def call(self, inputs, **kwargs):
"""Forward pass for bipartite generation."""
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)
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature),
inputs.dtype)
scale = tf.cast(utils.one_hot_argmax(scale, self.temperature),
inputs.dtype)
inverse_scale = utils.multiplicative_inverse(
scale, self.vocab_size)
shifted_inputs = utils.one_hot_minus(inputs, loc)
masked_outputs = (1. - mask) * utils.one_hot_multiply(
shifted_inputs, inverse_scale)
elif net.shape[-1] == self.vocab_size:
loc = net
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature),
inputs.dtype)
masked_outputs = (1. - mask) * utils.one_hot_minus(inputs, loc)
else:
raise ValueError(
'Output of layer does not have compatible dimensions.')
outputs = masked_inputs + masked_outputs
return outputs
def _initial_call(self, new_inputs, length, **kwargs):
"""Returns Tensor of shape [..., 1, vocab_size].
Args:
new_inputs: Tensor of shape [..., vocab_size], the new input to generate
its output.
length: Length of final desired sequence.
**kwargs: Optional keyword arguments to layer.
"""
inputs = new_inputs[..., tf.newaxis, :]
# TODO(trandustin): To handle variable lengths, extend MADE to subset its
# input and output layer weights rather than pad inputs.
batch_ndims = inputs.shape.ndims - 2
padded_inputs = tf.pad(
inputs, paddings=[[0, 0]] * batch_ndims + [[0, length - 1], [0, 0]])
net = self.layer(padded_inputs, **kwargs)
if net.shape[-1] == 2 * self.vocab_size:
loc, scale = tf.split(net, 2, axis=-1)
loc = loc[..., 0:1, :]
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature), inputs.dtype)
scale = scale[..., 0:1, :]
scale = tf.cast(utils.one_hot_argmax(scale, self.temperature),
inputs.dtype)
inverse_scale = utils.multiplicative_inverse(scale, self.vocab_size)
shifted_inputs = utils.one_hot_minus(inputs, loc)
outputs = utils.one_hot_multiply(shifted_inputs, inverse_scale)
elif net.shape[-1] == self.vocab_size:
loc = net
loc = loc[..., 0:1, :]
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature), inputs.dtype)
outputs = utils.one_hot_minus(inputs, loc)
else:
raise ValueError('Output of layer does not have compatible dimensions.')
return outputs
def _per_timestep_call(self, current_outputs, new_inputs, length, timestep,
**kwargs):
"""Returns Tensor of shape [..., timestep+1, vocab_size].
Args:
current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far
generated sequence Tensor.
new_inputs: Tensor of shape [..., vocab_size], the new input to generate
its output given current_outputs.
length: Length of final desired sequence.
timestep: Current timestep.
**kwargs: Optional keyword arguments to layer.
"""
inputs = tf.concat([current_outputs, new_inputs[..., tf.newaxis, :]],
axis=-2)
# TODO(trandustin): To handle variable lengths, extend MADE to subset its
# input and output layer weights rather than pad inputs.
batch_ndims = inputs.shape.ndims - 2
padded_inputs = tf.pad(inputs,
paddings=[[0, 0]] * batch_ndims +
[[0, length - timestep - 1], [0, 0]])
net = self.layer(padded_inputs, **kwargs)
if net.shape[-1] == 2 * self.vocab_size:
loc, scale = tf.split(net, 2, axis=-1)
loc = loc[..., :(timestep + 1), :]
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature),
inputs.dtype)
scale = scale[..., :(timestep + 1), :]
scale = tf.cast(utils.one_hot_argmax(scale, self.temperature),
inputs.dtype)
inverse_scale = utils.multiplicative_inverse(
scale, self.vocab_size)
shifted_inputs = utils.one_hot_minus(inputs, loc)
new_outputs = utils.one_hot_multiply(shifted_inputs, inverse_scale)
elif net.shape[-1] == self.vocab_size:
loc = net
loc = loc[..., :(timestep + 1), :]
loc = tf.cast(utils.one_hot_argmax(loc, self.temperature),
inputs.dtype)
new_outputs = utils.one_hot_minus(inputs, loc)
else:
raise ValueError(
'Output of layer does not have compatible dimensions.')
outputs = tf.concat([current_outputs, new_outputs[..., -1:, :]],
axis=-2)
if not tf.executing_eagerly():
outputs.set_shape([None] * batch_ndims +
[timestep + 1, self.vocab_size])
return outputs