quant_api.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""
Quantization APIs

Generally these APIs can be applied directly to any model
with Linear modules to obtain quantized linear ops. The intended
usage involves applying torch.compile to the model afterwards
both because primitives were designed based on the fusions that
come along with it and because that is how we access the intended quantized
and mixed GEMM kernels
"""
from functools import partial
import warnings
import torch
import torchao
import torch.nn as nn
import torch.nn.functional as F
from typing import Any, Callable, Union, Dict, Optional, Literal, Tuple
import types

from torchao.dtypes.uintx.Uintx import UintxLayoutType
from torchao.dtypes import (
    to_affine_quantized_intx,
    to_affine_quantized_floatx,
    TensorCoreTiledLayoutType,
    PlainLayoutType,
    AffineQuantizedTensor,
    SemiSparseLayoutType,
    Float8LayoutType,
    MarlinSparseLayoutType,
)
from torchao.utils import (
    TORCH_VERSION_AT_LEAST_2_4,
    TORCH_VERSION_AT_LEAST_2_5,
    unwrap_tensor_subclass,
)
from .subclass import (
    QuantizedLinearWeightBase,
)

from .linear_activation_quantized_tensor import (
    LinearActivationQuantizedTensor,
    to_linear_activation_quantized,
)

from .quant_primitives import (
    MappingType,
    ZeroPointDomain,
)
from .weight_only import WeightOnlyInt8QuantLinear
from .unified import Quantizer, TwoStepQuantizer
from .GPTQ import (
    Int4WeightOnlyGPTQQuantizer,
    Int4WeightOnlyQuantizer,
)
from .utils import _get_per_token_block_size
import logging
from .autoquant import autoquant, AutoQuantizableLinearWeight
from torchao.quantization.observer import AffineQuantizedObserverBase
from torchao.quantization.linear_activation_weight_observer import (
    LinearActivationWeightObservedTensor,
)
from torchao.float8.inference import Float8MMConfig

from torchao.quantization.observer import PerTensor, PerRow, get_block_size

logger = logging.getLogger(__name__)

__all__ = [
    "swap_conv2d_1x1_to_linear",
    "Quantizer",
    "TwoStepQuantizer",
    "Int4WeightOnlyGPTQQuantizer",
    "Int4WeightOnlyQuantizer",
    "autoquant",
    "_get_subclass_inserter",
    "quantize_",
    "int8_dynamic_activation_int4_weight",
    "int8_dynamic_activation_int8_weight",
    "int8_dynamic_activation_int8_semi_sparse_weight",
    "int4_weight_only",
    "int8_weight_only",
    "float8_weight_only",
    "uintx_weight_only",
    "fpx_weight_only",
    "float8_dynamic_activation_float8_weight",
]

from .GPTQ import (
    Int8DynActInt4WeightQuantizer,
    Int8DynActInt4WeightGPTQQuantizer,

)
__all__ += [
    "Int8DynActInt4WeightQuantizer",
    "Int8DynActInt4WeightGPTQQuantizer",
]

### TO BE DEPRECATED START
from .subclass import (
    Int4WeightOnlyQuantizedLinearWeight,
    Int8DynamicallyQuantizedLinearWeight,
    Int8WeightOnlyQuantizedLinearWeight,
)

def _in_features_greater_than_16(mod, *args):
    return hasattr(mod, "in_features") and mod.in_features > 16

def change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
    """
    Converts all linear weight tensors to the `Int8DynamicallyQuantizedLinearWeight`
    Tensor subclass, effectively applying the same form of quantization
    as apply_dynamic_quant while not modifying the linear modules.
    """
    if TORCH_VERSION_AT_LEAST_2_4:
        raise ImportError("This API is deprecated for pytorch 2.4+, please checkout quantization/README.md for most up to date APIs")

    if filter_fn is None:
        filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
            *args
        )

    _replace_with_custom_fn_if_matches_filter(
        model, _get_subclass_inserter(Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs), filter_fn
    )


def change_linear_weights_to_int8_woqtensors(model, filter_fn=None, **kwargs):
    """
    Converts all linear weight tensors to the
    `Int8WeightOnlyQuantizedLinearWeight` tensor subclass,
    effectively applying the same form of quantization
    as apply_weight_only_int8_quant while not modifying the linear modules.
    """
    if TORCH_VERSION_AT_LEAST_2_4:
        raise ImportError("This API is deprecated for pytorch 2.4+, please checkout quantization/README.md for most up to date APIs")

