9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
81 lines
3.7 KiB
Python
81 lines
3.7 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 intel_extension_for_pytorch as ipex
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import time
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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# you could tune the prompt based on your own model,
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# prompt format is tuned based on the output example in this link:
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# https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0#usage-instructions
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SOLAR_PROMPT_FORMAT = "<s>### User:\n{prompt}\n### Assistant:\n"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for SOLAR model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="upstage/SOLAR-10.7B-Instruct-v1.0",
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help='The huggingface repo id for the SOLAR 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 in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained 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 = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True,
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use_cache=True)
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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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prompt = SOLAR_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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# ipex 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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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with BigDL-LLM INT4 optimizations
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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, '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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