Package upstream model with reproducible local MMEB evaluation

bb2b5cc verified 11 days ago

14.5 kB

import inspect

from ..core_model_loading import ConversionOps
from ..quantizers.quantizers_utils import get_module_from_name, should_convert_module
from ..utils import (
    get_available_devices,
    is_accelerate_available,
    is_bitsandbytes_available,
    is_torch_available,
    logging,
)


if is_bitsandbytes_available():
    import bitsandbytes as bnb

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

    from ..pytorch_utils import Conv1D

if is_accelerate_available():
    import accelerate
    from accelerate.hooks import add_hook_to_module, remove_hook_from_module

logger = logging.get_logger(__name__)


class Bnb4bitQuantize(ConversionOps):
    def __init__(self, hf_quantizer):
        self.hf_quantizer = hf_quantizer

    def convert(
        self,
        input_dict: dict[str, list[torch.Tensor]],
        full_layer_name: str | None = None,
        model: torch.nn.Module | None = None,
        **kwargs,
    ) -> dict[str, torch.Tensor]:
        """
        we need to store some parameters to create the quantized weight. For example, bnb requires 6 values that are stored in the checkpoint to recover the quantized weight. So we store them in a dict that it stored in hf_quantizer for now as we can't save it in the op since we create an op per tensor.
        """
        value = list(input_dict.values())[0]
        value = value[0]

        # update param name to get the weights instead of the quantized stats
        module, _ = get_module_from_name(model, full_layer_name)

        # Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization.
        # Since weights are saved in the correct "orientation", we skip transposing when loading.
        if issubclass(module.source_cls, Conv1D):
            value = value.T

        old_value = model.get_parameter_or_buffer(full_layer_name)
        new_value = bnb.nn.Params4bit(value, requires_grad=False, **old_value.__dict__).to(value.device)
        module._is_hf_initialized = True
        return {full_layer_name: new_value}


class Bnb4bitDeserialize(ConversionOps):
    def __init__(self, hf_quantizer):
        self.hf_quantizer = hf_quantizer

    def convert(
        self,
        input_dict: dict[str, list[torch.Tensor]],
        model: torch.nn.Module | None = None,
        full_layer_name: str | None = None,
        **kwargs,
    ) -> dict[str, torch.Tensor]:
        """
        Deserialization of bnb keys. We need 6 keys to recreate the quantized weights
        """
        if len(input_dict) == 1:
            return input_dict

        for key, value in input_dict.items():
            if isinstance(value, list):
                input_dict[key] = value[0]

        key_weight = "weight"
        weight = input_dict.pop(key_weight)
        module, _ = get_module_from_name(model, full_layer_name)
        new_value = bnb.nn.Params4bit.from_prequantized(
            data=weight,
            quantized_stats=input_dict,
            requires_grad=False,
            device=weight.device,
            module=module,
        )
        module._is_hf_initialized = True
        return {key_weight: new_value}


class Bnb8bitQuantize(ConversionOps):
    def __init__(self, hf_quantizer):
        self.hf_quantizer = hf_quantizer

    def convert(
        self,
        input_dict: dict[str, list[torch.Tensor]],
        model: torch.nn.Module | None = None,
        full_layer_name: str | None = None,
        **kwargs,
    ) -> dict[str, torch.Tensor]:
        value = list(input_dict.values())[0]
        value = value[0] if isinstance(value, list) else value

        module, _ = get_module_from_name(model, full_layer_name)

        # Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization.
        # Since weights are saved in the correct "orientation", we skip transposing when loading.
        if issubclass(module.source_cls, Conv1D):
            value = value.T
        value_device = value.device
        kwargs = model.get_parameter_or_buffer(full_layer_name).__dict__
        kwargs.pop("SCB", None)
        new_value = bnb.nn.Int8Params(value.to("cpu"), requires_grad=False, **kwargs).to(value_device)
        return {full_layer_name: new_value}


class Bnb8bitDeserialize(ConversionOps):
    def __init__(self, hf_quantizer):
        self.hf_quantizer = hf_quantizer

    def convert(
        self,
        input_dict: dict[str, list[torch.Tensor]],
        model: torch.nn.Module | None = None,
        full_layer_name: str | None = None,
        **kwargs,
    ) -> dict[str, torch.Tensor]:
        """
        Deserialization of bnb keys.
        """
        if len(input_dict) == 1:
            # special case when we only fetched the weight
            # since we collected keys, we need to return it like that
            return input_dict

        for key, value in input_dict.items():
            if isinstance(value, list):
                input_dict[key] = value[0]

        module, _ = get_module_from_name(model, full_layer_name)

        key_weight = "weight"
        weight = input_dict[key_weight]
        kwargs = model.get_parameter_or_buffer(full_layer_name).__dict__
        kwargs["SCB"] = input_dict["SCB"]
        new_value = bnb.nn.Int8Params(weight, requires_grad=False, **kwargs).to(weight.device)
        module._is_hf_initialized = True
        return {key_weight: new_value}


def replace_with_bnb_linear(
    model: torch.nn.Module,
    modules_to_not_convert: list[str] | None = None,
    quantization_config=None,
    pre_quantized=False,
):
    """
    A helper function to replace all `torch.nn.Linear` modules by bnb modules from the `bitsandbytes` library.
    Args:
        model (`torch.nn.Module`):
            The model to convert, can be any `torch.nn.Module` instance.
        modules_to_not_convert (`list[str]`, 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 (`BitsAndBytesConfig`):
            The quantization config object that contains the quantization parameters.
        pre_quantized (`book`, defaults to `False`):
            Whether the model is pre-quantized or not
    """
    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
        new_module = None
        with torch.device("meta"):
            if isinstance(module, Conv1D) or type(module) is nn.Linear:
                if isinstance(module, Conv1D):
                    in_features, out_features = module.weight.shape
                else:
                    in_features = module.in_features
                    out_features = module.out_features
                if quantization_config.quantization_method() == "llm_int8":
                    new_module = bnb.nn.Linear8bitLt(
                        in_features,
                        out_features,
                        module.bias is not None,
                        has_fp16_weights=quantization_config.llm_int8_has_fp16_weight,
                        threshold=quantization_config.llm_int8_threshold,
                    )
                    if pre_quantized:
                        # this is kind of an edge case when supporting both loading and quantization ...
                        # we need to set the right dtype as we cast the checkpoint with the dtype of the meta model
                        new_module.weight.data = new_module.weight.data.to(dtype=torch.int8)
                else:
                    new_module = bnb.nn.Linear4bit(
                        in_features,
                        out_features,
                        module.bias is not None,
                        quantization_config.bnb_4bit_compute_dtype,
                        compress_statistics=quantization_config.bnb_4bit_use_double_quant,
                        quant_type=quantization_config.bnb_4bit_quant_type,
                        quant_storage=quantization_config.bnb_4bit_quant_storage,
                    )
                    if pre_quantized:
                        # same here
                        new_module.weight.data = new_module.weight.data.to(
                            dtype=quantization_config.bnb_4bit_quant_storage
                        )
                if new_module is not None:
                    # Store the module class in case we need to transpose the weight later
                    new_module.source_cls = type(module)
                    # Force requires grad to False to avoid unexpected errors
                    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


# Copied from PEFT: https://github.com/huggingface/peft/blob/47b3712898539569c02ec5b3ed4a6c36811331a1/src/peft/utils/integrations.py#L41
def dequantize_bnb_weight(weight: "torch.nn.Parameter", state=None):
    """
    Helper function to dequantize 4bit or 8bit bnb weights.
    If the weight is not a bnb quantized weight, it will be returned as is.
    """
    if not isinstance(weight, torch.nn.Parameter):
        raise TypeError(f"Input weight should be of type nn.Parameter, got {type(weight)} instead")

    cls_name = weight.__class__.__name__
    if cls_name not in ("Params4bit", "Int8Params"):
        return weight

