chore: import upstream snapshot with attribution

2026-07-13 13:23:58 +08:00
commit 770d92cb1f
694 changed files with 114634 additions and 0 deletions
@@ -0,0 +1,112 @@
+"""Command line entrypoint of weight conversion."""
+
+import argparse
+from pathlib import Path
+from typing import Union
+
+from mlc_llm.interface.convert_weight import convert_weight
+from mlc_llm.interface.help import HELP
+from mlc_llm.model import MODELS
+from mlc_llm.quantization import QUANTIZATION
+from mlc_llm.support.argparse import ArgumentParser
+from mlc_llm.support.auto_config import detect_config, detect_model_type
+from mlc_llm.support.auto_device import detect_device
+from mlc_llm.support.auto_weight import detect_weight
+
+
+def main(argv):
+    """Parse command line argumennts and apply quantization."""
+
+    def _parse_source(path: Union[str, Path], config_path: Path) -> Path:
+        if path == "auto":
+            return config_path.parent
+        path = Path(path)
+        if not path.exists():
+            raise argparse.ArgumentTypeError(f"Model source does not exist: {path}")
+        return path
+
+    def _parse_output(path: Union[str, Path]) -> Path:
+        path = Path(path)
+        if not path.is_dir():
+            path.mkdir(parents=True, exist_ok=True)
+        return path
+
+    def _parse_lora_adapter(path: Union[str, Path]) -> Path:
+        path = Path(path)
+        if not path.exists() or not path.is_dir():
+            raise argparse.ArgumentTypeError(f"LoRA adapter directory does not exist: {path}")
+        return path
+
+    parser = ArgumentParser("MLC AutoLLM Quantization Framework")
+    parser.add_argument(
+        "config",
+        type=detect_config,
+        help=HELP["config"] + " (required)",
+    )
+    parser.add_argument(
+        "--quantization",
+        type=str,
+        required=True,
+        choices=list(QUANTIZATION.keys()),
+        help=HELP["quantization"] + " (required, choices: %(choices)s)",
+    )
+    parser.add_argument(
+        "--model-type",
+        type=str,
+        default="auto",
+        choices=["auto", *list(MODELS.keys())],
+        help=HELP["model_type"] + ' (default: "%(default)s")',
+    )
+    parser.add_argument(
+        "--device",
+        default="auto",
+        type=detect_device,
+        help=HELP["device_quantize"] + ' (default: "%(default)s")',
+    )
+    parser.add_argument(
+        "--source",
+        type=str,
+        default="auto",
+        help=HELP["source"] + ' (default: "%(default)s")',
+    )
+    parser.add_argument(
+        "--source-format",
+        type=str,
+        choices=["auto", "huggingface-torch", "huggingface-safetensor", "awq"],
+        default="auto",
+        help=HELP["source_format"] + ' (default: "%(default)s", choices: %(choices)s")',
+    )
+    parser.add_argument(
+        "--output",
+        "-o",
+        type=_parse_output,
+        required=True,
+        help=HELP["output_quantize"] + " (required)",
+    )
+    parser.add_argument(
+        "--lora-adapter",
+        type=_parse_lora_adapter,
+        default=None,
+        help=(
+            "Path to a LoRA adapter directory in PEFT format. "
+            "When provided, adapter weights are merged into the base model before quantization."
+        ),
+    )
+
+    parsed = parser.parse_args(argv)
+    parsed.source, parsed.source_format = detect_weight(
+        _parse_source(parsed.source, parsed.config),
+        parsed.config,
+        parsed.source_format,
+    )
+    model = detect_model_type(parsed.model_type, parsed.config)
+    convert_weight(
+        config=parsed.config,
+        quantization=QUANTIZATION[parsed.quantization],
+        model=model,
+        device=parsed.device,
+        source=parsed.source,
+        source_format=parsed.source_format,
+        output=parsed.output,
+        lora_adapter=parsed.lora_adapter,
+    )