73 lines
3.1 KiB
Python
73 lines
3.1 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, time
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import pytest
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel, AutoModelForSpeechSeq2Seq
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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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(AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_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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model.to('cpu') # deallocate gpu memory
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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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#def test_transformers_auto_model_for_speech_seq2seq_int4():
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# from transformers import WhisperProcessor
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# from datasets import load_from_disk
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# model_path = os.environ.get('WHISPER_TINY_ORIGIN_PATH')
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# dataset_path = os.environ.get('SPEECH_DATASET_PATH')
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# processor = WhisperProcessor.from_pretrained(model_path)
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# ds = load_from_disk(dataset_path)
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# sample = ds[0]["audio"]
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# input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
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# input_features = input_features.to(device)
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# model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True, optimize_model=True)
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# model = model.to(device)
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# predicted_ids = model.generate(input_features)
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# # decode token ids to text
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# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
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# model.to('cpu')
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# print('Output:', transcription)
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# assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
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if __name__ == '__main__':
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pytest.main([__file__])
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