[API] Optimize clip_grad_norm #69661
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zyfncg
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Nov 25, 2024
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Summary of this PR: 1. upload DPA-1 related code 2. merge much develop code 3. add all eager composite operators except `softmax_grad`, `p_norm_grad`, `split_grad`, and `concat_grad` to the composite operator blacklist(<https://github.com/deepmodeling/deepmd-kit/pull/4414/files#diff-e678abb052b278f8a479f8d13b839a9ec0effd9923478a850bc13758f918e1e9R134-R148>) to significantly improve model execution speed (reducing the time taken from 100% more than PyTorch to about 10% to 15% more). related PR: lanpa/tensorboardX#728 ### Training curve:  ### Accuracy test(left: paddle, right: torch):  Ralated optimization of Paddle framework: - [x] PaddlePaddle/Paddle#69349 - [x] PaddlePaddle/Paddle#69333 - [x] PaddlePaddle/Paddle#69479 - [x] PaddlePaddle/Paddle#69515 - [x] PaddlePaddle/Paddle#69487 - [x] PaddlePaddle/Paddle#69661 - [x] PaddlePaddle/Paddle#69660 - [x] PaddlePaddle/Paddle#69596 - [x] PaddlePaddle/Paddle#69556 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## Release Notes - **New Features** - Introduced several new classes for molecular descriptors, including `DescrptDPA1`, `DescrptBlockSeAtten`, and `LayerNorm`, enhancing the modeling capabilities for molecular simulations. - Added new JSON configuration files for model parameters and multitask models related to water simulations. - Implemented new test classes for validating the functionality of the `DPAtomicModel` and various descriptor classes. - Added new test classes for evaluating denoising models, including `TestDenoiseModelDPA1` and `TestDenoiseModelDPA2`. - Enhanced the `ModelWrapper` class to clarify the handling of model parameters and state management. - **Bug Fixes** - Improved internal logic for handling model state saving and loading, ensuring consistency in outputs. - **Documentation** - Enhanced type hints and return annotations across various classes and methods for better clarity. - **Tests** - Expanded the testing framework with new test cases for denoising models and descriptor functionalities, ensuring robust validation of features. - Activated previously skipped tests for energy models, improving test coverage. - Enhanced multitask training tests with new configuration handling and test classes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Support DPA-2 in paddle backend. This PR will be updated after #4414 is merged. ### Training curve:  ### Accuracy test(left: paddle, right: torch):  Ralated optimization of Paddle framework: - [x] PaddlePaddle/Paddle#69349 - [x] PaddlePaddle/Paddle#69333 - [x] PaddlePaddle/Paddle#69479 - [x] PaddlePaddle/Paddle#69515 - [x] PaddlePaddle/Paddle#69487 - [x] PaddlePaddle/Paddle#69661 - [x] PaddlePaddle/Paddle#69660 - [x] PaddlePaddle/Paddle#69596 - [x] PaddlePaddle/Paddle#69556 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced new classes for molecular descriptors: `DescrptDPA2`, `DescrptBlockRepformers`, `DescrptSeTTebd`, and `DescrptBlockSeTTebd`. - Added new functions for tensor operations and descriptor management, enhancing the capabilities of the module. - Updated JSON configurations for multitask models to refine selection criteria and data paths. - **Bug Fixes** - Improved error handling and parameter validation across various descriptor classes. - **Documentation** - Enhanced test coverage for new descriptor functionalities and configurations. - **Tests** - Added new test classes to validate the functionality of `DescrptDPA2` and multitask training scenarios. - Expanded test capabilities for descriptor classes based on installed dependencies. - Updated existing tests to support new configurations and functionalities. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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PR Category
Performance Optimization
PR Types
Improvements
Description
Pcard-75624
梯度裁剪时，调用
grad_避免额外的GPU拷贝，同时使用multiply_替换multiply优化前
优化后
