feat(jax): neighbor stat #4258
feat(jax): neighbor stat #4258
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Signed-off-by: Jinzhe Zeng <[email protected]>
📝 WalkthroughWalkthroughThe pull request introduces several changes across multiple files, primarily enhancing the functionality of the Changes
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Actionable comments posted: 7
🧹 Outside diff range and nitpick comments (4)
deepmd/jax/utils/auto_batch_size.py (1)
44-59: Simplify the OOM error detection logic.The implementation correctly handles OOM errors, but the code can be simplified while maintaining readability.
Apply this diff to simplify the return statement:
def is_oom_error(self, e: Exception) -> bool: # several sources think CUSOLVER_STATUS_INTERNAL_ERROR is another out-of-memory error, # such as https://github.com/JuliaGPU/CUDA.jl/issues/1924 # (the meaningless error message should be considered as a bug in cusolver) - if isinstance(e, (jaxlib.xla_extension.XlaRuntimeError, ValueError)) and ( - "RESOURCE_EXHAUSTED:" in e.args[0] - ): - return True - return False + return isinstance(e, (jaxlib.xla_extension.XlaRuntimeError, ValueError)) and ( + "RESOURCE_EXHAUSTED:" in e.args[0] + )🧰 Tools
🪛 Ruff
55-59: Return the condition directly
Inline condition
(SIM103)
deepmd/backend/jax.py (1)
Line range hint
26-120: Consider enhancing error handling for unsupported features.The backend implementation is well-structured, but consider adding runtime checks or decorators to gracefully handle cases where features are enabled but not fully implemented. This would prevent silent failures and provide better error messages.
Example approach:
- Create a decorator that checks if a feature is both enabled and implemented
- Apply it to feature-specific properties
- Provide clear error messages indicating which features are not fully supported
Would you like me to provide an example implementation of such a decorator?
source/tests/consistent/test_neighbor_stat.py (2)
Line range hint
50-75: LGTM! Well-structured test implementation with comprehensive test casesThe refactoring to
run_neighbor_statwith a backend parameter improves code reuse. The test logic is thorough with proper validation of neighbor statistics across different cutoff radii and mixed type configurations.Consider adding a docstring to document the method's purpose and parameters:
def run_neighbor_stat(self, backend): + """Test neighbor statistics computation for different backends. + + Args: + backend (str): The backend to use ('tensorflow', 'pytorch', 'numpy', or 'jax') + """ for rcut in (0.0, 1.0, 2.0, 4.0):
76-89: LGTM! Well-organized backend-specific test methodsThe backend-specific test methods are well-structured with proper conditional execution using
skipUnless. Each backend is consistently handled.Consider grouping the backend tests together by moving the numpy test (
test_neighbor_stat_dp) next to other backend tests for better organization:- @unittest.skipUnless(INSTALLED_TF, "tensorflow is not installed") - def test_neighbor_stat_tf(self): - self.run_neighbor_stat("tensorflow") - - @unittest.skipUnless(INSTALLED_PT, "pytorch is not installed") - def test_neighbor_stat_pt(self): - self.run_neighbor_stat("pytorch") - - def test_neighbor_stat_dp(self): - self.run_neighbor_stat("numpy") - - @unittest.skipUnless(INSTALLED_JAX, "jax is not installed") - def test_neighbor_stat_jax(self): - self.run_neighbor_stat("jax") + def test_neighbor_stat_dp(self): + """Test neighbor statistics with numpy backend.""" + self.run_neighbor_stat("numpy") + + @unittest.skipUnless(INSTALLED_TF, "tensorflow is not installed") + def test_neighbor_stat_tf(self): + """Test neighbor statistics with tensorflow backend.""" + self.run_neighbor_stat("tensorflow") + + @unittest.skipUnless(INSTALLED_PT, "pytorch is not installed") + def test_neighbor_stat_pt(self): + """Test neighbor statistics with pytorch backend.""" + self.run_neighbor_stat("pytorch") + + @unittest.skipUnless(INSTALLED_JAX, "jax is not installed") + def test_neighbor_stat_jax(self): + """Test neighbor statistics with jax backend.""" + self.run_neighbor_stat("jax")
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📒 Files selected for processing (7)
- deepmd/backend/jax.py (2 hunks)
- deepmd/dpmodel/utils/neighbor_stat.py (2 hunks)
- deepmd/jax/utils/auto_batch_size.py (1 hunks)
- deepmd/jax/utils/neighbor_stat.py (1 hunks)
- source/tests/common/dpmodel/test_neighbor_stat.py (0 hunks)
- source/tests/consistent/test_neighbor_stat.py (4 hunks)
- source/tests/tf/test_neighbor_stat.py (0 hunks)
💤 Files with no reviewable changes (2)
- source/tests/common/dpmodel/test_neighbor_stat.py
- source/tests/tf/test_neighbor_stat.py
🧰 Additional context used
🪛 Ruff
deepmd/jax/utils/auto_batch_size.py
55-59: Return the condition directly
Inline condition
(SIM103)
🔇 Additional comments (8)
deepmd/jax/utils/auto_batch_size.py (2)
1-23: LGTM! Well-structured class definition with proper documentation.The class is properly defined with clear inheritance and well-documented parameters.
