初始化项目，由ModelHub XC社区提供模型
Model: CausalLM/7B Source: Original Platform
This commit is contained in:
ModelHub XC
632
eval/evaluate_chatml_ceval.py
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632
eval/evaluate_chatml_ceval.py
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import os
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import pandas as pd
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import numpy as np
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import argparse
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import datasets
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import torch
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import re
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from thefuzz import process
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from typing import List
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from tqdm import tqdm
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from transformers.trainer_utils import set_seed
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from typing import Tuple, List, Union, Iterable
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import numpy as np
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import torch
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import torch.nn.functional as F
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from transformers import PreTrainedTokenizer
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from transformers import logging
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from transformers.generation import LogitsProcessor
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from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
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HistoryType = List[Tuple[str, str]]
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TokensType = List[int]
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BatchTokensType = List[List[int]]
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def make_context(
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tokenizer: PreTrainedTokenizer,
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query: str,
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history: List[Tuple[str, str]] = None,
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system: str = "",
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max_window_size: int = 6144,
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chat_format: str = "chatml",
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):
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if history is None:
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history = []
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im_start, im_end = "<|im_start|>", "<|im_end|>"
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im_start_tokens = [tokenizer.im_start_id]
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im_end_tokens = [tokenizer.im_end_id]
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nl_tokens = tokenizer.encode("\n")
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def _tokenize_str(role, content):
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return f"{role}\n{content}", tokenizer.encode(
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role
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) + nl_tokens + tokenizer.encode(content)
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system_text, system_tokens_part = _tokenize_str("system", system)
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system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
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raw_text = ""
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context_tokens = []
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for turn_query, turn_response in reversed(history):
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query_text, query_tokens_part = _tokenize_str("user", turn_query)
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query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
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response_text, response_tokens_part = _tokenize_str(
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"assistant", turn_response
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)
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response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
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next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
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prev_chat = (
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f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
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)
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current_context_size = (
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len(system_tokens) + len(next_context_tokens) + len(context_tokens)
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)
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if current_context_size < max_window_size:
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context_tokens = next_context_tokens + context_tokens
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raw_text = prev_chat + raw_text
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else:
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break
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context_tokens = system_tokens + context_tokens
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raw_text = f"{im_start}{system_text}{im_end}" + raw_text
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context_tokens += (
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nl_tokens
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+ im_start_tokens
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+ _tokenize_str("user", query)[1]
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+ im_end_tokens
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+ nl_tokens
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+ im_start_tokens
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+ tokenizer.encode("assistant")
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+ nl_tokens
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)
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raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
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return raw_text, context_tokens
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def chat(
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model,
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tokenizer: PreTrainedTokenizer,
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query: str,
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history: Optional[HistoryType],
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system: str = "You are a helpful assistant.",
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append_history: bool = True
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) -> Tuple[str, HistoryType]:
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if history is None:
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history = []
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raw_text, context_tokens = make_context(
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tokenizer,
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query,
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history=history,
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system=system,
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max_window_size=6144,
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chat_format = "chatml",
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)
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stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
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input_ids = torch.tensor([context_tokens]).cuda()
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outputs = model.generate(
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input_ids,
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# stop_words_ids = stop_words_ids,
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return_dict_in_generate = False,
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)
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response = decode_tokens(
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outputs[0],
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tokenizer,
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raw_text_len=len(raw_text),
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context_length=len(context_tokens),
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chat_format='chatml',
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verbose=False,
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)
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if append_history:
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history.append((query, response))
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return response, history
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def decode_tokens(
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tokens: Union[torch.LongTensor, TokensType],
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tokenizer: PreTrainedTokenizer,
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raw_text_len: int,
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context_length: int,
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chat_format: str = "chatml",
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verbose: bool = False,
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return_end_reason: bool = False,
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) -> str:
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if torch.is_tensor(tokens):
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tokens = tokens.cpu().numpy().tolist()
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return _decode_chatml(
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tokens,
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stop_words=[],
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eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
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tokenizer=tokenizer,
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raw_text_len=raw_text_len,
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context_length=context_length,
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verbose=verbose,
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return_end_reason=return_end_reason,
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)
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def _decode_chatml(
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tokens: List[int],
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*,
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stop_words: List[str],
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eod_token_ids: List[int],
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tokenizer: PreTrainedTokenizer,
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raw_text_len: int,
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context_length: int,
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verbose: bool = False,
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return_end_reason: bool = False,
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chat_format = "chatml",
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):
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end_reason = f"Gen length {len(tokens)}"
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eod_token_idx = context_length
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for eod_token_idx in range(context_length, len(tokens)):
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if tokens[eod_token_idx] in eod_token_ids:
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end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
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break
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trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx])[raw_text_len:]
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if verbose:
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print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens)[raw_text_len:])
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print("\nRaw Generate:", trim_decode_tokens)
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print("\nEnd Reason:", end_reason)
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for stop_word in stop_words:
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trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
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trim_decode_tokens = trim_decode_tokens.strip()
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if verbose:
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print("\nGenerate:", trim_decode_tokens)
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if return_end_reason:
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return trim_decode_tokens, end_reason
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else:
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return trim_decode_tokens
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def load_models_tokenizer(args):
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
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model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
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model.generation_config.do_sample = False # use greedy decoding
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return model, tokenizer
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def process_before_extraction(gen, question, choice_dict):
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# Example Prompt:
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# 关于传输层的面向连接服务的特性是____。
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# A. 既不保证可靠，也不保证按序交付
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# B. 不保证可靠，但保证按序交付
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# C. 保证可靠，但不保证按序交付
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# D. 既保证可靠，也保证按序交付
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# Example Model Output：
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# 关于传输层的面向连接服务的特性是既保证可靠，也保证按序交付
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# Processed Output:
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# 答案是D
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question_split = question.rstrip("。").split("。")[-1].split("_")
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# replacing the question
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if len(question_split[0].strip()) > 4:
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gen = gen.replace(question_split[0], "答案是")
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if len(question_split[-1].strip()) > 4:
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gen = gen.replace(question_split[-1], "")
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# replace the choice by letter in the generated sentence
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# from longest one to shortest one
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for key, val in sorted(choice_dict.items(), key=lambda x: len(x[1]), reverse=True):
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gen = gen.replace(val.rstrip("。"), key)
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return gen
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def count_substr(gen, pattern):
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return len(re.findall(pattern, gen))
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def extract_choice(gen, prompt, choice_list):
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# 答案是A | 选项是A | 应该选A选项
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res = re.search(
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r"(?:(?:选|选择|选定)[：:]?\s*|(?:(?:答案|选项)(?![^ABCD]{0,10}?(?:不|非)[^ABCD]{0,10}?(?:是|选|为|：|:|】))[^ABCD]{0,10}?(?:是|选|为|：|:|】))[^ABCD]{0,10}?)(A|B|C|D)(?:选项)?(?:\)|。|\.|，|,|．|、|A|B|C|D|$|：|:|\)|）)",
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gen,
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)
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# A选项正确 | A选项符合题意
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if res is None:
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res = re.search(
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r"(A|B|C|D)(?:选?项)?(?![^ABCD]{0,4}?(?:不|非)[^ABCD]{0,4}?(?:正确|对[的，。：]|符合))[^ABCD]{0,4}?(?:正确|对[的，。：]|符合)",
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gen,
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)
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# 直接输出 A
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if res is None:
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res = re.search(r"^[\(（]?(A|B|C|D)(?:。|\)|）|\.|，|,|．|：|:|$)", gen)
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# 获取第一个出现的字母
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if res is None:
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res = re.search(r"(?<![a-zA-Z])(A|B|C|D)(?![a-zA-Z=])", gen)
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if res is None:
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return choices[choice_list.index(process.extractOne(gen, choice_list)[0])]
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return res.group(1)
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def format_example(line):
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example = line["question"] + "\n\n"
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for choice in choices:
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example += f'{choice}. {line[f"{choice}"]}\n'
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return example
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def extract_answer(response, row):
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prompt = row["question"]
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gen = process_before_extraction(
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response, prompt, {choice: row[choice] for choice in choices}
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)
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if not isinstance(prompt, str):
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prompt = prompt[0]
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pred = extract_choice(gen, prompt, [row[choice] for choice in choices])
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return pred
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@torch.no_grad()
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def eval_subject(
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model,
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tokenizer,
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subject_name,
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test_df,
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save_result_dir=None,
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overwrite=False,
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**kwargs
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):
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result_path = os.path.join(save_result_dir, f"{subject_name}_result.csv")
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if not overwrite and os.path.exists(result_path):
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print(f"{result_path} existed, skip!")
