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commented
2 months ago Walkthrough
## Walkthrough The recent updates in the `deepmd` module aim to enhance reproducibility by introducing optional `seed` parameters in various classes and functions, allowing users to control random number generation for improved consistency. Additionally, new tensor initialization methods have been added to provide diverse strategies for effectively initializing neural network weights. ## Changes | Files/Paths | Change Summary | |---------------------------------------------------|---------------------------------------------------------------------------------------------------| | `deepmd/pt/model/descriptor/dpa1.py` | Added `seed` parameter to `DescrptDPA1` class for random seed initialization. | | `deepmd/pt/model/descriptor/dpa2.py` | Added `seed` parameter in `DPA2` class affecting parameter initialization and type embedding. | | `deepmd/pt/model/network/init.py` | Introduced various tensor initialization functions based on `torch.nn.init`. | | `source/tests/pt/model/test_forward_lower.py` | Doubled selection for virtual atoms in `setUp` function to avoid large errors in different setups. | ## Possibly related issues - **Issue deepmodeling/deepmd-kit#3467**: The changes in this update align with enforcing explicit seeds for random methods to ensure reproducibility, particularly within the `deepmd/pt` module.Recent Review Details
**Configuration used: CodeRabbit UI** **Review profile: CHILL**Commits
Files that changed from the base of the PR and between ac73c23342d741fb822006b118faaa8628afc865 and ad594b62b34e02996a9348e7dcb6876759899597.Files selected for processing (4)
* deepmd/pt/model/descriptor/dpa1.py (4 hunks) * deepmd/pt/model/descriptor/dpa2.py (6 hunks) * deepmd/pt/model/network/init.py (1 hunks) * source/tests/pt/model/test_forward_lower.py (2 hunks)Files skipped from review as they are similar to previous changes (2)
* deepmd/pt/model/descriptor/dpa1.py * deepmd/pt/model/descriptor/dpa2.pyAdditional comments not posted (11)
source/tests/pt/model/test_forward_lower.py (2)
`170-171`: Doubling the `sel` parameter for virtual atoms in tests seems justified to avoid large errors. Ensure this adjustment aligns with expected model behavior. --- `181-183`: Adjusting `nsel` parameters in `repinit` and `repformer` for virtual atoms in tests is noted. Verify that these changes do not adversely affect the model's expected behavior.deepmd/pt/model/network/init.py (9)
`182-193`: Using a no_grad wrapper in `zeros_` function aligns with best practices for tensor initialization. --- `196-207`: Using a no_grad wrapper in `ones_` function is correctly implemented. --- `210-226`: Proper use of no_grad wrapper and handling of torch function overrides in `constant_` function. --- `229-254`: Correct implementation of no_grad wrapper and torch function overrides in `normal_` function. --- `257-286`: Detailed and correct implementation of the truncated normal distribution in `trunc_normal_` function. --- `347-390`: Correct implementation of `kaiming_normal_` function using the Kaiming normal distribution. --- `393-422`: Correct implementation of `xavier_uniform_` function using the Xavier uniform distribution. --- `425-454`: Correct implementation of `xavier_normal_` function using the Xavier normal distribution. --- `289-344`: Implementation of `kaiming_uniform_` function is correct. Verify the calculations of `fan` and `gain` to ensure they are accurate.Verification successful
--- The implementations of `calculate_gain` and `_calculate_correct_fan` have been successfully extracted. Both functions appear to be correctly implemented according to the expected formulas for Kaiming initialization. - `calculate_gain` correctly returns the gain values for different nonlinearities, including `relu`, `leaky_relu`, and others. - `_calculate_correct_fan` correctly calculates the fan value based on the mode (`fan_in` or `fan_out`). The calculations of `fan` and `gain` in the `kaiming_uniform_` function are accurate.Scripts Executed
The following scripts were executed for the analysis: --- Script: ```shell #!/bin/bash # Description: Verify the calculations of `fan` and `gain` in `kaiming_uniform_`. # Test: Search for usage of `fan` and `gain` calculations. Expect: Accurate calculations. rg --type python $'fan|gain' ``` Length of output: 60 --- --- Script: ```shell #!/bin/bash # Description: Verify the calculations of `fan` and `gain` in `kaiming_uniform_`. # Test: Search for definitions and usages of `fan` and `gain` in the codebase. rg 'fan|gain' ``` Length of output: 24010 --- --- Script: ```shell #!/bin/bash # Description: Extract the implementations of `calculate_gain` and `_calculate_correct_fan` from `deepmd/pt/model/network/init.py`. # Extract the implementation of `calculate_gain`. rg --pcre2 --multiline --multiline-dotall 'def calculate_gain\([^)]*\):.*?def ' deepmd/pt/model/network/init.py # Extract the implementation of `_calculate_correct_fan`. rg --pcre2 --multiline --multiline-dotall 'def _calculate_correct_fan\([^)]*\):.*?def ' deepmd/pt/model/network/init.py ``` Length of output: 3364
codecov[bot]
wanghan-iapcm
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Summary by CodeRabbit
New Features
Improvements
seedparameter in initialization methods and classes.get_generatorfunction for random seed management.Bug Fixes