    _replace_with_custom_fn_if_matches_filter(
        model,
        _get_subclass_inserter(Int8WeightOnlyQuantizedLinearWeight, enable_parametrization=False, **kwargs),
        _is_linear if filter_fn is None else filter_fn,
    )

def change_linear_weights_to_int4_woqtensors(model, groupsize=128, inner_k_tiles=8, filter_fn=None):
    """
    Converts all linear weight tensors to the
    `Int4WeightOnlyQuantizedLinearWeight` tensor subclass,
    effectively applying the same form of quantization
    as apply_dynamic_quant while not modifying the linear modules.
    Args:
        `groupsize`: parameter for quantization, controls the granularity of quantization, smaller
         size is more fine grained, choices are [256, 128, 64, 32]
        `inner_k_tiles`: parameter for int4 mm kernel, choices are [8, 4, 2]
    """
    if TORCH_VERSION_AT_LEAST_2_4:
        raise ImportError("This API is deprecated for pytorch 2.4+, please checkout quantization/README.md for most up to date APIs")

    if filter_fn is None:
        filter_fn = _is_linear

    _replace_with_custom_fn_if_matches_filter(
        model,
        _get_subclass_inserter(Int4WeightOnlyQuantizedLinearWeight, enable_parametrization=False, groupsize=groupsize, inner_k_tiles=inner_k_tiles),
        filter_fn,
    )

### TO BE DEPRECATED END


def _replace_with_custom_fn_if_matches_filter(
    model,
    replacement_fn,
    filter_fn,
    cur_fqn="",
    device=None,
) -> None:
    """
    Recursively replaces each child module in `model` with the result of `replacement_fn(child)`
    if `filter_fn(child)` returns `True`.

    Args:
        model (torch.nn.Module): The model containing modules to be replaced.
        replacement_fn (Callable[[torch.nn.Module], torch.nn.Module]): The function to replace matching modules.
        filter_fn (Callable[[torch.nn.Module], bool]): The filter function to determine which modules to replace.
        cur_fqn (str, optional): The current fully qualified name of the module being processed. Defaults to "".
        device (device, optional): Device to move the model to before applying `filter_fn`. Defaults to None.

    Returns:
        None
    """
    if filter_fn(model, cur_fqn[:-1]):
        if device is not None:
            model.to(device=device)  # move to device before quantization
        model = replacement_fn(model)
        return model
    else:
        for name, child in model.named_children():
            new_child = _replace_with_custom_fn_if_matches_filter(
                child, replacement_fn, filter_fn, f"{cur_fqn}{name}.", device
            )
            if new_child is not child:
                setattr(model, name, new_child)
        if device is not None:
            model.to(device=device)  # move parent module to device
        return model


def _is_linear(mod, *args):
    # avoid circular dependencies
    from torchao.quantization.prototype.qat.affine_fake_quantized_tensor import (
        AffineFakeQuantizedTensor,
    )

    # adding weight tensor subclass isinstance check to make sure the weight is only quantized once
    # when it is shared by multiple linear modules
    return (
        isinstance(mod, torch.nn.Linear)
        and hasattr(mod, "weight")
        and not isinstance(mod.weight, QuantizedLinearWeightBase)
        and not isinstance(mod.weight, AutoQuantizableLinearWeight)
        and not isinstance(mod.weight, AffineQuantizedTensor)
        and not isinstance(mod.weight, LinearActivationQuantizedTensor)
        and not isinstance(mod.weight, AffineFakeQuantizedTensor)
    )

import torch.nn.utils.parametrize as parametrize

def _get_subclass_inserter(cls, enable_parametrization=False, **kwargs):
    """
    Returns a function which inserts the given subclass into all linear modules
    in the model. The inserted module will have its weight set to the result of
    `cls(mod.weight, **kwargs)`. If parametrization is enabled then this will be done using
    torch.nn.utils.parametrize instead of directly setting the attribute on the module.

