# -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 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.
"""Conductance converters for the phenomenological noise models for inference."""
from typing import Dict, List, Optional, Tuple
from torch import abs as torch_abs
from torch import Tensor
from torch.autograd import no_grad
from aihwkit.inference.converter.base import BaseConductanceConverter
_ZERO_CLIP = 1e-7
[docs]class SinglePairConductanceConverter(BaseConductanceConverter):
r"""Single pair of devices.
Assuming a single pair of devices per cross-point, taking positive
and negative weights, respectively, where one device is always at
0.
Args:
g_max: In :math:`\mu S`, the maximal conductance, ie the value
the absolute max of the weights will be mapped to.
g_min: In :math:`\mu S`, the minimal conductance, ie the value
the logical zero of the weights will be mapped to.
"""
def __init__(self, g_max: Optional[float] = None, g_min: Optional[float] = None):
self.g_max = 25.0 if g_max is None else g_max
self.g_min = 0.0 if g_min is None else g_min
self.scale_ratio = None
if self.g_max < 0.0:
raise ValueError("g_max should be a positive value")
if self.g_min < 0.0:
raise ValueError("g_min should be a positive value")
if self.g_min >= self.g_max:
raise ValueError("g_min should be smaller than g_max")
def __str__(self) -> str:
return "{}(g_max={:1.2f}, g_min={:1.2f})".format(
self.__class__.__name__, self.g_max, self.g_min
)
[docs] @no_grad()
def convert_to_conductances(self, weights: Tensor) -> Tuple[List[Tensor], Dict]:
abs_max = torch_abs(weights).max()
scale_ratio = (self.g_max - self.g_min) / abs_max.clamp(min=_ZERO_CLIP)
scaled_weights = weights * scale_ratio
conductances = [
scaled_weights.clamp(min=0.0, max=self.g_max) + self.g_min,
(-scaled_weights).clamp(min=0.0, max=self.g_max) + self.g_min,
]
params = {"scale_ratio": scale_ratio}
return conductances, params
[docs] @no_grad()
def convert_back_to_weights(self, conductances: List[Tensor], params: Dict) -> Tensor:
if len(conductances) != 2:
raise ValueError("conductances must contain exactly two elements")
if "scale_ratio" not in params:
raise ValueError("params do not contain scale_ratio")
weights = ((conductances[0] - self.g_min) - (conductances[1] - self.g_min)) / params[
"scale_ratio"
]
return weights