Update utils.py

utils.py

@@ -1,4 +1,29 @@
-from app_modules.presets import *
+from __future__ import annotations
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
+import logging
+import json
+import os
+import datetime
+import hashlib
+import csv
+import requests
+import re
+import html
+import markdown2
+import torch 
+import sys
+import gc
+from pygments.lexers import guess_lexer, ClassNotFound
+
+import gradio as gr
+from pypinyin import lazy_pinyin
+import tiktoken
+import mdtex2html
+from markdown import markdown
+from pygments import highlight
+from pygments.lexers import guess_lexer,get_lexer_by_name
+from pygments.formatters import HtmlFormatter
+from beschreibungen import *
 
 logging.basicConfig(
     level=logging.INFO,
@@ -180,7 +205,6 @@ def cancel_outputing():
     return "Stop Done"
 
 def transfer_input(inputs):
-    # 一次性返回，降低延迟
     textbox = reset_textbox()
     return (
         inputs,
@@ -203,79 +227,7 @@ shared_state = State()
 
 
 
-# Greedy Search
-def greedy_search(input_ids: torch.Tensor,
-                  model: torch.nn.Module,
-                  tokenizer: transformers.PreTrainedTokenizer,
-                  stop_words: list,
-                  max_length: int,
-                  temperature: float = 1.0,
-                  top_p: float = 1.0,
-                  top_k: int = 25) -> Iterator[str]:
-    generated_tokens = []
-    past_key_values = None
-    current_length = 1
-    for i in range(max_length):
-        with torch.no_grad():
-            if past_key_values is None:
-                outputs = model(input_ids)
-            else:
-                outputs = model(input_ids[:, -1:], past_key_values=past_key_values)
-            logits = outputs.logits[:, -1, :]
-            past_key_values = outputs.past_key_values
-
-            # apply temperature
-            logits /= temperature
-    
-            probs = torch.softmax(logits, dim=-1)
-            # apply top_p
-            probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
-            probs_sum = torch.cumsum(probs_sort, dim=-1)
-            mask = probs_sum - probs_sort > top_p
-            probs_sort[mask] = 0.0
-    
-            # apply top_k
-            #if top_k is not None:
-            #    probs_sort1, _ = torch.topk(probs_sort, top_k)
-            #    min_top_probs_sort = torch.min(probs_sort1, dim=-1, keepdim=True).values
-            #    probs_sort = torch.where(probs_sort < min_top_probs_sort, torch.full_like(probs_sort, float(0.0)), probs_sort)
-    
-            probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
-            next_token = torch.multinomial(probs_sort, num_samples=1)
-            next_token = torch.gather(probs_idx, -1, next_token)
-    
-            input_ids = torch.cat((input_ids, next_token), dim=-1)
-    
-            generated_tokens.append(next_token[0].item())
-            text = tokenizer.decode(generated_tokens)
-    
-            yield text
-            if any([x in text for x in stop_words]):
-                del past_key_values
-                del logits
-                del probs
-                del probs_sort
-                del probs_idx
-                del probs_sum
-                gc.collect()
-                return 
-
-def generate_prompt_with_history(text,history,tokenizer,max_length=2048):
-    prompt = "The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n[|Human|]Hello!\n[|AI|]Hi!"   
-    history = ["\n[|Human|]{}\n[|AI|]{}".format(x[0],x[1]) for x in history]
-    history.append("\n[|Human|]{}\n[|AI|]".format(text))
-    history_text = ""
-    flag = False
-    for x in history[::-1]:
-        if tokenizer(prompt+history_text+x, return_tensors="pt")['input_ids'].size(-1) <= max_length:
-            history_text = x + history_text
-            flag = True
-        else:
-            break
-    if flag:
-        return  prompt+history_text,tokenizer(prompt+history_text, return_tensors="pt")
-    else:
-        return None
+
 
 
 def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
@@ -289,57 +241,3 @@ def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
 
 
 
-def load_tokenizer_and_model(base_model,adapter_model=None,load_8bit=False):
-    if torch.cuda.is_available():
-        device = "cuda"
-    else:
-        device = "cpu"
-
-    try:
-        if torch.backends.mps.is_available():
-            device = "mps"
-    except:  # noqa: E722
-        pass
-    tokenizer = LlamaTokenizer.from_pretrained(base_model)
-    if device == "cuda":
-        model = LlamaForCausalLM.from_pretrained(
-            base_model,
-            load_in_8bit=load_8bit,
-            torch_dtype=torch.float16,
-            device_map="auto",
-        )
-        if adapter_model is not None:
-            model = PeftModel.from_pretrained(
-                model,
-                adapter_model,
-                torch_dtype=torch.float16,
-            )
-    elif device == "mps":
-        model = LlamaForCausalLM.from_pretrained(
-            base_model,
-            device_map={"": device},
-            torch_dtype=torch.float16,
-        )
-        if adapter_model is not None:
-            model = PeftModel.from_pretrained(
-                model,
-                adapter_model,
-                device_map={"": device},
-                torch_dtype=torch.float16,
-            )
-    else:
-        model = LlamaForCausalLM.from_pretrained(
-            base_model, device_map={"": device}, low_cpu_mem_usage=True
-        )
-        if adapter_model is not None:
-            model = PeftModel.from_pretrained(
-                model,
-                adapter_model,
-                device_map={"": device},
-            )
-
-    if not load_8bit:
-        model.half()  # seems to fix bugs for some users.
-
-    model.eval()
-    return tokenizer,model,device