# -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Convolution layers."""
from typing import Optional, Tuple, Union, List
from torch import Tensor, arange, cat, float64, int32, ones
from torch.nn import Unfold
from torch.nn.functional import pad
from torch.nn.modules.conv import _ConvNd, Conv1d, Conv2d, Conv3d
from torch.nn.modules.utils import _single, _pair, _triple
from aihwkit.nn.functions import AnalogIndexedFunction
from aihwkit.nn.modules.base import AnalogModuleBase, RPUConfigAlias
from aihwkit.simulator.configs import SingleRPUConfig
class _AnalogConvNd(AnalogModuleBase, _ConvNd):
"""Base class for convolution layers."""
__constants__ = ['stride', 'padding', 'dilation', 'groups',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_size', 'in_features', 'out_features',
'realistic_read_write', 'weight_scaling_omega',
'digital_bias', 'analog_bias', 'use_bias']
in_channels: int
out_channels: int
kernel_size: Tuple[int, ...]
stride: Tuple[int, ...]
padding: Tuple[int, ...]
dilation: Tuple[int, ...]
realistic_read_write: bool
weight_scaling_omega: float
transposed: bool
output_padding: Tuple[int, ...]
groups: int
padding_mode: str
fold_indices: Tensor
input_size: float
in_features: int
out_features: int
digital_bias: bool
analog_bias: bool
use_bias: bool
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Tuple[int, ...],
stride: Tuple[int, ...],
padding: Tuple[int, ...],
dilation: Tuple[int, ...],
transposed: bool,
output_padding: Tuple[int, ...],
groups: int,
bias: bool,
padding_mode: str,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# pylint: disable=too-many-arguments, too-many-locals
if groups != 1:
raise ValueError('Only one group is supported')
if padding_mode != 'zeros':
raise ValueError('Only "zeros" padding mode is supported')
# Call super() after tile creation, including ``reset_parameters``.
_ConvNd.__init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding, groups, bias,
padding_mode)
# Create the tile and set the analog.
if rpu_config is None:
rpu_config = SingleRPUConfig()
AnalogModuleBase.__init__(
self,
self.get_tile_size(in_channels, groups, kernel_size),
out_channels,
bias,
realistic_read_write,
rpu_config.mapping
)
self.analog_tile = self._setup_tile(rpu_config)
# Register analog tile
self.register_analog_tile(self.analog_tile)
# Set weights from the reset_parameters
self.set_weights(self.weight, self.bias, remap_weights=True,
weight_scaling_omega=weight_scaling_omega)
# Set the index matrices.
self.fold_indices = Tensor().detach()
self.input_size = 0
self.tensor_view = (-1,) # type: Tuple[int, ...]
# Unregister weight/bias as a parameter but keep it for syncs
self.unregister_parameter('weight')
if self.analog_bias:
self.unregister_parameter('bias')
def get_tile_size(
self,
in_channels: int,
groups: int,
kernel_size: Tuple[int, ...]
) -> int:
"""Calculate the tile size."""
raise NotImplementedError
def get_image_size(self, size: int, i: int) -> int:
"""Calculate the output image sizes."""
nom = (size + 2 * self.padding[i] - self.dilation[i] * (self.kernel_size[i] - 1) - 1)
return nom // self.stride[i] + 1
def reset_parameters(self) -> None:
"""Reset the parameters (weight and bias)."""
super().reset_parameters()
if self.analog_tile_count():
self.set_weights(self.weight, self.bias)
def recalculate_indexes(self, x_input: Tensor) -> None:
"""Calculate and set the indexes of the analog tile."""
self.fold_indices, image_sizes, self.input_size = \
self._calculate_indexes(x_input, self.in_channels)
self.analog_tile.set_indexed(self.fold_indices, image_sizes)
def _calculate_indexes(self, x_input: Tensor,
in_channels: int) -> Tuple[Tensor, List[int], int]:
"""Calculate and return the fold indexes and sizes.
