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https://github.com/NVIDIA/TensorRT-LLM.git
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e06f537e08
* Update TensorRT-LLM --------- Co-authored-by: erenup <ping.nie@pku.edu.cn> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
66 lines
1.5 KiB
Python
66 lines
1.5 KiB
Python
import gc
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import sys
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import traceback
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from dataclasses import dataclass, field
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from functools import wraps
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from pathlib import Path
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from typing import List
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import torch
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@dataclass
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class GenerationOutput:
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text: str = ""
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token_ids: List[int] = field(default_factory=list)
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logprobs: List[float] = field(default_factory=list)
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def print_colored(message, color: str = None):
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colors = dict(
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grey="\x1b[38;20m",
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yellow="\x1b[33;20m",
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red="\x1b[31;20m",
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bold_red="\x1b[31;1m",
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bold_green="\033[1;32m",
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)
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reset = "\x1b[0m"
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if color:
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sys.stderr.write(colors[color] + message + reset)
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else:
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sys.stderr.write(message)
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def file_with_suffix_exists(directory, suffix) -> bool:
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path = Path(directory)
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for file_path in path.glob(f'*{suffix}'):
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if file_path.is_file():
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return True
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return False
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def print_traceback_on_error(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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traceback.print_exc()
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raise e
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return wrapper
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def get_device_count() -> int:
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return torch.cuda.device_count() if torch.cuda.is_available() else 0
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def release_gc():
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''' Release memory allocated by PyTorch and Python garbage collector explicitly and immediately.
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This could be used when some states might be kept in memory even after the variables are deleted.
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'''
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gc.collect()
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torch.cuda.empty_cache()
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