mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-20 22:32:07 +00:00
430 lines
14 KiB
Python
430 lines
14 KiB
Python
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import os
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import psutil
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import signal
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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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from huggingface_hub import hf_hub_download
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from text_generation_server.utils.adapter import parse_lora_adapters
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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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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):
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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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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",
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json_output: bool = False,
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otlp_endpoint: Optional[str] = None,
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otlp_service_name: str = "text-generation-inference.server",
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max_input_tokens: Optional[int] = 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(otlp_service_name=otlp_service_name, otlp_endpoint=otlp_endpoint)
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lora_adapters = parse_lora_adapters(os.getenv("LORA_ADAPTERS"))
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# TODO: enable lora with cuda graphs. for now disable cuda graphs if lora is enabled
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# and warn the user
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if lora_adapters:
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logger.warning("LoRA adapters enabled (experimental feature).")
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if "CUDA_GRAPHS" in os.environ:
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logger.warning(
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"LoRA adapters incompatible with CUDA Graphs. Disabling CUDA Graphs."
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)
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global CUDA_GRAPHS
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CUDA_GRAPHS = None
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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 = "bfloat16" if dtype is None else dtype.value
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logger.info(f"quantize={quantize}")
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if dtype is not None and quantize not in {
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None,
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"bitsandbytes",
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"bitsandbytes-nf4",
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"bitsandbytes-fp4",
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}:
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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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logger.info("CLI SHARDED = {} DTYPE = {}".format(sharded, dtype))
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if sharded:
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tgi_file = Path(__file__).resolve().parent / "tgi_service.py"
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num_shard = int(os.getenv("WORLD_SIZE", "1"))
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logger.info("CLI SHARDED = {}".format(num_shard))
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import subprocess
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cmd = (
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f"deepspeed --num_nodes 1 --num_gpus {num_shard} --no_local_rank {tgi_file}"
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)
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cmd += f" --model_id {model_id} --revision {revision} --sharded {sharded}"
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cmd += f" --dtype {dtype} --trust_remote_code {trust_remote_code} --uds_path {uds_path}"
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cmd += f" --quantize {quantize} --max_input_tokens {max_input_tokens}"
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if speculate is not None:
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cmd += f"--speculate {speculate}"
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logger.info("CLI server start deepspeed ={} ".format(cmd))
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sys.stdout.flush()
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sys.stderr.flush()
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with subprocess.Popen(cmd, shell=True, executable="/bin/bash") as proc:
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do_terminate = False
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current_handler = signal.getsignal(signal.SIGTERM)
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def terminate_handler(sig, frame):
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nonlocal do_terminate
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do_terminate = True
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if callable(current_handler):
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current_handler(sig, frame)
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signal.signal(signal.SIGTERM, terminate_handler)
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finished = False
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while not finished:
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try:
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if do_terminate:
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parent = psutil.Process(proc.pid)
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all_procs = parent.children(recursive=True) + [parent]
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for p in all_procs:
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try:
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p.terminate()
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except psutil.NoSuchProcess:
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pass
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_, alive = psutil.wait_procs(all_procs, timeout=30)
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for p in alive:
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p.kill()
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do_terminate = False
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proc.wait(timeout=3)
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except subprocess.TimeoutExpired:
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pass
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else:
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finished = True
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sys.stdout.flush()
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sys.stderr.flush()
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if proc.returncode != 0:
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logger.error(f"{cmd} exited with status = {proc.returncode}")
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return proc.returncode
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else:
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server.serve(
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model_id,
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lora_adapters,
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revision,
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sharded,
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quantize,
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speculate,
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dtype,
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trust_remote_code,
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uds_path,
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max_input_tokens,
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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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merge_lora: 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, 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(
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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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# TODO: maybe reverse the default value of merge_lora?