    if cls_name == "Params4bit":
        output_tensor = bnb.functional.dequantize_4bit(weight.data, weight.quant_state)
        return output_tensor

    if state.SCB is None:
        state.SCB = weight.SCB

    if hasattr(bnb.functional, "int8_vectorwise_dequant"):
        # Use bitsandbytes API if available (requires v0.45.0+)
        dequantized = bnb.functional.int8_vectorwise_dequant(weight.data, state.SCB)
    else:
        # Multiply by (scale/127) to dequantize.
        dequantized = weight.data * state.SCB.view(-1, 1) * 7.874015718698502e-3

    return dequantized


def _create_accelerate_new_hook(old_hook):
    r"""
    Creates a new hook based on the old hook. Use it only if you know what you are doing !
    This method is a copy of: https://github.com/huggingface/peft/blob/748f7968f3a31ec06a1c2b0328993319ad9a150a/src/peft/utils/other.py#L245
    with some changes
    """
    old_hook_cls = getattr(accelerate.hooks, old_hook.__class__.__name__)
    old_hook_attr = old_hook.__dict__
    filtered_old_hook_attr = {}
    old_hook_init_signature = inspect.signature(old_hook_cls.__init__)
    for k in old_hook_attr:
        if k in old_hook_init_signature.parameters:
            filtered_old_hook_attr[k] = old_hook_attr[k]
    new_hook = old_hook_cls(**filtered_old_hook_attr)
    return new_hook


def dequantize_and_replace(model, quantization_config=None, dtype=None):
    """
    Converts a quantized model into its dequantized original version. The newly converted model will have
    some performance drop compared to the original model before quantization - use it only for specific usecases
    such as QLoRA adapters merging.
    Returns the converted model.
    """
    quant_method = quantization_config.quantization_method()

    target_cls = bnb.nn.Linear8bitLt if quant_method == "llm_int8" else bnb.nn.Linear4bit
    for module_name, module in model.named_modules():
        if isinstance(module, target_cls):
            with torch.device("meta"):
                bias = getattr(module, "bias", None)
                new_module = torch.nn.Linear(module.in_features, module.out_features, bias=bias is not None)
            state = module.state if quant_method == "llm_int8" else None
            new_module.weight = torch.nn.Parameter(dequantize_bnb_weight(module.weight, state))
            weight = dequantize_bnb_weight(module.weight, state)
            if dtype is None:
                logger.warning_once(
                    f"The modules are dequantized in {weight.dtype}. If you want to change the dtype, please specify `dtype` in `dequantize`. "
                )
            else:
                logger.warning_once(f"The modules are dequantized in {weight.dtype} and casted to {dtype}.")
                weight = weight.to(dtype)
            new_module.weight = torch.nn.Parameter(weight)
            if bias is not None:
                new_module.bias = bias
            if hasattr(module, "_hf_hook"):
                old_hook = module._hf_hook
                new_hook = _create_accelerate_new_hook(old_hook)
                remove_hook_from_module(module)
                add_hook_to_module(new_module, new_hook)
            new_module.to(module.weight.device)
            model.set_submodule(module_name, new_module)
            has_been_replaced = True

    if not has_been_replaced:
        logger.warning(
            "For some reason the model has not been properly dequantized. You might see unexpected behavior."
        )
    return model


def validate_bnb_backend_availability(raise_exception=False):
    """
    Validates if the available devices are supported by bitsandbytes, optionally raising an exception if not.
    """
    bnb_supported_devices = getattr(bnb, "supported_torch_devices", set())
    available_devices = set(get_available_devices())

    if not available_devices.intersection(bnb_supported_devices):
        if raise_exception:
            err_msg = (
                f"None of the available devices `available_devices = {available_devices or None}` are supported by the bitsandbytes version you have installed: `bnb_supported_devices = {bnb_supported_devices}`. "
                "Please check the docs to see if the backend you intend to use is available and how to install it: https://huggingface.co/docs/bitsandbytes/main/en/installation"
            )
            raise RuntimeError(err_msg)

