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import torch
from torch import Tensor
from torch.ao.quantization.experimental.observer import APoTObserver
from torch.ao.quantization.fake_quantize import FakeQuantizeBase
from torch.ao.quantization.experimental.fake_quantize_function import fake_quantize_function
class APoTFakeQuantize(FakeQuantizeBase):
alpha: Tensor
gamma: Tensor
quantization_levels: Tensor
level_indices: Tensor
def __init__(self, observer=APoTObserver, **observer_kwargs):
super().__init__()
self.activation_post_process = observer(**observer_kwargs)
self.dtype = self.activation_post_process.dtype
def calculate_qparams(self, signed=False): # type: ignore[override]
return self.activation_post_process.calculate_qparams(signed=signed)
def forward(self, X: torch.Tensor): # type: ignore[override]
if self.observer_enabled[0] == 1:
self.activation_post_process.forward(X)
result = self.activation_post_process.calculate_qparams(signed=False)
self.alpha = result[0]
self.gamma = result[1]
self.quantization_levels = result[2]
self.level_indices = result[3]
if self.fake_quant_enabled[0] == 1:
assert (self.alpha is not None
and self.gamma is not None
and self.quantization_levels is not None
and self.level_indices is not None), "Must set qparams for fake quant"
X = fake_quantize_function.apply(X, self.alpha, self.gamma, self.quantization_levels, self.level_indices)
return X
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