Upload evaluate_chatml_mmlu.py

eval/evaluate_chatml_mmlu.py

@@ -0,0 +1,391 @@
+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
+
+'''
+wget https://people.eecs.berkeley.edu/~hendrycks/data.tar
+mkdir data/mmlu
+mv data.tar data/mmlu
+cd data/mmlu; tar xf data.tar
+cd ../../
+
+pip install thefuzz
+python eval/evaluate_chat_mmlu.py -d data/mmlu/data/
+'''
+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)