def __init__(self, config_):
super(Sequence, self).__init__()
self.config = config_
self.is_train = self.config.is_train
# input_size = self.config.input_size
# hidden_size = self.config.hidden_size
# rnn_hidden_size = self.config.rnn_hidden_size
self.beam_width = self.config.beam_width
self.max_decode_step = self.config.max_decode_step
# modules
self.en_embedder = Embedder(
num_embeddings=self.config.en_vocab_size + 4,
embedding_dim=self.config.embedding_dim).to(self.config.gpu)
self.cn_embedder = Embedder(
num_embeddings=self.config.cn_vocab_size + 4,
embedding_dim=self.config.embedding_dim).to(self.config.gpu)
self.encoder = RNNEncoder(config_)
self.decoder = RNNDecoder(config_)
self.softmax = torch.nn.Softmax(dim=-1)
# loss two input sized of bs, s_len, n_class and bs, s_len
self.nll_loss = torch.nn.NLLLoss(
weight=torch.tensor([0] + [1] * (self.config.cn_vocab_size + 3),
dtype=torch.float32)).to(self.config.gpu)
def build_neural_topic_model(self, corpus):
self.build_trans_mat(corpus.OUTPUT.itos, corpus.TOPIC.itos)
self.topic_embedder = Embedder(num_embeddings=self.topic_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.topic_layer = nn.Sequential(
self.topic_embedder,
nn.Linear(self.embed_size, self.hidden_size),
nn.Tanh(),
)
hidden = get_mlp([self.topic_vocab_size, 800, self.embed_size],
'Sigmoid')
normal = NormalParameter(self.embed_size, 50)
h_to_z = nn.Sequential(nn.Linear(50, self.topic_num), nn.Dropout(0.2))
topics = Topics(self.topic_num, self.topic_vocab_size)
penalty = 0.8
self.ntm = NTMR(hidden=hidden,
normal=normal,
h_to_z=h_to_z,
topics=topics,
embedding=self.topic_embedder,
penalty=penalty)
def __init__(self,
vocab,
hidden_size,
hop=1,
attn_mode='dot',
padding_idx=None):
super(EncoderMemNN, self).__init__()
self.num_vocab = vocab
self.max_hops = hop
self.hidden_size = hidden_size
self.attn_mode = attn_mode
self.padding_idx = padding_idx
for hop in range(self.max_hops + 1):
C = Embedder(self.num_vocab,
self.hidden_size,
padding_idx=self.padding_idx)
C.weight.data.normal_(0, 0.1)
self.add_module("C_{}".format(hop), C)
for hop in range(self.max_hops):
A = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode=self.attn_mode,
return_attn_only=True)
self.add_module("A_{}".format(hop), A)
self.C = AttrProxy(self, "C_")
self.A = AttrProxy(self, "A_")
self.softmax = nn.Softmax(dim=1)
def __init__(self,
src_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
with_bridge=False,
dropout=0.0,
use_gpu=False,
pretrain_epoch=5):
super(Entity_Seq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.with_bridge = with_bridge
self.dropout = dropout
self.use_gpu = use_gpu
self.pretrain_epoch = pretrain_epoch
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge1 = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh(),
)
self.bridge2 = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh(),
)
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
dropout=self.dropout)
# Loss Definition
if self.use_gpu:
self.cuda()
def __init__(self,
vocab_size,
hidden_size,
embed_units,
max_hop=3,
padding_idx=None,
n_layers=1,
attn_mode='mlp',
dropout=0.0,
use_gpu=False):
super(Mem2Seq, self).__init__()
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.embed_units = embed_units
self.max_hop = max_hop
self.padding_idx = padding_idx
self.attn_mode = attn_mode
self.dropout = dropout
self.use_gpu = use_gpu
self.n_layers = n_layers
enc_embedder = Embedder(num_embeddings=self.vocab_size,
embedding_dim=self.embed_units,
padding_idx=self.padding_idx)
self.rnn_encoder = RNNEncoder(input_size=self.embed_units,
hidden_size=self.hidden_size,
embedder=enc_embedder,
dropout=self.dropout)
self.mem_encoder = EncoderMemNN(vocab=self.vocab_size,
hidden_size=self.hidden_size,
hop=self.max_hop,
attn_mode='general',
padding_idx=self.padding_idx)
self.decoder = DecoderMemNN(vocab=self.vocab_size,
embedding_dim=self.hidden_size,
hidden_size=self.hidden_size,
hop=self.max_hop,
dropout=self.dropout,
padding_idx=self.padding_idx)
if self.padding_idx is not None:
self.weight = torch.ones(self.vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self, input_size, hidden_size, dropout, padding_idx,
n_layers=1, use_gpu=False):
super(ContextRNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.n_layers = n_layers
self.dropout = dropout
self.dropout_layer = nn.Dropout(dropout)
self.padding_idx = padding_idx
self.embedding = Embedder(
input_size, hidden_size, padding_idx=self.padding_idx)
self.gru = nn.GRU(hidden_size, hidden_size, n_layers,
dropout=dropout, bidirectional=True)
self.W = nn.Linear(2 * hidden_size, hidden_size)
def __init__(self, vocab, embedding_dim, hidden_size, hop,
dropout=0.0, num_layers=1, padding_idx=None, attn_mode=None):
super(DecoderMemNN, self).__init__()
self.num_vocab = vocab
self.max_hops = hop
self.embedding_dim = embedding_dim
self.hidden_size = hidden_size
self.dropout = dropout
self.num_layers = num_layers
self.padding_idx = padding_idx
self.attn_mode = attn_mode
self.rnn_input_size = self.embedding_dim
self.out_input_size = self.hidden_size
for hop in range(self.max_hops + 1):
C = Embedder(self.num_vocab, embedding_dim,
padding_idx=self.padding_idx)
C.weight.data.normal_(0, 0.1)
self.add_module("C_{}".format(hop), C)
self.C = AttrProxy(self, "C_")
if self.attn_mode is not None:
self.attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode=self.attn_mode,
project=False)
self.rnn_input_size += self.hidden_size
self.softmax = nn.Softmax(dim=1)
self.log_softmax = nn.LogSoftmax(dim=-1)
self.W = nn.Linear(self.embedding_dim, 1)
self.W1 = nn.Linear(2 * self.embedding_dim, self.num_vocab)
self.gru = nn.GRU(input_size=self.rnn_input_size,
hidden_size=self.embedding_dim,
num_layers=self.num_layers,
dropout=self.dropout if self.num_layers > 1 else 0,
batch_first=True)
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False,
use_bow=False,
use_kd=False,
use_dssm=False,
use_posterior=False,
weight_control=False,
use_pg=False,
use_gs=False,
concat=False,
pretrain_epoch=0):
super(KnowledgeSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.use_bow = use_bow
self.use_dssm = use_dssm
self.weight_control = weight_control
self.use_kd = use_kd
self.use_pg = use_pg
self.use_gs = use_gs
self.use_posterior = use_posterior
self.pretrain_epoch = pretrain_epoch
self.baseline = 0
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size), nn.Tanh())
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
knowledge_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
knowledge_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.knowledge_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=knowledge_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.prior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.posterior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
concat=concat)
self.log_softmax = nn.LogSoftmax(dim=-1)
self.softmax = nn.Softmax(dim=-1)
self.sigmoid = nn.Sigmoid()
self.softplus = nn.Softplus()
if self.use_bow:
self.bow_output_layer = nn.Sequential(
nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size), nn.Tanh(),
nn.Linear(in_features=self.hidden_size,
out_features=self.tgt_vocab_size),
nn.LogSoftmax(dim=-1))
if self.use_dssm:
self.dssm_project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size)
self.mse_loss = torch.nn.MSELoss(reduction='mean')
if self.use_kd:
self.knowledge_dropout = nn.Dropout()
if self.padding_idx is not None:
self.weight = torch.ones(self.tgt_vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
self.kl_loss = torch.nn.KLDivLoss(size_average=True)
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False,
use_bow=False,
use_kd=False,
use_dssm=False,
use_posterior=False,
weight_control=False,
use_pg=False,
use_gs=False,
concat=False,
pretrain_epoch=0):
super(KnowledgeSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.use_bow = use_bow
self.use_dssm = use_dssm
self.weight_control = weight_control
self.use_kd = use_kd
self.use_pg = use_pg
self.use_gs = use_gs
self.use_posterior = use_posterior
self.pretrain_epoch = pretrain_epoch
self.baseline = 0
bc = BertClient()