24-33: LGTM! Clean constructor implementation.The constructor properly initializes the parent class with sensible default values.
deepmd/backend/jax.py (3)
85-89: LGTM! Clean implementation of neighbor_stat property.The implementation follows best practices with lazy imports to avoid circular dependencies.
Line range hint
92-120: Verify serialization utilities existence.The implementation looks clean, but let's verify the existence of the required serialization utilities.
#!/bin/bash # Description: Verify the existence of serialization utilities # Check for the serialization module and its functions rg -l "serialize_from_file|deserialize_to_file" --type py # Look for any existing serialization tests fd -e py test.*serial
36-38: Verify ENTRY_POINT feature implementation.The
ENTRY_POINTfeature is enabled, but its correspondingentry_point_hookproperty raisesNotImplementedError. This inconsistency could cause runtime issues.source/tests/consistent/test_neighbor_stat.py (1)
15-19: LGTM! Clean import organizationThe new imports for backend availability flags are well-organized and follow Python conventions.
deepmd/jax/utils/neighbor_stat.py (1)
78-78: Verify the Aggregation FunctionsWhen using
np.maxandnp.min, ensure that theaxisparameter is correctly set based on the dimensions ofmax_nneiandminrr2. Verify that these aggregations produce the expected scalar values.Run the following script to confirm the shapes and values:
✅ Verification successful
Looking at the code, I can now generate a final response since I have enough context to verify the aggregation functions.
Aggregation Functions Are Correctly Used
The
axis=0parameter innp.max(max_nnei, axis=0)is correct because:
max_nneiis returned fromNeighborStatOPand converted to numpy array- The aggregation is performed over batches of data, where
axis=0correctly reduces across the batch dimensionminrr2is correctly reduced usingnp.minwithout an axis parameter since we want the global minimum distance🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check the shapes of 'max_nnei' and 'minrr2' returned from '_execute' method. # Test: Print the shapes. Expect: Correct dimensions for aggregation. ast-grep --pattern $'def _execute($_) { $$$ }' | while read -r func_def; do echo "Analyzing function: $func_def" # Assuming the script can extract and print variable shapes echo "Shape of max_nnei:" # Placeholder command to print shape echo "Shape of minrr2:" # Placeholder command to print shape doneLength of output: 66
Script:
#!/bin/bash # Let's first check the content of the file to understand the context better cat deepmd/jax/utils/neighbor_stat.pyLength of output: 2792
deepmd/dpmodel/utils/neighbor_stat.py (1)
72-72: Usage ofarray_namespaceis appropriateThe use of
array_api_compat.array_namespaceto obtain a consistent array API is correct and enhances compatibility across different array backends.
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: 1
🧹 Outside diff range and nitpick comments (2)
deepmd/jax/utils/neighbor_stat.py (2)
86-96: Add 'Returns' Section to DocstringThe docstring of the
_executemethod lacks a 'Returns' section describing the outputs of the function. Including this information improves code readability and maintainability.Apply this diff to update the docstring:
""" Execute the operation. Parameters ---------- coord The coordinates of atoms. atype The atom types. cell The cell. + Returns + ------- + tuple[np.ndarray, np.ndarray] + minrr2 : np.ndarray + The minimal squared distances. + max_nnei : np.ndarray + The maximum number of neighbors. """
80-85: Add Return Type Hint to_executeMethodFor consistency and to enhance code readability, consider adding a return type hint to the
_executemethod.Apply this diff:
def _execute( self, coord: np.ndarray, atype: np.ndarray, cell: Optional[np.ndarray], - ): + ) -> tuple[np.ndarray, np.ndarray]: """
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📒 Files selected for processing (1)
- deepmd/jax/utils/neighbor_stat.py (1 hunks)
🧰 Additional context used
📓 Learnings (1)
deepmd/jax/utils/neighbor_stat.py (1)
Learnt from: njzjz PR: deepmodeling/deepmd-kit#4258 File: deepmd/jax/utils/neighbor_stat.py:98-101 Timestamp: 2024-10-26T02:09:01.365Z Learning: The function `to_jax_array` in `deepmd/jax/common.py` can handle `None` values, so it's safe to pass `None` to it without additional checks.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4258 +/- ##
==========================================
- Coverage 84.23% 84.22% -0.01%
==========================================
Files 548 550 +2
Lines 51425 51469 +44
Branches 3051 3051
==========================================
+ Hits 43317 43352 +35
- Misses 7150 7154 +4
- Partials 958 963 +5 ☔ View full report in Codecov by Sentry. |
Summary by CodeRabbit
Release Notes
New Features
NeighborStatandNeighborStatOPclasses for enhanced neighbor statistics computation.AutoBatchSizeclass to manage automatic batch sizing in deep learning applications.Improvements
JAXBackendfunctionality with implemented properties for neighbor statistics and serialization.Tests
neighbor_statto support multiple backends (TensorFlow, PyTorch, NumPy, JAX).