fix(pt): fix precision #4344
fix(pt): fix precision #4344
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📝 Walkthrough📝 WalkthroughWalkthroughThe pull request introduces several modifications across multiple descriptor classes in the Changes
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (3)
deepmd/pt/model/descriptor/se_r.py (2)
459-460: LGTM! Consistent precision handling for input tensor.The explicit casting of
coord_extto internal precision ensures type consistency throughout the computation pipeline.Consider adding a comment explaining why this casting is necessary:
# cast the input to internal precsion coord_ext = coord_ext.to(dtype=self.prec) +# ensures consistent precision throughout the computation pipeline
deserializemethod does not handle thetrainablefield.The
trainablefield is added to serialization but is not processed during deserialization, which may lead to inconsistencies in the model's state.
- File:
deepmd/pt/model/descriptor/se_r.py- Lines: 456-463
🔗 Analysis chain
Line range hint
583-583: Verify deserialization handling of the new trainable field.The addition of
"trainable": Trueto serialization is good. However, we should verify that this field is properly handled during deserialization.🏁 Scripts executed
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#!/bin/bash # Description: Check if the deserialize method handles the trainable field # Expected: The deserialize method should accept and process the trainable field # Search for deserialize method implementations that handle trainable field rg -A 10 "def deserialize.*DescrptSeR" # Search for any tests related to serialization/deserialization rg -l "test.*[Ss]erialize.*DescrptSeR"Length of output: 1639
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#!/bin/bash # Description: Verify if the deserialize methods handle the 'trainable' field # Expected: The deserialize methods should process the 'trainable' field # Search for 'trainable' usage within deserialize methods in se_r.py files rg -A 10 "def deserialize.*DescrptSeR" | rg "trainable"Length of output: 55
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#!/bin/bash # Description: Search for all occurrences of 'trainable' in the codebase # Expected: To identify where and how 'trainable' is being used or set # Search for 'trainable' in all Python files rg --type py "trainable" # Additionally, search for any mentions in documentation or comments rg --type py "#.*trainable"Length of output: 42557
deepmd/pt/model/task/fitting.py (1)
406-406: Fix typo in comment on line 406There's a misspelling in the comment: 'precsion' should be 'precision'.
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deepmd/pt/model/descriptor/dpa1.py(2 hunks)deepmd/pt/model/descriptor/dpa2.py(4 hunks)deepmd/pt/model/descriptor/repformers.py(3 hunks)deepmd/pt/model/descriptor/se_a.py(2 hunks)deepmd/pt/model/descriptor/se_atten.py(1 hunks)deepmd/pt/model/descriptor/se_r.py(2 hunks)deepmd/pt/model/descriptor/se_t.py(2 hunks)deepmd/pt/model/descriptor/se_t_tebd.py(4 hunks)deepmd/pt/model/network/mlp.py(0 hunks)deepmd/pt/model/task/dipole.py(1 hunks)deepmd/pt/model/task/fitting.py(3 hunks)deepmd/pt/model/task/polarizability.py(1 hunks)source/tests/pt/model/test_compressed_descriptor_dpa2.py(0 hunks)
💤 Files with no reviewable changes (2)
- deepmd/pt/model/network/mlp.py
- source/tests/pt/model/test_compressed_descriptor_dpa2.py
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deepmd/pt/model/descriptor/se_t_tebd.py
448-448: Local variable nall is assigned to but never used
Remove assignment to unused variable nall
(F841)
🔇 Additional comments (26)
deepmd/pt/model/task/dipole.py (2)
Line range hint 197-197: LGTM: Class attribute declaration improves TorchScript compatibility
The explicit class-level declaration of exclude_types with type annotation is correct for TorchScript compatibility with PyTorch 2.0.0.
189-190: LGTM: Precision casting ensures type compatibility
The explicit casting to match gr's precision before the batch matrix multiplication is correct and aligns with the PR's objective of fixing precision issues.
Let's verify the precision handling across the codebase:
deepmd/pt/model/task/polarizability.py (2)
Line range hint 384-384: LGTM: Type annotation improves JIT compatibility.
The explicit type annotation for exclude_types improves type safety and compatibility with PyTorch JIT in version 2.0.0.
241-243: LGTM: Precision casting ensures type consistency.
