feat(pt/dp): support case embedding and sharable fitting #4417
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📝 WalkthroughWalkthroughThe changes in this pull request primarily introduce a new method Changes
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Actionable comments posted: 12
🧹 Outside diff range and nitpick comments (25)
deepmd/dpmodel/fitting/ener_fitting.py (1)
Line range hint
35-78: Consider documenting data ID usage patternsThe addition of
numb_dataidsupport enables new model sharing capabilities. Consider:
- Documenting recommended patterns for data ID assignment
- Adding examples of how to effectively use this feature in multi-task scenarios
- Updating model serialization documentation to reflect this change
source/tests/pt/model/water/multitask_sharefit.json (3)
27-37: Consider optimizing the fitting network architectureThe current configuration has:
numb_dataid: 2correctly matches the number of models- The fitting network uses identical layer sizes [240, 240, 240]
Consider using a progressive reduction in layer sizes (e.g., [240, 180, 120]) which might provide better feature abstraction while reducing the number of parameters.
41-52: Consider differentiating model configurationsBoth models are currently using identical configurations, including the same fitting network dataid (":1"). This setup might not fully leverage the benefits of multitask learning. Consider:
- Using different dataid values (":1" and ":2")
- Adjusting
data_stat_nbatchdifferently for each model based on their respective dataset sizes
63-82: Review loss weighting strategyThe loss configuration shows:
- Identical settings for both models
- High initial force preference (1000 → 1)
- Disabled volume preference
Consider:
- Adjusting preferences based on each model's specific task
- Enabling volume preference for better structural prediction
- Adding a schedule for preference transition
deepmd/pt/model/task/property.py (1)
86-86: Add documentation for the new parameter.The
numb_dataidparameter is missing from the docstring. Please update the class documentation to include:numb_dataid : int, optional Number of data identifiers. Defaults to 0.deepmd/pt/model/task/invar_fitting.py (1)
Line range hint
94-146: Overall implementation looks good but needs documentation.The implementation of
numb_dataidsupport is clean and follows existing patterns. However, please ensure:
- Add docstring updates to document the new
numb_dataidparameter- Update the class docstring to reflect the version 3 compatibility requirement
- Consider adding examples of how to use the new data identification feature
source/tests/universal/dpmodel/fitting/test_fitting.py (1)
Line range hint
42-54: LGTM with a minor suggestion for the docstring.The addition of
numb_dataidis consistent with the PR objectives. Consider making the docstring more explicit about the shared value:- numb_param=0, # test numb_fparam, numb_aparam and numb_dataid together + numb_param=0, # shared value for testing numb_fparam, numb_aparam and numb_dataid🧰 Tools
🪛 Ruff (0.7.0)
39-39: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
deepmd/dpmodel/atomic_model/dp_atomic_model.py (1)
68-73: Add type hints and enhance documentation.The implementation looks good, but could benefit from some improvements:
- def set_dataid(self, data_idx): + def set_dataid(self, data_idx: int) -> None: """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + Parameters + ---------- + data_idx : int + The data identification index to be set """ self.fitting.set_dataid(data_idx)deepmd/dpmodel/atomic_model/make_base_atomic_model.py (1)
71-77: Add type hints and enhance documentation forset_dataid.While the implementation is good, consider these improvements to make the interface more robust:
- Add type hints for the
data_idxparameter- Enhance the docstring with parameter type, valid range, and error conditions
@abstractmethod -def set_dataid(self, data_idx) -> None: +def set_dataid(self, data_idx: int) -> None: """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + Parameters + ---------- + data_idx : int + The index used to identify the data. Must be a non-negative integer. + + Raises + ------ + ValueError + If data_idx is negative or exceeds the maximum allowed value. """ passdeepmd/pt/model/task/ener.py (1)
83-85: Consider documenting version changes and migration pathThe version bump to 3 indicates a breaking change in the serialization format. Consider:
- Documenting the version change in the changelog
- Providing migration instructions for users with existing serialized models
- Adding tests to verify backward compatibility handling
deepmd/dpmodel/fitting/invar_fitting.py (1)
126-126: Document thenumb_dataidparameter in the class docstring.The new parameter
numb_dataidshould be documented in the Parameters section of the class docstring to maintain consistency with the existing comprehensive documentation.Add this to the Parameters section of the docstring:
Parameters ---------- + numb_dataid : int, optional + Number of data identifiers for the fitting process. Defaults to 0. bias_atom Bias for each element.Also applies to: 159-159
deepmd/pt/model/atomic_model/dp_atomic_model.py (1)
96-101: Add error handling for edge cases.The implementation looks good and aligns with the PR objectives. However, consider adding error handling for robustness:
def set_dataid(self, data_idx): """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + Parameters + ---------- + data_idx : int + The data identification index to be set + + Raises + ------ + AttributeError + If fitting_net is None """ + if self.fitting_net is None: + raise AttributeError("Cannot set dataid: fitting_net is None") self.fitting_net.set_dataid(data_idx)The suggested changes:
- Add type hints and parameter documentation
- Add error handling for the case when fitting_net is None
- Document the potential exception
deepmd/dpmodel/atomic_model/pairtab_atomic_model.py (2)
123-130: LGTM! Consider enhancing the documentation.The implementation correctly indicates that data identification is not supported for this model type. However, it would be helpful to extend the docstring to explain why data identification is not supported for the
PairTabAtomicModelclass, helping users understand the limitation.Consider updating the docstring to:
def set_dataid(self, data_idx): """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + This functionality is not supported for PairTabAtomicModel as it uses pre-computed + tabulated values and does not involve a fitting process that would benefit from + data identification. """
123-130: Good architectural alignment with other atomic models.The implementation properly follows the interface contract by implementing the
set_dataidmethod, while clearly indicating its limitations. This maintains consistency with other atomic models while properly handling the specific constraints of thePairTabAtomicModel.source/tests/pt/test_multitask.py (1)
Line range hint
236-273: Consider increasing test coverage for shared fittingWhile the test implementation is correct, running for only 1 step (
numb_steps = 1) might not thoroughly verify the shared fitting behavior, especially parameter sharing during training.Consider:
- Increasing
numb_stepsto ensure parameters remain properly shared throughout training- Adding assertions to verify the shared parameters after multiple training steps
deepmd/dpmodel/atomic_model/linear_atomic_model.py (1)
137-144: Enhance documentation and error handlingWhile the implementation correctly propagates the data identification to sub-models, consider the following improvements:
Enhance the docstring by:
- Specifying the type of
data_idxparameter- Adding a return type annotation (-> None)
- Documenting potential exceptions
Add error handling to gracefully handle potential exceptions from sub-models.
- def set_dataid(self, data_idx): + def set_dataid(self, data_idx: int) -> None: """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + Parameters + ---------- + data_idx : int + The data identification index to be set + + Raises + ------ + ValueError + If data_idx is invalid + RuntimeError + If setting dataid fails for any sub-model """ + if not isinstance(data_idx, int) or data_idx < 0: + raise ValueError(f"data_idx must be a non-negative integer, got {data_idx}") + for model in self.models: - model.set_dataid(data_idx) + try: + model.set_dataid(data_idx) + except Exception as e: + raise RuntimeError(f"Failed to set dataid for model {model}: {str(e)}")deepmd/pt/model/atomic_model/pairtab_atomic_model.py (1)
144-151: Enhance docstring with rationale for unsupported featureThe implementation correctly raises NotImplementedError, but the docstring could be more informative by explaining why data identification is not supported for this model type.
def set_dataid(self, data_idx): """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + This feature is not supported in PairTabAtomicModel as it uses pre-computed + tabulated values for pairwise interactions, which are independent of data + identification. """ raise NotImplementedError( "Data identification not supported for PairTabAtomicModel!" )deepmd/dpmodel/fitting/general_fitting.py (1)
446-458: Consider caching the tiled dataid tensorThe current implementation tiles the dataid tensor for every call. Consider caching the tiled tensor if the shape (nf, nloc) remains constant across multiple calls, which is common in batch processing scenarios.
if self.numb_dataid > 0: assert self.dataid is not None - dataid = xp.tile(xp.reshape(self.dataid, [1, 1, -1]), [nf, nloc, 1]) + # Cache the reshaped dataid + if not hasattr(self, '_reshaped_dataid'): + self._reshaped_dataid = xp.reshape(self.dataid, [1, 1, -1]) + # Tile the cached tensor + dataid = xp.tile(self._reshaped_dataid, [nf, nloc, 1]) xx = xp.concat( [xx, dataid], axis=-1, )deepmd/pt/model/task/fitting.py (3)
218-226: Document the rationale for zeros initializationThere's a commented line suggesting an alternative initialization using an identity matrix. Consider documenting why zeros initialization was chosen over the identity matrix approach, or if the identity matrix approach would be more appropriate.
368-376: Add parameter validation in set_dataidConsider adding validation for the data_idx parameter to ensure it's within the valid range [0, numb_dataid).
def set_dataid(self, data_idx): """ Set the data identification of this fitting net by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. """ + if not 0 <= data_idx < self.numb_dataid: + raise ValueError(f"data_idx must be in range [0, {self.numb_dataid})") self.dataid = torch.eye(self.numb_dataid, dtype=self.prec, device=device)[ data_idx ]
507-519: LGTM: Consider caching the tiled dataid tensorThe dataid concatenation is implemented correctly. However, since the dataid tensor is constant during forward passes, consider caching the tiled version to avoid repeated operations.
if self.numb_dataid > 0: assert self.dataid is not None - dataid = torch.tile(self.dataid.reshape([1, 1, -1]), [nf, nloc, 1]) + if not hasattr(self, '_cached_dataid') or self._cached_dataid.shape[:2] != (nf, nloc): + self._cached_dataid = torch.tile(self.dataid.reshape([1, 1, -1]), [nf, nloc, 1]) + dataid = self._cached_dataid xx = torch.cat( [xx, dataid], dim=-1, )deepmd/dpmodel/model/make_model.py (1)
555-557: Add type hints and documentation for the new method.The new
set_dataidmethod should include type hints and documentation to maintain consistency with the rest of the codebase.- def set_dataid(self, data_idx): - self.atomic_model.set_dataid(data_idx) + def set_dataid(self, data_idx: int) -> None: + """Set the data identifier for the atomic model. + + Parameters + ---------- + data_idx : int + The data identifier to be set on the atomic model. + """ + self.atomic_model.set_dataid(data_idx)deepmd/pt/model/atomic_model/linear_atomic_model.py (2)
161-168: Enhance docstring with type hints and example usage.The docstring should include parameter type hints and an example usage for better clarity.
def set_dataid(self, data_idx): """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + Parameters + ---------- + data_idx : int + The data identification index to be set. + + Example + ------- + >>> model = LinearEnergyAtomicModel(...) + >>> model.set_dataid(0) """
161-168: Consider adding error handling for invalid data_idx.The method should validate the data_idx parameter and handle potential errors from sub-models.
def set_dataid(self, data_idx): """ Set the data identification of this atomic model by the given data_idx, typically concatenated with the output of the descriptor and fed into the fitting net. """ + if not isinstance(data_idx, (int, torch.Tensor)): + raise TypeError(f"data_idx must be an integer or tensor, got {type(data_idx)}") for model in self.models: - model.set_dataid(data_idx) + try: + model.set_dataid(data_idx) + except Exception as e: + raise RuntimeError(f"Failed to set data_idx for model {model.__class__.__name__}: {str(e)}")deepmd/utils/argcheck.py (1)
Line range hint
1436-1469: LGTM! Thenumb_dataidparameter is consistently implemented across fitting functions.The implementation:
- Adds proper documentation explaining the parameter's purpose for multitask models
- Uses consistent default value of 0
- Maintains consistent parameter type (int) and optional status
- Clearly indicates PyTorch-only support
Consider documenting the following in the codebase:
- Examples of how to use data identification embeddings in multitask scenarios
- Best practices for choosing the dimension of data identification embeddings
- Performance implications of using data identification embeddings
Also applies to: 1520-1543, 1575-1593, 1624-1659, 1702-1730
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📒 Files selected for processing (29)
deepmd/dpmodel/atomic_model/dp_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/linear_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/make_base_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/dpmodel/fitting/dipole_fitting.py(3 hunks)deepmd/dpmodel/fitting/dos_fitting.py(3 hunks)deepmd/dpmodel/fitting/ener_fitting.py(3 hunks)deepmd/dpmodel/fitting/general_fitting.py(9 hunks)deepmd/dpmodel/fitting/invar_fitting.py(3 hunks)deepmd/dpmodel/fitting/polarizability_fitting.py(4 hunks)deepmd/dpmodel/fitting/property_fitting.py(3 hunks)deepmd/dpmodel/model/make_model.py(1 hunks)deepmd/pt/model/atomic_model/dp_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/linear_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/pt/model/model/make_model.py(1 hunks)deepmd/pt/model/task/dipole.py(3 hunks)deepmd/pt/model/task/dos.py(3 hunks)deepmd/pt/model/task/ener.py(3 hunks)deepmd/pt/model/task/fitting.py(10 hunks)deepmd/pt/model/task/invar_fitting.py(3 hunks)deepmd/pt/model/task/polarizability.py(4 hunks)deepmd/pt/model/task/property.py(3 hunks)deepmd/pt/train/training.py(2 hunks)deepmd/pt/train/wrapper.py(2 hunks)deepmd/utils/argcheck.py(10 hunks)source/tests/pt/model/water/multitask_sharefit.json(1 hunks)source/tests/pt/test_multitask.py(4 hunks)source/tests/universal/dpmodel/fitting/test_fitting.py(10 hunks)
🔇 Additional comments (53)
deepmd/pt/train/training.py (2)
268-268: Properly pass 'resuming' parameter to model initialization
The addition of the resuming parameter in the call to get_model_for_wrapper ensures that the model loading logic correctly handles resuming states during training.
1270-1285: Verify correct setting of data IDs for multitask models
The logic in get_model_for_wrapper appropriately sets data IDs for each model when not resuming. This ensures that data identification indices are correctly assigned during fresh multitask training sessions.
deepmd/dpmodel/fitting/ener_fitting.py (3)
59-59: LGTM!
The numb_dataid parameter is correctly passed to the superclass.
35-35: LGTM! Verify version compatibility
The addition of numb_dataid parameter with a default value maintains backward compatibility.
✅ Verification successful
Parameter addition is consistently implemented across fitting classes
The numb_dataid parameter with default value 0 is consistently implemented across all fitting classes in the codebase:
- Base class
GeneralFittingproperly handles the parameter - All derived fitting classes (Energy, Property, DOS, Dipole, Polarizability, etc.) correctly pass it to their superclass
- Parameter is well-documented in
deepmd/utils/argcheck.pyas "The dimension of the data identification embedding" - Tests in
source/tests/universal/dpmodel/fitting/test_fitting.pyverify this functionality
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The following scripts were executed for the analysis:
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78-78: Verify version bump documentation
The version compatibility check has been updated from 2 to 3, which indicates a breaking change. This change needs to be properly documented.
✅ Verification successful
Version bump is part of a consistent update across fitting modules
The version compatibility check update from 2 to 3 in ener_fitting.py is part of a broader, consistent change across the PyTorch and dpmodel fitting modules. The search results show:
-
All fitting modules in
deepmd/dpmodel/fitting/use version 3:ener_fitting.py: version 3invar_fitting.py: version 3property_fitting.py: version 3dipole_fitting.py: version 3dos_fitting.py: version 3polarizability_fitting.py: version 4
-
Similarly, the corresponding PyTorch task modules in
deepmd/pt/model/task/have matching version numbers.
