Add VRAM monitoring

This commit is contained in:
EyeDeck 2022-09-17 00:49:31 -04:00 committed by AUTOMATIC1111
parent 1fc1c537c7
commit ed6787ca2f
4 changed files with 112 additions and 2 deletions

77
modules/memmon.py Normal file
View file

@ -0,0 +1,77 @@
import threading
import time
from collections import defaultdict
import torch
class MemUsageMonitor(threading.Thread):
run_flag = None
device = None
disabled = False
opts = None
data = None
def __init__(self, name, device, opts):
threading.Thread.__init__(self)
self.name = name
self.device = device
self.opts = opts
self.daemon = True
self.run_flag = threading.Event()
self.data = defaultdict(int)
def run(self):
if self.disabled:
return
while True:
self.run_flag.wait()
torch.cuda.reset_peak_memory_stats()
self.data.clear()
if self.opts.memmon_poll_rate <= 0:
self.run_flag.clear()
continue
self.data["min_free"] = torch.cuda.mem_get_info()[0]
while self.run_flag.is_set():
free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug?
self.data["min_free"] = min(self.data["min_free"], free)
time.sleep(1 / self.opts.memmon_poll_rate)
def dump_debug(self):
print(self, 'recorded data:')
for k, v in self.read().items():
print(k, -(v // -(1024 ** 2)))
print(self, 'raw torch memory stats:')
tm = torch.cuda.memory_stats(self.device)
for k, v in tm.items():
if 'bytes' not in k:
continue
print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
print(torch.cuda.memory_summary())
def monitor(self):
self.run_flag.set()
def read(self):
free, total = torch.cuda.mem_get_info()
self.data["total"] = total
torch_stats = torch.cuda.memory_stats(self.device)
self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
self.data["system_peak"] = total - self.data["min_free"]
return self.data
def stop(self):
self.run_flag.clear()
return self.read()

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@ -12,6 +12,7 @@ from modules.paths import script_path, sd_path
from modules.devices import get_optimal_device
import modules.styles
import modules.interrogate
import modules.memmon
sd_model_file = os.path.join(script_path, 'model.ckpt')
if not os.path.exists(sd_model_file):
@ -138,6 +139,7 @@ class Options:
"show_progressbar": OptionInfo(True, "Show progressbar"),
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step":1}),
"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
@ -217,3 +219,6 @@ class TotalTQDM:
total_tqdm = TotalTQDM()
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
mem_mon.start()

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@ -119,6 +119,7 @@ def save_files(js_data, images, index):
def wrap_gradio_call(func):
def f(*args, **kwargs):
shared.mem_mon.monitor()
t = time.perf_counter()
try:
@ -135,8 +136,19 @@ def wrap_gradio_call(func):
elapsed = time.perf_counter() - t
mem_stats = {k:-(v//-(1024*1024)) for k,v in shared.mem_mon.stop().items()}
active_peak = mem_stats['active_peak']
reserved_peak = mem_stats['reserved_peak']
sys_peak = '?' if opts.memmon_poll_rate <= 0 else mem_stats['system_peak']
sys_total = mem_stats['total']
sys_pct = '?' if opts.memmon_poll_rate <= 0 else round(sys_peak/sys_total * 100, 2)
vram_tooltip = "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.&#013;" \
"Torch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.&#013;" \
"Sys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%)."
# last item is always HTML
res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>" \
f"<p class='vram' title='{vram_tooltip}'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p></div>"
shared.state.interrupted = False

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@ -1,5 +1,21 @@
.output-html p {margin: 0 0.5em;}
.performance { font-size: 0.85em; color: #444; }
.performance {
font-size: 0.85em;
color: #444;
display: flex;
justify-content: space-between;
white-space: nowrap;
}
.performance .time {
margin-right: 0;
}
.performance .vram {
margin-left: 0;
text-align: right;
}
#generate{
min-height: 4.5em;