mirror of
https://github.com/QwenLM/Qwen.git
synced 2026-05-20 16:35:47 +08:00
fix format problems in evaluation code; update ceval extraction rules
This commit is contained in:
feihu.hf
@@ -11,39 +11,46 @@ from tqdm import tqdm
|
||||
from transformers.trainer_utils import set_seed
|
||||
|
||||
|
||||
'''
|
||||
"""
|
||||
wget https://huggingface.co/datasets/haonan-li/cmmlu/resolve/main/cmmlu_v1_0_1.zip
|
||||
mkdir data/cmmlu
|
||||
mv cmmlu_v1_0_1.zip data/cmmlu
|
||||
cd data/cmmlu; unzip cmmlu_v1_0_1.zip
|
||||
cd ../../
|
||||
python evaluate_cmmlu.py -d data/cmmlu/
|
||||
'''
|
||||
"""
|
||||
|
||||
|
||||
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)
|
||||
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
|
||||
)
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
def format_example(line, include_answer=True):
|
||||
example = '问题：' + line['Question']
|
||||
example = "问题：" + line["Question"]
|
||||
for choice in choices:
|
||||
example += f'\n{choice}. {line[f"{choice}"]}'
|
||||
|
||||
if include_answer:
|
||||
example += '\n答案：' + line["Answer"] + '\n\n'
|
||||
example += "\n答案：" + line["Answer"] + "\n\n"
|
||||
else:
|
||||
example += '\n答案：'
|
||||
example += "\n答案："
|
||||
return example
|
||||
|
||||
|
||||
def generate_few_shot_prompt(k, subject, dev_df):
|
||||
prompt = ''
|
||||
prompt = ""
|
||||
if k == -1:
|
||||
k = dev_df.shape[0]
|
||||
for i in range(k):
|
||||
@@ -55,35 +62,37 @@ def generate_few_shot_prompt(k, subject, dev_df):
|
||||
|
||||
|
||||
def get_logits(tokenizer, model, inputs: List[str]):
|
||||
input_ids = tokenizer(inputs, padding=False)['input_ids']
|
||||
input_ids = tokenizer(inputs, padding=False)["input_ids"]
|
||||
input_ids = torch.tensor(input_ids, device=model.device)
|
||||
tokens = {'input_ids': input_ids}
|
||||
tokens = {"input_ids": input_ids}
|
||||
|
||||
outputs = model(input_ids)['logits']
|
||||
outputs = model(input_ids)["logits"]
|
||||
logits = outputs[:, -1, :]
|
||||
log_probs = torch.nn.functional.softmax(logits, dim=-1)
|
||||
return log_probs, {'tokens': tokens}
|
||||
return log_probs, {"tokens": tokens}
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def eval_subject(
|
||||
model,
|
||||
tokenizer,
|
||||
subject_name,
|
||||
test_df,
|
||||
k=5,
|
||||
dev_df=None,
|
||||
few_shot=False,
|
||||
save_result_dir=None,
|
||||
**kwargs
|
||||
model,
|
||||
tokenizer,
|
||||
subject_name,
|
||||
test_df,
|
||||
k=5,
|
||||
dev_df=None,
|
||||
few_shot=False,
|
||||
save_result_dir=None,
|
||||
**kwargs,
|
||||
):
|
||||
result = []
|
||||
score = []
|
||||
|
||||
few_shot_prompt = generate_few_shot_prompt(
|
||||
k, subject_name, dev_df) if few_shot else []
|
||||
all_probs = {'prob_A': [], 'prob_B': [], 'prob_C': [], 'prob_D': []}
|
||||
if args.debug: print(f"few_shot_prompt: {few_shot_prompt}")
|
||||
few_shot_prompt = (
|
||||
generate_few_shot_prompt(k, subject_name, dev_df) if few_shot else []
|
||||
)
|
||||
all_probs = {"prob_A": [], "prob_B": [], "prob_C": [], "prob_D": []}
|
||||
if args.debug:
|
||||
print(f"few_shot_prompt: {few_shot_prompt}")
|
||||
|
||||
for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
|
||||
question = format_example(row, include_answer=False)
|
||||
@@ -94,51 +103,56 @@ def eval_subject(
|
||||
logits = output.flatten()
|
||||
|
||||
softval = torch.nn.functional.softmax(
|
||||
torch.tensor(
|
||||
[
|
||||
logits[tokenizer("A")['input_ids']],
|
||||
logits[tokenizer("B")['input_ids']],
|
||||
logits[tokenizer("C")['input_ids']],
|
||||
logits[tokenizer("D")['input_ids']],
|
||||
]
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
torch.tensor(
|
||||
[
|
||||
logits[tokenizer("A")["input_ids"]],