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deepmd/dpmodel/fitting/dos_fitting.py (2)
39-39: LGTM: Constructor changes for data ID support
The addition of numb_dataid parameter is well-implemented with proper type hints and default value, maintaining backward compatibility while enabling new functionality.
Also applies to: 64-64
Line range hint 78-85: Verify deserialization of numb_dataid field
While the version compatibility check is updated correctly, we should verify that the numb_dataid field is properly handled during deserialization.
Let's check how other fitting classes handle this field:
✅ Verification successful
Based on the search results, I can now provide a final response:
The numb_dataid field is properly handled during deserialization
The code review shows that:
- The
numb_dataidfield is properly handled in the parentGeneralFittingclass which all fitting classes inherit from - The field is:
- Initialized with a default value of 0 in all fitting classes
- Properly passed through constructors
- Included in the serialization via
serialize()method - Correctly deserialized through the parent class's
deserialize()method
- The field is used consistently across the codebase for data identification purposes
🏁 Scripts executed
The following scripts were executed for the analysis:
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# Description: Check how numb_dataid is handled in deserialization across fitting classes
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rg -B 5 -A 5 'numb_dataid.*deserialize' deepmd/dpmodel/fitting/
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source/tests/pt/model/water/multitask_sharefit.json (2)
11-14: Verify the sel parameters for water system
The selection parameters [46, 92] seem unusually high for a water system (O, H, B). These parameters typically represent the maximum number of neighbors considered, but water molecules usually have much fewer interactions.
✅ Verification successful
The sel parameters [46, 92] are appropriate for water systems
Based on the search results, the selection parameters [46, 92] are consistently used across multiple water system configurations in the codebase:
- These exact values appear in many water-related configurations including:
examples/water/se_e2_a/input.jsonexamples/water_tensor/dipole/dipole_input.jsonexamples/water_tensor/polar/polar_input.json- Multiple test configurations under
source/tests/tf/water_*
The widespread and consistent use of these selection parameters [46, 92] in water system configurations across the codebase indicates this is a validated parameter choice for water systems, contrary to the initial concern.
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The following scripts were executed for the analysis:
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56-60: Review learning rate decay parameters
The current learning rate configuration:
- Starts at 2e-4
- Decays by 0.98 every 5000 steps
- Stops at 3.51e-8 (very small)
This aggressive decay might cause training instability. Consider:
- Increasing the decay_steps or decay_rate
- Using a higher stop_lr (~1e-6)
deepmd/pt/model/task/dos.py (2)
104-104: Verify version compatibility across the codebase.
The version compatibility check has been updated from 2 to 3, which indicates a breaking change.
Let's verify version consistency across related files:
✅ Verification successful
Version compatibility check is consistent across the codebase
The version compatibility check with version 3 is consistently implemented across all related files in both deepmd/dpmodel and deepmd/pt/model directories. All deserialize methods and descriptors uniformly use check_version_compatibility(version, 3, 1), indicating this is an intentional, coordinated update rather than an inconsistency.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check version compatibility checks across the codebase
# Expected: All related classes should use consistent version numbers
# Search for version compatibility checks
rg "check_version_compatibility.*3.*1" -B 2 -A 2
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fd "version|VERSION" --type f --exec rg "version.*=.*3" {} \;
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50-50: LGTM! Verify integration with data identification system.
The addition of numb_dataid parameter and its propagation to the parent class is implemented correctly.
Let's verify the consistency of this parameter across related classes:
Also applies to: 77-77
✅ Verification successful
Based on the search results, I can now provide a final response:
The numb_dataid parameter is consistently implemented across the codebase
The addition of numb_dataid parameter in DOSFittingNet is part of a well-integrated data identification system that:
- Is consistently implemented across all fitting networks (ener, dos, dipole, polarizability, etc.)
- Has proper documentation indicating it's for multitask model training with data identification embeddings
- Has consistent default value of 0 across all implementations
- Is properly handled in the base
Fittingclass with appropriate tensor initialization and propagation
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The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check if numb_dataid is consistently implemented across fitting classes
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def __init__($$$, numb_dataid: int = 0, $$$) {
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deepmd/pt/model/task/property.py (1)
106-106: LGTM!
The numb_dataid parameter is correctly passed to the parent class constructor.
deepmd/dpmodel/fitting/property_fitting.py (3)
81-81: LGTM: Clean implementation of numb_dataid parameter
The addition of the numb_dataid parameter is well-structured with proper type annotation and default value, maintaining backward compatibility. The parameter is correctly forwarded to the parent class.
Also applies to: 103-103
Line range hint 81-103: Verify consistency across fitting classes
Let's ensure the numb_dataid implementation is consistent across all fitting classes.
Also applies to: 116-116
✅ Verification successful
The previous search pattern might have been too specific. Let's try a broader search to find all fitting classes and their initialization parameters, particularly focusing on numb_dataid usage.
Based on the search results, I can now generate the final response as we have sufficient evidence about the numb_dataid implementation across fitting classes.
The numb_dataid parameter implementation is consistent across fitting classes
The implementation is verified to be consistent across the codebase with the following observations:
- All fitting classes use the same parameter signature:
numb_dataid: int = 0 - The parameter is properly initialized in parent classes and consistently used in child classes
- The implementation includes proper handling of dataid tensors/arrays when
numb_dataid > 0 - The serialization/deserialization of the parameter is consistent across classes
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for consistent implementation across fitting classes
echo "Checking numb_dataid parameter in other fitting classes..."
ast-grep --pattern 'class $_(InvarFitting) {
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def __init__($$$, numb_dataid: int = 0, $$$) {
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}'
echo "Checking version compatibility in other fitting classes..."
fd fitting.py -x rg -A 2 "check_version_compatibility.*3"
Length of output: 1202
Script:
#!/bin/bash
# Let's try a different pattern to find fitting classes and their numb_dataid parameter
echo "Searching for fitting class definitions..."
fd fitting.py -x ast-grep --pattern 'class $_ {
$$$
def __init__($$$) {
$$$
}
$$$
}'
echo "Searching for numb_dataid usage..."
fd fitting.py -x rg -B 2 -A 2 "numb_dataid"
Length of output: 7198
116-116: Verify version compatibility handling
The version check has been updated from 2 to 3, which could affect the loading of existing serialized models.
Let's check for version handling across the codebase:
✅ Verification successful
Version compatibility change is properly handled
The version update from 2 to 3 is well-managed in the codebase:
- The change is consistently applied across all deserialize methods in both PyTorch and TensorFlow implementations
- The model compatibility documentation shows there's a clear upgrade path using
dp convert-fromutility - The version check implementation in
deepmd/utils/version.pyensures backward compatibility by accepting a minimal version (defaulting to 1)
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other version checks and migration logic
echo "Checking for version compatibility patterns..."
rg -A 2 "check_version_compatibility.*3"
echo "Checking for migration utilities or documentation..."
fd -e py -e md . -x rg -l "migration|upgrade|version.*2.*3"
Length of output: 3563
Script:
#!/bin/bash
# Let's check the version compatibility function implementation
echo "Checking version compatibility implementation..."
rg -A 5 "def check_version_compatibility"
# Let's also check for any version-related changes in recent commits
echo "Checking version-related changes in recent history..."
git log -n 5 --oneline --grep="version"
# Let's check the documentation for version compatibility
echo "Checking version compatibility documentation..."
cat doc/troubleshooting/model-compatability.md
Length of output: 1762
Script:
#!/bin/bash
# Let's check the implementation of the version compatibility function
echo "Checking version compatibility function content..."
cat deepmd/utils/version.py
# Let's check for any conversion utilities
echo "Checking for conversion utilities..."
rg -l "convert.*from" --type py
# Let's check if there are any tests related to version compatibility
echo "Checking version-related tests..."
fd -e py test -x rg -l "test.*version|version.*test"
Length of output: 3021
deepmd/pt/model/task/invar_fitting.py (2)
117-117: Verify parent class implementation of numb_dataid.
The parameter is correctly passed to the parent class. Let's verify the parent class implementation.
✅ Verification successful
Parameter numb_dataid is correctly implemented in parent class
The parent class GeneralFitting in both PyTorch and native implementations correctly handles the numb_dataid parameter:
- Both implementations accept
numb_dataidin their constructor with default value of 0 - Both initialize
self.dataidbuffer/attribute whennumb_dataid > 0 - Both properly concatenate dataid to the input tensor during forward/call operations
- Both have consistent serialization/deserialization support for the parameter
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check parent class implementation
# Expected: Find GeneralFitting class and verify numb_dataid handling
# Search for GeneralFitting class definition and its usage of numb_dataid
ast-grep --pattern 'class GeneralFitting($_):
$$$
def __init__($params) {
$$$
}
$$$
}'
Length of output: 100
Script:
#!/bin/bash
# Let's try a different approach to find the parent class implementation
# First, let's locate the file containing GeneralFitting
rg -l "class GeneralFitting"
# Then search for numb_dataid usage in constructor
rg "def __init__.*numb_dataid" -A 5
Length of output: 142
Script:
#!/bin/bash
# Now that we found the files, let's examine their content
echo "=== Content of deepmd/pt/model/task/fitting.py ==="
cat deepmd/pt/model/task/fitting.py
echo -e "\n=== Content of deepmd/dpmodel/fitting/general_fitting.py ==="
cat deepmd/dpmodel/fitting/general_fitting.py
Length of output: 40337
94-94: Verify the usage of numb_dataid parameter.
The new parameter is properly typed and follows the existing parameter pattern. Let's verify its usage across the codebase to ensure consistent implementation.
✅ Verification successful
Parameter numb_dataid is consistently implemented across the codebase
The parameter is properly integrated into the codebase with consistent implementation:
- It's used in all fitting classes (energy, dipole, polarizability, DOS, etc.) with the same signature and default value of 0
- The parameter is well-documented in
deepmd/utils/argcheck.pyas "The dimension of the data identification embedding for multitask models" - The base implementation in
deepmd/pt/model/task/fitting.pyhandles the parameter correctly:- Initializes data identification tensors when
numb_dataid > 0 - Properly includes it in the input dimension calculations
- Initializes data identification tensors when
- The training module (
deepmd/pt/train/training.py) validates that all models have consistentnumb_dataidvalues
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check how numb_dataid is used across the codebase
# Expected: Find other fitting classes and their usage of numb_dataid
# Search for other fitting classes using numb_dataid
rg -l "numb_dataid" --type python
# Search for actual usage of the parameter in code
ast-grep --pattern 'numb_dataid = $_'
Length of output: 104
Script:
#!/bin/bash
# Let's try different search approaches
# Search for files containing numb_dataid
rg -l "numb_dataid"
# Search for actual usage with context
rg -A 3 "numb_dataid"
# Search for class definitions that might use this parameter
ast-grep --pattern 'class $_ {
def __init__($$$, numb_dataid: int$$$) {
$$$
}
}'
Length of output: 23938
source/tests/universal/dpmodel/fitting/test_fitting.py (2)
Line range hint 81-216: Implementation is consistently applied across all fitting types.
The addition of numb_dataid is uniformly implemented across all fitting parameter functions (Dos, Dipole, Polar, Property), maintaining consistency in:
- Parameter documentation
- Input dictionary structure
- Test parameterization
This consistency ensures that data identification is properly tested across all fitting types.
Line range hint 220-231: Verify test coverage for numb_dataid parameter.
The parameterization tests numb_dataid with values 0 and 2, which is consistent with the existing tests for numb_fparam and numb_aparam. Let's verify that these test cases provide adequate coverage.
✅ Verification successful
Based on the comprehensive review of the test files, I can now generate the final response:
Test coverage for numb_dataid parameter is adequate.
The parameter is well-tested through the following mechanisms:
- It's tested alongside
numb_fparamandnumb_aparamwith values 0 and 2 across all fitting types (Energy, DOS, Dipole, Polar, and Property) - The test coverage is implemented through parameterized tests that verify the parameter's functionality across different configurations:
- Different precision types ("float64", "float32", "bfloat16")
- Mixed and non-mixed types (True/False)
- Different backends (TF, PyTorch, JAX, Array API)
- The parameter is consistently tested in combination with other related parameters like
exclude_typesandprecision
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for additional tests that might be relevant to dataid functionality
rg -l "numb_dataid" "source/tests"
# Look for any existing test assertions related to dataid
rg "assert.*dataid" "source/tests"
Length of output: 174
Script:
#!/bin/bash
# Let's examine the test file content to understand the test coverage
cat source/tests/universal/dpmodel/fitting/test_fitting.py
# Also check the JSON file that mentions numb_dataid
cat source/tests/pt/model/water/multitask_sharefit.json
Length of output: 9279
Script:
#!/bin/bash
# Let's check the test base class to see how these parameters are used in tests
cat source/tests/common/cases/fitting/fitting.py
Length of output: 127
Script:
#!/bin/bash
# Let's try to find the correct path for the fitting test base class
fd -t f "fitting.py" source/tests
# And check for any other test files that might be testing the fitting functionality
rg -l "class.*Fitting.*Test" source/tests
Length of output: 1421
Script:
#!/bin/bash
# Let's check the base test class and consistent test cases
cat source/tests/universal/common/cases/fitting/fitting.py
cat source/tests/consistent/fitting/common.py
cat source/tests/consistent/fitting/test_ener.py
Length of output: 11456
🧰 Tools
🪛 Ruff (0.7.0)
39-39: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
deepmd/pt/model/task/dipole.py (3)
84-84: LGTM: Parameter addition follows best practices
The new numb_dataid parameter is well-integrated with proper type hints and a sensible default value.
107-107: LGTM: Proper parent class initialization
The numb_dataid parameter is correctly forwarded to the parent class constructor.
133-133: Verify version compatibility across the codebase
The version check has been updated from 2 to 3. This change needs verification to ensure:
- Consistent version numbers across related fitting classes
- Proper handling of existing serialized models
✅ Verification successful
Version compatibility change is consistent with most task modules
The version check update from 2 to 3 in dipole.py aligns with most other task modules:
property.py,invar_fitting.py,dos.py,ener.pyall use version 3- Only
polarizability.pyuses a higher version (4)
This indicates the version bump is part of a coordinated update across the codebase and maintains consistency with related classes.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check version compatibility across fitting classes
# Check version numbers in other fitting classes
echo "Checking version numbers in fitting classes..."
rg -A 1 'check_version_compatibility.*version.*\d+.*\d+.*\d+' deepmd/pt/model/task/
# Check for any migration utilities or version handling code
echo "Checking for version migration code..."
rg -l 'deserialize|serialize' deepmd/pt/model/task/
Length of output: 1573
deepmd/dpmodel/atomic_model/dp_atomic_model.py (1)
68-73: Verify fitting component integration.
Let's ensure the set_dataid method is properly integrated with the fitting component.
✅ Verification successful
Integration of set_dataid method is properly implemented
The verification shows that:
- The
set_dataidmethod is consistently implemented across all fitting components in bothptanddpmodelpaths - The method is properly propagated through the model hierarchy:
- Called by training code (
deepmd/pt/train/training.py) - Implemented in base models and atomic models
- Correctly delegated to the fitting components
- Called by training code (
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify the set_dataid method implementation in fitting components
# Check if all fitting components implement set_dataid
echo "Checking fitting components for set_dataid implementation..."
ast-grep --pattern 'class $class implements BaseFitting {
$$$
set_dataid($$$) {
$$$
}
$$$
}'
# Check for any direct usage of set_dataid
echo "Checking usage of set_dataid..."
rg "set_dataid" -A 2
Length of output: 4471
deepmd/dpmodel/atomic_model/make_base_atomic_model.py (1)
71-77: Verify implementations across model types.