    Args:
        cls (torch.Tensor): The class to insert as a child module.
        kwargs (Any): Any additional arguments for the constructor.
    """
    constructor = kwargs.pop("constructor", "subclass_constructor")
    from_float = kwargs.pop("method", "from_float")
    def insert_subclass(lin):
        if enable_parametrization:
            lin.weight = torch.nn.Parameter(cls.from_float(lin.weight, **kwargs), requires_grad=False)
            _, args = lin.weight.__tensor_flatten__()
            parametrize.register_parametrization(lin, "weight", getattr(cls, constructor)(*args))
        else:
            lin.weight = torch.nn.Parameter(
                # cls.from_float(...)
                getattr(cls, from_float)(lin.weight, **kwargs), requires_grad=False
            )
        return lin

    return insert_subclass


def swap_conv2d_1x1_to_linear(model, filter_fn=None):
    """
    Changes all conv2d 1x1 modules to equivalent linear modules so that they can then be quantized.
    """

    class PermuteSandwich(torch.nn.Module):
        def __init__(self, mod):
            super().__init__()
            self.mod = mod

        def forward(self, *args):
            return self.mod(args[0].permute(0, 2, 3, 1)).permute(-0, 3, 1, 2)

    def replace_conv2d_1x1(conv):
        assert conv.kernel_size == (1, 1)
        lin = torch.nn.Linear(
            conv.in_channels, conv.out_channels, bias=(conv.bias is None)
        )
        lin.weight = torch.nn.Parameter(conv.weight.squeeze(-1, -2))
        lin.bias = conv.bias
        return PermuteSandwich(lin)

    if filter_fn is None:
        filter_fn = lambda mod, *args: isinstance(
            mod, torch.nn.Conv2d
        ) and mod.kernel_size == (1, 1)

    _replace_with_custom_fn_if_matches_filter(
        model, replace_conv2d_1x1, filter_fn=filter_fn
    )
def insert_observers_(
    model: nn.Module,
    input_observer: Optional[AffineQuantizedObserverBase],
    weight_observer: Optional[AffineQuantizedObserverBase],
    *,
    filter_fn: Optional[Callable[[torch.nn.Module, str], bool]] = None,
):
    """
    Converts the weight of a linear module to a LinearActivationWeightObservedTensor.

    This function wraps the weight of the given linear module with a LinearActivationWeightObservedTensor,
    which enables observation of both input and weight tensors during forward passes.
    The wrapped weight is then re-wrapped as a nn.Parameter to maintain compatibility
    with PyTorch's module system.

    Example::

    ```
        import torch
        import torch.nn as nn
        from torchao.quantization.linear_observer_tensor import insert_observers_
        from torchao.quantization.observer import (
            AffineQuantizedMinMaxObserver,
            PerTensor,
            MappingType
        )

        # Create observers
        input_observer = AffineQuantizedMinMaxObserver(
            MappingType.SYMMETRIC,
            torch.float8_e4m3fn,
            granularity_type=PerTensor(),
            eps=torch.finfo(torch.float32).eps,
            scale_dtype=torch.float,
            zero_point_dtype=torch.int,
            zero_point_domain=None,
        )

        # Create a linear module
        linear_module = nn.Linear(10, 20)

        # Convert the linear module's weight to an observed tensor
        insert_observers_(linear_module, input_observer, weight_observer=None)

        # The linear_module can now be used as usual, with observers calculating statistics
        output = linear_module(torch.randn(10, 10))

        # Get the scale and zero point of the input observer
        scale, zero_point = linear_module.weight.input_observer.calculate_qparams()
    ```

    Args:
        model (nn.Module): The nn.Module to convert.
        input_observer (Optional[AffineQuantizedObserverBase]): Observer for input tensor.
        weight_observer (Optional[AffineQuantizedObserverBase]): Observer for weight tensor.
        filter_fn (Optional[Callable[[torch.nn.Module, str], bool]]): Filter function to select which modules to convert.
            If not provided, all linear modules will be converted. This function should take a module and its fully qualified name.

