fix(pt): make int rcut safe after jit op
#4222
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📝 WalkthroughWalkthroughThe changes involve modifications to multiple classes across several files, primarily focusing on ensuring that the Changes
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (1)
deepmd/pt/utils/neighbor_stat.py (1)
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1-195: Summary: Performance optimizations introduced with potential compatibility concerns.This PR introduces several performance optimizations to the
NeighborStatclass:
- JIT compilation of the
NeighborStatOPinstance.- Use of
AutoBatchSizefor potential memory and performance improvements.- Explicit type conversion for
rcutinNeighborStatOP.These changes align well with the PR objective to make int
rcutsafe after JIT op. However, there are two areas that require attention:
- The parameter name change from
mixed_typetomixed_typesmay break backward compatibility.- The performance impact of using
AutoBatchSizeshould be verified, especially for large datasets.Please address these concerns and consider adding appropriate documentation or deprecation warnings if necessary.
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📒 Files selected for processing (1)
- deepmd/pt/utils/neighbor_stat.py (1 hunks)
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🔇 Additional comments (5)
deepmd/pt/utils/neighbor_stat.py (5)
47-47: LGTM: Explicit float conversion ensures type consistency.The explicit conversion of
rcutto float is a good practice. It ensures type consistency forself.rcut, which is crucial for numerical operations and aligns with the PR objective to make intrcutsafe after jit op.
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131-135: Verify backward compatibility of parameter name change.The parameter
mixed_typehas been renamed tomixed_typesin theNeighborStatclass signature. While this improves consistency with theNeighborStatOPclass, it may break backward compatibility for existing code that uses this class.Please ensure that this change doesn't break any existing code. Consider adding a deprecation warning if backward compatibility is a concern.
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140-142: LGTM: JIT compilation improves performance.The use of
torch.jit.scriptto compile theNeighborStatOPinstance is a good optimization. This aligns with the PR objective of making the operation safe after JIT compilation and can potentially improve performance.
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144-172: Verify the impact ofAutoBatchSizeusage in theiteratormethod.The
iteratormethod has been significantly modified to useAutoBatchSize. While this can help manage memory usage and performance, it's important to ensure that the functionality remains the same and that performance is indeed improved.Please provide benchmarks or test results demonstrating the performance impact of this change, especially for large datasets.
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174-195: LGTM: New_executemethod handles tensor conversions correctly.The new
_executemethod effectively handles the conversion between NumPy arrays and PyTorch tensors, which is necessary for using the JIT-compiled operation. The method also correctly handles the optionalcellparameter.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4222 +/- ##
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- Coverage 84.55% 84.55% -0.01%
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Files 537 537
Lines 51237 51237
Branches 3047 3047
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- Hits 43324 43322 -2
- Misses 6965 6967 +2
Partials 948 948 ☔ View full report in Codecov by Sentry. |
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Does it only affect neighbor stat? Does it affect dp freeze? |
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I've tried adding type annotation So maybe we can not avoid explictly setting float in each descriptor before PyTorch fix this bugs. See pytorch/issues/130682. |
iProzd
requested review from
wanghan-iapcm and
njzjz
and removed request for
njzjz
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (6)
source/tests/pt/model/test_jit.py (1)
148-163: Consider adding a docstring to clarify test purpose.The setUp implementation correctly tests integer
rcuthandling by converting the values explicitly. This directly addresses the issue #3974 where integerrcutvalues were causing runtime errors.Consider adding a docstring to make the test's purpose more explicit:
class TestEnergyModelDPA2IntRcut(unittest.TestCase, JITTest): + """Test that the model handles integer rcut values correctly. + + This test ensures that the model works with integer rcut values, + addressing the issue where torch.jit.script would fail when rcut + was not explicitly a float. + """ def setUp(self):deepmd/pt/model/descriptor/se_r.py (1)
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31-76: Consider adding type hints forrcutparameters.While the float conversion fixes the immediate issue, adding type hints would provide better documentation and enable static type checking.
