def maybe_int_bias(module: QuantWBIOL):
if module.bias is not None:
if module.is_bias_quant_enabled:
bias = module.int_bias(float_datatype=True)
else:
bias = module.bias
bias = torch.t(bias).detach()
else:
bias = None
return bias
def maybe_int_bias(module: QuantWBIOL):
if module.bias is not None:
if module.is_bias_quant_enabled:
bias = module.int_bias(float_datatype=True)
else:
bias = module.bias
bias_shape = [1] * len(module.weight.shape)
bias_shape[1] = -1
# shape should broadcast with activations along channel dim
bias = bias.view(bias_shape).detach()
else:
bias = None
return bias
def int_bias(module: QuantWBIOL):
if module.bias is not None:
int_bias = module.int_bias(float_datatype=False).detach()
return int_bias.type(torch.int32)
else:
return None
def int_bias(module: QuantWBIOL):
if module.bias is not None:
return module.int_bias(float_datatype=False).detach()
else:
return None