def bias(self, ext, module, g_inp, g_out, backproped):
bp_strategy = ext.get_backprop_strategy()
if BackpropStrategy.is_batch_average(bp_strategy):
return self._bias_for_batch_average(module, backproped)
elif BackpropStrategy.is_sqrt(bp_strategy):
return self._bias_for_sqrt(module, backproped)
def bias(self, ext, module, g_inp, g_out, backproped):
self.check_parameters(ext, module)
bp_strategy = ext.get_backprop_strategy()
if BackpropStrategy.is_batch_average(bp_strategy):
return self._bias_for_batch_average(backproped)
elif BackpropStrategy.is_sqrt(bp_strategy):
return self._factor_from_sqrt(backproped)
def weight(self, ext, module, g_inp, g_out, backproped):
self.check_parameters(ext, module)
bp_strategy = ext.get_backprop_strategy()
if BackpropStrategy.is_batch_average(bp_strategy):
return self._weight_for_batch_average(ext, module, backproped)
elif BackpropStrategy.is_sqrt(bp_strategy):
return self._weight_for_sqrt(ext, module, backproped)
def backpropagate(self, ext, module, g_inp, g_out, backproped):
bp_strategy = ext.get_backprop_strategy()
if BackpropStrategy.is_batch_average(bp_strategy):
return self.backpropagate_batch_average(ext, module, g_inp, g_out,
backproped)
elif BackpropStrategy.is_sqrt(bp_strategy):
return self.backpropagate_sqrt(ext, module, g_inp, g_out,
backproped)
def weight(self, ext, module, g_inp, g_out, backproped):
if module.groups != 1:
raise NotImplementedError(
f"groups ≠ 1 is not supported by {ext.__class__.__name__} " +
f"(got {module.groups}).")
bp_strategy = ext.get_backprop_strategy()
if BackpropStrategy.is_batch_average(bp_strategy):
return self._weight_for_batch_average(ext, module, backproped)
elif BackpropStrategy.is_sqrt(bp_strategy):
return self._weight_for_sqrt(ext, module, backproped)