Let's ensure all atomic model implementations properly implement the new set_dataid method with consistent behavior.
✅ Verification successful
Let me try a different search strategy to find the implementations.
All atomic model implementations properly implement the required method
The verification shows that all classes inheriting from BaseAtomicModel have implemented the set_dataid method consistently:
- DPAtomicModel
- PairTabAtomicModel
- LinearEnergyAtomicModel
Each implementation includes the proper docstring and follows the same signature as the abstract method. The implementations exist in both the PyTorch (pt) and core (dpmodel) versions of the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check implementations of set_dataid across atomic models
# Expected results: All atomic model classes should implement set_dataid
# Find all potential atomic model implementations
echo "Searching for atomic model implementations..."
rg -l "class.*\(.*BAM.*\)" --type py
# Check for set_dataid implementations
echo "Checking set_dataid implementations..."
ast-grep --pattern 'class $CLASS {
$$$
def set_dataid($ARGS) {
$$$
}
$$$
}'
Length of output: 316
Script:
#!/bin/bash
# Search for class definitions that inherit from BaseAtomicModel
echo "Searching for classes inheriting from BaseAtomicModel..."
rg "class.*\(.*BaseAtomicModel.*\)" -A 5 --type py
# Search specifically for set_dataid method implementations
echo -e "\nSearching for set_dataid implementations..."
rg "def set_dataid" -B 2 -A 2 --type py
Length of output: 8848
deepmd/pt/train/wrapper.py (1)
Line range hint 115-131: Verify model structure compatibility
The changes assume all models have an atomic_model attribute. Let's verify this assumption across the codebase.
✅ Verification successful
Based on the extensive search results, I can now make a definitive assessment of the atomic_model structure across the codebase:
All model implementations consistently follow the atomic_model pattern
- The codebase has a well-structured hierarchy of atomic models:
- Base class:
BaseAtomicModeldefined indeepmd/dpmodel/atomic_model/base_atomic_model.py - Multiple specialized implementations like
DPAtomicModel,DPEnergyAtomicModel,DPPolarAtomicModeletc. - All model classes properly inherit and implement the atomic_model interface
- The attribute access pattern using
__getattr__is consistently implemented across all model types
- Base class:
The code changes in the wrapper are safe as they rely on a standardized atomic_model structure that is uniformly present across the entire codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if all model implementations have the expected atomic_model structure
# Search for class definitions that might be used with ModelWrapper
ast-grep --pattern 'class $name($_) {
$$$
def forward($$$) {
$$$
}
$$$
}'
# Search for atomic_model references to understand the expected structure
rg "atomic_model" -A 5 -B 5
Length of output: 112437
deepmd/dpmodel/fitting/dipole_fitting.py (2)
98-98: LGTM: Clean parameter addition
The new numb_dataid parameter is well-placed and properly typed with a sensible default value.
134-134: LGTM: Proper parent class initialization
The numb_dataid parameter is correctly passed to the parent class constructor.
deepmd/pt/model/task/ener.py (2)
53-53: LGTM: Parameter addition follows consistent pattern
The addition of numb_dataid parameter with proper typing and default value aligns with the existing parameter pattern and supports the new dataid functionality.
71-71: LGTM: Correct parameter propagation to superclass
The numb_dataid parameter is properly passed to the superclass constructor, maintaining the inheritance chain.
deepmd/dpmodel/fitting/invar_fitting.py (1)
188-188: LGTM! Version compatibility update is appropriate.
The version check update from 2 to 3 properly reflects the breaking change in the serialization format due to the addition of the numb_dataid parameter.
deepmd/pt/model/task/polarizability.py (2)
Line range hint 196-207: Verify version consistency across serialization methods
The version bump from 3 to 4 and compatibility check update look good. This aligns with the addition of the numb_dataid parameter.
Let's verify version consistency across related files:
✅ Verification successful
Version consistency verified across the codebase
The version bump from 3 to 4 in the polarizability fitting module is consistent across both implementations:
deepmd/pt/model/task/polarizability.py: version 4deepmd/dpmodel/fitting/polarizability_fitting.py: version 4
Both files maintain backward compatibility by accepting versions 1 through 4 in their deserialize methods. The version bump is isolated to polarizability-related modules and doesn't affect other components, which maintain their respective versions.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check version numbers in serialize/deserialize methods
# Look for other fitting classes and their version numbers
# Search for version numbers in serialize methods
rg -A 1 '@version.*=.*[0-9]+' --type py
# Search for version compatibility checks
rg -A 1 'check_version_compatibility.*[0-9]+.*[0-9]+.*[0-9]+' --type py
Length of output: 7971
Line range hint 88-132: Verify consistent usage of numb_dataid across fitting classes
The addition of numb_dataid parameter looks good. The default value and superclass propagation are correctly implemented.
Let's verify the consistency across other fitting classes:
✅ Verification successful
Based on the search results, I can now generate the final response since we have enough information to verify the consistency of numb_dataid usage across fitting classes:
The numb_dataid parameter is consistently implemented
The parameter is correctly implemented across all fitting classes with consistent default value of 0 and proper inheritance chain:
- All fitting classes inherit from either
GeneralFittingorInvarFitting - The parameter is consistently passed to the parent class in all implementations
- Default value of 0 is maintained across all classes (EnergyFittingNet, DOSFittingNet, DipoleFittingNet, PropertyFittingNet, PolarFittingNet)
- Parameter type and position in constructor signature is consistent
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if numb_dataid is consistently implemented across fitting classes
# Look for other fitting classes and their numb_dataid parameter
# Search for fitting class definitions and their numb_dataid parameter
ast-grep --pattern 'class $_(GeneralFitting):
$$$
def __init__($$$, numb_dataid: int = 0, $$$):
$$$'
Length of output: 110
Script:
#!/bin/bash
# Let's try a different approach to find fitting classes and their numb_dataid parameter
# First, find all Python files that might contain fitting classes
echo "=== Files containing 'Fitting' classes ==="
rg "class.*Fitting" --type py
echo -e "\n=== Checking numb_dataid parameter in constructors ==="
# Look for constructor definitions with numb_dataid parameter
rg "def __init__.*numb_dataid.*=" --type py -A 2
Length of output: 8488
Script:
#!/bin/bash
# Let's check the implementation of GeneralFitting class since it's the parent class
echo "=== Checking GeneralFitting implementation ==="
rg "class GeneralFitting" -A 20 --type py
echo -e "\n=== Checking PolarFittingNet and similar classes ==="
# Look for specific fitting classes and their constructors
for class in "PolarFittingNet" "DipoleFittingNet" "EnergyFittingNet" "DOSFittingNet" "PropertyFittingNet"; do
echo -e "\n--- $class implementation ---"
rg "class $class.*:.*\n.*def __init__" -A 10 --type py
done
Length of output: 5422
Script:
#!/bin/bash
# Let's fix the regex and use multiline mode
echo "=== Checking fitting class implementations ==="
for class in "PolarFittingNet" "DipoleFittingNet" "EnergyFittingNet" "DOSFittingNet" "PropertyFittingNet"; do
echo -e "\n--- $class implementation ---"
rg "class $class.*:" --multiline --type py -A 30
done
echo -e "\n=== Checking numb_dataid in GeneralFitting constructor ==="
rg "def __init__.*numb_dataid.*:.*$" --multiline --type py -B 5 -A 10 deepmd/pt/model/task/fitting.py
Length of output: 37190
deepmd/pt/model/atomic_model/dp_atomic_model.py (1)
96-101: Verify integration with tests and other atomic models.
Let's ensure proper test coverage and consistent implementation across atomic models.
deepmd/dpmodel/fitting/polarizability_fitting.py (3)
104-104: LGTM: Parameter addition is consistent and well-implemented
The addition of numb_dataid parameter follows the established pattern across fitting classes and is properly integrated into both the constructor signature and superclass initialization.
Also applies to: 154-154
192-192: LGTM: Version update aligns with feature addition
The version bump from 3 to 4 appropriately tracks the addition of dataid support while maintaining backward compatibility.
203-203: LGTM: Version compatibility check properly updated
The version compatibility check is correctly updated to handle versions 1 through 4, maintaining backward compatibility while supporting the new dataid feature.
source/tests/pt/test_multitask.py (3)
55-55: LGTM: Well-structured parameter comparison for shared fitting
The implementation correctly handles shared fitting by:
- Adding a configurable share_fitting flag
- Excluding model-specific parameters (bias_atom_e, dataid) from comparison
Also applies to: 70-75
42-48: Verify the existence of multitask_sharefit.json template file
The template loading looks good, but we should verify that the file exists to prevent runtime errors.
✅ Verification successful
Template file exists at the expected location
The multitask_sharefit.json template file exists at ./source/tests/pt/model/water/multitask_sharefit.json, which is consistent with the path being constructed in the code relative to __file__. The template loading implementation is correct and safe.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if the template file exists
# Expected: File should exist in the water directory
fd -t f "multitask_sharefit.json" -x echo "Template file found: {}"
Length of output: 142
Line range hint 1-394: Verify test coverage for shared fitting functionality
Let's ensure we have adequate test coverage for the new shared fitting functionality.
✅ Verification successful
Let me gather more information about the test coverage by examining the implementation details.
Let me check the test configuration files to understand the test coverage better.
Test coverage for shared fitting functionality is adequate
The test coverage for shared fitting functionality is comprehensive with:
- Dedicated test configuration (
multitask_sharefit.json) that properly defines shared fitting networks TestMultiTaskSeASharefitclass that explicitly tests shared fitting with appropriate assertions- Proper verification of parameter sharing in
test_multitask_trainmethod - Clear distinction between shared and non-shared fitting configurations
The TODOs found in other test files are unrelated to the multitask shared fitting functionality.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check test coverage and related test files
# Expected: Should find related test files and verify coverage
# Find all related test files
echo "Related test files:"
fd -t f "test.*\.py" -x grep -l "share_fitting"
# Check for any TODOs or missing test cases
echo -e "\nPotential missing test cases:"
rg -i "todo|fixme|xxx" -g "test_*"
Length of output: 405
Script:
#!/bin/bash
# Check implementation of shared fitting functionality
ast-grep --pattern 'def test_multitask_train' -A 10
# Check test class implementations
rg "class.*Test.*ShareFit" -A 5
# Check for any other shared fitting related implementations
rg "share_fitting" -A 2 -B 2
Length of output: 1371
Script:
#!/bin/bash
# Check the multitask template files
echo "Content of multitask.json:"
cat source/tests/pt/water/multitask.json
echo -e "\nContent of multitask_sharefit.json:"
cat source/tests/pt/water/multitask_sharefit.json
# Check if there are any other test configurations
echo -e "\nOther test configurations:"
fd -t f "multitask.*\.json"
Length of output: 6216
deepmd/dpmodel/atomic_model/linear_atomic_model.py (1)
137-144: Verify sub-model compatibility and version handling
Please ensure:
- All possible sub-model types implement the
set_dataidmethod - Version compatibility checks are updated to handle this new feature
✅ Verification successful
Let me gather more specific information about the set_dataid implementation across models.
Let me check one more aspect to verify the inheritance chain and method implementations.
Based on the gathered information, I can now provide the final response:
Implementation of set_dataid is properly handled across the model hierarchy
The verification shows:
set_dataidis defined as an abstract method in the base class (make_base_atomic_model.py)- All atomic model implementations properly handle the method:
linear_atomic_model.py: Correctly propagates to sub-modelsdp_atomic_model.py: Properly delegates to fitting netpairtab_atomic_model.py: Explicitly raises NotImplementedError as intended
Version compatibility is also properly maintained with @version checks in the relevant model implementations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify that all atomic model implementations have set_dataid method
# Find all atomic model implementations
echo "Checking for atomic model implementations..."
rg -l "class.*AtomicModel.*\("
# Check for set_dataid implementations
echo "Checking for set_dataid implementations..."
ast-grep --pattern 'class $CLASS extends $_ {
$$$
set_dataid($ARGS) {
$$$
}
$$$
}'
# Check version compatibility handling
echo "Checking version compatibility..."
rg "check_version_compatibility|@version" --type py
Length of output: 18104
Script:
#!/bin/bash
# Search for set_dataid method implementations in atomic models
echo "Checking set_dataid implementations in atomic models..."
ast-grep --pattern 'def set_dataid($ARGS) {
$$$
}'
# Search for base class implementation
echo -e "\nChecking base class implementation..."
rg -A 5 "class.*BaseAtomicModel" deepmd/dpmodel/atomic_model/base_atomic_model.py
Length of output: 562
Script:
#!/bin/bash
# Check BaseAtomicModel_ implementation and inheritance
echo "Checking BaseAtomicModel_ implementation..."
rg -B 2 -A 10 "class BaseAtomicModel_" --type py
# Check all atomic model classes that might implement set_dataid
echo -e "\nChecking set_dataid in model files..."
rg -B 2 -A 5 "def set_dataid" deepmd/dpmodel/atomic_model/ deepmd/pt/model/atomic_model/ deepmd/jax/atomic_model/
Length of output: 5424
deepmd/dpmodel/fitting/general_fitting.py (4)
108-108: LGTM: Constructor changes for data ID support
The addition of numb_dataid parameter is well-integrated into the constructor, maintaining backward compatibility with a default value of 0.
Also applies to: 131-131
176-179: LGTM: Data ID initialization is consistent
The initialization of self.dataid follows the established pattern and properly handles both zero and non-zero cases with appropriate precision.
185-185: LGTM: Input dimension calculation updated correctly
The addition of self.numb_dataid to the input dimension calculation is properly integrated.
308-308: LGTM: Serialization properly updated
The serialization changes are complete with version increment and proper inclusion of new fields.
Also applies to: 316-316, 325-325
deepmd/pt/model/task/fitting.py (5)
70-71: LGTM: Consistent dataid sharing implementation
The addition of dataid sharing follows the same pattern as bias_atom_e, ensuring consistent parameter sharing between instances.
144-144: LGTM: Well-documented numb_dataid parameter
The numb_dataid parameter is properly initialized with a default value of 0, maintaining backward compatibility, and is well-documented in the class docstring.
Also applies to: 166-166
231-231: LGTM: Correct input dimension update
The input dimension is properly updated to include the dataid features.
389-390: LGTM: Consistent dataid accessor implementation
The dataid handling in setitem and getitem follows the same pattern as other parameters, maintaining consistency.
Also applies to: 407-408
291-291: Verify version compatibility handling
The version has been incremented from 2 to 3 with the addition of dataid-related fields. Ensure that the deserialize method properly handles both version 2 and 3 formats.
Also applies to: 299-299, 308-308
deepmd/dpmodel/model/make_model.py (1)
555-557: Verify consistent implementation across related classes.
Since this is part of a larger change to support data identification, let's verify that all required classes have implemented the set_dataid method consistently.
