# -*- coding:utf-8 -*- 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 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 from 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'
{highlighted_code}
' 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'

{html.escape(userinput)}

' + 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("$", "$") def replace_leading_tabs_and_spaces(line): new_line = [] for char in line: if char == "\t": new_line.append(" ") elif char == " ": new_line.append(" ") 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(): return [], [], "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 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: 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_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