    Returns:
        nn.Linear: The modified linear module with its weight wrapped in a LinearActivationWeightObservedTensor.
    """

    def convert_to_linear_observer(linear_module: nn.Linear):
        # Wrap the weight with LinearActivationWeightObservedTensor and then with nn.Parameter
        linear_module.weight = nn.Parameter(
            LinearActivationWeightObservedTensor.from_float(
                linear_module.weight,
                input_observer=input_observer,
                weight_observer=weight_observer,
            ),
            requires_grad=linear_module.weight.requires_grad,
        )
        return linear_module

    _replace_with_custom_fn_if_matches_filter(
        model,
        convert_to_linear_observer,
        _is_linear if filter_fn is None else filter_fn,
    )


def _quantization_type(weight: torch.Tensor):
    if isinstance(weight, AffineQuantizedTensor):
        return f"{weight.__class__.__name__}({weight._quantization_type()})"

    if isinstance(weight, LinearActivationQuantizedTensor):
        return f"{weight.__class__.__name__}(activation={weight.input_quant_func}, weight={_quantization_type(weight.original_weight_tensor)})"

    if type(weight) is torch.Tensor:
        return "not quantized"

    return "not recognized"

def _linear_extra_repr(self):
    return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight={_quantization_type(self.weight)}"

def _get_linear_subclass_inserter(constructor, *, allow_requires_grad=False, **kwargs):
    """Helper function to apply the constructor that quantizes the weight Tensor (with additional kwargs)
    to the weight of linear module
    """
    def insert_subclass(lin):
        requires_grad = allow_requires_grad and lin.weight.requires_grad
        lin.weight = torch.nn.Parameter(constructor(lin.weight, **kwargs), requires_grad=requires_grad)
        lin.extra_repr = types.MethodType(_linear_extra_repr, lin)
        return lin

    return insert_subclass

def quantize_(
    model: torch.nn.Module,
    apply_tensor_subclass: Callable[[torch.nn.Module], torch.nn.Module],
    filter_fn: Optional[Callable[[torch.nn.Module, str], bool]] = None,
    set_inductor_config: bool = True,
    device: Optional[torch.types.Device] = None,
):
    """Convert the weight of linear modules in the model with `apply_tensor_subclass`, model is modified inplace

    Args:
        model (torch.nn.Module): input model
        apply_tensor_subclass (Callable[[torch.nn.Module], torch.nn.Module]): function that applies tensor subclass conversion to the weight of a module and return the module (e.g. convert the weight tensor of linear to affine quantized tensor)
        filter_fn (Optional[Callable[[torch.nn.Module, str], bool]]): function that takes a nn.Module instance and fully qualified name of the module, returns True if we want to run `apply_tensor_subclass` on
        the weight of the module
        set_inductor_config (bool, optional): Whether to automatically use recommended inductor config settings (defaults to True)
        device (device, optional): Device to move module to before applying `filter_fn`. This can be set to `"cuda"` to speed up quantization. The final model will be on the specified `device`.
            Defaults to None (do not change device).

    Example::

        import torch
        import torch.nn as nn
        from torchao import quantize_

        # 1. quantize with some predefined `apply_tensor_subclass` method that corresponds to
        # optimized execution paths or kernels (e.g. int4 tinygemm kernel)
        # also customizable with arguments
        # currently options are
        # int8_dynamic_activation_int4_weight (for executorch)
        # int8_dynamic_activation_int8_weight (optimized with int8 mm op and torch.compile)
        # int4_weight_only (optimized with int4 tinygemm kernel and torch.compile)
        # int8_weight_only (optimized with int8 mm op and torch.compile
        from torchao.quantization.quant_api import int4_weight_only

        m = nn.Sequential(nn.Linear(32, 1024), nn.Linear(1024, 32))
        quantize_(m, int4_weight_only(group_size=32))

        # 2. write your own new apply_tensor_subclass
        # You can also add your own apply_tensor_subclass by manually calling tensor subclass constructor
        # on weight

        from torchao.dtypes import to_affine_quantized_intx

        # weight only uint4 asymmetric groupwise quantization
        groupsize = 32
        apply_weight_quant = lambda x: to_affine_quantized_intx(
          x, "asymmetric", (1, groupsize), torch.int32, 0, 15, 1e-6,
          zero_point_dtype=torch.bfloat16, preserve_zero=False, zero_point_domain="float")

        def apply_weight_quant_to_linear(linear):
            linear.weight = torch.nn.Parameter(apply_weight_quant(linear.weight), requires_grad=False)
            return linear

