b0338c5529
* Add --modelscope option for glm-v4 and MiniCPM-V-2_6 * glm-edge * minicpm-v-2_6:don't use model_hub=modelscope when use lowbit; internvl2 --------- Co-authored-by: ATMxsp01 <shou.xu@intel.com>
97 lines
3.9 KiB
Python
97 lines
3.9 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 ipex_llm.transformers import AutoModelForCausalLM
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for GLM-Edge 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 GLM-Edge model to be downloaded'
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', or the path to the checkpoint folder')
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parser.add_argument('--prompt', type=str, default="AI是什么？",
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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('--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/glm-edge-4b-chat" if args.modelscope else "THUDM/glm-edge-4b-chat")
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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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optimize_model=True,
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trust_remote_code=True,
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use_cache=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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# Generate predicted tokens
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with torch.inference_mode():
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# The following code for generation is adapted from https://huggingface.co/THUDM/glm-edge-1.5b-chat#inference
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message = [{"role": "user", "content": args.prompt}]
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inputs = tokenizer.apply_chat_template(
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message,
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return_tensors="pt",
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add_generation_prompt=True,
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return_dict=True,
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).to("xpu")
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generate_kwargs = {
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"input_ids": inputs["input_ids"],
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"attention_mask": inputs["attention_mask"],
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"max_new_tokens": args.n_predict,
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"do_sample": False,
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}
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# ipex_llm model needs a warmup, then inference time can be accurate
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output = model.generate(**generate_kwargs)
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st = time.time()
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output = model.generate(**generate_kwargs)
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torch.xpu.synchronize()
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end = time.time()
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output_str = tokenizer.decode(output[0][inputs.input_ids.shape[1]:], 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(args.prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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