88 lines
4.1 KiB
Python
88 lines
4.1 KiB
Python
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#
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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 ipex_llm.transformers import AutoModel, AutoModelForCausalLM, init_pipeline_parallel
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from transformers import AutoTokenizer
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init_pipeline_parallel()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-13b-chat-hf",
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help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) 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="Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun",
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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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parser.add_argument('--low-bit', type=str, default='sym_int4', help='The quantization type the model will convert to.')
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parser.add_argument('--gpu-num', type=int, default=2, help='GPU number to use')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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low_bit = args.low_bit
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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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try:
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_low_bit=low_bit,
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True,
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torch_dtype=torch.float16,
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pipeline_parallel_stages=args.gpu_num)
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except:
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model = AutoModel.from_pretrained(model_path,
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load_in_low_bit=low_bit,
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True,
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pipeline_parallel_stages=args.gpu_num)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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local_rank = torch.distributed.get_rank()
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# Generate predicted tokens
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with torch.inference_mode():
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input_ids = tokenizer.encode(args.prompt, return_tensors="pt").to(f'xpu:{local_rank}')
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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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if local_rank == args.gpu_num - 1:
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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(f"First token cost {model.first_token_time:.4f} s and rest tokens cost average {model.rest_cost_mean:.4f} s")
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print('-'*20, 'Prompt', '-'*20)
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print(args.prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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