Args:
x_input: input matrix
in_channels: number of input channel
Returns:
fold_indices: indices for the analog tile
image_sizes: image sizes for the analog tile
input_size: size of the current input
"""
raise NotImplementedError
def forward(self, x_input: Tensor) -> Tensor:
"""Compute the forward pass."""
input_size = x_input.numel() / x_input.size(0)
if not self.fold_indices.numel() or self.input_size != input_size:
self.recalculate_indexes(x_input)
out = AnalogIndexedFunction.apply(
self.analog_tile.get_analog_ctx(), x_input,
self.analog_tile.shared_weights, not self.training)
out = self.analog_tile.apply_out_scaling(out, self.tensor_view)
if self.digital_bias:
return out + self.bias.view(*self.tensor_view)
return out
[docs]class AnalogConv1d(_AnalogConvNd):
"""1D convolution layer that uses an analog tile.
Applies a 1D convolution over an input signal composed of several input
planes, using an analog tile for its forward, backward and update passes.
Note:
The tensor parameters of this layer (``.weight`` and ``.bias``) are not
guaranteed to contain the same values as the internal weights and biases
stored in the analog tile. Please use ``set_weights`` and
``get_weights`` when attempting to read or modify the weight/bias. This
read/write process can simulate the (noisy and inexact) analog writing
and reading of the resistive elements.
Args:
in_channels: number of channels in the input image.
out_channels: number of channels produced by the convolution.
kernel_size: size of the convolving kernel.
stride: stride of the convolution.
padding: zero-padding added to both sides of the input.
dilation: spacing between kernel elements.
groups: number of blocked connections from input channels to output
channels.
bias: whether to use a bias row on the analog tile or not.
padding_mode: padding strategy. Only ``'zeros'`` is supported.
rpu_config: resistive processing unit configuration.
realistic_read_write: whether to enable realistic read/write
for setting initial weights and during reading of the weights.
weight_scaling_omega: If non-zero, the analog weights will be
scaled by ``weight_scaling_omega`` divided by the absolute
maximum value of the original weight matrix.
"""
# pylint: disable=abstract-method
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple],
stride: Union[int, Tuple] = 1,
padding: Union[int, Tuple] = 0,
dilation: Union[int, Tuple] = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# pylint: disable=too-many-arguments
kernel_size = _single(kernel_size)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
if dilation != _single(1):
raise ValueError('Only dilation = 1 is supported')
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, # type: ignore
False, _single(0), groups, bias, padding_mode,
rpu_config, realistic_read_write, weight_scaling_omega
)
self.tensor_view = (-1, 1)
[docs] @classmethod
def from_digital(
cls,
module: Conv1d,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
) -> 'AnalogConv1d':
"""Return an AnalogConv1d layer from a torch Conv1d layer.
Args:
module: The torch module to convert. All layers that are
defined in the ``conversion_map``.
rpu_config: RPU config to apply to all converted tiles.
Applied to all converted tiles.
realistic_read_write: Whether to use closed-loop programming
when setting the weights. Applied to all converted tiles.
Note:
Make sure that the weight max and min settings of the
device support the desired analog weight range.
Returns:
an AnalogConv1d layer based on the digital Conv1d ``module``.
"""
analog_module = cls(module.in_channels,
module.out_channels,
module.kernel_size,
module.stride,
module.padding,
module.dilation,
module.groups,
module.bias is not None,
module.padding_mode,
rpu_config,
realistic_read_write,
)
analog_module.set_weights(module.weight, module.bias)
return analog_module
[docs] def get_tile_size(
self,
in_channels: int,
groups: int,
kernel_size: Tuple[int, ...]
) -> int:
"""Calculate the tile size."""
return (in_channels // groups) * kernel_size[0]
def _calculate_indexes(self, x_input: Tensor,
in_channels: int) -> Tuple[Tensor, List[int], int]:
"""Calculate and return the fold indexes and sizes.
