Source code for torch.ao.nn.quantized.modules.activation
# mypy: allow-untyped-defs
import torch
from warnings import warn
__all__ = [
"ReLU6",
"Hardswish",
"ELU",
"LeakyReLU",
"Sigmoid",
"Softmax",
"MultiheadAttention",
"PReLU"
]
[docs]class ReLU6(torch.nn.ReLU):
r"""Applies the element-wise function:
:math:`\text{ReLU6}(x) = \min(\max(x_0, x), q(6))`, where :math:`x_0` is the
zero_point, and :math:`q(6)` is the quantized representation of number 6.
Args:
inplace: can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
.. image:: ../scripts/activation_images/ReLU6.png
Examples::
>>> m = nn.quantized.ReLU6()
>>> input = torch.randn(2)
>>> # xdoctest: +SKIP
>>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32)
>>> output = m(input)
"""
def __init__(self, inplace=False):
super().__init__(inplace)
self.inplace = inplace
def forward(self, input):
return torch.ops.quantized.relu6(input, self.inplace)
def _get_name(self):
return 'QuantizedReLU6'
@staticmethod
def from_float(mod, use_precomputed_fake_quant=False):
return ReLU6(mod.inplace)
[docs]class Hardswish(torch.nn.Hardswish):
r"""This is the quantized version of :class:`~torch.nn.Hardswish`.
Args:
scale: quantization scale of the output tensor
zero_point: quantization zero point of the output tensor
"""
def __init__(self, scale, zero_point, device=None, dtype=None):
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
def forward(self, input):
return torch.ops.quantized.hardswish(input, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedHardswish'
@staticmethod
def from_float(mod, use_precomputed_fake_quant=False):
scale, zero_point = mod.activation_post_process.calculate_qparams()
return Hardswish(float(scale), int(zero_point))
@classmethod
def from_reference(cls, mod, scale, zero_point):
return cls(float(scale), int(zero_point))
[docs]class ELU(torch.nn.ELU):
r"""This is the quantized equivalent of :class:`~torch.nn.ELU`.
Args:
scale: quantization scale of the output tensor
zero_point: quantization zero point of the output tensor
alpha: the alpha constant
"""
def __init__(self, scale, zero_point, alpha=1.):
super().__init__(alpha)
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
return torch.ao.nn.quantized.functional.elu(
input, self.scale, self.zero_point, self.alpha)
def _get_name(self):
return 'QuantizedELU'
@staticmethod
def from_float(mod, use_precomputed_fake_quant=False):
scale, zero_point = mod.activation_post_process.calculate_qparams()
return ELU(float(scale), int(zero_point), mod.alpha)
@classmethod
def from_reference(cls, mod, scale, zero_point):
return cls(float(scale), int(zero_point), mod.alpha)
[docs]class LeakyReLU(torch.nn.LeakyReLU):
r"""This is the quantized equivalent of :class:`~torch.nn.LeakyReLU`.
Args:
scale: quantization scale of the output tensor
zero_point: quantization zero point of the output tensor
negative_slope: Controls the angle of the negative slope. Default: 1e-2
"""
def __init__(self, scale: float, zero_point: int, negative_slope: float = 1e-2,
inplace: bool = False, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__(negative_slope, inplace)
self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
def forward(self, input):
return torch.ops.quantized.leaky_relu(
input, self.negative_slope, self.inplace, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedLeakyReLU'
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
scale, zero_point = mod.activation_post_process.calculate_qparams()
return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
@classmethod
def from_reference(cls, mod, scale, zero_point):
return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
[docs]class Sigmoid(torch.nn.Sigmoid):
r"""This is the quantized equivalent of :class:`~torch.nn.Sigmoid`.
Args:
scale: quantization scale of the output tensor
zero_point: quantization zero point of the output tensor
"""
def __init__(self, output_scale: float, output_zero_point: int):
super().__init__()
self.output_scale = output_scale
self.output_zero_point = output_zero_point
def forward(self, input):
return torch.ops.quantized.sigmoid(input, self.output_scale, self.output_zero_point)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
output_scale, output_zero_point = mod.activation_post_process.calculate_qparams()
return cls(float(output_scale), int(output_zero_point))
class Softmax(torch.nn.Softmax):
r"""This is the quantized version of :class:`~torch.nn.Softmax`.
