llmcompressor.pipelines.layer_sequential.pipeline

@CalibrationPipeline.register("layer_sequential")
class LayerSequentialPipeline(CalibrationPipeline):
    @staticmethod
    def __call__(
        model: torch.nn.Module, dataloader: DataLoader, dataset_args: "DatasetArguments"
    ):
        """
        Run a layer-wise sequential data pipeline according to the following steps:

        1. Layers are identified according to `sequential_targets`
        2. A hook is attached to the first layer. This hook raises an exception which is
            then caught and used to capture the input arguments to the first layer
        3. The inputs to the first layer are used to calibrate the first layer, and the
            output of the previous layer is used as inputs to calibrate the next layer

        This pipeline requires that the model have distinct layers defined in its
        architecture and that the outputs of the previous layer are exactly the inputs
        to the next layer. This is violated by encoder-decoder architectures, among
        others.

        If your model architecture violates these assumptions, consider using the
        sequential pipeline (see llmcompressor.pipelines.sequential). Architectures
        which are known to fail these assumptions include GPT-J and most vision models

        :param model: model being calibrated
        :param dataloader: loads data for calibration
        :param dataset_args: dataset arguments relevant to pipelines
        """
        session = active_session()

        # find layers
        modifiers = session.get_modifiers()
        sequential_targets, _ = get_targets_from_modifiers(modifiers, model)
        layers = match_modules(model, sequential_targets)

        LifecycleCallbacks.calibration_epoch_start()

        with calibration_forward_context(model), DisableQuantization(model):
            # prepare intermediates cache
            intermediates: IntermediatesCache = capture_first_layer_intermediates(
                model, layers[0], dataloader
            )

            num_layers = len(layers)
            for layer_index, layer in enumerate(layers):
                # prepare tqdm description texts
                calib_desc = f"({layer_index + 1}/{num_layers}): Calibrating"
                prop_desc = f"({layer_index + 1}/{num_layers}): Propagating"

                # do an preliminary pass to trigger modifier hooks
                for batch_idx in tqdm.tqdm(range(len(dataloader)), desc=calib_desc):
                    inputs = intermediates.fetch(batch_idx)
                    layer(**inputs)

                # trigger compression
                LifecycleCallbacks.sequential_epoch_end()

                # this pass does not trigger modifier hooks
                # and is only used for capturing outputs from newly compressed modules
                with HooksMixin.disable_hooks():
                    for batch_idx in tqdm.tqdm(range(len(dataloader)), desc=prop_desc):
                        inputs = intermediates.fetch(batch_idx)
                        output = layer(**inputs)

                        if layer_index < num_layers - 1:
                            next_layer = layers[layer_index + 1]
                            output = to_next_layer_kwargs(output, next_layer)
                            output = maybe_inject_pos_embeddings(
                                output, next_layer, inputs
                            )

                            intermediates.delete(batch_idx)
                            intermediates.update(batch_idx, output)

            # redudant, finish any remaining compression
            LifecycleCallbacks.calibration_epoch_end()