Args:
x_input: input matrix
in_channels: number of input channel
Returns:
fold_indices: indices for the analog tile
image_sizes: image sizes for the analog tile
input_size: size of the current input
"""
input_size = x_input.numel() / x_input.size(0)
# pytorch just always uses NCHW order
fold_indices = arange(2, x_input.size(2) + 2, dtype=float64).detach()
shape = [1] + [1] + list(x_input.shape[2:])
fold_indices = fold_indices.reshape(*shape)
if not all(item == 0 for item in self.padding):
fold_indices = pad(fold_indices, pad=[self.padding[0], self.padding[0]],
mode='constant', value=0)
unfold = fold_indices.unfold(2, self.kernel_size[0], self.stride[0]).clone()
fold_indices = unfold.reshape(-1, self.kernel_size[0]).transpose(0, 1).flatten().round()
# concatenate the matrix index for different channels
fold_indices_orig = fold_indices.clone()
for i in range(in_channels - 1):
fold_indices_tmp = fold_indices_orig.clone()
for j in range(fold_indices_orig.size(0)):
if fold_indices_orig[j] != 0:
fold_indices_tmp[j] += (input_size / in_channels) * (i + 1)
fold_indices = cat([fold_indices, fold_indices_tmp], dim=0).clone()
fold_indices = fold_indices.to(dtype=int32)
if self.analog_bias:
out_image_size = fold_indices.numel() // (self.kernel_size[0])
fold_indices = cat((fold_indices, ones(out_image_size, dtype=int32)), 0)
fold_indices = fold_indices.to(x_input.device)
x_height = x_input.size(2)
d_height = self.get_image_size(x_height, 0)
image_sizes = [in_channels, x_height, d_height]
return (fold_indices, image_sizes, input_size)
[docs]class AnalogConv2d(_AnalogConvNd):
"""2D convolution layer that uses an analog tile.
Applies a 2D convolution over an input signal composed of several input
planes, using an analog tile for its forward, backward and update passes.
Note:
The tensor parameters of this layer (``.weight`` and ``.bias``) are not
guaranteed to contain the same values as the internal weights and biases
stored in the analog tile. Please use ``set_weights`` and
``get_weights`` when attempting to read or modify the weight/bias. This
read/write process can simulate the (noisy and inexact) analog writing
and reading of the resistive elements.
Args:
in_channels: number of channels in the input image.
out_channels: number of channels produced by the convolution.
kernel_size: size of the convolving kernel.
stride: stride of the convolution.
padding: zero-padding added to both sides of the input.
dilation: spacing between kernel elements.
groups: number of blocked connections from input channels to output
channels.
bias: whether to use a bias row on the analog tile or not.
padding_mode: padding strategy. Only ``'zeros'`` is supported.
rpu_config: resistive processing unit configuration.
realistic_read_write: whether to enable realistic read/write
for setting initial weights and read out of weights.
weight_scaling_omega: If non-zero, the analog weights will be
scaled by ``weight_scaling_omega`` divided by the absolute
maximum value of the original weight matrix.
"""
# pylint: disable=abstract-method
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple],
stride: Union[int, Tuple] = 1,
padding: Union[int, Tuple] = 0,
dilation: Union[int, Tuple] = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# pylint: disable=too-many-arguments
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, # type: ignore
False, _pair(0), groups, bias, padding_mode,
rpu_config, realistic_read_write, weight_scaling_omega
)
self.tensor_view = (-1, 1, 1)
[docs] @classmethod
def from_digital(
cls,
module: Conv2d,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
) -> 'AnalogConv2d':
"""Return an AnalogConv2d layer from a torch Conv2d layer.
Args:
module: The torch module to convert. All layers that are
defined in the ``conversion_map``.
rpu_config: RPU config to apply to all converted tiles.
Applied to all converted tiles.
realistic_read_write: Whether to use closed-loop programming
when setting the weights. Applied to all converted tiles.