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score = []
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for (_, datarow), (_, resultrow) in zip(
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test_df.iterrows(), pd.read_csv(result_path).iterrows()
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):
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pred = extract_answer(resultrow["model_response"], datarow)
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correct = 1 if pred == datarow["answer"] else 0
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score.append(correct)
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correct_ratio = 100 * sum(score) / len(score)
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return correct_ratio
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responses = []
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result = []
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score = []
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for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
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question = format_example(row)
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response, _ = chat(
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model,
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tokenizer,
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question,
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history=None,
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)
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print(question)
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print(response)
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pred = extract_answer(response, row)
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print(pred)
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print("======================")
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if "answer" in row:
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correct = 1 if pred == row["answer"] else 0
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score.append(correct)
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if args.debug:
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print(f'{question} pred: {pred} ref: {row["answer"]}')
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responses.append(response)
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result.append(pred)
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if score:
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correct_ratio = 100 * sum(score) / len(score)
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if args.debug:
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print(subject_name, correct_ratio)
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else:
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correct_ratio = 0
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if save_result_dir:
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test_df["model_response"] = responses
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test_df["model_output"] = result
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if score:
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test_df["correctness"] = score
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os.makedirs(save_result_dir, exist_ok=True)
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test_df.to_csv(result_path, encoding="utf-8", index=False)
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return correct_ratio
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def cal_ceval(res):
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acc_sum_dict = dict()
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acc_norm_sum_dict = dict()
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cnt_dict = dict()
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acc_sum = 0.0
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cnt = 0
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hard_cnt = 0
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hard_acc_sum = 0.0
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for tt in res.keys():
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name = tt.split("-")[-1]
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acc_sum += float(res[tt])
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cnt += 1
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class_ = TASK_NAME_MAPPING[name][2]
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if class_ not in acc_sum_dict:
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acc_sum_dict[class_] = 0.0
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acc_norm_sum_dict[class_] = 0.0
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cnt_dict[class_] = 0.0
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if name in hard_list:
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hard_cnt += 1
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hard_acc_sum += float(res[tt])
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acc_sum_dict[class_] += float(res[tt])
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cnt_dict[class_] += 1
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print("\n\n\n")
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for k in ["STEM", "Social Science", "Humanities", "Other"]:
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if k in cnt_dict:
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print("%s acc: %.2f " % (k, acc_sum_dict[k] / cnt_dict[k]))
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if hard_cnt > 0:
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print("Hard acc:%.2f " % (hard_acc_sum / hard_cnt))
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print("AVERAGE acc:%.2f " % (acc_sum / cnt))
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TASK_NAME_MAPPING = {
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"computer_network": ["Computer Network", "\u8ba1\u7b97\u673a\u7f51\u7edc", "STEM"],
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"operating_system": ["Operating System", "\u64cd\u4f5c\u7cfb\u7edf", "STEM"],
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"computer_architecture": [
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"Computer Architecture",
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"\u8ba1\u7b97\u673a\u7ec4\u6210",
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"STEM",
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],
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"college_programming": ["College Programming", "\u5927\u5b66\u7f16\u7a0b", "STEM"],
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"college_physics": ["College Physics", "\u5927\u5b66\u7269\u7406", "STEM"],
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"college_chemistry": ["College Chemistry", "\u5927\u5b66\u5316\u5b66", "STEM"],
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"advanced_mathematics": [
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"Advanced Mathematics",
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"\u9ad8\u7b49\u6570\u5b66",
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"STEM",
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],
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"probability_and_statistics": [
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"Probability and Statistics",
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"\u6982\u7387\u7edf\u8ba1",
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"STEM",
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],
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"discrete_mathematics": [
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"Discrete Mathematics",
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"\u79bb\u6563\u6570\u5b66",
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"STEM",
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],
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"electrical_engineer": [
