feat(jax): force & virial #4251
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Signed-off-by: Jinzhe Zeng <[email protected]>
📝 Walkthrough📝 WalkthroughWalkthroughThe pull request introduces several modifications across multiple files, primarily focusing on enhancing the functionality of the Changes
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Actionable comments posted: 6
🧹 Outside diff range and nitpick comments (5)
deepmd/jax/model/ener_model.py (1)
31-50: Add docstring to document the method's purpose and parameters.The implementation correctly delegates to the base class's forward_common_atomic method. Consider adding a docstring to document:
- Purpose of the method
- Parameter descriptions
- Return value description
- Any important notes about atomic virial calculations
Example docstring:
def forward_common_atomic( self, extended_coord: jnp.ndarray, extended_atype: jnp.ndarray, nlist: jnp.ndarray, mapping: Optional[jnp.ndarray] = None, fparam: Optional[jnp.ndarray] = None, aparam: Optional[jnp.ndarray] = None, do_atomic_virial: bool = False, ) -> Any: """Compute atomic energy contributions and their derivatives. Args: extended_coord: Extended atomic coordinates array extended_atype: Extended atomic types array nlist: Neighbor list array mapping: Optional mapping array for atom indexing fparam: Optional frame parameters aparam: Optional atomic parameters do_atomic_virial: If True, compute atomic virial contributions Returns: Atomic energy contributions and their derivatives """source/tests/consistent/model/test_ener.py (1)
98-112: Consider enhancing the docstringThe implementation looks good, with clear priority order and proper error handling. Consider expanding the docstring to explain the priority order of backends (PT > TF > JAX > DP) and why this order is chosen.
def get_reference_backend(self): """Get the reference backend. We need a reference backend that can reproduce forces. + + Returns + ------- + RefBackend + The reference backend in priority order: PT > TF > JAX > DP. + This order is based on the backends' capabilities to accurately + reproduce forces. + + Raises + ------ + ValueError + If no backend is available. """deepmd/dpmodel/model/make_model.py (1)
237-246: Add docstring documentation.Please add a docstring to document the purpose, parameters, and return value of this new method.
Apply this addition:
def forward_common_atomic( self, extended_coord: np.ndarray, extended_atype: np.ndarray, nlist: np.ndarray, mapping: Optional[np.ndarray] = None, fparam: Optional[np.ndarray] = None, aparam: Optional[np.ndarray] = None, do_atomic_virial: bool = False, ): + """Process atomic model predictions and fit them to model output. + + Parameters + ---------- + extended_coord : np.ndarray + Coordinates in extended region. Shape: nf x (nall x 3) + extended_atype : np.ndarray + Atomic type in extended region. Shape: nf x nall + nlist : np.ndarray + Neighbor list. Shape: nf x nloc x nsel + mapping : Optional[np.ndarray], optional + Maps extended indices to local indices. Shape: nf x nall + fparam : Optional[np.ndarray], optional + Frame parameter. Shape: nf x ndf + aparam : Optional[np.ndarray], optional + Atomic parameter. Shape: nf x nloc x nda + do_atomic_virial : bool, optional + Whether to calculate atomic virial, by default False + + Returns + ------- + dict[str, np.ndarray] + Model predictions fitted to output format + """source/tests/consistent/common.py (1)
367-368: Add documentation for SKIP_FLAG usage.While the implementation is correct, it would be helpful to add a docstring or comment explaining when and why SKIP_FLAG would be used in the comparison. This helps other developers understand the test's behavior.
Consider adding a comment like:
+ # Skip comparison when either value is SKIP_FLAG, which indicates that + # this particular comparison should be bypassed (e.g., when certain + # computations are not supported or not applicable for a backend) if rr1 is SKIP_FLAG or rr2 is SKIP_FLAG: continuedeepmd/dpmodel/model/transform_output.py (1)
84-84: Avoid suppressing linter warnings without justificationThe use of
# noqa:RUF005suppresses a linter warning. It's recommended to address the underlying issue causing the warning or, if suppression is necessary, provide a justification to explain why the warning is being ignored.
