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41 changes: 23 additions & 18 deletions openseek/competition/LongContext-ICL-Annotation/src/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,20 +4,20 @@

# from method import build_prompt, select_examples, annotate

from method import build_prompt, select_examples
from method import build_prompt, select_examples, select_examples_M05, select_examples_M19, select_examples_M20, build_prompt_cot, build_prompt_by_task_type

from method import annotate_nvidia as annotate # For Nvidia GPU
# from method import annotate_ascend as annotate # For Huawei Ascend
# from method import annotate_nvidia as annotate # For Nvidia GPU
from method import annotate_ascend as annotate # For Huawei Ascend

TASK_FILES = {
1: './data/openseek-1_closest_integers.json',
2: './data/openseek-2_count_nouns_verbs.json',
3: './data/openseek-3_collatz_conjecture.json',
4: './data/openseek-4_conala_concat_strings.json',
5: './data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json',
6: './data/openseek-6_mnli_same_genre_classification.json',
7: './data/openseek-7_jeopardy_answer_generation_all.json',
8: '../data/openseek-8_kernel_generation.json',
1: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-1_closest_integers.json',
2: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-2_count_nouns_verbs.json',
3: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-3_collatz_conjecture.json',
4: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-4_conala_concat_strings.json',
5: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json',
6: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-6_mnli_same_genre_classification.json',
7: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-7_jeopardy_answer_generation_all.json',
8: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-8_kernel_generation.json',
Comment on lines +13 to +20

@gemini-code-assist gemini-code-assist Bot May 29, 2026

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medium

Hardcoding absolute paths like /root/OpenSeek/... reduces the portability of the codebase, as it will fail on any environment where the directory structure is different. It is highly recommended to use relative paths (as was done previously) or construct paths dynamically relative to the project root.

Suggested change
1: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-1_closest_integers.json',
2: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-2_count_nouns_verbs.json',
3: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-3_collatz_conjecture.json',
4: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-4_conala_concat_strings.json',
5: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json',
6: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-6_mnli_same_genre_classification.json',
7: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-7_jeopardy_answer_generation_all.json',
8: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-8_kernel_generation.json',
1: './data/openseek-1_closest_integers.json',
2: './data/openseek-2_count_nouns_verbs.json',
3: './data/openseek-3_collatz_conjecture.json',
4: './data/openseek-4_conala_concat_strings.json',
5: './data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json',
6: './data/openseek-6_mnli_same_genre_classification.json',
7: './data/openseek-7_jeopardy_answer_generation_all.json',
8: './data/openseek-8_kernel_generation.json',

}

def parser_args():
Expand All @@ -30,7 +30,7 @@ def parser_args():
default='../outputs/',
help='Prefix path to save the evaluation logs.')
parser.add_argument('--tokenizer_path', type=str,
default='/share/project/wuhaiming/spaces/data_agent/OpenSeek-main/openseek/competition/LongContext-ICL-Annotation/src/Qwen3-4B')
default='/root/Qwen3-4B')
args = parser.parse_args()
return args

Expand All @@ -48,7 +48,7 @@ def evaluate(task_id:int,

task_name = task_dict['task_name']
task_description = task_dict['Definition'][0]
icl_examples = task_dict['examples'][:100]
icl_examples = task_dict['examples'][:50]
test_samples = task_dict['test_samples']

version = 1
Expand All @@ -62,26 +62,31 @@ def evaluate(task_id:int,
pass

examples_str = None
for test_sample in tqdm(test_samples, desc=f'Evaluation on Task {task_id}: {task_name}'):
for sample_idx, test_sample in enumerate(tqdm(test_samples, desc=f'Evaluation on Task {task_id}: {task_name}')):
test_record = dict()

test_sample_id = test_sample['id']
test_record['test_sample_id'] = test_sample_id


text2annotate = test_sample['input']
prompt = build_prompt(task_description, text2annotate)

# M03优化：使用任务分型Prompt路由系统
# 根据任务类型自动选择最合适的prompt策略
prompt = build_prompt_by_task_type(task_id, task_description, text2annotate)

if examples_str is None:
examples_str = select_examples(icl_examples, task_description, text2annotate)
# M19优化：使用检索+重试+后处理组合策略
examples_str = select_examples_M19(icl_examples, task_description, text2annotate, task_id, sample_idx)
Comment on lines 78 to +80

@gemini-code-assist gemini-code-assist Bot May 29, 2026

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high

There is a logical contradiction here. The newly introduced select_examples_M19 is designed as a dynamic retrieval strategy (as stated in its docstring: "每个样本都重新选择示例" / "re-selects examples for each sample"). However, because of the if examples_str is None: guard, select_examples_M19 is only called once during the first iteration of the loop. For all subsequent test samples, the same examples_str is reused, completely bypassing the dynamic retrieval logic.

If you remove the if examples_str is None: guard to make it truly dynamic, select_examples_M19 will be called on every iteration. This will introduce a severe performance bottleneck because select_examples_M19 currently loads the tokenizer from disk on every single call.

To fix both issues:

  1. Remove the if examples_str is None: guard so that examples are retrieved dynamically per sample.
  2. Update select_examples_M19 (and other retrieval functions) to use a cached tokenizer singleton (similar to get_tokenizer_m20()) instead of calling AutoTokenizer.from_pretrained on every invocation.
        # M19优化：使用检索+重试+后处理组合策略 (动态检索每个样本)
        examples_str = select_examples_M19(icl_examples, task_description, text2annotate, task_id, sample_idx)

input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str+'\n\n')

# tokenized_input = qwen_tokenizer(input_prompt, return_tensors="pt")
# if tokenized_input['input_ids'].shape[1] > max_input_length:
# test_record['prediction'] = None
# else:
# prediction = annotate(input_prompt)
# prediction = annotate(input_prompt, task_id)
# test_record['prediction'] = prediction
prediction = annotate(input_prompt)
prediction = annotate(input_prompt, task_id)
test_record['prediction'] = prediction
with open(output_file, 'a') as f:
f.write(json.dumps(test_record)+'\n')
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