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test.py
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test.py
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import teval.evaluators as evaluator_factory
from teval.utils.meta_template import meta_template_dict
from lagent.llms.huggingface import HFTransformerCasualLM, HFTransformerChat
from lagent.llms.openai import GPTAPI
import argparse
import mmengine
import os
from tqdm import tqdm
import shutil
import random
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', type=str, default='data/instruct_v1.json')
parser.add_argument('--model_type', type=str, choices=['api', 'hf'], default='hf')
# hf means huggingface, if you want to use huggingface model, you should specify the path of the model
parser.add_argument('--model_display_name', type=str, default="")
# if not set, it will be the same as the model type, only inference the output_name of the result
parser.add_argument('--resume', action='store_true')
parser.add_argument('--out_name', type=str, default='tmp.json')
parser.add_argument('--out_dir', type=str, default="work_dirs/")
# [api model name: 'gpt-3.5-turbo-16k', 'gpt-4-1106-preview', 'claude-2.1', 'chat-bison-001']
parser.add_argument('--model_path', type=str, help="path to huggingface model / api model name")
parser.add_argument('--eval', type=str, choices=['instruct', 'reason', 'plan', 'retrieve', 'review', 'understand', 'rru'])
parser.add_argument('--test_num', type=int, default=-1, help='number of samples to test, -1 means all')
parser.add_argument('--prompt_type', type=str, default='json', choices=['json', 'str'])
parser.add_argument('--meta_template', type=str, default='qwen')
parser.add_argument('--batch_size', type=int, default=1)
args = parser.parse_args()
return args
def load_dataset(dataset_path, out_dir, is_resume=False, tmp_folder_name='tmp'):
dataset = mmengine.load(dataset_path)
total_num = len(dataset)
# possible filter here
tested_num = 0
if is_resume:
file_list = os.listdir(os.path.join(out_dir, tmp_folder_name))
for filename in file_list:
if filename.split('.')[0] in dataset:
tested_num += 1
file_id = filename.split('.')[0]
dataset.pop(file_id)
else:
print(f"Warning: {filename} not in dataset, remove it from cache")
os.remove(os.path.join(out_dir, tmp_folder_name, filename))
return dataset, tested_num, total_num
def split_special_tokens(text):
text = text.split('<eoa>')[0]
text = text.split('<TOKENS_UNUSED_1>')[0]
text = text.split('<|im_end|>')[0]
text = text.split('\nuser')[0]
text = text.split('\nassistant')[0]
text = text.split('\nUSER')[0]
text = text.split('[INST]')[0]
text = text.split('<|user|>')[0]
text = text.strip()
if text.startswith('```json'):
text = text[len('```json'):]
text = text.strip('`').strip()
return text
def infer(dataset, llm, out_dir, tmp_folder_name='tmp', test_num = 1, batch_size=1):
random_list = list(dataset.keys())[:test_num]
batch_infer_list = []; batch_infer_ids = []
for idx in tqdm(random_list):
prompt = dataset[idx]['origin_prompt']
batch_infer_list.append(prompt)
batch_infer_ids.append(idx)
# batch inference
if len(batch_infer_ids) == batch_size or idx == len(random_list) - 1:
predictions = llm.chat(batch_infer_list, do_sample=False)
for ptr, prediction in enumerate(predictions):
if not isinstance(prediction, str):
print("Warning: the output of llm is not a string, force to convert it into str")
prediction = str(prediction)
prediction = split_special_tokens(prediction)
data_ptr = batch_infer_ids[ptr]
dataset[data_ptr]['prediction'] = prediction
mmengine.dump(dataset[data_ptr], os.path.join(out_dir, tmp_folder_name, f'{data_ptr}.json'))
batch_infer_ids = []; batch_infer_list = []
# load results from cache
results = dict()
file_list = os.listdir(os.path.join(out_dir, tmp_folder_name))
for filename in file_list:
file_id = filename.split('.')[0]
results[file_id] = mmengine.load(os.path.join(out_dir, tmp_folder_name, filename))
return results
if __name__ == '__main__':
args = parse_args()
os.makedirs(args.out_dir, exist_ok=True)
tmp_folder_name = os.path.splitext(args.out_name)[0]
os.makedirs(os.path.join(args.out_dir, tmp_folder_name), exist_ok=True)
dataset, tested_num, total_num = load_dataset(args.dataset_path, args.out_dir, args.resume, tmp_folder_name=tmp_folder_name)
if args.test_num == -1:
test_num = max(total_num - tested_num, 0)
else:
test_num = max(min(args.test_num - tested_num, total_num - tested_num), 0)
output_file_path = os.path.join(args.out_dir, args.out_name)
if test_num != 0:
if args.model_type == 'api':
# if you want to use GPT, please refer to lagent for how to pass your key to GPTAPI class
llm = GPTAPI(args.model_path)
# elif args.model_type.startswith('claude'):
# llm = ClaudeAPI(args.model_type)
elif args.model_type == 'hf':
meta_template = meta_template_dict.get(args.meta_template)
if "chatglm" in args.model_display_name:
llm = HFTransformerChat(path=args.model_path, meta_template=meta_template)
else:
llm = HFTransformerCasualLM(path=args.model_path, meta_template=meta_template, max_new_tokens=512)
print(f"Tested {tested_num} samples, left {test_num} samples, total {total_num} samples")
prediction = infer(dataset, llm, args.out_dir, tmp_folder_name=tmp_folder_name, test_num=test_num, batch_size=args.batch_size)
# dump prediction to out_dir
mmengine.dump(prediction, os.path.join(args.out_dir, args.out_name))
if args.eval:
if args.model_display_name == "":
model_display_name = args.model_type
else:
model_display_name = args.model_display_name
os.makedirs(args.out_dir, exist_ok=True)
eval_mapping = dict(
instruct="InstructEvaluator",
plan="PlanningEvaluator",
review="ReviewEvaluator",
reason="ReasonRetrieveUnderstandEvaluator",
retrieve="ReasonRetrieveUnderstandEvaluator",
understand="ReasonRetrieveUnderstandEvaluator",
rru="ReasonRetrieveUnderstandEvaluator"
)
if "_zh" in args.dataset_path:
bert_score_model = "thenlper/gte-large-zh"
json_path = os.path.join(args.out_dir, model_display_name + '_' + str(args.test_num) + '_zh.json')
else:
bert_score_model = "all-mpnet-base-v2"
json_path = os.path.join(args.out_dir, model_display_name + '_' + str(args.test_num) + '.json')
evaluator_class = getattr(evaluator_factory, eval_mapping[args.eval])
evaluator = evaluator_class(output_file_path, default_prompt_type=args.prompt_type, eval_type = args.eval, bert_score_model=bert_score_model)
if os.path.exists(json_path):
results = mmengine.load(json_path)
else:
results = dict()
eval_results = evaluator.evaluate()
print(eval_results)
results[args.eval + '_' + args.prompt_type] = eval_results
print(f"Writing Evaluation Results to {json_path}")
mmengine.dump(results, json_path)