chore: rename j_must_have to j_deprecated and only warn about deprecated keys

[njzjz]

Fix #3523.

Summary by CodeRabbit

• Refactor

  • Simplified code by removing the j_must_have function and directly accessing dictionary keys in various test files.
  • Replaced j_must_have with direct dictionary access for improved code readability and maintenance.

• Chores

  • Updated test files to directly access dictionary values, enhancing code readability and maintainability.

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Commits

Files that changed from the base of the PR and between c7d560f and fa8b2a6.

Walkthrough

The changes involve the removal of the j_must_have function from various files in the deepmd project and replacing its usage with direct dictionary access. Additionally, the j_must_have function has been renamed to j_deprecated in deepmd/common.py, with updated parameter names and an optional deprecated_key parameter to issue warnings for deprecated keys.

Changes

File(s)	Change Summary
deepmd/common.py	Removed j_must_have function, renamed it to j_deprecated with updated parameters and added an optional deprecated_key.
deepmd/tf/common.py	Removed j_must_have from the list of exported entities.
deepmd/tf/entrypoints/train.py, deepmd/tf/train/trainer.py	Removed j_must_have function calls and directly accessed dictionary keys.
deepmd/utils/compat.py, deepmd/utils/data_system.py	Replaced j_must_have with j_deprecated and updated direct dictionary key access.
source/tests/tf/...	Removed j_must_have imports and calls, replaced with direct dictionary key access in multiple test files.

Assessment against linked issues

Objective (Issue #3523)	Addressed	Explanation
Rename j_must_have to j_deprecated and update parameters	✅
Add optional deprecated_key parameter to j_deprecated	✅
Remove j_must_have usage and replace with direct dictionary access	✅

The changes meet the primary objectives of renaming the j_must_have function to j_deprecated, adding an optional deprecated_key parameter, and updating the codebase to remove j_must_have usage in favor of direct dictionary access.

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[github-advanced-security]

CodeQL found more than 20 potential problems in the proposed changes. Check the Files changed tab for more details.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 82.48%. Comparing base (7b16911) to head (fa8b2a6).
Report is 134 commits behind head on devel.

Additional details and impacted files

@@            Coverage Diff             @@
##            devel    #3816      +/-   ##
==========================================
- Coverage   82.48%   82.48%   -0.01%     
==========================================
  Files         516      516              
  Lines       49057    49057              
  Branches     2987     2986       -1     
==========================================
- Hits        40466    40465       -1     
- Misses       7680     7681       +1     
  Partials      911      911              

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[coderabbitai]

Actionable comments posted: 28

Outside diff range and nitpick comments (4)

deepmd/utils/data_system.py (1)

794-797: Add detailed documentation for the get_batch method to clarify its behavior and parameters.

deepmd/tf/train/trainer.py (3)

Line range hint 93-125: Consider adding error handling for missing keys in jdata.

Using direct dictionary access (jdata["key"]) can lead to KeyError if the key is missing. Consider using jdata.get("key", default_value) to provide default values or handle missing keys gracefully.

---

Line range hint 184-186: Refine the assertion error message in build method.

The assertion assert isinstance(self.fitting, EnerFitting) could be more informative. Consider raising a specific exception with a clear error message if the condition is not met.

- assert isinstance(self.fitting, EnerFitting), "Data in mixed_type format must use ener fitting!"
+ if not isinstance(self.fitting, EnerFitting):
+     raise TypeError("Data in mixed_type format must use EnerFitting. Current fitting type: {}".format(type(self.fitting).__name__))

---

Line range hint 1015-1017: Consider replacing tf.py_func with tf.data API for better performance and compatibility.

tf.py_func is not recommended as it does not support graph optimizations and may not be compatible with TensorFlow 2.x. Consider using tf.data API for building data loading operations, which is more efficient and compatible with newer TensorFlow versions.

[coderabbitai]

Actionable comments posted: 4

[coderabbitai]

Actionable comments posted: 27

[njzjz]

@coderabbitai resolve

[coderabbitai]

Actions Performed

Comments resolved.