def make_base_state_vars(self, name, output):
"""
make state vars for input from outside the loop (automatic tile_batch)
:param str name: layer name
:param output: Data
:rtype: Data
"""
rec_layer = self.parent_rec_layer
from TFUtil import tile_transposed
output.placeholder = tile_transposed(
rec_layer.create_state_var(name="base_value_%s" % name, initial_value=output.placeholder, data_shape=output),
axis=output.batch_dim_axis,
multiples=self.parent_tile_multiples_var.read_value())
from TFUtil import DimensionTag
for i, size in list(output.size_placeholder.items()):
dim_tag = DimensionTag.get_tag_from_size_tensor(size)
if not dim_tag:
print("Warning, no defined dim tag on %r, axis %i" % (name, output.get_batch_axis(i)), file=log.v2)
dim_tag = output.get_dim_tag(output.get_batch_axis(i))
dim_tag.set_tag_on_size_tensor(size)
new_size = rec_layer.create_state_var(name="base_size%i_%s" % (i, name), initial_value=size)
new_size = tile_transposed(new_size, axis=0, multiples=self.parent_tile_multiples_var.read_value())
dim_tag.set_tag_on_size_tensor(new_size)
output.size_placeholder[i] = new_size
return output
def tile_batch_op(self, repetitions):
"""
:param tf.Tensor repetitions:
:return: op which assigns the tiled value of the previous var value
:rtype: tf.Operation
"""
if self.var_data_shape.batch_dim_axis is None:
return tf.no_op(name="tile_batch_state_var_no_op_%s" % self.name)
# See also Data.copy_extend_with_beam.
from TFUtil import tile_transposed
tiled_value = tile_transposed(
self.var.read_value(), axis=self.var_data_shape.batch_dim_axis, multiples=repetitions)
return tf.assign(self.var, tiled_value, name="tile_batch_state_var_%s" % self.name).op