enc_embedder = bc.encode(['你好', '吃饭了么'])
# enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
# embedding_dim=self.embed_size, padding_idx=self.padding_idx) # Embedder(30004, 300, padding_idx=0)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh()) #定义一个linear层 ,通过Squential将网络层和激活函数结合起来，输出激活后的网络节点
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
knowledge_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
knowledge_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.knowledge_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=knowledge_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.prior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.posterior_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="dot")
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
concat=concat)
self.log_softmax = nn.LogSoftmax(dim=-1)
self.softmax = nn.Softmax(dim=-1)
self.sigmoid = nn.Sigmoid()
self.softplus = nn.Softplus()
"""
Softplus():a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
"""
if self.use_bow:
self.bow_output_layer = nn.Sequential(
nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size), nn.Tanh(),
nn.Linear(in_features=self.hidden_size,
out_features=self.tgt_vocab_size),
nn.LogSoftmax(dim=-1))
if self.use_dssm:
self.dssm_project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size)
self.mse_loss = torch.nn.MSELoss(reduction='mean')
if self.use_kd:
self.knowledge_dropout = nn.Dropout()
if self.padding_idx is not None:
self.weight = torch.ones(self.tgt_vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(
weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean') #量化真实回复与基线生成的回复的不同 :NLLLoss() 负对数似然
self.kl_loss = torch.nn.KLDivLoss(
size_average=True
) #KLDivLoss() #select related background knowledge -> lead the conversation
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self, corpus, config):
super(Seq2Seq, self).__init__()
self.src_vocab_size = corpus.SRC.vocab_size
self.tgt_vocab_size = corpus.TGT.vocab_size
self.output_vocab_size = corpus.OUTPUT.vocab_size
self.topic_vocab_size = corpus.TOPIC.vocab_size
self.padding_idx = corpus.padding_idx
self.embed_size = config.embed_size
self.hidden_size = config.hidden_size
self.num_layers = config.num_layers
self.bidirectional = config.bidirectional
self.attn_mode = config.attn_mode
self.attn_hidden_size = config.attn_hidden_size
self.with_bridge = config.with_bridge
self.tie_embedding = config.tie_embedding
self.dropout = config.dropout
self.use_gpu = config.use_gpu
self.decoder_attention_channels = config.decoder_attention_channels
self.topic_k = config.topic_k
self.topic_num = config.topic_num
self.decoder_attention_channels = config.decoder_attention_channels
self.use_ntm = config.use_ntm
# topic
self.build_neural_topic_model(corpus)
self.t_to_feature = nn.Sequential(
nn.Linear(self.embed_size * len(self.decoder_attention_channels),
self.hidden_size),
nn.Tanh(),
nn.Dropout(p=self.dropout),
)
# encoder
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
# bridge
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh(),
)
# decoder
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
topic_vocab_size_ = self.topic_vocab_size
if config.without_topic_project:
topic_vocab_size_ = None
if config.without_fd is not None and config.without_fd is True:
RNNDecoder = RNNDecoder_noFd
topic_vocab_size_ = None
else:
RNNDecoder = RNNDecoder_full
self.decoder = RNNDecoder(
input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.output_vocab_size,
topic_size=topic_vocab_size_,
trans_mat=self.trans_mat,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
attn_hidden_size=self.attn_hidden_size,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
tgt_unk_idx=corpus.OUTPUT.itos.index('<unk>'),
attention_channels=self.decoder_attention_channels,
)
# Loss Definition
if self.padding_idx is not None:
weight = torch.ones(self.output_vocab_size)
weight[self.padding_idx] = 0
else:
weight = None
self.nll_loss = NLLLoss(weight=weight,
ignore_index=self.padding_idx,
reduction='mean')