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"Electrical Engineer",
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"\u6ce8\u518c\u7535\u6c14\u5de5\u7a0b\u5e08",
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"STEM",
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],
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"metrology_engineer": [
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"Metrology Engineer",
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"\u6ce8\u518c\u8ba1\u91cf\u5e08",
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"STEM",
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],
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"high_school_mathematics": [
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"High School Mathematics",
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"\u9ad8\u4e2d\u6570\u5b66",
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"STEM",
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],
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"high_school_physics": ["High School Physics", "\u9ad8\u4e2d\u7269\u7406", "STEM"],
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"high_school_chemistry": [
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"High School Chemistry",
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"\u9ad8\u4e2d\u5316\u5b66",
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"STEM",
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],
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"high_school_biology": ["High School Biology", "\u9ad8\u4e2d\u751f\u7269", "STEM"],
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"middle_school_mathematics": [
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"Middle School Mathematics",
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"\u521d\u4e2d\u6570\u5b66",
|
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"STEM",
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],
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"middle_school_biology": [
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"Middle School Biology",
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"\u521d\u4e2d\u751f\u7269",
|
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"STEM",
|
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],
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"middle_school_physics": [
|
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"Middle School Physics",
|
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"\u521d\u4e2d\u7269\u7406",
|
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"STEM",
|
||||
],
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"middle_school_chemistry": [
|
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"Middle School Chemistry",
|
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"\u521d\u4e2d\u5316\u5b66",
|
||||
"STEM",
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],
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"veterinary_medicine": ["Veterinary Medicine", "\u517d\u533b\u5b66", "STEM"],
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"college_economics": [
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"College Economics",
|
||||
"\u5927\u5b66\u7ecf\u6d4e\u5b66",
|
||||
"Social Science",
|
||||
],
|
||||
"business_administration": [
|
||||
"Business Administration",
|
||||
"\u5de5\u5546\u7ba1\u7406",
|
||||
"Social Science",
|
||||
],
|
||||
"marxism": [
|
||||
"Marxism",
|
||||
"\u9a6c\u514b\u601d\u4e3b\u4e49\u57fa\u672c\u539f\u7406",
|
||||
"Social Science",
|
||||
],
|
||||
"mao_zedong_thought": [
|
||||
"Mao Zedong Thought",
|
||||
"\u6bdb\u6cfd\u4e1c\u601d\u60f3\u548c\u4e2d\u56fd\u7279\u8272\u793e\u4f1a\u4e3b\u4e49\u7406\u8bba\u4f53\u7cfb\u6982\u8bba",
|
||||
"Social Science",
|
||||
],
|
||||
"education_science": ["Education Science", "\u6559\u80b2\u5b66", "Social Science"],
|
||||
"teacher_qualification": [
|
||||
"Teacher Qualification",
|
||||
"\u6559\u5e08\u8d44\u683c",
|
||||
"Social Science",
|
||||
],
|
||||
"high_school_politics": [
|
||||
"High School Politics",
|
||||
"\u9ad8\u4e2d\u653f\u6cbb",
|
||||
"Social Science",
|
||||
],
|
||||
"high_school_geography": [
|
||||
"High School Geography",
|
||||
"\u9ad8\u4e2d\u5730\u7406",
|
||||
"Social Science",
|
||||
],
|
||||
"middle_school_politics": [
|
||||
"Middle School Politics",
|
||||
"\u521d\u4e2d\u653f\u6cbb",
|
||||
"Social Science",
|
||||
],
|
||||
"middle_school_geography": [
|
||||
"Middle School Geography",
|
||||
"\u521d\u4e2d\u5730\u7406",
|
||||
"Social Science",
|
||||
],
|
||||
"modern_chinese_history": [
|
||||
"Modern Chinese History",
|
||||
"\u8fd1\u4ee3\u53f2\u7eb2\u8981",
|
||||
"Humanities",
|
||||
],
|
||||
"ideological_and_moral_cultivation": [
|
||||
"Ideological and Moral Cultivation",
|
||||
"\u601d\u60f3\u9053\u5fb7\u4fee\u517b\u4e0e\u6cd5\u5f8b\u57fa\u7840",
|
||||
"Humanities",
|
||||
],
|
||||
"logic": ["Logic", "\u903b\u8f91\u5b66", "Humanities"],
|
||||
"law": ["Law", "\u6cd5\u5b66", "Humanities"],
|
||||
"chinese_language_and_literature": [
|
||||
"Chinese Language and Literature",
|
||||
"\u4e2d\u56fd\u8bed\u8a00\u6587\u5b66",
|
||||
"Humanities",
|
||||
],
|
||||
"art_studies": ["Art Studies", "\u827a\u672f\u5b66", "Humanities"],
|
||||
"professional_tour_guide": [
|
||||
"Professional Tour Guide",
|
||||
"\u5bfc\u6e38\u8d44\u683c",
|
||||
"Humanities",
|
||||
],
|
||||
"legal_professional": [
|
||||
"Legal Professional",
|
||||
"\u6cd5\u5f8b\u804c\u4e1a\u8d44\u683c",
|
||||
"Humanities",
|
||||
],
|
||||
"high_school_chinese": [
|
||||
"High School Chinese",
|
||||
"\u9ad8\u4e2d\u8bed\u6587",
|
||||
"Humanities",
|
||||
],
|
||||
"high_school_history": [
|
||||
"High School History",
|
||||
"\u9ad8\u4e2d\u5386\u53f2",
|
||||
"Humanities",
|
||||
],
|
||||
"middle_school_history": [
|
||||
"Middle School History",
|
||||
"\u521d\u4e2d\u5386\u53f2",
|
||||
"Humanities",
|
||||
],
|
||||
"civil_servant": ["Civil Servant", "\u516c\u52a1\u5458", "Other"],
|
||||
"sports_science": ["Sports Science", "\u4f53\u80b2\u5b66", "Other"],
|
||||
"plant_protection": ["Plant Protection", "\u690d\u7269\u4fdd\u62a4", "Other"],
|
||||
"basic_medicine": ["Basic Medicine", "\u57fa\u7840\u533b\u5b66", "Other"],
|
||||
"clinical_medicine": ["Clinical Medicine", "\u4e34\u5e8a\u533b\u5b66", "Other"],
|
||||
"urban_and_rural_planner": [
|
||||
"Urban and Rural Planner",
|
||||
"\u6ce8\u518c\u57ce\u4e61\u89c4\u5212\u5e08",
|
||||
"Other",
|
||||
],
|
||||
"accountant": ["Accountant", "\u6ce8\u518c\u4f1a\u8ba1\u5e08", "Other"],
|
||||
"fire_engineer": [
|
||||
"Fire Engineer",
|
||||
"\u6ce8\u518c\u6d88\u9632\u5de5\u7a0b\u5e08",
|
||||
"Other",
|
||||
],
|
||||
"environmental_impact_assessment_engineer": [
|
||||
"Environmental Impact Assessment Engineer",
|
||||
"\u73af\u5883\u5f71\u54cd\u8bc4\u4ef7\u5de5\u7a0b\u5e08",
|
||||
"Other",
|
||||
],
|
||||
"tax_accountant": ["Tax Accountant", "\u7a0e\u52a1\u5e08", "Other"],
|
||||
"physician": ["Physician", "\u533b\u5e08\u8d44\u683c", "Other"],
|
||||
}
|
||||
hard_list = [
|
||||
"advanced_mathematics",
|
||||
"discrete_mathematics",
|
||||
"probability_and_statistics",
|
||||
"college_physics",
|
||||
"college_chemistry",
|
||||
"high_school_mathematics",
|
||||
"high_school_physics",
|
||||
"high_school_chemistry",
|
||||
]
|
||||
choices = ["A", "B", "C", "D"]
|
||||
|
||||
|
||||
def main(args):
|
||||
print("loading model weights")
|
||||
if args.checkpoint_path:
|
||||
model, tokenizer = load_models_tokenizer(args)
|
||||
else:
|
||||
model, tokenizer = None, None
|
||||
print("model loaded")
|
||||
dev_result = {}
|
||||
for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
|
||||
val_file_path = os.path.join(
|
||||
args.eval_data_path, "val", f"{subject_name}_val.csv"
|
||||
)
|
||||
val_df = pd.read_csv(val_file_path)
|
||||
|
||||
score = eval_subject(
|
||||
model,
|
||||
tokenizer,
|
||||
subject_name,
|
||||
val_df,
|
||||
save_result_dir="outs_chat/ceval_eval_result",
|
||||
overwrite=args.overwrite,
|
||||
)
|
||||
dev_result[subject_name] = score
|
||||
cal_ceval(dev_result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Test HF checkpoint.")
