remove unuse file

mineru/backend/hybrid/utils.py

@@ -1,97 +0,0 @@
-import os
-
-from loguru import logger
-from packaging import version
-
-from mineru.utils.check_sys_env import is_windows_environment, is_linux_environment
-from mineru.utils.config_reader import get_device
-from mineru.utils.model_utils import get_vram
-
-
-def enable_custom_logits_processors() -> bool:
-    import torch
-    from vllm import __version__ as vllm_version
-
-    if torch.cuda.is_available():
-        major, minor = torch.cuda.get_device_capability()
-        # 正确计算Compute Capability
-        compute_capability = f"{major}.{minor}"
-    elif hasattr(torch, 'npu') and torch.npu.is_available():
-        compute_capability = "8.0"
-    else:
-        logger.info("CUDA not available, disabling custom_logits_processors")
-        return False
-
-    # 安全地处理环境变量
-    vllm_use_v1_str = os.getenv('VLLM_USE_V1', "1")
-    if vllm_use_v1_str.isdigit():
-        vllm_use_v1 = int(vllm_use_v1_str)
-    else:
-        vllm_use_v1 = 1
-
-    if vllm_use_v1 == 0:
-        logger.info("VLLM_USE_V1 is set to 0, disabling custom_logits_processors")
-        return False
-    elif version.parse(vllm_version) < version.parse("0.10.1"):
-        logger.info(f"vllm version: {vllm_version} < 0.10.1, disable custom_logits_processors")
-        return False
-    elif version.parse(compute_capability) < version.parse("8.0"):
-        if version.parse(vllm_version) >= version.parse("0.10.2"):
-            logger.info(f"compute_capability: {compute_capability} < 8.0, but vllm version: {vllm_version} >= 0.10.2, enable custom_logits_processors")
-            return True
-        else:
-            logger.info(f"compute_capability: {compute_capability} < 8.0 and vllm version: {vllm_version} < 0.10.2, disable custom_logits_processors")
-            return False
-    else:
-        logger.info(f"compute_capability: {compute_capability} >= 8.0 and vllm version: {vllm_version} >= 0.10.1, enable custom_logits_processors")
-        return True
-
-
-def set_lmdeploy_backend(device_type: str) -> str:
-    if device_type.lower() in ["ascend", "maca", "camb"]:
-        lmdeploy_backend = "pytorch"
-    elif device_type.lower() in ["cuda"]:
-        import torch
-        if not torch.cuda.is_available():
-            raise ValueError("CUDA is not available.")
-        if is_windows_environment():
-            lmdeploy_backend = "turbomind"
-        elif is_linux_environment():
-            major, minor = torch.cuda.get_device_capability()
-            compute_capability = f"{major}.{minor}"
-            if version.parse(compute_capability) >= version.parse("8.0"):
-                lmdeploy_backend = "pytorch"
-            else:
-                lmdeploy_backend = "turbomind"
-        else:
-            raise ValueError("Unsupported operating system.")
-    else:
-        raise ValueError(f"Unsupported lmdeploy device type: {device_type}")
-    return lmdeploy_backend
-
-
-def set_default_gpu_memory_utilization() -> float:
-    from vllm import __version__ as vllm_version
-    if version.parse(vllm_version) >= version.parse("0.11.0"):
-        return 0.7
-    else:
-        return 0.5
-
-
-def set_default_batch_size() -> int:
-    try:
-        device = get_device()
-        gpu_memory = get_vram(device)
-
-        if gpu_memory >= 16:
-            batch_size = 8
-        elif gpu_memory >= 8:
-            batch_size = 4
-        else:
-            batch_size = 1
-        logger.info(f'gpu_memory: {gpu_memory} GB, batch_size: {batch_size}')
-
-    except Exception as e:
-        logger.warning(f'Error determining VRAM: {e}, using default batch_ratio: 1')
-        batch_size = 1
-    return batch_size