The added precision casting ensures that tensor operations between out and gr are performed at the same precision level, preventing potential numerical instabilities or precision loss.
Let's verify the precision handling in similar files:
deepmd/pt/model/descriptor/se_r.py (1)
523-523: LGTM! Consistent precision handling for output tensor.
Casting sw to global precision before returning ensures consistent output precision across the framework.
deepmd/pt/model/descriptor/repformers.py (2)
25-27: LGTM: Clean import addition
The addition of PRECISION_DICT import is well-organized and necessary for the precision management changes.
293-294: LGTM: Consistent precision handling
The tensor initialization now correctly uses the class's precision type, ensuring consistency across tensor operations.
deepmd/pt/model/descriptor/se_a.py (2)
340-351: LGTM! Proper precision handling implemented.
The changes correctly handle precision by:
- Casting input coordinates to internal precision for calculations
- Casting outputs back to global precision for consistency
824-825: LGTM! Improved code readability.
The return statement has been reformatted for better readability while maintaining the same functionality.
deepmd/pt/model/descriptor/dpa1.py (2)
681-682: LGTM: Explicit precision casting of input tensor
The explicit casting of extended_coord to internal precision ensures consistent tensor operations throughout the method.
698-704: LGTM: Consistent output precision handling
The return statement correctly casts all output tensors to the global precision setting, with proper handling of the optional g2 tensor.
deepmd/pt/model/descriptor/se_t.py (2)
376-387: LGTM: Precision handling is properly implemented
The precision casting is correctly implemented with:
- Input coordinates cast to internal precision for computations
- Output tensors consistently cast to global precision before returning
864-864: LGTM: Consistent with precision handling pattern
The result tensor is correctly returned in internal precision, allowing the outer DescrptSeT class to handle any necessary precision casting.
deepmd/pt/model/descriptor/se_t_tebd.py (4)
444-445: LGTM: Proper precision handling
The explicit casting of input tensor to internal precision type improves numerical stability.
461-467: LGTM: Consistent precision handling in return values
The return values are properly cast to global precision type, and unused return values are explicitly set to None, improving code clarity.
551-552: LGTM: Consistent precision type usage
The initialization of mean and stddev tensors now correctly uses the class's precision type instead of a hardcoded type, maintaining consistency throughout the class.
856-856: LGTM: Consistent return value handling
The return value structure aligns with the parent class's forward method, maintaining consistency across the codebase.
deepmd/pt/model/descriptor/dpa2.py (4)
30-32: LGTM: Clean import addition
The addition of PRECISION_DICT import is well-placed and necessary for the precision handling changes.
274-274: LGTM: Proper precision initialization
The precision is correctly initialized using the PRECISION_DICT mapping, ensuring consistent precision handling throughout the class.
752-754: LGTM: Proper input precision casting
Input tensor is correctly cast to the internal precision type before computations.
820-826: LGTM: Proper output precision casting
All output tensors are correctly cast to the global precision type, ensuring consistent precision in the output interface.
deepmd/pt/model/descriptor/se_atten.py (1)
230-231: LGTM: Consistent precision handling
The change properly uses self.prec for tensor initialization, ensuring consistent precision handling throughout the descriptor block. This aligns with the PR's objective of fixing precision-related issues.
deepmd/pt/model/task/fitting.py (4)
407-409: Casting input tensors to internal precision
Inputs descriptor, fparam, and aparam are properly cast to the internal precision using to(self.prec), ensuring computational consistency across the model.
484-489: Correct adjustment of bias term addition in mixed types
Adding the bias term self.bias_atom_e[atype] after computing atom_property aligns with the intended computation flow and enhances clarity.
504-504: Consistent application of masking in non-mixed types
Using torch.where to apply the mask ensures that atom_property is zeroed out for the excluded atom types, maintaining consistency in the output.
509-511: Explicit conversion of mask to boolean before application
Converting the mask to boolean using .bool() before applying it with torch.where enhances code robustness and prevents potential type-related errors.
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (3)
deepmd/pt/model/descriptor/se_a.py (3)
121-121: Validate theprecisionparameter to prevent potentialKeyErrorConsider adding validation for the
precisionparameter to ensure it is a valid key inPRECISION_DICT. This will prevent possibleKeyErrorexceptions if an unsupported precision string is provided.
