import torch
from torch.nn.parameter import Parameter
from mqbench.fake_quantize.quantize_base import QuantizeBase
[docs]class PACTFakeQuantize(QuantizeBase):
def __init__(self, observer, alpha=6.0, **observer_kwargs):
super(PACTFakeQuantize, self).__init__(observer, **observer_kwargs)
self.alpha = Parameter(torch.tensor([alpha]))
if not self.is_symmetric_quant:
self.n_alpha = Parameter(torch.tensor([-alpha]))
self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))
self.register_buffer('zero_point', torch.tensor([0], dtype=torch.int))
[docs] def forward(self, X):
if self.observer_enabled[0] == 1:
self.activation_post_process(X.detach())
X = torch.where(X > self.alpha, self.alpha, X)
self.activation_post_process.max_val.data.fill_(self.alpha.data[0])
if X.min() < 0:
if self.is_symmetric_quant:
X = torch.where(X < -self.alpha, -self.alpha, X)
self.activation_post_process.min_val.data.fill_(-self.alpha[0].data)
else:
X = torch.where(X < self.n_alpha, self.n_alpha, X)
self.activation_post_process.min_val.data.fill_(self.n_alpha[0].data)
else:
self.activation_post_process.min_val.data.fill_(0.)
_scale, _zero_point = self.activation_post_process.calculate_qparams()
_scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
if self.scale.shape != _scale.shape:
self.scale.resize_(_scale.shape)
self.zero_point.resize_(_zero_point.shape)
self.scale.copy_(_scale)
self.zero_point.copy_(_zero_point)
if self.fake_quant_enabled[0] == 1:
X = torch.fake_quantize_per_tensor_affine(
X, self.scale.item(), int(self.zero_point.item()), self.quant_min, self.quant_max)
return X