def __init__( self, - rcut, - rcut_smth, + rcut: Union[int, float], + rcut_smth: Union[int, float], sel, neuron=[25, 50, 100],deepmd/pt/model/descriptor/repformers.py (1)
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42-196: Consider adding type hints forrcutparametersTo improve type safety and documentation, consider adding type hints for
rcutandrcut_smthparameters and updating their docstring descriptions.def __init__( self, - rcut, - rcut_smth, + rcut: float, + rcut_smth: float, sel: int, ntypes: int, nlayers: int = 3, g1_dim=128,Also update the docstring:
Parameters ---------- - rcut : float + rcut : float The cut-off radius. - rcut_smth : float + rcut_smth : float Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth.deepmd/pt/model/descriptor/se_t.py (3)
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432-448: Consider adding more type hints for better JIT compatibility.While the class uses some type hints, adding more explicit type hints for class attributes and method parameters would improve static type checking and JIT compatibility. This is especially important given that the PR aims to fix JIT-related issues.
Consider adding type hints for class attributes:
class DescrptBlockSeT(DescriptorBlock): ndescrpt: Final[int] __constants__: ClassVar[list] = ["ndescrpt"] + rcut: Final[float] + rcut_smth: Final[float] + neuron: Final[list[int]] + filter_neuron: Final[list[int]] + set_davg_zero: Final[bool] + activation_function: Final[str]
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676-684: Add input validation for tensor shapes and types.The forward method accepts multiple tensor inputs but lacks explicit validation of their shapes and types. Adding validation would help catch errors earlier and provide clearer error messages.
Consider adding validation at the start of the forward method:
def forward( self, nlist: torch.Tensor, extended_coord: torch.Tensor, extended_atype: torch.Tensor, extended_atype_embd: Optional[torch.Tensor] = None, mapping: Optional[torch.Tensor] = None, ): + # Validate input tensors + if not torch.is_tensor(nlist) or not torch.is_tensor(extended_coord) or not torch.is_tensor(extended_atype): + raise TypeError("Input arguments must be torch.Tensor") + if len(nlist.shape) != 3: + raise ValueError(f"Expected nlist to have 3 dimensions, got {len(nlist.shape)}") + if len(extended_coord.shape) != 2 or extended_coord.shape[1] % 3 != 0: + raise ValueError(f"Invalid shape for extended_coord: {extended_coord.shape}") del extended_atype_embd, mapping
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587-605: Enhance path handling in compute_input_stats.The method handles paths but could benefit from more robust validation and error handling for file system operations.
Consider adding path validation and error handling:
def compute_input_stats( self, merged: Union[Callable[[], list[dict]], list[dict]], path: Optional[DPPath] = None, ): + # Validate path if provided + if path is not None: + try: + if not path.parent.exists(): + raise ValueError(f"Parent directory does not exist: {path.parent}") + if path.exists() and not path.is_dir(): + raise ValueError(f"Path exists but is not a directory: {path}") + except Exception as e: + raise RuntimeError(f"Failed to validate path: {e}") from e env_mat_stat = EnvMatStatSe(self) if path is not None: path = path / env_mat_stat.get_hash()
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📒 Files selected for processing (9)
- deepmd/pt/model/atomic_model/pairtab_atomic_model.py (1 hunks)
- deepmd/pt/model/descriptor/repformer_layer.py (1 hunks)
- deepmd/pt/model/descriptor/repformers.py (1 hunks)
- deepmd/pt/model/descriptor/se_a.py (1 hunks)
- deepmd/pt/model/descriptor/se_atten.py (1 hunks)
- deepmd/pt/model/descriptor/se_r.py (1 hunks)
- deepmd/pt/model/descriptor/se_t.py (1 hunks)
- deepmd/pt/model/descriptor/se_t_tebd.py (1 hunks)
- source/tests/pt/model/test_jit.py (1 hunks)
🧰 Additional context used
🔇 Additional comments (14)
source/tests/pt/model/test_jit.py (2)
147-147: LGTM! Class structure follows established patterns.The test class correctly inherits from both
unittest.TestCaseandJITTest, maintaining consistency with other test classes in the file.
165-166: LGTM! Proper cleanup implementation.The tearDown method correctly delegates to the parent class, maintaining consistent cleanup behavior across all test classes.
deepmd/pt/model/descriptor/se_r.py (2)
77-78: LGTM! Type safety improvement forrcutparameters.The explicit float conversion addresses the issue #3974 by ensuring that integer values for
rcutandrcut_smthare safely converted to floats.
