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import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Dict, Any, List
from torch.nn.common_types import _size_1_t
from .utils import ReferenceQuantizedModule
__all__ = ['Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d']
class _ConvNd(torch.nn.modules.conv._ConvNd, ReferenceQuantizedModule):
""" A reference version of nn.quantized.Conv2d
we will not pack the parameters in this module, since weight packing is an
optimization for quantized backends supported in PyTorch (fbgemm/qnnpack),
this is useful when user want to use this module in other backends like Glow.
"""
__annotations__ = {"bias": Optional[torch.Tensor]}
_IS_REFERENCE = True
@staticmethod
def from_float(cls, float_conv, weight_qparams):
qref_conv = cls(
float_conv.in_channels,
float_conv.out_channels,
float_conv.kernel_size, # type: ignore[arg-type]
float_conv.stride, # type: ignore[arg-type]
float_conv.padding, # type: ignore[arg-type]
float_conv.dilation, # type: ignore[arg-type]
float_conv.groups,
float_conv.bias is not None, # type: ignore[arg-type]
float_conv.padding_mode,
device=float_conv.weight.device,
dtype=float_conv.weight.dtype,
weight_qparams=weight_qparams)
qref_conv.weight = torch.nn.Parameter(float_conv.weight.detach())
if float_conv.bias is not None:
qref_conv.bias = torch.nn.Parameter(float_conv.bias.detach())
return qref_conv
class Conv1d(_ConvNd, nn.Conv1d):
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t = 1,
padding: _size_1_t = 0,
dilation: _size_1_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = "zeros",
device=None,
dtype=None,
weight_qparams: Optional[Dict[str, Any]] = None):
nn.Conv1d.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, padding_mode, device, dtype)
self._init_weight_qparams(weight_qparams, device)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
we have:
w(float) -- quant - dequant \
x(float) ------------- F.conv1d ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.conv1d --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized conv1d
"""
weight_quant_dequant = self.get_weight()
result = F.conv1d(
x, weight_quant_dequant, self.bias, self.stride,
self.padding, self.dilation, self.groups)
return result
def _get_name(self):
return "QuantizedConv1d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
return _ConvNd.from_float(cls, float_conv, weight_qparams)
class Conv2d(_ConvNd, nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros',
device=None,
dtype=None,
weight_qparams: Optional[Dict[str, Any]] = None):
nn.Conv2d.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, padding_mode, device, dtype)
self._init_weight_qparams(weight_qparams, device)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
we have:
w(float) -- quant - dequant \
x(float) ------------- F.conv2d ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.conv2d --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized conv2d
"""
weight_quant_dequant = self.get_weight()
result = F.conv2d(
x, weight_quant_dequant, self.bias, self.stride,
self.padding, self.dilation, self.groups)
return result
def _get_name(self):
return "QuantizedConv2d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
return _ConvNd.from_float(cls, float_conv, weight_qparams)
class Conv3d(_ConvNd, nn.Conv3d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode="zeros",
device=None,
dtype=None,
weight_qparams: Optional[Dict[str, Any]] = None):
nn.Conv3d.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, padding_mode, device, dtype)
self._init_weight_qparams(weight_qparams, device)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
we have:
w(float) -- quant - dequant \
x(float) ------------- F.conv3d ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.conv3d --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized conv3d
"""
weight_quant_dequant = self.get_weight()
result = F.conv3d(
x, weight_quant_dequant, self.bias, self.stride,
self.padding, self.dilation, self.groups)
return result
def _get_name(self):
return "QuantizedConv3d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
return _ConvNd.from_float(cls, float_conv, weight_qparams)
class _ConvTransposeNd(_ConvNd, torch.nn.modules.conv._ConvTransposeNd):
""" A reference version of nn.quantized.ConvTranspose2d
we will not pack the parameters in this module, since weight packing is an
optimization for quantized backends supported in PyTorch (fbgemm/qnnpack),
this is useful when user want to use this module in other backends like Glow.
