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author | AUTOMATIC1111 <16777216c@gmail.com> | 2023-05-22 04:15:34 +0000 |
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committer | GitHub <noreply@github.com> | 2023-05-22 04:15:34 +0000 |
commit | 8137bdba61fd57cc1ddae801f6080d51e13d70c5 (patch) | |
tree | c5a02e9f9ae57c9f0ff8499379c6cc61a97c094e /modules/devices.py | |
parent | a862428902c4aecde8852761c3a4d95c196885cb (diff) | |
parent | 3366e494a1147e570d8527eea19da88edb3a1e0c (diff) | |
download | stable-diffusion-webui-gfx803-8137bdba61fd57cc1ddae801f6080d51e13d70c5.tar.gz stable-diffusion-webui-gfx803-8137bdba61fd57cc1ddae801f6080d51e13d70c5.tar.bz2 stable-diffusion-webui-gfx803-8137bdba61fd57cc1ddae801f6080d51e13d70c5.zip |
Merge branch 'dev' into text-drag-fix
Diffstat (limited to 'modules/devices.py')
-rw-r--r-- | modules/devices.py | 20 |
1 files changed, 19 insertions, 1 deletions
diff --git a/modules/devices.py b/modules/devices.py index c705a3cb..1ed6ffdc 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,5 +1,7 @@ import sys import contextlib +from functools import lru_cache + import torch from modules import errors @@ -65,7 +67,7 @@ def enable_tf32(): # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 - if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]): + if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True @@ -154,3 +156,19 @@ def test_for_nans(x, where): message += " Use --disable-nan-check commandline argument to disable this check." raise NansException(message) + + +@lru_cache +def first_time_calculation(): + """ + just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and + spends about 2.7 seconds doing that, at least wih NVidia. + """ + + x = torch.zeros((1, 1)).to(device, dtype) + linear = torch.nn.Linear(1, 1).to(device, dtype) + linear(x) + + x = torch.zeros((1, 1, 3, 3)).to(device, dtype) + conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) + conv2d(x) |