Args:
dim: A dimension along which Softmax will be computed (so every slice along dim will sum to 1).
scale: quantization scale of the output tensor
zero_point: quantization zero point of the output tensor
"""
def __init__(self, dim=None, scale=1.0, zero_point=0):
super().__init__()
self.dim = dim
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
dim = self.dim
if dim is None:
stacklevel = 3
# Note: adding the mypy ignore on _get_softmax_dim seems less bad
# than making `_get_softmax_dim` an official API.
dim = torch.nn.functional._get_softmax_dim( # type: ignore[attr-defined]
"softmax", input.dim(), stacklevel)
return torch.ops.quantized.softmax(
input, dim, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedSoftmax'
@staticmethod
def from_float(mod, use_precomputed_fake_quant=False):
scale, zero_point = mod.activation_post_process.calculate_qparams()
return Softmax(mod.dim, float(scale), int(zero_point))
@classmethod
def from_reference(cls, mod, scale, zero_point):
return cls(mod.dim, float(scale), int(zero_point))
class MultiheadAttention(torch.ao.nn.quantizable.MultiheadAttention):
_FLOAT_MODULE = torch.ao.nn.quantizable.MultiheadAttention
def _get_name(self):
return "QuantizedMultiheadAttention"
@classmethod
def from_float(cls, other):
# The whole flow is float -> observed -> quantized
# This class does observed -> quantized only
raise NotImplementedError("It looks like you are trying to convert a "
"non-observed MHA module. Please, see "
"the examples on quantizable MHAs.")
@classmethod
def from_observed(cls, other):
converted = torch.ao.quantization.convert(other, mapping=None,
inplace=False,
remove_qconfig=True,
convert_custom_config_dict=None)
converted.__class__ = cls
# Remove the parameters for the bias_k and bias_v to quantize them
# TODO: This is a potential source of accuracy drop.
# quantized cat takes the scale and zp of the first
# element, which might lose the precision in the bias_k
# and the bias_v (which are cat'ed with k/v being first).
if converted.bias_k is not None:
bias_k = converted._parameters.pop('bias_k')
sc, zp = torch._choose_qparams_per_tensor(bias_k,
reduce_range=False)
bias_k = torch.quantize_per_tensor(bias_k, sc, zp, torch.quint8)
setattr(converted, 'bias_k', bias_k) # noqa: B010
if converted.bias_v is not None:
bias_v = converted._parameters.pop('bias_v')
sc, zp = torch._choose_qparams_per_tensor(bias_k, # type: ignore[possibly-undefined]
reduce_range=False)
bias_v = torch.quantize_per_tensor(bias_v, sc, zp, torch.quint8)
setattr(converted, 'bias_v', bias_v) # noqa: B010
del converted.in_proj_weight
del converted.in_proj_bias
return converted
class PReLU(torch.nn.Module):
r"""This is the quantized equivalent of :class:`~torch.nn.PReLU`.
Args:
scale: quantization scale of the output tensor
zero_point: quantization zero point of the output tensor
num_parameters: number of parameters: 1, or the number of channels at input. Default: 1
"""
def __init__(self, output_scale: float, output_zero_point: int,
num_parameters: int = 1) -> None:
super().__init__()
self.num_parameters = num_parameters
self.scale = output_scale
self.zero_point = output_zero_point
w = torch.randn(num_parameters, dtype=torch.float)
qw = torch.quantize_per_tensor(w, scale=1.0, zero_point=0, dtype=torch.quint8)
self.set_weight(qw)
def set_weight(self, w: torch.Tensor) -> None:
self.weight = w
def forward(self, input: torch.Tensor) -> torch.Tensor:
return torch.ops.quantized.prelu(input, self.weight, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedPReLU'
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
scale, zero_point = mod.activation_post_process.calculate_qparams()
qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
float_wt = mod.weight.float()
observer = mod.qconfig.weight()
observer(float_wt)
if observer.dtype != torch.quint8:
warn(
f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}"
)
wt_scale, wt_zp = observer.calculate_qparams()
qweight = torch.quantize_per_tensor(
float_wt, float(wt_scale), int(wt_zp), torch.quint8)
qprelu.set_weight(qweight)
return qprelu
@classmethod
def from_reference(cls, mod, scale, zero_point):
qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
float_wt = mod.weight.float()
observer = mod.qconfig.weight()
observer(float_wt)
if observer.dtype != torch.quint8:
warn(
f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}"
)
wt_scale, wt_zp = observer.calculate_qparams()
qweight = torch.quantize_per_tensor(
float_wt, float(wt_scale), int(wt_zp), torch.quint8)
qprelu.set_weight(qweight)
return qprelu
