venv/lib/python3.13/site-packages/transformers/integrations/spqr.py

# Copyright 2024 The HuggingFace Team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"SpQR (Sparse-Quantized Representation) integration file"

from ..utils import is_accelerate_available, is_spqr_available, is_torch_available


if is_torch_available():
    import torch.nn as nn


def replace_with_spqr_linear(
    model,
    quantization_config=None,
    modules_to_not_convert=None,
    current_key_name=None,
    has_been_replaced=False,
):
    """
    Public method that recursively replaces the Linear layers of the given model with SpQR quantized layers.
    `accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the
    conversion has been successful or not.

    Args:
        model (`torch.nn.Module`):
            The model to convert, can be any `torch.nn.Module` instance.
        quantization_config (`SpQRConfig`):
            The quantization config object that contains the quantization parameters.
        modules_to_not_convert (`list[str]`, *optional*):
            A list of nn.Linear weights to not convert. If a parameter path is in the list (e.g. `lm_head.weight`), the corresponding module will not be
            converted.
        current_key_name (`list`, *optional*):
            A list that contains the current key name. This is used for recursion and should not be passed by the user.
        has_been_replaced (`bool`, *optional*):
            A boolean that indicates if the conversion has been successful or not. This is used for recursion and
            should not be passed by the user.
    """
    if modules_to_not_convert is None:
        modules_to_not_convert = []

    if is_accelerate_available():
        from accelerate import init_empty_weights
    if is_spqr_available():
        from spqr_quant import QuantizedLinear

    for name, module in model.named_children():
        if current_key_name is None:
            current_key_name = []
        current_key_name.append(name)

        if isinstance(module, nn.Linear):
            # Check if the current key is not in the `modules_to_not_convert`
            if ".".join(current_key_name) + ".weight" not in modules_to_not_convert:
                with init_empty_weights():
                    tensor_name = ".".join(current_key_name)

                    shapes = quantization_config.shapes
                    shapes_keys = shapes.keys()

                    shapes_valid = (
                        f"{tensor_name}.dense_weights.shape" in shapes_keys
                        and f"{tensor_name}.row_offsets.shape" in shapes_keys
                        and f"{tensor_name}.col_vals.shape" in shapes_keys
                        and f"{tensor_name}.in_perm.shape" in shapes_keys
                    )

                    if not shapes_valid:
                        raise ValueError(
                            f"The SpQR quantization config does not contain the shape "
                            f"configuration for {tensor_name}. This indicates that the "
                            f"configuration is either invalid or corrupted."
                        )

                    dense_weights_shape = shapes[f"{tensor_name}.dense_weights.shape"]
                    row_offsets_shape = shapes[f"{tensor_name}.row_offsets.shape"]
                    col_vals_shape = shapes[f"{tensor_name}.col_vals.shape"]
                    in_perm_shape = shapes[f"{tensor_name}.in_perm.shape"]

                    in_features = module.in_features
                    out_features = module.out_features

                    model._modules[name] = QuantizedLinear.create_placehodler(
                        rows=out_features,
                        cols=in_features,
                        bits=quantization_config.bits,
                        beta1=quantization_config.beta1,
                        beta2=quantization_config.beta2,
                        dense_weights_shape=dense_weights_shape,
                        row_offsets_shape=row_offsets_shape,
                        col_vals_shape=col_vals_shape,
                        in_perm_shape=in_perm_shape,
                    )
                    has_been_replaced = True

                    # Store the module class in case we need to transpose the weight later
                    model._modules[name].source_cls = type(module)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(False)
            else:
                pass
        if len(list(module.children())) > 0:
            _, has_been_replaced = replace_with_spqr_linear(
                module,
                quantization_config=quantization_config,
                modules_to_not_convert=modules_to_not_convert,
                current_key_name=current_key_name,
                has_been_replaced=has_been_replaced,
            )
        # Remove the last key for recursion
        current_key_name.pop(-1)
    return model, has_been_replaced