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
import datasets
from datasets import load_dataset
import evaluate


def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
    for stop_word in stop_words:
        if s.endswith(stop_word):
            return True
        for i in range(1, len(stop_word)):
            if s.endswith(stop_word[:i]):
                return True
    return False

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!"   
    prompt = "Das folgende ist eine Unterhaltung zwischen einem Menschen und einem KI-Assistenten, der Baize genannt wird. Baize ist ein open-source KI-Assistent, der von UCSD entwickelt wurde. Der Mensch und der KI-Assistent chatten abwechselnd miteinander in deutsch. Die Antworten des KI Assistenten sind immer so ausführlich wie möglich und in Markdown Schreibweise und in deutscher Sprache. Wenn nötig übersetzt er sie ins Deutsche. Die Antworten des KI-Assistenten vermeiden Themen und Antworten zu unethischen, kontroversen oder sensiblen Themen. Die Antworten sind immer sehr höflich formuliert..\n[|Human|]Hallo!\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 load_tokenizer_and_model(base_model, load_8bit=False):
    if torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast = True)
    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 

########################################
#Predict
def predict(model,
            tokenizer,
            device,
            text,
            history,
            top_p,
            temperature,
            max_length_tokens,
            max_context_length_tokens,):
    if text=="":
        return "Leer"
    try:
        model
    except:
        return [[text,"No Model Found"]]

    inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens)
    if inputs is None:
        return "Too long"
    else:
        prompt,inputs=inputs
        begin_length = len(prompt)
        
    input_ids = inputs["input_ids"][:,-max_context_length_tokens:].to(device)
    torch.cuda.empty_cache()

    #torch.no_grad() bedeutet, dass für die betreffenden tensoren keine Ableitungen berechnet werden bei der backpropagation 
    #hier soll das NN ja auch nicht geändert werden 8backprop ist nicht nötig), da es um interference-prompts geht!
    with torch.no_grad():
        antwort=[[""],[""]]
        #die vergangenen prompts werden alle als Tupel in history abgelegt sortiert nach 'Human' und 'AI'- dass sind daher auch die stop-words, die den jeweils nächsten Eintrag kennzeichnen        
        for x in greedy_search(input_ids,model,tokenizer,stop_words=["[|Human|]", "[|AI|]"],max_length=max_length_tokens,temperature=temperature,top_p=top_p):
            if is_stop_word_or_prefix(x,["[|Human|]", "[|AI|]"]) is False:
                if "[|Human|]" in x:
                    x = x[:x.index("[|Human|]")].strip()
                if "[|AI|]" in x:
                    x = x[:x.index("[|AI|]")].strip() 
                x = x.strip()   
                a, b=   [[y[0],convert_to_markdown(y[1])] for y in history]+[[text, convert_to_markdown(x)]],history + [[text,x]]
                antwort = antwort + a

    del input_ids
    gc.collect()
    torch.cuda.empty_cache()
    
    try:
        return antwort
    except:
        pass



#Funktion, die der trainer braucht, um das Training zu evaluieren - mit einer Metrik
def compute_metrics(eval_pred):
    #Metrik berechnen, um das training messen zu können - wird es besser???
    metric = evaluate.load("accuracy")   #3 Arten von gegebener Metrik: f1  oder roc_auc  oder accuracy
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    #Call compute on metric to calculate the accuracy of your predictions. 
    #Before passing your predictions to compute, you need to convert the predictions to logits (remember all Transformers models return logits):
    return metric.compute(predictions=predictions, references=labels)


def compute_metrics2(p):
    pred, labels = p
    pred = np.argmax(pred, axis=1)

    accuracy = accuracy_score(y_true=labels, y_pred=pred)
    recall = recall_score(y_true=labels, y_pred=pred)
    precision = precision_score(y_true=labels, y_pred=pred)
    f1 = f1_score(y_true=labels, y_pred=pred)

    return {"accuracy": accuracy, "precision": precision, "recall": recall, "f1": f1}



                
def convert_to_markdown(text):
    text = text.replace("$","&#36;")
    def replace_leading_tabs_and_spaces(line):
        new_line = []
        
        for char in line:
            if char == "\t":
                new_line.append("&#9;")
            elif char == " ":
                new_line.append("&nbsp;")
            else:
                break
        return "".join(new_line) + line[len(new_line):]

    markdown_text = ""
    lines = text.split("\n")
    in_code_block = False

    for line in lines:
        if in_code_block is False and line.startswith("```"):
            in_code_block = True
            markdown_text += f"{line}\n"
        elif in_code_block is True and line.startswith("```"):
            in_code_block = False
            markdown_text += f"{line}\n"
        elif in_code_block:
            markdown_text += f"{line}\n"
        else:
            line = replace_leading_tabs_and_spaces(line)
            line = re.sub(r"^(#)", r"\\\1", line)
            markdown_text += f"{line}  \n"

    return markdown_text


#Datasets encodieren - in train und val Sets
class Dataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels=None):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        if self.labels:
            item["labels"] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.encodings["input_ids"])


#######################################################
#Fine-Tuning
#######################################################

#load Dataset
def daten_laden(name):
    return load_dataset("alexkueck/tis",   delimiter=";", column_names=["id", "text"])