fix(tf): fix model out_bias deserialize #4350
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Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (1)
deepmd/tf/model/model.py (1)
891-892: Specify explicit data type forout_stdto ensure consistencyConsider specifying the data type explicitly in the
np.onescall forout_stdto avoid potential issues with default data types across different NumPy versions or platforms. This enhances code portability and eliminates the need to suppress linter warnings.Apply this diff to specify the data type and remove the linter suppression:
"out_bias": out_bias, - "out_std": np.ones([1, ntypes, dict_fit["dim_out"]]), # pylint: disable=no-explicit-dtype + "out_std": np.ones([1, ntypes, dict_fit["dim_out"]], dtype=GLOBAL_NP_FLOAT_PRECISION)
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📒 Files selected for processing (2)
deepmd/tf/model/model.py(5 hunks)source/tests/consistent/model/test_ener.py(1 hunks)
🔇 Additional comments (5)
source/tests/consistent/model/test_ener.py (1)
144-146: Verify test coverage for bias deserialization
The modification to increment out_bias by 1.0 appears to be testing the bias deserialization fix. However, we should ensure comprehensive test coverage.
Consider adding explicit assertions to verify:
- The bias value is correctly serialized
- The bias value is correctly deserialized
- The model produces expected outputs with modified bias
Example test structure:
def test_bias_serialization(self):
# Arrange
model = get_model_pt(data)
original_bias = model.atomic_model.out_bias.copy()
# Act
model.atomic_model.out_bias += 1.0
serialized = model.serialize()
deserialized = get_model_pt(serialized)
# Assert
np.testing.assert_array_equal(
deserialized.atomic_model.out_bias,
original_bias + 1.0
)✅ Verification successful
Let me check the content of test_change_bias.py since it seems directly relevant to our verification.
Based on the review of both test files, I can now provide the final response:
Bias modification is well-tested across multiple test suites
The modification to increment out_bias by 1.0 in test_ener.py is part of a comprehensive test coverage for bias functionality, complemented by dedicated test cases in test_change_bias.py which includes:
- Testing bias changes with data statistics
- Testing bias changes with system files
- Testing user-defined bias modifications
- Proper serialization/deserialization verification
The current modification serves as an additional verification point in the cross-backend consistency tests.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check test coverage for bias-related functionality
# Look for other bias-related tests and verify coverage
# Search for bias-related test cases
echo "Searching for bias-related tests..."
rg -l 'out_bias|bias_atom' 'source/tests'
# Search for bias-related functionality in implementation
echo "Searching for bias implementation..."
rg -l 'out_bias|bias_atom' 'source' --glob '!tests/**'
Length of output: 3969
Script:
#!/bin/bash
# Check the content of test_change_bias.py
echo "Content of test_change_bias.py:"
cat source/tests/pt/test_change_bias.py
# Also check test_ener.py to understand the full context
echo -e "\nContent of test_ener.py:"
cat source/tests/consistent/model/test_ener.py
Length of output: 18917
deepmd/tf/model/model.py (4)
19-21: Importing GLOBAL_NP_FLOAT_PRECISION is appropriate
The import of GLOBAL_NP_FLOAT_PRECISION from deepmd.env is necessary for specifying the data type in subsequent NumPy array initializations.
812-822: Correct adjustment of bias_atom_e during deserialization
The code properly adjusts bias_atom_e by adding the reshaped out_bias during deserialization, ensuring the model variables are correctly updated without modifying the original data structures.
831-834: Explicitly handling unsupported keys enhances error reporting
By checking for non-empty atom_exclude_types and pair_exclude_types and raising NotImplementedError, the code now explicitly informs users when they attempt to use unsupported features, improving clarity and maintainability.
867-877: Proper handling of bias_atom_e and out_bias during serialization
The code correctly reshapes bias_atom_e to form out_bias, resets bias_atom_e to zeros with appropriate dimensions, and initializes out_bias when bias_atom_e is not present. This ensures consistency in the serialized model data.
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (1)
source/tests/consistent/model/test_ener.py (1)
144-146: Consider adding a test for out_bias serialization/deserialization.Since this change is related to fixing model out_bias deserialization, it would be beneficial to add explicit tests verifying that the out_bias values are correctly preserved after serialization and deserialization cycles.
Would you like me to help generate test cases for verifying out_bias serialization/deserialization?
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📒 Files selected for processing (1)
source/tests/consistent/model/test_ener.py(1 hunks)
🔇 Additional comments (1)
source/tests/consistent/model/test_ener.py (1)
144-146: LGTM! Uniform initialization of out_bias implemented correctly.
The suggested initialization of out_bias using uniform_() has been implemented as discussed in the previous review.
Signed-off-by: Jinzhe Zeng <[email protected]>
per discussion
Summary by CodeRabbit
New Features
PTbackend in the energy model tests.Bug Fixes
Documentation