|
||||
logits[tokenizer("B")["input_ids"]],
|
||||
logits[tokenizer("C")["input_ids"]],
|
||||
logits[tokenizer("D")["input_ids"]],
|
||||
]
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
if softval.dtype in {torch.bfloat16, torch.float16}:
|
||||
softval = softval.to(dtype=torch.float32)
|
||||
probs = softval.detach().cpu().numpy()
|
||||
|
||||
for i, choice in enumerate(choices):
|
||||
all_probs[f'prob_{choice}'].append(probs[i])
|
||||
all_probs[f"prob_{choice}"].append(probs[i])
|
||||
pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]
|
||||
|
||||
if 'Answer' in row:
|
||||
correct = 1 if pred == row['Answer'] else 0
|
||||
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"]}')
|
||||
if args.debug:
|
||||
print(f'{question} pred: {pred} ref: {row["Answer"]}')
|
||||
result.append(pred)
|
||||
|
||||
if score:
|
||||
correct_ratio = 100 * sum(score) / len(score)
|
||||
if args.debug: print(subject_name, correct_ratio)
|
||||
if args.debug:
|
||||
print(subject_name, correct_ratio)
|
||||
else:
|
||||
correct_ratio = 0
|
||||
if save_result_dir:
|
||||
test_df['model_output'] = result
|
||||
test_df["model_output"] = result
|
||||
for i, choice in enumerate(choices):
|
||||
test_df[f'prob_{choice}'] = (all_probs[f'prob_{choice}'])
|
||||
test_df[f"prob_{choice}"] = all_probs[f"prob_{choice}"]
|
||||
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)
|
||||
test_df.to_csv(
|
||||
os.path.join(save_result_dir, f"{subject_name}_result.csv"),
|
||||
encoding="utf-8",
|
||||
index=False,
|
||||
)
|
||||
|
||||
return correct_ratio
|
||||
|
||||
|
||||
def cal_cmmlu(res):
|
||||
print('\n\n\n')
|
||||
res = {k.split('-')[-1]:float(v) for k,v in res.items()}
|
||||
print("\n\n\n")
|
||||
res = {k.split("-")[-1]: float(v) for k, v in res.items()}
|
||||
for k, v in TASK_NAME_MAPPING.items():
|
||||
avg_acc = np.mean(list(map(lambda x: res[x], v)))
|
||||
print(f"{k} acc: {avg_acc:.2f}")
|
||||
@@ -147,85 +161,103 @@ def cal_cmmlu(res):
|
||||
|
||||
|
||||
subcategories = {
|
||||
"agronomy": ['other'],
|
||||
"anatomy": ['biology'],
|
||||
"ancient_chinese": ['linguistics','china specific'],
|
||||
"arts": ['arts'],
|
||||
"astronomy": ['physics'],
|
||||
"business_ethics": ['business'],
|
||||
"chinese_civil_service_exam": ['politics','china specific'],
|
||||
"chinese_driving_rule": ['other','china specific'],
|
||||
"chinese_food_culture": ['culture','china specific'],
|
||||
"chinese_foreign_policy": ['politics','china specific'],
|
||||
"chinese_history":['history','china specific'],
|
||||
"chinese_literature": ['literature','china specific'],
|
||||
"chinese_teacher_qualification": ['education','china specific'],
|
||||
"college_actuarial_science":['math'],
|
||||
"college_education":['education'],
|
||||
"college_engineering_hydrology": ['engineering'],
|
||||
"college_law": ['law'],
|
||||
"college_mathematics": ['math'],
|
||||
"college_medical_statistics":['statistics'],
|
||||
"clinical_knowledge": ['other'],
|
||||
"college_medicine": ['other'],
|
||||
"computer_science": ['computer science'],
|
||||
"computer_security": ['other'],
|
||||
"conceptual_physics": ['physics'],
|
||||
"construction_project_management": ['other','china specific'],
|
||||
"economics": ['economics'],
|
||||
"education": ['education'],
|
||||
"elementary_chinese":['linguistics','china specific'],
|
||||
"elementary_commonsense":['other','china specific'],
|
||||
"elementary_information_and_technology": ['other'],
|
||||
"electrical_engineering": ['engineering'],
|
||||
"elementary_mathematics": ['math'],
|
||||
"ethnology": ['culture','china specific'],
|
||||
"food_science": ['other'],
|
||||