|
||||
parser.add_argument(
|
||||
"-c",
|
||||
"--checkpoint-path",
|
||||
type=str,
|
||||
help="Checkpoint path",
|
||||
default="Qwen/Qwen-7B-Chat",
|
||||
)
|
||||
parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed")
|
||||
|
||||
# Provide extra arguments required for tasks
|
||||
group = parser.add_argument_group(title="Evaluation options")
|
||||
group.add_argument(
|
||||
"-d", "--eval_data_path", type=str, required=True, help="Path to eval data"
|
||||
)
|
||||
group.add_argument(
|
||||
"--debug", action="store_true", default=False, help="Print infos."
|
||||
)
|
||||
group.add_argument(
|
||||
"--overwrite",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Overwrite existed results",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
set_seed(args.seed)
|
||||
|
||||
main(args)
|
||||
358
eval/evaluate_chatml_gsm8k.py
Normal file
358
eval/evaluate_chatml_gsm8k.py
Normal file
@@ -0,0 +1,358 @@
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
import argparse
|
||||
import numpy as np
|
||||
import tqdm
|
||||
from datasets import load_from_disk, load_dataset
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
from transformers.generation import GenerationConfig
|
||||
|
||||
import os
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import argparse
|
||||
import datasets
|
||||
import torch
|
||||
import re
|
||||
from thefuzz import process
|
||||
from typing import List
|
||||
from tqdm import tqdm
|
||||
from transformers.trainer_utils import set_seed
|
||||
|
||||
from typing import Tuple, List, Union, Iterable
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from transformers import PreTrainedTokenizer
|
||||
from transformers import logging
|
||||
from transformers.generation import LogitsProcessor
|
||||
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
|
||||
HistoryType = List[Tuple[str, str]]
|
||||
TokensType = List[int]
|
||||
BatchTokensType = List[List[int]]
|
||||
|
||||
def make_context(
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = None,
|
||||
system: str = "",
|
||||
max_window_size: int = 6144,
|
||||
chat_format: str = "chatml",
|
||||
):
|
||||
if history is None:
|
||||
history = []
|
||||
|
||||
im_start, im_end = "<|im_start|>", "<|im_end|>"
|
||||
im_start_tokens = [tokenizer.im_start_id]
|
||||
im_end_tokens = [tokenizer.im_end_id]
|
||||
nl_tokens = tokenizer.encode("\n")
|
||||
|
||||
def _tokenize_str(role, content):
|
||||
return f"{role}\n{content}", tokenizer.encode(
|
||||
role
|
||||
) + nl_tokens + tokenizer.encode(content)
|
||||
|
||||
system_text, system_tokens_part = _tokenize_str("system", system)
|
||||
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
|
||||
|
||||
raw_text = ""
|
||||
context_tokens = []
|
||||
|
||||
for turn_query, turn_response in reversed(history):
|
||||
query_text, query_tokens_part = _tokenize_str("user", turn_query)
|
||||
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
|
||||
response_text, response_tokens_part = _tokenize_str(
|
||||
"assistant", turn_response
|
||||
)
|
||||
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
|
||||
|
||||
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
|
||||
prev_chat = (
|
||||
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
|
||||
)
|
||||
|
||||
current_context_size = (
|
||||
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
|
||||
)
|
||||
if current_context_size < max_window_size:
|
||||
context_tokens = next_context_tokens + context_tokens
|
||||
raw_text = prev_chat + raw_text
|
||||
else:
|
||||
break
|
||||
|
||||
context_tokens = system_tokens + context_tokens
|
||||
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
|
||||
context_tokens += (
|
||||
nl_tokens
|
||||
+ im_start_tokens
|
||||
+ _tokenize_str("user", query)[1]
|
||||
+ im_end_tokens
|
||||
+ nl_tokens
|
||||
+ im_start_tokens
|
||||
+ tokenizer.encode("assistant")
|
||||
+ nl_tokens
|
||||
)
|
||||
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
|
||||
|
||||
return raw_text, context_tokens
|
||||
|
||||
def chat(
|
||||
model,
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
query: str,
|
||||
history: Optional[HistoryType],
|
||||
system: str = "You are a helpful assistant.",
|
||||
append_history: bool = True
|
||||
) -> Tuple[str, HistoryType]:
|
||||
|
||||
|
||||
if history is None:
|
||||
history = []
|
||||
|
||||
raw_text, context_tokens = make_context(
|
||||
tokenizer,
|
||||
query,
|
||||
history=history,
|
||||
system=system,
|
||||
max_window_size=6144,
|
||||
chat_format = "chatml",
|
||||
)
|
||||
|
||||
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
|
||||
input_ids = torch.tensor([context_tokens]).cuda()
|
||||
outputs = model.generate(
|
||||
input_ids,
|
||||
# stop_words_ids = stop_words_ids,
|
||||
return_dict_in_generate = False,
|
||||
)
|
||||
|
||||
response = decode_tokens(
|
||||
outputs[0],
|
||||
tokenizer,
|
||||
raw_text_len=len(raw_text),
|
||||
context_length=len(context_tokens),
|
||||
chat_format='chatml',
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
if append_history:
|
||||
history.append((query, response))
|
||||
|
||||
return response, history
|
||||
|
||||
def decode_tokens(
|
||||
tokens: Union[torch.LongTensor, TokensType],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
context_length: int,
|
||||
chat_format: str = "chatml",
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
) -> str:
|
||||
if torch.is_tensor(tokens):
|
||||
tokens = tokens.cpu().numpy().tolist()
|
||||
|
||||
|
||||
return _decode_chatml(
|
||||
tokens,
|
||||
stop_words=[],
|
||||
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
|
||||
tokenizer=tokenizer,
|
||||
raw_text_len=raw_text_len,
|
||||
context_length=context_length,
|
||||
verbose=verbose,
|
||||
return_end_reason=return_end_reason,
|
||||
)
|
||||
|
||||
|
||||
def _decode_chatml(
|
||||
tokens: List[int],
|
||||
*,
|
||||
stop_words: List[str],
|
||||
eod_token_ids: List[int],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
context_length: int,
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
chat_format = "chatml",
|
||||
):
|
||||
end_reason = f"Gen length {len(tokens)}"
|
||||
eod_token_idx = context_length
|
||||
for eod_token_idx in range(context_length, len(tokens)):
