ccc18eefb5
* Add Modelscope option for GPU model chatglm3 * Update readme * Update readme * Update readme * Update readme * format update --------- Co-authored-by: ATMxsp01 <shou.xu@intel.com>
85 lines
3.5 KiB
Python
85 lines
3.5 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 ipex_llm.transformers import AutoModel
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Stream Chat for ChatGLM3 model')
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parser.add_argument('--repo-id-or-model-path', type=str,
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help='The Hugging Face or ModelScope repo id for the ChatGLM3 model to be downloaded'
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', or the path to the 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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parser.add_argument('--modelscope', action="store_true", default=False,
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help="Use models from modelscope")
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args = parser.parse_args()
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if args.modelscope:
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from modelscope import AutoTokenizer
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model_hub = 'modelscope'
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else:
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from transformers import AutoTokenizer
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model_hub = 'huggingface'
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model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
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("ZhipuAI/chatglm3-6b" if args.modelscope else "THUDM/chatglm3-6b")
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disable_stream = args.disable_stream
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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 = AutoModel.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True,
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optimize_model=True,
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model_hub=model_hub)
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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,
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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_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=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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