77-78: Verify similar patterns in other descriptor classes.Let's check if other descriptor classes need similar float conversion for
rcutparameters.✅ Verification successful
Let me gather more information about the
rcut_smthparameter since we see consistent float conversion forrcutacross descriptor classes.
Float conversion is consistently implemented across descriptor classes
The verification shows that both
rcutandrcut_smthparameters are consistently converted to float across all descriptor classes that use them. Specifically:
- All 7 descriptor classes that use
rcutproperly convert it to float- All 7 descriptor classes that use
rcut_smthproperly convert it to float- The classes are: se_t_tebd, se_t, se_r, se_atten, se_a, repformers, and repformer_layer
The float conversion pattern in the reviewed file matches the pattern used throughout the codebase, confirming this is the correct implementation.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for descriptor classes that use rcut parameter echo "Searching for descriptor classes with rcut parameter..." rg -l "def __init__.*rcut" "deepmd/pt/model/descriptor/" # Search for float conversions of rcut in those files echo -e "\nChecking float conversion patterns..." rg "rcut\s*=\s*float\(" "deepmd/pt/model/descriptor/" # Look for potential type-related errors with rcut echo -e "\nChecking for potential type-related errors..." rg "TypeError.*rcut" "deepmd/pt/model/descriptor/"Length of output: 975
Script:
#!/bin/bash # Search for rcut_smth parameter in descriptor classes echo "Searching for rcut_smth parameter usage..." rg "rcut_smth" "deepmd/pt/model/descriptor/" # Search for float conversions of rcut_smth echo -e "\nChecking float conversion patterns for rcut_smth..." rg "rcut_smth\s*=\s*float\(" "deepmd/pt/model/descriptor/" # Search for __init__ methods with rcut_smth parameter echo -e "\nChecking __init__ methods with rcut_smth..." rg "def __init__.*rcut_smth" "deepmd/pt/model/descriptor/"Length of output: 8870
deepmd/pt/model/atomic_model/pairtab_atomic_model.py (2)
77-78: LGTM: Type safety fix forrcutparameterThe explicit float conversion of
rcutensures type safety and fixes the JIT compilation issue. This change aligns with the parameter's documented type in the class docstring.
77-78: Verify type safety in dependent codeThe
rcutparameter is used in energy calculations and serialization. Let's verify there are no other type-related issues in the codebase.✅ Verification successful
Based on the search results, I can now generate the final response since I have enough information about the usage of
rcutacross the codebase.Type safety is properly enforced for
rcutparameterThe codebase consistently handles
rcutparameter type safety through:
- Explicit float type casting in PyTorch models (e.g.
self.rcut = float(rcut))- Proper type declarations in TensorFlow models using
GLOBAL_TF_FLOAT_PRECISION- Consistent float handling in utility functions and descriptors
- Default value of 6.0 is defined as float in argument checks
- Proper numeric comparisons in distance calculations (e.g.
rr >= self.rcut)The type conversion in
pairtab_atomic_model.pyaligns with the codebase's type safety practices and has no negative impact.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for other usages of rcut parameter that might need similar type safety fixes rg -g '!*.pyc' -g '!*test*' -A 2 -B 2 '\brcut\b.*=|=.*\brcut\b'Length of output: 89019
deepmd/pt/model/descriptor/repformers.py (2)
196-197: LGTM: Type safety fix forrcutparametersThe explicit float conversion of
rcutandrcut_smthparameters addresses the type safety issue mentioned in #3974, ensuring compatibility withtorch.jit.script.
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290-291: Verify type compatibility in dependent componentsThe float-converted
rcutparameters are used inRepformerLayerandprod_env_mat. Let's verify that these components handle float types correctly.Also applies to: 449-456
deepmd/pt/model/descriptor/se_a.py (2)
398-399: LGTM! Type safety fix implemented correctly.The explicit float conversion of
rcutandrcut_smthparameters addresses the runtime error issue (#3974) when integer values are provided.
Line range hint
607-613: Verify the fix resolves the runtime error.The type conversion at initialization should prevent the runtime error in
prod_env_mat. Let's verify this by checking for any remaining integerrcutusage in the codebase.✅ Verification successful
Let me generate a new script to properly check for integer
rcutassignments using a different regex pattern.The previous results from
ast-grepshowed that in the PyTorch implementation (deepmd/pt/), allrcutassignments are properly usingfloat()conversion. However, we need to verify there are no integer assignments in function calls or other contexts that might bypass this protection.