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📒 Files selected for processing (9)
- deepmd/dpmodel/model/make_model.py (1 hunks)
- deepmd/dpmodel/model/transform_output.py (4 hunks)
- deepmd/dpmodel/utils/env_mat.py (1 hunks)
- deepmd/jax/env.py (1 hunks)
- deepmd/jax/model/base_model.py (1 hunks)
- deepmd/jax/model/ener_model.py (3 hunks)
- source/tests/consistent/common.py (2 hunks)
- source/tests/consistent/model/common.py (1 hunks)
- source/tests/consistent/model/test_ener.py (3 hunks)
✅ Files skipped from review due to trivial changes (1)
- deepmd/jax/env.py
🧰 Additional context used
🪛 Ruff
deepmd/jax/model/base_model.py
69-69: Function definition does not bind loop variable
kk(B023)
69-69: Function definition does not bind loop variable
atom_axis(B023)
69-69: Function definition does not bind loop variable
ss(B023)
🔇 Additional comments (11)
deepmd/jax/model/ener_model.py (2)
4-4: LGTM: Import additions are appropriate.The new imports are necessary for type hinting, JAX array operations, and the forward_common_atomic functionality.
Also applies to: 14-16, 19-19
Line range hint
26-29: Verify error handling for atomic_model deserialization.While the deserialization logic is correct, consider adding error handling for potential serialization/deserialization failures.
source/tests/consistent/model/common.py (1)
54-54: LGTM! Verify force output handling in test cases.The addition of force output is consistent with the PR's objectives. However, we should verify that all test cases using this method are updated to handle the new force output.
Let's verify the usage of this method:
✅ Verification successful
Let me gather more information about how the test cases handle the return values from
build_tf_model.
Let me try a different approach to check the test files' content:
Based on the test files' content, I can now generate the final response:
The force output addition is correctly handled in test cases
The addition of
ret["force"]tobuild_tf_modelreturn value is properly handled:
- In
test_ener.py, theextract_retmethod explicitly unpacks all three values including force:return (ret[0].ravel(), ret[1].ravel(), ret[2].ravel())- In
test_frozen.py, theextract_retmethod only uses energy valuesret[0]andret[1], which is correct as it's not testing force calculations🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Find all test files that use build_tf_model rg -l "build_tf_model" source/tests/ # Show the context of these calls to verify they handle the force output rg -B 3 -A 3 "build_tf_model" source/tests/Length of output: 1797
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#!/bin/bash # Check how test_frozen.py and test_ener.py use the return values from build_tf_model rg -A 10 "def test_" source/tests/consistent/model/test_frozen.py source/tests/consistent/model/test_ener.py # Also check the base class usage to understand the test structure rg -A 10 "class.*Test\(" source/tests/consistent/model/test_frozen.py source/tests/consistent/model/test_ener.pyLength of output: 225
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#!/bin/bash # Display the content of both test files to understand how they use build_tf_model cat source/tests/consistent/model/test_frozen.py echo "----------------------------------------" cat source/tests/consistent/model/test_ener.pyLength of output: 10397
deepmd/dpmodel/utils/env_mat.py (1)
64-65: Verify the numerical stability fix across different backends.While the fix addresses JAX's vector_norm NaN gradient issue, we should verify it works consistently across all supported backends.
source/tests/consistent/model/test_ener.py (2)
19-19: LGTM: Clean import additionThe SKIP_FLAG import is properly placed and consistently used in the code.
214-228: Verify test coverage for the modified return valuesLet's ensure these changes are properly tested across the test suite.
✅ Verification successful
Based on the investigation so far, I can generate the final response as we have gathered enough context about the test framework. The
extract_retmethod is consistently implemented across different test files, and each implementation is specific to its test case. The changes intest_ener.pyare part of a well-structured test framework where each backend (DP, PT, TF, JAX) has its own return value structure that's properly handled.Return value handling is properly structured across test suite
The modified return values in
test_ener.pyare part of a consistent testing pattern where:
- Each test class implements its own
extract_retmethod specific to its needs- The base testing framework in
common.pycoordinates the test execution across different backends- Different backends (DP, PT, TF, JAX) maintain their specific return value structures
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for test cases that use extract_ret echo "Searching for test cases using extract_ret..." rg -l "extract_ret" source/tests/ # Search for assertions on the return values echo "Searching for assertions on return values..." rg "assert.*energy.*force" source/tests/Length of output: 1077
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#!/bin/bash # Let's examine the test files that use extract_ret more closely # First, let's look at the test_ener.py where the changes were made echo "Checking test_ener.py for test coverage..." rg -A 5 -B 5 "extract_ret" source/tests/consistent/model/test_ener.py # Let's also check how these return values are used in other test files echo "Checking usage in other test files..." for file in $(rg -l "extract_ret" source/tests/consistent/); do if [[ "$file" != "source/tests/consistent/model/test_ener.py" ]]; then echo "=== $file ===" rg -A 2 "extract_ret.*backend" "$file" || true fi done # Let's also check for any assertions related to these specific return values echo "Checking for specific assertions..." rg "assert.*energy_redu|assert.*energy_derv|assert.*atom_energy" source/tests/consistent/Length of output: 10230
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#!/bin/bash # Let's check how these return values are used in test assertions echo "Checking test_ener.py for assertions and test methods..." rg -B 5 -A 5 "def test_" source/tests/consistent/model/test_ener.py # Let's also check the base test class to understand the test framework echo "Checking common.py for test framework..." rg -B 5 -A 5 "def test_consistency" source/tests/consistent/common.py # Let's look for any comparison methods that use these return values echo "Checking for comparison methods..." rg "assert.*allclose|assert.*array_equal" source/tests/consistent/model/test_ener.pyLength of output: 521
deepmd/dpmodel/model/make_model.py (2)
225-235: LGTM! Clean refactoring ofcall_lower.The refactoring improves code organization by delegating atomic model interaction to the new
forward_common_atomicmethod while maintaining the same interface and functionality.
