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refact: the DPA2 descriptor #3758
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Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Duo <[email protected]>
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Signed-off-by: Duo <[email protected]>
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CodeQL found more than 20 potential problems in the proposed changes. Check the Files changed tab for more details.
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Actionable comments posted: 11
Out of diff range and nitpick comments (1)
deepmd/pt/model/descriptor/repformer_layer.py (1)
271-290
: Review the initialization parameters ofAtten2Map
.Consider adding default values for parameters such as
has_gate
andsmooth
in theAtten2Map
class to enhance usability and avoid potential errors if these parameters are not provided.
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Actionable comments posted: 5
Out of diff range and nitpick comments (2)
deepmd/pt/model/descriptor/repformer_layer.py (2)
32-67
: Consider adding type hints for_scale
and_mode
in theget_residual
function to improve code readability and maintainability.
494-494
: Plan to refactorgrrg
for reuse across descriptors.The method
grrg
could be reused in different descriptors. Plan a refactoring in a future PR to improve code reuse and maintainability.
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Actionable comments posted: 7
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Actionable comments posted: 0
Out of diff range and nitpick comments (1)
deepmd/pt/model/descriptor/repformer_layer.py (1)
32-67
: Consider adding type hints for all parameters in theget_residual
function to enhance code readability and maintainability.
- Refact the DPA2 descriptor in PyTorch with clearer interface - Support residual - Remove bn - Add numpy implement <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Added a new descriptor class `DescrptDPA2` implementing DPA-2 functionality for computing descriptors and representations based on input coordinates and atom types. - Expanded supported backends for DPA-2 descriptor to include DP in addition to PyTorch. - **Documentation** - Updated the supported backends information in the documentation for the DPA-2 descriptor to reflect the addition of DP backend support. - Added a reference to the model implementation and a training example link in the DPA-2 descriptor documentation. - **Tests** - Introduced test cases for the `DescrptDPA2` class in different frameworks like TensorFlow, PyTorch, and DeepMD to cover various parameters and configurations. - Validated the functionality of the `DescrptDPA2` descriptor class for deep learning models in the test case class `TestDescrptDPA2`. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Duo <[email protected]> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Refact the DPA2 descriptor in PyTorch with clearer interface - Support residual - Remove bn - Add numpy implement <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Added a new descriptor class `DescrptDPA2` implementing DPA-2 functionality for computing descriptors and representations based on input coordinates and atom types. - Expanded supported backends for DPA-2 descriptor to include DP in addition to PyTorch. - **Documentation** - Updated the supported backends information in the documentation for the DPA-2 descriptor to reflect the addition of DP backend support. - Added a reference to the model implementation and a training example link in the DPA-2 descriptor documentation. - **Tests** - Introduced test cases for the `DescrptDPA2` class in different frameworks like TensorFlow, PyTorch, and DeepMD to cover various parameters and configurations. - Validated the functionality of the `DescrptDPA2` descriptor class for deep learning models in the test case class `TestDescrptDPA2`. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Duo <[email protected]> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Summary by CodeRabbit
New Features
DescrptDPA2
implementing DPA-2 functionality for computing descriptors and representations based on input coordinates and atom types.Documentation
Tests
DescrptDPA2
class in different frameworks like TensorFlow, PyTorch, and DeepMD to cover various parameters and configurations.DescrptDPA2
descriptor class for deep learning models in the test case classTestDescrptDPA2
.