0284801fbd
* Set BIGDL_IMPORT_IPEX default to true, i.e., auto import IPEX for XPU. * Remove import intel_extension_for_pytorch as ipex from GPU example. * Add support for bigdl-core-xe-21.
74 lines
2.8 KiB
Python
74 lines
2.8 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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import numpy as np
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from transformers import AutoModel, AutoTokenizer
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from bigdl.llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Stream Chat for ChatGLM2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/chatglm2-6b",
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help='The huggingface repo id for the ChatGLM2 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--question', type=str, default="晚上睡不着应该怎么办",
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help='Qustion you want to ask')
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parser.add_argument('--disable-stream', action="store_true",
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help='Disable stream chat')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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disable_stream = args.disable_stream
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# Load model
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model = AutoModel.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 BigDL-LLM optimization on model
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model = optimize_model(model)
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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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with torch.inference_mode():
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prompt = args.question
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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=32)
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# start inference
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if disable_stream:
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# Chat
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response, history = model.chat(tokenizer, args.question, history=[])
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print('-'*20, 'Chat Output', '-'*20)
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print(response)
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else:
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# Stream chat
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response_ = ""
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print('-'*20, 'Stream Chat Output', '-'*20)
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for response, history in model.stream_chat(tokenizer, args.question, history=[]):
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print(response.replace(response_, ""), end="")
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response_ = response
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