        # apply to modules under block0 submodule
        def filter_fn(module: nn.Module, fqn: str) -> bool:
            return isinstance(module, nn.Linear)

        m = nn.Sequential(nn.Linear(32, 1024), nn.Linear(1024, 32))
        quantize_(m, apply_weight_quant_to_linear, filter_fn)

    """
    if set_inductor_config:
        torchao.quantization.utils.recommended_inductor_config_setter()

    _replace_with_custom_fn_if_matches_filter(
        model,
        apply_tensor_subclass,
        _is_linear if filter_fn is None else filter_fn,
        device=device,
    )

def _int8_asymm_per_token_quant(x: torch.Tensor) -> torch.Tensor:
    """This is defined here instead of local function to support serialization
    """
    mapping_type = MappingType.ASYMMETRIC
    target_dtype = torch.int8
    return to_affine_quantized_intx(x, mapping_type, _get_per_token_block_size(x), target_dtype)

def apply_int8_dynamic_activation_int4_weight_quant(weight, group_size=32, mapping_type=MappingType.SYMMETRIC):
    """This is defined here instead of local function to support serialization
    """
    if weight.shape[-1] % group_size != 0:
        return weight

    # weight settings
    block_size = (1, group_size)
    target_dtype = torch.int8
    eps = torch.finfo(torch.float32).eps
    quant_min = -8
    quant_max = 7

    # input settings
    input_quant_func = _int8_asymm_per_token_quant

    weight = to_affine_quantized_intx(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps)
    weight = to_linear_activation_quantized(weight, input_quant_func)
    return weight

def int8_dynamic_activation_int4_weight(group_size=32, mapping_type=MappingType.SYMMETRIC):
    """Applies int8 dynamic per token asymmetric activation quantization and int4 per group weight symmetric quantization to linear
    This is used to produce a model for executorch backend, but currently executorch did not
    support lowering for the quantized model from this flow yet

    Args:
        `group_size`: parameter for quantization, controls the granularity of quantization, smaller
         size is more fine grained
    """
    return _get_linear_subclass_inserter(apply_int8_dynamic_activation_int4_weight_quant, group_size=group_size, mapping_type=mapping_type)


def int4_weight_only(group_size=128, layout_type=TensorCoreTiledLayoutType(inner_k_tiles=8), use_hqq=False):
    """
    Applies uint4 weight-only asymmetric per-group quantization to linear layers, using
    "tensor_core_tiled" layout for speedup with tinygemm kernel

    Note:
        This is targeting `tinygemm` int4mm kernel (`torch.ops.aten._weight_int4pack_mm`), the main difference
        of quantization algorithm compared to the more traditional type of integer quantization is the following:
        1). zero_point is in floating point domain instead of integer domain (`zero_point_domain`=`ZeroPointDomain.FLOAT`)
        2). floating point zero does not have to be exactly representable (`preserve_zero`=False in `choose_qparams_affine`)
        please follow the relevant code in `choose_qparams_affine`, `quantize_affine` and `dequantize_affine`
        to learn about how the quantization parameters are chosen and how the Tensor is quantized/dequantized for tinygemm

    Args:
        `group_size`: parameter for quantization, controls the granularity of quantization, smaller
         size is more fine grained, choices are [256, 128, 64, 32]
        `layout_type`: layout type for quantized tensor, default is `TensorCoreTiledLayoutType(inner_k_tiles=8)`
        `use_hqq`: whether to use hqq or default quantization mode, default is False
    """
    def apply_int4_weight_only_quant(weight):
        if weight.shape[-1] % group_size != 0:
            logger.info(
                f"Skipping quantizing weight with int4 weight only quantization because the shape of weight {weight.shape} is not compatible with group_size {group_size}"
            )
            return weight

        mapping_type = MappingType.ASYMMETRIC
        block_size = (1, group_size)
        target_dtype = torch.int32
        quant_min = 0
        quant_max = 15
        eps = 1e-6
        preserve_zero = False
        zero_point_dtype = torch.bfloat16
        zero_point_domain = ZeroPointDomain.FLOAT