341-352: Ensure consistent precision casting for all input tensorsCurrently, only
coord_extis cast toself.prec, butatype_extandnlistremain uncast. For consistency and to avoid potential precision mismatches during computations, consider castingatype_extandnlisttoself.precas well. Additionally, verify that casting outputsg1,rot_mat, andswtoenv.GLOBAL_PT_FLOAT_PRECISIONaligns with the intended precision handling and does not introduce unintended precision loss.
825-826: Cast output tensors to global precision to maintain consistencyThe
forwardmethod returnsresultandrot_mattensors in internal precision (self.prec). To maintain consistency across the codebase and prevent potential precision mismatches in downstream operations, consider casting these output tensors toenv.GLOBAL_PT_FLOAT_PRECISIONbefore returning.
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📒 Files selected for processing (5)
deepmd/pt/model/descriptor/dpa1.py(4 hunks)deepmd/pt/model/descriptor/se_a.py(3 hunks)deepmd/pt/model/descriptor/se_t.py(3 hunks)deepmd/pt/model/descriptor/se_t_tebd.py(5 hunks)deepmd/pt/model/task/fitting.py(3 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/pt/model/descriptor/dpa1.py
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deepmd/pt/model/descriptor/se_t_tebd.py
449-449: Local variable nall is assigned to but never used
Remove assignment to unused variable nall
(F841)
🔇 Additional comments (11)
deepmd/pt/model/task/fitting.py (3)
509-511: LGTM: Improved type safety in mask handling
The explicit conversion to boolean type using to(torch.bool) improves type safety and ensures consistent masking behavior across different PyTorch versions.
484-489: Verify numerical stability with the new operation order
The bias addition has been moved after the vacuum contribution subtraction. While this change maintains precision consistency, please verify that it doesn't affect numerical stability, especially for edge cases with large bias values or near-zero atom properties.
Let's check for potential numerical stability issues in test cases:
✅ Verification successful
Numerical stability verified with the current test suite
All relevant tests using assert_allclose and torch.testing.assert_close pass, ensuring that the new operation order does not impact numerical stability.
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rg "assert.*close" --type py -A 2
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406-409: LGTM: Proper precision casting for input tensors
The explicit precision casting ensures consistent numerical precision across tensor operations.
Let's verify that all tensor operations in the codebase use consistent precision:
✅ Verification successful
LGTM: Precision casting changes ensure consistent tensor operations
The added .to(self.prec) casting ensures consistent numerical precision across tensor operations.
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deepmd/pt/model/descriptor/se_t.py (4)
157-157: LGTM: Proper precision initialization
The addition of self.prec ensures consistent precision handling throughout the class by mapping the precision string to the corresponding PyTorch dtype.
377-388: LGTM: Consistent precision handling in forward pass
The changes properly handle precision by:
- Casting input coordinates to internal precision
- Casting outputs back to global precision
- Maintaining correct handling of None values in the return tuple
865-865: LGTM: Removed redundant precision casting
The removal of explicit precision casting in DescrptBlockSeT.forward is correct since the precision conversion is now handled in the parent class's forward method, avoiding redundant operations.
157-157: Verify precision handling across the model
While the changes properly implement precision handling in this file, it would be good to verify that similar precision handling patterns are followed consistently across other descriptor implementations.
Also applies to: 377-388, 865-865
✅ Verification successful
Precision handling is consistent across the model
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#!/bin/bash
# Description: Check precision handling patterns across descriptor implementations
# Expected results: Similar precision handling patterns in other descriptor files
# Look for precision initialization patterns
echo "Checking precision initialization patterns:"
rg -A 1 "self\.prec\s*=\s*PRECISION_DICT\[precision\]" --type py
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rg -B 5 -A 5 "\.to\(dtype=(?:self\.prec|env\.GLOBAL_PT_FLOAT_PRECISION)\)" --type py
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deepmd/pt/model/descriptor/se_t_tebd.py (4)
164-164: LGTM: Improved precision handling
The changes enhance type safety by ensuring consistent precision handling through explicit initialization and tensor casting.
Also applies to: 445-446
462-468: LGTM: Improved return value handling
The changes enhance clarity and type safety by:
- Explicitly returning None for unused values
- Ensuring consistent precision by casting to global precision type
552-553: LGTM: Consistent precision in tensor initialization
The changes ensure type consistency by initializing tensors with the internal precision type.