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# currently by default we don't merge the weights with the base model
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if merge_lora:
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try:
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hf_hub_download(
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model_id, revision=revision, filename="adapter_config.json"
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)
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utils.download_and_unload_peft(
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model_id, revision, trust_remote_code=trust_remote_code
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)
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is_local_model = True
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utils.weight_files(model_id, revision, extension)
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return
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except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
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pass
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else:
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try:
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utils.peft.download_peft(
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model_id, revision, trust_remote_code=trust_remote_code
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)
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except Exception:
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pass
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try:
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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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)
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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)
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if base_model_id and base_model_id != model_id:
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try:
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logger.info(f"Downloading parent model {base_model_id}")
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download_weights(
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model_id=base_model_id,
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revision="main",
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extension=extension,
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auto_convert=auto_convert,
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logger_level=logger_level,
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json_output=json_output,
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trust_remote_code=trust_remote_code,
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)
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except Exception:
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pass
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except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
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pass
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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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elif (Path(model_id) / "adapter_config.json").exists():
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# Try to load as a local PEFT model
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try:
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utils.download_and_unload_peft(
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model_id, revision, trust_remote_code=trust_remote_code
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)
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utils.weight_files(model_id, revision, extension)
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return
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except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
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pass
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elif (Path(model_id) / "config.json").exists():
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# Try to load as a local Medusa model
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try:
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import json
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config = Path(model_id) / "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)
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if base_model_id:
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try:
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logger.info(f"Downloading parent model {base_model_id}")
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download_weights(
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model_id=base_model_id,
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revision="main",
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extension=extension,
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auto_convert=auto_convert,
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logger_level=logger_level,
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json_output=json_output,
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trust_remote_code=trust_remote_code,
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)
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except Exception:
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pass
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except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
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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:
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local_pt_files = utils.weight_files(model_id, revision, ".bin")
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except Exception:
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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":
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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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if not trust_remote_code:
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logger.warning(
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"🚨🚨BREAKING CHANGE in 2.0🚨🚨: Safetensors conversion is disabled without `--trust-remote-code` because "
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"Pickle files are unsafe and can essentially contain remote code execution!"
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"Please check for more information here: https://huggingface.co/docs/text-generation-inference/basic_tutorials/safety",
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)
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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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import transformers
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import json
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if is_local_model:
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config_filename = os.path.join(model_id, "config.json")
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else:
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config_filename = hf_hub_download(
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model_id, revision=revision, filename="config.json"
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)
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with open(config_filename, "r") as f:
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|
|
config = json.load(f)
|
||
|
|
architecture = config["architectures"][0]
|
||
|
|
|
||
|
|
class_ = getattr(transformers, architecture)
|
||
|
|
|
||
|
|
# Name for this varible depends on transformers version.
|
||
|
|
discard_names = getattr(class_, "_tied_weights_keys", [])
|
||
|
|
|
||
|
|
except Exception:
|
||
|
|
discard_names = []
|
||
|
|
# Convert pytorch weights to safetensors
|
||
|
|
utils.convert_files(local_pt_files, local_st_files, discard_names)
|
||
|
|
|
||
|
|
|
||
|
|
@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,
|
||
|
|
groupsize: int = 128,
|
||
|
|
):
|
||
|
|
if revision is None:
|
||
|
|
revision = "main"
|
||
|
|
download_weights(
|
||
|
|
model_id=model_id,
|
||
|
|
revision=revision,
|
||
|
|
logger_level=logger_level,
|
||
|
|
json_output=json_output,
|
||
|
|
)
|
||
|
|
from text_generation_server.layers.gptq.quantize import quantize
|
||
|
|
|
||
|
|
quantize(
|
||
|
|
model_id=model_id,
|
||
|
|
bits=4,
|
||
|
|
groupsize=groupsize,
|
||
|
|
output_dir=output_dir,
|
||
|
|
revision=revision,
|
||
|
|
trust_remote_code=trust_remote_code,
|
||
|
|
upload_to_model_id=upload_to_model_id,
|
||
|
|
percdamp=percdamp,
|
||
|
|
act_order=act_order,
|
||
|
|
sym=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
app()
|