diffusers/quantizers/quantization_config.py

#!/usr/bin/env python
# coding=utf-8

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Adapted from
https://github.com/huggingface/transformers/blob/52cb4034ada381fe1ffe8d428a1076e5411a8026/src/transformers/utils/quantization_config.py
"""

from __future__ import annotations

import copy
import importlib.metadata
import json
import os
import warnings
from dataclasses import dataclass
from enum import Enum
from typing import Any, Callable

from packaging import version

from ..utils import deprecate, is_torch_available, is_torchao_version, logging


if is_torch_available():
    import torch

logger = logging.get_logger(__name__)


class QuantizationMethod(str, Enum):
    BITS_AND_BYTES = "bitsandbytes"
    GGUF = "gguf"
    NUNCHAKU_LITE = "nunchaku_lite"
    TORCHAO = "torchao"
    QUANTO = "quanto"
    MODELOPT = "modelopt"
    AUTOROUND = "auto-round"
    SDNQ = "sdnq"


@dataclass
class QuantizationConfigMixin:
    """
    Mixin class for quantization config
    """

    quant_method: QuantizationMethod
    _exclude_attributes_at_init = []

    @classmethod
    def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
        """
        Instantiates a [`QuantizationConfigMixin`] from a Python dictionary of parameters.

        Args:
            config_dict (`dict[str, Any]`):
                Dictionary that will be used to instantiate the configuration object.
            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                Whether or not to return a list of unused keyword arguments. Used for `from_pretrained` method in
                `PreTrainedModel`.
            kwargs (`dict[str, Any]`):
                Additional parameters from which to initialize the configuration object.

        Returns:
            [`QuantizationConfigMixin`]: The configuration object instantiated from those parameters.
        """

        config = cls(**config_dict)

        to_remove = []
        for key, value in kwargs.items():
            if hasattr(config, key):
                setattr(config, key, value)
                to_remove.append(key)
        for key in to_remove:
            kwargs.pop(key, None)

        if return_unused_kwargs:
            return config, kwargs
        else:
            return config

    def to_json_file(self, json_file_path: str | os.PathLike):
        """
        Save this instance to a JSON file.

        Args:
            json_file_path (`str` or `os.PathLike`):
                Path to the JSON file in which this configuration instance's parameters will be saved.
            use_diff (`bool`, *optional*, defaults to `True`):
                If set to `True`, only the difference between the config instance and the default
                `QuantizationConfig()` is serialized to JSON file.
        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            config_dict = self.to_dict()
            json_string = json.dumps(config_dict, indent=2, sort_keys=True) + "\n"

            writer.write(json_string)

    def to_dict(self) -> dict[str, Any]:
        """
        Serializes this instance to a Python dictionary. Returns:
            `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
        """
        return copy.deepcopy(self.__dict__)

    def __iter__(self):
        """allows `dict(obj)` for situations where obj may be a dict or QuantizationConfigMixin"""
        for attr, value in copy.deepcopy(self.__dict__).items():
            yield attr, value

    def __repr__(self):
        return f"{self.__class__.__name__} {self.to_json_string()}"

    def to_json_string(self, use_diff: bool = True) -> str:
        """
        Serializes this instance to a JSON string.

        Args:
            use_diff (`bool`, *optional*, defaults to `True`):
                If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`
                is serialized to JSON string.

        Returns:
            `str`: String containing all the attributes that make up this configuration instance in JSON format.
        """
        if use_diff is True:
            config_dict = self.to_diff_dict()
        else:
            config_dict = self.to_dict()
        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"

    def update(self, **kwargs):
        """
        Updates attributes of this class instance with attributes from `kwargs` if they match existing attributes,
        returning all the unused kwargs.

        Args:
            kwargs (`dict[str, Any]`):
                Dictionary of attributes to tentatively update this class.

        Returns:
            `dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
        """
        to_remove = []
        for key, value in kwargs.items():
            if hasattr(self, key):
                setattr(self, key, value)
                to_remove.append(key)

        # Remove all the attributes that were updated, without modifying the input dict
        unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
        return unused_kwargs


@dataclass
class BitsAndBytesConfig(QuantizationConfigMixin):
    """
    This is a wrapper class about all possible attributes and features that you can play with a model that has been
    loaded using `bitsandbytes`.

    This replaces `load_in_8bit` or `load_in_4bit` therefore both options are mutually exclusive.

    Currently only supports `LLM.int8()`, `FP4`, and `NF4` quantization. If more methods are added to `bitsandbytes`,
    then more arguments will be added to this class.