"genetics": ['biology'],
|
||||
"global_facts": ['global'],
|
||||
"high_school_biology": ['biology'],
|
||||
"high_school_chemistry": ['chemistry'],
|
||||
"high_school_geography": ['geography'],
|
||||
"high_school_mathematics": ['math'],
|
||||
"high_school_physics": ['physics'],
|
||||
"high_school_politics": ['politics','china specific'],
|
||||
"human_sexuality": ['other'],
|
||||
"international_law": ['law'],
|
||||
"journalism": ['sociology'],
|
||||
"jurisprudence": ['law'],
|
||||
"legal_and_moral_basis": ['other'],
|
||||
"logical": ['philosophy'],
|
||||
"machine_learning": ['computer science'],
|
||||
"management": ['business'],
|
||||
"marketing": ['business'],
|
||||
"marxist_theory": ['philosophy'],
|
||||
"modern_chinese": ['linguistics','china specific'],
|
||||
"nutrition": ['other'],
|
||||
"philosophy": ['philosophy'],
|
||||
"professional_accounting": ['business'],
|
||||
"professional_law": ['law'],
|
||||
"professional_medicine": ['other'],
|
||||
"professional_psychology": ['psychology'],
|
||||
"public_relations": ['politics'],
|
||||
"security_study": ['politics'],
|
||||
"sociology": ['culture'],
|
||||
"sports_science": ['other'],
|
||||
"traditional_chinese_medicine": ['other','china specific'],
|
||||
"virology": ['biology'],
|
||||
"world_history":['history'],
|
||||
"world_religions": ['global'],
|
||||
"agronomy": ["other"],
|
||||
"anatomy": ["biology"],
|
||||
"ancient_chinese": ["linguistics", "china specific"],
|
||||
"arts": ["arts"],
|
||||
"astronomy": ["physics"],
|
||||
"business_ethics": ["business"],
|
||||
"chinese_civil_service_exam": ["politics", "china specific"],
|
||||
"chinese_driving_rule": ["other", "china specific"],
|
||||
"chinese_food_culture": ["culture", "china specific"],
|
||||
"chinese_foreign_policy": ["politics", "china specific"],
|
||||
"chinese_history": ["history", "china specific"],
|
||||
"chinese_literature": ["literature", "china specific"],
|
||||
"chinese_teacher_qualification": ["education", "china specific"],
|
||||
"college_actuarial_science": ["math"],
|
||||
"college_education": ["education"],
|
||||
"college_engineering_hydrology": ["engineering"],
|
||||
"college_law": ["law"],
|
||||
"college_mathematics": ["math"],
|
||||
"college_medical_statistics": ["statistics"],
|
||||
"clinical_knowledge": ["other"],
|
||||
"college_medicine": ["other"],
|
||||
"computer_science": ["computer science"],
|
||||
"computer_security": ["other"],
|
||||
"conceptual_physics": ["physics"],
|
||||
"construction_project_management": ["other", "china specific"],
|
||||
"economics": ["economics"],
|
||||
"education": ["education"],
|
||||
"elementary_chinese": ["linguistics", "china specific"],
|
||||
"elementary_commonsense": ["other", "china specific"],
|
||||
"elementary_information_and_technology": ["other"],
|
||||
"electrical_engineering": ["engineering"],
|
||||
"elementary_mathematics": ["math"],
|
||||
"ethnology": ["culture", "china specific"],
|
||||
"food_science": ["other"],
|
||||
"genetics": ["biology"],
|
||||
"global_facts": ["global"],
|
||||
"high_school_biology": ["biology"],
|
||||
"high_school_chemistry": ["chemistry"],
|
||||
"high_school_geography": ["geography"],
|
||||
"high_school_mathematics": ["math"],
|
||||
"high_school_physics": ["physics"],
|
||||
"high_school_politics": ["politics", "china specific"],
|
||||
"human_sexuality": ["other"],
|
||||
"international_law": ["law"],
|
||||
"journalism": ["sociology"],
|
||||
"jurisprudence": ["law"],
|
||||
"legal_and_moral_basis": ["other"],
|
||||
"logical": ["philosophy"],
|
||||
"machine_learning": ["computer science"],
|
||||
"management": ["business"],
|
||||
"marketing": ["business"],
