cli.py 10.7 KB
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import os
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import sys
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import typer

from pathlib import Path
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from loguru import logger
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from typing import Optional
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from enum import Enum
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from huggingface_hub import hf_hub_download
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app = typer.Typer()


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class Quantization(str, Enum):
    bitsandbytes = "bitsandbytes"
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    bitsandbytes_nf4 = "bitsandbytes-nf4"
    bitsandbytes_fp4 = "bitsandbytes-fp4"
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    gptq = "gptq"
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    awq = "awq"
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    eetq = "eetq"
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    exl2 = "exl2"
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    fp8 = "fp8"
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    marlin = "marlin"
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class Dtype(str, Enum):
    float16 = "float16"
    bloat16 = "bfloat16"


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@app.command()
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def serve(
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    model_id: str,
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    revision: Optional[str] = None,
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    sharded: bool = False,
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    quantize: Optional[Quantization] = None,
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    speculate: Optional[int] = None,
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    dtype: Optional[Dtype] = None,
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    trust_remote_code: bool = False,
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    uds_path: Path = "/tmp/text-generation-server",
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    logger_level: str = "INFO",
    json_output: bool = False,
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    otlp_endpoint: Optional[str] = None,
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):
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    if sharded:
        assert (
            os.getenv("RANK", None) is not None
        ), "RANK must be set when sharded is True"
        assert (
            os.getenv("WORLD_SIZE", None) is not None
        ), "WORLD_SIZE must be set when sharded is True"
        assert (
            os.getenv("MASTER_ADDR", None) is not None
        ), "MASTER_ADDR must be set when sharded is True"
        assert (
            os.getenv("MASTER_PORT", None) is not None
        ), "MASTER_PORT must be set when sharded is True"

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    # Remove default handler
    logger.remove()
    logger.add(
        sys.stdout,
        format="{message}",
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        filter="text_generation_server",
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        level=logger_level,
        serialize=json_output,
        backtrace=True,
        diagnose=False,
    )
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    # Import here after the logger is added to log potential import exceptions
    from text_generation_server import server
    from text_generation_server.tracing import setup_tracing

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    # Setup OpenTelemetry distributed tracing
    if otlp_endpoint is not None:
        setup_tracing(shard=os.getenv("RANK", 0), otlp_endpoint=otlp_endpoint)

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    # Downgrade enum into str for easier management later on
    quantize = None if quantize is None else quantize.value
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    dtype = None if dtype is None else dtype.value
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    if dtype is not None and quantize not in {
        None,
        "bitsandbytes",
        "bitsandbytes-nf4",
        "bitsandbytes-fp4",
    }:
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        raise RuntimeError(
            "Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
        )
    server.serve(
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        model_id,
        revision,
        sharded,
        quantize,
        speculate,
        dtype,
        trust_remote_code,
        uds_path,
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    )
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@app.command()
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def download_weights(
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    model_id: str,
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    revision: Optional[str] = None,
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    extension: str = ".safetensors",
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    auto_convert: bool = True,
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    logger_level: str = "INFO",
    json_output: bool = False,
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    trust_remote_code: bool = False,
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):
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    # Remove default handler
    logger.remove()
    logger.add(
        sys.stdout,
        format="{message}",
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        filter="text_generation_server",
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        level=logger_level,
        serialize=json_output,
        backtrace=True,
        diagnose=False,
    )

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    # Import here after the logger is added to log potential import exceptions
    from text_generation_server import utils

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    # Test if files were already download
    try:
        utils.weight_files(model_id, revision, extension)
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        logger.info("Files are already present on the host. " "Skipping download.")
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        return
    # Local files not found
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    except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
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        pass

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    is_local_model = (Path(model_id).exists() and Path(model_id).is_dir()) or os.getenv(
        "WEIGHTS_CACHE_OVERRIDE", None
    ) is not None

    if not is_local_model:
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        try:
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            adapter_config_filename = hf_hub_download(
                model_id, revision=revision, filename="adapter_config.json"
            )
            utils.download_and_unload_peft(
                model_id, revision, trust_remote_code=trust_remote_code
            )
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            is_local_model = True
            utils.weight_files(model_id, revision, extension)
            return
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        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
            pass

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        try:
            import json
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            config = hf_hub_download(
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                model_id, revision=revision, filename="config.json"
            )
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            with open(config, "r") as f:
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                config = json.load(f)

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            base_model_id = config.get("base_model_name_or_path", None)
            if base_model_id and base_model_id != model_id:
                try:
                    logger.info(f"Downloading parent model {base_model_id}")
                    download_weights(
                        model_id=base_model_id,
                        revision="main",
                        extension=extension,
                        auto_convert=auto_convert,
                        logger_level=logger_level,
                        json_output=json_output,
                        trust_remote_code=trust_remote_code,
                    )
                except Exception:
                    pass
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        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
            pass

