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import unittest |
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from typing import Any, Dict, Optional |
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import torch |
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from swift.llm import (DatasetMeta, InferRequest, Model, ModelGroup, ModelMeta, PtEngine, RequestConfig, |
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ResponsePreprocessor, TemplateMeta, get_model_tokenizer_with_flash_attn, load_dataset, |
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register_dataset, register_model, register_template) |
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class CustomPreprocessor(ResponsePreprocessor): |
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prompt = """Task: Based on the given two sentences, provide a similarity score between 0.0 and 5.0. |
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Sentence 1: {text1} |
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Sentence 2: {text2} |
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Similarity score: """ |
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def preprocess(self, row: Dict[str, Any]) -> Optional[Dict[str, Any]]: |
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return super().preprocess({ |
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'query': self.prompt.format(text1=row['text1'], text2=row['text2']), |
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'response': f"{row['label']:.1f}" |
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}) |
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register_dataset( |
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DatasetMeta( |
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ms_dataset_id='swift/stsb', |
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hf_dataset_id='SetFit/stsb', |
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preprocess_func=CustomPreprocessor(), |
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)) |
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register_template( |
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TemplateMeta( |
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template_type='custom', |
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prefix=['<extra_id_0>System\n{{SYSTEM}}\n'], |
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prompt=['<extra_id_1>User\n{{QUERY}}\n<extra_id_1>Assistant\n'], |
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chat_sep=['\n'])) |
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register_model( |
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ModelMeta( |
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model_type='custom', |
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model_groups=[ |
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ModelGroup([Model('AI-ModelScope/Nemotron-Mini-4B-Instruct', 'nvidia/Nemotron-Mini-4B-Instruct')]) |
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], |
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template='custom', |
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get_function=get_model_tokenizer_with_flash_attn, |
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ignore_patterns=['nemo'])) |
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class TestCustom(unittest.TestCase): |
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def test_custom_model(self): |
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infer_request = InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]) |
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request_config = RequestConfig(max_tokens=512, temperature=0) |
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engine = PtEngine('AI-ModelScope/Nemotron-Mini-4B-Instruct', torch.float16) |
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response = engine.infer([infer_request], request_config) |
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swift_response = response[0].choices[0].message.content |
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engine.default_template.template_backend = 'jinja' |
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response = engine.infer([infer_request], request_config) |
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jinja_response = response[0].choices[0].message.content |
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assert swift_response == jinja_response, (f'swift_response: {swift_response}\njinja_response: {jinja_response}') |
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print(f'response: {swift_response}') |
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def test_custom_dataset(self): |
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dataset = load_dataset(['swift/stsb'])[0] |
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assert len(dataset) == 5749 |
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assert list(dataset[0].keys()) == ['messages'] |
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print(f'dataset: {dataset}') |
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print(f'dataset[0]: {dataset[0]}') |
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if __name__ == '__main__': |
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unittest.main() |
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