utils.py

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 torch 
import sys
import gc
from pygments.lexers import guess_lexer, ClassNotFound
import gradio as gr
from pygments import highlight
from pygments.lexers import guess_lexer,get_lexer_by_name
from pygments.formatters import HtmlFormatter
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM



def reset_state():
    return [], [], "Reset Done"

def reset_textbox():
    return gr.update(value=""),""

def cancel_outputing():
    return "Stop Done"

def transfer_input(inputs):
    textbox = reset_textbox()
    return (
        inputs,
        gr.update(value=""),
        gr.Button.update(visible=True),
    )

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





tokenizer = AutoTokenizer.from_pretrained("project-baize/baize-v2-7b")

model = AutoModelForCausalLM.from_pretrained("project-baize/baize-v2-7b")

def load_tokenizer_and_model(base_model,load_8bit=False):
    if torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    tokenizer = AutoTokenizer.from_pretrained.from_pretrained(base_model, use_fast = False)
    if device == "cuda":
        model = AutoModelForCausalLM.from_pretrained(
            base_model,
            load_in_8bit=load_8bit,
            torch_dtype=torch.float16,
            device_map="auto",
        )
    else:
        model = AutoModelForCausalLM.from_pretrained(
            base_model, device_map={"": device}, low_cpu_mem_usage=True
        )

    if not load_8bit:
        model.half()  # seems to fix bugs for some users.

    model.eval()
    return tokenizer,model,device

# 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