BigDL-LLM PyTorch API#

Optimize Model#

You can run any PyTorch model with optimize_model through only one-line code change to benefit from BigDL-LLM optimization, regardless of the library or API you are using.

bigdl.llm.optimize_model(model, low_bit='sym_int4', optimize_llm=True, modules_to_not_convert=None, replace_embedding=False)[source]#

A method to optimize any pytorch model.

Parameters

  • model – The original PyTorch model (nn.module)

  • low_bit – Supported low-bit options are “sym_int4”, “asym_int4”, “sym_int5”, “asym_int5” or “sym_int8”.

  • optimize_llm – Whether to further optimize llm model.

  • modules_to_not_convert – list of str value, modules (nn.Module) that are skipped when conducting model optimizations. Default to be None.

  • replace_embedding – Whether to replace the Embedding layer, may need to set it to True when running BigDL-LLM on GPU on Windows. Default to be False.

Returns

The optimized model.

>>> # Take OpenAI Whisper model as an example
>>> from bigdl.llm import optimize_model
>>> model = whisper.load_model('tiny') # Load whisper model under pytorch framework
>>> model = optimize_model(model) # With only one line code change
>>> # Use the optimized model without other API change
>>> result = model.transcribe(audio, verbose=True, language="English")
>>> # (Optional) you can also save the optimized model by calling 'save_low_bit'
>>> model.save_low_bit(saved_dir)

Load Optimized Model#

To avoid high resource consumption during the loading processes of the original model, we provide save/load API to support the saving of model after low-bit optimization and the loading of the saved low-bit model. Saving and loading operations are platform-independent, regardless of their operating systems.

bigdl.llm.optimize.load_low_bit(model, model_path)[source]#

Load the optimized pytorch model.

Parameters

  • model – The PyTorch model instance

  • model_path – The path of saved optimized model

Returns

The optimized model.

>>> # Example 1:
>>> # Take ChatGLM2-6B model as an example
>>> # Make sure you have saved the optimized model by calling 'save_low_bit'
>>> from bigdl.llm.optimize import low_memory_init, load_low_bit
>>> with low_memory_init(): # Fast and low cost by loading model on meta device
>>>     model = AutoModel.from_pretrained(saved_dir,
>>>                                       torch_dtype="auto",
>>>                                       trust_remote_code=True)
>>> model = load_low_bit(model, saved_dir) # Load the optimized model

>>> # Example 2:
>>> # If the model doesn't fit 'low_memory_init' method,
>>> # alternatively, you can obtain the model instance through traditional loading method.
>>> # Take OpenAI Whisper model as an example
>>> # Make sure you have saved the optimized model by calling 'save_low_bit'
>>> from bigdl.llm.optimize import load_low_bit
>>> model = whisper.load_model('tiny') # A model instance through traditional loading method
>>> model = load_low_bit(model, saved_dir) # Load the optimized model