ab40607b87
* Add gpu workflow and a transformers API inference test * Set device-specific env variables in script instead of workflow * Fix status message --------- Co-authored-by: sgwhat <ge.song@intel.com>
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import pytest
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
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from transformers import LlamaTokenizer, AutoTokenizer
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device = os.environ['DEVICE']
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print(f'Running on {device}')
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if device == 'xpu':
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import intel_extension_for_pytorch as ipex
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@pytest.mark.parametrize('prompt, answer', [
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('What is the capital of France?\n\n','Paris')
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])
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@pytest.mark.parametrize('Model, Tokenizer, model_path',[
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(AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
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(AutoModel, AutoTokenizer, os.environ.get('CHATGLM2_6B_ORIGIN_PATH')),
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(AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
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])
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def test_completion(Model, Tokenizer, model_path, prompt, answer):
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tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True)
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model = model.to(device)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
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output = model.generate(input_ids, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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assert answer in output_str
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if __name__ == '__main__':
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pytest.main([__file__])
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