86 lines
3.7 KiB
Python
86 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 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://huggingface.co/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py#L15
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DOLLY_V2_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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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 Dolly v2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v2-12b",
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help='The huggingface repo id for the Dolly v2 (e.g. `databricks/dolly-v2-7b` and `databricks/dolly-v2-12b`) 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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# 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, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = DOLLY_V2_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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end_key_token_id=tokenizer.encode("### End")[0]
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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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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=end_key_token_id)
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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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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=end_key_token_id)
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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, 'Output', '-'*20)
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print(output_str)
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