Note:
Make sure that the weight max and min settings of the
device support the desired analog weight range.
Returns:
an AnalogConv2d layer based on the digital Conv2d ``module``.
"""
analog_module = cls(module.in_channels,
module.out_channels,
module.kernel_size,
module.stride,
module.padding,
module.dilation,
module.groups,
module.bias is not None,
module.padding_mode,
rpu_config,
realistic_read_write,
)
analog_module.set_weights(module.weight, module.bias)
return analog_module
[docs] def get_tile_size(
self,
in_channels: int,
groups: int,
kernel_size: Tuple[int, ...]
) -> int:
"""Calculate the tile size."""
return (in_channels // groups) * kernel_size[0] * kernel_size[1]
def _calculate_indexes(self, x_input: Tensor,
in_channels: int) -> Tuple[Tensor, List[int], int]:
"""Calculate and return the fold indexes and sizes.
Args:
x_input: input matrix
in_channels: number of input channel
Returns:
fold_indices: indices for the analog tile
image_sizes: image sizes for the analog tile
input_size: size of the current input
"""
input_size = x_input.numel() / x_input.size(0)
# pytorch just always uses NCHW order
fold_indices = arange(2, input_size + 2, dtype=float64).detach()
shape = [1] + list(x_input.shape[1:])
fold_indices = fold_indices.reshape(*shape)
unfold = Unfold(kernel_size=self.kernel_size,
stride=self.stride,
padding=self.padding,
dilation=self.dilation)
fold_indices = unfold(fold_indices).flatten().round().to(dtype=int32)
if self.analog_bias:
out_image_size = fold_indices.numel() // (self.kernel_size[0] * self.kernel_size[1])
fold_indices = cat((fold_indices, ones(out_image_size, dtype=int32)), 0)
fold_indices = fold_indices.to(x_input.device)
x_height = x_input.size(2)
x_width = x_input.size(3)
d_height = self.get_image_size(x_height, 0)
d_width = self.get_image_size(x_width, 1)
image_sizes = [in_channels, x_height, x_width, d_height, d_width]
return (fold_indices, image_sizes, input_size)
[docs]class AnalogConv3d(_AnalogConvNd):
"""3D convolution layer that uses an analog tile.
Applies a 3D convolution over an input signal composed of several input
planes, using an analog tile for its forward, backward and update passes.
Note:
The tensor parameters of this layer (``.weight`` and ``.bias``) are not
guaranteed to contain the same values as the internal weights and biases
stored in the analog tile. Please use ``set_weights`` and
``get_weights`` when attempting to read or modify the weight/bias. This
read/write process can simulate the (noisy and inexact) analog writing
and reading of the resistive elements.
Args:
in_channels: number of channels in the input image.
out_channels: number of channels produced by the convolution.
kernel_size: size of the convolving kernel.
stride: stride of the convolution.
padding: zero-padding added to both sides of the input.
dilation: spacing between kernel elements.
groups: number of blocked connections from input channels to output
channels.
bias: whether to use a bias row on the analog tile or not.
padding_mode: padding strategy. Only ``'zeros'`` is supported.
rpu_config: resistive processing unit configuration.
realistic_read_write: whether to enable realistic read/write
for setting initial weights and read out of weights.
weight_scaling_omega: If non-zero, the analog weights will be
scaled by ``weight_scaling_omega`` divided by the absolute
maximum value of the original weight matrix.
"""
# pylint: disable=abstract-method
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple],
stride: Union[int, Tuple] = 1,
padding: Union[int, Tuple] = 0,
dilation: Union[int, Tuple] = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
weight_scaling_omega: Optional[float] = None,
):
# pylint: disable=too-many-arguments
kernel_size = _triple(kernel_size)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
if dilation != _triple(1):
raise ValueError('Only dilation = 1 is supported')
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, # type: ignore
False, _triple(0), groups, bias, padding_mode,
rpu_config, realistic_read_write, weight_scaling_omega
)
self.tensor_view = (-1, 1, 1, 1)
[docs] @classmethod
def from_digital(
cls,
module: Conv3d,
rpu_config: Optional[RPUConfigAlias] = None,
realistic_read_write: bool = False,
) -> 'AnalogConv3d':
"""Return an AnalogConv3d layer from a torch Conv3d layer.
