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