utils/app_modules/utils.py

# -*- coding:utf-8 -*-
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
import logging
import json
from transformers import AutoTokenizer, AutoModel
import os
import datetime
import hashlib
import csv
import requests
import re
import html
from collections.abc import Iterator
import markdown2
import torch
import sys
from pygments.lexers import guess_lexer, ClassNotFound

import gradio as gr
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
import transformers
from peft import PeftModel

from transformers import GenerationConfig#, LlamaForCausalLM, LlamaTokenizer
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../')
sys.path.append(path)
from utils.app_modules.presets import *

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
)


def markdown_to_html_with_syntax_highlight(md_str):
    def replacer(match):
        lang = match.group(1) or "text"
        code = match.group(2)
        lang = lang.strip()
        # print(1,lang)
        if lang == "text":
            lexer = guess_lexer(code)
            lang = lexer.name
            # print(2,lang)
        try:
            lexer = get_lexer_by_name(lang, stripall=True)
        except ValueError:
            lexer = get_lexer_by_name("python", stripall=True)
        formatter = HtmlFormatter()
        # print(3,lexer.name)
        highlighted_code = highlight(code, lexer, formatter)

        return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'

    code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
    md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)

    html_str = markdown(md_str)
    return html_str


def normalize_markdown(md_text: str) -> str:
    lines = md_text.split("\n")
    normalized_lines = []
    inside_list = False

    for i, line in enumerate(lines):
        if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
            if not inside_list and i > 0 and lines[i - 1].strip() != "":
                normalized_lines.append("")
            inside_list = True
            normalized_lines.append(line)
        elif inside_list and line.strip() == "":
            if i < len(lines) - 1 and not re.match(
                r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
            ):
                normalized_lines.append(line)
            continue
        else:
            inside_list = False
            normalized_lines.append(line)

    return "\n".join(normalized_lines)


def convert_mdtext(md_text):
    code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
    inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
    code_blocks = code_block_pattern.findall(md_text)
    non_code_parts = code_block_pattern.split(md_text)[::2]

    result = []
    for non_code, code in zip(non_code_parts, code_blocks + [""]):
        if non_code.strip():
            non_code = normalize_markdown(non_code)
            if inline_code_pattern.search(non_code):
                result.append(markdown(non_code, extensions=["tables"]))
            else:
                result.append(mdtex2html.convert(non_code, extensions=["tables"]))
        if code.strip():
            # _, code = detect_language(code)  # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题
            # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题
            code = f"\n```{code}\n\n```"
            code = markdown_to_html_with_syntax_highlight(code)
            result.append(code)
    result = "".join(result)
    result += ALREADY_CONVERTED_MARK
    return result


def convert_asis(userinput):
    return (
        f'<p style="white-space:pre-wrap;">{html.escape(userinput)}</p>'
        + ALREADY_CONVERTED_MARK
    )


def detect_converted_mark(userinput):
    if userinput.endswith(ALREADY_CONVERTED_MARK):
        return True
    else:
        return False


def detect_language(code):
    if code.startswith("\n"):
        first_line = ""
    else:
        first_line = code.strip().split("\n", 1)[0]
    language = first_line.lower() if first_line else ""
    code_without_language = code[len(first_line) :].lstrip() if first_line else code
    return language, code_without_language


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 += "```\n"
        elif in_code_block is True and line.startswith("```"):
            in_code_block = False
            markdown_text += "```\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


def add_language_tag(text):
    def detect_language(code_block):
        try:
            lexer = guess_lexer(code_block)
            return lexer.name.lower()
        except ClassNotFound:
            return ""

    code_block_pattern = re.compile(r"(```)(\w*\n[^`]+```)", re.MULTILINE)

    def replacement(match):
        code_block = match.group(2)
        if match.group(2).startswith("\n"):
            language = detect_language(code_block)
            if language:
                return f"```{language}{code_block}```"
            else:
                return f"```\n{code_block}```"
        else:
            return match.group(1) + code_block + "```"

    text2 = code_block_pattern.sub(replacement, text)
    return text2


def delete_last_conversation(chatbot, history):
    if len(chatbot) > 0:
        chatbot.pop()

    if len(history) > 0:
        history.pop()

    return (
        chatbot,
        history,
        "Delete Done",
    )


def reset_state(history):
    return [], history[1:], "Reset Done"


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


def cancel_outputing():
    shared_state.interrupt()
    textbox = reset_textbox()
    return "Stop Done"


def transfer_input(inputs):
    # 一次性返回，降低延迟
    textbox = reset_textbox()
    return (
        inputs,
        gr.update(value=""),
        gr.Button.update(visible=True),
        gr.Button.update(visible=True),
    )


class State:
    interrupted = False

    def interrupt(self):
        self.interrupted = True

    def recover(self):
        self.interrupted = False


shared_state = State()


def sample_decode(
    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]):
            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 SiliconGirlfriend (Your personal and empathetic lover and companion).  The human and the AI assistant take turns chatting. Human statements start with Human: and AI assistant statements start with Assistant:. 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!\Assistant: Hi!"
    prompt = "你是一名AI助手，名字是Silicon Girlfriend，有同情心的情感陪伴者。以下是一个用户与助手的对话。人类和AI助手轮番对话。人类的问题以Human: 开头，而Assistant的回答以Assistant:开头。Assistant会提供对问题尽可能详细的回复且回复以Markdown的形式呈现。\
    Assistant积极向上，语言亲和富含情感，能给用户情感支持。请以如下形式开展对话：\n Human: 你好!\Assistant: 你好!"
    history = ["\nHuman: {}\nAssistant: {}".format(x[0], x[1]) for x in history]
    history.append("\nHuman: {}\nAssistant: ".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:
    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 load_tokenizer_full_model(model_path,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(model_path)
    if device == "cuda":
        model = LlamaForCausalLM.from_pretrained(
            model_path,
            load_in_8bit=load_8bit,
            torch_dtype=torch.float16,
            device_map="auto",
        )
    elif device == "mps":
        model = LlamaForCausalLM.from_pretrained(
            model_path,
            load_in_8bit=load_8bit,
            device_map={"": device},
            torch_dtype=torch.float16,
        )
    else:
        model = LlamaForCausalLM.from_pretrained(
            model_path,
            load_in_8bit=load_8bit,
            device_map={"": device}, 
            low_cpu_mem_usage=True
        )
    if not load_8bit and device != "cpu":
        model.half()  # seems to fix bugs for some users.
    model.to(device)
    model.eval()
    return tokenizer, model,device

def load_chatglm_tokenizer_and_model(model_path):
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
    model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
    model = model.eval()
    return tokenizer, model

def load_tokenizer_and_model(base_model, adapter_model, 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",
        )
        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,
        )
        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
        )
        model = PeftModel.from_pretrained(
            model,
            adapter_model,
            device_map={"": device},
        )

    if not load_8bit and device != "cpu":
        model.half()  # seems to fix bugs for some users.

    model.eval()
    return tokenizer, model, device