if self.use_gpu:
self.cuda()
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False):
super(HSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.sub_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.hiera_encoder = RNNEncoder(input_size=self.hidden_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.encoder = HRNNEncoder(self.sub_encoder, self.hiera_encoder)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh(),
)
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
attn_hidden_size=self.attn_hidden_size,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout)
# Loss Definition
if self.padding_idx is not None:
weight = torch.ones(self.tgt_vocab_size)
weight[self.padding_idx] = 0
else:
weight = None
self.nll_loss = NLLLoss(weight=weight,
ignore_index=self.padding_idx,
reduction='mean')
if self.use_gpu:
self.cuda()
def __init__(self, corpus, config):
super(Seq2Seq, self).__init__()
self.src_vocab_size = corpus.SRC.vocab_size
self.tgt_vocab_size = corpus.TGT.vocab_size
self.embed_size = config.embed_size
self.hidden_size = config.hidden_size
self.padding_idx = corpus.padding_idx
self.num_layers = config.num_layers
self.bidirectional = config.bidirectional
self.attn_mode = config.attn_mode
self.attn_hidden_size = config.attn_hidden_size
self.with_bridge = config.with_bridge
self.tie_embedding = config.tie_embedding
self.dropout = config.dropout
self.use_gpu = config.use_gpu
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size),
nn.Tanh(),
)
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
attn_hidden_size=self.attn_hidden_size,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout)
# Loss Definition
if self.padding_idx is not None:
weight = torch.ones(self.tgt_vocab_size)
weight[self.padding_idx] = 0
else:
weight = None
self.nll_loss = NLLLoss(weight=weight,
ignore_index=self.padding_idx,
reduction='mean')
if self.use_gpu:
self.cuda()
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
tie_embedding=False,
margin=None,
with_project=False,
dropout=0.0,
use_gpu=False):
super(DSSM, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.margin = margin
self.with_project = with_project
src_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.src_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=src_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_project:
self.project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size,
bias=False)
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
tgt_embedder = src_embedder
else:
tgt_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.tgt_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=tgt_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.use_gpu:
self.cuda()
def __init__(self,
vocab_size,
embed_units,
hidden_size,
padding_idx=None,
num_layers=1,
max_hop=3,
bidirectional=True,
attn_mode='mlp',
dropout=0.0,
use_gpu=False):
super(MemNet, self).__init__()
self.vocab_size = vocab_size
self.embed_units = embed_units
self.padding_idx = padding_idx
self.hidden_size = hidden_size
self.num_layers = num_layers
self.max_hop = max_hop
self.bidirectional = bidirectional
self.dropout = dropout
self.attn_mode = attn_mode
self.use_gpu = use_gpu
enc_embedder = Embedder(num_embeddings=self.vocab_size,
embedding_dim=self.embed_units,
padding_idx=self.padding_idx)
dec_embedder = enc_embedder
self.rnn_encoder = RNNEncoder(input_size=self.embed_units,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.mem_encoder = EncoderMemNN(vocab=self.vocab_size,
hidden_size=self.hidden_size,
hop=self.max_hop,
attn_mode='general',
padding_idx=self.padding_idx)
self.decoder = RNNDecoder(input_size=self.embed_units,
hidden_size=self.hidden_size,
output_size=self.vocab_size,
embedder=dec_embedder,
attn_mode=self.attn_mode,
attn_hidden_size=self.hidden_size,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout)
self.softmax = nn.Softmax(dim=-1)
if self.padding_idx is not None:
self.weight = torch.ones(self.vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self,
src_vocab_size,
tgt_vocab_size,