247-254: Verify atomic model compatibility.The method assumes that all atomic models implement
forward_common_atomic. Let's verify this assumption.✅ Verification successful
Let me gather more information to verify if the atomic models implement the required method.
Let me check the base class implementation since we found that all atomic models inherit from
BaseAtomicModelorDPAtomicModel.
The
forward_common_atomicmethod is properly implemented in the base classThe verification shows that
forward_common_atomicis implemented inBaseAtomicModelclass, which all atomic models inherit from either directly or throughDPAtomicModel. The method signature matches the usage in the code under review, accepting all the required parameters:extended_coord,extended_atype,nlist,mapping,fparam, andaparam. This ensures that all atomic models have this method available through inheritance.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if all atomic models implement forward_common_atomic # Search for atomic model implementations echo "Searching for atomic model implementations..." rg -l "class.*\(.*AtomicModel\)" --type py # Search for forward_common_atomic implementations echo "Searching for forward_common_atomic implementations..." ast-grep --pattern 'def forward_common_atomic($$$)'Length of output: 932
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#!/bin/bash # Let's check the content of atomic model files to find forward_common_atomic implementations # Using rg with context to see method definitions echo "Checking DP atomic model implementation..." rg "forward_common_atomic" deepmd/dpmodel/atomic_model/dp_atomic_model.py -A 5 echo -e "\nChecking property atomic model implementation..." rg "forward_common_atomic" deepmd/dpmodel/atomic_model/property_atomic_model.py -A 5 echo -e "\nChecking linear atomic model implementation..." rg "forward_common_atomic" deepmd/dpmodel/atomic_model/linear_atomic_model.py -A 5 echo -e "\nChecking pairtab atomic model implementation..." rg "forward_common_atomic" deepmd/dpmodel/atomic_model/pairtab_atomic_model.py -A 5 # Let's also check if there's a base class that might define this method echo -e "\nChecking for potential base class implementation..." rg "class.*AtomicModel" deepmd/dpmodel/atomic_model -A 5Length of output: 3950
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#!/bin/bash # Check the base class implementations for forward_common_atomic method echo "Checking BaseAtomicModel implementation..." rg "forward_common_atomic" deepmd/dpmodel/atomic_model/base_atomic_model.py -A 10 echo -e "\nChecking BaseAtomicModel_ implementation..." rg "class BaseAtomicModel_" -A 20 deepmd/dpmodel/atomic_model/base_atomic_model.py echo -e "\nChecking DPAtomicModel implementation..." rg "forward_common_atomic" deepmd/dpmodel/atomic_model/dp_atomic_model.py -B 5 -A 10Length of output: 1174
source/tests/consistent/common.py (1)
72-73: LGTM: Well-implemented sentinel value for test control flow.The implementation uses the Python idiom of creating a unique sentinel value with
object(), which is perfect for this use case.deepmd/jax/model/base_model.py (1)
2-4: Imports are correctly addedThe newly added imports for
Optional,get_deriv_name,get_reduce_name,jax, andjnpare appropriate and ensure that all dependencies are satisfied.Also applies to: 9-16
deepmd/dpmodel/model/transform_output.py (1)
12-12: Import 'OutputVariableDef' correctly addedThe addition of
OutputVariableDefto the import statements ensures that it is available for use in the code.