✅ Verification successful
Implementation is consistent across the codebase
The verification shows that set_dataid is properly implemented across all relevant classes:
- Abstract method defined in base class
make_base_atomic_model.py - Consistent implementations in all atomic models:
dp_atomic_model.pylinear_atomic_model.pypairtab_atomic_model.py
- Proper delegation in model classes (
make_model.py) - Implementation in fitting classes
- All implementations include proper documentation
The method under review follows the established pattern of delegating to the atomic model, which is consistent with other implementations in the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for consistent implementation of set_dataid across related classes
# Search for class definitions and their set_dataid implementations
echo "Checking set_dataid implementations across classes:"
ast-grep --pattern 'class $CLASS_NAME {
$$$
set_dataid($PARAM) {
$$$
}
$$$
}'
# Search for any TODO or unimplemented set_dataid methods
echo -e "\nChecking for unimplemented or TODO set_dataid methods:"
rg -A 2 'def set_dataid.*:.*(\#.*TODO|raise\s+NotImplementedError|pass\s*$)'
Length of output: 408
Script:
#!/bin/bash
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deepmd/pt/model/atomic_model/linear_atomic_model.py (1)
161-168: Verify set_dataid implementation in all model classes.
Let's verify that all model classes in the inheritance chain implement the set_dataid method to ensure consistent behavior.
✅ Verification successful
Implementation of set_dataid is consistent across model hierarchy
The verification shows that set_dataid is properly implemented across the model hierarchy:
- The abstract method is defined in
BaseAtomicModel(deepmd/dpmodel/atomic_model/make_base_atomic_model.py) - Concrete implementations exist in all required model classes:
- DPAtomicModel (deepmd/dpmodel/atomic_model/dp_atomic_model.py)
- LinearAtomicModel (deepmd/dpmodel/atomic_model/linear_atomic_model.py)
- PairTabAtomicModel (deepmd/dpmodel/atomic_model/pairtab_atomic_model.py)
- The method is also properly propagated through model wrappers (make_model)
- The implementation is consistent across both PyTorch and DPModel backends
The implementation follows the composite pattern correctly, with each model properly delegating the call to its components.
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deepmd/pt/model/model/make_model.py (1)
517-519: LGTM: Clean implementation of data ID delegation.
The set_dataid method correctly delegates the data ID setting to the underlying atomic model, maintaining the class's responsibility hierarchy.
Let's verify the consistency of set_dataid implementation across the codebase:
✅ Verification successful
Implementation of set_dataid is consistent across the codebase
The verification shows that:
- The
set_dataidmethod is consistently implemented across all atomic models (dp_atomic_model.py,linear_atomic_model.py,pairtab_atomic_model.py) - The method signature is uniform, taking a single
data_idxparameter - The delegation pattern is correctly followed in both PyTorch (
pt) and base model implementations - The method is properly used in the training module for setting data identification
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deepmd/utils/argcheck.py (1)
Line range hint 1436-1469: Verify PyTorch-only implementation of numb_dataid
The parameter is marked as PyTorch-only. Let's verify there are no TensorFlow dependencies.
✅ Verification successful
Based on the gathered evidence, I can now provide the final response:
PyTorch-only implementation of numb_dataid is correctly marked
The verification confirms that:
- The parameter is only implemented in PyTorch files under
deepmd/pt/ - No implementation exists in TensorFlow-specific directories
- The implementation is properly isolated to PyTorch modules with data identification embedding support
- The universal test suite handles this parameter appropriately without TensorFlow dependencies
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🧹 Outside diff range and nitpick comments (3)
examples/water_multi_task/pytorch_example/input_torch_sharefit.json (2)
76-76: Document the new fitting network reference syntaxThe syntax
shared_fit_with_id:1appears to use a new format with:1suffix. Consider adding documentation to explain this syntax and its implications.Also applies to: 81-81
93-110: Document the loss configuration choicesA few points to consider:
- Both models use identical loss configurations. If this is intentional, please document the reasoning.
- Volume preferences are set to 0. Consider documenting why volume is not included in the loss calculation.
doc/train/multi-task-training.md (1)
59-65: Consider enhancing the documentation with additional details.To make the documentation even more helpful, consider adding:
- Trade-offs between the two approaches to help users choose the most appropriate one
- Guidelines on when to use each approach based on use cases
- Explanation of how different
shared_levelvalues affect the sharing behaviorExample addition:
2. **Descriptor and fitting network sharing with data identification**: - Share the descriptor with `shared_level`=0. - Share the fitting network with `shared_level`=1. - {ref}`numb_dataid <model[standard]/fitting_net[ener]/numb_dataid>` must be set to the number of model branches, which will distinguish different data tasks using a one-hot embedding. - See [here](../../examples/water_multi_task/pytorch_example/input_torch_sharefit.json) for an example. + + **Choosing between approaches:** + - Use "Descriptor sharing only" when your tasks require different fitting networks due to significantly different output properties or scales + - Use "Descriptor and fitting network sharing" when your tasks are similar but need task-specific adaptations + + The `shared_level` parameter controls the degree of parameter sharing: + - Level 0: Complete sharing of all parameters + - Level 1: Partial sharing with task-specific adaptations
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🔇 Additional comments (7)
source/tests/common/test_examples.py (2)
67-67: LGTM! Good test coverage for sharable fitting.
The addition of input_torch_sharefit.json to input_files_multi appropriately extends test coverage for the new sharable fitting functionality while leveraging the existing multi-task testing infrastructure.
67-67: Verify the new JSON file.
Let's ensure the new configuration file exists and follows the expected structure for multi-task configurations.
✅ Verification successful
JSON file structure verified and valid for multi-task configuration
The JSON file exists and follows the expected structure for multi-task configurations with:
- Proper model configuration with shared descriptors and fitting networks
- Two water models (water_1 and water_2) sharing the same descriptor and fitting network
- Complete training configuration with proper loss functions and data paths
- Appropriate shared fitting configuration through
shared_fit_with_idmechanism
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examples/water_multi_task/pytorch_example/input_torch_sharefit.json (4)
67-67: Verify numb_dataid matches model count
The numb_dataid value of 2 correctly matches the number of models (water_1 and water_2) that share the fitting network.
73-82: Consider consolidating identical model configurations
Both water_1 and water_2 models use identical configurations. If this is intentional, consider adding a comment explaining why two separate models are needed. If not, consider consolidating them into a single model definition.
85-91: LGTM: Learning rate configuration is well-structured
The exponential decay configuration with the specified parameters provides a good balance for training stability and convergence.
148-153: Consider adjusting training parameters
The configuration uses reasonable defaults for steps and frequencies, but consider:
- 100,000 steps might be excessive for this setup - verify if this matches your convergence requirements
- Equal save_freq and disp_freq (100) might generate too many checkpoints - consider increasing save_freq
doc/train/multi-task-training.md (1)
59-65: Well-documented approaches with clear examples!
The documentation clearly explains the two approaches for multi-task training and provides example configurations for each approach. The technical requirements, especially for the data identification approach, are well-specified.
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
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🧹 Outside diff range and nitpick comments (3)
source/tests/pt/test_multitask.py (3)
45-51: Add error handling for template file loadingWhile the code follows existing patterns, consider adding explicit error handling for file operations to provide clearer error messages if the template file is missing.
- with open(multitask_sharefit_template_json) as f: - multitask_sharefit_template = json.load(f) + try: + with open(multitask_sharefit_template_json) as f: + multitask_sharefit_template = json.load(f) + except FileNotFoundError: + raise FileNotFoundError(f"Multitask sharefit template not found at {multitask_sharefit_template_json}") + except json.JSONDecodeError: + raise ValueError(f"Invalid JSON format in {multitask_sharefit_template_json}")
58-58: LGTM! Consider adding docstring for share_fitting flagThe implementation correctly handles shared fitting parameters with appropriate exclusions. Consider adding a docstring to document the purpose of the share_fitting flag and its impact on parameter comparison.
class MultiTaskTrainTest: + """Base class for multitask training tests. + + Attributes: + share_fitting: When True, enables shared fitting network parameters between models, + except for bias_atom_e and dataid parameters. + """Also applies to: 73-78
239-277: Add test class documentationThe implementation looks good, but would benefit from documentation explaining the test scenarios and expectations for shared fitting.
class TestMultiTaskSeASharefit(unittest.TestCase, MultiTaskTrainTest): + """Test case for multitask training with shared fitting networks. + + This test verifies that: + 1. Models correctly share fitting network parameters + 2. Excluded parameters (bias_atom_e, dataid) remain model-specific + 3. Training proceeds correctly with shared fitting networks + """
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🧹 Outside diff range and nitpick comments (6)
deepmd/tf/fit/dipole.py (2)
87-87: Update docstring to document the new parameter.The
numb_dataidparameter is missing from the class docstring. Please add documentation for this parameter.Add the following to the docstring parameters section:
Parameters ---------- + numb_dataid : int + Number of data identifiers, not supported in TensorFlow implementation
128-129: Consider handling TensorFlow limitations at a higher level.Currently, the TensorFlow limitation for data IDs is handled within this class. Consider moving this validation to a higher level in the architecture, possibly during the model configuration phase, to make the limitation more visible and maintainable.
This would:
- Make the limitation more discoverable
- Centralize framework-specific restrictions
- Simplify future updates if TensorFlow adds support
deepmd/tf/fit/dos.py (2)
114-114: Enhance the error message for unsupported numb_dataid.The error message could be more informative by explaining why dataid is not supported in TensorFlow and suggesting potential alternatives.
- if numb_dataid > 0: - raise ValueError("numb_dataid is not supported in TensorFlow.") + if numb_dataid > 0: + raise ValueError( + "numb_dataid > 0 is not supported in TensorFlow implementation. " + "Consider using a different backend that supports data identification, " + "or set numb_dataid=0 to disable this feature." + )Also applies to: 136-138
679-679: LGTM! Consider documenting version changes.The serialization changes are consistent and maintain backward compatibility. The version bump from 2 to 3 correctly reflects the addition of the dataid field.
Consider adding a comment explaining the version history:
# Version history: # 1: Initial version # 2: <previous changes> # 3: Added support for dataid fieldAlso applies to: 706-716, 739-739
deepmd/tf/fit/polar.py (2)
98-98: Validate the newnumb_dataidparameter implementation.The implementation raises a ValueError if
numb_dataid > 0, indicating that this feature is not supported in TensorFlow. However, the error message could be more informative.Consider enhancing the error message to provide more context:
- raise ValueError("numb_dataid is not supported in TensorFlow.") + raise ValueError("Data ID feature is not supported in TensorFlow implementation. Consider using other backends that support this feature.")Also applies to: 166-172
166-172: Validate error handling consistency.The error handling for unsupported features (
numb_fparam,numb_aparam,numb_dataid) follows a consistent pattern, which is good. However, consider grouping these checks together for better maintainability.Consider refactoring to group the validation checks:
- if numb_fparam > 0: - raise ValueError("numb_fparam is not supported in the dipole fitting") - if numb_aparam > 0: - raise ValueError("numb_aparam is not supported in the dipole fitting") - if numb_dataid > 0: - raise ValueError("numb_dataid is not supported in TensorFlow.") + unsupported_params = { + 'numb_fparam': "not supported in the dipole fitting", + 'numb_aparam': "not supported in the dipole fitting", + 'numb_dataid': "not supported in TensorFlow" + } + for param_name, value in unsupported_params.items(): + if locals()[param_name] > 0: + raise ValueError(f"{param_name} is {value}")
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🔇 Additional comments (9)
deepmd/tf/fit/dipole.py (3)
Line range hint 392-402: LGTM! Version bump and serialization changes are appropriate.
The version increment and addition of numb_dataid to the serialized data follow best practices:
- Version bumped from 2 to 3 to reflect the schema change
- New field added consistently with similar fields
436-436: LGTM! Version compatibility check is correctly updated.
The version compatibility check is properly updated to handle the new version 3 while maintaining backward compatibility with versions 1 and 2.
128-129: Verify TensorFlow limitation for data ID support.
The error message states that data IDs are not supported in TensorFlow. Let's verify this limitation.
✅ Verification successful
Data ID feature is confirmed to be PyTorch-only, TensorFlow limitation is correct
The code search results confirm that:
- Data ID feature is explicitly documented as PyTorch-only in the codebase (see
doc_only_pt_supported + doc_numb_dataidin argcheck.py) - All TensorFlow fitting implementations (ener.py, polar.py, dipole.py, dos.py) consistently raise the "not supported in TensorFlow" error
- The feature is fully implemented in PyTorch path (deepmd/pt/) with working data ID functionality
- Documentation (multi-task-training.md) describes this as a PyTorch feature for multi-task model training
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deepmd/tf/fit/polar.py (2)
633-633: Verify version compatibility check implementation.
The version compatibility check has been updated to support version 4 while maintaining backward compatibility with version 1 for PT compatibility.
585-585: Version increment needs validation across dependent modules.
The version has been incremented from 3 to 4 to accommodate the new numb_dataid parameter. This change requires careful validation to ensure backward compatibility.
Also applies to: 595-595
✅ Verification successful
Let's examine the version usage more specifically in the relevant model files.
Based on the analysis of the codebase, I can now provide a final response:
Version compatibility is properly handled across the codebase
The version increment from 3 to 4 in the polarizability module is safe because:
- The codebase has a robust version compatibility checking system through
check_version_compatibility()function that validates version changes across all modules - The polarizability module explicitly checks version compatibility in both TF and PT implementations:
deepmd/pt/model/task/polarizability.py:check_version_compatibility(data.pop("@version", 1), 4, 1)deepmd/tf/fit/polar.py:check_version_compatibility(data.pop("@version", 1), 4, 1)
- The version increment accommodates the new
numb_dataidparameter while maintaining backward compatibility with version 1 through 4
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deepmd/tf/fit/ener.py (4)
Line range hint 159-196: LGTM: Constructor changes properly handle the new parameter.
The addition of numb_dataid parameter with validation is well-implemented. The error message clearly indicates that this feature is not supported in TensorFlow.
885-885: LGTM: Version compatibility properly updated.
Version number is correctly bumped from 2 to 3 to reflect the addition of the new numb_dataid parameter. The version compatibility check is properly updated.
Also applies to: 911-911
921-921: LGTM: Serialization format properly updated.
The serialization format is correctly updated to include the new numb_dataid parameter and dataid field, while maintaining backward compatibility.
Also applies to: 953-953
Line range hint 159-196: Verify consistent handling of numb_dataid.
Let's verify that all code paths properly handle the new parameter.
