Upload app1.py

app1.py

@@ -23,10 +23,6 @@ from transformers import AutoTokenizer, AutoModel
 from model.modeling_llada import LLaDAModelLM
 
 def chat(args):  
-    model = LLaDAModelLM.from_pretrained('GSAI-ML/LLaDA-8B-Instruct', trust_remote_code=True, torch_dtype=torch.float16, device_map = 'auto').eval()
-    tokenizer = AutoTokenizer.from_pretrained('GSAI-ML/LLaDA-8B-Instruct', trust_remote_code=True)
-    device = next(iter(model.parameters())).device.type
-
     gen_length = args.gen_length
     steps = args.steps
     print('*' * 66)
@@ -34,36 +30,36 @@ def chat(args):
     print('*' * 66)
 
     conversation_num = 0
-    while True:
-        #user_input = input("Enter your question: ")
-        user_input = args.question
+    #while True:
+    #user_input = input("Enter your question: ")
+    user_input = args.question
 
-        m = [{"role": "user", "content": user_input}]
-        user_input = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False)
-        input_ids = tokenizer(user_input)['input_ids']
-        input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)
+    m = [{"role": "user", "content": user_input}]
+    user_input = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False)
+    input_ids = tokenizer(user_input)['input_ids']
+    input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)
 
-        if conversation_num == 0:
-            prompt = input_ids
-        else:
-            prompt = torch.cat([prompt, input_ids[:, 1:]], dim=1)
-        print(f'use cache: {args.use_cache} use cache position: {args.if_cache_position} threshold: {args.threshold} block size: {args.block_size}')
-        if args.use_cache:
-            if args.if_cache_position:
-                out, nfe = generate_with_dual_cache(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
-            else:
-                out, nfe = generate_with_prefix_cache(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
+    if conversation_num == 0:
+        prompt = input_ids
+    else:
+        prompt = torch.cat([prompt, input_ids[:, 1:]], dim=1)
+    print(f'use cache: {args.use_cache} use cache position: {args.if_cache_position} threshold: {args.threshold} block size: {args.block_size}')
+    if args.use_cache:
+        if args.if_cache_position:
+            out, nfe = generate_with_dual_cache(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
         else:
-            out, nfe = generate(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
+            out, nfe = generate_with_prefix_cache(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
+    else:
+        out, nfe = generate(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
 
-        answer = tokenizer.batch_decode(out[:, prompt.shape[1]:], skip_special_tokens=True)[0]
-        print(f"Bot's reply: {answer}")
-        print(f"Number of forward passes: {nfe}")
+    answer = tokenizer.batch_decode(out[:, prompt.shape[1]:], skip_special_tokens=True)[0]
+    print(f"Bot's reply: {answer}")
+    print(f"Number of forward passes: {nfe}")
 
-        # remove the <EOS>
-        prompt = out[out != 126081].unsqueeze(0)
-        conversation_num += 1
-        print('-----------------------------------------------------------------------')
+    # remove the <EOS>
+    prompt = out[out != 126081].unsqueeze(0)
+    conversation_num += 1
+    #print('-----------------------------------------------------------------------')
 
 
 if __name__ == "__main__":