Generating Public and Internal headers

There are 3 main components of the Headergen. The first component are the YAML files that contain all the function header information and are separated by header specification and standard. The second component are the classes that are created for each component of the function header: macros, enumerations, types, function, arguments, and objects. The third component is the Python script that uses the class representation to deserialize YAML files into its specific components and then reserializes the components into the function header. The Python script also combines the generated header content with header definitions and extra macro and type inclusions from the .h.def file.

Instructions

Required Versions:

  • Python Version: 3.8

  • PyYAML Version: 5.1

  1. Keep full-build mode on when building, otherwise headers will not be generated.

  2. Once the build is complete, enter in the command line within the build directory ninja check-hdrgen to ensure that the integration tests are passing.

  3. Then enter in the command line ninja libc to generate headers. Headers will be in build/projects/libc/include or build/libc/include in a runtime build. Sys spec headers will be located in build/projects/libc/include/sys.

To add a function to the YAML files, you can either manually enter it in the YAML file corresponding to the header it belongs to or add it through the command line.

To add through the command line:

  1. Make sure you are in the llvm-project directory.

  2. Enter in the command line:

    python3 libc/hdrgen/yaml_to_classes.py
libc/hdrgen/yaml/[yaml_file.yaml] --add_function "<return_type>" <function_name> "<function_arg1, function_arg2>" <standard> <guard> <attribute>

    Example:

    python3 libc/hdrgen/yaml_to_classes.py
libc/hdrgen/yaml/ctype.yaml --add_function "char" example_function
"int, void, const void" stdc example_float example_attribute

    Keep in mind only the return_type and arguments have quotes around them. If you do not have any guards or attributes you may enter “null” for both.

  3. Check the YAML file that the added function is present. You will also get a generated header file with the new addition in the hdrgen directory to examine.

If you want to sort the functions alphabetically you can check out libc/hdrgen/yaml_functions_sorted.py.

Testing

Headergen has an integration test that you may run once you have configured your CMake within the build directory. In the command line, enter the following: ninja check-hdrgen. The integration test is one test that ensures the process of YAML to classes to generate headers works properly. If there are any new additions on formatting headers, make sure the test is updated with the specific addition.

Integration Test can be found in: libc/hdrgen/tests/test_integration.py

File to modify if adding something to formatting: libc/hdrgen/tests/expected_output/test_header.h

Common Errors

  1. Missing function specific component

    Example:

    "/llvm-project/libc/hdrgen/yaml_to_classes.py", line 67, in yaml_to_classes function_data["return_type"]

    If you receive this error or any error pertaining to function_data[function_specific_component] while building the headers that means the function specific component is missing within the YAML files. Through the call stack, you will be able to find the header file which has the issue. Ensure there is no missing function specific component for that YAML header file.

  2. CMake Error: require argument to be specified

    Example:

    CMake Error at:
/llvm-project/libc/cmake/modules/LLVMLibCHeaderRules.cmake:86 (message):
'add_gen_hdr2' rule requires GEN_HDR to be specified.
Call Stack (most recent call first):
/llvm-project/libc/include/CMakeLists.txt:22 (add_gen_header2)
/llvm-project/libc/include/CMakeLists.txt:62 (add_header_macro)

    If you receive this error, there is a missing YAML file, h_def file, or header name within the libc/include/CMakeLists.txt. The last line in the error call stack will point to the header where there is a specific component missing. Ensure the correct style and required files are present:

    [header_name]
    [../libc/hdrgen/yaml/[yaml_file.yaml]
    [header_name.h.def]
    [header_name.h]
    DEPENDS
    {Necessary Depend Files}

  3. Command line: expected arguments

    Example:

    usage: yaml_to_classes.py [-h] [--output_dir OUTPUT_DIR] [--h_def_file H_DEF_FILE]
[--add_function RETURN_TYPE NAME ARGUMENTS STANDARDS GUARD ATTRIBUTES]
[--e ENTRY_POINTS] [--export-decls]
yaml_file
yaml_to_classes.py:
error: argument --add_function: expected 6 arguments

    In the process of adding a function, you may run into an issue where the command line is requiring more arguments than what you currently have. Ensure that all components of the new function are filled. Even if you do not have a guard or attribute, make sure to put null in those two areas.

  4. Object has no attribute

    Example:

    File "/llvm-project/libc/hdrgen/header.py", line 60, in __str__ for
function in self.functions: AttributeError: 'HeaderFile' object has no
attribute 'functions'

    When running ninja libc in the build directory to generate headers you may receive the error above. Essentially this means that in libc/hdrgen/header.py there is a missing attribute named functions. Make sure all function components are defined within this file and there are no missing functions to add these components.

  5. Unknown type name

    Example:

    /llvm-project/build/projects/libc/include/sched.h:20:25: error: unknown type
name 'size_t'; did you mean 'time_t'?
20 | int_sched_getcpucount(size_t, const cpu_set_t*) __NOEXCEPT
 |           ^
/llvm-project/build/projects/libc/include/llvm-libc-types/time_t.h:15:24:
note: 'time_t' declared here
15 | typedef __INT64_TYPE__ time_t;
|                    ^

    During the header generation process errors like the one above may occur because there are missing types for a specific header file. Check the YAML file corresponding to the header file and make sure all the necessary types that are being used are input into the types as well. Delete the specific header file from the build folder and re-run ninja libc to ensure the types are being recognized.

  6. Test Integration Errors

    Sometimes the integration test will fail but that still means the process is working unless the comparison between the output and expected_output is not showing. If that is the case make sure in libc/hdrgen/tests/test_integration.py there are no missing arguments that run through the script.

    If the integration tests are failing due to mismatching of lines or small errors in spacing that is nothing to worry about. If this is happening while you are making a new change to the formatting of the headers, then ensure the expected output file libc/hdrgen/tests/expected_output/test_header.h has the changes you are applying.