Also applies to: 885-953
✅ Verification successful
Parameter numb_dataid is correctly handled with proper error messages in TensorFlow and full support in PyTorch
The verification shows consistent handling of numb_dataid across the codebase:
- TensorFlow implementation explicitly raises ValueError when numb_dataid > 0 with consistent error message "numb_dataid is not supported in TensorFlow"
- PyTorch implementation fully supports numb_dataid with proper initialization and usage:
- Initializes dataid buffer when numb_dataid > 0
- Properly extends input dimension by numb_dataid
- Handles dataid in forward pass by tiling and concatenating with input
- Used in multi-task training as documented in doc/train/multi-task-training.md
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🧹 Outside diff range and nitpick comments (2)
doc/train/multi-task-training.md (2)
56-58: Enhance documentation of theshared_levelparameter.The explanation of the
shared_levelparameter could be more comprehensive. Consider adding:
- Valid values for
shared_level- How different values affect parameter sharing
- Examples of when to use different shared_level values
- and use the user-defined integer `shared_level` in the code to share the corresponding module to varying degrees. - (The default value for `shared_level` is set to 0. If default sharing is enabled, all parameters in the descriptor will be shared, - whereas in the fitting network, all parameters will be shared except for `bias_atom_e` and `dataid`.). + and use the user-defined integer `shared_level` in the code to control parameter sharing: + - `shared_level=0` (default): Full sharing. All parameters in the descriptor will be shared. + In the fitting network, all parameters will be shared except for `bias_atom_e` and `dataid`. + - `shared_level=1`: [Document behavior] + - `shared_level=2`: [Document behavior] + + Example use cases: + - Use `shared_level=0` when [explain scenario] + - Use `shared_level=1` when [explain scenario]
60-65: Enhance the multi-task training approaches section.While the two approaches are clearly listed, the documentation would be more helpful with:
- Pros and cons of each approach
- Small inline examples demonstrating the key differences
- Guidelines for choosing between approaches
2. **Descriptor and fitting network sharing with data identification**: - Share the descriptor and the fitting network with `shared_level`=0. - {ref}`numb_dataid <model[standard]/fitting_net[ener]/numb_dataid>` must be set to the number of model branches, which will distinguish different data tasks using a one-hot embedding. - - See [here](../../examples/water_multi_task/pytorch_example/input_torch_sharefit.json) for an example. + - See [here](../../examples/water_multi_task/pytorch_example/input_torch_sharefit.json) for a complete example. + + **Choosing between approaches:** + - Use approach 1 when [explain scenario], as it provides [benefits] + - Use approach 2 when [explain scenario], as it provides [benefits] + + **Quick example of approach 1:** + ```json + { + "model": { + "shared_dict": { + "descriptor": { ... } + }, + "model_dict": { ... } + } + } + ``` + + **Quick example of approach 2:** + ```json + { + "model": { + "shared_dict": { + "descriptor": { ... }, + "fitting_net": { ... } + }, + "model_dict": { + "model1": { + "fitting_net": { + "numb_dataid": 2 + } + } + } + } + } + ```
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Actionable comments posted: 4
🧹 Outside diff range and nitpick comments (15)
examples/water_multi_task/pytorch_example/input_torch_sharefit.json (2)
59-69: Document the shared_fit_with_id configurationThe
shared_fit_with_idconfiguration introduces a new concept withnumb_caseid, but its purpose and usage are not clearly documented. This was also noted in a previous review comment.Consider adding documentation to explain:
- The purpose and significance of
numb_caseid- How the ID sharing mechanism works between models
- The relationship between
numb_caseidand the number of models
117-146: Document the data sharing strategyBoth models use identical training data paths. If this is intentional for the multi-task learning setup:
- Document why the same training data is appropriate for both models
- Explain how the shared data benefits the multi-task learning process
- Consider adding a comment in the configuration explaining the data sharing strategy
deepmd/pt/model/task/invar_fitting.py (1)
94-94: Document the newnumb_caseidparameterThe new parameter
numb_caseidis added but not documented in the class docstring. Please add parameter documentation following the existing NumPy docstring format.Add this to the Parameters section of the class docstring:
Parameters ---------- + numb_caseid : int, optional + Number of case identifiers. Defaults to 0. activation_function : strAlso applies to: 117-117
doc/train/multi-task-training.md (1)
60-65: Consider enhancing the multitask training approaches documentation.The documentation clearly explains the two approaches but could be enhanced by adding:
- Guidance on when to use each approach (trade-offs, use cases)
- Whether it's possible to mix both approaches in the same training setup
- Examples of how the
numb_caseidvalue relates to the number of model branchessource/tests/pt/test_multitask.py (2)
45-51: Add error handling for template file loadingConsider adding explicit error handling for the case when the template file is missing.
- with open(multitask_sharefit_template_json) as f: - multitask_sharefit_template = json.load(f) + try: + with open(multitask_sharefit_template_json) as f: + multitask_sharefit_template = json.load(f) + except FileNotFoundError: + raise FileNotFoundError( + f"Shared fitting template file not found: {multitask_sharefit_template_json}" + ) + except json.JSONDecodeError: + raise ValueError( + f"Invalid JSON in shared fitting template: {multitask_sharefit_template_json}" + )
239-274: Add class documentationPlease add a docstring to explain the purpose of this test class and what aspects of shared fitting it verifies.
class TestMultiTaskSeASharefit(unittest.TestCase, MultiTaskTrainTest): + """Test class for verifying shared fitting functionality in multi-task training. + + This class tests the scenario where multiple models share the same fitting network + parameters, except for bias_atom_e and caseid parameters which remain model-specific. + """Consider adding specific test methods
While inheriting test methods from MultiTaskTrainTest is good, consider adding specific test methods to verify shared fitting behavior.
def test_shared_fitting_parameters(self) -> None: """Verify that appropriate parameters are shared between models.""" self.config = update_deepmd_input(self.config, warning=True) self.config = normalize(self.config, multi_task=True) trainer = get_trainer(deepcopy(self.config), shared_links=self.shared_links) # Get parameters before training initial_params = trainer.wrapper.model.state_dict() # Run training trainer.run() # Get parameters after training trained_params = trainer.wrapper.model.state_dict() # Verify shared parameters were updated together for key in trained_params: if ('fitting_net' in key and 'bias_atom_e' not in key and 'caseid' not in key): if 'model_1' in key: paired_key = key.replace('model_1', 'model_2') torch.testing.assert_close( trained_params[key], trained_params[paired_key], msg=f"Parameters not shared: {key} vs {paired_key}" )deepmd/tf/fit/dipole.py (2)
123-123: Consider enhancing the error messageWhile the validation is correct, the error message could be more informative by explaining why case IDs are not supported in TensorFlow or suggesting alternatives.
- raise ValueError("numb_caseid is not supported in TensorFlow.") + raise ValueError("numb_caseid > 0 is not supported in TensorFlow backend. Consider using other backends that support multi-task learning.")Also applies to: 128-129
87-87: Consider adding documentation about TensorFlow limitationsThe class docstring should document why
numb_caseidis not supported in TensorFlow to help users understand the limitation.Add to the docstring:
Parameters ---------- + numb_caseid : int, optional + Number of case IDs for multi-task learning. Currently not supported in TensorFlow backend + due to architectural limitations. Default is 0. sel_type : list[int]deepmd/dpmodel/fitting/general_fitting.py (1)
446-458: Improve assertion message for caseid checkThe implementation is correct, but the assertion message could be more descriptive.
- assert self.caseid is not None + assert self.caseid is not None, "caseid should not be None when numb_caseid > 0"deepmd/pt/model/task/fitting.py (2)
135-135: Add documentation for thenumb_caseidparameter.The
numb_caseidparameter has been added to the constructor, but its documentation is missing from the class docstring.Add the following to the class docstring under the Parameters section:
numb_aparam : int Number of atomic parameters. + numb_caseid : int + Number of case identifiers. activation_function : str Activation function.Also applies to: 157-157
209-217: Remove commented-out initialization code.The commented-out line suggests an alternative initialization using an identity matrix. If this is not needed, it should be removed to maintain clean code.
self.register_buffer( "caseid", torch.zeros(self.numb_caseid, dtype=self.prec, device=device), - # torch.eye(self.numb_caseid, dtype=self.prec, device=device)[0], )deepmd/tf/fit/polar.py (1)
171-172: Enhance error message clarityThe current error message could be more informative. Consider expanding it to explain why case IDs aren't supported in TensorFlow and what alternatives are available.
- raise ValueError("numb_caseid is not supported in TensorFlow.") + raise ValueError("Case IDs (numb_caseid > 0) are not supported in TensorFlow. Consider using PyTorch implementation for case ID support.")deepmd/pt/train/training.py (2)
1270-1286: Add docstring to document the resuming parameter.The function's behavior regarding case ID initialization should be clearly documented.
Add this docstring:
def get_model_for_wrapper(_model_params, resuming=False): + """Get model(s) for the wrapper with optional case ID initialization. + + Args: + _model_params: Model parameters dictionary + resuming: If True, skip case ID initialization for multi-task models + + Returns: + Single model or dictionary of models for multi-task training + """
1289-1308: Add type hints to improve code clarity.The function's return type and parameters should be clearly typed.
Add type hints:
-def get_caseid_config(_model_params): +def get_caseid_config(_model_params: dict) -> tuple[bool, dict[str, int]]:deepmd/tf/fit/ener.py (1)
953-953: Clarify the purpose of'caseid'set toNonein serializationThe
'caseid'variable is serialized with a value ofNone. Please confirm if this is intended. If it's not currently used, consider omitting it or providing a comment explaining its future use.
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📒 Files selected for processing (34)
deepmd/dpmodel/atomic_model/dp_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/linear_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/make_base_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/dpmodel/fitting/dipole_fitting.py(3 hunks)deepmd/dpmodel/fitting/dos_fitting.py(3 hunks)deepmd/dpmodel/fitting/ener_fitting.py(3 hunks)deepmd/dpmodel/fitting/general_fitting.py(9 hunks)deepmd/dpmodel/fitting/invar_fitting.py(3 hunks)deepmd/dpmodel/fitting/polarizability_fitting.py(4 hunks)deepmd/dpmodel/fitting/property_fitting.py(3 hunks)deepmd/dpmodel/model/make_model.py(1 hunks)deepmd/pt/model/atomic_model/dp_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/linear_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/pt/model/model/make_model.py(1 hunks)deepmd/pt/model/task/dipole.py(3 hunks)deepmd/pt/model/task/dos.py(3 hunks)deepmd/pt/model/task/ener.py(3 hunks)deepmd/pt/model/task/fitting.py(10 hunks)deepmd/pt/model/task/invar_fitting.py(3 hunks)deepmd/pt/model/task/polarizability.py(4 hunks)deepmd/pt/model/task/property.py(3 hunks)deepmd/pt/train/training.py(2 hunks)deepmd/tf/fit/dipole.py(5 hunks)deepmd/tf/fit/dos.py(6 hunks)deepmd/tf/fit/ener.py(6 hunks)deepmd/tf/fit/polar.py(5 hunks)deepmd/utils/argcheck.py(10 hunks)doc/train/multi-task-training.md(1 hunks)examples/water_multi_task/pytorch_example/input_torch_sharefit.json(1 hunks)source/tests/pt/model/water/multitask_sharefit.json(1 hunks)source/tests/pt/test_multitask.py(3 hunks)source/tests/universal/dpmodel/fitting/test_fitting.py(10 hunks)
🚧 Files skipped from review as they are similar to previous changes (24)
- deepmd/dpmodel/atomic_model/dp_atomic_model.py
- deepmd/dpmodel/atomic_model/linear_atomic_model.py
- deepmd/dpmodel/atomic_model/make_base_atomic_model.py
- deepmd/dpmodel/atomic_model/pairtab_atomic_model.py
- deepmd/dpmodel/fitting/dipole_fitting.py
- deepmd/dpmodel/fitting/dos_fitting.py
- deepmd/dpmodel/fitting/ener_fitting.py
- deepmd/dpmodel/fitting/invar_fitting.py
- deepmd/dpmodel/fitting/polarizability_fitting.py
- deepmd/dpmodel/fitting/property_fitting.py
- deepmd/dpmodel/model/make_model.py
- deepmd/pt/model/atomic_model/dp_atomic_model.py
- deepmd/pt/model/atomic_model/linear_atomic_model.py
- deepmd/pt/model/atomic_model/pairtab_atomic_model.py
- deepmd/pt/model/model/make_model.py
- deepmd/pt/model/task/dipole.py
- deepmd/pt/model/task/dos.py
- deepmd/pt/model/task/ener.py
- deepmd/pt/model/task/polarizability.py
- deepmd/pt/model/task/property.py
- deepmd/tf/fit/dos.py
- deepmd/utils/argcheck.py
- source/tests/pt/model/water/multitask_sharefit.json
- source/tests/universal/dpmodel/fitting/test_fitting.py
🔇 Additional comments (26)
examples/water_multi_task/pytorch_example/input_torch_sharefit.json (4)
72-84: LGTM: Model configuration properly utilizes shared components
The model dictionary correctly references the shared components for both water models, which is appropriate for multi-task learning with shared parameters.
85-91: LGTM: Learning rate configuration is well-defined
The exponential decay configuration with specified start/stop rates and decay steps is appropriate for this training setup.
136-146:
Add validation data for water_2
The water_2 model is missing validation data, which could affect model evaluation and training monitoring.
92-111: Verify if identical loss configurations are intended
Both water_1 and water_2 use identical loss configurations. While this might be intentional for multi-task learning, consider:
- Documenting why identical loss weights are appropriate
- Whether different weights might be beneficial for different tasks
✅ Verification successful
Based on the search results, I can now generate the final response since I have enough context about multi-task configurations in the codebase:
Identical loss configurations are intentional for this multi-task setup
The identical loss configurations between water_1 and water_2 are appropriate because:
- Both models share the same fitting network (using "shared_fit_with_id" configuration)
- The codebase's multi-task implementation (
deepmd/pt/train/training.py) supports identical loss weights for shared architectures - This is a standard pattern seen in other multi-task examples (e.g.,
source/tests/pt/model/water/multitask_sharefit.json)
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for similar multi-task configurations in the codebase
rg -l "start_pref_[efv]" | xargs rg "model_dict|loss_dict" -C 5
Length of output: 21862
deepmd/pt/model/task/invar_fitting.py (1)
146-146: Version compatibility documentation needed
A previous review comment already addresses the version compatibility documentation requirements.
source/tests/pt/test_multitask.py (1)
58-58: LGTM! Well-structured parameter comparison logic
The changes properly handle shared fitting parameters while correctly excluding bias and case ID parameters from comparison.
Also applies to: 73-78
deepmd/tf/fit/dipole.py (3)
87-87: LGTM: Parameter addition follows existing patterns
The new numb_caseid parameter is correctly added with appropriate type hints and a sensible default value.
392-392: LGTM: Proper version management and serialization
The version bump and serialization changes are appropriate for the new parameter addition, maintaining backward compatibility through proper versioning.
Also applies to: 402-402
436-436: Verify version compatibility across dependent modules
The version check is correctly updated, but we should verify that all dependent modules are compatible with version 3 of the serialized format.
✅ Verification successful
Based on the comprehensive search results, I can now provide a final response:
Version compatibility is properly maintained across the codebase
The version check for DipoleFitting is consistent with other fitting modules in both TF and PyTorch implementations:
- All fitting modules (ener, dos, polar, dipole) use version 3 as maximum and version 1 as minimum
- The version compatibility check is implemented uniformly across the codebase
- The serialization format version 3 is properly declared in the dipole.py file
🏁 Scripts executed
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Script:
#!/bin/bash
# Description: Check for other modules that might deserialize DipoleFittingSeA data
# to ensure they can handle version 3
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rg -l "deserialize.*DipoleFitting"
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rg "check_version_compatibility.*DipoleFitting"
Length of output: 86
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#!/bin/bash
# Let's broaden the search to find all version compatibility checks and serialization patterns
# This will help understand the versioning system across the codebase
# Search for all version compatibility checks
rg "check_version_compatibility" -A 2
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rg -i "dipole.*(?:serial|deserial)" -A 2
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rg "@version" "deepmd/tf/fit/dipole.py" -A 2
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deepmd/dpmodel/fitting/general_fitting.py (5)
108-108: LGTM: Constructor changes are well-implemented
The addition of numb_caseid parameter and its initialization follow the existing pattern and are properly documented.
Also applies to: 131-131
176-179: LGTM: Proper initialization of caseid
The initialization logic follows the same pattern as other similar parameters and correctly handles the precision.
185-185: LGTM: Input dimension calculation properly updated
The input dimension calculation correctly includes the case identification dimension.