|
||||
if tokens[eod_token_idx] in eod_token_ids:
|
||||
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
|
||||
break
|
||||
|
||||
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx])[raw_text_len:]
|
||||
if verbose:
|
||||
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens)[raw_text_len:])
|
||||
print("\nRaw Generate:", trim_decode_tokens)
|
||||
print("\nEnd Reason:", end_reason)
|
||||
for stop_word in stop_words:
|
||||
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
|
||||
trim_decode_tokens = trim_decode_tokens.strip()
|
||||
if verbose:
|
||||
print("\nGenerate:", trim_decode_tokens)
|
||||
|
||||
if return_end_reason:
|
||||
return trim_decode_tokens, end_reason
|
||||
else:
|
||||
return trim_decode_tokens
|
||||
|
||||
|
||||
|
||||
def load_models_tokenizer(args):
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
from transformers.generation import GenerationConfig
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
|
||||
model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
|
||||
model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
|
||||
model.generation_config.do_sample = False # use greedy decoding
|
||||
return model, tokenizer
|
||||
|
||||
'''
|
||||
python eval/evaluate_chat_gsm8k.py [--use-fewshot]
|
||||
'''
|
||||
|
||||
INVALID_ANS = "[invalid]"
|
||||
DEVICE = "cuda:0"
|
||||
|
||||
def doc_to_text(doc, use_fewshot):
|
||||
if use_fewshot:
|
||||
context = (
|
||||
"Question: Angelo and Melanie want to plan how many hours over the next week they should study together for their test next week. They have 2 chapters of their textbook to study and 4 worksheets to memorize. They figure out that they should dedicate 3 hours to each chapter of their textbook and 1.5 hours for each worksheet. If they plan to study no more than 4 hours each day, how many days should they plan to study total over the next week if they take a 10-minute break every hour, include 3 10-minute snack breaks each day, and 30 minutes for lunch each day?\nLet's think step by step\n"
|
||||
"Angelo and Melanie think they should dedicate 3 hours to each of the 2 chapters, 3 hours x 2 chapters = 6 hours total.\nFor the worksheets they plan to dedicate 1.5 hours for each worksheet, 1.5 hours x 4 worksheets = 6 hours total.\nAngelo and Melanie need to start with planning 12 hours to study, at 4 hours a day, 12 / 4 = 3 days.\nHowever, they need to include time for breaks and lunch. Every hour they want to include a 10-minute break, so 12 total hours x 10 minutes = 120 extra minutes for breaks.\nThey also want to include 3 10-minute snack breaks, 3 x 10 minutes = 30 minutes.\nAnd they want to include 30 minutes for lunch each day, so 120 minutes for breaks + 30 minutes for snack breaks + 30 minutes for lunch = 180 minutes, or 180 / 60 minutes per hour = 3 extra hours.\nSo Angelo and Melanie want to plan 12 hours to study + 3 hours of breaks = 15 hours total.\nThey want to study no more than 4 hours each day, 15 hours / 4 hours each day = 3.75\nThey will need to plan to study 4 days to allow for all the time they need.\nThe answer is 4\n\n"
|
||||
"Question: Mark's basketball team scores 25 2 pointers, 8 3 pointers and 10 free throws. Their opponents score double the 2 pointers but half the 3 pointers and free throws. What's the total number of points scored by both teams added together?\nLet's think step by step\n"
|
||||
"Mark's team scores 25 2 pointers, meaning they scored 25*2= 50 points in 2 pointers.\nHis team also scores 6 3 pointers, meaning they scored 8*3= 24 points in 3 pointers\nThey scored 10 free throws, and free throws count as one point so they scored 10*1=10 points in free throws.\nAll together his team scored 50+24+10= 84 points\nMark's opponents scored double his team's number of 2 pointers, meaning they scored 50*2=100 points in 2 pointers.\nHis opponents scored half his team's number of 3 pointers, meaning they scored 24/2= 12 points in 3 pointers.\nThey also scored half Mark's team's points in free throws, meaning they scored 10/2=5 points in free throws.\nAll together Mark's opponents scored 100+12+5=117 points\nThe total score for the game is both team's scores added together, so it is 84+117=201 points\nThe answer is 201\n\n"
|
||||
"Question: Bella has two times as many marbles as frisbees. She also has 20 more frisbees than deck cards. If she buys 2/5 times more of each item, what would be the total number of the items she will have if she currently has 60 marbles?\nLet's think step by step\n"
|
||||
"When Bella buys 2/5 times more marbles, she'll have increased the number of marbles by 2/5*60 = 24\nThe total number of marbles she'll have is 60+24 = 84\nIf Bella currently has 60 marbles, and she has two times as many marbles as frisbees, she has 60/2 = 30 frisbees.\nIf Bella buys 2/5 times more frisbees, she'll have 2/5*30 = 12 more frisbees.\nThe total number of frisbees she'll have will increase to 30+12 = 42\nBella also has 20 more frisbees than deck cards, meaning she has 30-20 = 10 deck cards\nIf she buys 2/5 times more deck cards, she'll have 2/5*10 = 4 more deck cards.\nThe total number of deck cards she'll have is 10+4 = 14\nTogether, Bella will have a total of 14+42+84 = 140 items\nThe answer is 140\n\n"
|
||||
"Question: A group of 4 fruit baskets contains 9 apples, 15 oranges, and 14 bananas in the first three baskets and 2 less of each fruit in the fourth basket. How many fruits are there?\nLet's think step by step\n"
|
||||
"For the first three baskets, the number of apples and oranges in one basket is 9+15=24\nIn total, together with bananas, the number of fruits in one basket is 24+14=38 for the first three baskets.\nSince there are three baskets each having 38 fruits, there are 3*38=114 fruits in the first three baskets.\nThe number of apples in the fourth basket is 9-2=7\nThere are also 15-2=13 oranges in the fourth basket\nThe combined number of oranges and apples in the fourth basket is 13+7=20\nThe fourth basket also contains 14-2=12 bananas.\nIn total, the fourth basket has 20+12=32 fruits.\nThe four baskets together have 32+114=146 fruits.\nThe answer is 146\n\n"
|
||||
f"Question: {doc['question']}\nLet's think step by step"
|
||||
)
|
||||
else:
|
||||
