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feat(jax): force & virial #4251

Merged
merged 8 commits into from
Oct 29, 2024
Merged

feat(jax): force & virial #4251

merged 8 commits into from
Oct 29, 2024

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njzjz
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@njzjz njzjz commented Oct 25, 2024

Summary by CodeRabbit

Release Notes

  • New Features

    • Introduced new methods forward_common_atomic in multiple classes to enhance atomic model predictions and derivative calculations.
    • Added a new function get_leading_dims for better handling of output dimensions.
    • Added a new function scatter_sum for performing reduction operations on tensors.
    • Updated test methods to include flexible handling of results with the new SKIP_FLAG variable.
  • Bug Fixes

    • Improved numerical stability in calculations by ensuring small values are handled appropriately.
  • Tests

    • Expanded test outputs to include additional data like forces and virials for more comprehensive testing.
    • Enhanced backend handling in tests to accommodate new return values based on backend availability.

Signed-off-by: Jinzhe Zeng <[email protected]>
@njzjz njzjz requested review from iProzd and wanghan-iapcm October 25, 2024 02:02
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coderabbitai bot commented Oct 25, 2024

📝 Walkthrough
📝 Walkthrough

Walkthrough

The pull request introduces several modifications across multiple files, primarily focusing on enhancing the functionality of the CM class in make_model.py by adding a new method, forward_common_atomic, and refactoring the existing call_lower method. Additionally, it introduces a new function, get_leading_dims, in transform_output.py, and modifies the _make_env_mat function in env_mat.py for numerical stability. Changes also include updates to the EnergyModel class in ener_model.py and the addition of new methods in test files to improve backend handling and output extraction.

Changes

File Change Summary
deepmd/dpmodel/model/make_model.py - Added method forward_common_atomic in CM class.
- Refactored call_lower to use forward_common_atomic and updated return value processing.
deepmd/dpmodel/model/transform_output.py - Added method get_leading_dims to compute leading dimensions.
- Modified communicate_extended_output to enhance derivative handling.
deepmd/dpmodel/utils/env_mat.py - Modified _make_env_mat to improve numerical stability by replacing small values in diff.
deepmd/jax/env.py - Added commented-out line for enabling JAX NaN debugging.
deepmd/jax/model/base_model.py - Added method forward_common_atomic to enhance atomic model predictions and derivatives.
deepmd/jax/model/ener_model.py - Added method forward_common_atomic in EnergyModel class.
- Modified __setattr__ to deserialize atomic_model.
source/tests/consistent/common.py - Added global variable SKIP_FLAG for conditional test assertions.
source/tests/consistent/model/common.py - Updated build_tf_model method to include ret["force"] in return values.
source/tests/consistent/model/test_ener.py - Added method get_reference_backend for backend handling.
- Updated extract_ret to include SKIP_FLAG and adjust return values based on backend.

Possibly related PRs

  • feat(jax): energy model (no grad support) #4226: The changes in this PR involve modifications to the forward_common_atomic method in the BaseAtomicModel class, which is relevant to the new forward_common_atomic method added in the main PR's CM class. Both methods share similar parameters and functionality, indicating a direct relationship between the two.

Suggested reviewers

  • wanghan-iapcm
  • iProzd

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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 docstring

The 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:
         continue
deepmd/dpmodel/model/transform_output.py (1)

84-84: Avoid suppressing linter warnings without justification

The use of # noqa:RUF005 suppresses 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.

📜 Review details

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Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 02580c2 and 0517b59.

📒 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"] to build_tf_model return value is properly handled:

  • In test_ener.py, the extract_ret method explicitly unpacks all three values including force: return (ret[0].ravel(), ret[1].ravel(), ret[2].ravel())
  • In test_frozen.py, the extract_ret method only uses energy values ret[0] and ret[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


Script:

#!/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.py

Length of output: 225


Script:

#!/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.py

Length 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 addition

The SKIP_FLAG import is properly placed and consistently used in the code.


214-228: Verify test coverage for the modified return values

Let'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_ret method is consistently implemented across different test files, and each implementation is specific to its test case. The changes in test_ener.py are 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.py are part of a consistent testing pattern where:

  • Each test class implements its own extract_ret method specific to its needs
  • The base testing framework in common.py coordinates 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


Script:

#!/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


Script:

#!/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.py

Length of output: 521

deepmd/dpmodel/model/make_model.py (2)

225-235: LGTM! Clean refactoring of call_lower.

The refactoring improves code organization by delegating atomic model interaction to the new forward_common_atomic method 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 BaseAtomicModel or DPAtomicModel.


The forward_common_atomic method is properly implemented in the base class

The verification shows that forward_common_atomic is implemented in BaseAtomicModel class, which all atomic models inherit from either directly or through DPAtomicModel. The method signature matches the usage in the code under review, accepting all the required parameters: extended_coord, extended_atype, nlist, mapping, fparam, and aparam. 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


Script:

#!/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 5

Length of output: 3950


Script:

#!/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 10

Length 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 added

The newly added imports for Optional, get_deriv_name, get_reduce_name, jax, and jnp are 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 added

The addition of OutputVariableDef to the import statements ensures that it is available for use in the code.

source/tests/consistent/model/common.py Outdated Show resolved Hide resolved
deepmd/dpmodel/utils/env_mat.py Outdated Show resolved Hide resolved
source/tests/consistent/model/test_ener.py Outdated Show resolved Hide resolved
deepmd/jax/model/base_model.py Outdated Show resolved Hide resolved
deepmd/dpmodel/model/transform_output.py Outdated Show resolved Hide resolved
deepmd/dpmodel/model/transform_output.py Outdated Show resolved Hide resolved
Signed-off-by: Jinzhe Zeng <[email protected]>
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codecov bot commented Oct 25, 2024

Codecov Report

Attention: Patch coverage is 97.46835% with 2 lines in your changes missing coverage. Please review.