        # Sparse Marlin only supports symmetric quantization.
        # NOTE: If we start having lots of layouts that require different configurations, 
        # we should consider moving this logic somewhere else.
        if isinstance(layout_type, MarlinSparseLayoutType):
            mapping_type = MappingType.SYMMETRIC
            preserve_zero = True
            zero_point_domain = ZeroPointDomain.INT

        return to_affine_quantized_intx(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain, layout_type=layout_type, use_hqq=use_hqq)

    return _get_linear_subclass_inserter(apply_int4_weight_only_quant)


def int8_weight_only():
    """
    Applies int8 weight-only symmetric per-channel quantization to linear layers.
    """
    def apply_int8wo_quant(weight):
        mapping_type = MappingType.SYMMETRIC
        target_dtype = torch.int8
        eps = torch.finfo(torch.float32).eps
        zero_point_dtype = torch.int64
        block_size = (1, weight.shape[1])
        return to_affine_quantized_intx(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)

    return _get_linear_subclass_inserter(apply_int8wo_quant)

def _int8_symm_per_token_reduced_range_quant(x: torch.Tensor) -> torch.Tensor:
    mapping_type = MappingType.SYMMETRIC
    target_dtype = torch.int8
    eps = 1e-5
    quant_min = -127
    quant_max = 127
    return to_affine_quantized_intx(x, mapping_type, _get_per_token_block_size(x), target_dtype, eps=eps, quant_min=quant_min, quant_max=quant_max, scale_dtype=torch.float32 if x.dtype == torch.float16 else None)


def int8_dynamic_activation_int8_weight(layout_type=PlainLayoutType()):
    """
    Applies int8 dynamic symmetric per-token activation and int8 per-channel weight
    quantization to linear layers
    """
    def apply_int8_dynamic_activation_int8_weight_quant(weight):
        in_features = weight.shape[1]
        # int8 dynamic quantization only has benefit when in_feature > 16
        if in_features <= 16:
            logger.info(
                f"Skipping applying int8_dynamic_activation_int8_weight to weight of shape {weight.shape}"
                f" because `in_feature` is <= 16: {in_features}")
            return weight

        # weight settings
        mapping_type = MappingType.SYMMETRIC
        def get_weight_block_size(x):
            return (1, x.shape[1])
        target_dtype = torch.int8
        eps = torch.finfo(torch.float32).eps
        zero_point_dtype = torch.int64

        # input settings
        input_quant_func = _int8_symm_per_token_reduced_range_quant

        block_size = get_weight_block_size(weight)
        weight = to_affine_quantized_intx(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype, layout_type=layout_type)
        weight = to_linear_activation_quantized(weight, input_quant_func)
        return weight

    return _get_linear_subclass_inserter(apply_int8_dynamic_activation_int8_weight_quant)


def int8_dynamic_activation_int8_semi_sparse_weight():
    """
    Applies int8 dnynamic symmetric per-token activation and int8 per-channel weight
    quantization + 2:4 sparsity to linear layers.
    """
    warnings.warn("""int8_dyanmic_activation_int8_semi_sparse_weight() will be deprecated at a later release. Please use the layout_type kwarg in int8_dynamic_activation_int8_weight instead.

    from torchao.dtypes import SemiSparseLayoutType
    int8_dynamic_activation_int8_weight(layout_type=SemiSparseLayoutType()""")

    return int8_dynamic_activation_int8_weight(layout_type=SemiSparseLayoutType())


def float8_weight_only(weight_dtype: torch.dtype = torch.float8_e4m3fn):
    """
    Applies float8 weight-only symmetric per-channel quantization to linear layers.

    Args:
        weight_dtype (torch.dtype): The target data type for weight quantization. Default is torch.float8_e4m3fn.

    Note:
        The actual matmul will be computed in original precision of the weight tensor.

