from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from torch._jit_internal import Optional # noqa: F401
import torch.nn as nn
import torch.nn.intrinsic as nni
from torch.nn.modules import Module
from torch.nn.quantized.modules.utils import _quantize_weight
[docs]class Quantize(Module):
r"""Quantizes an incoming tensor
Args:
`out_scale`: scale of the output Quantized Tensor
`out_zero_point`: zero_point of output Quantized Tensor
`out_dtype`: data type of output Quantized Tensor
Attributes:
`out_scale`, `out_zero_point`, `out_dtype`
Examples::
>>> t = torch.tensor([[1., -1.], [1., -1.]])
>>> scale, zero_point, dtype = 1.0, 2, torch.qint8
>>> qm = Quantize(scale, zero_point, dtype)
>>> qt = qm(t)
>>> print(qt)
tensor([[ 1., -1.],
[ 1., -1.]], size=(2, 2), dtype=torch.qint8, scale=1.0, zero_point=2)
"""
def __init__(self, scale, zero_point, dtype):
super(Quantize, self).__init__()
self.register_buffer('scale', torch.tensor([scale]))
self.register_buffer('zero_point', torch.tensor([zero_point], dtype=torch.long))
self.dtype = dtype
def forward(self, X):
return torch.quantize_per_tensor(X, float(self.scale),
int(self.zero_point), self.dtype)
@staticmethod
def from_float(mod):
assert hasattr(mod, 'observer')
scale, zero_point = mod.observer.calculate_qparams()
return Quantize(scale.float().item(), zero_point.long().item(), mod.observer.dtype)
def extra_repr(self):
return 'scale={}, zero_point={}, dtype={}'.format(self.scale, self.zero_point, self.dtype)
[docs]class DeQuantize(Module):
r"""Dequantizes an incoming tensor
Examples::
>>> input = torch.tensor([[1., -1.], [1., -1.]])
>>> scale, zero_point, dtype = 1.0, 2, torch.qint8
>>> qm = Quantize(scale, zero_point, dtype)
>>> quantized_input = qm(input)
>>> dqm = DeQuantize()
>>> dequantized = dqm(quantized_input)
>>> print(dequantized)
tensor([[ 1., -1.],
[ 1., -1.]], dtype=torch.float32)
"""
def __init__(self):
super(DeQuantize, self).__init__()
def forward(self, Xq):
return Xq.dequantize()
@staticmethod
def from_float(mod):
return DeQuantize()
[docs]class Linear(torch.nn.Module):
r"""
A quantized linear module with quantized tensor as inputs and outputs.
We adopt the same interface as `torch.nn.Linear`, please see
https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
Similar to :class:`~torch.nn.Linear`, attributes will be randomly
initialized at module creation time and will be overwritten later
Attributes:
weight (Tensor): the non-learnable quantized weights of the module of
shape :math:`(\text{out\_features}, \text{in\_features})`.
bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
If :attr:`bias` is ``True``, the values are initialized to zero.
scale: `scale` parameter of output Quantized Tensor, type: double
zero_point: `zero_point` parameter for output Quantized Tensor, type: long
Examples::
>>> m = nn.quantized.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
_FLOAT_MODULE = nn.Linear
def __init__(self, in_features, out_features, bias_=True):
super(Linear, self).__init__()
