# coding=utf-8
r"""Quantized convolution modules."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from typing import Optional, List
import torch
import torch.nn as nn
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.qat as nniqat
from torch._ops import ops
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn.quantized.modules.utils import _pair_from_first
from torch.nn.quantized.modules.utils import _quantize_weight
from torch.nn.utils import fuse_conv_bn_weights
class _ConvNd(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation,
transposed, output_padding,
groups, bias,
padding_mode='zeros'):
super(_ConvNd, self).__init__()
if padding_mode != 'zeros':
raise NotImplementedError(
"Currently only zero-padding is supported by quantized conv")
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.transposed = transposed
self.output_padding = output_padding
self.groups = groups
self.padding_mode = padding_mode
# Initialize as NCHW. set_weight will internally transpose to NHWC.
qweight = torch._empty_affine_quantized(
[out_channels, in_channels // self.groups] + list(kernel_size),
scale=1, zero_point=0, dtype=torch.qint8)
bias_float = (
torch.zeros(out_channels, dtype=torch.float) if bias else None)
self.set_weight_bias(qweight, bias_float)
self.scale = 1.0
self.zero_point = 0
def extra_repr(self):
s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}, scale={scale}, zero_point={zero_point}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1,) * len(self.dilation):
s += ', dilation={dilation}'
if self.output_padding != (0,) * len(self.output_padding):
s += ', output_padding={output_padding}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias() is None:
s += ', bias=False'
return s.format(**self.__dict__)
# ===== 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(_ConvNd, 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_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
self.transposed,
self.output_padding,
self.groups,
self.padding_mode,
w,
b,
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(_ConvNd, self)._load_from_state_dict(
state_dict, prefix, local_metadata, False, missing_keys,
unexpected_keys, error_msgs)
@torch.jit.export
def __setstate__(self, state):
self.in_channels = state[0]
self.out_channels = state[1]
self.kernel_size = state[2]
self.stride = state[3]
self.padding = state[4]
self.dilation = state[5]
self.transposed = state[6]
self.output_padding = state[7]
self.groups = state[8]
self.padding_mode = state[9]
self.set_weight_bias(state[10], state[11])
self.scale = state[12]
self.zero_point = state[13]
self.training = state[14]
class Conv1d(_ConvNd):
r"""Applies a 1D convolution over a quantized input signal composed of
several quantized input planes.
For details on input arguments, parameters, and implementation see
:class:`~torch.nn.Conv1d`.
.. note::
Only `zeros` is supported for the :attr:`padding_mode` argument.
.. note::
Only `torch.quint8` is supported for the input data type.
Attributes:
weight (Tensor): packed tensor derived from the learnable weight
parameter.
scale (Tensor): scalar for the output scale
zero_point (Tensor): scalar for the output zero point
See :class:`~torch.nn.Conv1d` for other attributes.
Examples::
>>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
>>> input = torch.randn(20, 16, 100)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
dtype=torch.quint32)
>>> output = m(q_input)
"""
_FLOAT_MODULE = nn.Conv1d
# We are using Conv2d to run the Conv1d. For that we need to know which
# dimension is squeezed/unsqueezed.
_SQUEEZE_DIM = -2 # -2 is faster than -1.
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros'):
kernel_size = list(_pair_from_first(kernel_size))
kernel_size[self._SQUEEZE_DIM] = 1
kernel_size = tuple(kernel_size)
stride = list(_pair_from_first(stride))
stride[self._SQUEEZE_DIM] = 1
stride = tuple(stride)
padding = list(_pair_from_first(padding))
padding[self._SQUEEZE_DIM] = 0
padding = tuple(padding)
dilation = list(_pair_from_first(dilation))
dilation[self._SQUEEZE_DIM] = 1
dilation = tuple(dilation)
super(Conv1d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _single(0), groups, bias, padding_mode)
def _get_name(self):
return 'QuantizedConv1d'
def set_weight_bias(self, w, b):
# type: (torch.Tensor, Optional[torch.Tensor]) -> None
# Check if need to unsqueeze
if len(w.shape) == 3:
w = w.unsqueeze(dim=self._SQUEEZE_DIM)
self._packed_params = torch.ops.quantized.conv2d_prepack(
w, b, self.stride, self.padding, self.dilation, self.groups)
def _weight_bias(self):
w, b = torch.ops.quantized.conv2d_unpack(self._packed_params)
w = w.squeeze(dim=self._SQUEEZE_DIM)
return w, b
def weight(self):
return self._weight_bias()[0]
def bias(self):
return self._weight_bias()[1]
def forward(self, input):
# Temporarily using len(shape) instead of ndim due to JIT issue
# https://github.com/pytorch/pytorch/issues/23890
if len(input.shape) != 3:
raise ValueError("Input shape must be `(N, C, L)`!")
