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Make cudaMalloc backed opt-in #9300

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Make cudaMalloc backed opt-in #9300

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1 change: 0 additions & 1 deletion comfy/cli_args.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,6 @@ def __call__(self, parser, namespace, values, option_string=None):
parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
cm_group = parser.add_mutually_exclusive_group()
cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")


fp_group = parser.add_mutually_exclusive_group()
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15 changes: 10 additions & 5 deletions cuda_malloc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import importlib.util
from comfy.cli_args import args
import subprocess
import logging

#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
def get_gpu_names():
Expand Down Expand Up @@ -50,7 +51,7 @@ def enum_display_devices():
"GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60"
}

def cuda_malloc_supported():
def device_cuda_malloc_supported():
try:
names = get_gpu_names()
except:
Expand All @@ -62,8 +63,8 @@ def cuda_malloc_supported():
return False
return True


if not args.cuda_malloc:
def cuda_malloc_supported():
software_supported = False
try:
version = ""
torch_spec = importlib.util.find_spec("torch")
Expand All @@ -76,16 +77,20 @@ def cuda_malloc_supported():
version = module.__version__

if int(version[0]) >= 2 and "+cu" in version: #enable by default for torch version 2.0 and up only on cuda torch
args.cuda_malloc = cuda_malloc_supported()
software_supported = True
except:
pass

return (software_supported and device_cuda_malloc_supported())

if args.cuda_malloc and not args.disable_cuda_malloc:
if args.cuda_malloc:
env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
if env_var is None:
env_var = "backend:cudaMallocAsync"
else:
env_var += ",backend:cudaMallocAsync"

if not cuda_malloc_supported():
logging.warning("WARNING: this card most likely does not support cuda-malloc\n")

os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var
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