272-273: LGTM: Dictionary access methods properly updated
The addition of 'caseid' to setitem and getitem is consistent with the existing pattern.
Also applies to: 290-291
308-308: LGTM: Serialization properly handles case identification
The version bump and addition of case identification fields to serialization are appropriate and consistent.
Also applies to: 316-316, 325-325
deepmd/pt/model/task/fitting.py (5)
67-67: LGTM: Comment accurately reflects parameter sharing behavior.
The comment clearly documents that both bias_atom_e and caseid are excluded from parameter sharing when shared_level is 0.
221-222: LGTM: Input dimension calculation correctly includes case identifiers.
The numb_caseid is properly added to the input dimension calculation for the neural network.
282-282: LGTM: Serialization properly handles case identification data.
The serialization has been correctly updated to include case identification data with appropriate version bump.
Also applies to: 290-290, 299-299
380-381: LGTM: Consistent attribute access implementation.
The caseid attribute is properly handled in both __setitem__ and __getitem__ methods, maintaining consistency with other attributes.
Also applies to: 398-399
498-510: LGTM: Case identification properly integrated into forward pass.
The case identification is correctly concatenated to the input tensor, with proper handling of both normal and zero inputs. The implementation follows the same pattern as fparam and aparam handling.
deepmd/tf/fit/polar.py (3)
98-98: LGTM: Constructor changes for case ID support
The addition of numb_caseid parameter is well-implemented with:
- Safe default value of 0
- Early validation to prevent unsupported usage in TensorFlow
- Proper initialization in the class
Also applies to: 166-166, 171-172
595-595: LGTM: Proper serialization of new parameter
The numb_caseid field is correctly added to the serialized data, maintaining consistency with the constructor parameters.
585-585: Clarify version compatibility comment
While the version increment is appropriate for the new parameter, the comment "to allow PT version" needs elaboration. Consider documenting:
- What PT version means
- Why this compatibility is needed
- Any specific version requirements
Also applies to: 633-634
✅ Verification successful
Let me gather more context about the version changes and PT references.
Let me check for any version-related changes in the model implementation and serialization.
Based on the gathered information, I can now provide a final response:
PT version refers to PyTorch backend compatibility
The version increment from 3 to 4 in the polarizability fitting module was necessary to maintain compatibility with the PyTorch backend implementation. This is evident from:
- The version check is used in both TensorFlow (
PolarFittingSeA) and PyTorch (PolarFittingPT) implementations - The comment "to allow PT version" refers to PyTorch version compatibility
- The version increment ensures proper model serialization/deserialization across different backend implementations
🏁 Scripts executed
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#!/bin/bash
# Check for other version compatibility comments
rg -A 2 "PT version"
Length of output: 327
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#!/bin/bash
# Check for version-related changes and context
rg -B 2 -A 2 '@version.*[0-9]' deepmd/tf/fit/polar.py
# Look for PT/PyTorch related imports or configurations
rg -B 2 -A 2 'torch|pytorch|PT' deepmd/tf/fit/polar.py
# Check the commit history for version changes
git log -p deepmd/tf/fit/polar.py | rg -B 2 -A 2 '@version|PT version'
# Look for version documentation
fd README.md | xargs rg -i 'version|compatibility'
Length of output: 1022
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#!/bin/bash
# Check for model serialization and version handling
ast-grep --pattern 'def serialize($$$)' deepmd/tf/fit/polar.py
# Look for version compatibility checks
rg -B 3 -A 3 'check_version_compatibility' --type py
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fd polar.py | xargs rg -B 2 -A 2 'class.*Polar'
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deepmd/tf/fit/ener.py (4)
194-196: Validation for numb_caseid is appropriate
The validation check for numb_caseid ensures that users are informed when an unsupported value is provided in TensorFlow. The error handling is correctly implemented.
885-885: Versioning updated appropriately for deserialization
The check_version_compatibility call has been updated to reflect the new version (3), ensuring that the deserialization process is compatible with the updated data structure.
911-911: Version number increased to 3 in serialization
The version number in the serialized data is correctly updated to 3 to match the changes in the data structure.
921-921: numb_caseid parameter is correctly included in serialization
The addition of numb_caseid to the serialized data ensures that this parameter is preserved when the model is saved and loaded.
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (3)
source/tests/pt/model/water/multitask.json (1)
Line range hint
1-185: Consider enhancing the test configurationAs this appears to be a test configuration, consider the following improvements:
- Use different model architectures or hyperparameters for
model_1andmodel_2to better test the "sharable fitting" feature- Reduce
numb_stepsfrom 100000 to a smaller number suitable for testing- Add comments explaining the purpose of this test configuration and how it validates the dataid and sharable fitting features
source/tests/pt/model/water/multitask_sharefit.json (1)
41-54: Consider diversifying model configurationsBoth models use identical configurations which might limit the learning capacity of the multitask setup. Consider:
- Using different descriptor parameters for each model
- Adjusting the neuron configurations to capture different aspects of the system
deepmd/pt/model/task/fitting.py (1)
213-213: Remove commented code.The commented initialization approach should be removed if it's not being used.
📜 Review details
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📒 Files selected for processing (13)
deepmd/dpmodel/atomic_model/dp_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/linear_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/make_base_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/dpmodel/fitting/general_fitting.py(9 hunks)deepmd/dpmodel/model/make_model.py(1 hunks)deepmd/pt/model/atomic_model/dp_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/linear_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/pt/model/model/make_model.py(1 hunks)deepmd/pt/model/task/fitting.py(10 hunks)source/tests/pt/model/water/multitask.json(1 hunks)source/tests/pt/model/water/multitask_sharefit.json(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (10)
- deepmd/dpmodel/atomic_model/dp_atomic_model.py
- deepmd/dpmodel/atomic_model/linear_atomic_model.py
- deepmd/dpmodel/atomic_model/make_base_atomic_model.py
- deepmd/dpmodel/atomic_model/pairtab_atomic_model.py
- deepmd/dpmodel/fitting/general_fitting.py
- deepmd/dpmodel/model/make_model.py
- deepmd/pt/model/atomic_model/dp_atomic_model.py
- deepmd/pt/model/atomic_model/linear_atomic_model.py
- deepmd/pt/model/atomic_model/pairtab_atomic_model.py
- deepmd/pt/model/model/make_model.py
🔇 Additional comments (11)
source/tests/pt/model/water/multitask.json (1)
13-14: Please verify the selection parameters for atom types
The sel array has been modified to include a new value 4. Given that my_type_map contains three atom types ["O", "H", "B"], please verify:
- The relationship between the
selarray [46, 92, 4] and the atom types - Whether these numbers are appropriate for the respective atom types
- How this change relates to the dataid support mentioned in the PR title
✅ Verification successful
Let me analyze the search results and generate additional verification scripts to understand the relationship between the sel parameters and atom types better.
The search results show that most water-related configurations use [46, 92] for the sel parameter, but we need to understand:
- The documentation of what these numbers represent
- The specific type_map configuration in this test case to verify the atom type order
- Whether the addition of '4' is consistent with other multitask configurations
This will help us make a definitive assessment about the changes.
Let me search for the complete content of the multitask configuration to understand the full context of the type mapping.
The documentation clearly explains that sel represents the maximum number of type-i neighbors of an atom, and we see that standard water configurations use [46, 92] for O and H atoms. We need to see the complete multitask configuration to understand:
- The definition of
my_type_mapto confirm the atom types order - Whether the addition of '4' corresponds to the third atom type (B)
- If this pattern is consistent with other multitask configurations
Selection parameters are correctly configured for the multitask model
The sel array [46, 92, 4] is correctly aligned with the my_type_map ["O", "H", "B"] configuration, where:
- 46 represents the maximum number of O-type neighbors
- 92 represents the maximum number of H-type neighbors
- 4 represents the maximum number of B-type neighbors
This configuration is consistent with:
- The documentation which explains that
selvalues represent the maximum number of type-i neighbors - The standard water model configuration [46, 92] for O and H atoms
- The same configuration being used in the related
multitask_sharefit.json
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for similar descriptor configurations in test files
rg -A 10 '"type":\s*"se_e2_a"' --type json
# Look for documentation or examples of sel parameter usage
fd -e md -e rst . | xargs rg -l "sel.*parameter|descriptor.*sel"
Length of output: 17998
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#!/bin/bash
# Check the content of the model documentation for sel parameter
cat doc/model/sel.md
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rg -A 5 '"type_map"' source/tests/pt/model/water/
Length of output: 4509
Script:
#!/bin/bash
# Get the complete content of the multitask.json to see the my_type_map definition
cat source/tests/pt/model/water/multitask.json
# Look for other multitask examples that might have similar sel configurations
rg -A 10 '"my_type_map"' --type json
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source/tests/pt/model/water/multitask_sharefit.json (6)
56-63: LGTM: Learning rate configuration is well structured
The exponential decay strategy with the given parameters provides a good balance between initial learning and convergence.
64-83: LGTM: Loss configuration aligns with water system requirements
The energy-based loss with high initial force preference is appropriate for water system modeling.
89-123: **** The existing review comment about optimizing training data configuration and batch size is still valid.
125-128: **** The existing review comment about increasing the number of training steps is still valid.
11-15: Verify the sel parameter configuration
The sel parameter [46, 92, 4] appears to be inconsistent with the number of atom types in my_type_map ["O", "H", "B"]. Each element in sel should correspond to the selection of neighbors for each atom type.
1-134: Verify the shared fitting implementation
While the overall structure of the configuration is well-organized, please ensure that the shared fitting implementation (numb_caseid: 2) is properly tested with different scenarios:
- Different input data distributions
- Various atom type combinations
- Edge cases in the fitting process
✅ Verification successful
Let me gather more information about the test coverage and implementation details.
Let me check the documentation content and other test configurations for a complete understanding.
Shared fitting configuration is correctly implemented
The shared fitting implementation in the configuration is properly set up and aligns with the documented best practices:
- The
numb_caseid: 2correctly matches the number of model branches (model_1 and model_2) - The configuration follows the documented approach of "Descriptor and fitting network sharing with data identification"
- Equal task sampling weights (0.5/0.5) ensure balanced training between the two models
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other shared fitting test configurations
rg -A 5 '"numb_caseid"' --glob '**/tests/**/*.json'
Length of output: 466
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#!/bin/bash
# Check for test data variations and atom types
rg -l "data_0" --glob '**/tests/**/*.json'
# Look for other multitask configurations
rg -l "model_prob" --glob '**/tests/**/*.json'
# Search for any documentation or examples of shared fitting
fd -e md -e rst . | xargs rg -l "shared.*fit|numb_caseid"
Length of output: 529
Script:
#!/bin/bash
# Check documentation content
cat doc/train/multi-task-training.md
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cat source/tests/pt/model/water/multitask.json
# Check data directory structure
fd -t d "data_0" source/tests/pt/model/water/
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deepmd/pt/model/task/fitting.py (4)
67-67: LGTM: Comment accurately reflects parameter sharing behavior.
The comment correctly documents that both bias_atom_e and caseid are excluded from parameter sharing when shared_level is 0.
359-367: LGTM with existing validation suggestion.
The implementation correctly creates a one-hot encoded tensor for case identification.
282-282: LGTM: Proper versioning and serialization.
The version increment and addition of new fields (numb_caseid and caseid) to serialization are handled correctly.
Also applies to: 290-290, 299-299
498-510: LGTM: Verify tensor shapes in production.
The case identification concatenation is implemented correctly. However, it's important to verify the tensor shapes in production, especially for edge cases with different batch sizes.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4417 +/- ##
==========================================
+ Coverage 83.27% 83.30% +0.02%
==========================================
Files 667 667
Lines 61445 61533 +88
Branches 3486 3486
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+ Hits 51166 51257 +91
+ Misses 9152 9149 -3
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (9)
doc/train/multi-task-training.md (2)
56-63: Enhance the shared_level parameter documentationThe shared_level documentation needs additional clarity:
- Add an example showing the complete syntax for specifying different shared levels
- Explain the colon syntax in
"my_descriptor:shared_level"- Include practical examples for each sharing level (0 and 1)
Consider adding this example:
{ "model": { "descriptor": { "type": "se_e2_a", "shared_key": "my_descriptor:1" // Level 1: shares only type embedding } } }🧰 Tools
🪛 LanguageTool
[misspelling] ~62-~62: Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.
Context: ...escriptors support all levels (e.g., se_a only supports level 0) - - For fitting ...(EN_A_VS_AN)
64-69: Enhance the multitask approaches documentationConsider adding:
- A comparison table showing pros/cons of each approach
- Guidelines for choosing between the approaches
- More details about how numb_caseid affects model behavior and data handling
Consider adding this comparison:
| Aspect | Descriptor-only Sharing | Full Sharing with Data ID | |--------|------------------------|---------------------------| | Use Case| Different fitting requirements | Similar fitting patterns | | Memory | Higher (separate fitting nets) | Lower (shared parameters) | | Flexibility | More flexible | More efficient |deepmd/pt/model/task/fitting.py (2)
211-219: Consider alternative initialization strategy.The current implementation initializes
caseidwith zeros, but there's a commented-out alternative usingtorch.eye. Consider if the identity matrix initialization might be more appropriate as it would provide unique, orthogonal case identifiers by default.- torch.zeros(self.numb_caseid, dtype=self.prec, device=device), + torch.eye(self.numb_caseid, dtype=self.prec, device=device)[0],
500-512: Consider adding shape assertions for debugging.The implementation correctly handles case identification concatenation. Consider adding shape assertions to help with debugging:
if self.numb_caseid > 0: assert self.caseid is not None caseid = torch.tile(self.caseid.reshape([1, 1, -1]), [nf, nloc, 1]) + assert caseid.shape == (nf, nloc, self.numb_caseid), f"Expected shape {(nf, nloc, self.numb_caseid)}, got {caseid.shape}" xx = torch.cat( [xx, caseid], dim=-1, )deepmd/tf/fit/dos.py (2)
77-78: Verify the error message for unsupported featureThe error message "numb_caseid is not supported in TensorFlow" could be more informative. Consider providing guidance on alternatives or explaining why it's not supported.
- raise ValueError("numb_caseid is not supported in TensorFlow.") + raise ValueError("numb_caseid is not supported in TensorFlow. Please use PyTorch backend for this feature or consider using alternative approaches for case identification.")Also applies to: 116-116, 138-140
Line range hint
1-745: Consider architectural documentation for TensorFlow limitationsThe code introduces a feature flag (
numb_caseid) that is explicitly unsupported in TensorFlow. This architectural decision should be documented:
- Why is this feature only supported in PyTorch?
- What are the technical limitations in TensorFlow?
- Is there a migration path for users who need this feature?
Consider adding a section in the documentation explaining these architectural decisions and providing guidance for users.
deepmd/tf/fit/polar.py (1)
173-174: Consider adding a TODO comment for future backend support.The error message indicates TensorFlow-specific limitation. Consider adding a TODO comment to document potential future support in other backends.
if numb_caseid > 0: + # TODO: Add support for case IDs in other backends raise ValueError("numb_caseid is not supported in TensorFlow.")deepmd/pt/model/descriptor/dpa2.py (2)
407-408: Consider updating documentation to reflect supported sharing levelsThe
share_paramsmethod now supportsshared_levelvalues of 0 and 1. To improve clarity, please update the method's docstring or comments to specify the supportedshared_levelvalues and their intended behaviors.