context = doc["question"]
|
||||
return context
|
||||
|
||||
|
||||
def decode(tokens_list, tokenizer, raw_text_len):
|
||||
sents = []
|
||||
for tokens in tokens_list:
|
||||
tokens = tokens.cpu().numpy().tolist()
|
||||
sent = tokenizer.tokenizer.decode(tokens[raw_text_len:])
|
||||
sent = sent.split("<|endoftext|>")[0]
|
||||
sent = sent.split("\n\n\n")[0]
|
||||
sent = sent.split("\n\n")[0]
|
||||
sent = sent.split("Question:")[0]
|
||||
sents.append(sent)
|
||||
return sents
|
||||
|
||||
|
||||
def generate_sample(model, tokenizer, question):
|
||||
response, _ = chat(
|
||||
model,
|
||||
tokenizer,
|
||||
question,
|
||||
history=None,
|
||||
)
|
||||
print(question)
|
||||
print("-------------")
|
||||
print(response)
|
||||
print("=============")
|
||||
return response
|
||||
|
||||
|
||||
def extract_answer_hf(completion):
|
||||
def _get_last_digit(s):
|
||||
_PAT_LAST_DIGIT = re.compile(
|
||||
r"(?<=(\s|[\$%#{]))([+-])?(?=(\S))(0|([1-9](\d*|\d{0,2}(,\d{3})*)))?(\.\d*[1-9])?(?=(\s|[.,}]|$))"
|
||||
)
|
||||
match = list(_PAT_LAST_DIGIT.finditer(s))
|
||||
if match:
|
||||
last_digit = match[-1].group().replace(",", "").replace("+", "")
|
||||
# print(f"The last digit in {s} is {last_digit}")
|
||||
else:
|
||||
last_digit = None
|
||||
print(f"No digits found in {s!r}")
|
||||
return last_digit
|
||||
|
||||
job_gen = completion.strip(".").replace("\n", "\\n")
|
||||
last_digit = _get_last_digit(job_gen)
|
||||
if last_digit is not None:
|
||||
return eval(last_digit)
|
||||
return INVALID_ANS
|
||||
|
||||
|
||||
def extract_answer(completion):
|
||||
try:
|
||||
last_number = re.findall(r"\d+", completion)[-1]
|
||||
return eval(last_number)
|
||||
except:
|
||||
return INVALID_ANS
|
||||
|
||||
|
||||
def is_correct(completion, answer):
|
||||
gold = extract_answer(answer)
|
||||
assert gold != INVALID_ANS, "No ground truth answer found in the document."
|
||||
return extract_answer(completion) == gold
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Test HF checkpoint.")
|
||||
parser.add_argument(
|
||||
"-c",
|
||||
"--checkpoint-path",
|
||||
type=Path,
|
||||
help="Checkpoint path",
|
||||
default="Qwen/Qwen-7B-Chat",
|
||||
)
|
||||
parser.add_argument("-f", "--sample-input-file", type=str, default=None)
|
||||
parser.add_argument(
|
||||
"-o", "--sample-output-file", type=str, default="gsm8k_res.jsonl"
|
||||
)
|
||||
parser.add_argument("--use-fewshot", action="store_true")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.sample_input_file is not None:
|
||||
dataset = load_from_disk(args.sample_input_file) # or:
|
||||
else:
|
||||
dataset = load_dataset("gsm8k", "main")
|
||||
|
||||
print("Loading tokenizer ...")
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
args.checkpoint_path, trust_remote_code=True, bf16=True, use_flash_attn=True
|
||||
)
|
||||
|
||||
print("Loading model ...")
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
args.checkpoint_path, device_map="auto", trust_remote_code=True
|
||||
).eval()
|
||||
model.generation_config = GenerationConfig.from_pretrained(
|
||||
args.checkpoint_path, trust_remote_code=True
|
||||
)
|
||||
model.generation_config.do_sample = False # use greedy decoding
|
||||
|
||||
test = dataset["test"]
|
||||
|
||||
f_output = open(args.sample_output_file, "w", encoding="utf-8")
|
||||
tot_length = test.num_rows
|
||||
acc_res = []
|
||||
for doc in tqdm(test):
|
||||
context = doc_to_text(doc, args.use_fewshot)
|
||||
print(context)
|
||||
completion = generate_sample(model, tokenizer, context)
|
||||
answer = doc["answer"]
|
||||
acc = is_correct(completion, answer)
|
||||
doc["completion"] = completion
|
||||
doc["acc"] = acc
|
||||
f_output.write(json.dumps(doc, ensure_ascii=False) + "\n")
|
||||
f_output.flush()
|
||||
acc_res.append(acc)
|
||||
|
||||
f_output.close()
|
||||
print("4-shot Acc: " if args.use_fewshot else "Zero-shot Acc", np.mean(acc_res))
|
||||
381
eval/evaluate_chatml_mmlu.py
Normal file
381
eval/evaluate_chatml_mmlu.py
Normal file
@@ -0,0 +1,381 @@
|
||||
import os
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import argparse
|
||||
import datasets
|
||||
import torch
|
||||
import re
|
||||
from thefuzz import process
|
||||
from typing import List
|
||||
from tqdm import tqdm
|
||||
from transformers.trainer_utils import set_seed
|
||||
|
||||
from typing import Tuple, List, Union, Iterable
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from transformers import PreTrainedTokenizer
|
||||
from transformers import logging
|
||||
from transformers.generation import LogitsProcessor
|
||||
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
|
||||
HistoryType = List[Tuple[str, str]]
|
||||
TokensType = List[int]
|
||||
BatchTokensType = List[List[int]]
|
||||
|
||||
def make_context(
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = None,
|
||||
system: str = "",
|
||||
max_window_size: int = 6144,
|
||||
chat_format: str = "chatml",
|
||||
):
|
||||
if history is None:
|
||||
history = []
|
||||
|
||||
im_start, im_end = "<|im_start|>", "<|im_end|>"
|
||||
im_start_tokens = [tokenizer.im_start_id]
|
||||
im_end_tokens = [tokenizer.im_end_id]
|
||||
nl_tokens = tokenizer.encode("\n")
|
||||
|
||||
def _tokenize_str(role, content):
|
||||
return f"{role}\n{content}", tokenizer.encode(
|
||||
role
|
||||
) + nl_tokens + tokenizer.encode(content)
|
||||
|
||||
system_text, system_tokens_part = _tokenize_str("system", system)
|
||||
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
|
||||
|
||||
raw_text = ""
|
||||
context_tokens = []
|
||||
|
||||
for turn_query, turn_response in reversed(history):
|
||||
query_text, query_tokens_part = _tokenize_str("user", turn_query)
|
||||
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
|
||||
response_text, response_tokens_part = _tokenize_str(
|
||||
"assistant", turn_response
|
||||
)
|
||||
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
|
||||
|
||||
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
|
||||
prev_chat = (
|
||||
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
|
||||
)
|
||||
|
||||
current_context_size = (
|
||||
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
|
||||
)
|
||||
if current_context_size < max_window_size:
|
||||
