c619315131
* First push of bigdl-llm example for gptneox model family * Add some args and other small updates * Small updates * Add example for llama family models * Small fix * Small fix * Update for batch_decode api and change default model for llama example * Small fix * Small fix * Small fix * Small model family name fix and add example for bloom * Small fix * Small default prompt fix * Small fix * Change default prompt * Add sample output for inference * Hide example inference time
89 lines
3.2 KiB
Python
89 lines
3.2 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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import time
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import argparse
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def convert_and_load(repo_id_or_model_path, n_threads):
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from bigdl.llm.ggml.transformers import AutoModelForCausalLM
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# here you may input the HuggingFace repo id directly as the value of `pretrained_model_name_or_path`.
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# This will allow the pre-trained model to be downloaded directly from the HuggingFace repository.
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# The downloaded model will then be converted to binary format with int4 dtype weights,
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# and saved into the cache_dir folder.
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#
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# if you already have the pre-trained model downloaded, you can provide the path to
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# the downloaded folder as the value of `pretrained_model_name_or_path``
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llm = AutoModelForCausalLM.from_pretrained(
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pretrained_model_name_or_path=repo_id_or_model_path,
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model_family='bloom',
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dtype='int4',
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cache_dir='./',
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n_threads=n_threads)
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# if you want to explicitly convert the pre-trained model, you can use the `convert_model` API
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# to convert the downloaded Huggungface checkpoint first,
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# and then load the binary checkpoint directly.
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#
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# from bigdl.llm.ggml import convert_model
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#
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# model_path = repo_id_or_model_path
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# output_ckpt_path = convert_model(
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# input_path=model_path,
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# output_path='./',
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# dtype='int4',
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# model_family='bloom')
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#
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# llm = AutoModelForCausalLM.from_pretrained(
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# pretrained_model_name_or_path=output_ckpt_path,
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# model_family='bloom',
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# n_threads=n_threads)
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return llm
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def inference(llm, prompt):
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st = time.time()
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output = llm(prompt,
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max_tokens=32)
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print(f'Inference time (fast forward): {time.time()-st} s')
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print(f'Output:\n{output}')
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def main():
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parser = argparse.ArgumentParser(description='BLOOM pipeline example')
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parser.add_argument('--thread-num', type=int, default=2, required=True,
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help='Number of threads to use for inference')
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parser.add_argument('--repo-id-or-model-path', type=str, default="bigscience/bloomz-7b1",
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help='The huggingface repo id for BLOOM family model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default='Q: What is AI? A:',
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help='Prompt to infer')
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args = parser.parse_args()
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# Step 1: convert and load int4 model
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llm = convert_and_load(repo_id_or_model_path=args.repo_id_or_model_path, n_threads=args.thread_num)
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# Step 2: conduct inference
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inference(llm=llm, prompt=args.prompt)
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if __name__ == '__main__':
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main()
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