mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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from multiprocessing import Event, Process, Queue
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from tensorrt_llm.logger import logger
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from tensorrt_llm.profiler import (MemUnitType, bytes_to_target_unit,
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device_memory_info)
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class MemoryMonitor:
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def __init__(self, query_interval=0.1):
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self.query_interval = query_interval # second(s)
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self.mem_monitor_process = None
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# bytes
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self._peak_host_memory = 0
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self._peak_device_memory = 0
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self.device_handles = {}
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self.signal_event = Event() # Sending signal to subprocess
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self.peak_mem_queue = Queue() # Receiving results from subprocess
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def start(self):
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self.mem_monitor_process = Process(target=self._upd_peak_memory_usage,
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args=(self.signal_event,
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self.peak_mem_queue))
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self.mem_monitor_process.start()
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logger.debug("Launched memory monitor subprocess.")
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def kill(self):
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if self.mem_monitor_process is not None:
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self.mem_monitor_process.kill()
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logger.debug("Memory monitor subprocess is killed.")
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def stop(self):
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self.signal_event.set()
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logger.debug("Sent signal to stop memory monitor subprocess.")
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self._peak_device_memory = max(self._peak_device_memory,
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self.peak_mem_queue.get())
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self.mem_monitor_process.join()
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self.mem_monitor_process = None
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logger.debug("Memory monitor subprocess joined.")
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def _upd_peak_memory_usage(self, signal_event, peak_mem_queue):
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peak_used, _, _ = device_memory_info()
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while not signal_event.is_set():
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used, _, _ = device_memory_info()
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peak_used = max(used, peak_used)
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peak_mem_queue.put(peak_used)
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def get_peak_memory_usage(self, unit: MemUnitType = 'GiB'):
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return bytes_to_target_unit(self._peak_host_memory, unit), \
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bytes_to_target_unit(self._peak_device_memory, unit)
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