def __init__(self,
src_vocab_size,
trg_vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.,
bos_id=0,
eos_id=1,
beam_size=4,
max_out_len=256):
args = dict(locals())
args.pop("self")
args.pop("__class__", None)
self.bos_id = args.pop("bos_id")
self.beam_size = args.pop("beam_size")
self.max_out_len = args.pop("max_out_len")
self.num_layers = num_layers
super(Seq2SeqAttnInferModel, self).__init__(**args)
# Dynamic decoder for inference
self.beam_search_decoder = nn.BeamSearchDecoder(
self.decoder.lstm_attention.cell,
start_token=bos_id,
end_token=eos_id,
beam_size=beam_size,
embedding_fn=self.decoder.embedder,
output_fn=self.decoder.output_layer)
def __init__(self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
bos_id=0,
eos_id=1,
beam_size=4,
max_out_len=256):
self.bos_id = bos_id
self.beam_size = beam_size
self.max_out_len = max_out_len
self.num_layers = num_layers
super(Seq2SeqAttnInferModel,
self).__init__(vocab_size, embed_dim, hidden_size, num_layers,
eos_id)
# Dynamic decoder for inference
self.beam_search_decoder = nn.BeamSearchDecoder(
self.decoder.lstm_attention.cell,
start_token=bos_id,
end_token=eos_id,
beam_size=beam_size,
embedding_fn=self.decoder.embedder,
output_fn=self.decoder.output_layer)
def forward(self, trg):
# Encoder
latent_z = paddle.normal(shape=(trg.shape[0], self.latent_size))
dec_first_hidden_cell = self.fc(latent_z)
dec_first_hidden, dec_first_cell = paddle.split(
dec_first_hidden_cell, 2, axis=-1)
if self.num_layers > 1:
dec_first_hidden = paddle.split(dec_first_hidden, self.num_layers)
dec_first_cell = paddle.split(dec_first_cell, self.num_layers)
else:
dec_first_hidden = [dec_first_hidden]
dec_first_cell = [dec_first_cell]
dec_initial_states = [[h, c]
for h, c in zip(dec_first_hidden, dec_first_cell)]
output_fc = lambda x: F.one_hot(
paddle.multinomial(
F.softmax(paddle.squeeze(
self.decoder.output_fc(x),[1]))),num_classes=self.vocab_size)
latent_z = nn.BeamSearchDecoder.tile_beam_merge_with_batch(
latent_z, self.beam_size)
decoder = nn.BeamSearchDecoder(
cell=self.decoder.lstm.cell,
start_token=self.start_token,
end_token=self.end_token,
beam_size=self.beam_size,
embedding_fn=self.decoder.trg_embedder,
output_fn=output_fc)
outputs, _ = nn.dynamic_decode(
decoder,
inits=dec_initial_states,
max_step_num=self.max_out_len,
latent_z=latent_z)
return outputs