    """
    from torchao.dtypes import to_affine_quantized_floatx

    def apply_float8wo_quant(weight):
        block_size = (1, weight.shape[1])
        return to_affine_quantized_floatx(
            input_float=weight,
            block_size=block_size,
            target_dtype=weight_dtype,
            scale_dtype=None,
            layout_type=Float8LayoutType(mm_config=None),
        )

    return _get_linear_subclass_inserter(apply_float8wo_quant)


_fp8_granularities = Union[PerTensor, PerRow]


# Validate and process granularity input
def _normalize_granularity(
    granularity: Optional[
        Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]]
    ]
) -> Tuple[_fp8_granularities, _fp8_granularities]:
    if granularity is None:
        return (PerTensor(), PerTensor())
    elif isinstance(granularity, (PerTensor, PerRow)):
        return (granularity, granularity)
    elif isinstance(granularity, tuple) and len(granularity) == 2:
        if not (
            isinstance(granularity[0], (PerTensor, PerRow))
            and isinstance(granularity[1], (PerTensor, PerRow))
        ):
            raise ValueError(f"Invalid granularity types: {granularity}, only PerTensor or PerRow are supported.")
        if not isinstance(granularity[0], type(granularity[1])):
            raise ValueError(
                f"Different granularities for activation and weight are not supported: {granularity}, only PerTensor or PerRow are supported."
            )
        return granularity
    else:
        raise ValueError(f"Invalid granularity specification: {granularity}, only PerTensor or PerRow are supported.")


def _input_quant_func_dyanmic_fp8(
    x: torch.Tensor,
    activation_granularity: _fp8_granularities,
    activation_dtype: torch.dtype,
):
    if isinstance(activation_granularity, PerRow):
        assert (
            x.dtype == torch.bfloat16
        ), "PerRow quantization only works for bfloat16 precision input activation"

    block_size = get_block_size(x.shape, activation_granularity)
    activation = to_affine_quantized_floatx(
        input_float=x,
        block_size=block_size,
        target_dtype=activation_dtype,
        scale_dtype=torch.float32,
        layout_type=Float8LayoutType(mm_config=None),  # Config is stored on weight
    )
    return activation


def float8_dynamic_activation_float8_weight(
    activation_dtype: torch.dtype = torch.float8_e4m3fn,
    weight_dtype: torch.dtype = torch.float8_e4m3fn,
    granularity: Optional[
        Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]]
    ] = None,
    mm_config: Optional[Float8MMConfig] = None,
):
    """
    Applies float8 dynamic symmetric quantization to both activations and weights of linear layers.

    Args:
        activation_dtype (torch.dtype): The target data type for activation quantization. Default is torch.float8_e4m3fn.
        weight_dtype (torch.dtype): The target data type for weight quantization. Default is torch.float8_e4m3fn.
        granularity:
            The granularity for quantization. Can be either a single granularity (applied to both
            activations and weights) or a tuple of two granularities (one for activations, one for weights).
            If None, defaults to PerTensor for both. Currently both quantizations need to be the same type. And 
            only PerTensor and PerRow are supported.
        mm_config (Float8MMConfig): Configuration for the matrix multiplication. Default uses fast accumulation.

    """
    if mm_config is None:
        mm_config = Float8MMConfig(use_fast_accum=True)

    activation_granularity, weight_granularity = _normalize_granularity(granularity)

    def apply_float8_dynamic_activation_quant(weight: torch.Tensor):
        if isinstance(weight_granularity, PerRow):
            assert (
                weight.dtype == torch.bfloat16
            ), "PerRow quantization only works for bfloat16 precision input weight"

        block_size = get_block_size(weight.shape, weight_granularity)
        quantized_weight = to_affine_quantized_floatx(
            input_float=weight,
            block_size=block_size,
            target_dtype=weight_dtype,
            scale_dtype=torch.float32,
            layout_type=Float8LayoutType(mm_config=mm_config),
        )

        input_quant_func = partial(
            _input_quant_func_dyanmic_fp8,
            activation_granularity=activation_granularity,
            activation_dtype=activation_dtype,
        )

        quantized_weight = to_linear_activation_quantized(
            quantized_weight, input_quant_func
        )
        return quantized_weight

    return _get_linear_subclass_inserter(apply_float8_dynamic_activation_quant)


def uintx_weight_only(dtype, group_size=64, pack_dim=-1, use_hqq=False):
    """
    Applies uintx weight-only asymmetric per-group quantization to linear layers, using uintx quantization where
    x is the number of bits specified by `dtype`