    Args:
        load_in_8bit (`bool`, *optional*, defaults to `False`):
            This flag is used to enable 8-bit quantization with LLM.int8().
        load_in_4bit (`bool`, *optional*, defaults to `False`):
            This flag is used to enable 4-bit quantization by replacing the Linear layers with FP4/NF4 layers from
            `bitsandbytes`.
        llm_int8_threshold (`float`, *optional*, defaults to 6.0):
            This corresponds to the outlier threshold for outlier detection as described in `LLM.int8() : 8-bit Matrix
            Multiplication for Transformers at Scale` paper: https://huggingface.co/papers/2208.07339 Any hidden states
            value that is above this threshold will be considered an outlier and the operation on those values will be
            done in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5],
            but there are some exceptional systematic outliers that are very differently distributed for large models.
            These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of
            magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6,
            but a lower threshold might be needed for more unstable models (small models, fine-tuning).
        llm_int8_skip_modules (`list[str]`, *optional*):
            An explicit list of the modules that we do not want to convert in 8-bit. This is useful for models such as
            Jukebox that has several heads in different places and not necessarily at the last position. For example
            for `CausalLM` models, the last `lm_head` is typically kept in its original `dtype`.
        llm_int8_enable_fp32_cpu_offload (`bool`, *optional*, defaults to `False`):
            This flag is used for advanced use cases and users that are aware of this feature. If you want to split
            your model in different parts and run some parts in int8 on GPU and some parts in fp32 on CPU, you can use
            this flag. This is useful for offloading large models such as `google/flan-t5-xxl`. Note that the int8
            operations will not be run on CPU.
        llm_int8_has_fp16_weight (`bool`, *optional*, defaults to `False`):
            This flag runs LLM.int8() with 16-bit main weights. This is useful for fine-tuning as the weights do not
            have to be converted back and forth for the backward pass.
        bnb_4bit_compute_dtype (`torch.dtype` or str, *optional*, defaults to `torch.float32`):
            This sets the computational type which might be different than the input type. For example, inputs might be
            fp32, but computation can be set to bf16 for speedups.
        bnb_4bit_quant_type (`str`,  *optional*, defaults to `"fp4"`):
            This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types
            which are specified by `fp4` or `nf4`.
        bnb_4bit_use_double_quant (`bool`, *optional*, defaults to `False`):
            This flag is used for nested quantization where the quantization constants from the first quantization are
            quantized again.
        bnb_4bit_quant_storage (`torch.dtype` or str, *optional*, defaults to `torch.uint8`):
            This sets the storage type to pack the quanitzed 4-bit prarams.
        kwargs (`dict[str, Any]`, *optional*):
            Additional parameters from which to initialize the configuration object.
    """

    _exclude_attributes_at_init = ["_load_in_4bit", "_load_in_8bit", "quant_method"]

    def __init__(
        self,
        load_in_8bit=False,
        load_in_4bit=False,
        llm_int8_threshold=6.0,
        llm_int8_skip_modules=None,
        llm_int8_enable_fp32_cpu_offload=False,
        llm_int8_has_fp16_weight=False,
        bnb_4bit_compute_dtype=None,
        bnb_4bit_quant_type="fp4",
        bnb_4bit_use_double_quant=False,
        bnb_4bit_quant_storage=None,
        **kwargs,
    ):
        self.quant_method = QuantizationMethod.BITS_AND_BYTES

        if load_in_4bit and load_in_8bit:
            raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")

        self._load_in_8bit = load_in_8bit
        self._load_in_4bit = load_in_4bit
        self.llm_int8_threshold = llm_int8_threshold
        self.llm_int8_skip_modules = llm_int8_skip_modules
        self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload
        self.llm_int8_has_fp16_weight = llm_int8_has_fp16_weight
        self.bnb_4bit_quant_type = bnb_4bit_quant_type
        self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant

        if bnb_4bit_compute_dtype is None:
            self.bnb_4bit_compute_dtype = torch.float32
        elif isinstance(bnb_4bit_compute_dtype, str):
            self.bnb_4bit_compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
        elif isinstance(bnb_4bit_compute_dtype, torch.dtype):
            self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype
        else:
            raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype")

        if bnb_4bit_quant_storage is None:
            self.bnb_4bit_quant_storage = torch.uint8
        elif isinstance(bnb_4bit_quant_storage, str):
            if bnb_4bit_quant_storage not in [
                "float16",
                "float32",
                "int8",
                "uint8",
                "float64",
                "bfloat16",
            ]:
                raise ValueError(
                    "`bnb_4bit_quant_storage` must be a valid string (one of 'float16', 'float32', 'int8', 'uint8', 'float64', 'bfloat16') "
                )
            self.bnb_4bit_quant_storage = getattr(torch, bnb_4bit_quant_storage)
        elif isinstance(bnb_4bit_quant_storage, torch.dtype):
            self.bnb_4bit_quant_storage = bnb_4bit_quant_storage
        else:
            raise ValueError("bnb_4bit_quant_storage must be a string or a torch.dtype")

        if kwargs and not all(k in self._exclude_attributes_at_init for k in kwargs):
            logger.warning(f"Unused kwargs: {list(kwargs.keys())}. These kwargs are not used in {self.__class__}.")

