411d896636
* ut * transformers api first ut * name * dir issue * use chatglm instead of chatglm2 * omp * set omp in sh * source * taskset * test * test omp * add test
55 lines
1.9 KiB
Python
55 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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from bigdl.llm.models import Llama, Bloom, Gptneox, Starcoder
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from bigdl.llm.utils import get_avx_flags
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import unittest
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import os
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import time
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import torch
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
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from transformers import LlamaTokenizer, AutoTokenizer
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class TestTransformersAPI(unittest.TestCase):
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def setUp(self):
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thread_num = os.environ.get('THREAD_NUM')
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if thread_num is not None:
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self.n_threads = int(thread_num)
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else:
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self.n_threads = 2
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def test_transformers_int4(self):
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model_path = os.environ.get('ORIGINAL_CHATGLM_6B_PATH')
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_str = "晚上睡不着应该怎么办"
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with torch.inference_mode():
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st = time.time()
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input_ids = tokenizer.encode(input_str, return_tensors="pt")
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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end = time.time()
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print('Prompt:', input_str)
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print('Output:', output_str)
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print(f'Inference time: {end-st} s')
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if __name__ == '__main__':
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unittest.main()
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