Transformers documentation

Contribute new quantization method

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Contribute new quantization method

Transformers supports and integrates many quantization methods such as QLoRA, GPTQ, LLM.int8, and AWQ. However, there are other quantization approaches that are not yet integrated. To make adding and using these quantization methods with Transformers models easier, you should use the HfQuantizer class. The HfQuantizer is designed as an internal helper class for adding a quantization method instead of something you apply to every PyTorch module.

This guide will show you how to integrate a new quantization method with the HfQuantizer class.

Requirements

Before integrating a new quantization method into Transformers, ensure the method you are trying to add meets the following prerequisites. Only quantization methods that can be run with PyTorch modules are currently supported.

  • The quantization method is available through a Python package that is pip-installable by anyone (it is also fine if you can only install the package from source). Ideally, pre-compiled kernels are included in the pip package.
  • The method can run on commonly-used hardware (CPU, GPU, …).
  • The method is wrapped in a nn.Module (e.g., Linear8bitLt, Linear4bit), and the quantized linear layer should have the following definition:
class Linear4bit(nn.Module):
    def __init__(self, ...):
        ...
    
    def forward(self, x):
        return my_4bit_kernel(x, self.weight, self.bias)

This way, Transformers models can be easily quantized by replacing some instances of nn.Linear with a target class.

  • The quantization method should be serializable. You can save the quantized weights locally or push them to the Hub.
  • Make sure the package that contains the quantization kernels/primitive is stable (no frequent breaking changes).

For some quantization methods, they may require “pre-quantizing” the models through data calibration (e.g., AWQ). In this case, we prefer to only support inference in Transformers and let the third-party library maintained by the ML community deal with the model quantization itself.

Build a new HFQuantizer class

  1. Create a new quantization config class inside src/transformers/utils/quantization_config.py and make sure to expose the new quantization config inside Transformers main init by adding it to the _import_structure object of src/transformers/init.py.

  2. Create a new file inside src/transformers/quantizers/ named quantizer_your_method.py, and make it inherit from src/transformers/quantizers/base.py::HfQuantizer. Make sure to add the new quantizer and quantization config in the quantization auto-mapping in src/transformers/quantizers/auto.py.

  3. Define the following class attributes/property methods for your quantization method:

  • requires_calibration: Whether the quantization method requires a data calibration process. If set to True, you can only support inference (with quantized weights) and not inference and quantization.
  • required_packages: A list of strings of the required packages to use the quantized weights. You might need to define some new utility methods such as is_auto_awq_available in transformers/src/utils/import_utils.py.
  • requires_parameters_quantization: Only required if your quantization method requires extra attention to the underlying nn.Parameter object. For example, bitsandbytes uses Params4bit and Int8Param, which requires some extra attention when quantizing the model. Most of the recent quantization method packs int2/int4 weights inside torch.uint8 weights, so this flag should not be really required (set to False by default).
  • is_serializable: A property method to determine whether the method is serializable or not.
  • is_trainable: A property method to determine whether you can fine-tune models on top of the quantization method (with or without PEFT approaches).
  1. Write the validate_environment and update_torch_dtype methods. These methods are called before creating the quantized model to ensure users use the right configuration. You can have a look at how this is done on other quantizers.

  2. Write the _process_model_before_weight_loading method. In Transformers, the quantized models are initialized first on the "meta" device before loading the weights. This means the _process_model_before_weight_loading method takes care of manipulating the model skeleton to replace some modules (e.g., nn.Linear) with the target modules (quantization modules). You can define a module replacement logic or any other utility method by creating a new file in transformers/src/integrations/ and exposing the relevant methods in that folder’s __init__.py file. The best starting point would be to have a look at another quantization methods such as quantizer_awq.py.

  3. Write the _process_model_after_weight_loading method. This method enables implementing additional features that require manipulating the model after loading the weights.

  4. Document everything! Make sure your quantization method is documented in the docs/source/en/quantization.md file.

  5. Add tests! You should add tests by first adding the package in our nightly Dockerfile inside docker/transformers-quantization-latest-gpu and then adding a new test file in tests/quantization/xxx. Feel free to check out how it is implemented for other quantization methods.