        self.post_init()

    @property
    def load_in_4bit(self):
        return self._load_in_4bit

    @load_in_4bit.setter
    def load_in_4bit(self, value: bool):
        if not isinstance(value, bool):
            raise TypeError("load_in_4bit must be a boolean")

        if self.load_in_8bit and value:
            raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
        self._load_in_4bit = value

    @property
    def load_in_8bit(self):
        return self._load_in_8bit

    @load_in_8bit.setter
    def load_in_8bit(self, value: bool):
        if not isinstance(value, bool):
            raise TypeError("load_in_8bit must be a boolean")

        if self.load_in_4bit and value:
            raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
        self._load_in_8bit = value

    def post_init(self):
        r"""
        Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
        """
        if not isinstance(self.load_in_4bit, bool):
            raise TypeError("load_in_4bit must be a boolean")

        if not isinstance(self.load_in_8bit, bool):
            raise TypeError("load_in_8bit must be a boolean")

        if not isinstance(self.llm_int8_threshold, float):
            raise TypeError("llm_int8_threshold must be a float")

        if self.llm_int8_skip_modules is not None and not isinstance(self.llm_int8_skip_modules, list):
            raise TypeError("llm_int8_skip_modules must be a list of strings")
        if not isinstance(self.llm_int8_enable_fp32_cpu_offload, bool):
            raise TypeError("llm_int8_enable_fp32_cpu_offload must be a boolean")

        if not isinstance(self.llm_int8_has_fp16_weight, bool):
            raise TypeError("llm_int8_has_fp16_weight must be a boolean")

        if self.bnb_4bit_compute_dtype is not None and not isinstance(self.bnb_4bit_compute_dtype, torch.dtype):
            raise TypeError("bnb_4bit_compute_dtype must be torch.dtype")

        if not isinstance(self.bnb_4bit_quant_type, str):
            raise TypeError("bnb_4bit_quant_type must be a string")

        if not isinstance(self.bnb_4bit_use_double_quant, bool):
            raise TypeError("bnb_4bit_use_double_quant must be a boolean")

        if self.load_in_4bit and not version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse(
            "0.39.0"
        ):
            raise ValueError(
                "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"
            )

    def is_quantizable(self):
        r"""
        Returns `True` if the model is quantizable, `False` otherwise.
        """
        return self.load_in_8bit or self.load_in_4bit

    def quantization_method(self):
        r"""
        This method returns the quantization method used for the model. If the model is not quantizable, it returns
        `None`.
        """
        if self.load_in_8bit:
            return "llm_int8"
        elif self.load_in_4bit and self.bnb_4bit_quant_type == "fp4":
            return "fp4"
        elif self.load_in_4bit and self.bnb_4bit_quant_type == "nf4":
            return "nf4"
        else:
            return None

    def to_dict(self) -> dict[str, Any]:
        """
        Serializes this instance to a Python dictionary. Returns:
            `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
        """
        output = copy.deepcopy(self.__dict__)
        output["bnb_4bit_compute_dtype"] = str(output["bnb_4bit_compute_dtype"]).split(".")[1]
        output["bnb_4bit_quant_storage"] = str(output["bnb_4bit_quant_storage"]).split(".")[1]
        output["load_in_4bit"] = self.load_in_4bit
        output["load_in_8bit"] = self.load_in_8bit

        return output

    def __repr__(self):
        config_dict = self.to_dict()
        return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"

    def to_diff_dict(self) -> dict[str, Any]:
        """
        Removes all attributes from config which correspond to the default config attributes for better readability and
        serializes to a Python dictionary.

        Returns:
            `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        config_dict = self.to_dict()

        # get the default config dict
        default_config_dict = BitsAndBytesConfig().to_dict()

        serializable_config_dict = {}

        # only serialize values that differ from the default config
        for key, value in config_dict.items():
            if value != default_config_dict[key]:
                serializable_config_dict[key] = value

        return serializable_config_dict


@dataclass
class GGUFQuantizationConfig(QuantizationConfigMixin):
    """This is a config class for GGUF Quantization techniques.

    Args:
        compute_dtype: (`torch.dtype`, defaults to `torch.float32`):
            This sets the computational type which might be different than the input type. For example, inputs might be
            fp32, but computation can be set to bf16 for speedups.

