def __init__(self, hps, embed):
"""
:param hps:
word_emb_dim: word embedding dimension
sent_max_len: max token number in the sentence
output_channel: output channel for cnn
min_kernel_size: min kernel size for cnn
max_kernel_size: max kernel size for cnn
word_embedding: bool, use word embedding or not
embedding_path: word embedding path
embed_train: bool, whether to train word embedding
cuda: bool, use cuda or not
:param vocab: FastNLP.Vocabulary
"""
super(Encoder, self).__init__()
self._hps = hps
self.sent_max_len = hps.sent_max_len
embed_size = hps.word_emb_dim
sent_max_len = hps.sent_max_len
input_channels = 1
out_channels = hps.output_channel
min_kernel_size = hps.min_kernel_size
max_kernel_size = hps.max_kernel_size
width = embed_size
# word embedding
self.embed = embed
# position embedding
self.position_embedding = nn.Embedding.from_pretrained(
get_sinusoid_encoding_table(sent_max_len + 1,
embed_size,
padding_idx=0),
freeze=True)
# cnn
self.convs = nn.ModuleList([
nn.Conv2d(input_channels,
out_channels,
kernel_size=(height, width))
for height in range(min_kernel_size, max_kernel_size + 1)
])
print("[INFO] Initing W for CNN.......")
for conv in self.convs:
init_weight_value = 6.0
init.xavier_normal_(conv.weight.data,
gain=np.sqrt(init_weight_value))
fan_in, fan_out = Encoder.calculate_fan_in_and_fan_out(
conv.weight.data)
std = np.sqrt(init_weight_value) * np.sqrt(2.0 /
(fan_in + fan_out))
def __init__(self, hps):
super(ELMoEndoer2, self).__init__()
self._hps = hps
self._cuda = hps.cuda
self.sent_max_len = hps.sent_max_len
from allennlp.modules.elmo import Elmo
elmo_dim = 1024
options_file = "/remote-home/dqwang/ELMo/elmo_2x4096_512_2048cnn_2xhighway_5.5B_options.json"
weight_file = "/remote-home/dqwang/ELMo/elmo_2x4096_512_2048cnn_2xhighway_5.5B_weights.hdf5"
# elmo_dim = 512
# options_file = "/remote-home/dqwang/ELMo/elmo_2x2048_256_2048cnn_1xhighway_options.json"
# weight_file = "/remote-home/dqwang/ELMo/elmo_2x2048_256_2048cnn_1xhighway_weights.hdf5"
embed_size = hps.word_emb_dim
sent_max_len = hps.sent_max_len
input_channels = 1
out_channels = hps.output_channel
min_kernel_size = hps.min_kernel_size
max_kernel_size = hps.max_kernel_size
width = embed_size
# elmo embedding
self.elmo = Elmo(options_file, weight_file, 1, dropout=0)
self.embed_proj = nn.Linear(elmo_dim, embed_size)
# position embedding
self.position_embedding = nn.Embedding.from_pretrained(
get_sinusoid_encoding_table(sent_max_len + 1,
embed_size,
padding_idx=0),
freeze=True)
# cnn
self.convs = nn.ModuleList([
nn.Conv2d(input_channels,
out_channels,
kernel_size=(height, width))
for height in range(min_kernel_size, max_kernel_size + 1)
])
logger.info("[INFO] Initing W for CNN.......")
for conv in self.convs:
init_weight_value = 6.0
init.xavier_normal_(conv.weight.data,
gain=np.sqrt(init_weight_value))
fan_in, fan_out = Encoder.calculate_fan_in_and_fan_out(
conv.weight.data)
std = np.sqrt(init_weight_value) * np.sqrt(2.0 /
(fan_in + fan_out))
def __init__(self, hps):
super(BertEncoder, self).__init__()
from pytorch_pretrained_bert.modeling import BertModel
self._hps = hps
self.sent_max_len = hps.sent_max_len
self._cuda = hps.cuda
embed_size = hps.word_emb_dim
sent_max_len = hps.sent_max_len
input_channels = 1
out_channels = hps.output_channel
min_kernel_size = hps.min_kernel_size
max_kernel_size = hps.max_kernel_size
width = embed_size
# word embedding
self._bert = BertModel.from_pretrained(
"/remote-home/dqwang/BERT/pre-train/uncased_L-24_H-1024_A-16")
self._bert.eval()
for p in self._bert.parameters():
p.requires_grad = False
self.word_embedding_proj = nn.Linear(4096, embed_size)
# position embedding
self.position_embedding = nn.Embedding.from_pretrained(
get_sinusoid_encoding_table(sent_max_len + 1,
embed_size,
padding_idx=0),
freeze=True)
# cnn
self.convs = nn.ModuleList([
nn.Conv2d(input_channels,
out_channels,
kernel_size=(height, width))
for height in range(min_kernel_size, max_kernel_size + 1)
])
logger.info("[INFO] Initing W for CNN.......")
for conv in self.convs:
init_weight_value = 6.0
init.xavier_normal_(conv.weight.data,
gain=np.sqrt(init_weight_value))
fan_in, fan_out = Encoder.calculate_fan_in_and_fan_out(
conv.weight.data)
std = np.sqrt(init_weight_value) * np.sqrt(2.0 /
(fan_in + fan_out))
def __init__(self, hps, embed):
"""
:param hps:
min_kernel_size: min kernel size for cnn encoder
max_kernel_size: max kernel size for cnn encoder
output_channel: output_channel number for cnn encoder
hidden_size: hidden size for transformer
n_layers: transfromer encoder layer
n_head: multi head attention for transformer
ffn_inner_hidden_size: FFN hiddens size
atten_dropout_prob: dropout size
doc_max_timesteps: max sentence number of the document
:param vocab:
"""
super(TransformerModel, self).__init__()
self._hps = hps
self.encoder = Encoder(hps, embed)
self.sent_embedding_size = (hps.max_kernel_size - hps.min_kernel_size +
1) * hps.output_channel
self.hidden_size = hps.hidden_size
self.n_head = hps.n_head
self.d_v = self.d_k = int(self.hidden_size / self.n_head)
self.d_inner = hps.ffn_inner_hidden_size
self.num_layers = hps.n_layers
self.projection = nn.Linear(self.sent_embedding_size, self.hidden_size)
self.sent_pos_embed = nn.Embedding.from_pretrained(
get_sinusoid_encoding_table(hps.doc_max_timesteps + 1,
self.hidden_size,
padding_idx=0),
freeze=True)
self.layer_stack = nn.ModuleList([
EncoderLayer(self.hidden_size,
self.d_inner,
self.n_head,
self.d_k,
self.d_v,
dropout=hps.atten_dropout_prob)
for _ in range(self.num_layers)
])
self.wh = nn.Linear(self.hidden_size, 2)