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        # Try to download weights from the hub
        try:
            filenames = utils.weight_hub_files(model_id, revision, extension)
            utils.download_weights(filenames, model_id, revision)
            # Successfully downloaded weights
            return

        # No weights found on the hub with this extension
        except utils.EntryNotFoundError as e:
            # Check if we want to automatically convert to safetensors or if we can use .bin weights instead
            if not extension == ".safetensors" or not auto_convert:
                raise e

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    elif (Path(model_id) / "adapter_config.json").exists():
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        # Try to load as a local PEFT model
        try:
            utils.download_and_unload_peft(
                model_id, revision, trust_remote_code=trust_remote_code
            )
            utils.weight_files(model_id, revision, extension)
            return
        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
            pass
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    elif (Path(model_id) / "config.json").exists():
        # Try to load as a local Medusa model
        try:
            import json

            config = Path(model_id) / "config.json"
            with open(config, "r") as f:
                config = json.load(f)

            base_model_id = config.get("base_model_name_or_path", None)
            if base_model_id:
                try:
                    logger.info(f"Downloading parent model {base_model_id}")
                    download_weights(
                        model_id=base_model_id,
                        revision="main",
                        extension=extension,
                        auto_convert=auto_convert,
                        logger_level=logger_level,
                        json_output=json_output,
                        trust_remote_code=trust_remote_code,
                    )
                except Exception:
                    pass
        except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
            pass
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    # Try to see if there are local pytorch weights
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    try:
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        # Get weights for a local model, a hub cached model and inside the WEIGHTS_CACHE_OVERRIDE
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        try:
            local_pt_files = utils.weight_files(model_id, revision, ".bin")
        except Exception:
            local_pt_files = utils.weight_files(model_id, revision, ".pt")
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    # No local pytorch weights
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    except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
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        if extension == ".safetensors":
            logger.warning(
                f"No safetensors weights found for model {model_id} at revision {revision}. "
                f"Downloading PyTorch weights."
            )
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        # Try to see if there are pytorch weights on the hub
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        pt_filenames = utils.weight_hub_files(model_id, revision, ".bin")
        # Download pytorch weights
        local_pt_files = utils.download_weights(pt_filenames, model_id, revision)
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    if auto_convert:
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        if not trust_remote_code:
            logger.warning(
                f"🚨🚨BREAKING CHANGE in 2.0🚨🚨: Safetensors conversion is disabled without `--trust-remote-code` because "
                f"Pickle files are unsafe and can essentially contain remote code execution!"
                f"Please check for more information here: https://huggingface.co/docs/text-generation-inference/basic_tutorials/safety",
            )

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        logger.warning(
            f"No safetensors weights found for model {model_id} at revision {revision}. "
            f"Converting PyTorch weights to safetensors."
        )

        # Safetensors final filenames
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        local_st_files = [
            p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors"
            for p in local_pt_files
        ]
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        try:
            import transformers
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            import json
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            if is_local_model:
                config_filename = os.path.join(model_id, "config.json")
            else:
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                config_filename = hf_hub_download(
                    model_id, revision=revision, filename="config.json"
                )
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            with open(config_filename, "r") as f:
                config = json.load(f)
            architecture = config["architectures"][0]
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            class_ = getattr(transformers, architecture)

            # Name for this varible depends on transformers version.
            discard_names = getattr(class_, "_tied_weights_keys", [])

        except Exception as e:
            discard_names = []
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        # Convert pytorch weights to safetensors
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        utils.convert_files(local_pt_files, local_st_files, discard_names)
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@app.command()
def quantize(
    model_id: str,
    output_dir: str,
    revision: Optional[str] = None,
    logger_level: str = "INFO",
    json_output: bool = False,
    trust_remote_code: bool = False,
    upload_to_model_id: Optional[str] = None,
    percdamp: float = 0.01,
    act_order: bool = False,
):
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    if revision is None:
        revision = "main"
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    download_weights(
        model_id=model_id,
        revision=revision,
        logger_level=logger_level,
        json_output=json_output,
    )
    from text_generation_server.utils.gptq.quantize import quantize

    quantize(
        model_id=model_id,
        bits=4,
        groupsize=128,
        output_dir=output_dir,
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        revision=revision,
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        trust_remote_code=trust_remote_code,
        upload_to_model_id=upload_to_model_id,
        percdamp=percdamp,
        act_order=act_order,
    )


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if __name__ == "__main__":
    app()