def reverse(self, inputs, **kwargs):
"""Reverse pass for the inverse bipartite transformation."""
if not self.built:
self._maybe_build(inputs)
inputs = tf.convert_to_tensor(inputs)
batch_ndims = inputs.shape.ndims - 2
mask = tf.reshape(tf.cast(self.mask, inputs.dtype),
[1] * batch_ndims + [-1, 1])
masked_inputs = mask * inputs
net = self.layer(masked_inputs, **kwargs)
if net.shape[-1] == 2 * self.vocab_size:
loc, scale = tf.split(net, 2, axis=-1)
scale = tf.cast(utils.one_hot_argmax(scale, self.temperature),
inputs.dtype)
scaled_inputs = utils.one_hot_multiply(inputs, scale)
elif net.shape[-1] == self.vocab_size:
loc = net
scaled_inputs = inputs
else:
raise ValueError(
'Output of layer does not have compatible dimensions.')
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature),
inputs.dtype)
masked_outputs = (1. - mask) * utils.one_hot_add(loc, scaled_inputs)
outputs = masked_inputs + masked_outputs
return outputs
def reverse(self, inputs, **kwargs):
"""Reverse pass returning the inverse autoregressive transformation."""
if not self.built:
self._maybe_build(inputs)
net = self.layer(inputs, **kwargs)
if net.shape[-1] == 2 * self.vocab_size:
loc, scale = tf.split(net, 2, axis=-1)
scale = tf.cast(utils.one_hot_argmax(scale, self.temperature),
inputs.dtype)
scaled_inputs = utils.one_hot_multiply(inputs, scale)
elif net.shape[-1] == self.vocab_size:
loc = net
scaled_inputs = inputs
else:
raise ValueError('Output of layer does not have compatible dimensions.')
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature), inputs.dtype)
outputs = utils.one_hot_add(loc, scaled_inputs)
return outputs