        logger.warning("No supported devices found for bitsandbytes")
        return False
    return True

	import inspect

	from ..core_model_loading import ConversionOps
	from ..quantizers.quantizers_utils import get_module_from_name, should_convert_module
	from ..utils import (
	get_available_devices,
	is_accelerate_available,
	is_bitsandbytes_available,
	is_torch_available,
	logging,
	)


	if is_bitsandbytes_available():
	import bitsandbytes as bnb

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

	from ..pytorch_utils import Conv1D

	if is_accelerate_available():
	import accelerate
	from accelerate.hooks import add_hook_to_module, remove_hook_from_module

	logger = logging.get_logger(__name__)


	class Bnb4bitQuantize(ConversionOps):
	def __init__(self, hf_quantizer):
	self.hf_quantizer = hf_quantizer

	def convert(
	self,
	input_dict: dict[str, list[torch.Tensor]],
	full_layer_name: str \| None = None,
	model: torch.nn.Module \| None = None,
	**kwargs,
	) -> dict[str, torch.Tensor]:
	"""
	we need to store some parameters to create the quantized weight. For example, bnb requires 6 values that are stored in the checkpoint to recover the quantized weight. So we store them in a dict that it stored in hf_quantizer for now as we can't save it in the op since we create an op per tensor.
	"""
	value = list(input_dict.values())[0]
	value = value[0]

	# update param name to get the weights instead of the quantized stats
	module, _ = get_module_from_name(model, full_layer_name)

	# Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization.
	# Since weights are saved in the correct "orientation", we skip transposing when loading.
	if issubclass(module.source_cls, Conv1D):
	value = value.T

	old_value = model.get_parameter_or_buffer(full_layer_name)
	new_value = bnb.nn.Params4bit(value, requires_grad=False, **old_value.__dict__).to(value.device)
	module._is_hf_initialized = True
	return {full_layer_name: new_value}


	class Bnb4bitDeserialize(ConversionOps):
	def __init__(self, hf_quantizer):
	self.hf_quantizer = hf_quantizer

	def convert(
	self,
	input_dict: dict[str, list[torch.Tensor]],
	model: torch.nn.Module \| None = None,
	full_layer_name: str \| None = None,
	**kwargs,
	) -> dict[str, torch.Tensor]:
	"""
	Deserialization of bnb keys. We need 6 keys to recreate the quantized weights
	"""
	if len(input_dict) == 1:
	return input_dict

	for key, value in input_dict.items():
	if isinstance(value, list):
	input_dict[key] = value[0]

	key_weight = "weight"
	weight = input_dict.pop(key_weight)
	module, _ = get_module_from_name(model, full_layer_name)
	new_value = bnb.nn.Params4bit.from_prequantized(
	data=weight,
	quantized_stats=input_dict,
	requires_grad=False,
	device=weight.device,
	module=module,
	)
	module._is_hf_initialized = True
	return {key_weight: new_value}


	class Bnb8bitQuantize(ConversionOps):
	def __init__(self, hf_quantizer):
	self.hf_quantizer = hf_quantizer

	def convert(
	self,
	input_dict: dict[str, list[torch.Tensor]],
	model: torch.nn.Module \| None = None,
	full_layer_name: str \| None = None,
	**kwargs,
	) -> dict[str, torch.Tensor]:
	value = list(input_dict.values())[0]
	value = value[0] if isinstance(value, list) else value

	module, _ = get_module_from_name(model, full_layer_name)

	# Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization.
	# Since weights are saved in the correct "orientation", we skip transposing when loading.
	if issubclass(module.source_cls, Conv1D):
	value = value.T
	value_device = value.device
	kwargs = model.get_parameter_or_buffer(full_layer_name).__dict__
	kwargs.pop("SCB", None)
	new_value = bnb.nn.Int8Params(value.to("cpu"), requires_grad=False, **kwargs).to(value_device)
	return {full_layer_name: new_value}


	class Bnb8bitDeserialize(ConversionOps):
	def __init__(self, hf_quantizer):
	self.hf_quantizer = hf_quantizer

	def convert(
	self,
	input_dict: dict[str, list[torch.Tensor]],
	model: torch.nn.Module \| None = None,
	full_layer_name: str \| None = None,
	**kwargs,
	) -> dict[str, torch.Tensor]:
	"""
	Deserialization of bnb keys.
	"""
	if len(input_dict) == 1:
	# special case when we only fetched the weight
	# since we collected keys, we need to return it like that
	return input_dict

	for key, value in input_dict.items():
	if isinstance(value, list):
	input_dict[key] = value[0]

	module, _ = get_module_from_name(model, full_layer_name)

	key_weight = "weight"
	weight = input_dict[key_weight]
	kwargs = model.get_parameter_or_buffer(full_layer_name).__dict__
	kwargs["SCB"] = input_dict["SCB"]
	new_value = bnb.nn.Int8Params(weight, requires_grad=False, **kwargs).to(weight.device)
	module._is_hf_initialized = True
	return {key_weight: new_value}


	def replace_with_bnb_linear(
	model: torch.nn.Module,
	modules_to_not_convert: list[str] \| None = None,
	quantization_config=None,
	pre_quantized=False,
	):
	"""
	A helper function to replace all `torch.nn.Linear` modules by bnb modules from the `bitsandbytes` library.

	Args:
	model (`torch.nn.Module`):
	The model to convert, can be any `torch.nn.Module` instance.
	modules_to_not_convert (`list[str]`, 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 (`BitsAndBytesConfig`):
	The quantization config object that contains the quantization parameters.
	pre_quantized (`book`, defaults to `False`):
	Whether the model is pre-quantized or not
	"""
	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
	new_module = None
	with torch.device("meta"):
	if isinstance(module, Conv1D) or type(module) is nn.Linear:
	if isinstance(module, Conv1D):
	in_features, out_features = module.weight.shape
	else:
	in_features = module.in_features
	out_features = module.out_features
	if quantization_config.quantization_method() == "llm_int8":
	new_module = bnb.nn.Linear8bitLt(
	in_features,
	out_features,
	module.bias is not None,
	has_fp16_weights=quantization_config.llm_int8_has_fp16_weight,
	threshold=quantization_config.llm_int8_threshold,
	)
	if pre_quantized:
	# this is kind of an edge case when supporting both loading and quantization ...
	# we need to set the right dtype as we cast the checkpoint with the dtype of the meta model
	new_module.weight.data = new_module.weight.data.to(dtype=torch.int8)
	else:
	new_module = bnb.nn.Linear4bit(
	in_features,
	out_features,
	module.bias is not None,
	quantization_config.bnb_4bit_compute_dtype,
	compress_statistics=quantization_config.bnb_4bit_use_double_quant,
	quant_type=quantization_config.bnb_4bit_quant_type,
	quant_storage=quantization_config.bnb_4bit_quant_storage,
	)
	if pre_quantized:
	# same here
	new_module.weight.data = new_module.weight.data.to(
	dtype=quantization_config.bnb_4bit_quant_storage
	)
	if new_module is not None:
	# Store the module class in case we need to transpose the weight later
	new_module.source_cls = type(module)
	# Force requires grad to False to avoid unexpected errors
	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


	# Copied from PEFT: https://github.com/huggingface/peft/blob/47b3712898539569c02ec5b3ed4a6c36811331a1/src/peft/utils/integrations.py#L41
	def dequantize_bnb_weight(weight: "torch.nn.Parameter", state=None):
	"""
	Helper function to dequantize 4bit or 8bit bnb weights.

