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add cot eval scripts

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熊兮
2025-06-23 10:20:34 +08:00
parent 7052081ea2
commit 52427fc0d2
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configs/cot_eval_api.json Normal file
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{
"job_type": "cot_eval_api",
"dataset": {
"input_path": "cot_input.json",
"output_path": "cot_output.json"
},
"inference":{
"base_url": "ENDPOINT",
"api_key": "TOKEN",
"max_new_tokens": 8196
}
}

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easydistill/eval/cot_eval.py Normal file
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# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import json, jsonlines
import argparse
import logging
import os
import re
from tqdm import tqdm
from openai import OpenAI
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def build_cot_prompts(instruction, output):
rv_prompt_template = (
"You are an expert judge tasked with evaluating the Reasoning Verbosity of a Chain-of-Thought (CoT) "
"for a given problem and its answer. Reasoning Verbosity Evaluation Focus: Assess how well the CoT’s "
"length and step complexity match the problem’s inherent difficulty. An optimal chain is neither "
"missing essential steps nor padded with needless digressions. A simple question should be solved "
"with a brief, direct chain; a challenging one may justifiably require a longer path with reflection "
"and error-checking. Scoring Guidelines (0-9):\n"
"0-1 Minimal verbosity, straightforward expression with little to no elaboration.\n"
"2-3 Clear and concise reasoning with necessary explanations.\n"
"4-5 Moderate verbosity with detailed explanations and thorough reasoning.\n"
"6-7 Extensive verbosity with comprehensive justification and exploration of complex connections.\n"
"8-9 High verbosity with deep, exhaustive exploration of reasoning; involves extensive elaboration, nested justifications, "
"and consideration of counterarguments or alternative perspectives.\n"
"Given Problem, Answer with hain-of-Thought, you will:\n"
"1. Analyze the Reasoning Verbosity\n"
"2. Determine score using the above criteria\n"
"3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
f"Problem: {instruction}\n"
f"Answer with Chain-of-Thought: {output}"
)
cd_prompt_template = (
"You are an expert judge assessing the Cognitive Difficulty of a Chain-of-Thought (CoT) "
"for a given problem and its answer. Cognitive Difficulty Evaluation Focus: The level of "
"reasoning competence required for a model to follow and reproduce the chain faithfully. "
"Judge the reasoning approach, techniques, and overall difficulty. Higher scores correspond "
"to more advanced concepts, abstractions, or multi-layer reasoning patterns. "
"Scoring Guidelines (0-9):\n"
"0-1 Elementary facts or a single trivial operation.\n"
"2-3 Multi-step arithmetic, explicit enumeration, basic rule chaining.\n"
"4-5 Early-undergraduate logic/algebra; one non-obvious insight.\n"
"6-7 Advanced undergraduate techniques (determinants, dynamic programming, layered code reasoning, etc).\n"
"8-9 Graduate-level abstraction, nested proofs, intricate algorithmic analysis.\n"
"Given Problem, Answer with hain-of-Thought, you will:\n"
"1. Analyze the Cognitive Difficulty\n"
"2. Determine score using the above criteria\n"
"3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
f"Problem: {instruction}\n"
f"Answer with Chain-of-Thought: {output}"
)
lc_prompt_template = (
"You are a rigorous logical validator analyzing problem-solving components. "
"Your task is to separately assess the validity of the reasoning process and final solution. "
"Given Problem, Answer with hain-of-Thought, you will:\n"
"1. Verify stepwise logical coherence and soundness\n"
"2. Confirm all critical problem constraints are properly addressed\n"
"3. Check for self-contradictions or unsupported leaps in logic\n"
"4. Verify the process can actually derive the proposed solution\n"
"5. Output ONLY the 1/0 answer (1 for true, 0 for false) for logical correctness, place your answer in <score></score>\n"
f"Problem: {instruction}\n"
f"Answer with Chain-of-Thought: {output}"
)
return rv_prompt_template, cd_prompt_template, lc_prompt_template
def extract_score(text):
match = re.search(r"<score>(\d+)</score>", text)
if match:
return int(match.group(1))
else:
return -1
def read_json_fields(filename):
try:
with open(filename, 'r') as file:
data = json.load(file)
return data
except FileNotFoundError:
logging.error("The file was not found.")
except json.JSONDecodeError:
logging.error("There was an error decoding the JSON file.")
except Exception as e:
logging.error(f"An error occurred: {e}")
def write_data_to_json_file(data, file_path):
try:
with open(file_path, 'w') as file:
json.dump(data, file, ensure_ascii=False, indent=4)
logging.info(f"Data successfully written to {file_path}")
except Exception as e:
logging.error(f"An error occurred: {e}")
def generate_teacher_response_api(data_list, config):
client = OpenAI(
api_key = config["inference"]["api_key"],
base_url = config["inference"]["base_url"]
)
models = client.models.list()
model = models.data[0].id
logging.info(model)
outcomes = []
for sample in tqdm(data_list, desc="Call remote model and generating responses"):
instruction = sample["instruction"]
output = sample["output"]
rv_prompt_template, cd_prompt_template, lc_prompt_template = build_cot_prompts(instruction, output)
def generate_score(sample, model, config):
message = [
{'role': 'user', 'content': sample}
]
completion = client.chat.completions.create(
messages = message,
model = model,
max_completion_tokens = config["inference"]["max_new_tokens"]
)
result = completion.choices[0].message.content
score = extract_score(result)
return score
rv_score = generate_score(rv_prompt_template, model, config)
cd_score = generate_score(cd_prompt_template, model, config)
lc_score = generate_score(lc_prompt_template, model, config)
if lc_score == 1:
lc_score = True
else:
lc_score =False
outcomes.append(
{
'instruction': instruction,
'output': output,
"reasoning_verbosity": rv_score,
"cognitive_difficulty": cd_score,
"logical_correctness": lc_score
}
)
write_data_to_json_file(outcomes, config["dataset"]["output_path"])
def infer_with_teacher_model(config):
logging.info('Generating distillation data from the teacher model!')
data_list = read_json_fields(config["dataset"]["input_path"])
generate_teacher_response_api(data_list, config)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True, help='path to the json config file')
args = parser.parse_args()
config = json.load(open(args.config))
infer_with_teacher_model(config)
if __name__ == "__main__":
main()