def __init__(self, params):
super(MTEncoderV1, self).__init__(params)
p = self.params
assert p.packed_input is False, (
'Packed inputs are not yet supported for '
'MTEncoderV1.')
with tf.variable_scope(p.name):
if p.cc_schedule is not None:
self.CreateChild('cc_schedule', p.cc_schedule)
self.CreateChild('emb', p.emb)
rnn_layers_params = []
# L0 is a bi-directional lstm.
# L0's forward lstm cell
params = (p.lstm_tpl.Copy()
if p.lstm_tpl_bidi is None else p.lstm_tpl_bidi.Copy())
params.name = 'L0_rnn_fwd'
params.num_input_nodes = p.emb.embedding_dim
params.num_output_nodes = p.lstm_cell_size
forward_lstm = params
# L0's backward lstm cell
params = params.Copy()
params.name = 'L0_rnn_bak'
backward_lstm = params
# L0 layer.
params = model_helper.CreateBidirectionalRNNParams(
self.params, forward_lstm, backward_lstm)
params.name = 'L0'
rnn_layers_params.append(params)
# The latter layers are all uni-directional lstm.
input_size = 2 * p.lstm_cell_size
for i in range(1, p.num_lstm_layers):
# Forward lstm cell.
cell = (p.lstm_tpl.Copy()
if p.lstm_tpl_uni is None else p.lstm_tpl_uni.Copy())
cell.name = 'L%d_rnn' % i
cell.num_input_nodes = input_size
cell.num_output_nodes = p.lstm_cell_size
# Forward lstm layer.
params = model_helper.CreateUnidirectionalRNNParams(
self.params, cell)
params.name = 'L%d' % i
rnn_layers_params.append(params)
input_size = p.lstm_cell_size
self.CreateChildren('rnn', rnn_layers_params)
dropout_p = layers.DropoutLayer.Params().Set(
name='dropout_layer',
keep_prob=1.0 - p.dropout_prob,
random_seed=p.random_seed +
84828474 if p.random_seed else None)
self.CreateChild('dropout', dropout_p)
def __init__(self, params):
super(MTEncoderUniRNN, self).__init__(params)
p = self.params
assert p.packed_input is False, (
'Packed inputs are not yet supported for '
'MTEncoderUniRNN.')
with tf.variable_scope(p.name):
if p.cc_schedule is None:
self.cc_schedule = None
else:
self.CreateChild('cc_schedule', p.cc_schedule)
self.CreateChild('emb', p.emb)
rnn_layers_params = []
num_input_nodes = p.emb.embedding_dim
for i in range(p.num_lstm_layers):
cell = p.lstm_tpl.Copy()
cell.name = 'L%d_rnn' % i
cell.num_input_nodes = num_input_nodes
cell.num_output_nodes = p.lstm_cell_size
params = model_helper.CreateUnidirectionalRNNParams(
self.params, cell)
params.name = 'L%d' % i
rnn_layers_params.append(params)
num_input_nodes = cell.num_output_nodes
self.CreateChildren('rnn', rnn_layers_params)
dropout_p = layers.DropoutLayer.Params().Set(
name='dropout_layer',
keep_prob=1.0 - p.dropout_prob,
random_seed=p.random_seed +
827366448 if p.random_seed else None)
self.CreateChild('dropout', dropout_p)
if p.is_transparent:
transparent_params = p.transparent_merger_tpl.Copy()
transparent_params.name = 'transparent'
transparent_params.num_sources = p.num_lstm_layers
self.CreateChild('transparent_merger', transparent_params)
def __init__(self, params):
super(MTDecoderV1, self).__init__(params)
p = self.params
assert p.emb.vocab_size == p.softmax.num_classes
with tf.variable_scope(p.name):
if p.cc_schedule is None:
self.cc_schedule = None
else:
self.CreateChild('cc_schedule', p.cc_schedule)
if py_utils.use_tpu():
emb_device = self.cluster.WorkerDeviceInModelSplit(0)
else:
emb_device = ''
with tf.device(emb_device):
self.CreateChild('emb', p.emb)
p.attention.dtype = p.dtype
p.attention.source_dim = p.source_dim
p.attention.query_dim = p.rnn_cell_dim
p.attention.packed_input = p.packed_input
if p.attention.params_init is None:
p.attention.params_init = py_utils.WeightInit.Gaussian(
1. / math.sqrt(p.attention.source_dim + p.attention.query_dim))
atten_params = p.attention.Copy()
params = p.atten_rnn_cell_tpl.Copy()
params.name = 'atten_rnn'
params.dtype = p.dtype
params.reset_cell_state = p.packed_input
params.num_input_nodes = p.emb.embedding_dim + p.attention.source_dim
params.num_output_nodes = p.rnn_cell_dim
atten_rnn_cell = params.Copy()
params = p.atten_rnn_cls.Params()
params.name = 'frnn_with_atten'
params.dtype = p.dtype
params.cell = atten_rnn_cell
params.attention = atten_params
params.output_prev_atten_ctx = p.use_prev_atten_ctx
params.packed_input = p.packed_input
params.use_zero_atten_state = p.use_zero_atten_state
params.atten_context_dim = p.attention.source_dim
self.CreateChild('frnn_with_atten', params)
# TODO(zhifengc): Avoid this?
self._rnn_attn = self.frnn_with_atten.rnn_cell
self._atten = self.frnn_with_atten.attention
rnn_layers_params = []
for i in range(1, p.rnn_layers):
params = p.rnn_cell_tpl.Copy()
params.name = 'rnn%d' % i
params.dtype = p.dtype
params.num_input_nodes = p.rnn_cell_dim + p.attention.source_dim
params.num_output_nodes = p.rnn_cell_dim
params.reset_cell_state = p.packed_input
rnn_cell_p = params
params = model_helper.CreateUnidirectionalRNNParams(
self.params, rnn_cell_p)
params.name = 'frnn%d' % i
params.packed_input = p.packed_input
rnn_layers_params.append(params)
self.CreateChildren('frnn', rnn_layers_params)
p.softmax.dtype = p.dtype
if p.feed_attention_context_vec_to_softmax:
p.softmax.input_dim = p.rnn_cell_dim + p.attention.source_dim
else:
p.softmax.input_dim = p.rnn_cell_dim
self.CreateChild('softmax', p.softmax)
def CreateUnidirectionalRNNParams(self, forward_p):
return model_helper.CreateUnidirectionalRNNParams(
self.params, forward_p)
