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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, build_prompt_cot, build_prompt_by_task_type

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

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medium

The imports build_prompt and build_prompt_cot are not used anywhere in main.py. Removing them keeps the imports clean and avoids confusion.

Suggested change
from method import build_prompt, select_examples, build_prompt_cot, build_prompt_by_task_type
from method import select_examples, 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',
}

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)
# 使用混合上下文长度策略：传递task_id和sample_idx
examples_str = select_examples(icl_examples, task_description, text2annotate, task_id, sample_idx)
input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str+'\n\n')
Comment on lines 78 to 81

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

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high

The mixed context length strategy is currently broken because examples_str is cached as a single string initialized to None outside the loop. As a result, select_examples is only called once (at sample_idx == 0), meaning the 30k context length is used for all subsequent samples, and the 8k optimization is never triggered.

To fix this, we can repurpose examples_str as a dictionary cache to store both the large (30k/16k) and small (8k) versions of the few-shot examples, and select the appropriate one based on the current sample_idx.

Suggested change
if examples_str is None:
examples_str = select_examples(icl_examples, task_description, text2annotate)
# 使用混合上下文长度策略：传递task_id和sample_idx
examples_str = select_examples(icl_examples, task_description, text2annotate, task_id, sample_idx)
input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str+'\n\n')
if not isinstance(examples_str, dict):
examples_str = {}
cache_key = "large" if (task_id == 8 or sample_idx < 50) else "small"
if cache_key not in examples_str:
examples_str[cache_key] = select_examples(icl_examples, task_description, text2annotate, task_id, sample_idx, qwen_tokenizer)
input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str[cache_key] + '\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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