neural_compressor.torch.algorithms.weight_only.teq

Module Contents

Classes

TrainableEquivalentTransformation

Weight-only quantization, Trainable Equivalent Transformation (TEQ).

TEQuantizer

The base quantizer for all algorithm quantizers.
class neural_compressor.torch.algorithms.weight_only.teq.TrainableEquivalentTransformation(model, weight_config={}, absorb_to_layer={}, folding=True, example_inputs=None)[source]

Weight-only quantization, Trainable Equivalent Transformation (TEQ).

class neural_compressor.torch.algorithms.weight_only.teq.TEQuantizer(quant_config, folding, absorb_to_layer, example_inputs)[source]

The base quantizer for all algorithm quantizers.

The Quantizer unifies the interfaces across various quantization algorithms, including GPTQ, RTN, etc. Given a float model, Quantizer apply the quantization algorithm to the model according to the quant_config.

To implement a new quantization algorithm,, inherit from Quantizer and implement the following methods:

  • prepare: prepare a given model for convert.

  • convert: convert a prepared model to a quantized model.

Note: quantize and execute are optional for new quantization algorithms.