    """

    def __init__(self, compute_dtype: "torch.dtype" | None = None):
        self.quant_method = QuantizationMethod.GGUF
        self.compute_dtype = compute_dtype
        self.pre_quantized = True

        # TODO: (Dhruv) Add this as an init argument when we can support loading unquantized checkpoints.
        self.modules_to_not_convert = None

        if self.compute_dtype is None:
            self.compute_dtype = torch.float32


@dataclass
class NunchakuLiteQuantizationConfig(QuantizationConfigMixin):
    """Configuration for loading Nunchaku Lite checkpoints.

    Nunchaku Lite support in Diffusers loads prequantized checkpoints. To create a compatible checkpoint, use
    [`diffuse-compressor`](https://github.com/rootonchair/diffuse-compressor) to choose or adapt a target configuration
    for the model architecture, quantize and export the transformer, and package it as a Diffusers pipeline with the
    compact `quantization_config` stored in `config.json`.

    The exported state dict must match the target Diffusers model architecture exactly. Checkpoints quantized with
    fused QKV projections won't load into a model config that expects separate Q, K, and V projection modules.

    Example compact `config.json` config:

    ```json
    {
      "_class_name": "ErnieImageTransformer2DModel",
      "quantization_config": {
        "quant_method": "nunchaku_lite",
        "compute_dtype": "bfloat16",
        "svdq_w4a4": {
          "precision": "nvfp4",
          "group_size": 16,
          "rank": 32,
          "targets": ["layers.0.self_attention.to_q"]
        },
        "awq_w4a16": {
          "precision": "int4",
          "group_size": 64,
          "targets": ["final_linear"]
        }
      }
    }
    ```

    Args:
        compute_dtype (`torch.dtype`, defaults to `torch.bfloat16`):
            Runtime dtype used by the floating-point buffers in the quantized modules.
        svdq_w4a4 (`dict`, *optional*):
            Explicit SVDQ W4A4 target configuration with `precision`, `group_size`, `rank`, and `targets`.
        awq_w4a16 (`dict`, *optional*):
            Explicit AWQ W4A16 target configuration with `precision`, `group_size`, and `targets`.
    """

    def __init__(
        self,
        compute_dtype: "torch.dtype" | str | None = None,
        svdq_w4a4: dict[str, Any] | None = None,
        awq_w4a16: dict[str, Any] | None = None,
        **kwargs,
    ):
        self.quant_method = QuantizationMethod.NUNCHAKU_LITE
        if compute_dtype is None:
            compute_dtype = torch.bfloat16
        if isinstance(compute_dtype, str):
            if not hasattr(torch, compute_dtype):
                raise ValueError(f"Unsupported Nunchaku compute dtype: {compute_dtype!r}.")
            compute_dtype = getattr(torch, compute_dtype)
        if not isinstance(compute_dtype, torch.dtype):
            raise ValueError("Nunchaku compute_dtype must be a string or a torch.dtype.")
        self.compute_dtype = compute_dtype
        self.pre_quantized = True
        self.svdq_w4a4 = svdq_w4a4
        self.awq_w4a16 = awq_w4a16

        self.post_init()

    def post_init(self):
        if self.svdq_w4a4 is None and self.awq_w4a16 is None:
            raise ValueError(
                "Nunchaku compact quantization config must include `svdq_w4a4.targets` or `awq_w4a16.targets`."
            )

        for op, raw in (("svdq_w4a4", self.svdq_w4a4), ("awq_w4a16", self.awq_w4a16)):
            if raw is None:
                continue
            if not isinstance(raw, dict):
                raise ValueError(f"Nunchaku compact config section {op!r} must be a JSON object.")

            for key, expected_type in (("precision", str), ("group_size", int), ("targets", list)):
                if key not in raw:
                    raise ValueError(f"Nunchaku compact config section {op!r} is missing required field {key!r}.")
                if not isinstance(raw[key], expected_type):
                    raise ValueError(
                        f"Nunchaku compact config section {op!r} field {key!r} must be {expected_type.__name__}."
                    )

            precision = raw["precision"]
            group_size = raw["group_size"]
            targets = raw["targets"]
            if precision not in ("int4", "nvfp4"):
                raise ValueError(f"Unsupported Nunchaku precision {precision!r} for {op!r}.")
            if group_size <= 0:
                raise ValueError(f"Nunchaku compact config section {op!r} must have positive group_size.")
            if not targets:
                raise ValueError(f"Nunchaku compact config section {op!r} must contain at least one target.")
            if not all(isinstance(target, str) for target in targets):
                raise ValueError(f"Nunchaku compact config section {op!r} targets must be strings.")

