94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
import os
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import sys
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from loguru import logger
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from mineru.backend.vlm.utils import set_lmdeploy_backend
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from mineru.utils.models_download_utils import auto_download_and_get_model_root_path
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def main():
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args = sys.argv[1:]
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has_port_arg = False
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has_gpu_memory_utilization_arg = False
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has_log_level_arg = False
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device_type = ""
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lm_backend = ""
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# 检查现有参数
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indices_to_remove = []
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for i, arg in enumerate(args):
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if arg == "--server-port" or arg.startswith("--server-port="):
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has_port_arg = True
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if arg == "--cache-max-entry-count" or arg.startswith("--cache-max-entry-count="):
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has_gpu_memory_utilization_arg = True
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if arg == "--log-level" or arg.startswith("--log-level="):
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has_log_level_arg = True
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if arg == "--backend" or arg == "--lmdeploy-backend":
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if i + 1 < len(args):
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lm_backend = args[i + 1]
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indices_to_remove.extend([i, i + 1])
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elif arg.startswith("--backend=") or arg.startswith("--lmdeploy-backend="):
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lm_backend = arg.split("=", 1)[1]
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indices_to_remove.append(i)
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if arg == "--device" or arg == "--lmdeploy-device":
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if i + 1 < len(args):
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device_type = args[i + 1]
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indices_to_remove.extend([i, i + 1])
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elif arg.startswith("--device=") or arg.startswith("--lmdeploy-device="):
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device_type = arg.split("=", 1)[1]
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indices_to_remove.append(i)
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# 从后往前删除,避免索引错位
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for i in sorted(set(indices_to_remove), reverse=True):
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args.pop(i)
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# 添加默认参数
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if not has_port_arg:
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args.extend(["--server-port", "30000"])
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if not has_gpu_memory_utilization_arg:
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args.extend(["--cache-max-entry-count", "0.5"])
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if not has_log_level_arg:
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args.extend(["--log-level", "ERROR"])
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device_type = os.getenv("MINERU_LMDEPLOY_DEVICE", device_type)
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if device_type == "":
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device_type = "cuda"
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elif device_type not in ["cuda", "ascend", "maca", "camb"]:
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raise ValueError(f"Unsupported lmdeploy device type: {device_type}")
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lm_backend = os.getenv("MINERU_LMDEPLOY_BACKEND", lm_backend)
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if lm_backend == "":
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lm_backend = set_lmdeploy_backend(device_type)
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elif lm_backend not in ["pytorch", "turbomind"]:
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raise ValueError(f"Unsupported lmdeploy backend: {lm_backend}")
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logger.info(f"lmdeploy device is: {device_type}, lmdeploy backend is: {lm_backend}")
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if lm_backend == "pytorch":
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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args.extend(["--device", device_type])
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args.extend(["--backend", lm_backend])
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model_path = auto_download_and_get_model_root_path("/", "vlm")
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# logger.debug(args)
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# 重构参数，将模型路径作为位置参数
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sys.argv = [sys.argv[0]] + ["serve", "api_server", model_path] + args
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if os.getenv('OMP_NUM_THREADS') is None:
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os.environ["OMP_NUM_THREADS"] = "1"
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# 启动 lmdeploy 服务器
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print(f"start lmdeploy server: {sys.argv}")
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# 使用os.system调用启动lmdeploy服务器
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os.system("lmdeploy " + " ".join(sys.argv[1:]))
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if __name__ == "__main__":
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main()
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