input = input.unsqueeze(dim=self._SQUEEZE_DIM)
output = ops.quantized.conv2d(
input, self._packed_params, self.stride, self.padding,
self.dilation, self.groups, self.scale, self.zero_point)
return output.squeeze(dim=self._SQUEEZE_DIM)
@classmethod
def from_float(cls, mod):
r"""Creates 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
"""
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.'
weight_post_process = mod.qconfig.weight()
weight_post_process(mod.weight)
act_scale, act_zp = mod.activation_post_process.calculate_qparams()
assert weight_post_process.dtype == torch.qint8, \
'Weight observer must have a dtype of qint8'
qweight = _quantize_weight(mod.weight.float(), weight_post_process)
qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
mod.stride, mod.padding, mod.dilation, mod.groups,
mod.bias is not None, mod.padding_mode)
qconv.set_weight_bias(qweight, mod.bias)
qconv.scale = float(act_scale)
qconv.zero_point = int(act_zp)
return qconv
[docs]class Conv2d(_ConvNd):
r"""Applies a 2D convolution over a quantized input signal composed of
several quantized input planes.
For details on input arguments, parameters, and implementation see
:class:`~torch.nn.Conv2d`.
.. note::
Only `zeros` is supported for the :attr:`padding_mode` argument.
.. note::
Only `torch.quint8` is supported for the input data type.
Attributes:
weight (Tensor): packed tensor derived from the learnable weight
parameter.
scale (Tensor): scalar for the output scale
zero_point (Tensor): scalar for the output zero point
See :class:`~torch.nn.Conv2d` for other attributes.
Examples::
>>> # With square kernels and equal stride
>>> m = nn.quantized.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> output = m(q_input)
"""
_FLOAT_MODULE = nn.Conv2d
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros'):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _pair(0), groups, bias, padding_mode)
def _get_name(self):
return 'QuantizedConv2d'
def set_weight_bias(self, w, b):
# type: (torch.Tensor, Optional[torch.Tensor]) -> None
self._packed_params = torch.ops.quantized.conv2d_prepack(
w, b, self.stride, self.padding, self.dilation, self.groups)
def _weight_bias(self):
return torch.ops.quantized.conv2d_unpack(self._packed_params)
def weight(self):
(w, _) = torch.ops.quantized.conv2d_unpack(self._packed_params)
return w
def bias(self):
(_, b) = torch.ops.quantized.conv2d_unpack(self._packed_params)
return b
def forward(self, input):
# Temporarily using len(shape) instead of ndim due to JIT issue
# https://github.com/pytorch/pytorch/issues/23890
if len(input.shape) != 4:
raise ValueError("Input shape must be `(N, C, H, W)`!")
return ops.quantized.conv2d(
input, self._packed_params, self.stride, self.padding,
self.dilation, self.groups, self.scale, self.zero_point)
[docs] @classmethod
def from_float(cls, mod):
r"""Creates 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) == cls.__QAT_MODULE, ' nnq.' + cls.__name__ + \
# '.from_float only works for ' + cls.__QAT_MODULE.__name__
if type(mod) == nniqat.ConvBn2d:
mod.weight, mod.bias = fuse_conv_bn_weights(
mod.weight, mod.bias, mod.running_mean, mod.running_var,
mod.eps, mod.gamma, mod.beta)
assert hasattr(mod, 'activation_post_process'), \
'Input QAT module must have observer attached'
weight_post_process = mod.weight_fake_quant
activation_post_process = mod.activation_post_process
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.'