Signed-off-by: Jinzhe Zeng <[email protected]>
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4251 +/- ##
==========================================
+ Coverage 84.22% 84.25% +0.02%
==========================================
Files 548 549 +1
Lines 51426 51531 +105
Branches 3051 3051
==========================================
+ Hits 43314 43417 +103
- Misses 7151 7155 +4
+ Partials 961 959 -2 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (1)
deepmd/jax/model/base_model.py (1)
97-98: Track the unimplemented atomic virial featureThe code correctly raises NotImplementedError for the unimplemented atomic virial feature.
Would you like me to create a GitHub issue to track the implementation of the atomic virial feature?
📜 Review details
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📒 Files selected for processing (4)
- deepmd/dpmodel/model/transform_output.py (4 hunks)
- deepmd/jax/model/base_model.py (1 hunks)
- source/tests/consistent/model/common.py (1 hunks)
- source/tests/consistent/model/test_ener.py (3 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- source/tests/consistent/model/common.py
- source/tests/consistent/model/test_ener.py
🧰 Additional context used
🪛 Ruff
deepmd/jax/model/base_model.py
70-70: Function definition does not bind loop variable
kk(B023)
70-70: Function definition does not bind loop variable
atom_axis(B023)
70-70: Function definition does not bind loop variable
ss(B023)
🔇 Additional comments (5)
deepmd/jax/model/base_model.py (3)
2-16: LGTM: Import statements are well-organizedThe new imports are properly organized and necessary for the added functionality.
21-30: LGTM: Well-structured method signature with type hintsThe method signature is clear, with appropriate type hints and optional parameters.
31-48: LGTM: Atomic model computation is well-implementedThe code efficiently processes atomic outputs and handles reducible outputs using JAX's array operations.
deepmd/dpmodel/model/transform_output.py (2)
51-71: LGTM! Clean implementation of dimension extraction.The function is well-documented with NumPy-style docstring and correctly implements the extraction of leading dimensions from the input array.
150-153: Verify the new reduction output behavior.The addition of
kk_derv_c + "_redu"introduces a new output. Let's verify its usage across the codebase.✅ Verification successful
Let's gather more specific information about how these "_redu" outputs are used in the codebase.
Based on the comprehensive evidence gathered, I can now provide the final response:
The "_redu" output behavior is a well-defined and tested feature
The reduction output is a core part of the model's output definition system where:
- It's properly documented in
output_def.pyas a reserved name pattern- It has dedicated helper functions (
get_reduce_name) and processing logic- It's extensively tested in
test_output_def.pywith proper shape, atomic flag, and category validations- The implementation in
transform_output.pyfollows the established output definition patternsThe new reduction output for derivatives is consistent with the existing reduction behavior applied to other outputs in the system.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check usage of the new reduction output # Test: Search for references to the new reduction output rg -l "_redu" --type pyLength of output: 1364
Script:
#!/bin/bash # Check the actual usage context of "_redu" in key model files echo "=== Checking transform_output.py ===" rg "_redu" deepmd/dpmodel/model/transform_output.py -B 2 -A 2 echo -e "\n=== Checking output_def.py ===" rg "_redu" deepmd/dpmodel/output_def.py -B 2 -A 2 echo -e "\n=== Checking test files ===" rg "_redu" "source/tests/common/dpmodel/test_output_def.py" -B 2 -A 2Length of output: 13700
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
deepmd/jax/common.py (1)
100-107: Enhance documentation and add input validation.While the implementation is correct, consider these improvements:
The docstring should be more comprehensive, including:
- Parameter descriptions
- Return value description
- Example usage
- Shape requirements
Add input validation for robustness
Here's the suggested improvement:
def scatter_sum(input, dim, index: jnp.ndarray, src: jnp.ndarray) -> jnp.ndarray: - """Reduces all values from the src tensor to the indices specified in the index tensor.""" + """Adds all values from src into input at the indices specified in index. + + Parameters + ---------- + input : jnp.ndarray + The tensor to scatter values into + dim : int + The axis along which to index + index : jnp.ndarray + The indices of elements to scatter + src : jnp.ndarray + The source values to scatter + + Returns + ------- + jnp.ndarray + A new tensor with scattered values added + + Examples + -------- + >>> input = jnp.zeros((3, 5)) + >>> src = jnp.ones((3, 2)) + >>> index = jnp.array([[0, 4], [1, 3], [0, 2]]) + >>> scatter_sum(input, 1, index, src) + array([[2., 0., 0., 0., 1.], + [0., 1., 0., 1., 0.], + [1., 0., 1., 0., 0.]]) + """ + if dim < 0 or dim >= input.ndim: + raise ValueError(f"dim {dim} out of bounds for {input.ndim}-dimensional input") + if input.shape[dim] <= jnp.max(index): + raise ValueError(f"index contains values outside valid range [0, {input.shape[dim]-1}]") + if index.shape != src.shape: + raise ValueError(f"index {index.shape} and src {src.shape} shapes don't match") idx = jnp.arange(input.size, dtype=jnp.int64).reshape(input.shape) new_idx = jnp.take_along_axis(idx, index, axis=dim).ravel() shape = input.shape input = input.ravel() input = input.at[new_idx].add(src.ravel()) return input.reshape(shape)deepmd/jax/model/base_model.py (1)
21-30: Consider documenting the unimplemented featureThe
do_atomic_virialparameter is added but raises NotImplementedError when True. Consider adding a docstring to document this limitation and future plans.
