| |
| |
| |
| |
| license_statement = "NLP demo software by HyperbeeAI. Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. hello@hyperbee.ai" |
| print("imported functions.py") |
| print(license_statement) |
| print("") |
|
|
| import torch, sys |
| import torch.nn as nn |
| from torch.autograd import Function |
|
|
| class Q_ud(Function): |
| @staticmethod |
| def forward(_, x, xb): |
| factor = 2**(xb-1) |
| return x.mul(factor).add(.5).floor().div(factor) |
|
|
| class Q_u(Function): |
| @staticmethod |
| def forward(_, x, xb): |
| factor = 2**(8-xb) |
| return x.mul(factor).add(.5).floor() |
|
|
| class Q_d(Function): |
| @staticmethod |
| def forward(_, x, xb): |
| factor = 2**(xb-1) |
| return x.div(factor).add(.5).floor() |
|
|
| class quantization(nn.Module): |
| def __init__(self, xb = 8, mode='updown', wide=False): |
| super().__init__() |
| self.xb = xb |
| self.mode = mode |
| self.wide = wide |
|
|
| def forward(self, x): |
| if(self.mode=='updown'): |
| return Q_ud.apply(x, self.xb) |
| elif(self.mode=='down'): |
| if(self.wide): |
| return Q_d.apply(x, self.xb - 5) |
| else: |
| return Q_d.apply(x, self.xb) |
| elif(self.mode=='up'): |
| return Q_u.apply(x, self.xb) |
| else: |
| print('wrong quantization mode. exiting') |
| sys.exit() |
|
|
| class clamping_hw(nn.Module): |
| def __init__(self, xb = 8, wide=False): |
| super().__init__() |
| if(wide): |
| self.min_val = -2**(30-1) |
| self.max_val = 2**(30-1)-1 |
| else: |
| self.min_val = -2**(xb-1) |
| self.max_val = 2**(xb-1)-1 |
|
|
| def forward(self, x): |
| return x.clamp(min=self.min_val, max=self.max_val) |
|
|
| |
| |
| def linear_functional(x, weight, bias, _stride, _padding): |
| |
| return nn.functional.linear(x, weight, bias) |
|
|
