78 lines
3.2 KiB
Python
78 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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#
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import torch
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import time
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import argparse
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from ipex_llm import optimize_model
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# you could tune the prompt based on your own model,
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# Refer to https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct
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PROMPT_FORMAT = """
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You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
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### Instruction:
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{prompt}
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### Response:
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for deepseek model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="deepseek-ai/deepseek-coder-6.7b-instruct",
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help='The huggingface repo id for the deepseek (e.g. `deepseek-ai/deepseek-coder-6.7b-instruct`) 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="What is AI?",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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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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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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).eval()
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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tokenizer.pad_token = tokenizer.eos_token
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# With only one line to enable IPEX-LLM optimization on model
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# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the optimize_model function.
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# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
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model = optimize_model(model,
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cpu_embedding=True)
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model = model.to('xpu')
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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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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torch.xpu.synchronize()
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end = time.time()
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output = output.cpu()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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