aceea4dc29
* re-open pr to run on latest runner * re-add examples and ut * rename ut and move deprecate to warning instead of raising an error info * ut fix
113 lines
5 KiB
Python
113 lines
5 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 unittest
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import os
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import pytest
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import time
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import torch
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from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq
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from transformers import 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_auto_model_int4(self):
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model_path = os.environ.get('ORIGINAL_CHATGLM2_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 = "Tell me the capital of France.\n\n"
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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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res = 'Paris' in output_str
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self.assertTrue(res)
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def test_transformers_auto_model_for_causal_lm_int4(self):
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model_path = os.environ.get('ORIGINAL_REPLIT_CODE_PATH')
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_str = 'def hello():\n print("hello world")\n'
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
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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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res = '\nhello()' in output_str
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self.assertTrue(res)
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def test_transformers_auto_model_for_speech_seq2seq_int4(self):
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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from datasets import load_from_disk
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model_path = os.environ.get('ORIGINAL_WHISPER_TINY_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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model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
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with torch.inference_mode():
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st = time.time()
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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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end = time.time()
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print('Output:', transcription)
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print(f'Inference time: {end-st} s')
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res = 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
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self.assertTrue(res)
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def test_transformers_chatglm_for_causallm(self):
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from bigdl.llm.transformers import ChatGLMForCausalLM
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model_path = os.environ.get('ORIGINAL_CHATGLM2_6B_PATH')
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model = ChatGLMForCausalLM.from_pretrained(model_path, native=False, 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 = "Tell me the capital of France.\n\n"
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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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res = 'Paris' in output_str
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self.assertTrue(res)
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if __name__ == '__main__':
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pytest.main([__file__])
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