78 lines
No EOL
3.1 KiB
Python
78 lines
No EOL
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 torch
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import time
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import argparse
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import numpy as np
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from transformers import AutoTokenizer
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ZIYA_PROMPT_FORMAT = "<human>: \n{prompt}\n<bot>: \n"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Ziya model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="IDEA-CCNL/Ziya-Coding-34B-v1.0",
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help='The huggingface repo id for the Ziya 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="def quick_sort(arr):\n",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=128,
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help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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from transformers import AutoModelForCausalLM
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from ipex_llm import optimize_model
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# enabling `use_cache=True` allows the model to utilize the previous
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# key/values attentions to speed up decoding;
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# to obtain optimal performance with IPEX-LLM `optimization_model` API optimizations,
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# it is important to set use_cache=True for Ziya models
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model = AutoModelForCausalLM.from_pretrained(model_path,
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trust_remote_code=True,
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use_cache=True)
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model = optimize_model(model)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = ZIYA_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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st = time.time()
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict,
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do_sample = True,
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top_p = 0.85,
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temperature = 0.8,
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repetition_penalty = 0.95,
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eos_token_id = tokenizer.eos_token_id,
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pad_token_id = tokenizer.pad_token_id,
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)
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end = time.time()
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output_str = tokenizer.batch_decode(output)[0]
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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