silvere
commented
1 year ago install nvida cuda toolset first. and then reinstall pytorch with cuda enabled. works fine for me.
Open masoningithub opened 1 year ago
silvere
commented
1 year ago install nvida cuda toolset first. and then reinstall pytorch with cuda enabled. works fine for me.
chat1q2w3e4r5t
commented
1 year ago 1.quick start
python visual_chatgpt.py --load ImageCaptioning_cpu,Text2Image_cpu
python visual_chatgpt.py --load "ImageCaptioning_cuda:0,Text2Image_cuda:0"
2.change the default value for cpu users
# for cpu user
parser.add_argument('--load', type=str, default="ImageCaptioning_cpu,Text2Image_cpu")It is the simple method ,but cannot use the cuda
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ D:\Python\visual-chatgpt\visual_chatgpt.py:1051 in │
│ │
│ 1048 │ parser.add_argument('--load', type=str, default="ImageCaptioning_cuda:0,Text2Image_c │
│ 1049 │ args = parser.parse_args() │
│ 1050 │ loaddict = {e.split('')[0].strip(): e.split('_')[1].strip() for e in args.load.spl │
│ ❱ 1051 │ bot = ConversationBot(load_dict=load_dict) │
│ 1052 │ with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo: │
│ 1053 │ │ chatbot = gr.Chatbot(elem_id="chatbot", label="Visual ChatGPT") │
│ 1054 │ │ state = gr.State([]) │
│ │
│ D:\Python\visual-chatgpt\visual_chatgpt.py:987 in init │
│ │
│ 984 │ │ self.models = {} │
│ 985 │ │ # Load Basic Foundation Models │
│ 986 │ │ for class_name, device in load_dict.items(): │
│ ❱ 987 │ │ │ self.models[class_name] = globals()class_name │
│ 988 │ │ │
│ 989 │ │ # Load Template Foundation Models │
│ 990 │ │ for class_name, module in globals().items(): │
│ │
│ D:\Python\visual-chatgpt\visual_chatgpt.py:313 in init │
│ │
│ 310 │ │ self.device = device │
│ 311 │ │ self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 │
│ 312 │ │ self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning │
│ ❱ 313 │ │ self.model = BlipForConditionalGeneration.from_pretrained( │
│ 314 │ │ │ "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype).to(se │
│ 315 │ │
│ 316 │ @prompts(name="Get Photo Description", │
│ │
│ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\transformers\modeling_utils.py:1811 in to │
│ │
│ 1808 │ │ │ │ " model has already been set to the correct devices and casted to the co │
│ 1809 │ │ │ ) │
│ 1810 │ │ else: │
│ ❱ 1811 │ │ │ return super().to(*args, *kwargs) │
│ 1812 │ │
│ 1813 │ def half(self, args): │
│ 1814 │ │ # Checks if the model has been loaded in 8-bit │
│ │
│ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\nn\modules\module.py:927 in to │
│ │
│ 924 │ │ │ │ │ │ │ non_blocking, memory_format=convert_to_format) │
│ 925 │ │ │ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else No │
│ 926 │ │ │
│ ❱ 927 │ │ return self._apply(convert) │
│ 928 │ │
│ 929 │ def register_backward_hook( │
│ 930 │ │ self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]] │
│ │
│ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\nn\modules\module.py:579 in _apply │
│ │
│ 576 │ │
│ 577 │ def _apply(self, fn): │
│ 578 │ │ for module in self.children(): │
│ ❱ 579 │ │ │ module._apply(fn) │
│ 580 │ │ │
│ 581 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 582 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\nn\modules\module.py:579 in _apply │
│ │
│ 576 │ │
│ 577 │ def _apply(self, fn): │
│ 578 │ │ for module in self.children(): │
│ ❱ 579 │ │ │ module._apply(fn) │
│ 580 │ │ │
│ 581 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 582 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\nn\modules\module.py:579 in _apply │
│ │
│ 576 │ │
│ 577 │ def _apply(self, fn): │
│ 578 │ │ for module in self.children(): │
│ ❱ 579 │ │ │ module._apply(fn) │
│ 580 │ │ │
│ 581 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 582 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\nn\modules\module.py:602 in _apply │
│ │
│ 599 │ │ │ # track autograd history of
param_applied, so we have to use │ │ 600 │ │ │ #with torch.no_grad():│ │ 601 │ │ │ with torch.no_grad(): │ │ ❱ 602 │ │ │ │ param_applied = fn(param) │ │ 603 │ │ │ should_use_set_data = compute_should_use_set_data(param, param_applied) │ │ 604 │ │ │ if should_use_set_data: │ │ 605 │ │ │ │ param.data = param_applied │ │ │ │ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\nn\modules\module.py:925 in convert │ │ │ │ 922 │ │ │ if convert_to_format is not None and t.dim() in (4, 5): │ │ 923 │ │ │ │ return t.to(device, dtype if t.is_floating_point() or t.is_complex() els │ │ 924 │ │ │ │ │ │ │ non_blocking, memory_format=convert_to_format) │ │ ❱ 925 │ │ │ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else No │ │ 926 │ │ │ │ 927 │ │ return self._apply(convert) │ │ 928 │ │ │ │ F:\Programs\miniconda3\envs\visgpt\lib\site-packages\torch\cuda__init__.py:211 in _lazy_init │ │ │ │ 208 │ │ │ │ "Cannot re-initialize CUDA in forked subprocess. To use CUDA with " │ │ 209 │ │ │ │ "multiprocessing, you must use the 'spawn' start method") │ │ 210 │ │ if not hasattr(torch._C, '_cuda_getDeviceCount'): │ │ ❱ 211 │ │ │ raise AssertionError("Torch not compiled with CUDA enabled") │ │ 212 │ │ if _cudart is None: │ │ 213 │ │ │ raise AssertionError( │ │ 214 │ │ │ │ "libcudart functions unavailable. It looks like you have a broken build? │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ AssertionError: Torch not compiled with CUDA enabled