"""
@staticmethod
def from_float(cls, float_conv, weight_qparams):
qref_conv = cls(
float_conv.in_channels,
float_conv.out_channels,
float_conv.kernel_size, # type: ignore[arg-type]
float_conv.stride, # type: ignore[arg-type]
float_conv.padding, # type: ignore[arg-type]
float_conv.output_padding, # type: ignore[arg-type]
float_conv.groups,
float_conv.bias is not None, # type: ignore[arg-type]
float_conv.dilation, # type: ignore[arg-type]
float_conv.padding_mode,
device=float_conv.weight.device,
dtype=float_conv.weight.dtype,
weight_qparams=weight_qparams)
qref_conv.weight = torch.nn.Parameter(float_conv.weight.detach())
if float_conv.bias is not None:
qref_conv.bias = torch.nn.Parameter(float_conv.bias.detach())
return qref_conv
class ConvTranspose1d(_ConvTransposeNd, nn.ConvTranspose1d):
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t = 1,
padding: _size_1_t = 0,
output_padding: _size_1_t = 0,
groups: int = 1,
bias: bool = True,
dilation: _size_1_t = 1,
padding_mode: str = "zeros",
device=None,
dtype=None,
weight_qparams: Optional[Dict[str, Any]] = None):
nn.ConvTranspose1d.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode, device, dtype)
self._init_weight_qparams(weight_qparams, device)
def forward(self, x: torch.Tensor, output_size: Optional[List[int]] = None) -> torch.Tensor:
"""
we have:
w(float) -- quant - dequant \
x(float) ------------- F.convTranspose1d ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.convTranspose1d --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized conv1d
"""
assert isinstance(self.padding, tuple)
# One cannot replace List by Tuple or Sequence in "_output_padding" because
# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
output_padding = self._output_padding(
input, output_size, self.stride, self.padding, self.kernel_size, self.dilation) # type: ignore[arg-type]
weight_quant_dequant = self.get_weight()
result = F.conv_transpose1d(
x, weight_quant_dequant, self.bias, self.stride,
self.padding, output_padding, self.groups, self.dilation)
return result
def _get_name(self):
return "QuantizedConvTranspose1d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
class ConvTranspose2d(_ConvTransposeNd, nn.ConvTranspose2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0,
groups=1, bias=True, dilation=1,
padding_mode='zeros',
device=None,
dtype=None,
weight_qparams: Optional[Dict[str, Any]] = None):
nn.ConvTranspose2d.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode, device, dtype)
self._init_weight_qparams(weight_qparams, device)
def forward(self, x: torch.Tensor, output_size: Optional[List[int]] = None) -> torch.Tensor:
"""
we have:
w(float) -- quant - dequant \
x(float) ------------- F.convTranspose2d ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.convTranspose2d --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized conv2d
"""
assert isinstance(self.padding, tuple)
# One cannot replace List by Tuple or Sequence in "_output_padding" because
# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
output_padding = self._output_padding(
input, output_size, self.stride, self.padding, self.kernel_size, self.dilation) # type: ignore[arg-type]
weight_quant_dequant = self.get_weight()
result = F.conv_transpose2d(
x, weight_quant_dequant, self.bias, self.stride,
self.padding, output_padding, self.groups, self.dilation)
return result
def _get_name(self):
return "QuantizedConvTranspose2d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
class ConvTranspose3d(_ConvTransposeNd, nn.ConvTranspose3d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0,
groups=1, bias=True, dilation=1,
padding_mode="zeros",
device=None,
dtype=None,
weight_qparams: Optional[Dict[str, Any]] = None):
nn.ConvTranspose3d.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode, device, dtype)
self._init_weight_qparams(weight_qparams, device)
def forward(self, x: torch.Tensor, output_size: Optional[List[int]] = None) -> torch.Tensor:
"""
we have:
w(float) -- quant - dequant \
x(float) ------------- F.convTranspose3d ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.convTranspose3d --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized conv3d
"""
assert isinstance(self.padding, tuple)
# One cannot replace List by Tuple or Sequence in "_output_padding" because
# TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
output_padding = self._output_padding(
input, output_size, self.stride, self.padding, self.kernel_size, self.dilation) # type: ignore[arg-type]
weight_quant_dequant = self.get_weight()
result = F.conv_transpose3d(
x, weight_quant_dequant, self.bias, self.stride,
self.padding, output_padding, self.groups, self.dilation)
return result
def _get_name(self):
return "QuantizedConvTranspose3d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
|
