Inference

Optimum Intel can be used to load models from the Hub and create pipelines to run inference with IPEX optimizations (including patching with custom operators, weight prepacking and graph mode) on a variety of Intel processors. For now support is only enabled for CPUs.

Loading

You can load your model and apply IPEX optimizations (including weight prepacking and graph mode). For supported architectures like LLaMA, BERT and ViT, further optimizations will be applied by patching the model to use custom operators. For now, support is only enabled for CPUs and the original model will be exported via TorchScript. In the future torch.compile will be used and model exported via TorchScript will get deprecated.

  import torch
  from transformers import AutoTokenizer, pipeline
- from transformers import AutoModelForCausalLM
+ from optimum.intel import IPEXModelForCausalLM

  model_id = "gpt2"
- model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+ model = IPEXModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, export=True)
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
  results = pipe("He's a dreadful magician and")

As shown in the table below, each task is associated with a class enabling to automatically load your model.