            if op == "svdq_w4a4":
                if "rank" not in raw:
                    raise ValueError(f"Nunchaku compact config section {op!r} is missing required field 'rank'.")
                if not isinstance(raw["rank"], int):
                    raise ValueError(f"Nunchaku compact config section {op!r} field 'rank' must be int.")
                if raw["rank"] < 0:
                    raise ValueError(f"Nunchaku compact config section {op!r} must have non-negative rank.")
                expected_group_size = 16 if precision == "nvfp4" else 64
                if group_size != expected_group_size:
                    raise ValueError(
                        f"Nunchaku SVDQ config with precision={precision!r} requires "
                        f"group_size={expected_group_size}, got {group_size}."
                    )
            elif precision != "int4":
                raise ValueError("Nunchaku AWQ target requires precision='int4'.")

    def to_dict(self) -> dict[str, Any]:
        output = super().to_dict()
        output["compute_dtype"] = str(output["compute_dtype"]).split(".")[1]
        return output


@dataclass
class TorchAoConfig(QuantizationConfigMixin):
    """This is a config class for torchao quantization/sparsity techniques.

    Args:
        quant_type (`AOBaseConfig`):
            An `AOBaseConfig` subclass instance specifying the quantization type. See the [torchao
            documentation](https://docs.pytorch.org/ao/main/api_ref_quantization.html#inference-apis-for-quantize) for
            available config classes (e.g. `Int4WeightOnlyConfig`, `Int8WeightOnlyConfig`, `Float8WeightOnlyConfig`,
            `Float8DynamicActivationFloat8WeightConfig`, etc.).
        modules_to_not_convert (`list[str]`, *optional*, default to `None`):
            The list of modules to not quantize, useful for quantizing models that explicitly require to have some
            modules left in their original precision.

    Example:
        ```python
        from diffusers import FluxTransformer2DModel, TorchAoConfig
        from torchao.quantization import Int8WeightOnlyConfig

        quantization_config = TorchAoConfig(Int8WeightOnlyConfig())
        transformer = FluxTransformer2DModel.from_pretrained(
            "black-forest-labs/Flux.1-Dev",
            subfolder="transformer",
            quantization_config=quantization_config,
            torch_dtype=torch.bfloat16,
        )
        ```
    """

    def __init__(
        self,
        quant_type: "AOBaseConfig",  # noqa: F821
        modules_to_not_convert: list[str] | None = None,
        **kwargs,
    ) -> None:
        self.quant_method = QuantizationMethod.TORCHAO
        self.quant_type = quant_type
        self.modules_to_not_convert = modules_to_not_convert

        self.post_init()

    def post_init(self):
        if is_torchao_version("<", "0.15.0"):
            raise ValueError("TorchAoConfig requires torchao >= 0.15.0. Please upgrade with `pip install -U torchao`.")

        from torchao.quantization.quant_api import AOBaseConfig

        if not isinstance(self.quant_type, AOBaseConfig):
            raise TypeError(f"quant_type must be an AOBaseConfig instance, got {type(self.quant_type).__name__}")

    def to_dict(self):
        """Convert configuration to a dictionary."""
        d = super().to_dict()

        # Handle AOBaseConfig serialization
        from torchao.core.config import config_to_dict

        # For now we assume there is 1 config per Transformer, however in the future
        # we may want to support a config per fqn.
        # See: https://docs.pytorch.org/ao/stable/api_reference/generated/torchao.quantization.quantize_.html
        d["quant_type"] = {"default": config_to_dict(self.quant_type)}

        return d

    @classmethod
    def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
        """Create configuration from a dictionary."""
        if not is_torchao_version(">=", "0.15.0"):
            raise NotImplementedError("TorchAoConfig requires torchao >= 0.15.0 for construction from dict")
        config_dict = config_dict.copy()
        quant_type = config_dict.pop("quant_type")

        # Check if we only have one key which is "default"
        # In the future we may update this
        assert len(quant_type) == 1 and "default" in quant_type, (
            "Expected only one key 'default' in quant_type dictionary"
        )
        quant_type = quant_type["default"]

        # Deserialize quant_type if needed
        from torchao.core.config import config_from_dict

        quant_type = config_from_dict(quant_type)

        return cls(quant_type=quant_type, **config_dict)

    def get_apply_tensor_subclass(self):
        """Create the appropriate quantization method based on configuration."""
        return self.quant_type

    def __repr__(self):
        config_dict = self.to_dict()
        return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"


@dataclass
class QuantoConfig(QuantizationConfigMixin):
    """
    This is a wrapper class about all possible attributes and features that you can play with a model that has been
    loaded using `quanto`.

