def __init__(self, vocab_size, hidden_size=1024, num_layers=4, dropout=0.2,
batch_first=False, share_embedding=True, fusion=True):
"""
Constructor for the GNMT v2 model.
:param vocab_size: size of vocabulary (number of tokens)
:param hidden_size: internal hidden size of the model
:param num_layers: number of layers, applies to both encoder and
decoder
:param dropout: probability of dropout (in encoder and decoder)
:param batch_first: if True the model uses (batch,seq,feature) tensors,
if false the model uses (seq, batch, feature)
:param share_embedding: if True embeddings are shared between encoder
and decoder
"""
super(GNMT, self).__init__(batch_first=batch_first)
if share_embedding:
embedder = nn.Embedding(vocab_size, hidden_size,
padding_idx=config.PAD)
nn.init.uniform_(embedder.weight.data, -0.1, 0.1)
else:
embedder = None
self.encoder = ResidualRecurrentEncoder(vocab_size, hidden_size,
num_layers, dropout,
batch_first, embedder)
self.decoder = ResidualRecurrentDecoder(vocab_size, hidden_size,
num_layers, dropout,
batch_first, embedder,
fusion=fusion)
def __init__(self, vocab_size, hidden_size=512, num_layers=8, bias=True,
dropout=0.2, batch_first=False, math='fp32',
share_embedding=False):
"""
Constructor for the GNMT v2 model.
:param vocab_size: size of vocabulary (number of tokens)
:param hidden_size: internal hidden size of the model
:param num_layers: number of layers, applies to both encoder and
decoder
:param bias: globally enables or disables bias in encoder and decoder
:param dropout: probability of dropout (in encoder and decoder)
:param batch_first: if True the model uses (batch,seq,feature) tensors,
if false the model uses (seq, batch, feature)
:param math: arithmetic type, 'fp32' or 'fp16'
:param share_embedding: if True embeddings are shared between encoder
and decoder
"""
super(GNMT, self).__init__(batch_first=batch_first)
if share_embedding:
embedder = nn.Embedding(vocab_size, hidden_size, padding_idx=config.PAD)
else:
embedder = None
self.encoder = ResidualRecurrentEncoder(vocab_size, hidden_size,
num_layers, bias, dropout,
batch_first, embedder)
self.decoder = ResidualRecurrentDecoder(vocab_size, hidden_size,
num_layers, bias, dropout,
batch_first, math, embedder)