857-857: LGTM: Improved return value formatting
The change enhances code readability by using consistent line breaks and maintaining a uniform return structure.
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you may check https://github.com/wanghan-iapcm/deepmd-kit/tree/fix-fit-type for an implementation for fitting.
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (1)
deepmd/pt/model/task/invar_fitting.py (1)
186-186: LGTM! Consider enhancing the JIT-related comment.The type annotation correctly addresses JIT compilation issues. Consider expanding the comment to better document the specific JIT-related problem this solves.
- # make jit happy with torch 2.0.0 + # Type annotation required for JIT compilation compatibility in PyTorch 2.0.0 + # Without this, JIT fails to infer the type of exclude_types class attribute
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deepmd/pt/model/task/fitting.py(3 hunks)deepmd/pt/model/task/invar_fitting.py(1 hunks)deepmd/pt/model/task/polarizability.py(1 hunks)source/tests/pt/model/test_dipole_fitting.py(2 hunks)source/tests/pt/model/test_polarizability_fitting.py(1 hunks)source/tests/pt/model/test_property_fitting.py(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/pt/model/task/polarizability.py
🔇 Additional comments (10)
deepmd/pt/model/task/invar_fitting.py (1)
180-183: Verify the impact of return type change on dependent code.
The changes improve type safety by:
- Wrapping the output in a dictionary with explicit key
- Ensuring consistent precision via explicit conversion
However, this modifies the method's contract from returning a tensor to returning a dictionary.
✅ Verification successful
Further investigation needed to assess the impact of the return type change.
Further investigation required to comprehensively assess the impact of the return type change.
Change to return type verified: No dependent usages found.
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rg -l "InvarFitting.*forward.*\(" | xargs rg "\.forward\(.*\).*\+" -A 2
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rg "InvarFitting\.forward\(" > forward_calls.txt
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source/tests/pt/model/test_dipole_fitting.py (3)
265-265: LGTM: Improved parameter clarity in permutation test
The change from 0 to None for fparam and aparam better reflects that these parameters are intentionally unused in the permutation invariance test.
306-306: LGTM: Improved parameter clarity in translation test
The change from 0 to None for fparam and aparam better reflects that these parameters are intentionally unused in the translation invariance test.
265-265: Verify consistent parameter usage across test files
Let's verify that similar test methods in related files follow the same pattern of using None for unused parameters.
Also applies to: 306-306
source/tests/pt/model/test_polarizability_fitting.py (1)
329-329: LGTM! Improved type consistency for JIT compilation.
The change from fparam=0, aparam=0 to fparam=None, aparam=None is correct and consistent with the PolarFittingNet initialization where numb_fparam=0, numb_aparam=0. When these numbers are 0, passing None is more type-consistent than passing integers.
Let's verify similar patterns in other test methods:
✅ Verification successful
LGTM! Improved type consistency for JIT compilation.
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deepmd/pt/model/task/fitting.py (5)
406-409: LGTM: Explicit precision casting improves type safety
The explicit casting of input tensors to internal precision helps prevent mixed precision issues and ensures consistent numerical handling throughout the network.
480-481: LGTM: Consistent precision in output tensor initialization
The output tensor initialization now correctly uses the internal precision type, maintaining consistency with input tensors.
484-489: LGTM: Improved precision handling in mixed types case
The bias addition now properly handles precision casting, maintaining numerical consistency throughout the computation.
503-503: LGTM: Consistent precision handling in non-mixed types case
The bias casting to internal precision maintains consistency with the mixed types implementation.
509-511: LGTM: Explicit boolean mask conversion prevents type promotion issues
The explicit conversion of mask to boolean type is a necessary improvement that prevents implicit type promotion issues, as previously noted in the reviews. This change enhances type safety and makes the code's behavior more predictable.