    Args:
        `dtype`: torch.uint1 to torch.uint7 sub byte dtypes
        `group_size`: parameter for quantization, controls the granularity of quantization, smaller
         size is more fine grained, defaults to 64
        `pack_dim`: the dimension we use for packing, defaults to -1
        `use_hqq`: whether to use hqq algorithm or the default algorithm to quantize the weight
    """
    from torchao.quantization.quant_primitives import _DTYPE_TO_QVALUE_BOUNDS

    SUPPORTED_DTYPES = {torch.uint1, torch.uint2, torch.uint3, torch.uint4, torch.uint5, torch.uint6, torch.uint7, torch.uint8}
    assert dtype in SUPPORTED_DTYPES, f"Unsupported dtype for hqq: {dtype}"

    def apply_uintx_weight_only_quant(weight, dtype):
        mapping_type = MappingType.ASYMMETRIC
        block_size = (1, group_size)

        if use_hqq:
            if dtype == torch.uint4:
                logger.warn(f"Recommended to use `int4_weight_only(group_size, use_hqq=True)` for the best performance")
            quant_min, quant_max = _DTYPE_TO_QVALUE_BOUNDS[dtype]
            dtype = torch.uint8
            eps = None
            zero_point_dtype = None
            zero_point_domain = ZeroPointDomain.FLOAT
            preserve_zero = False
            layout_type = PlainLayoutType()
        else:
            quant_min, quant_max = None, None
            eps = torch.finfo(torch.float32).eps
            zero_point_dtype = torch.int32
            zero_point_domain = ZeroPointDomain.INT
            preserve_zero = True
            layout_type = UintxLayoutType(dtype=dtype, pack_dim=pack_dim)

        return to_affine_quantized_intx(
            weight, mapping_type, block_size, dtype,
            quant_min=quant_min, quant_max=quant_max,
            eps=eps, zero_point_dtype=zero_point_dtype,
            zero_point_domain=zero_point_domain,
            preserve_zero=preserve_zero,
            layout_type=layout_type,
            use_hqq=use_hqq,
        )

    return _get_linear_subclass_inserter(apply_uintx_weight_only_quant, dtype=dtype)

def fpx_weight_only(ebits: int, mbits: int):
    """Sub-byte floating point dtypes defined by `ebits`: exponent bits and `mbits`: mantissa bits
    e.g. fp6_e3_m2, fp6_e2_m3, ...
    The packing format and kernels are from the fp6-llm paper: https://arxiv.org/abs/2401.14112
    github repo: https://github.com/usyd-fsalab/fp6_llm, now renamed to quant-llm
    For more details for packing please see: :class:`~torchao.dtypes.fpx.FpxTensorCoreAQTLayout`

    This is experimental, will be merged with `to_affine_quantized_floatx`
    in the future
    """

    def apply_quant_llm(weight: torch.Tensor) -> torch.Tensor:
        from torchao.dtypes.fpx import FpxTensorCoreLayoutType
        from torchao.dtypes import to_affine_quantized_fpx

        assert weight.dim() == 2, f"fpx only works for 2-d Tensor, got: {weight.dim()}"
        out_dim, in_dim = weight.shape
        if (in_dim % 64 != 0) or (out_dim % 256 != 0):
            logger.info(
                f"Skipping floatx quantization float{ebits + mbits + 1}_{ebits}_{mbits} because "
                f"the shape is not compatible with the kernel: in_dim={in_dim}, out_dim={out_dim} "
                "expected in_dim % 64 == 0 and out_dim % 256 == 0")
            return weight

        layout_type = FpxTensorCoreLayoutType(ebits, mbits)
        return to_affine_quantized_fpx(weight, layout_type)
    return _get_linear_subclass_inserter(apply_quant_llm)


if TORCH_VERSION_AT_LEAST_2_5:
    torch.serialization.add_safe_globals([_int8_asymm_per_token_quant, _int8_symm_per_token_reduced_range_quant])