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a58be34384
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12770413 * add prompt and lora * add adapter * add prefix * add tests * adapter smoke test passed * prompt test passed * support model id in petl * migrate chatglm6b * add train script for chatglm6b * move gen_kwargs to finetune.py * add chatglm2 * add model definination
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import torch
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from modelscope import read_config
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.models.base import Model
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from modelscope.msdatasets import MsDataset
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from modelscope.pipelines import pipeline
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from modelscope.swift import Swift
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from modelscope.swift.adapter import AdapterConfig
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from modelscope.swift.prompt import PromptConfig
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import ModelFile, Tasks
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from modelscope.utils.test_utils import test_level
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class TestPrompt(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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@unittest.skipUnless(test_level() >= 0, 'skip in this level')
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def test_prompt_smoke_test(self):
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dataset = MsDataset.load(
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'clue', subset_name='afqmc',
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split='train').to_hf_dataset().select(range(2))
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model_dir = snapshot_download(
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'damo/nlp_structbert_sentence-similarity_chinese-tiny')
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model = Model.from_pretrained(model_dir, adv_grad_factor=None)
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cfg_file = os.path.join(model_dir, 'configuration.json')
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model_cfg = os.path.join(model_dir, 'config.json')
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model_cfg = read_config(model_cfg)
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prompt_config = PromptConfig(
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dim=model_cfg.hidden_size,
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module_layer_name=r'.*layer\.\d+$',
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embedding_pos=0,
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attention_mask_pos=1)
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model = Swift.prepare_model(model, prompt_config)
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kwargs = dict(
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model=model,
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cfg_file=cfg_file,
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train_dataset=dataset,
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eval_dataset=dataset,
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work_dir=self.tmp_dir)
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
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def pipeline_sentence_similarity(model_dir):
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model = Model.from_pretrained(model_dir)
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prompt_config.pretrained_weights = output_dir
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Swift.prepare_model(model, prompt_config)
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model.eval()
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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity, model=model)
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return pipeline_ins(input=('test', 'this is a test'))
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output1 = pipeline_sentence_similarity(
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'damo/nlp_structbert_sentence-similarity_chinese-tiny')
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print(output1)
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if __name__ == '__main__':
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unittest.main()
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