mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
143 lines
5.0 KiB
Python
143 lines
5.0 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import contextlib
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from dataclasses import dataclass
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import paddle
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import pynvml
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from fastdeploy.platforms import current_platform
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@dataclass
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class PaddleMemoryInfo:
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# Max memory reserved by Paddle
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max_reserved: int = 0
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# Max memory allocated by Paddle
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max_allocated: int = 0
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# Current memory reserved by Paddle
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current_reserved: int = 0
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# Current memory allocated by Paddle
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current_allocated: int = 0
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class GPUMemoryChecker:
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def __init__(
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self,
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device: int = 0, # logic device id
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device_id: int = 0, # physical device id
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print_debug_info: bool = True,
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):
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self.gpu_memory_info = None
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self.paddle_memory_info = None
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self.device = device
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self.device_id = device_id
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self.print_debug_info = print_debug_info
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if current_platform.is_iluvatar():
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self.gpu_memory_handle = None
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else:
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pynvml.nvmlInit()
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self.gpu_memory_handle = pynvml.nvmlDeviceGetHandleByIndex(self.device_id)
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def __del__(self):
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""" """
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if self.gpu_memory_handle is None:
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pass
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else:
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pynvml.nvmlShutdown()
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def _print_memory_info(
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self,
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debug_title: str = "",
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):
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"""Print debug info"""
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print(
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f"\n{debug_title}:",
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f"\n\tDevice Total memory: {self.gpu_memory_info.total}",
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f"\n\tDevice Used memory: {self.gpu_memory_info.used}",
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f"\n\tDevice Free memory: {self.gpu_memory_info.free}",
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f"\n\tPaddle max memory Reserved: {self.paddle_memory_info.max_reserved}",
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f"\n\tPaddle max memory Allocated: {self.paddle_memory_info.max_allocated}",
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f"\n\tPaddle memory Reserved: {self.paddle_memory_info.current_reserved}",
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f"\n\tPaddle memory Allocated: {self.paddle_memory_info.current_reserved}",
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)
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def get_gpu_memory_info(self):
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"""Get Device memory information"""
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current_meminfo = pynvml.nvmlDeviceGetMemoryInfo(self.gpu_memory_handle)
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return current_meminfo
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def get_paddle_memory_info(self) -> PaddleMemoryInfo:
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"""Get GPU memory information managed by Paddle"""
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current_paddle_memory_info = PaddleMemoryInfo()
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current_paddle_memory_info.max_reserved = paddle.device.cuda.max_memory_reserved(self.device)
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current_paddle_memory_info.max_allocated = paddle.device.cuda.max_memory_allocated(self.device)
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current_paddle_memory_info.reserved = paddle.device.cuda.memory_reserved(self.device)
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current_paddle_memory_info.allocated = paddle.device.cuda.memory_allocated(self.device)
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return current_paddle_memory_info
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def _check_memory(self):
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"""Check current device memory usage with pre checkpoint"""
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current_gpu_memory_info = self.get_gpu_memory_info()
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current_paddle_memory_info = self.get_paddle_memory_info()
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if self.gpu_memory_info is not None and self.paddle_memory_info is not None:
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assert (
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current_paddle_memory_info.max_reserved <= self.paddle_memory_info.max_reserved
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), f"Memory Check Failed! Current checkpoint Padddle memory usage ({current_paddle_memory_info.max_reserved}) must be less than or equal to the previous one ({self.paddle_memory_info.max_reserved})."
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assert (
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current_gpu_memory_info.used <= self.gpu_memory_info.used
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), f"Memory Check Failed! Current checkpoint GPU memory usage ({current_gpu_memory_info.used}) must be less than or equal to the previous one ({self.gpu_memory_info.used})."
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self.gpu_memory_info = current_gpu_memory_info
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self.paddle_memory_info = current_paddle_memory_info
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def add_check_point(
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self,
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debug_title: str = "",
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):
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"""Add checkpoints for GPU memory usage"""
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self._check_memory()
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if self.print_debug_info:
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self._print_memory_info(debug_title)
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def create_guard(default_value):
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_state = default_value
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@contextlib.contextmanager
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def state_guard(current_state):
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nonlocal _state
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old_state = _state
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_state = current_state
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try:
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yield
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finally:
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_state = old_state
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def get_state():
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return _state
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return state_guard, get_state
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sot_warmup_guard, in_sot_warmup_mode = create_guard(False)
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profile_run_guard, in_profile_run_mode = create_guard(False)
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