    Args:
        weights_dtype (`str`, *optional*, defaults to `"int8"`):
            The target dtype for the weights after quantization. Supported values are ("float8","int8","int4","int2")
       modules_to_not_convert (`list`, *optional*, default to `None`):
            The list of modules to not quantize, useful for quantizing models that explicitly require to have some
            modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
    """

    def __init__(
        self,
        weights_dtype: str = "int8",
        modules_to_not_convert: list[str] | None = None,
        **kwargs,
    ):
        deprecation_message = "`QuantoConfig` is deprecated and will be removed in version 1.0.0."
        deprecate("QuantoConfig", "1.0.0", deprecation_message)
        self.quant_method = QuantizationMethod.QUANTO
        self.weights_dtype = weights_dtype
        self.modules_to_not_convert = modules_to_not_convert

        self.post_init()

    def post_init(self):
        r"""
        Safety checker that arguments are correct
        """
        accepted_weights = ["float8", "int8", "int4", "int2"]
        if self.weights_dtype not in accepted_weights:
            raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights_dtype}")


@dataclass
class NVIDIAModelOptConfig(QuantizationConfigMixin):
    """This is a config class to use nvidia modelopt for quantization.

    Args:
        quant_type (`str`):
            The type of quantization we want to use, following is how to use:
                **weightquant_activationquant ==> FP8_FP8** In the above example we have use FP8 for both weight and
                activation quantization. Following are the all the options:
                    - FP8
                    - INT8
                    - INT4
                    - NF4
                    - NVFP4
        modules_to_not_convert (`list[str]`, *optional*, default to `None`):
            The list of modules to not quantize, useful for quantizing models that explicitly require to have some
        weight_only (`bool`, *optional*, default to `False`):
            If set to `True`, the quantization will be applied only to the weights of the model.
        channel_quantize (`int`, *optional*, default to `None`):
            The channel quantization axis, useful for quantizing models across different axes.
        block_quantize (`int`, *optional*, default to `None`):
            The block size, useful to further quantize each channel/axes into blocks.
        scale_channel_quantize (`int`, *optional*, default to `None`):
            The scale channel quantization axis, useful for quantizing calculated scale across different axes.
        scale_block_quantize (`int`, *optional*, default to `None`):
            The scale block size, useful for quantizing each scale channel/axes into blocks.
        algorithm (`str`, *optional*, default to `"max"`):
            The algorithm to use for quantization, currently only supports `"max"`.
        forward_loop (`Callable`, *optional*, default to `None`):
            The forward loop function to use for calibration during quantization.
        modelopt_config (`dict`, *optional*, default to `None`):
            The modelopt config, useful for passing custom configs to modelopt.
        disable_conv_quantization (`bool`, *optional*, default to `False`):
            If set to `True`, the quantization will be disabled for convolutional layers.
        kwargs (`dict[str, Any]`, *optional*):
            Additional parameters which are to be used for calibration.
    """

    quanttype_to_numbits = {
        "FP8": (4, 3),
        "INT8": 8,
        "INT4": 4,
        "NF4": 4,
        "NVFP4": (2, 1),
    }
    quanttype_to_scalingbits = {
        "NF4": 8,
        "NVFP4": (4, 3),
    }

    def __init__(
        self,
        quant_type: str,
        modules_to_not_convert: list[str] | None = None,
        weight_only: bool = True,
        channel_quantize: int | None = None,
        block_quantize: int | None = None,
        scale_channel_quantize: int | None = None,
        scale_block_quantize: int | None = None,
        algorithm: str = "max",
        forward_loop: Callable | None = None,
        modelopt_config: dict | None = None,
        disable_conv_quantization: bool = False,
        **kwargs,
    ) -> None:
        self.quant_method = QuantizationMethod.MODELOPT
        self._normalize_quant_type(quant_type)
        self.modules_to_not_convert = modules_to_not_convert
        self.weight_only = weight_only
        self.channel_quantize = channel_quantize
        self.block_quantize = block_quantize
        self.calib_cfg = {
            "method": algorithm,
            # add more options here if needed
        }
        self.forward_loop = forward_loop
        self.scale_channel_quantize = scale_channel_quantize
        self.scale_block_quantize = scale_block_quantize
        self.modelopt_config = self.get_config_from_quant_type() if not modelopt_config else modelopt_config
        self.disable_conv_quantization = disable_conv_quantization

    def check_model_patching(self, operation: str = "loading"):
        # ModelOpt imports diffusers internally. This is here to prevent circular imports
        from modelopt.torch.opt.plugins.huggingface import _PATCHED_CLASSES

        if len(_PATCHED_CLASSES) == 0:
            warning_msg = (
                f"Not {operation} weights in modelopt format. This might cause unreliable behavior."
                "Please make sure to run the following code before loading/saving model weights:\n\n"
                "    from modelopt.torch.opt import enable_huggingface_checkpointing\n"
                "    enable_huggingface_checkpointing()\n"
            )
            warnings.warn(warning_msg)

    def _normalize_quant_type(self, quant_type: str) -> str:
        """
        Validates and normalizes the quantization type string.

        Splits the quant_type into weight and activation components, verifies them against supported types, and
        replaces unsupported values with safe defaults.

