bfa1367149
* Change installation address Change former address: "https://docs.conda.io/en/latest/miniconda.html#" to new address: "https://conda-forge.org/download/" for 63 occurrences under python\llm\example * Change Prompt Change "Anaconda Prompt" to "Miniforge Prompt" for 1 occurrence * Create and update model minicpm * Update model minicpm Update model minicpm under GPU/PyTorch-Models * Update readme and generate.py change "prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)" and delete "pip install transformers==4.37.0 " * Update comments for minicpm GPU Update comments for generate.py at minicpm GPU * Add CPU example for MiniCPM * Update minicpm README for CPU * Update README for MiniCPM and Llama3 * Update Readme for Llama3 CPU Pytorch * Update and fix comments for MiniCPM
81 lines
3.4 KiB
Python
81 lines
3.4 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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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for MiniCPM model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-2B-sft-bf16",
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help='The huggingface repo id for the MiniCPM 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="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(model_path,
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trust_remote_code=True,
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torch_dtype='auto',
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low_cpu_mem_usage=True,
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use_cache=True)
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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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model = model.to('xpu')
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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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# here the prompt formatting refers to: https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16/blob/79fbb1db171e6d8bf77cdb0a94076a43003abd9e/modeling_minicpm.py#L1320
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chat = [
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{ "role": "user", "content": args.prompt },
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]
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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# ipex_llm model needs a warmup, then inference time can be accurate
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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# start inference
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st = time.time()
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output = model.generate(input_ids,
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do_sample=False,
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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_str = tokenizer.decode(output[0], skip_special_tokens=False)
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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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