def step(self):
        """Move to the next stage of quantization."""
        if self.quantization_step >= len(self.layers_to_quantize):
            # Maybe we just finished the last training step or we loaded
            # a checkpoint for an iterative PQ model which previously
            # finished training. Either way, don't quantize again.
            return

        logger.info(
            "quantizing model (step={}; layers_to_quantize[step]={})".format(
                self.quantization_step, self.layers_to_quantize[self.quantization_step]
            )
        )
        quantized_layers = pq.quantize_model_(
            self.trainer.get_model(),
            self.size_tracker,
            self.layers_to_quantize,
            self.block_sizes_config,
            self.n_centroids_config,
            step=self.quantization_step,
        )
        logger.info("quantized layers: {}".format(quantized_layers))
        logger.info(self.size_tracker)

        self.quantization_step += 1

        # reintialize the Trainer since model parameters have changed
        self.trainer.reinitialize()
Move to the next stage of quantization.
structured_prompting/fairseq-version/fairseq/fairseq/quantization_utils.py