image-match/python/py/Lib/site-packages/transformers/integrations/aqlm.py

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# Licensed under the Apache License, Version 2.0 (the "License");
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"AQLM (Additive Quantization of Language Model) integration file"

from ..quantizers.quantizers_utils import should_convert_module
from ..utils import is_torch_available, logging


if is_torch_available():
    import torch
    import torch.nn as nn

logger = logging.get_logger(__name__)


def replace_with_aqlm_linear(model, modules_to_not_convert: list[str] | None = None, quantization_config=None):
    """
    Public method that recursively replaces the Linear layers of the given model with AQLM quantized layers.

    Args:
        model (`torch.nn.Module`):
            The model to convert, can be any `torch.nn.Module` instance.
        modules_to_not_convert (`list[str]`, *optional*, defaults to `None`):
            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.
        quantization_config (`AqlmConfig`):
            The quantization config object that contains the quantization parameters.
    """
    from aqlm import QuantizedLinear

    has_been_replaced = False
    # we need this to correctly materialize the weights during quantization
    for module_name, module in model.named_modules():
        if not should_convert_module(module_name, modules_to_not_convert):
            continue
        with torch.device("meta"):
            if isinstance(module, nn.Linear):
                new_module = QuantizedLinear(
                    module.in_features,
                    module.out_features,
                    bias=module.bias is not None,
                    in_group_size=quantization_config.in_group_size,
                    out_group_size=quantization_config.out_group_size,
                    num_codebooks=quantization_config.num_codebooks,
                    nbits_per_codebook=quantization_config.nbits_per_codebook,
                )
                new_module.source_cls = type(module)
                new_module.requires_grad_(False)
                model.set_submodule(module_name, new_module)
                has_been_replaced = True

    if not has_been_replaced:
        logger.warning(
            "You are loading your model using eetq but no linear modules were found in your model."
            " Please double check your model architecture, or submit an issue on github if you think this is"
            " a bug."
        )

    return model