        Args:
            quant_type (str): The input quantization type string (e.g., 'FP8_INT8').

        Returns:
            str: A valid quantization type string (e.g., 'FP8_INT8' or 'FP8').
        """
        parts = quant_type.split("_")
        w_type = parts[0]
        act_type = parts[1] if len(parts) > 1 else None
        if len(parts) > 2:
            logger.warning(f"Quantization type {quant_type} is not supported. Picking FP8_INT8 as default")
            w_type = "FP8"
            act_type = None
        else:
            if w_type not in NVIDIAModelOptConfig.quanttype_to_numbits:
                logger.warning(f"Weight Quantization type {w_type} is not supported. Picking FP8 as default")
                w_type = "FP8"
            if act_type is not None and act_type not in NVIDIAModelOptConfig.quanttype_to_numbits:
                logger.warning(f"Activation Quantization type {act_type} is not supported. Picking INT8 as default")
                act_type = None
        self.quant_type = w_type + ("_" + act_type if act_type is not None else "")

    def get_config_from_quant_type(self) -> dict[str, Any]:
        """
        Get the config from the quantization type.
        """
        import modelopt.torch.quantization as mtq

        BASE_CONFIG = {
            "quant_cfg": {
                "*weight_quantizer": {"fake_quant": False},
                "*input_quantizer": {},
                "*output_quantizer": {"enable": False},
                "*q_bmm_quantizer": {},
                "*k_bmm_quantizer": {},
                "*v_bmm_quantizer": {},
                "*softmax_quantizer": {},
                **mtq.config._default_disabled_quantizer_cfg,
            },
            "algorithm": self.calib_cfg,
        }

        quant_cfg = BASE_CONFIG["quant_cfg"]
        if self.weight_only:
            for k in quant_cfg:
                if "*weight_quantizer" not in k and not quant_cfg[k]:
                    quant_cfg[k]["enable"] = False

        parts = self.quant_type.split("_")
        w_type = parts[0]
        act_type = parts[1].replace("A", "") if len(parts) > 1 else None
        for k in quant_cfg:
            if k not in mtq.config._default_disabled_quantizer_cfg and "enable" not in quant_cfg[k]:
                if k == "*input_quantizer":
                    if act_type is not None:
                        quant_cfg[k]["num_bits"] = NVIDIAModelOptConfig.quanttype_to_numbits[act_type]
                    continue
                quant_cfg[k]["num_bits"] = NVIDIAModelOptConfig.quanttype_to_numbits[w_type]

        if self.block_quantize is not None and self.channel_quantize is not None:
            quant_cfg["*weight_quantizer"]["block_sizes"] = {self.channel_quantize: self.block_quantize}
            quant_cfg["*input_quantizer"]["block_sizes"] = {
                self.channel_quantize: self.block_quantize,
                "type": "dynamic",
            }
        elif self.channel_quantize is not None:
            quant_cfg["*weight_quantizer"]["axis"] = self.channel_quantize
            quant_cfg["*input_quantizer"]["axis"] = self.channel_quantize
            quant_cfg["*input_quantizer"]["type"] = "dynamic"

        # Only fixed scaling sizes are supported for now in modelopt
        if self.scale_channel_quantize is not None and self.scale_block_quantize is not None:
            if w_type in NVIDIAModelOptConfig.quanttype_to_scalingbits:
                quant_cfg["*weight_quantizer"]["block_sizes"].update(
                    {
                        "scale_bits": NVIDIAModelOptConfig.quanttype_to_scalingbits[w_type],
                        "scale_block_sizes": {self.scale_channel_quantize: self.scale_block_quantize},
                    }
                )
            if act_type and act_type in NVIDIAModelOptConfig.quanttype_to_scalingbits:
                quant_cfg["*input_quantizer"]["block_sizes"].update(
                    {
                        "scale_bits": NVIDIAModelOptConfig.quanttype_to_scalingbits[act_type],
                        "scale_block_sizes": {self.scale_channel_quantize: self.scale_block_quantize},
                    }
                )

        return BASE_CONFIG


@dataclass
class AutoRoundConfig(QuantizationConfigMixin):
    """Configuration class for AutoRound quantization.

    AutoRound is a weight-only quantization algorithm that uses sign gradient descent to jointly optimize weight
    rounding and min-max values. This config targets the W4A16 (4-bit weights, 16-bit activations) setting.

