227 lines
6.9 KiB
Python
227 lines
6.9 KiB
Python
import os
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import sys
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import typer
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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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app = typer.Typer()
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class Quantization(str, Enum):
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bitsandbytes = "bitsandbytes"
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bitsandbytes_nf4 = "bitsandbytes-nf4"
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bitsandbytes_fp4 = "bitsandbytes-fp4"
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gptq = "gptq"
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ct2 = "ct2"
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class Dtype(str, Enum):
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float16 = "float16"
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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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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",
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json_output: bool = False,
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otlp_endpoint: Optional[str] = None,
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):
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if sharded:
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assert (
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os.getenv("RANK", None) is not None
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), "RANK must be set when sharded is True"
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assert (
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os.getenv("WORLD_SIZE", None) is not None
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), "WORLD_SIZE must be set when sharded is True"
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assert (
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os.getenv("MASTER_ADDR", None) is not None
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), "MASTER_ADDR must be set when sharded is True"
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assert (
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os.getenv("MASTER_PORT", None) is not None
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), "MASTER_PORT must be set when sharded is True"
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# Remove default handler
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logger.remove()
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logger.add(
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sys.stdout,
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format="{message}",
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filter="text_generation_server",
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level=logger_level,
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serialize=json_output,
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backtrace=True,
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diagnose=False,
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)
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# Import here after the logger is added to log potential import exceptions
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from text_generation_server import server
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from text_generation_server.tracing import setup_tracing
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# Setup OpenTelemetry distributed tracing
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if otlp_endpoint is not None:
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setup_tracing(shard=os.getenv("RANK", 0), otlp_endpoint=otlp_endpoint)
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# Downgrade enum into str for easier management later on
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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 is not None and quantize != Quantization.ct2:
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raise RuntimeError(
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"Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
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)
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server.serve(
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model_id, revision, sharded, quantize, 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",
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json_output: bool = False,
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trust_remote_code: bool = False
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):
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# Remove default handler
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logger.remove()
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logger.add(
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sys.stdout,
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format="{message}",
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filter="text_generation_server",
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level=logger_level,
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serialize=json_output,
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backtrace=True,
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diagnose=False,
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)
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# Import here after the logger is added to log potential import exceptions
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from text_generation_server import utils
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# Test if files were already download
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try:
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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
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# Local files not found
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except (utils.LocalEntryNotFoundError, FileNotFoundError):
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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(
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"WEIGHTS_CACHE_OVERRIDE", None
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) is not None
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if not is_local_model:
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# Try to download weights from the hub
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try:
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filenames = utils.weight_hub_files(model_id, revision, extension)
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utils.download_weights(filenames, model_id, revision)
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# Successfully downloaded weights
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return
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# No weights found on the hub with this extension
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except utils.EntryNotFoundError as e:
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# Check if we want to automatically convert to safetensors or if we can use .bin weights instead
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if not extension == ".safetensors" or not auto_convert:
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raise e
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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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local_pt_files = utils.weight_files(model_id, revision, ".bin")
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# No local pytorch weights
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except utils.LocalEntryNotFoundError:
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if extension == ".safetensors":
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logger.warning(
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f"No safetensors weights found for model {model_id} at revision {revision}. "
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f"Downloading PyTorch weights."
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)
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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")
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# Download pytorch weights
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local_pt_files = utils.download_weights(pt_filenames, model_id, revision)
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if auto_convert:
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logger.warning(
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f"No safetensors weights found for model {model_id} at revision {revision}. "
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f"Converting PyTorch weights to safetensors."
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)
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# Safetensors final filenames
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local_st_files = [
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p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors"
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for p in local_pt_files
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]
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try:
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from transformers import AutoConfig
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import transformers
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config = AutoConfig.from_pretrained(
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model_id,
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revision=revision,
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trust_remote_code=trust_remote_code
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)
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architecture = config.architectures[0]
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class_ = getattr(transformers, architecture)
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# Name for this varible depends on transformers version.
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discard_names = getattr(class_, "_tied_weights_keys", [])
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discard_names.extend(getattr(class_, "_keys_to_ignore_on_load_missing", []))
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except Exception as e:
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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()
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def quantize(
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model_id: str,
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output_dir: str,
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revision: Optional[str] = None,
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logger_level: str = "INFO",
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json_output: bool = False,
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trust_remote_code: bool = False,
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upload_to_model_id: Optional[str] = None,
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percdamp: float = 0.01,
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act_order: bool = False,
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):
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if revision is None:
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revision = "main"
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download_weights(
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model_id=model_id,
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revision=revision,
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logger_level=logger_level,
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json_output=json_output,
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)
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from text_generation_server.utils.gptq.quantize import quantize
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quantize(
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model_id=model_id,
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bits=4,
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groupsize=128,
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output_dir=output_dir,
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revision=revision,
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trust_remote_code=trust_remote_code,
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upload_to_model_id=upload_to_model_id,
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percdamp=percdamp,
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act_order=act_order,
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)
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if __name__ == "__main__":
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app()
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