update

pilot/model/guanaco_llm.py

@@ -1,73 +1,68 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
 import torch
-import transformers
-from transformers import GenerationConfig
-from pilot.model.llm_utils import Iteratorize, Stream
+from threading import Thread
+from transformers import TextIteratorStreamer, StoppingCriteriaList, StoppingCriteria
+from pilot.conversation import ROLE_ASSISTANT, ROLE_USER
 
-
-def guanaco_generate_output(model, tokenizer, params, device, context_len=2048, stream_interval=2):
+def guanaco_generate_output(model, tokenizer, params, device, context_len=2048):
     """Fork from fastchat: https://github.com/KohakuBlueleaf/guanaco-lora/blob/main/generate.py"""
-    prompt = params["prompt"]
-    inputs = tokenizer(prompt, return_tensors="pt")
-    input_ids = inputs["input_ids"].to(device)
-    temperature = (0.5,)
-    top_p = (0.95,)
-    top_k = (45,)
-    max_new_tokens = (128,)
-    stream_output = True
+    stop = params.get("stop", "###")
+    messages = params["prompt"].split(stop)
 
-    generation_config = GenerationConfig(
-        temperature=temperature,
-        top_p=top_p,
-        top_k=top_k,
+
+    hist = []
+    for i in range(1, len(messages) - 2, 2):
+        hist.append(
+            (
+                messages[i].split(ROLE_USER + ":")[1],
+                messages[i + 1].split(ROLE_ASSISTANT + ":")[1],
+            )
+        )
+    
+
+    text = + "".join(["".join([f"### USER: {item[0]}\n",f"### Assistant: {item[1]}\n",])for item in hist[:-1]])
+    text += "".join(["".join([f"### USER: {hist[-1][0]}\n",f"### Assistant: {hist[-1][1]}\n",])])
+
+
+    query = messages[-2].split(ROLE_USER + ":")[1]
+    print("Query Message: ", query)
+
+    input_ids = tokenizer(query, return_tensors="pt").input_ids
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    stop_token_ids = [0]
+    class StopOnTokens(StoppingCriteria):
+        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+            for stop_id in stop_token_ids:
+                if input_ids[0][-1] == stop_id:
+                    return True
+            return False
+        
+    stop = StopOnTokens()
+
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        max_new_tokens=512,
+        temperature=1.0,
+        do_sample=True,
+        top_k=1,        
+        streamer=streamer,
+        repetition_penalty=1.7,
+        stopping_criteria=StoppingCriteriaList([stop])
     )
 
-    generate_params = {
-        "input_ids": input_ids,
-        "generation_config": generation_config,
-        "return_dict_in_generate": True,
-        "output_scores": True,
-        "max_new_tokens": max_new_tokens,
-    }
 
-    # if stream_output:
-    #     # Stream the reply 1 token at a time.
-    #     # This is based on the trick of using 'stopping_criteria' to create an iterator,
-    #     # from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.
+    t1 = Thread(target=model.generate, kwargs=generate_kwargs)
+    t1.start()
 
-    #     def generate_with_callback(callback=None, **kwargs):
-    #         kwargs.setdefault("stopping_criteria", transformers.StoppingCriteriaList())
-    #         kwargs["stopping_criteria"].append(Stream(callback_func=callback))
-    #         with torch.no_grad():
-    #             model.generate(**kwargs)
+    generator =  model.generate(**generate_kwargs)
+    for output in generator:
+        # new_tokens = len(output) - len(input_ids[0])
+        decoded_output = tokenizer.decode(output)
+        if output[-1] in [tokenizer.eos_token_id]:
+            break
 
-    #     def generate_with_streaming(**kwargs):
-    #         return Iteratorize(generate_with_callback, kwargs, callback=None)
+        out = decoded_output.split("### Response:")[-1].strip()
 
-    #     with generate_with_streaming(**generate_params) as generator:
-    #         for output in generator:
-    #             # new_tokens = len(output) - len(input_ids[0])
-    #             decoded_output = tokenizer.decode(output)
+        yield out
 
-    #             if output[-1] in [tokenizer.eos_token_id]:
-    #                 break
-
-    #             yield decoded_output.split("### Response:")[-1].strip()
-    #     return  # early return for stream_output
-
-    with torch.no_grad():
-        generation_output = model.generate(
-            input_ids=input_ids,
-            generation_config=generation_config,
-            return_dict_in_generate=True,
-            output_scores=True,
-            max_new_tokens=max_new_tokens,
-        )
-
-    s = generation_output.sequences[0]
-    print(f"debug_sequences,{s}", s)
-    output = tokenizer.decode(s)
-    print(f"debug_output,{output}", output)
-    yield output.split("### Response:")[-1].strip()