TensorRT-LLMs/docs/source/helper.py

import logging
import math
from itertools import chain
from pathlib import Path


def underline(title: str, character: str = "=") -> str:
    return f"{title}\n{character * len(title)}"


def generate_title(filename: str) -> str:
    with open(filename) as f:
        # fine the first line that contains '###'
        for line in f:
            if '###' in line:
                title = line[3:].strip()
                break
        assert title is not None, f"No title found in {filename}"
    return underline(title)


def generate_examples():
    root_dir = Path(__file__).parent.parent.parent.resolve()
    ignore_list = {'__init__.py', 'quickstart_example.py'}
    doc_dir = root_dir / "docs/source/examples"

    # Source paths for LLMAPI examples
    llmapi_script_dir = root_dir / "examples/llm-api"
    llmapi_script_paths = sorted(
        llmapi_script_dir.glob("*.py"),
        # The autoPP example should be at the end since it is a preview example
        key=lambda x: math.inf if 'llm_auto_parallel' in x.stem else 0)
    llmapi_script_paths += sorted(llmapi_script_dir.glob("*.sh"))

    llmapi_script_paths = [
        i for i in llmapi_script_paths if i.name not in ignore_list
    ]
    # Destination paths for LLMAPI examples
    llmapi_doc_paths = [
        doc_dir / f"{path.stem}.rst" for path in llmapi_script_paths
    ]
    llmapi_script_base_url = "https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llm-api"

    # Path for trtllm-serve examples
    serve_script_dir = root_dir / "examples/serve"
    serve_script_paths = sorted(
        chain(serve_script_dir.glob("*.py"), serve_script_dir.glob("*.sh")))
    serve_script_paths = [
        i for i in serve_script_paths if i.name not in ignore_list
    ]
    serve_doc_paths = [
        doc_dir / f"{path.stem}.rst" for path in serve_script_paths
    ]
    serve_script_base_url = "https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/serve"

    # Generate the example docs for each example script
    def write_script(base_url, script_paths, doc_paths, extra_content=""):
        for script_path, doc_path in zip(script_paths, doc_paths):
            if script_path.name in ignore_list:
                logging.warning(f"Ignoring file: {script_path.name}")
                continue
            script_url = f"{base_url}/{script_path.name}"

            # Determine language based on file extension
            language = "python" if script_path.suffix == ".py" else "bash"

            # Make script_path relative to doc_path and call it include_path
            include_path = '../../..' / script_path.relative_to(root_dir)

            # For Python files, use generate_title to extract title from comments
            # For shell scripts, use filename as title
            if script_path.suffix == ".py":
                title = generate_title(script_path)
            else:
                # Create a title from the filename (remove extension and replace underscores with spaces)
                title_text = script_path.stem.replace('_', ' ').title()
                title = underline(title_text)

            content = (f"{title}\n\n"
                       f"{extra_content}"
                       f"Source {script_url}.\n\n"
                       f".. literalinclude:: {include_path}\n"
                       f"    :language: {language}\n"
                       "    :linenos:\n")
            with open(doc_path, "w+") as f:
                f.write(content)

    # Generate the toctree for LLMAPI example scripts
    write_script(llmapi_script_base_url, llmapi_script_paths, llmapi_doc_paths)
    with open(doc_dir / "llm_examples_index.template.rst_") as f:
        examples_index = f.read()

    with open(doc_dir / "llm_api_examples.rst", "w+") as f:
        example_docs = "\n   ".join(path.stem for path in llmapi_script_paths)
        f.write(examples_index.replace(r"%EXAMPLE_DOCS%", example_docs)\
                .replace(r"%EXAMPLE_NAME%", "LLM Examples"))

    # Generate the toctree for trtllm-serve example scripts
    trtllm_serve_content = "Refer to the `trtllm-serve documentation <https://nvidia.github.io/TensorRT-LLM/commands/trtllm-serve.html>`_ for starting a server.\n\n"
    write_script(serve_script_base_url, serve_script_paths, serve_doc_paths,
                 trtllm_serve_content)
    with open(doc_dir / "trtllm_serve_examples.rst", "w+") as f:
        example_docs = "\n   ".join(path.stem for path in serve_script_paths)
        f.write(examples_index.replace(r"%EXAMPLE_DOCS%", example_docs)\
                .replace(r"%EXAMPLE_NAME%", "Online Serving Examples"))

    with open(doc_dir / "index.rst") as f:
        examples_index = f.read()

    with open(doc_dir / "index.rst", "w+") as f:
        example_docs = "\n    ".join(path.stem for path in llmapi_script_paths)
        f.write(examples_index.replace(r"%EXAMPLE_DOCS%", example_docs))


def extract_all_and_eval(file_path):
    ''' Extract the __all__ variable from a Python file.
    This is a trick to make the CI happy even the tensorrt_llm lib is not available.
    NOTE: This requires the __all__ variable to be defined at the end of the file.
    '''
    with open(file_path, 'r') as file:
        content = file.read()

    lines = content.split('\n')
    filtered_line_begin = 0

    for i, line in enumerate(lines):
        if line.startswith("__all__"):
            filtered_line_begin = i
            break

    code_to_eval = '\n'.join(lines[filtered_line_begin:])

    local_vars = {}
    exec(code_to_eval, {}, local_vars)
    return local_vars


def generate_llmapi():
    root_dir = Path(__file__).parent.parent.parent.resolve()

    # Destination paths
    doc_dir = root_dir / "docs/source/llm-api"
    doc_dir.mkdir(exist_ok=True)
    doc_path = doc_dir / "reference.rst"

    llmapi_all_file = root_dir / "tensorrt_llm/llmapi/__init__.py"
    public_classes_names = extract_all_and_eval(llmapi_all_file)['__all__']

    content = underline("API Reference", "-") + "\n\n"
    for cls_name in public_classes_names:
        cls_name = cls_name.strip()
        options = [
            "    :members:", "    :undoc-members:", "    :show-inheritance:"
        ]

        if cls_name != 'LLM':  # Conditionally add :special-members: __init__
            options.append("    :special-members: __init__")

        if cls_name in ['TrtLLM', 'TorchLLM', 'LLM']:
            options.append("    :inherited-members:")

        content += f".. autoclass:: tensorrt_llm.llmapi.{cls_name}\n"
        content += "\n".join(options) + "\n\n"

    with open(doc_path, "w+") as f:
        f.write(content)