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273799c85d
* enable fsdp2 benchmark on XPU Signed-off-by: Matrix YAO <matrix.yao@intel.com> * add deterministic Signed-off-by: Matrix YAO <matrix.yao@intel.com> --------- Signed-off-by: Matrix YAO <matrix.yao@intel.com>
131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
# Copyright 2025 The HuggingFace Team. 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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import gc
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import json
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import os
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import threading
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import time
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import psutil
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import torch
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from accelerate import PartialState
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class MemoryTracker:
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def __init__(
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self,
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device: torch.device,
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output_directory: str,
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run_name: str,
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save_memory_snapshot: bool,
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log_interval: float = 0.01,
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):
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"""Class for tracking gpu and cpu memory usage of the process.
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Args:
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device (`torch.device`):
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PyTorch device to monitor.
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output_directory (`str`):
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Directory to save the memory usage data to, will be created if it doesn't exist.
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run_name (`str`):
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Name of the run, will be used to name the output files.
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save_memory_snapshot (`bool`):
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Whether to also save `torch.cuda.memory._dump_snapshot` to the output directory.
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log_interval (`float`, *optional*):
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Interval in seconds between memory measurements. Defaults to 0.01.
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"""
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self.log_interval = log_interval
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self.save_memory_snapshot = save_memory_snapshot
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self.output_directory = output_directory
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self.run_name = run_name
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self.timestamps = []
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self.allocated_memory = []
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self.reserved_memory = []
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self.virtual_memory = []
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self.start_time = None
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self.running = False
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self._thread = None
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self._state = PartialState()
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self._process = psutil.Process()
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self._device = device
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self.torch_accelerator_module = getattr(torch, device.type, torch.cuda)
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def _monitor(self):
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self.start_time = time.time()
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while self.running:
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allocated = self.torch_accelerator_module.memory_allocated(self._device) / (1024 * 1024)
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reserved = self.torch_accelerator_module.memory_reserved(self._device) / (1024 * 1024)
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virtual_memory = self._process.memory_info().rss / (1024 * 1024)
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self.allocated_memory.append(allocated)
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self.reserved_memory.append(reserved)
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self.virtual_memory.append(virtual_memory)
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self.timestamps.append(time.time() - self.start_time)
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time.sleep(self.log_interval)
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def start(self):
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gc.collect()
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self.torch_accelerator_module.empty_cache()
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if self.output_directory:
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os.makedirs(self.output_directory, exist_ok=True)
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if self.save_memory_snapshot:
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self.torch_accelerator_module.memory._record_memory_history()
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self.running = True
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self._thread = threading.Thread(target=self._monitor)
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self._thread.daemon = True
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self._thread.start()
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def stop(self):
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self.running = False
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if self._thread:
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self._thread.join()
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if self.save_memory_snapshot and self._state.is_main_process and self.output_directory:
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output_file = os.path.join(self.output_directory, f"{self.run_name}_memory_snapshot.pkl")
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self.torch_accelerator_module.memory._dump_snapshot(output_file)
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if self._state.is_main_process and self.output_directory:
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path = os.path.join(self.output_directory, f"{self.run_name}_memory_usage.json")
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with open(path, "w") as f:
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json.dump(
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{
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"timestamps": self.timestamps,
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"allocated_memory": self.allocated_memory,
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"reserved_memory": self.reserved_memory,
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"virtual_memory": self.virtual_memory,
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},
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f,
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)
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if self.save_memory_snapshot:
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self.torch_accelerator_module.memory._record_memory_history(False)
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self.torch_accelerator_module.empty_cache()
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@property
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def peak_allocated_memory(self):
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return max(self.allocated_memory)
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@property
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def peak_reserved_memory(self):
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return max(self.reserved_memory)
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