context_tokens = next_context_tokens + context_tokens
|
||||
raw_text = prev_chat + raw_text
|
||||
else:
|
||||
break
|
||||
|
||||
context_tokens = system_tokens + context_tokens
|
||||
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
|
||||
context_tokens += (
|
||||
nl_tokens
|
||||
+ im_start_tokens
|
||||
+ _tokenize_str("user", query)[1]
|
||||
+ im_end_tokens
|
||||
+ nl_tokens
|
||||
+ im_start_tokens
|
||||
+ tokenizer.encode("assistant")
|
||||
+ nl_tokens
|
||||
)
|
||||
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
|
||||
|
||||
return raw_text, context_tokens
|
||||
|
||||
def chat(
|
||||
model,
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
query: str,
|
||||
history: Optional[HistoryType],
|
||||
system: str = "You are a helpful assistant.",
|
||||
append_history: bool = True
|
||||
) -> Tuple[str, HistoryType]:
|
||||
|
||||
|
||||
if history is None:
|
||||
history = []
|
||||
|
||||
raw_text, context_tokens = make_context(
|
||||
tokenizer,
|
||||
query,
|
||||
history=history,
|
||||
system=system,
|
||||
max_window_size=6144,
|
||||
chat_format = "chatml",
|
||||
)
|
||||
|
||||
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
|
||||
input_ids = torch.tensor([context_tokens]).cuda()
|
||||
outputs = model.generate(
|
||||
input_ids,
|
||||
# stop_words_ids = stop_words_ids,
|
||||
return_dict_in_generate = False,
|
||||
)
|
||||
|
||||
response = decode_tokens(
|
||||
outputs[0],
|
||||
tokenizer,
|
||||
raw_text_len=len(raw_text),
|
||||
context_length=len(context_tokens),
|
||||
chat_format='chatml',
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
if append_history:
|
||||
history.append((query, response))
|
||||
|
||||
return response, history
|
||||
|
||||
def decode_tokens(
|
||||
tokens: Union[torch.LongTensor, TokensType],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
context_length: int,
|
||||
chat_format: str = "chatml",
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
) -> str:
|
||||
if torch.is_tensor(tokens):
|
||||
tokens = tokens.cpu().numpy().tolist()
|
||||
|
||||
|
||||
return _decode_chatml(
|
||||
tokens,
|
||||
stop_words=[],
|
||||
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
|
||||
tokenizer=tokenizer,
|
||||
raw_text_len=raw_text_len,
|
||||
context_length=context_length,
|
||||
verbose=verbose,
|
||||
return_end_reason=return_end_reason,
|
||||
)
|
||||
|
||||
|
||||
def _decode_chatml(
|
||||
tokens: List[int],
|
||||
*,
|
||||
stop_words: List[str],
|
||||
eod_token_ids: List[int],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
context_length: int,
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
chat_format = "chatml",
|
||||
):
|
||||
end_reason = f"Gen length {len(tokens)}"
|
||||
eod_token_idx = context_length
|
||||
for eod_token_idx in range(context_length, len(tokens)):
|
||||
if tokens[eod_token_idx] in eod_token_ids:
|
||||
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
|
||||
break
|
||||
|
||||
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx])[raw_text_len:]
|
||||
if verbose:
|
||||
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens)[raw_text_len:])
|
||||
print("\nRaw Generate:", trim_decode_tokens)
|
||||
print("\nEnd Reason:", end_reason)
|
||||
for stop_word in stop_words:
|
||||
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
|
||||
trim_decode_tokens = trim_decode_tokens.strip()
|
||||
if verbose:
|
||||
print("\nGenerate:", trim_decode_tokens)
|
||||
|
||||
if return_end_reason:
|
||||
return trim_decode_tokens, end_reason
|
||||
else:
|
||||
return trim_decode_tokens
|
||||
|
||||
|
||||
|
||||
def load_models_tokenizer(args):
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
from transformers.generation import GenerationConfig
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
|
||||
model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
|
||||
model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
|
||||
model.generation_config.do_sample = False # use greedy decoding
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
def format_example(line):
|
||||
example = 'The following is a multiple-choice question. Please choose the most suitable one among A, B, C and D as the answer to this question.\n\n' + line['question'] + "\n"
|
||||
for choice in choices:
|
||||
example += f'{choice}. {line[f"{choice}"]}\n'
|
||||
return example
|
||||
|
||||
|
||||
def process_before_extraction(gen, choice_dict):
|
||||
# replace the choice by letter in the generated sentence
|
||||
# from longest one to shortest one
|
||||
for key, val in sorted(choice_dict.items(), key=lambda x: len(x[1]), reverse=True):
|
||||
pattern = re.compile(re.escape(val.rstrip(".")), re.IGNORECASE)
|
||||
gen = pattern.sub(key, gen)
|
||||
return gen
|
||||
|
||||
def extract_choice(gen, choice_list):
|
||||
# answer is A | choice is A | choose A
|
||||
res = re.search(r"(?:(?:[Cc]hoose)|(?:(?:[Aa]nswer|[Cc]hoice)(?![^ABCD]{0,20}?(?:n't|not))[^ABCD]{0,10}?\b(?:|is|:|be))\b)[^ABCD]{0,20}?\b(A|B|C|D)\b", gen)
|
||||
|
||||
# A is correct | A is right
|
||||
if res is None:
|
||||
res = re.search(r"\b(A|B|C|D)\b(?![^ABCD]{0,8}?(?:n't|not)[^ABCD]{0,5}?(?:correct|right))[^ABCD]{0,10}?\b(?:correct|right)\b", gen)
|
||||
|
||||
# straight answer: A
|
||||
if res is None:
|
||||
res = re.search(r"^(A|B|C|D)(?:\.|,|:|$)", gen)
|
||||
|
||||
# simply extract the first appearred letter
|
||||
if res is None:
|
||||
res = re.search(r"(?<![a-zA-Z])(A|B|C|D)(?![a-zA-Z=])", gen)
|
||||
|
||||
if res is None:
|
||||
return choices[choice_list.index(process.extractOne(gen, choice_list)[0])]
|
||||
else:
|
||||
return res.group(1)
|
||||
|
||||
def extract_answer(response, row):
|
||||
gen = process_before_extraction(response, {choice: row[choice] for choice in choices})
|
||||
pred = extract_choice(gen, [row[choice] for choice in choices])
|
||||
return pred
|
||||
|
||||
@torch.no_grad()
|
||||
def eval_subject(
|
||||
model,
|
||||
tokenizer,
|
||||
subject_name,
|
||||
test_df,
|
||||
save_result_dir=None,
|
||||
overwrite=False,
|
||||
**kwargs
|
||||
):
|
||||
result_path = os.path.join(save_result_dir, f'{subject_name}_result.csv')
|
||||
if not overwrite and os.path.exists(result_path):
|
||||
print(f"{result_path} existed, skip!")
