From 2a3c0ab5d29b584d8d82eb90be71bb5a04e749fc Mon Sep 17 00:00:00 2001 From: Ryan Dick Date: Tue, 3 Oct 2023 11:28:19 -0400 Subject: [PATCH] Move MemorySnapshot to its own file. --- .../model_management/memory_snapshot.py | 66 +++++++++++++++++++ .../backend/model_management/model_cache.py | 61 +---------------- 2 files changed, 67 insertions(+), 60 deletions(-) create mode 100644 invokeai/backend/model_management/memory_snapshot.py diff --git a/invokeai/backend/model_management/memory_snapshot.py b/invokeai/backend/model_management/memory_snapshot.py new file mode 100644 index 0000000000..018987e1e6 --- /dev/null +++ b/invokeai/backend/model_management/memory_snapshot.py @@ -0,0 +1,66 @@ +import gc +from typing import Optional + +import psutil +import torch + +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]): + """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. + """ + self.process_ram = process_ram + self.vram = vram + + @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 + + return cls(process_ram, vram) + + +def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnapshot) -> str: + """Get a pretty string describing the difference between two `MemorySnapshot`s.""" + ram_diff = snapshot_2.process_ram - snapshot_1.process_ram + msg = f"RAM ({(ram_diff/GB):+.2f}): {(snapshot_1.process_ram/GB):.2f}GB -> {(snapshot_2.process_ram/GB):.2f}GB" + + vram_diff = None + if snapshot_1.vram is not None and snapshot_2.vram is not None: + vram_diff = snapshot_2.vram - snapshot_1.vram + + msg += f", VRAM ({(vram_diff/GB):+.2f}): {(snapshot_1.vram/GB):.2f}GB -> {(snapshot_2.vram/GB):.2f}GB" + + return msg diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 68ebff7862..663fd8b627 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -27,10 +27,10 @@ from dataclasses import dataclass, field from pathlib import Path from typing import Any, Dict, Optional, Type, Union, types -import psutil import torch import invokeai.backend.util.logging as logger +from invokeai.backend.model_management.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff from ..util.devices import choose_torch_device from .models import BaseModelType, ModelBase, ModelType, SubModelType @@ -535,62 +535,3 @@ class ModelCache(object): with open(hashpath, "w") as f: f.write(hash) return hash - - -class MemorySnapshot: - """A snapshot of RAM and VRAM usage. All values are in bytes.""" - - def __init__(self, process_ram: int, vram: Optional[int]): - """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. - """ - self.process_ram = process_ram - self.vram = vram - - @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 choose_torch_device() == torch.device("cuda"): - 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 - - return cls(process_ram, vram) - - -def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnapshot) -> str: - """Get a pretty string describing the difference between two `MemorySnapshot`s.""" - ram_diff = snapshot_2.process_ram - snapshot_1.process_ram - msg = f"RAM ({(ram_diff/GIG):+.2f}): {(snapshot_1.process_ram/GIG):.2f}GB -> {(snapshot_2.process_ram/GIG):.2f}GB" - - vram_diff = None - if snapshot_1.vram is not None and snapshot_2.vram is not None: - vram_diff = snapshot_2.vram - snapshot_1.vram - - msg += f", VRAM ({(vram_diff/GIG):+.2f}): {(snapshot_1.vram/GIG):.2f}GB -> {(snapshot_2.vram/GIG):.2f}GB" - - return msg