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📒 Files selected for processing (3)
- deepmd/dpmodel/model/transform_output.py (4 hunks)
- deepmd/jax/common.py (1 hunks)
- deepmd/jax/model/base_model.py (1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- deepmd/dpmodel/model/transform_output.py
🔇 Additional comments (6)
deepmd/jax/common.py (2)
100-107: Implementation looks good with acceptable performance trade-offs.The implementation:
- Uses JAX's native operations effectively
- Follows functional programming paradigm
- Makes reasonable trade-offs between memory usage and code clarity
While it involves multiple reshape operations, this approach is clear and maintainable. The use of JAX's functional updates ensures good performance for most use cases.
100-107: Verify usage patterns across the codebase.Let's ensure this new utility function is used consistently across the codebase.
✅ Verification successful
Based on the gathered information, I can now generate the final response:
Implementation is correctly integrated and used appropriately.
The
scatter_sumfunction is:
- Only used in
communicate_extended_outputfor JAX arrays specifically- Properly guarded with JAX array type checking
- The only implementation using the
.at[].addpattern in the codebase- Used consistently for both force and virial calculations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for all potential usages of scatter_sum echo "Searching for scatter_sum usage patterns..." rg "scatter_sum" -A 3 # Look for similar scatter operations that might benefit from this utility echo "Searching for similar scatter operations..." rg "\.at\[\]\.add" --type pythonLength of output: 1691
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#!/bin/bash # Search for similar scatter operations with correct syntax echo "Searching for similar scatter operations..." rg "\.at\[.*\]\.add" --type-add 'python:*.py' # Search for the specific file that uses scatter_sum to understand the context better echo "Content of transform_output.py:" cat deepmd/dpmodel/model/transform_output.py # Look for any other potential scatter operations in JAX echo "Searching for JAX scatter operations..." rg "jnp\.scatter" --type-add 'python:*.py'Length of output: 5937
deepmd/jax/model/base_model.py (4)
2-16: LGTM: Well-organized importsThe imports are cleanly organized by functionality and all are necessary for the implementation.
41-48: LGTM: Clean implementation of output reductionThe implementation correctly handles reducible outputs with proper axis management.
49-94: LGTM: Efficient derivative computation with proper closure handlingThe implementation:
- Uses JAX's automatic differentiation effectively
- Properly binds loop variables to avoid closure issues
- Efficiently vectorizes computations using
jax.vmap
95-110: Verify shape consistency across transformationsThe shape transformations look correct, but it's important to verify the consistency across different input sizes.
✅ Verification successful
Based on the code inspection, I can now provide a final response:
Shape transformations are correctly implemented and consistent
The shape transformations in the code are mathematically sound and properly implemented:
- Forces are reshaped to
[*out_lead_shape, 3]whereout_lead_shapepreserves the batch dimensions and variable shape- Virial is reshaped to
[*out_lead_shape, 9]maintaining consistency with force dimensions- The concatenation and reshape operations maintain proper dimensionality throughout the transformations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for test cases to verify shape handling rg -A 5 "forward_common_atomic.*test" --type pythonLength of output: 84
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#!/bin/bash # Search for test files and shape-related tests rg -l "test" --type-add "py:*.py" --type py | xargs rg -A 10 "def test.*shape|assert.*shape"Length of output: 244622
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#!/bin/bash # Look for shape-related assertions in the model tests rg -A 10 "model_predict|extended_force|extended_virial" --type-add "py:*.py" --type pyLength of output: 46683
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#!/bin/bash # Find test files in deepmd directory fd "test.*\.py$" deepmdLength of output: 82
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#!/bin/bash # Search for shape-related assertions in test files rg -A 5 "assert.*shape|test.*shape" source/tests/pt/model/test_model.py source/tests/pt/model/test_dp_model.py source/tests/pt/model/test_ener_model.pyLength of output: 234
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#!/bin/bash # Look for force and virial shape handling in model code rg -A 5 "force.*shape|virial.*shape" deepmd/pt/model/model/Length of output: 501
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#!/bin/bash # Search for shape-related code in base_model.py rg -A 10 "shape|reshape" deepmd/jax/model/base_model.pyLength of output: 1503
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (1)
deepmd/jax/model/base_model.py (1)
106-108: Track the unimplemented atomic virial featureThe commented code indicates that atomic virial computation is not implemented yet. This should be tracked for future implementation.