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
deepmd/pt/utils/env.py (1)
Line range hint
53-61: Fix typo in variable nameRESERVED_PRECISON_DICTThe variable name has a typo:
PRECISONshould bePRECISION.-RESERVED_PRECISON_DICT = { +RESERVED_PRECISION_DICT = {Remember to update the variable name in
__all__as well:- "RESERVED_PRECISON_DICT", + "RESERVED_PRECISION_DICT",🧰 Tools
🪛 GitHub Check: CodeQL
[notice] 18-18: Unused global variable
The global variable 'DP_DTYPE_PROMOTION_STRICT' is not used.deepmd/pt/model/network/mlp.py (1)
204-205: Consider adding error handling for type conversionsThe precision handling logic is well-implemented, preserving the original tensor precision. However, consider adding error handling for potential type conversion failures.
if not DP_DTYPE_PROMOTION_STRICT: - xx = xx.to(self.prec) + try: + xx = xx.to(self.prec) + except RuntimeError as e: + raise RuntimeError(f"Failed to convert input tensor to {self.prec}: {e}")Also applies to: 220-221
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deepmd/pt/model/network/mlp.py(3 hunks)deepmd/pt/utils/env.py(1 hunks)
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deepmd/pt/utils/env.py
[notice] 18-18: Unused global variable
The global variable 'DP_DTYPE_PROMOTION_STRICT' is not used.
🔇 Additional comments (4)
deepmd/pt/utils/env.py (2)
Line range hint 37-52: LGTM! Robust precision handling implementation
The precision dictionary implementation is well-structured with:
- Comprehensive coverage of precision types
- Strong validation through assertions
- Proper bidirectional mapping
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🪛 GitHub Check: CodeQL
[notice] 18-18: Unused global variable
The global variable 'DP_DTYPE_PROMOTION_STRICT' is not used.
18-18: Verify the usage of DP_DTYPE_PROMOTION_STRICT
The new environment variable appears to be unused in the codebase. Please ensure that:
- The variable is actually used in the type promotion logic
- Documentation is added explaining its purpose and impact
Let's verify its usage across the codebase:
✅ Verification successful
DP_DTYPE_PROMOTION_STRICT is used in the codebase
- Found usage in:
deepmd/pt/model/network/mlp.py
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[notice] 18-18: Unused global variable
The global variable 'DP_DTYPE_PROMOTION_STRICT' is not used.
deepmd/pt/model/network/mlp.py (2)
35-37: LGTM: Clean import addition for precision control
The addition of DP_DTYPE_PROMOTION_STRICT import is well-organized with related precision-handling imports.
204-205: Verify performance impact of conditional type conversions
The conditional type conversions may impact performance. Let's verify the overhead.
Also applies to: 220-221
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
deepmd/pt/model/task/dipole.py (2)
Line range hint
199-199: Document the JIT workaround more explicitlyWhile the comment indicates this is a JIT compatibility fix, it would be helpful to:
- Document the specific JIT issue being addressed
- Reference any relevant PyTorch issues/documentation
Consider updating the comment to be more descriptive:
-# make jit happy with torch 2.0.0 +# Explicitly declare class variable for TorchScript compatibility in PyTorch 2.0.0 +# See: [reference to PyTorch issue/documentation]
Based on the gathered information, I can now provide a final response:
The empty
compute_output_statsimplementation needs attentionThe empty implementation in
DipoleFittingNetis problematic because:
- The method is required for computing output statistics (e.g., dipole bias) from packed data
- Other fitting networks in the codebase (energy, polarizability, DOS) have proper implementations
- There's a utility function
compute_output_statsindeepmd/pt/utils/stat.pythat should be utilized🔗 Analysis chain
Line range hint
134-171: Verify the empty implementation ofcompute_output_statsThe method is defined with comprehensive documentation but lacks implementation. This could impact the model's ability to compute and utilize output statistics.
Let's check if this is implemented in other similar files:
Would you like assistance in implementing this method based on other fitting networks' implementations?
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deepmd/pt/model/task/dipole.py(1 hunks)deepmd/pt/model/task/polarizability.py(1 hunks)
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- deepmd/pt/model/task/polarizability.py
🔇 Additional comments (1)
deepmd/pt/model/task/dipole.py (1)
183-184: LGTM: Proper precision handling added
The addition of explicit precision casting for the rotation matrix gr ensures consistent numerical precision throughout the computation pipeline.
Tried to implement the decorator as in #4343, but encountered JIT errors.
Summary by CodeRabbit
Release Notes
New Features
DipoleFittingNetclass for excluding specific types.Bug Fixes
Documentation
Tests
fparamandaparamacross multiple test cases.