from swift.dataset import EncodePreprocessor, load_dataset from swift.model import get_processor from swift.template import TemplateInputs, get_template def test_template(): tokenizer = get_processor('Qwen/Qwen2-7B-Instruct') template = get_template(tokenizer) template_inputs = TemplateInputs.from_dict({ 'messages': [{ 'role': 'system', 'content': 'AAA' }, { 'role': 'user', 'content': 'BBB' }, { 'role': 'assistant', 'content': 'CCC' }, { 'role': 'user', 'content': 'DDD' }] }) inputs = template.encode(template_inputs) print(f'inputs.keys(): {inputs.keys()}') print(tokenizer.decode(inputs['input_ids'])) def test_mllm(): processor = get_processor('Qwen/Qwen2-VL-7B-Instruct') template = get_template(processor) template_inputs = TemplateInputs( chosen={ 'messages': [{ 'role': 'system', 'content': 'AAA' }, { 'role': 'user', 'content': 'BBB' }, { 'role': 'assistant', 'content': 'CCC' }, { 'role': 'user', 'content': 'DDD' }], 'images': ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png'] }) inputs = template.encode(template_inputs) print(f'inputs.keys(): {inputs.keys()}') print(template.safe_decode(inputs['input_ids'])) def _test_dataset_map(model_id: str, dataset_id: str): tokenizer = get_processor(model_id) template = get_template(tokenizer) dataset = load_dataset([dataset_id], num_proc=2)[0] # 1: 1500 # 16: 10766.36 examples/s new_dataset = EncodePreprocessor(template)(dataset, num_proc=4) print(f'new_dataset: {new_dataset}') print(template.safe_decode(new_dataset[0]['input_ids'])) print(template.safe_decode(new_dataset[1]['input_ids'])) def test_llm_dataset_map(): _test_dataset_map('Qwen/Qwen2-7B-Instruct', 'AI-ModelScope/alpaca-gpt4-data-zh') def test_mllm_dataset_map(): _test_dataset_map('Qwen/Qwen2-VL-7B-Instruct', 'modelscope/coco_2014_caption:validation#100') if __name__ == '__main__': test_template() test_mllm() test_llm_dataset_map() test_mllm_dataset_map()