change to gpu

app.py

@@ -28,7 +28,7 @@ delta_ckpt = torch.load(args['delta_ckpt_path'], map_location=torch.device('cpu'
 model.load_state_dict(delta_ckpt, strict=False)
 delta_ckpt = torch.load(args['anomalygpt_ckpt_path'], map_location=torch.device('cpu'))
 model.load_state_dict(delta_ckpt, strict=False)
-model = model.eval().to(torch.bfloat16)#.half()#.cuda()
+model = model.eval().to(torch.float16)#.half()#.cuda()
 # model.image_decoder = model.image_decoder.cuda()
 # model.prompt_learner = model.prompt_learner.cuda()
 

model/openllama.py

@@ -43,7 +43,7 @@ for obj in objs:
         for s in prompted_state:
             for template in prompt_templates:
                 prompted_sentence.append(template.format(s))
-        prompted_sentence = data.load_and_transform_text(prompted_sentence, torch.device('cpu'))#torch.cuda.current_device())
+        prompted_sentence = data.load_and_transform_text(prompted_sentence, torch.cuda.current_device())#torch.cuda.current_device())
         prompt_sentence_obj.append(prompted_sentence)
     prompt_sentences[obj] = prompt_sentence_obj
 
@@ -167,7 +167,7 @@ class OpenLLAMAPEFTModel(nn.Module):
         max_tgt_len = args['max_tgt_len']
         stage = args['stage']
 
-        self.device = torch.device('cpu') # torch.cuda.current_device()
+        self.device = torch.cuda.current_device()
 
         print (f'Initializing visual encoder from {imagebind_ckpt_path} ...')
 
@@ -211,11 +211,11 @@ class OpenLLAMAPEFTModel(nn.Module):
 
         # # device_map = infer_auto_device_map(self.llama_model, no_split_module_classes=["OPTDecoderLayer"], dtype="float16")
         # # print(device_map)
-        # device_map = {'model.embed_tokens': 0, 'model.layers.0': 0, 'model.layers.1': 0, 'model.layers.2': 0, 'model.layers.3': 0, 'model.layers.4': 0, 'model.layers.5': 0, 'model.layers.6': 0, 'model.layers.7': 0, 'model.layers.8': 0, 'model.layers.9': 0, 'model.layers.10.self_attn': 0, 'model.layers.10.mlp.gate_proj': 0, 'model.layers.10.mlp.down_proj': 'cpu', 'model.layers.10.mlp.up_proj': 'cpu', 'model.layers.10.mlp.act_fn': 'cpu', 'model.layers.10.input_layernorm': 'cpu', 'model.layers.10.post_attention_layernorm': 'cpu', 'model.layers.11': 'cpu', 'model.layers.12': 'cpu', 'model.layers.13': 'cpu', 'model.layers.14': 'cpu', 'model.layers.15': 'cpu', 'model.layers.16': 'cpu', 'model.layers.17': 'cpu', 'model.layers.18': 'cpu', 'model.layers.19': 'cpu', 'model.layers.20': 'cpu', 'model.layers.21': 'cpu', 'model.layers.22': 'cpu', 'model.layers.23': 'cpu', 'model.layers.24': 'disk', 'model.layers.25': 'disk', 'model.layers.26': 'disk', 'model.layers.27': 'disk', 'model.layers.28': 'disk', 'model.layers.29': 'disk', 'model.layers.30': 'disk', 'model.layers.31.self_attn': 'disk', 'model.layers.31.mlp.gate_proj': 'disk', 'model.layers.31.mlp.down_proj': 'disk', 'model.layers.31.mlp.up_proj': 'disk', 'model.layers.31.mlp.act_fn': 'disk', 'model.layers.31.input_layernorm': 'disk', 'model.layers.31.post_attention_layernorm': 'disk', 'model.norm': 'disk', 'lm_head': 'disk'}
+        device_map = {'model.embed_tokens': 0, 'model.layers.0': 0, 'model.layers.1': 0, 'model.layers.2': 0, 'model.layers.3': 0, 'model.layers.4': 0, 'model.layers.5': 0, 'model.layers.6': 0, 'model.layers.7': 0, 'model.layers.8': 0, 'model.layers.9': 0, 'model.layers.10.self_attn': 0, 'model.layers.10.mlp.gate_proj': 0, 'model.layers.10.mlp.down_proj': 'cpu', 'model.layers.10.mlp.up_proj': 'cpu', 'model.layers.10.mlp.act_fn': 'cpu', 'model.layers.10.input_layernorm': 'cpu', 'model.layers.10.post_attention_layernorm': 'cpu', 'model.layers.11': 'cpu', 'model.layers.12': 'cpu', 'model.layers.13': 'cpu', 'model.layers.14': 'cpu', 'model.layers.15': 'cpu', 'model.layers.16': 'cpu', 'model.layers.17': 'cpu', 'model.layers.18': 'cpu', 'model.layers.19': 'cpu', 'model.layers.20': 'cpu', 'model.layers.21': 'cpu', 'model.layers.22': 'cpu', 'model.layers.23': 'cpu', 'model.layers.24': 'disk', 'model.layers.25': 'disk', 'model.layers.26': 'disk', 'model.layers.27': 'disk', 'model.layers.28': 'disk', 'model.layers.29': 'disk', 'model.layers.30': 'disk', 'model.layers.31.self_attn': 'disk', 'model.layers.31.mlp.gate_proj': 'disk', 'model.layers.31.mlp.down_proj': 'disk', 'model.layers.31.mlp.up_proj': 'disk', 'model.layers.31.mlp.act_fn': 'disk', 'model.layers.31.input_layernorm': 'disk', 'model.layers.31.post_attention_layernorm': 'disk', 'model.norm': 'disk', 'lm_head': 'disk'}
         # # self.llama_model = load_checkpoint_and_dispatch(self.llama_model, vicuna_ckpt_path, device_map=device_map, offload_folder="offload", offload_state_dict = True)
         # # self.llama_model.to(torch.float16)
         # # try:
-        self.llama_model = AutoModelForCausalLM.from_pretrained(vicuna_ckpt_path, torch_dtype=torch.bfloat16, device_map='auto', offload_folder="offload", offload_state_dict = True)
+        self.llama_model = AutoModelForCausalLM.from_pretrained(vicuna_ckpt_path, torch_dtype=torch.float16, device_map=device_map, offload_folder="offload", offload_state_dict = True)
         # # except:
         #     pass
         # finally:
@@ -223,7 +223,7 @@ class OpenLLAMAPEFTModel(nn.Module):
         self.llama_model = get_peft_model(self.llama_model, peft_config)
         self.llama_model.print_trainable_parameters()
 
-        self.llama_tokenizer = LlamaTokenizer.from_pretrained(vicuna_ckpt_path, use_fast=False, torch_dtype=torch.bfloat16, device_map='auto', offload_folder="offload", offload_state_dict = True)
+        self.llama_tokenizer = LlamaTokenizer.from_pretrained(vicuna_ckpt_path, use_fast=False, torch_dtype=torch.float16, device_map='auto', offload_folder="offload", offload_state_dict = True)
         self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
         self.llama_tokenizer.padding_side = "right"
         print ('Language decoder initialized.')