The runtime error fix is properly implemented
Based on the verification results:
- In the PyTorch implementation (
deepmd/pt/), allrcutassignments are properly usingfloat()conversion, including the one inse_a.py.- The only integer
rcutassignments found are in test files, which is acceptable as these values are converted to float when passed to the actual implementation.- No problematic integer
rcutusage was found in function calls that could bypass the float conversion.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for any direct assignments to rcut that might bypass the float conversion ast-grep --pattern 'self.rcut = $_' # Search for any remaining integer rcut parameters in function calls rg 'rcut\s*=\s*\d+(?![.])'Length of output: 5896
Script:
#!/bin/bash # Search for rcut assignments with integer values (no decimal point) # Using a simpler pattern that matches digits between = and either whitespace, comma, or end of line rg 'rcut\s*=\s*\d+(?:\s|,|$)' # Also check for rcut parameters in function calls with integer values rg 'rcut\s*=\s*\d+\s*[,)]'Length of output: 396
deepmd/pt/model/descriptor/se_t.py (1)
449-450: LGTM: Type conversion fix properly addresses the JIT compatibility issue.The explicit float conversion of
rcutandrcut_smthparameters addresses the issue #3974 where integerrcutvalues were causing runtime errors during JIT compilation.deepmd/pt/model/descriptor/se_t_tebd.py (1)
515-516: LGTM! The float casting fixes the JIT compatibility issue.The explicit conversion of
rcutandrcut_smthto float addresses the runtime error when integer values are provided. This fix aligns with the PR objectives and ensures type safety during JIT operations.Let's verify that this fix resolves the issue by checking for any remaining integer type annotations or similar parameters in the codebase:
✅ Verification successful
Let me try different shell scripts to verify the changes properly.
The previous scripts had issues with file type recognition. These new scripts should help us verify:
- How rcut parameters are defined across the codebase
- How rcut assignments are handled in other files
- Any JIT-related patterns or issues
- Other float casting patterns that might be relevant
Based on the search results, I can now generate the final response:
The float casting fix is correctly implemented and consistent across the codebase
The verification shows that:
- All descriptor implementations in PyTorch consistently cast
rcutandrcut_smthto float, as seen in multiple files:
deepmd/pt/model/descriptor/se_t.pydeepmd/pt/model/descriptor/se_a.pydeepmd/pt/model/descriptor/se_r.pydeepmd/pt/model/descriptor/repformers.pydeepmd/pt/model/descriptor/repformer_layer.py- The JIT-related search confirms this is a necessary fix for PyTorch JIT compatibility, with numerous JIT-scripted models in the codebase.
- The float casting pattern is consistently used across the codebase for similar parameters that need type safety during JIT operations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for other potential rcut parameters that might need similar float casting rg --type python "def.*\(.*rcut.*:.*int.*\)" # Search for any TODO comments related to type casting or JIT compatibility rg --type python "TODO.*[jJ][iI][tT]|TODO.*type.*cast" # Search for similar parameter patterns that might need float casting ast-grep --pattern 'def $FUNC($PARAM: int, $REST) { $$$ rcut = $PARAM $$$ }'Length of output: 244
Script:
#!/bin/bash # Search for rcut parameter definitions in Python files rg "def.*\(.*rcut.*\)" --type=py # Look for similar rcut assignments in Python files rg "self\.rcut\s*=" --type=py -A 1 # Search for any JIT-related comments or issues rg "jit|JIT" --type=py # Find other potential float casting patterns rg "float\(" --type=pyLength of output: 46334
deepmd/pt/model/descriptor/se_atten.py (1)
152-153: LGTM! Type safety enhancement forrcutparameters.The explicit float casting of
rcutandrcut_smthparameters effectively addresses the type mismatch issue when integer values are provided. This change ensures type safety without altering the underlying logic.deepmd/pt/model/descriptor/repformer_layer.py (1)
608-609: LGTM: Safe type conversion for cutoff parameters.The explicit conversion of
rcutandrcut_smthto float during initialization ensures type safety and fixes the issue with integer inputs. This change aligns with similar updates across the codebase and properly addresses issue #3974.
Fix #3974.
Summary by CodeRabbit
New Features
rcutandrcut_smthparameters are always floats.Bug Fixes
Documentation