# workaround for sequential, ConvReLU2d should probably
# inherit from Conv2d instead
if type(mod) == nni.ConvReLU2d:
activation_post_process = mod[1].activation_post_process
mod = mod[0]
else:
activation_post_process = mod.activation_post_process
weight_post_process = mod.qconfig.weight()
weight_post_process(mod.weight)
act_scale, act_zp = activation_post_process.calculate_qparams()
assert weight_post_process.dtype == torch.qint8, \
'Weight observer must have a dtype of qint8'
qweight = _quantize_weight(mod.weight.float(), weight_post_process)
qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
mod.stride, mod.padding, mod.dilation, mod.groups,
mod.bias is not None, mod.padding_mode)
qconv.set_weight_bias(qweight, mod.bias)
qconv.scale = float(act_scale)
qconv.zero_point = int(act_zp)
return qconv
[docs]class Conv3d(_ConvNd):
r"""Applies a 3D convolution over a quantized input signal composed of
several quantized input planes.
For details on input arguments, parameters, and implementation see
:class:`~torch.nn.Conv3d`.
.. note::
Only `zeros` is supported for the :attr:`padding_mode` argument.
.. note::
Only `torch.quint8` is supported for the input data type.
Attributes:
weight (Tensor): packed tensor derived from the learnable weight
parameter.
scale (Tensor): scalar for the output scale
zero_point (Tensor): scalar for the output zero point
See :class:`~torch.nn.Conv3d` for other attributes.
Examples::
>>> # With square kernels and equal stride
>>> m = nn.quantized.Conv3d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
>>> input = torch.randn(20, 16, 56, 56, 56)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> output = m(q_input)
"""
_FLOAT_MODULE = nn.Conv3d
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros'):
kernel_size = _triple(kernel_size)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
super(Conv3d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _triple(0), groups, bias, padding_mode)
def _get_name(self):
return 'QuantizedConv3d'
def set_weight_bias(self, w, b):
# type: (torch.Tensor, Optional[torch.Tensor]) -> None
self._packed_params = torch.ops.quantized.conv3d_prepack(
w, b, self.stride, self.padding, self.dilation, self.groups)
def _weight_bias(self):
return torch.ops.quantized.conv3d_unpack(self._packed_params)
def weight(self):
(w, _) = torch.ops.quantized.conv3d_unpack(self._packed_params)
return w
def bias(self):
(_, b) = torch.ops.quantized.conv3d_unpack(self._packed_params)
return b
def forward(self, input):
# Temporarily using len(shape) instead of ndim due to JIT issue
# https://github.com/pytorch/pytorch/issues/23890
if len(input.shape) != 5:
raise ValueError("Input shape must be `(N, C, D, H, W)`!")
return ops.quantized.conv3d(
input, self._packed_params, self.stride, self.padding,
self.dilation, self.groups, self.scale, self.zero_point)
[docs] @classmethod
def from_float(cls, mod):
r"""Creates 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
"""
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.'
# Workaround for sequential, ConvReLU3d should probably inherit from
# Conv3d instead
if type(mod) == nni.ConvReLU3d:
activation_post_process = mod[1].activation_post_process
mod = mod[0]
else:
activation_post_process = mod.activation_post_process
weight_post_process = mod.qconfig.weight()
weight_post_process(mod.weight)
act_scale, act_zp = activation_post_process.calculate_qparams()
assert weight_post_process.dtype == torch.qint8, \
'Weight observer must have a dtype of qint8'
qweight = _quantize_weight(mod.weight.float(), weight_post_process)
qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
mod.stride, mod.padding, mod.dilation, mod.groups,
mod.bias is not None, mod.padding_mode)
qconv.set_weight_bias(qweight, mod.bias)
qconv.scale = float(act_scale)
qconv.zero_point = int(act_zp)
return qconv
# === Transposed Convolutions ===
# Note: The MRO should make sure that the `super` in the `_ConvNd` will be
# called, while the one in the `nn._ConvTransposeNd` it won't.
class _ConvTransposeNd(_ConvNd, nn.modules.conv._ConvTransposeNd):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding,
groups, bias, padding_mode):
if padding_mode != 'zeros':
raise ValueError('Only "zeros" padding mode is supported for {}'.format(self.__class__.__name__))
super(_ConvTransposeNd, self).__init__(
in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding,
groups, bias, padding_mode)
def _input_padding(self, kernel_size, dilation, padding):
# type: (List[int], List[int], List[int]) -> List[int]
res = torch.jit.annotate(List[int], [])
for kdx in range(len(kernel_size)):
pad = (dilation[kdx] * (kernel_size[kdx] - 1) - padding[kdx])
res.append(pad)
return res