	If the weight is not a bnb quantized weight, it will be returned as is.
	"""
	if not isinstance(weight, torch.nn.Parameter):
	raise TypeError(f"Input weight should be of type nn.Parameter, got {type(weight)} instead")

	cls_name = weight.__class__.__name__
	if cls_name not in ("Params4bit", "Int8Params"):
	return weight

	if cls_name == "Params4bit":
	output_tensor = bnb.functional.dequantize_4bit(weight.data, weight.quant_state)
	return output_tensor

	if state.SCB is None:
	state.SCB = weight.SCB

	if hasattr(bnb.functional, "int8_vectorwise_dequant"):
	# Use bitsandbytes API if available (requires v0.45.0+)
	dequantized = bnb.functional.int8_vectorwise_dequant(weight.data, state.SCB)
	else:
	# Multiply by (scale/127) to dequantize.
	dequantized = weight.data * state.SCB.view(-1, 1) * 7.874015718698502e-3

	return dequantized


	def _create_accelerate_new_hook(old_hook):
	r"""
	Creates a new hook based on the old hook. Use it only if you know what you are doing !
	This method is a copy of: https://github.com/huggingface/peft/blob/748f7968f3a31ec06a1c2b0328993319ad9a150a/src/peft/utils/other.py#L245
	with some changes
	"""
	old_hook_cls = getattr(accelerate.hooks, old_hook.__class__.__name__)
	old_hook_attr = old_hook.__dict__
	filtered_old_hook_attr = {}
	old_hook_init_signature = inspect.signature(old_hook_cls.__init__)
	for k in old_hook_attr:
	if k in old_hook_init_signature.parameters:
	filtered_old_hook_attr[k] = old_hook_attr[k]
	new_hook = old_hook_cls(**filtered_old_hook_attr)
	return new_hook


	def dequantize_and_replace(model, quantization_config=None, dtype=None):
	"""
	Converts a quantized model into its dequantized original version. The newly converted model will have
	some performance drop compared to the original model before quantization - use it only for specific usecases
	such as QLoRA adapters merging.

	Returns the converted model.
	"""
	quant_method = quantization_config.quantization_method()

	target_cls = bnb.nn.Linear8bitLt if quant_method == "llm_int8" else bnb.nn.Linear4bit
	for module_name, module in model.named_modules():
	if isinstance(module, target_cls):
	with torch.device("meta"):
	bias = getattr(module, "bias", None)
	new_module = torch.nn.Linear(module.in_features, module.out_features, bias=bias is not None)
	state = module.state if quant_method == "llm_int8" else None
	new_module.weight = torch.nn.Parameter(dequantize_bnb_weight(module.weight, state))
	weight = dequantize_bnb_weight(module.weight, state)
	if dtype is None:
	logger.warning_once(
	f"The modules are dequantized in {weight.dtype}. If you want to change the dtype, please specify `dtype` in `dequantize`. "
	)
	else:
	logger.warning_once(f"The modules are dequantized in {weight.dtype} and casted to {dtype}.")
	weight = weight.to(dtype)
	new_module.weight = torch.nn.Parameter(weight)
	if bias is not None:
	new_module.bias = bias
	if hasattr(module, "_hf_hook"):
	old_hook = module._hf_hook
	new_hook = _create_accelerate_new_hook(old_hook)
	remove_hook_from_module(module)
	add_hook_to_module(new_module, new_hook)
	new_module.to(module.weight.device)
	model.set_submodule(module_name, new_module)
	has_been_replaced = True

	if not has_been_replaced:
	logger.warning(
	"For some reason the model has not been properly dequantized. You might see unexpected behavior."
	)
	return model


	def validate_bnb_backend_availability(raise_exception=False):
	"""
	Validates if the available devices are supported by bitsandbytes, optionally raising an exception if not.
	"""
	bnb_supported_devices = getattr(bnb, "supported_torch_devices", set())
	available_devices = set(get_available_devices())

	if not available_devices.intersection(bnb_supported_devices):
	if raise_exception:
	err_msg = (
	f"None of the available devices `available_devices = {available_devices or None}` are supported by the bitsandbytes version you have installed: `bnb_supported_devices = {bnb_supported_devices}`. "
	"Please check the docs to see if the backend you intend to use is available and how to install it: https://huggingface.co/docs/bitsandbytes/main/en/installation"
	)
	raise RuntimeError(err_msg)

	logger.warning("No supported devices found for bitsandbytes")
	return False
	return True