class BnB8BitDiffusersQuantizer(DiffusersQuantizer):
    """
    8-bit quantization from bitsandbytes quantization method:
        before loading: converts transformer layers into Linear8bitLt during loading: load 16bit weight and pass to the
        layer object after: quantizes individual weights in Linear8bitLt into 8bit at fitst .cuda() call
    saving:
        from state dict, as usual; saves weights and 'SCB' component
    loading:
        need to locate SCB component and pass to the Linear8bitLt object
    """

    use_keep_in_fp32_modules = True
    requires_calibration = False

    def __init__(self, quantization_config, **kwargs):
        super().__init__(quantization_config, **kwargs)

        if self.quantization_config.llm_int8_skip_modules is not None:
            self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules

    def validate_environment(self, *args, **kwargs):
        if not torch.cuda.is_available():
            raise RuntimeError("No GPU found. A GPU is needed for quantization.")
        if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
            raise ImportError(
                "Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install 'accelerate>=0.26.0'`"
            )
        if not is_bitsandbytes_available() or is_bitsandbytes_version("<", "0.43.3"):
            raise ImportError(
                "Using `bitsandbytes` 8-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
            )

        if kwargs.get("from_flax", False):
            raise ValueError(
                "Converting into 8-bit weights from flax weights is currently not supported, please make"
                " sure the weights are in PyTorch format."
            )

        device_map = kwargs.get("device_map", None)
        if (
            device_map is not None
            and isinstance(device_map, dict)
            and not self.quantization_config.llm_int8_enable_fp32_cpu_offload
        ):
            device_map_without_no_convert = {
                key: device_map[key] for key in device_map.keys() if key not in self.modules_to_not_convert
            }
            if "cpu" in device_map_without_no_convert.values() or "disk" in device_map_without_no_convert.values():
                raise ValueError(
                    "Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the "
                    "quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules "
                    "in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to "
                    "`from_pretrained`. Check "
                    "https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu "
                    "for more details. "
                )

    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.adjust_max_memory
    def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
        # need more space for buffers that are created during quantization
        max_memory = {key: val * 0.90 for key, val in max_memory.items()}
        return max_memory

    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.update_torch_dtype
    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
        if torch_dtype is None:
            # We force the `dtype` to be float16, this is a requirement from `bitsandbytes`
            logger.info(
                "Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
                "requirements of `bitsandbytes` to enable model loading in 8-bit or 4-bit. "
                "Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
                " torch_dtype=torch.float16 to remove this warning.",
                torch_dtype,
            )
            torch_dtype = torch.float16
        return torch_dtype

    # # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.update_device_map
    # def update_device_map(self, device_map):
    #     if device_map is None:
    #         device_map = {"": torch.cuda.current_device()}
    #         logger.info(
    #             "The device_map was not initialized. "
    #             "Setting device_map to {'':torch.cuda.current_device()}. "
    #             "If you want to use the model for inference, please set device_map ='auto' "
    #         )
    #     return device_map

    def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
        if target_dtype != torch.int8:
            logger.info("target_dtype {target_dtype} is replaced by `torch.int8` for 8-bit BnB quantization")
        return torch.int8

    def check_if_quantized_param(
        self,
        model: "ModelMixin",
        param_value: "torch.Tensor",
        param_name: str,
        state_dict: Dict[str, Any],
        **kwargs,
    ):
        import bitsandbytes as bnb

        module, tensor_name = get_module_from_name(model, param_name)
        if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Int8Params):
            if self.pre_quantized:
                if param_name.replace("weight", "SCB") not in state_dict.keys():
                    raise ValueError("Missing quantization component `SCB`")
                if param_value.dtype != torch.int8:
                    raise ValueError(
                        f"Incompatible dtype `{param_value.dtype}` when loading 8-bit prequantized weight. Expected `torch.int8`."
                    )
            return True
        return False

    def create_quantized_param(
        self,
        model: "ModelMixin",
        param_value: "torch.Tensor",
        param_name: str,
        target_device: "torch.device",
        state_dict: Dict[str, Any],
        unexpected_keys: Optional[List[str]] = None,
    ):
        import bitsandbytes as bnb