409-410: Provide a more informative error message for unsupported sharing levelsIn the
elseblock, aNotImplementedErroris raised without an explanatory message. Consider adding an informative error message indicating the unsupportedshared_levelvalue and listing the supported values (0 and 1). This will aid users in debugging and understanding the limitations.
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deepmd/dpmodel/atomic_model/linear_atomic_model.py(2 hunks)deepmd/pt/model/atomic_model/linear_atomic_model.py(2 hunks)deepmd/pt/model/descriptor/dpa2.py(1 hunks)deepmd/pt/model/task/dipole.py(4 hunks)deepmd/pt/model/task/fitting.py(11 hunks)deepmd/pt/model/task/invar_fitting.py(4 hunks)deepmd/pt/model/task/polarizability.py(5 hunks)deepmd/pt/model/task/property.py(4 hunks)deepmd/tf/descriptor/se_a_ebd.py(1 hunks)deepmd/tf/fit/dipole.py(6 hunks)deepmd/tf/fit/dos.py(7 hunks)deepmd/tf/fit/ener.py(7 hunks)deepmd/tf/fit/polar.py(6 hunks)doc/train/multi-task-training.md(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (7)
- deepmd/dpmodel/atomic_model/linear_atomic_model.py
- deepmd/pt/model/atomic_model/linear_atomic_model.py
- deepmd/pt/model/task/dipole.py
- deepmd/pt/model/task/invar_fitting.py
- deepmd/pt/model/task/polarizability.py
- deepmd/pt/model/task/property.py
- deepmd/tf/fit/dipole.py
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doc/train/multi-task-training.md
[misspelling] ~62-~62: Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.
Context: ...escriptors support all levels (e.g., se_a only supports level 0) - - For fitting ...
(EN_A_VS_AN)
🔇 Additional comments (13)
deepmd/pt/model/task/fitting.py (6)
67-67: LGTM: Appropriate parameter sharing exclusion.
The modification correctly excludes both bias_atom_e and caseid from parameter sharing, which is appropriate as these parameters should remain instance-specific.
98-99: LGTM: Well-documented parameter addition.
The numb_caseid parameter is properly documented and has a safe default value of 0.
224-224: LGTM: Correct input dimension calculation.
The input dimension calculation correctly includes the case identification dimension.
361-369: LGTM: Proper one-hot encoding implementation.
The method correctly implements one-hot encoding for case identification using torch.eye.
382-383: LGTM: Consistent attribute access implementation.
The changes to __setitem__ and __getitem__ maintain consistency with the existing pattern.
Also applies to: 400-401
284-284: Verify version compatibility in deserialization.
The serialization version has been bumped and new fields added correctly. However, ensure that the deserialization method properly handles older versions without these fields.
Also applies to: 292-292, 301-301
✅ Verification successful
No version handling needed in deserialization
The deserialization method in GeneralFitting class is simple and doesn't require version handling because:
- It only extracts variables and networks from the serialized data
- All other fields are passed directly to the constructor as kwargs
- The new fields (
numb_caseidandcaseid) are optional with default values in the constructor
This design makes it naturally backward compatible - older versions without these fields will work correctly since the constructor handles missing parameters with defaults.
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deepmd/tf/descriptor/se_a_ebd.py (1)
61-62:
Implementation missing for documented parameter
The numb_caseid parameter is documented in the class docstring but not implemented in the class. This creates an inconsistency between the documentation and the actual implementation.
Let's verify how this parameter is used across the codebase:
Suggested implementation:
def __init__(
self,
rcut: float,
rcut_smth: float,
sel: list[int],
neuron: list[int] = [24, 48, 96],
axis_neuron: int = 8,
resnet_dt: bool = False,
trainable: bool = True,
seed: Optional[int] = None,
type_one_side: bool = True,
type_nchanl: int = 2,
type_nlayer: int = 1,
numb_aparam: int = 0,
+ numb_caseid: int = 0,
set_davg_zero: bool = False,
activation_function: str = "tanh",
precision: str = "default",
exclude_types: list[list[int]] = [],
**kwargs,
) -> None:
"""Constructor."""
DescrptSeA.__init__(
self,
rcut,
rcut_smth,
sel,
neuron=neuron,
axis_neuron=axis_neuron,
resnet_dt=resnet_dt,
trainable=trainable,
seed=seed,
type_one_side=type_one_side,
set_davg_zero=set_davg_zero,
activation_function=activation_function,
precision=precision,
)
self.type_nchanl = type_nchanl
self.type_nlayer = type_nlayer
self.type_one_side = type_one_side
self.numb_aparam = numb_aparam
+ self.numb_caseid = numb_caseiddeepmd/tf/fit/polar.py (3)
66-67: LGTM: Parameter addition and validation are well implemented.
The numb_caseid parameter is properly:
- Documented in the class docstring
- Added with a default value of 0
- Validated with a clear error message
Also applies to: 100-100, 168-168, 173-174
587-587: LGTM: Serialization changes are consistent.
The version increment and addition of numb_caseid to the serialized data are appropriate for maintaining backward compatibility.
Also applies to: 597-597
635-635: LGTM: Version compatibility check is properly updated.
The version compatibility check is correctly updated to support versions 1-4, maintaining backward compatibility while allowing the new version.
deepmd/tf/fit/ener.py (3)
120-121: LGTM: Parameter documentation is complete
The numb_caseid parameter is properly documented in the docstring.
913-913: Verify serialization/deserialization compatibility
The serialization format has been updated with new fields:
- Version number increased to 3
- Added
numb_caseidfield - Added
caseidto variables (set to None)
Ensure that:
- Deserialization handles both old and new formats
- Unit tests cover both formats
Also applies to: 923-923, 955-955
✅ Verification successful
Based on the search results, I can now provide a definitive response about the serialization changes:
Serialization version changes are properly handled
The codebase shows comprehensive version compatibility checks:
- The new version 3 is properly handled in deserialization with
check_version_compatibility()calls that accept version 3 as maximum version - Both PyTorch and TensorFlow implementations handle version compatibility
- The deserialization code explicitly handles both old and new formats by using default values for new fields:
check_version_compatibility(data.pop("@version", 1), 3, 1)pattern shows backward compatibility with version 1- New fields like
numb_caseidare safely handled through dictionary pop operations with defaults
The serialization changes follow good practices:
- Version number increment is appropriate for the schema changes
- Backward compatibility is maintained
- Version checks are consistent across the codebase
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for test_file in $(fd "test.*ener" -t f); do
echo "=== Checking $test_file ==="
rg -B 2 -A 5 "serialize|deserialize" "$test_file"
done
# Check implementation of deserialization to verify version handling
ast-grep --pattern 'def restore_descriptor($$$) {
$$$
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887-887: Verify version compatibility with existing models
The version check has been updated from 2 to 3. Ensure that:
- Existing models with version 2 can still be loaded
- Version 3 changes are documented
✅ Verification successful
Let me analyze the version compatibility checks across the codebase to verify the impact.
Let me check the implementation of the version compatibility function to understand its behavior.
Based on the analysis of the codebase, I can now generate the final response:
Version 3 update is safe and properly implemented
The version update from 2 to 3 is necessary to support the new numb_caseid field in PyTorch models for multi-task training. The change is safe because:
- The version compatibility check ensures backward compatibility by accepting any version between 1 and 3
- The
numb_caseidfeature is only supported in PyTorch models, and TensorFlow models explicitly raise an error ifnumb_caseid > 0 - The version update is consistently implemented across all fitting modules (ener, dos, dipole, etc.)
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (10)
doc/train/multi-task-training.md (2)
56-64: Minor improvements needed in shared_level documentationThe documentation is much clearer now, but there are a few minor points to address:
- Use "an" instead of "a" before "se_a" for correct grammar
- Consider explicitly stating that level 0 is the default when not specified
Apply these changes:
- - Not all descriptors support all levels (e.g., se_a only supports level 0) + - Not all descriptors support all levels (e.g., an se_a only supports level 0)🧰 Tools
🪛 LanguageTool
[misspelling] ~62-~62: Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.
Context: ...escriptors support all levels (e.g., se_a only supports level 0) - - For fitting ...(EN_A_VS_AN)
65-69: Consider expanding dim_case_embd documentationThe documentation clearly presents the two approaches, but the explanation of
dim_case_embdcould be more detailed. Consider adding:
- An explanation of how the one-hot embedding works
- A small example showing the embedding structure
- The implications of changing this parameter after training
Would you like me to propose expanded documentation for the
dim_case_embdparameter?source/tests/pt/test_multitask.py (2)
58-58: LGTM! Consider adding a docstring.The shared fitting parameter comparison logic is well implemented. Consider adding a docstring to explain the significance of excluding
bias_atom_eandcase_embdfrom the comparison.def test_multitask_train(self) -> None: + """Test multitask training with support for shared fitting. + + Note: + When share_fitting is enabled, all fitting network parameters are shared + except for bias_atom_e and case_embd which are task-specific. + """ # test multitask trainingAlso applies to: 73-78
239-277: LGTM! Add class documentation.The test class implementation is well-structured and follows the established patterns. Consider adding class-level documentation to explain the purpose of shared fitting tests.
class TestMultiTaskSeASharefit(unittest.TestCase, MultiTaskTrainTest): + """Test cases for multi-task training with shared fitting networks. + + This class verifies that: + 1. Models correctly share fitting network parameters + 2. Task-specific parameters (bias_atom_e, case_embd) remain independent + 3. The shared fitting configuration loads and trains successfully + """ def setUp(self) -> None:deepmd/tf/fit/polar.py (2)
173-174: Consider improving the error messageWhile the validation is correct, the error message could be more informative by explaining why case embeddings are not supported in TensorFlow and potentially suggesting alternatives.
- raise ValueError("dim_case_embd is not supported in TensorFlow.") + raise ValueError("Case embeddings (dim_case_embd > 0) are not supported in TensorFlow implementation. Please use PyTorch implementation for this feature.")
Line range hint
12-24: Consider adding type hints for imported componentsThe imports could benefit from explicit type hints to improve code maintainability and IDE support.
-from deepmd.tf.descriptor import ( - DescrptSeA, -) +from deepmd.tf.descriptor import ( + DescrptSeA, # type: tf.Module -)deepmd/tf/fit/ener.py (1)
120-121: Update error message to clarify PyTorch alternativeThe error message should be more informative about the alternative solution.
- raise ValueError("dim_case_embd is not supported in TensorFlow.") + raise ValueError("dim_case_embd is not supported in TensorFlow. Use PyTorch backend for case embeddings support.")Also applies to: 161-161, 196-198
deepmd/pt/train/training.py (2)
1270-1285: LGTM: Case embedding initialization logicThe implementation correctly handles case embedding initialization for multi-task models, with proper checks for:
- Multi-task configuration presence
- Training state (new vs resuming)
- Case embedding configuration validation
Consider adding docstring documentation to explain:
- The purpose of case embeddings
- The
resumingparameter's effect- Return value structure
def get_model_for_wrapper(_model_params, resuming=False): + """Initialize model with optional case embedding support for multi-task learning. + + Args: + _model_params: Model configuration parameters + resuming: If True, skip case embedding initialization as it's loaded from checkpoint + + Returns: + Single model instance or dict of models for multi-task setup + """
1289-1311: LGTM: Case embedding configuration validationThe implementation provides robust validation for case embeddings in multi-task models:
- Validates multi-task setup requirement
- Ensures consistent case embedding dimensions across models
- Returns clear configuration for embedding initialization
Consider enhancing the error message to be more descriptive:
- f"All models must have the same dimension of data identification, while the settings are: {numb_case_embd_list}" + f"Inconsistent case embedding dimensions across models. Found dimensions {dict(zip(sorted_model_keys, numb_case_embd_list))}. All models must have the same dimension."deepmd/utils/argcheck.py (1)
Line range hint
1436-1469: Consider refactoring the repeated dim_case_embd parameter definition.The implementation is correct, but the same parameter definition is duplicated across all fitting functions. Consider extracting it into a helper function to improve maintainability.
Example refactor:
def _add_case_embd_arg(): """Helper function to create the case embedding dimension argument.""" return Argument( "dim_case_embd", int, optional=True, default=0, doc=doc_only_pt_supported + doc_numb_case_embd, ) # Usage in fitting functions: def fitting_ener(): return [ # ... other arguments ... _add_case_embd_arg(), # ... remaining arguments ... ]
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deepmd/dpmodel/atomic_model/dp_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/linear_atomic_model.py(2 hunks)deepmd/dpmodel/atomic_model/make_base_atomic_model.py(1 hunks)deepmd/dpmodel/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/dpmodel/fitting/dipole_fitting.py(3 hunks)deepmd/dpmodel/fitting/dos_fitting.py(3 hunks)deepmd/dpmodel/fitting/ener_fitting.py(3 hunks)deepmd/dpmodel/fitting/general_fitting.py(9 hunks)deepmd/dpmodel/fitting/invar_fitting.py(3 hunks)deepmd/dpmodel/fitting/polarizability_fitting.py(4 hunks)deepmd/dpmodel/fitting/property_fitting.py(3 hunks)deepmd/dpmodel/model/make_model.py(1 hunks)deepmd/pt/model/atomic_model/dp_atomic_model.py(1 hunks)deepmd/pt/model/atomic_model/linear_atomic_model.py(2 hunks)deepmd/pt/model/atomic_model/pairtab_atomic_model.py(1 hunks)deepmd/pt/model/model/make_model.py(1 hunks)deepmd/pt/model/task/dipole.py(4 hunks)deepmd/pt/model/task/dos.py(3 hunks)deepmd/pt/model/task/ener.py(3 hunks)deepmd/pt/model/task/fitting.py(11 hunks)deepmd/pt/model/task/invar_fitting.py(4 hunks)deepmd/pt/model/task/polarizability.py(5 hunks)deepmd/pt/model/task/property.py(4 hunks)deepmd/pt/train/training.py(2 hunks)deepmd/tf/fit/dipole.py(6 hunks)deepmd/tf/fit/dos.py(7 hunks)deepmd/tf/fit/ener.py(7 hunks)deepmd/tf/fit/polar.py(6 hunks)deepmd/utils/argcheck.py(10 hunks)doc/train/multi-task-training.md(1 hunks)examples/water_multi_task/pytorch_example/input_torch_sharefit.json(1 hunks)source/tests/pt/model/water/multitask_sharefit.json(1 hunks)source/tests/pt/test_multitask.py(3 hunks)source/tests/universal/dpmodel/fitting/test_fitting.py(10 hunks)
🚧 Files skipped from review as they are similar to previous changes (27)
- deepmd/dpmodel/atomic_model/dp_atomic_model.py
- deepmd/dpmodel/atomic_model/linear_atomic_model.py
- deepmd/dpmodel/atomic_model/make_base_atomic_model.py
- deepmd/dpmodel/atomic_model/pairtab_atomic_model.py
- deepmd/dpmodel/fitting/dipole_fitting.py
- deepmd/dpmodel/fitting/dos_fitting.py
- deepmd/dpmodel/fitting/ener_fitting.py
- deepmd/dpmodel/fitting/invar_fitting.py
- deepmd/dpmodel/fitting/polarizability_fitting.py
- deepmd/dpmodel/fitting/property_fitting.py
- deepmd/dpmodel/model/make_model.py
- deepmd/pt/model/atomic_model/dp_atomic_model.py
- deepmd/pt/model/atomic_model/linear_atomic_model.py
- deepmd/pt/model/atomic_model/pairtab_atomic_model.py
- deepmd/pt/model/model/make_model.py
- deepmd/pt/model/task/dipole.py
- deepmd/pt/model/task/dos.py
- deepmd/pt/model/task/ener.py
- deepmd/pt/model/task/fitting.py
- deepmd/pt/model/task/invar_fitting.py
- deepmd/pt/model/task/polarizability.py
- deepmd/pt/model/task/property.py
- deepmd/tf/fit/dipole.py
- deepmd/tf/fit/dos.py
- examples/water_multi_task/pytorch_example/input_torch_sharefit.json
- source/tests/pt/model/water/multitask_sharefit.json
- source/tests/universal/dpmodel/fitting/test_fitting.py
🧰 Additional context used
🪛 LanguageTool
doc/train/multi-task-training.md
[misspelling] ~62-~62: Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.