Project coverage is 84.25%. Comparing base (02580c2) to head (19e0970).
Report is 16 commits behind head on devel.

Files with missing lines Patch % Lines
deepmd/dpmodel/model/transform_output.py 93.33% 2 Missing ⚠️
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.
📢 Have feedback on the report? Share it here.

Signed-off-by: Jinzhe Zeng <[email protected]>
@njzjz njzjz changed the title feat(jax): force feat(jax): force & virial Oct 25, 2024
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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 feature

The 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

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between d0b576f and 004b89a.

📒 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-organized

The new imports are properly organized and necessary for the added functionality.


21-30: LGTM: Well-structured method signature with type hints

The method signature is clear, with appropriate type hints and optional parameters.


31-48: LGTM: Atomic model computation is well-implemented

The 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.py as a reserved name pattern
  • It has dedicated helper functions (get_reduce_name) and processing logic
  • It's extensively tested in test_output_def.py with proper shape, atomic flag, and category validations
  • The implementation in transform_output.py follows the established output definition patterns

The 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 py

Length 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 2

Length of output: 13700

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deepmd/dpmodel/model/transform_output.py Outdated Show resolved Hide resolved
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Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0

🧹 Outside diff range and nitpick comments (2)
deepmd/jax/common.py (1)

100-107: Enhance documentation and add input validation.

While the implementation is correct, consider these improvements:

  1. The docstring should be more comprehensive, including:

    • Parameter descriptions
    • Return value description
    • Example usage
    • Shape requirements
  2. 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 feature

The do_atomic_virial parameter is added but raises NotImplementedError when True. Consider adding a docstring to document this limitation and future plans.

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  • 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_sum function is:

  • Only used in communicate_extended_output for JAX arrays specifically
  • Properly guarded with JAX array type checking
  • The only implementation using the .at[].add pattern in the codebase
  • Used consistently for both force and virial calculations
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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 python

Length of output: 1691


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echo "Searching for similar scatter operations..."
rg "\.at\[.*\]\.add" --type-add 'python:*.py'

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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 imports

The imports are cleanly organized by functionality and all are necessary for the implementation.


41-48: LGTM: Clean implementation of output reduction

The implementation correctly handles reducible outputs with proper axis management.


49-94: LGTM: Efficient derivative computation with proper closure handling

The 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 transformations

The 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] where out_lead_shape preserves 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
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Length of output: 244622


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# Look for shape-related assertions in the model tests
rg -A 10 "model_predict|extended_force|extended_virial" --type-add "py:*.py" --type py

Length of output: 46683


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fd "test.*\.py$" deepmd

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Length of output: 234


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rg -A 5 "force.*shape|virial.*shape" deepmd/pt/model/model/

Length of output: 501


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# Search for shape-related code in base_model.py
rg -A 10 "shape|reshape" deepmd/jax/model/base_model.py

Length of output: 1503

njzjz added 2 commits October 25, 2024 02:53
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 feature

The 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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  • deepmd/jax/model/base_model.py (1 hunks)
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deepmd/jax/model/base_model.py (2)

2-16: LGTM: Well-organized imports

The new imports are logically grouped and all imported symbols are utilized in the implementation.


21-40: LGTM: Well-structured method signature and initialization

The method is well-designed with:

  • Clear type hints for all parameters
  • Proper handling of optional parameters
  • Clean initialization of atomic predictions

deepmd/jax/model/base_model.py Outdated Show resolved Hide resolved
deepmd/jax/model/base_model.py Outdated Show resolved Hide resolved
njzjz added 2 commits October 26, 2024 03:19
Signed-off-by: Jinzhe Zeng <[email protected]>
@njzjz njzjz requested a review from wanghan-iapcm October 26, 2024 07:27
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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 unimplemented do_atomic_virial parameter

The do_atomic_virial parameter is added but not fully implemented yet (as seen in the commented-out code at line 103). Consider either:

  1. Removing the parameter until the feature is implemented, or
  2. Adding a docstring to clarify that this feature is planned but not yet available.

73-88: Document the shape transformations

The 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 calculation

The 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 TODO

The commented-out code block for atomic virial should be handled better. Consider:

  1. Moving it to a TODO comment at the top of the file
  2. Creating a GitHub issue to track this feature
  3. 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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  • deepmd/jax/model/base_model.py (1 hunks)
🔇 Additional comments (2)
deepmd/jax/model/base_model.py (2)

2-16: LGTM: Well-organized imports

The imports are properly organized and include all necessary dependencies for the new functionality.


75-82: Verify JAX transformation performance

The use of jax.vmap(jax.jacrev()) is correct, but consider verifying the performance implications:

  1. Profile the execution time with different batch sizes
  2. 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_x64 configuration
  • 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.

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rg -l "performance|optimization" --type md

Length of output: 1008


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#!/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"

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rg -B2 -A2 "jax.*config" 

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rg -l "benchmark|profile|timing" --type py

Length of output: 14035

@iProzd iProzd enabled auto-merge October 29, 2024 10:18
@iProzd iProzd added this pull request to the merge queue Oct 29, 2024
Merged via the queue into deepmodeling:devel with commit 159361d Oct 29, 2024
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