Args:
module: The torch module to convert. All layers that are
defined in the ``conversion_map``.
rpu_config: RPU config to apply to all converted tiles.
Applied to all converted tiles.
realistic_read_write: Whether to use closed-loop programming
when setting the weights. Applied to all converted tiles.
Note:
Make sure that the weight max and min settings of the
device support the desired analog weight range.
Returns:
an AnalogConv3d layer based on the digital Conv3d ``module``.
"""
analog_module = cls(module.in_channels,
module.out_channels,
module.kernel_size,
module.stride,
module.padding,
module.dilation,
module.groups,
module.bias is not None,
module.padding_mode,
rpu_config,
realistic_read_write,
)
analog_module.set_weights(module.weight, module.bias)
return analog_module
[docs] def get_tile_size(
self,
in_channels: int,
groups: int,
kernel_size: Tuple[int, ...]
) -> int:
"""Calculate the tile size."""
return (in_channels // groups) * (
kernel_size[0] * kernel_size[1] * kernel_size[2])
def _calculate_indexes(self, x_input: Tensor,
in_channels: int) -> Tuple[Tensor, List[int], int]:
"""Calculate and return the fold indexes and sizes.
Args:
x_input: input matrix
in_channels: then number of in channels
Returns:
fold_indices: indices for the analog tile
image_sizes: image sizes for the analog tile
input_size: size of the current input
"""
# pylint: disable=too-many-locals
input_size = x_input.numel() / x_input.size(0)
# pytorch just always uses NCDHW order
fold_indices = arange(2, x_input.size(2) * x_input.size(3) * x_input.size(4) + 2,
dtype=float64).detach()
shape = [1] + [1] + list(x_input.shape[2:])
fold_indices = fold_indices.reshape(*shape)
if not all(item == 0 for item in self.padding):
fold_indices = pad(fold_indices, pad=[
self.padding[2], self.padding[2],
self.padding[1], self.padding[1],
self.padding[0], self.padding[0]], mode='constant', value=0)
unfold = fold_indices.unfold(2, self.kernel_size[0], self.stride[0]). \
unfold(3, self.kernel_size[1], self.stride[1]). \
unfold(4, self.kernel_size[2], self.stride[2]).clone()
fold_indices = unfold.reshape(-1, self.kernel_size[0] * self.kernel_size[1] *
self.kernel_size[2]).transpose(0, 1).flatten().round()
# concatenate the matrix index for different channels
fold_indices_orig = fold_indices.clone()
for i in range(in_channels - 1):
fold_indices_tmp = fold_indices_orig.clone()
for j in range(fold_indices_orig.size(0)):
if fold_indices_orig[j] != 0:
fold_indices_tmp[j] += (input_size / in_channels) * (i + 1)
fold_indices = cat([fold_indices, fold_indices_tmp], dim=0).clone()
fold_indices = fold_indices.to(dtype=int32)
if self.analog_bias:
out_image_size = fold_indices.numel() // (self.kernel_size[0] *
self.kernel_size[1] *
self.kernel_size[2])
fold_indices = cat((fold_indices, ones(out_image_size, dtype=int32)), 0)
fold_indices = fold_indices.to(x_input.device)
x_depth = x_input.size(2)
x_height = x_input.size(3)
x_width = x_input.size(4)
d_depth = self.get_image_size(x_depth, 0)
d_height = self.get_image_size(x_height, 1)
d_width = self.get_image_size(x_width, 2)
image_sizes = [in_channels, x_depth, x_height, x_width, d_depth, d_height, d_width]
return (fold_indices, image_sizes, input_size)