Context: ...escriptors support all levels (e.g., se_a only supports level 0) - - For fitting ...
(EN_A_VS_AN)
🔇 Additional comments (11)
deepmd/dpmodel/fitting/general_fitting.py (5)
108-108: LGTM! Constructor changes are well-implemented.
The initialization of case embedding parameters follows the established pattern and includes proper type hints and default values.
Also applies to: 131-131, 176-179
185-185: LGTM! Input dimension calculation is correct.
The case embedding dimension is properly incorporated into the total input dimension calculation.
272-273: LGTM! Serialization changes are complete.
The version bump and addition of case embedding fields to serialization are properly implemented.
Also applies to: 290-291, 308-308, 316-316, 325-325
446-458: LGTM! Case embedding handling in _call_common is well-implemented.
The case embedding is properly concatenated to the input tensor and correctly handled in the vacuum contribution calculation.
232-238: 🛠️ Refactor suggestion
Add input validation for case_idx
Similar to the existing review comments for set_dataid and set_caseid, consider adding input validation for case_idx.
def set_case_embd(self, case_idx: int):
"""
Set the case embedding of this fitting net by the given case_idx,
typically concatenated with the output of the descriptor and fed into the fitting net.
"""
+ if not isinstance(case_idx, (int, np.integer)):
+ raise TypeError(f"case_idx must be an integer, got {type(case_idx)}")
+ if not 0 <= case_idx < self.dim_case_embd:
+ raise ValueError(f"case_idx must be in range [0, {self.dim_case_embd}), got {case_idx}")
self.case_embd = np.eye(self.dim_case_embd, dtype=self.prec)[case_idx]deepmd/tf/fit/polar.py (2)
66-67: LGTM: Parameter addition is well-documented
The new parameter dim_case_embd is properly added with clear documentation and a default value of 0.
Also applies to: 100-100
587-587: Verify version compatibility handling
The version update from 3 to 4 is properly implemented in both serialization and deserialization. However, let's verify there are no other version-dependent features that need handling.
Also applies to: 597-597, 635-635
✅ Verification successful
Let me analyze the version-related code further by searching for version handling logic.
Let me search for version-related code specifically in the polar fitting module to verify the version compatibility handling.
Based on the code analysis, I can now provide a definitive response about the version compatibility handling in the polar fitting module.
Version compatibility is properly implemented
The version update to 4 is correctly handled in both TensorFlow and PyTorch implementations:
- The TensorFlow implementation (polar.py) explicitly sets version 4 in serialization and checks version compatibility with maximum version 4 and minimum version 1
- The PyTorch implementation (polarizability.py) maintains the same version compatibility checks
- Both implementations use the standard version compatibility checking utility
- The version change is properly documented in the serialized data structure
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deepmd/tf/fit/ener.py (2)
887-887: LGTM: Version compatibility check updated correctly
The version bump from 2 to 3 is appropriate for the new case embedding feature.
913-913: LGTM: Serialization format updated consistently
The serialization changes properly handle the new case embedding feature:
- Version number updated to 3
- Added dim_case_embd field
- Added case_embd to variables (initialized as None)
Also applies to: 923-923, 955-955
deepmd/pt/train/training.py (1)
268-268: LGTM: Model initialization parameter addition
The addition of the resuming parameter helps control when case embeddings should be initialized, preventing unnecessary reinitialization when resuming training.
deepmd/utils/argcheck.py (1)
Line range hint 1520-1543: LGTM! Identical implementation pattern as previous segment.
The implementation follows the same correct pattern as in the energy fitting function.
Also applies to: 1575-1593, 1624-1659, 1702-1730
iProzd
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (1)
doc/train/multi-task-training.md (1)
65-70: Add context for choosing between multi-task approachesThe documentation clearly explains the two approaches but would benefit from additional context:
- When to use each approach (use cases)
- Trade-offs between the approaches
- Performance implications of using shared fitting networks vs. separate ones
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doc/train/multi-task-training.md(1 hunks)
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doc/train/multi-task-training.md
[misspelling] ~63-~63: Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.
Context: ...escriptors support all levels (e.g., se_a only supports level 0) - For fitting ...
(EN_A_VS_AN)
🪛 Markdownlint (0.35.0)
doc/train/multi-task-training.md
54-54: null
Spaces inside code span elements
(MD038, no-space-in-code)
56-56: null
Spaces inside code span elements
(MD038, no-space-in-code)
🔇 Additional comments (2)
doc/train/multi-task-training.md (2)
64-64: Expand the fitting nets sharing documentation
The current documentation for fitting nets sharing is too brief. Please consider adding:
- Explanation of why only level 0 is supported
- Details about how bias_atom_e and caseid parameters behave differently
- Example configuration showing fitting net sharing
59-63: Verify the supported shared_level values
The documentation states that valid shared_level values are 0-1, but previous documentation mentioned support for levels 0-3 with additional sharing behaviors for DPA2. Please clarify:
- If the reduction to levels 0-1 is intentional
- If DPA2-specific levels (2-3) should be documented separately
✅ Verification successful
Based on the search results, I can now generate the final response:
Documentation accurately reflects the implemented shared_level values
The documentation is correct in stating that valid shared_level values are 0-1. The codebase implementation shows:
- Most descriptors (se_a, se_t, se_r) only support level 0
- Only DPA1, DPA2, and se_t_tebd support both levels 0 and 1
- Any other level (2-3) raises NotImplementedError
- Level 0 shares all parameters
- Level 1 shares only type embedding
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rg -A 5 "shared_level.*=.*[0-9]"
Length of output: 8118
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[misspelling] ~63-~63: Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.
Context: ...escriptors support all levels (e.g., se_a only supports level 0) - For fitting ...
(EN_A_VS_AN)
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (8)
deepmd/pd/model/task/ener.py (1)
45-45: Add parameter documentation and validationThe new
dim_case_embdparameter needs documentation to explain its purpose and constraints. Also consider adding validation to ensure non-negative dimensions.def __init__( self, ntypes: int, dim_descrpt: int, neuron: list[int] = [128, 128, 128], bias_atom_e: Optional[paddle.Tensor] = None, resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, dim_case_embd: int = 0, activation_function: str = "tanh", precision: str = DEFAULT_PRECISION, + """ + Parameters + ---------- + dim_case_embd : int, optional + Dimension of case/task embedding for multi-task learning. + Must be non-negative. Default is 0 (no case embedding). + """ + if dim_case_embd < 0: + raise ValueError("dim_case_embd must be non-negative")deepmd/pd/model/task/invar_fitting.py (1)
60-62: Enhance parameter documentationThe docstring for
dim_case_embdshould be more descriptive:
- Add type hint in the description (
: int)- Document any validation rules (e.g., must be non-negative)
- Clarify when this feature will be supported
dim_case_embd : int - (Not supported yet) - Dimension of case specific embedding. + (Not supported yet - planned for future release) + Dimension of case specific embedding. Must be a non-negative integer. + When supported, this will enable case-specific embeddings for multi-task training.deepmd/pd/model/atomic_model/dp_atomic_model.py (2)
142-147: The implementation looks good but could be more robust.The method is well-structured and properly documented. However, consider these improvements for better robustness:
Consider applying these enhancements:
- def set_case_embd(self, case_idx: int): + def set_case_embd(self, case_idx: int) -> None: """ Set the case embedding of this atomic model by the given case_idx, typically concatenated with the output of the descriptor and fed into the fitting net. + + Parameters + ---------- + case_idx : int + The index of the case embedding to set. Must be non-negative. + + Raises + ------ + ValueError + If case_idx is negative + AttributeError + If fitting_net doesn't support case embedding """ + if case_idx < 0: + raise ValueError(f"case_idx must be non-negative, got {case_idx}") + if not hasattr(self.fitting_net, 'set_case_embd'): + raise AttributeError("fitting_net doesn't support case embedding") self.fitting_net.set_case_embd(case_idx)
142-147: Consider documenting the case embedding architectureThe implementation follows good architectural patterns, but the broader context of case embedding usage could be better documented.
Consider:
- Adding a section in the class docstring explaining the case embedding feature and its role in multi-task training
- Creating an architecture decision record (ADR) documenting:
- The motivation for case embeddings
- The chosen implementation approach
- Alternative approaches considered
- Impact on existing components
- Updating relevant documentation to explain how case embeddings affect model training and inference
deepmd/pd/model/task/fitting.py (2)
168-170: Consider enhancing the error message.While the validation is correct, the error message could be more informative by including when this feature will be supported or providing alternative approaches.
- raise ValueError("dim_case_embd is not supported yet in PaddlePaddle.") + raise ValueError( + "Case embedding (dim_case_embd > 0) is not supported yet in PaddlePaddle. " + "This feature is planned for a future release. For now, please use dim_case_embd=0." + )
361-367: Consider using a shared error message constant.To maintain consistency and ease future updates, consider defining the error message as a class constant.
+ _CASE_EMBD_ERROR = "Case embedding is not supported yet in PaddlePaddle." + def __init__(self, ...): if dim_case_embd > 0: - raise ValueError("dim_case_embd is not supported yet in PaddlePaddle.") + raise ValueError(self._CASE_EMBD_ERROR) def set_case_embd(self, case_idx: int): """ Set the case embedding of this fitting net by the given case_idx, typically concatenated with the output of the descriptor and fed into the fitting net. """ - raise NotImplementedError("set_case_embd is not supported yet in PaddlePaddle.") + raise NotImplementedError(self._CASE_EMBD_ERROR)deepmd/pd/model/model/make_model.py (2)
519-521: Add docstring and return type annotation.The method implementation looks correct, but could benefit from improved documentation:
- def set_case_embd(self, case_idx: int): + def set_case_embd(self, case_idx: int) -> None: + """Set the case embedding index for the atomic model. + + Parameters + ---------- + case_idx : int + The index of the case embedding to be set. + """ self.atomic_model.set_case_embd(case_idx)
519-521: Add test coverage for case embedding functionality.The new method should be covered by unit tests to ensure proper delegation and error handling.
Would you like me to help create unit tests for:
- Basic case embedding setting
- Error handling for invalid case indices
- Integration with the atomic model
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📒 Files selected for processing (5)
deepmd/pd/model/atomic_model/dp_atomic_model.py(1 hunks)deepmd/pd/model/model/make_model.py(1 hunks)deepmd/pd/model/task/ener.py(3 hunks)deepmd/pd/model/task/fitting.py(6 hunks)deepmd/pd/model/task/invar_fitting.py(3 hunks)
🔇 Additional comments (7)
deepmd/pd/model/task/ener.py (1)
63-63: LGTM: Parameter correctly propagated to parent class
The dim_case_embd parameter is properly passed to the parent class constructor.
deepmd/pd/model/task/invar_fitting.py (1)
Line range hint 32-35: Consider architectural implications of case embedding support
Since this is part of a larger feature for case embedding support:
- Document the overall architecture design for case embedding
- Consider adding integration tests for the feature when implemented
- Update the class docstring to mention case embedding capability in the class overview
Also applies to: 98-98
deepmd/pd/model/atomic_model/dp_atomic_model.py (1)
142-147: Verify case embedding implementation across components
Let's verify the integration of case embedding functionality across the codebase.
✅ Verification successful
Case embedding implementation is properly integrated across components
The verification shows that case embedding functionality is well-integrated across the codebase:
- The
set_case_embdmethod is consistently implemented in all atomic models (DP, Linear, PairTab) - The fitting networks properly handle case embeddings with appropriate initialization and propagation
- The feature is supported in PyTorch but explicitly marked as unsupported in TensorFlow/PaddlePaddle
- Test files and examples demonstrate usage with multi-task models
- Documentation in
doc/train/multi-task-training.mdproperly describes the feature
🏁 Scripts executed
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Script:
#!/bin/bash
# Description: Verify case embedding implementation and usage
# Check if fitting_net classes implement set_case_embd
echo "Checking for set_case_embd implementation in fitting networks:"
ast-grep --pattern 'class $_ {
$$$
set_case_embd($$$) {
$$$
}
$$$
}'
# Check for case embedding usage patterns
echo -e "\nChecking for case embedding usage patterns:"
rg "set_case_embd|dim_case_embd" -A 3
# Check for potential test files
echo -e "\nChecking for related test files:"
fd -g "*test*case*embd*.py"
Length of output: 42721
deepmd/pd/model/task/fitting.py (3)
106-108: LGTM: Documentation is clear and accurate.
The documentation clearly indicates that case embedding is not yet supported and provides a good description of the parameter's purpose.
146-146: LGTM: Parameter addition is well-placed.
The dim_case_embd parameter is appropriately placed in the argument list with a safe default value of 0.
284-284: LGTM: Serialization changes are consistent.
The version bump and addition of case embedding fields are appropriate for maintaining backward compatibility.
Also applies to: 292-292, 301-301
deepmd/pd/model/model/make_model.py (1)
519-521: Verify initialization and usage order of case embeddings.
Please ensure that:
- Case embeddings are properly initialized in the atomic model before this method is called
- The method is called at the appropriate time in the model's lifecycle
✅ Verification successful
Let me try a different search strategy to gather more information about the case embedding functionality.
Based on the search results, I can now provide a final response as the code analysis reveals the complete picture of case embedding initialization and usage:
Case embedding initialization and usage order is properly implemented.
The codebase shows that:
- Case embeddings are initialized during model creation with
dim_case_embdparameter in the fitting network - The
set_case_embdmethod is called at the right time during training throughdeepmd/pt/train/training.py, specifically:- Only called during multitask training from scratch (not during resuming)
- Called after model initialization but before training starts
- The case embedding index is properly managed through
get_case_embd_config
🏁 Scripts executed
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Script:
#!/bin/bash
# Description: Check for case embedding initialization and usage order
# Search for case embedding initialization
echo "Searching for case embedding initialization..."
rg -p "dim_case_embd|case_embd" --type python
# Search for set_case_embd usage
echo "Searching for set_case_embd usage..."
ast-grep --pattern 'set_case_embd($_)'
Length of output: 305
Script:
#!/bin/bash
# Search for case embedding related code with proper file extension
echo "Searching for case embedding initialization and usage..."
rg -p "case_embd" .
# Search for atomic model initialization and its properties
echo "Searching for atomic model initialization..."
rg -p "atomic_model" .
# Search specifically in the make_model.py file for context
echo "Checking make_model.py content..."
rg -p "class CM|atomic_model" deepmd/pd/model/model/make_model.py
Length of output: 34961
Summary by CodeRabbit
New Features
set_case_embdmethod across multiple atomic model classes to enhance case embedding functionality.dim_case_embdparameter in various fitting classes to support case-specific embedding dimensions.dim_case_embdin the output.share_paramsmethod in theDescrptDPA2class to streamline parameter sharing logic.Bug Fixes
Documentation
Tests