9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
80 lines
3.1 KiB
Python
80 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
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import pytest
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import tempfile
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import torch
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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mistral_model_path = os.environ.get('MISTRAL_ORIGIN_PATH')
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prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
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@pytest.mark.parametrize("Model, Tokenizer, model_path, prompt", [
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(AutoModelForCausalLM, AutoTokenizer, mistral_model_path, prompt)
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])
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def test_optimize_model(Model, Tokenizer, model_path, prompt):
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tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=False,
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trust_remote_code=True)
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logits_base_model = (model(input_ids)).logits
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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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logits_optimized_model = (model(input_ids)).logits
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diff = abs(logits_base_model - logits_optimized_model).flatten()
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assert any(diff) is False
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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, AutoTokenizer, mistral_model_path),
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])
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def test_load_low_bit_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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with tempfile.TemporaryDirectory() as tempdir:
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model.save_low_bit(tempdir)
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loaded_model = Model.load_low_bit(tempdir,
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optimize_model=True,
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trust_remote_code=True)
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with torch.inference_mode():
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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output = loaded_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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