        fp16_statistics_key = param_name.replace("weight", "SCB")
        fp16_weights_format_key = param_name.replace("weight", "weight_format")

        fp16_statistics = state_dict.get(fp16_statistics_key, None)
        fp16_weights_format = state_dict.get(fp16_weights_format_key, None)

        module, tensor_name = get_module_from_name(model, param_name)
        if tensor_name not in module._parameters:
            raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")

        old_value = getattr(module, tensor_name)

        if not isinstance(module._parameters[tensor_name], bnb.nn.Int8Params):
            raise ValueError(f"Parameter `{tensor_name}` should only be a `bnb.nn.Int8Params` instance.")
        if (
            old_value.device == torch.device("meta")
            and target_device not in ["meta", torch.device("meta")]
            and param_value is None
        ):
            raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {target_device}.")

        new_value = param_value.to("cpu")
        if self.pre_quantized and not self.is_serializable:
            raise ValueError(
                "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
                "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
            )

        kwargs = old_value.__dict__
        new_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(target_device)

        module._parameters[tensor_name] = new_value
        if fp16_statistics is not None:
            setattr(module.weight, "SCB", fp16_statistics.to(target_device))
            if unexpected_keys is not None:
                unexpected_keys.remove(fp16_statistics_key)

        # We just need to pop the `weight_format` keys from the state dict to remove unneeded
        # messages. The correct format is correctly retrieved during the first forward pass.
        if fp16_weights_format is not None and unexpected_keys is not None:
            unexpected_keys.remove(fp16_weights_format_key)

    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer._process_model_after_weight_loading with 4bit->8bit
    def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
        model.is_loaded_in_8bit = True
        model.is_8bit_serializable = self.is_serializable
        return model

    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer._process_model_before_weight_loading
    def _process_model_before_weight_loading(
        self,
        model: "ModelMixin",
        device_map,
        keep_in_fp32_modules: List[str] = [],
        **kwargs,
    ):
        from .utils import replace_with_bnb_linear

        load_in_8bit_fp32_cpu_offload = self.quantization_config.llm_int8_enable_fp32_cpu_offload

        # We may keep some modules such as the `proj_out` in their original dtype for numerical stability reasons
        self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules

        if not isinstance(self.modules_to_not_convert, list):
            self.modules_to_not_convert = [self.modules_to_not_convert]

        self.modules_to_not_convert.extend(keep_in_fp32_modules)

        # Extend `self.modules_to_not_convert` to keys that are supposed to be offloaded to `cpu` or `disk`
        if isinstance(device_map, dict) and len(device_map.keys()) > 1:
            keys_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]]

            if len(keys_on_cpu) > 0 and not load_in_8bit_fp32_cpu_offload:
                raise ValueError(
                    "If you want to offload some keys to `cpu` or `disk`, you need to set "
                    "`llm_int8_enable_fp32_cpu_offload=True`. Note that these modules will not be "
                    " converted to 8-bit but kept in 32-bit."
                )
            self.modules_to_not_convert.extend(keys_on_cpu)

        # Purge `None`.
        # Unlike `transformers`, we don't know if we should always keep certain modules in FP32
        # in case of diffusion transformer models. For language models and others alike, `lm_head`
        # and tied modules are usually kept in FP32.
        self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]

        model = replace_with_bnb_linear(
            model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config
        )
        model.config.quantization_config = self.quantization_config

    @property
    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.is_serializable
    def is_serializable(self):
        # Because we're mandating `bitsandbytes` 0.43.3.
        return True

    @property
    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.is_serializable
    def is_trainable(self) -> bool:
        # Because we're mandating `bitsandbytes` 0.43.3.
        return True

    def _dequantize(self, model):
        from .utils import dequantize_and_replace

        model = dequantize_and_replace(
            model, self.modules_to_not_convert, quantization_config=self.quantization_config
        )
        return model
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py