctly implemented
The use of conditional imports based on INSTALLED_JAX and INSTALLED_ARRAY_API_STRICT effectively handles optional dependencies, ensuring that the code remains robust whether or not these libraries are installed.
289-290: Assigning new descriptor classes aligns with existing patterns
The addition of jax_class and array_api_strict_class follows the established convention for managing different backend classes. This enhances the code's scalability and maintainability.
386-405: New evaluation methods are consistent with existing implementation
The eval_jax and eval_array_api_strict methods are properly defined and consistent with other evaluation methods like eval_dp and eval_pt. They correctly pass the necessary parameters for descriptor evaluation.
for more information, see https://pre-commit.ci
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
deepmd/dpmodel/descriptor/repformers.py (2)
8-20: Add docstrings to the new transpose functions.The new transpose functions look good, but they would benefit from docstrings explaining:
- Input tensor shape and meaning of each dimension
- Output tensor shape and how dimensions are reordered
- Example usage
Example docstring:
def xp_transpose_01423(x): """Transpose a 5D tensor from (d0,d1,d2,d3,d4) to (d0,d1,d4,d2,d3). Parameters ---------- x : array_like Input tensor of shape (batch, loc, nei1, nei2, feat) Returns ------- array_like Transposed tensor of shape (batch, loc, feat, nei1, nei2) """Also applies to: 48-68
1880-1883: Add comment explaining numpy conversion.Consider adding a comment explaining why residuals need to be converted to numpy arrays during serialization.
# Convert residuals to numpy arrays for serialization compatibility "g1_residual": [to_numpy_array(aa) for aa in self.g1_residual],
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deepmd/dpmodel/descriptor/repformers.py(31 hunks)deepmd/jax/descriptor/dpa2.py(1 hunks)source/tests/array_api_strict/descriptor/dpa2.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/jax/descriptor/dpa2.py
🧰 Additional context used
🪛 Ruff
deepmd/dpmodel/descriptor/repformers.py
424-424: Loop control variable idx not used within loop body
Rename unused idx to _idx
(B007)
🔇 Additional comments (8)
deepmd/dpmodel/descriptor/repformers.py (6)
Line range hint 392-404: LGTM! Clean refactoring to use array API.
The changes successfully migrate numpy operations to use the array API, making the code more backend-agnostic. The new transpose helper functions are used appropriately.
Also applies to: 412-450
460-477: LGTM! Comprehensive deserialization implementation.
The deserialize method properly handles the reconstruction of the complex object hierarchy.
478-525: LGTM! Complete serialization implementation.
The serialize method thoroughly captures the object state, including all configuration parameters and nested objects.
1528-1561: LGTM! Clean array operation refactoring.
The array operations have been properly migrated to use array_api_compat while maintaining the original logic.
Line range hint 1294-1474: LGTM! Well-structured residual initialization.
The residual initialization is properly implemented with:
- Separate lists for different components (g1, g2, h2)
- Consistent seeding for reproducibility
- Proper handling of initialization modes
423-427:
Fix unused loop variable.
🧰 Tools
🪛 Ruff
424-424: Loop control variable idx not used within loop body
Rename unused idx to _idx
(B007)
source/tests/array_api_strict/descriptor/dpa2.py (2)
35-57: Implementation of __setattr__ method is correct
The overridden __setattr__ method correctly handles attribute assignments with appropriate deserialization and type checking.
47-47:
Typo in attribute name 'g1_shape_tranform'
The attribute 'g1_shape_tranform' appears to be misspelled; it should be 'g1_shape_transform'.
Apply this diff to correct the typo:
- elif name in {"g1_shape_tranform", "tebd_transform"}:
+ elif name in {"g1_shape_transform", "tebd_transform"}:Run the following script to check for other occurrences of 'g1_shape_tranform' in the codebase:
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (1)
doc/model/dpa2.md (1)
Line range hint
11-24: Consider adding JAX-specific installation requirements.While the document includes PyTorch-specific installation requirements, it should also include any specific requirements or considerations for using the JAX backend.
Consider adding a new section:
## Requirements of installation {{ jax_icon }} If you plan to use the JAX backend, ensure you have installed JAX with the appropriate hardware support (CPU/GPU/TPU). See [JAX installation guide](https://github.com/google/jax#installation) for details.🧰 Tools
🪛 Markdownlint
7-7: null
Bare URL used(MD034, no-bare-urls)
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doc/model/dpa2.md(1 hunks)
🔇 Additional comments (2)
doc/model/dpa2.md (2)
1-1: LGTM: Backend support clearly documented.
The addition of JAX to the supported backends is clearly indicated both in the header and the note section.
Also applies to: 4-4
Line range hint 26-26: Verify data format compatibility with JAX backend.
The document states that DPA-2 supports both standard and mixed type data formats. Let's verify this is true for the JAX backend as well.
✅ Verification successful
Let me check the specific data format handling in the JAX implementation and tests.
Let me check one more aspect to verify the actual implementation of data format handling in the JAX backend for DPA2.
Based on the evidence gathered from the shell scripts, I can now provide a final response:
DPA-2 data format compatibility with JAX backend is verified
The JAX backend fully supports mixed type data formats for DPA-2, as evidenced by:
- JAX model implementation explicitly handles
mixed_typesparameter in its core functionality (deepmd/jax/model/hlo.py) - Test cases in test_dpa2.py confirm mixed type support through consistent testing across backends
- The JAX descriptor (DescrptDPA2) inherits from DescrptDPA2DP, maintaining format compatibility with the base implementation
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for data format handling in JAX implementation
rg -l "mixed.*type" --type py | xargs rg "jax" -l
Length of output: 566
Script:
#!/bin/bash
# Check JAX implementation for data format handling
rg "mixed.*type" -A 5 -B 5 deepmd/jax/model/model.py deepmd/jax/model/hlo.py
# Check test cases for DPA2 with JAX backend
rg "mixed.*type|data_format" -A 5 -B 5 source/tests/consistent/descriptor/test_dpa2.py
Length of output: 4283
Script:
#!/bin/bash
# Check DPA2 descriptor implementation for JAX
rg "class.*DPA2" -A 10 deepmd/jax/descriptor/dpa2.py
# Also check if there are any format-specific implementations
rg "format|mixed_type" deepmd/jax/descriptor/dpa2.py
Length of output: 621
🧰 Tools
🪛 Markdownlint
7-7: null
Bare URL used
(MD034, no-bare-urls)
Summary by CodeRabbit
New Features
DescrptDPA2,DescrptBlockRepformers, andDescrptBlockSeTTebd.Improvements
array_api_compat.Bug Fixes
Tests