|
||||
"marxist_theory": ["philosophy"],
|
||||
"modern_chinese": ["linguistics", "china specific"],
|
||||
"nutrition": ["other"],
|
||||
"philosophy": ["philosophy"],
|
||||
"professional_accounting": ["business"],
|
||||
"professional_law": ["law"],
|
||||
"professional_medicine": ["other"],
|
||||
"professional_psychology": ["psychology"],
|
||||
"public_relations": ["politics"],
|
||||
"security_study": ["politics"],
|
||||
"sociology": ["culture"],
|
||||
"sports_science": ["other"],
|
||||
"traditional_chinese_medicine": ["other", "china specific"],
|
||||
"virology": ["biology"],
|
||||
"world_history": ["history"],
|
||||
"world_religions": ["global"],
|
||||
}
|
||||
|
||||
categories = {
|
||||
"STEM": ["physics", "chemistry", "biology", "computer science", "math", "engineering", "statistics"],
|
||||
"STEM": [
|
||||
"physics",
|
||||
"chemistry",
|
||||
"biology",
|
||||
"computer science",
|
||||
"math",
|
||||
"engineering",
|
||||
"statistics",
|
||||
],
|
||||
"Humanities": ["history", "philosophy", "law", "arts", "literature", "global"],
|
||||
"Social Science": ['linguistics',"business", "politics", "culture", "economics", "geography", "psychology", "education", "sociology"],
|
||||
"Other":["other"],
|
||||
"Social Science": [
|
||||
"linguistics",
|
||||
"business",
|
||||
"politics",
|
||||
"culture",
|
||||
"economics",
|
||||
"geography",
|
||||
"psychology",
|
||||
"education",
|
||||
"sociology",
|
||||
],
|
||||
"Other": ["other"],
|
||||
"China specific": ["china specific"],
|
||||
}
|
||||
|
||||
TASK_NAME_MAPPING = defaultdict(list)
|
||||
for k,v in categories.items():
|
||||
for k, v in categories.items():
|
||||
for subject, subcat in subcategories.items():
|
||||
for c in subcat:
|
||||
if c in v:
|
||||
@@ -240,30 +272,52 @@ def main(args):
|
||||
|
||||
test_result = {}
|
||||
for subject_name in tqdm(subcategories.keys()):
|
||||
dev_file_path = os.path.join(args.eval_data_path, 'dev', f'{subject_name}.csv')
|
||||
test_file_path = os.path.join(args.eval_data_path, 'test', f'{subject_name}.csv')
|
||||
dev_file_path = os.path.join(args.eval_data_path, "dev", f"{subject_name}.csv")
|
||||
test_file_path = os.path.join(
|
||||
args.eval_data_path, "test", f"{subject_name}.csv"
|
||||
)
|
||||
dev_df = pd.read_csv(dev_file_path)
|
||||
test_df = pd.read_csv(test_file_path)
|
||||
|
||||
score = eval_subject(model, tokenizer, subject_name, dev_df=dev_df, test_df=test_df, k=5, few_shot=True,
|
||||
save_result_dir=f"outs/cmmlu_eval_result")
|
||||
score = eval_subject(
|
||||
model,
|
||||
tokenizer,
|
||||
subject_name,
|
||||
dev_df=dev_df,
|
||||
test_df=test_df,
|
||||
k=5,
|
||||
few_shot=True,
|
||||
save_result_dir=f"outs/cmmlu_eval_result",
|
||||
)
|
||||
test_result[subject_name] = score
|
||||
cal_cmmlu(test_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")
|
||||
parser.add_argument('-s', '--seed', type=int, default=1234, help='Random seed')
|
||||
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",
|
||||
)
|
||||
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("--max-seq-len", type=int, default=2048,
|
||||
help='Size of the output generated text.')
|
||||
group.add_argument("--debug", action='store_true', default=False,
|
||||
help='Print infos.')
|
||||
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(
|
||||
"--max-seq-len",
|
||||
type=int,
|
||||
default=2048,
|
||||
help="Size of the output generated text.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--debug", action="store_true", default=False, help="Print infos."
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
set_seed(args.seed)
|
||||
|
||||
Reference in New Issue
Block a user