def _normalize_sequence(length, inputs, layout, merge, in_layout=None):
assert inputs is not None, \
"unroll(inputs=None) has been deprecated. " \
"Please create input variables outside unroll."
axis = layout.find('T')
in_axis = in_layout.find('T') if in_layout is not None else axis
if isinstance(inputs, symbol.Symbol):
if merge is False:
assert len(inputs.list_outputs()) == 1, \
"unroll doesn't allow grouped symbol as input. Please convert " \
"to list with list(inputs) first or let unroll handle splitting."
inputs = list(
symbol.split(inputs,
axis=in_axis,
num_outputs=length,
squeeze_axis=1))
else:
assert length is None or len(inputs) == length
if merge is True:
inputs = [symbol.expand_dims(i, axis=axis) for i in inputs]
inputs = symbol.Concat(*inputs, dim=axis)
in_axis = axis
if isinstance(inputs, symbol.Symbol) and axis != in_axis:
inputs = symbol.swapaxes(inputs, dim0=axis, dim1=in_axis)
return inputs, axis
def __call__(self, inputs, states):
# inputs: (batch_size, decoder_num_hidden)
# for dot attention decoder_num_hidden must equal encoder_num_hidden
if len(states) > 1:
states = [symbol.concat(*states, dim=1)]
# source: (batch_size, seq_len, encoder_num_hidden)
source = states[0]
# (batch_size, decoder_num_hidden, 1)
inputs = symbol.expand_dims(inputs, axis=2)
# (batch_size, seq_len, 1)
scores = symbol.batch_dot(source, inputs)
# (batch_size, encoder_num_hidden)
return _attention_pooling(source, scores), states
def __call__(self, inputs, states):
return inputs, states + [symbol.expand_dims(inputs, axis=1)]