8153c3008e
* Add initial hf huggingface GPU example * Small fix * Add llama3 gpu pytorch model example * Add llama 3 hf transformers CPU example * Add llama 3 pytorch model CPU example * Fixes * Small fix * Small fixes * Small fix * Small fix * Add links * update repo id * change prompt tuning url * remove system header if there is no system prompt --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com> Co-authored-by: Yuwen Hu <54161268+Oscilloscope98@users.noreply.github.com>
93 lines
4.1 KiB
Python
93 lines
4.1 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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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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# here the prompt tuning refers to https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3
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DEFAULT_SYSTEM_PROMPT = """\
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"""
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def get_prompt(user_input: str, chat_history: list[tuple[str, str]],
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system_prompt: str) -> str:
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prompt_texts = [f'<|begin_of_text|>']
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if system_prompt != '':
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prompt_texts.append(f'<|start_header_id|>system<|end_header_id|>\n{system_prompt}<|eot_id|>')
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for history_input, history_response in chat_history:
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prompt_texts.append(f'<|start_header_id|>user<|end_header_id|>\n{history_input.strip()}<|eot_id|>')
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prompt_texts.append(f'<|start_header_id|>assistant<|end_header_id|>\n{history_response.strip()}<|eot_id|>')
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prompt_texts.append(f'<|start_header_id|>user<|end_header_id|>\n{user_input.strip()}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n')
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return ''.join(prompt_texts)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama3 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct",
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help='The huggingface repo id for the Llama3 (e.g. `meta-llama/Meta-Llama-3-8B-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(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.half().to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
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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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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=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 (skip_special_tokens=False)', '-'*20)
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print(output_str)
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