# We don't muck around with buffers or attributes or anything here
# to keep the module simple. *everything* is simply a Python attribute.
# Serialization logic is explicitly handled in the below serialization and
# deserialization modules
self.in_features = in_features
self.out_features = out_features
bias = None
if bias_:
bias = torch.zeros(out_features, dtype=torch.float)
qweight = torch._empty_affine_quantized(
[out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8)
self.set_weight_bias(qweight, bias)
self.scale = 1.0
self.zero_point = 0
def _get_name(self):
return 'QuantizedLinear'
def extra_repr(self):
return 'in_features={}, out_features={}, scale={}, zero_point={}'.format(
self.in_features, self.out_features, self.scale, self.zero_point
)
def forward(self, x):
return torch.ops.quantized.linear(
x, self._packed_params, self.scale, self.zero_point)
# ===== Serialization methods =====
# The special consideration here is that we have to unpack the weights into their
# regular QTensor form for serialization. Packed weights should not live
# outside the process in which they were created, rather they should be derived
# from the QTensor weight.
def _save_to_state_dict(self, destination, prefix, keep_vars):
super(Linear, self)._save_to_state_dict(destination, prefix, keep_vars)
(w, b) = self._weight_bias()
destination[prefix + 'weight'] = w
destination[prefix + 'scale'] = torch.tensor(self.scale)
destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)
destination[prefix + 'bias'] = b
@torch.jit.export
def __getstate__(self):
if not torch.jit.is_scripting():
raise RuntimeError('torch.save() is not currently supported for quantized modules.'
' See https://github.com/pytorch/pytorch/issues/24045.'
' Please use state_dict or torch.jit serialization.')
(w, b) = self._weight_bias()
return (
self.in_features,
self.out_features,
b,
w,
self.scale,
self.zero_point,
self.training
)
# ===== Deserialization methods =====
# Counterpart to the serialization methods, we must pack the serialized QTensor
# weight into its packed format for use by the FBGEMM ops.
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
self.set_weight_bias(state_dict[prefix + 'weight'], state_dict[prefix + 'bias'])
state_dict.pop(prefix + 'weight')
state_dict.pop(prefix + 'bias')
self.scale = float(state_dict[prefix + 'scale'])
state_dict.pop(prefix + 'scale')
self.zero_point = int(state_dict[prefix + 'zero_point'])
state_dict.pop(prefix + 'zero_point')
super(Linear, self)._load_from_state_dict(state_dict, prefix, local_metadata, False,
missing_keys, unexpected_keys, error_msgs)
@torch.jit.export
def __setstate__(self, state):
# type: (Tuple[int, int, Optional[torch.Tensor], torch.Tensor, float, int, bool]) -> None
self.in_features = state[0]
self.out_features = state[1]
self.set_weight_bias(state[3], state[2])
self.scale = state[4]
self.zero_point = state[5]
self.training = state[6]
# Function rather than property to make sure that JIT serialization doesn't
# register this as an attribute
def _weight_bias(self):
return torch.ops.quantized.linear_unpack(self._packed_params)
def weight(self):
(w, b) = torch.ops.quantized.linear_unpack(self._packed_params)
return w
def bias(self):
(w, b) = torch.ops.quantized.linear_unpack(self._packed_params)
return b
def set_weight_bias(self, w, b):
# type: (torch.Tensor, Optional[torch.Tensor]) -> None
self._packed_params = torch.ops.quantized.linear_prepack(w, b)
[docs] @classmethod
def from_float(cls, mod):
r"""Create a quantized module from a float module or qparams_dict
Args:
mod (Module): a float module, either produced by torch.quantization
utilities or provided by the user
"""
if hasattr(mod, 'weight_fake_quant'):
# assert type(mod) == QATLinear, 'training mode nnq.Linear.from_float only works for nn.qat.Linear'
weight_observer = mod.weight_fake_quant
activation_observer = mod.observer
else:
assert type(mod) == cls._FLOAT_MODULE, ' nnq.' + cls.__name__ + '.from_float only works for ' + \
cls._FLOAT_MODULE.__name__
assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
if type(mod) == nni.LinearReLU:
activation_observer = mod[1].observer
mod = mod[0]
else:
activation_observer = mod.observer
weight_observer = mod.qconfig.weight()
weight_observer(mod.weight)
act_scale, act_zp = activation_observer.calculate_qparams()
assert weight_observer.dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
qweight = _quantize_weight(mod.weight.float(), weight_observer)
qlinear = cls(mod.in_features, mod.out_features)
qlinear.set_weight_bias(qweight, mod.bias)
qlinear.scale = float(act_scale)
qlinear.zero_point = int(act_zp)
return qlinear