    Reference: https://github.com/intel/auto-round

    Args:
        bits (`int`, *optional*, defaults to `4`):
            The number of bits to quantize weights to. For W4A16 this should be 4.
        group_size (`int`, *optional*, defaults to `128`):
            The group size for weight quantization. Weights in each group share the same scale and zero-point. Common
            choices: 32, 64, 128, -1 (per-channel).
        sym (`bool`, *optional*, defaults to `True`):
            Whether to use symmetric quantization (zero-point fixed at 0) or asymmetric quantization (zero-point is
            learned).
        backend (`str`, *optional*, defaults to `"auto"`):
            The backend kernel to use for quantized inference. Available backends:
            - `"auto"`: Automatically select the best available backend for the current device.
            - `"torch"`: Pure PyTorch kernel — works on CPU and CUDA.
            - `"tritonv2"`: Triton-based kernel — requires CUDA.
            - `"exllamav2"`: Exllamav2 kernel via GPTQModel — requires CUDA and `gptqmodel>=5.8.0`. Offers good CUDA
              inference performance.
            - `"marlin"`: Marlin kernel via GPTQModel — requires CUDA and `gptqmodel>=5.8.0`. Offers the best CUDA
              inference performance.
        kwargs (`dict[str, Any]`, *optional*):
            Additional keyword arguments forwarded to AutoRound (e.g. `iters`, `seqlen`, `batch_size`, `lr`,
            `minmax_lr` for calibration when quantizing from scratch).
    """

    VALID_BACKENDS = ["auto", "torch", "tritonv2", "exllamav2", "marlin"]

    def __init__(
        self,
        bits: int = 4,
        group_size: int = 128,
        sym: bool = True,
        backend: str = "auto",
        **kwargs,
    ) -> None:
        self.quant_method = QuantizationMethod.AUTOROUND
        self._validate_backend(backend)
        self.bits = bits
        self.group_size = group_size
        self.sym = sym
        self.backend = backend
        for k, v in kwargs.items():
            setattr(self, k, v)

    def _validate_backend(self, backend):
        if backend not in self.VALID_BACKENDS:
            raise ValueError(f"Invalid backend '{backend}'. Valid options are: {self.VALID_BACKENDS}")

    def to_dict(self) -> dict:
        """Serialize the config to a JSON-compatible dict.

        Output: A dict containing all config fields. The `quant_method` is stored as its string value so it can be
        round-tripped through JSON.
        """
        output = super().to_dict()
        output["quant_method"] = output["quant_method"].value
        return output

    @classmethod
    def from_dict(cls, config_dict: dict, return_unused_kwargs: bool = False, **kwargs):
        """Instantiate an AutoRoundConfig from a dictionary.

        Input: config_dict with keys like bits, group_size, sym, etc. Output: An AutoRoundConfig instance (and
        optionally unused kwargs).
        """
        # Filter out keys that are not constructor parameters
        # (e.g. quant_method is set automatically)
        config_dict = {k: v for k, v in config_dict.items() if k != "quant_method"}
        return super().from_dict(config_dict, return_unused_kwargs=return_unused_kwargs, **kwargs)


class SDNQConfig(QuantizationConfigMixin):
    """Configuration class for SDNQ (SD.Next Quantization).

    The `sdnq` library ships its own diffusers-compatible config and quantizer; this class is a thin factory that
    defers to them so that `quant_method="sdnq"` checkpoints load natively with diffusers. All arguments are forwarded
    to `sdnq.SDNQConfig`. Requires the `sdnq` library: `pip install sdnq`.

    Reference: https://github.com/Disty0/sdnq

    Args:
        weights_dtype (`str`, *optional*, defaults to `"int8"`):
            The target dtype for the weights after quantization, e.g. `"int8"`, `"uint4"`, `"float8_e4m3fn"`. See
            `sdnq.common.accepted_weight_dtypes` for all supported values.
        group_size (`int`, *optional*, defaults to `0`):
            How many elements of a tensor share the same quantization group. `0` auto-selects based on `weights_dtype`,
            `-1` disables grouping and uses row-wise quantization.
        use_svd (`bool`, *optional*, defaults to `False`):
            Whether to apply the SVDQuant algorithm on top of SDNQ quantization.
        use_quantized_matmul (`bool`, *optional*, defaults to `False`):
            Whether to use quantized INT8 / FP8 / FP16 matmul on the forward pass instead of BF16 / FP16.
        modules_to_not_convert (`list`, *optional*, defaults to `None`):
            The list of modules to skip during quantization.
        kwargs (`dict[str, Any]`, *optional*):
            Additional keyword arguments forwarded to `sdnq.SDNQConfig` (e.g. `quantized_matmul_dtype`, `svd_rank`,
            `use_hadamard`, `quant_conv`, `quant_embedding`, `modules_dtype_dict`).
    """

    def __new__(cls, *args, **kwargs):
        from .sdnq.sdnq_quantizer import _check_sdnq_requirement

        _check_sdnq_requirement()
        from sdnq import SDNQConfig as SDNQLibConfig

        return SDNQLibConfig(*args, **kwargs)