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fix: replace hardcoded .cuda() with dynamic device selection #553

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fix: replace hardcoded .cuda() with dynamic device selection #553

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17 changes: 13 additions & 4 deletions basic_demo/cli_demo_sat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,14 @@
from utils.utils import chat, llama2_tokenizer, llama2_text_processor_inference, get_image_processor
from utils.models import CogAgentModel, CogVLMModel


def _get_device():
if torch.cuda.is_available():
return 'cuda'
if torch.backends.mps.is_available():
return 'mps'
return 'cpu'

def main():
parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default=2048, help='max length of the total sequence')
Expand Down Expand Up @@ -43,8 +51,8 @@ def main():
model_parallel_size=world_size,
mode='inference',
skip_init=True,
use_gpu_initialization=True if (torch.cuda.is_available() and args.quant is None) else False,
device='cpu' if args.quant else 'cuda',
use_gpu_initialization=True if (torch.cuda.is_available() and args.quant is None) else False, # Note: MPS init handled via device param
device='cpu' if args.quant else _get_device(),
**vars(args)
), overwrite_args={'model_parallel_size': world_size} if world_size != 1 else {})
model = model.eval()
Expand All @@ -59,8 +67,9 @@ def main():

if args.quant:
quantize(model, args.quant)
if torch.cuda.is_available():
model = model.cuda()
device = _get_device()
if device != 'cpu':
model = model.to(device)


model.add_mixin('auto-regressive', CachedAutoregressiveMixin())
Expand Down
16 changes: 13 additions & 3 deletions basic_demo/web_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,15 @@




def _get_device():
if torch.cuda.is_available():
return 'cuda'
if torch.backends.mps.is_available():
return 'mps'
return 'cpu'


DESCRIPTION = '''<h1 style='text-align: center'> <a href="https://github.com/THUDM/CogVLM">CogVLM / CogAgent</a> </h1>'''

NOTES = '<h3> This app is adapted from <a href="https://github.com/THUDM/CogVLM">https://github.com/THUDM/CogVLM</a>. It would be recommended to check out the repo if you want to see the detail of our model, CogVLM & CogAgent. </h3>'
Expand Down Expand Up @@ -75,7 +84,7 @@ def load_model(args):
bf16=args.bf16,
skip_init=True,
use_gpu_initialization=True if (torch.cuda.is_available() and args.quant is None) else False,
device='cpu' if args.quant else 'cuda'),
device='cpu' if args.quant else _get_device()),
overwrite_args={'model_parallel_size': world_size} if world_size != 1 else {}
)
model = model.eval()
Expand All @@ -88,8 +97,9 @@ def load_model(args):

if args.quant:
quantize(model, args.quant)
if torch.cuda.is_available():
model = model.cuda()
device = _get_device()
if device != 'cpu':
model = model.to(device)
model.add_mixin('auto-regressive', CachedAutoregressiveMixin())

text_processor_infer = llama2_text_processor_inference(tokenizer, args.max_length, model.image_length)
Expand Down