embed_size,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode="mlp",
attn_hidden_size=None,
with_bridge=False,
tie_embedding=False,
dropout=0.0,
use_gpu=False,
use_dssm=False,
weight_control=False,
use_pg=False,
concat=False,
pretrain_epoch=0,
with_label=False):
super(TwoStagePersonaSeq2Seq, self).__init__()
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.padding_idx = padding_idx
self.num_layers = num_layers
self.bidirectional = bidirectional
self.attn_mode = attn_mode
self.attn_hidden_size = attn_hidden_size
self.with_bridge = with_bridge
self.tie_embedding = tie_embedding
self.dropout = dropout
self.use_gpu = use_gpu
self.use_dssm = use_dssm
self.weight_control = weight_control
self.use_pg = use_pg
self.pretrain_epoch = pretrain_epoch
self.baseline = 0
self.with_label = with_label
self.task_id = 1
enc_embedder = Embedder(num_embeddings=self.src_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=enc_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size), nn.Tanh())
if self.tie_embedding:
assert self.src_vocab_size == self.tgt_vocab_size
dec_embedder = enc_embedder
persona_embedder = enc_embedder
else:
dec_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
persona_embedder = Embedder(num_embeddings=self.tgt_vocab_size,
embedding_dim=self.embed_size,
padding_idx=self.padding_idx)
self.persona_encoder = RNNEncoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
embedder=persona_embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.persona_attention = Attention(query_size=self.hidden_size,
memory_size=self.hidden_size,
hidden_size=self.hidden_size,
mode="general")
self.decoder = RNNDecoder(input_size=self.embed_size,
hidden_size=self.hidden_size,
output_size=self.tgt_vocab_size,
embedder=dec_embedder,
num_layers=self.num_layers,
attn_mode=self.attn_mode,
memory_size=self.hidden_size,
feature_size=None,
dropout=self.dropout,
concat=concat,
with_label=self.with_label)
self.key_linear = nn.Linear(in_features=self.embed_size,
out_features=self.hidden_size)
if self.use_dssm:
self.dssm_project = nn.Linear(in_features=self.hidden_size,
out_features=self.hidden_size)
self.mse_loss = torch.nn.MSELoss(reduction='mean')
self.mse_loss = torch.nn.MSELoss(reduction='mean')
if self.padding_idx is not None:
self.weight = torch.ones(self.tgt_vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
self.persona_loss = NLLLoss(weight=None, reduction='mean')
self.eps = 1e-7
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
def __init__(self,
vocab_size,
embed_units,
hidden_size,
padding_idx=None,
num_layers=1,
bidirectional=True,
attn_mode='mlp',
dropout=0.0,
with_bridge=True,
use_gpu=False):
super(PointerNet, self).__init__()
self.vocab_size = vocab_size
self.embed_units = embed_units
self.padding_idx = padding_idx
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bidirectional = bidirectional
self.dropout = dropout
self.attn_mode = attn_mode
self.with_bridge = with_bridge
self.use_gpu = use_gpu
embedder = Embedder(num_embeddings=self.vocab_size,
embedding_dim=self.embed_units,
padding_idx=self.padding_idx)
self.fact_encoder = RNNEncoder(input_size=self.embed_units,
hidden_size=self.hidden_size,
embedder=embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.hist_encoder = RNNEncoder(input_size=self.embed_units,
hidden_size=self.hidden_size,
embedder=embedder,
num_layers=self.num_layers,
bidirectional=self.bidirectional,
dropout=self.dropout)
self.decoder = PointerDecoder(input_size=self.embed_units,
hidden_size=self.hidden_size,
output_size=self.vocab_size,
embedder=embedder,
attn_mode=self.attn_mode,
dropout=self.dropout)
if self.with_bridge:
self.bridge = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size), nn.Tanh())
if self.padding_idx is not None:
self.weight = torch.ones(self.vocab_size)
self.weight[self.padding_idx] = 0
else:
self.weight = None
self.nll_loss = NLLLoss(weight=self.weight,
ignore_index=self.padding_idx,
reduction='mean')
self.kl_loss = torch.nn.KLDivLoss(size_average=True)
if self.use_gpu:
self.cuda()
self.weight = self.weight.cuda()