|
||||
score = []
|
||||
for (_, datarow), (_, resultrow) in zip(test_df.iterrows(), pd.read_csv(result_path).iterrows()):
|
||||
# pred = extract_answer(resultrow['model_response'], datarow)
|
||||
pred = resultrow['model_output']
|
||||
correct = 1 if pred == datarow['answer'] else 0
|
||||
score.append(correct)
|
||||
return score
|
||||
|
||||
result = []
|
||||
score = []
|
||||
|
||||
for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
|
||||
question = format_example(row)
|
||||
|
||||
response, history = chat(
|
||||
model,
|
||||
tokenizer,
|
||||
question,
|
||||
history=None,
|
||||
)
|
||||
print(question)
|
||||
print(response)
|
||||
pred = extract_answer(response, row)
|
||||
print(pred)
|
||||
print("======================")
|
||||
|
||||
if 'answer' in row:
|
||||
correct = 1 if pred == row['answer'] else 0
|
||||
score.append(correct)
|
||||
if args.debug: print(f'{question} pred: {pred} ref: {row["answer"]}')
|
||||
result.append(pred)
|
||||
|
||||
if save_result_dir:
|
||||
test_df['model_output'] = result
|
||||
test_df['model_response'] = response
|
||||
if score:
|
||||
test_df["correctness"] = score
|
||||
os.makedirs(save_result_dir, exist_ok=True)
|
||||
test_df.to_csv(os.path.join(
|
||||
save_result_dir, f'{subject_name}_result.csv'), encoding="utf-8", index=False)
|
||||
|
||||
return score
|
||||
|
||||
|
||||
def cal_mmlu(res):
|
||||
acc_sum_dict = dict()
|
||||
acc_norm_sum_dict = dict()
|
||||
cnt_dict = dict()
|
||||
acc_sum = 0.
|
||||
cnt = 0
|
||||
hard_cnt = 0
|
||||
hard_acc_sum = 0.
|
||||
|
||||
for class_ in TASK_NAME_MAPPING.keys():
|
||||
acc_sum_dict[class_] = 0.
|
||||
acc_norm_sum_dict[class_] = 0.
|
||||
cnt_dict[class_] = 0.
|
||||
|
||||
for tt in TASK_NAME_MAPPING[class_]:
|
||||
acc_sum += sum(res[tt])
|
||||
cnt += len(res[tt])
|
||||
|
||||
acc_sum_dict[class_] += sum(res[tt])
|
||||
cnt_dict[class_] += len(res[tt])
|
||||
|
||||
print('\n\n\n')
|
||||
for k in TASK_NAME_MAPPING.keys():
|
||||
if k in cnt_dict:
|
||||
print('%s ACC: %.2f ' % (
|
||||
k, acc_sum_dict[k] * 100 / cnt_dict[k]))
|
||||
print('AVERAGE ACC:%.2f ' % (acc_sum *100 / cnt))
|
||||
|
||||
|
||||
def main(args):
|
||||
print("loading model weights")
|
||||
if args.checkpoint_path is not None:
|
||||
model, tokenizer = load_models_tokenizer(args)
|
||||
else:
|
||||
model, tokenizer = None, None
|
||||
print("model loaded")
|
||||
|
||||
dev_result = {}
|
||||
for subject_name in tqdm(SUBJECTS):
|
||||
# val_file_path = os.path.join(args.eval_data_path, 'val', f'{subject_name}_val.csv')
|
||||
# dev_file_path = os.path.join(args.eval_data_path, 'dev', f'{subject_name}_dev.csv')
|
||||
test_file_path = os.path.join(args.eval_data_path, 'test', f'{subject_name}_test.csv')
|
||||
# val_df = pd.read_csv(val_file_path, names=['question','A','B','C','D','answer'])
|
||||
# dev_df = pd.read_csv(dev_file_path, names=['question','A','B','C','D','answer'])
|
||||
test_df = pd.read_csv(test_file_path, names=['question','A','B','C','D','answer'])
|
||||
|
||||
score = eval_subject(model, tokenizer, subject_name, test_df, save_result_dir=f"outs_chat/mmlu_eval_result", overwrite=args.overwrite)
|
||||
dev_result[subject_name] = score
|
||||
cal_mmlu(dev_result)
|
||||
|
||||
|
||||
TASK_NAME_MAPPING = {'stem': ['abstract_algebra', 'anatomy', 'astronomy', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_physics', 'computer_security', 'conceptual_physics', 'electrical_engineering', 'elementary_mathematics', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_mathematics', 'high_school_physics', 'high_school_statistics', 'machine_learning'],
|
||||
'Humanities': ['formal_logic', 'high_school_european_history', 'high_school_us_history', 'high_school_world_history', 'international_law', 'jurisprudence', 'logical_fallacies', 'moral_disputes', 'moral_scenarios', 'philosophy', 'prehistory', 'professional_law', 'world_religions'],
|
||||
'other': ['business_ethics', 'college_medicine', 'human_aging', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'nutrition', 'professional_accounting', 'professional_medicine', 'virology', 'global_facts', 'clinical_knowledge'],
|
||||
'social': ['econometrics', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_microeconomics', 'high_school_psychology', 'human_sexuality', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy']}
|
||||
SUBJECTS = [v for vl in TASK_NAME_MAPPING.values() for v in vl]
|
||||
choices = ["A", "B", "C", "D"]
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Test HF checkpoint.')
|
||||
parser.add_argument('-c', '--checkpoint-path', type=str, help='Checkpoint path', default="Qwen/Qwen-7B-Chat")
|
||||
parser.add_argument('-s', '--seed', type=int, default=1234, help='Random seed')
|
||||
|
||||
"""Provide extra arguments required for tasks."""
|
||||
group = parser.add_argument_group(title='Evaluation options')
|
||||
group.add_argument('-d', '--eval_data_path', type=str,
|
||||
help='Path to eval data')
|
||||
group.add_argument("--debug", action='store_true', default=False,
|
||||
help='Print infos.')
|
||||
group.add_argument("--overwrite", action='store_true', default=False,
|
||||
help='Overwrite existed results')
|
||||
|
||||
args = parser.parse_args()
|
||||
set_seed(args.seed)
|
||||
|
||||
main(args)
|
||||
Reference in New Issue
Block a user