[fix] Add trust_remote_code option to prepare_dataset. #6338
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benchmarks/cpp/prepare_dataset.py (2)
43-45: Fix line length violation.The error message exceeds the 120-character limit as flagged by static analysis.
- raise ValueError( - f"Cannot find a tokenizer from the given string because of {e}\nPlease set tokenizer to the directory that contains the tokenizer, or set to a model name in HuggingFace." - ) + raise ValueError( + f"Cannot find a tokenizer from the given string because of {e}\n" + f"Please set tokenizer to the directory that contains the tokenizer, " + f"or set to a model name in HuggingFace." + )
88-91: Consider enhancing the help text for security clarity.The CLI option is correctly implemented, but the help text could be more descriptive about the security implications.
-@click.option("--trust-remote-code", - is_flag=True, - default=False, - help="Trust remote code.") +@click.option("--trust-remote-code", + is_flag=True, + default=False, + help="Trust remote code when loading tokenizer (security risk).")
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benchmarks/cpp/prepare_dataset.py
44-44: Line too long (186 > 120)
(E501)
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🔇 Additional comments (6)
benchmarks/cpp/prepare_dataset.py (6)
19-19: LGTM! Proper migration from deprecated Pydantic API.The import correctly updates from the deprecated
field_validatorto the modernmodel_validator, which aligns with the validator method changes in the class.
31-31: LGTM! Secure default for remote code trust.The new field properly defaults to
False, following security best practices by not trusting remote code unless explicitly enabled.
35-36: LGTM! Correct migration to model_validator.The decorator and method signature correctly implement the modern Pydantic validation pattern using
model_validator(mode='after').
38-41: LGTM! Proper integration of trust_remote_code parameter.The tokenizer loading correctly incorporates the new
trust_remote_codefield, enhancing security control over remote code execution.
47-49: LGTM! Correct model validator return pattern.The method properly assigns the tokenizer instance to
self.tokenizerand returnsself, which is the correct pattern for Pydantic model validators.
108-109: LGTM! Consistent parameter passing.The new
trust_remote_codeparameter is correctly passed to the RootArgs constructor, following the established pattern.
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Signed-off-by: Frank Di Natale <[email protected]>
Signed-off-by: Frank Di Natale <[email protected]>
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Actionable comments posted: 0
🧹 Nitpick comments (2)
benchmarks/cpp/prepare_dataset.py (2)
35-49: LGTM: Proper Pydantic v2 migration with minor type consideration.The migration from
field_validatortomodel_validator(mode='after')is correctly implemented. The logic properly uses bothself.tokenizerandself.trust_remote_codefields to load the tokenizer with the new parameter.Consider updating the type annotation for the
tokenizerfield to reflect that it becomes a tokenizer instance after validation, or document this behavior for clarity.
43-45: Fix line length violation per static analysis.Line 44 exceeds the 120-character limit. Consider breaking the error message for better readability.
- raise ValueError( - f"Cannot find a tokenizer from the given string because of {e}\nPlease set tokenizer to the directory that contains the tokenizer, or set to a model name in HuggingFace." - ) + raise ValueError( + f"Cannot find a tokenizer from the given string because of {e}\n" + f"Please set tokenizer to the directory that contains the tokenizer, " + f"or set to a model name in HuggingFace." + )
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**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile = ...).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL = ...).
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a Python class in the constructor.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
benchmarks/cpp/prepare_dataset.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. The block should be prepended to the top of all files, including .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
benchmarks/cpp/prepare_dataset.py
🪛 Ruff (0.12.2)
benchmarks/cpp/prepare_dataset.py
44-44: Line too long (186 > 120)
(E501)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
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🔇 Additional comments (4)
benchmarks/cpp/prepare_dataset.py (4)
19-19: LGTM: Import updated correctly for Pydantic model validator.The addition of
model_validatorto the import is necessary for the migration from the deprecatedfield_validator.
31-31: LGTM: Security-conscious field addition.The new
trust_remote_codefield withFalsedefault is well-designed, requiring explicit opt-in for security.
88-92: LGTM: Well-designed CLI option with environment variable support.The
--trust-remote-codeflag is properly implemented with security-conscious defaults and flexible environment variable support.
109-110: LGTM: Constructor correctly updated with new parameter.The
trust_remote_codeparameter is properly passed from CLI to theRootArgsconstructor.
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Signed-off-by: Lanyu Liao <[email protected]>
Summary by CodeRabbit
New Features
--trust-remote-code, allowing users to control remote code trust when loading tokenizers. Defaults toFalse.Bug Fixes
Description
This PR adds the
--trust-remote-codeoption to dataset preparation. It adds a click flag and augments theRootArgsmodel class to use a model validator so that it can initialize the tokenizer with other options in the instance.Test Coverage
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