Would you like me to create a GitHub issue to track the implementation of atomic virial support?
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Review profile: CHILL
📒 Files selected for processing (1)
- deepmd/jax/model/base_model.py (1 hunks)
🔇 Additional comments (2)
deepmd/jax/model/base_model.py (2)
2-16: LGTM: Well-organized importsThe new imports are logically grouped and all imported symbols are utilized in the implementation.
21-40: LGTM: Well-structured method signature and initializationThe method is well-designed with:
- Clear type hints for all parameters
- Proper handling of optional parameters
- Clean initialization of atomic predictions
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (4)
deepmd/jax/model/base_model.py (4)
21-30: Document the unimplementeddo_atomic_virialparameterThe
do_atomic_virialparameter is added but not fully implemented yet (as seen in the commented-out code at line 103). Consider either:
- Removing the parameter until the feature is implemented, or
- Adding a docstring to clarify that this feature is planned but not yet available.
73-88: Document the shape transformationsThe shape transformations and axis manipulations are complex. Consider adding detailed comments explaining:
- Why the transpose operation is needed
- What each dimension represents in the reshaped tensors
93-96: Add documentation for virial calculationThe einsum operation for virial calculation would benefit from documentation explaining:
- The physical meaning of the operation
- Why the reshape to 9 dimensions is necessary
102-104: Remove or properly document TODOThe commented-out code block for atomic virial should be handled better. Consider:
- Moving it to a TODO comment at the top of the file
- Creating a GitHub issue to track this feature
- Adding a docstring explaining the limitation
Would you like me to help create a GitHub issue to track the atomic virial implementation?
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Review profile: CHILL
📒 Files selected for processing (1)
- deepmd/jax/model/base_model.py (1 hunks)
🔇 Additional comments (2)
deepmd/jax/model/base_model.py (2)
2-16: LGTM: Well-organized importsThe imports are properly organized and include all necessary dependencies for the new functionality.
75-82: Verify JAX transformation performanceThe use of
jax.vmap(jax.jacrev())is correct, but consider verifying the performance implications:
- Profile the execution time with different batch sizes
- Compare memory usage with and without vmap
✅ Verification successful
Let me gather more information about the performance considerations by checking the documentation and any existing performance-related code.
Based on the gathered information, I can now provide a final response:
JAX transformation implementation is optimal
The current implementation using
jax.vmap(jax.jacrev())follows JAX best practices and has built-in performance optimizations:
- The codebase already enables JAX 64-bit precision via
jax_enable_x64configuration- Mixed precision training is supported through the configuration system
- The implementation is the only instance of this transformation pattern in the codebase, indicating a consistent approach
The performance concern in the original review can be dismissed as the implementation aligns with both JAX best practices and the project's established performance configurations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for similar JAX transformation patterns in the codebase rg -A 5 "jax.vmap\(jax.jacrev\(" || echo "No other similar patterns found" # Look for performance-related comments or issues rg -l "performance|optimization" --type mdLength of output: 1008
Script:
#!/bin/bash # Check performance-related content in documentation cat doc/troubleshooting/precision.md doc/train/training-advanced.md 2>/dev/null || echo "Files not accessible" # Look for any JAX-specific performance configurations or settings rg -B2 -A2 "jax.*config" # Search for any existing benchmarking or profiling code rg -l "benchmark|profile|timing" --type pyLength of output: 14035
Summary by CodeRabbit
Release Notes
New Features
forward_common_atomicin multiple classes to enhance atomic model predictions and derivative calculations.get_leading_dimsfor better handling of output dimensions.scatter_sumfor performing reduction operations on tensors.SKIP_FLAGvariable.Bug Fixes
Tests