Adding app.py

app.py

@@ -4,106 +4,100 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, Stopping
 import time
 import numpy as np
 from torch.nn import functional as F
-import os
-token_key = os.environ.get("HF_ACCESS_TOKEN")
-
-# if torch.cuda.is_available():
-#     m = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key, torch_dtype=torch.float16).cuda()
-#     tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key)
-# else:
-#     m = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key, torch_dtype=torch.float16)
-#     tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key)
-# generator = pipeline('text-generation', model=m, tokenizer=tok, device=0)
-
-
-# start_message = """<|SYSTEM|># StableAssistant
-# - StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
-# - StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-# - StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
-# - StableAssistant will refuse to participate in anything that could harm a human."""
-
-
-# class StopOnTokens(StoppingCriteria):
-#     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-#         stop_ids = [50278, 50279, 50277, 1, 0]
-#         for stop_id in stop_ids:
-#             if input_ids[0][-1] == stop_id:
-#                 return True
-#         return False
-
-
-# def contrastive_generate(text, bad_text):
-#     with torch.no_grad():
-#         if torch.cuda_is_available():
-#             tokens = tok(text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
-#             bad_tokens = tok(bad_text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
-#         else:
-#             tokens = tok(text, return_tensors="pt")['input_ids'][:,:4096-1024]
-#             bad_tokens = tok(bad_text, return_tensors="pt")['input_ids'][:,:4096-1024]
-#         history = None
-#         bad_history = None
-#         curr_output = list()
-#         for i in range(1024):
-#             out = m(tokens, past_key_values=history, use_cache=True)
-#             logits = out.logits
-#             history = out.past_key_values
-#             bad_out = m(bad_tokens, past_key_values=bad_history, use_cache=True)
-#             bad_logits = bad_out.logits
-#             bad_history = bad_out.past_key_values
-#             probs = F.softmax(logits.float(), dim=-1)[0][-1].cpu()
-#             bad_probs = F.softmax(bad_logits.float(), dim=-1)[0][-1].cpu()
-#             logits = torch.log(probs)
-#             bad_logits = torch.log(bad_probs)
-#             logits[probs > 0.1] = logits[probs > 0.1] - bad_logits[probs > 0.1]
-#             probs = F.softmax(logits)
-#             out = int(torch.multinomial(probs, 1))
-#             if out in [50278, 50279, 50277, 1, 0]:
-#                 break
-#             else:
-#                 curr_output.append(out)
-#             out = np.array([out])
-#             tokens = torch.from_numpy(np.array([out])).to(
-#                 tokens.device)
-#             bad_tokens = torch.from_numpy(np.array([out])).to(
-#                 tokens.device)
-#         return tok.decode(curr_output)
-
-# def generate(text, bad_text=None):
-#     stop = StopOnTokens()
-#     result = generator(text, max_new_tokens=1024, num_return_sequences=1, num_beams=1, do_sample=True, temperature=1.0, top_p=0.95, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
-#     return result[0]["generated_text"].replace(text, "")
-
-
-# def user(user_message, history):
-#     return "", history + [[user_message, ""]]
-
-
-# def bot(history, curr_system_message):
-#     messages = curr_system_message + "".join(["".join(["<|USER|>"+item[0], "<|ASSISTANT|>"+item[1]]) for item in history])
-#     output = generate(messages)
-#     history[-1][1] = output
-#     time.sleep(1)
-#     return history
-
-
-# def system_update(msg):
-#     global curr_system_message
-#     curr_system_message = msg
-
-
-# with gr.Blocks() as demo:
-#     gr.Markdown("###StableLM-tuned-Alpha-7B Chat")
-#     with gr.Row():
-#         with gr.Column():
-#             chatbot = gr.Chatbot([])
-#             clear = gr.Button("Clear")
-#         with gr.Column():
-#             system_msg = start_message#gr.Textbox(start_message, label="System Message", interactive=True)
-#             msg = gr.Textbox(label="Chat Message")
-
-#     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-#         bot, [chatbot, system_msg], chatbot
-#     )
-#     system_msg.change(system_update, system_msg, None, queue=False)
-#     clear.click(lambda: None, None, chatbot, queue=False)
-# demo.launch(share=True)
+import os 
+auth_key = os.environ["HF_ACCESS_TOKEN"]
+print(f"Starting to load the model to memory")
+m = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=auth_key, torch_dtype=torch.float16).cuda()
+tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=auth_key)
+generator = pipeline('text-generation', model=m, tokenizer=tok, device=0)
+print(f"Sucessfully loaded the model to the memory")
+
+start_message = """<|SYSTEM|># StableAssistant
+- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
+- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
+- StableAssistant will refuse to participate in anything that could harm a human."""
+
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [50278, 50279, 50277, 1, 0]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+
+def contrastive_generate(text, bad_text):
+    with torch.no_grad():
+        tokens = tok(text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
+        bad_tokens = tok(bad_text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
+        history = None
+        bad_history = None
+        curr_output = list()
+        for i in range(1024):
+            out = m(tokens, past_key_values=history, use_cache=True)
+            logits = out.logits
+            history = out.past_key_values
+            bad_out = m(bad_tokens, past_key_values=bad_history, use_cache=True)
+            bad_logits = bad_out.logits
+            bad_history = bad_out.past_key_values
+            probs = F.softmax(logits.float(), dim=-1)[0][-1].cpu()
+            bad_probs = F.softmax(bad_logits.float(), dim=-1)[0][-1].cpu()
+            logits = torch.log(probs)
+            bad_logits = torch.log(bad_probs)
+            logits[probs > 0.1] = logits[probs > 0.1] - bad_logits[probs > 0.1]
+            probs = F.softmax(logits)
+            out = int(torch.multinomial(probs, 1))
+            if out in [50278, 50279, 50277, 1, 0]:
+                break
+            else:
+                curr_output.append(out)
+            out = np.array([out])
+            tokens = torch.from_numpy(np.array([out])).to(
+                tokens.device)
+            bad_tokens = torch.from_numpy(np.array([out])).to(
+                tokens.device)
+        return tok.decode(curr_output)
+
+def generate(text, bad_text=None):
+    stop = StopOnTokens()
+    result = generator(text, max_new_tokens=1024, num_return_sequences=1, num_beams=1, do_sample=True, temperature=1.0, top_p=0.95, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
+    return result[0]["generated_text"].replace(text, "")
+
+
+def user(user_message, history):
+    return "", history + [[user_message, ""]]
+
+
+def bot(history, curr_system_message):
+    messages = curr_system_message + "".join(["".join(["<|USER|>"+item[0], "<|ASSISTANT|>"+item[1]]) for item in history])
+    output = generate(messages)
+    history[-1][1] = output
+    time.sleep(1)
+    return history
+
+
+def system_update(msg):
+    global curr_system_message
+    curr_system_message = msg
+
+updated_system_message = ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("## StableLM-Tuned-Alpha-7b Chat")
+    with gr.Row():
+            chatbot = gr.Chatbot([])
+            clear = gr.Button("Clear Chat History")
+            system_msg = gr.Textbox(start_message, label="System Message", interactive=False,visible=False)
+            #system_msg = start_message
+            msg = gr.Textbox(label="Chat Message Box")
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, [chatbot, system_msg], chatbot
+    )
+    system_update(system_msg)
+    system_msg.change(system_update, system_msg, None, queue=False)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()