InvokeAI/invokeai/backend/model_management/memory_snapshot.py

100 lines
3.9 KiB
Python

import gc
from typing import Optional
import psutil
import torch
from invokeai.backend.model_management.libc_util import LibcUtil, Struct_mallinfo2
GB = 2**30 # 1 GB
class MemorySnapshot:
"""A snapshot of RAM and VRAM usage. All values are in bytes."""
def __init__(self, process_ram: int, vram: Optional[int], malloc_info: Optional[Struct_mallinfo2]):
"""Initialize a MemorySnapshot.
Most of the time, `MemorySnapshot` will be constructed with `MemorySnapshot.capture()`.
Args:
process_ram (int): CPU RAM used by the current process.
vram (Optional[int]): VRAM used by torch.
malloc_info (Optional[Struct_mallinfo2]): Malloc info obtained from LibcUtil.
"""
self.process_ram = process_ram
self.vram = vram
self.malloc_info = malloc_info
@classmethod
def capture(cls, run_garbage_collector: bool = True):
"""Capture and return a MemorySnapshot.
Note: This function has significant overhead, particularly if `run_garbage_collector == True`.
Args:
run_garbage_collector (bool, optional): If true, gc.collect() will be run before checking the process RAM
usage. Defaults to True.
Returns:
MemorySnapshot
"""
if run_garbage_collector:
gc.collect()
# According to the psutil docs (https://psutil.readthedocs.io/en/latest/#psutil.Process.memory_info), rss is
# supported on all platforms.
process_ram = psutil.Process().memory_info().rss
if torch.cuda.is_available():
vram = torch.cuda.memory_allocated()
else:
# TODO: We could add support for mps.current_allocated_memory() as well. Leaving out for now until we have
# time to test it properly.
vram = None
try:
malloc_info = LibcUtil().mallinfo2()
except (OSError, AttributeError):
# OSError: This is expected in environments that do not have the 'libc.so.6' shared library.
# AttributeError: This is expected in environments that have `libc.so.6` but do not have the `mallinfo2` (e.g. glibc < 2.33)
# TODO: Does `mallinfo` work?
malloc_info = None
return cls(process_ram, vram, malloc_info)
def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: Optional[MemorySnapshot]) -> str:
"""Get a pretty string describing the difference between two `MemorySnapshot`s."""
def get_msg_line(prefix: str, val1: int, val2: int):
diff = val2 - val1
return f"{prefix: <30} ({(diff/GB):+5.3f}): {(val1/GB):5.3f}GB -> {(val2/GB):5.3f}GB\n"
msg = ""
if snapshot_1 is None or snapshot_2 is None:
return msg
msg += get_msg_line("Process RAM", snapshot_1.process_ram, snapshot_2.process_ram)
if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None:
msg += get_msg_line("libc mmap allocated", snapshot_1.malloc_info.hblkhd, snapshot_2.malloc_info.hblkhd)
msg += get_msg_line("libc arena used", snapshot_1.malloc_info.uordblks, snapshot_2.malloc_info.uordblks)
msg += get_msg_line("libc arena free", snapshot_1.malloc_info.fordblks, snapshot_2.malloc_info.fordblks)
libc_total_allocated_1 = snapshot_1.malloc_info.arena + snapshot_1.malloc_info.hblkhd
libc_total_allocated_2 = snapshot_2.malloc_info.arena + snapshot_2.malloc_info.hblkhd
msg += get_msg_line("libc total allocated", libc_total_allocated_1, libc_total_allocated_2)
libc_total_used_1 = snapshot_1.malloc_info.uordblks + snapshot_1.malloc_info.hblkhd
libc_total_used_2 = snapshot_2.malloc_info.uordblks + snapshot_2.malloc_info.hblkhd
msg += get_msg_line("libc total used", libc_total_used_1, libc_total_used_2)
if snapshot_1.vram is not None and snapshot_2.vram is not None:
msg += get_msg_line("VRAM", snapshot_1.vram, snapshot_2.vram)
return msg