def __init__(self,
input_dim,
n_units,
n_layers,
bottleneck_dim,
dropout,
param_init=0.1):
super(SequenceSummaryNetwork, self).__init__()
self.n_layers = n_layers
self.ssn = nn.ModuleList()
self.ssn += [LinearND(input_dim, n_units, bias=False, dropout=dropout)]
for l in range(1, n_layers - 1, 1):
self.ssn += [
LinearND(n_units,
bottleneck_dim if l == n_layers - 2 else n_units,
bias=False,
dropout=dropout)
]
self.ssn += [
LinearND(bottleneck_dim, input_dim, bias=False, dropout=dropout)
]
# Initialize parameters
self.reset_parameters(param_init)
def __init__(self,
key_dim,
query_dim,
attn_type,
attn_dim,
dropout=0,
n_heads=4):
super(MultiheadAttentionMechanism, self).__init__()
self.attn_type = attn_type
assert attn_dim % n_heads == 0
self.d_k = attn_dim // n_heads
self.n_heads = n_heads
self.key = None
self.value = None
self.mask = None
# attention dropout applied AFTER the softmax layer
self.attn_dropout = nn.Dropout(p=dropout)
if attn_type == 'scaled_dot':
self.w_key = LinearND(key_dim, attn_dim, bias=False)
self.w_value = LinearND(key_dim, attn_dim, bias=False)
self.w_query = LinearND(query_dim, attn_dim, bias=False)
elif attn_type == 'add':
self.w_key = LinearND(key_dim, attn_dim, bias=True)
self.w_value = LinearND(key_dim, attn_dim, bias=False)
self.w_query = LinearND(query_dim, attn_dim, bias=False)
self.v = LinearND(attn_dim, n_heads, bias=False)
else:
raise NotImplementedError(attn_type)
self.w_out = LinearND(attn_dim, key_dim)
def __init__(self, key_dim, query_dim, attn_dim, dropout=0, n_heads=4):
super(MultiheadAttentionMechanism, self).__init__()
self.d_k = attn_dim // n_heads
self.n_heads = n_heads
self.key = None
self.value = None
self.mask = None
# attention dropout applied AFTER the softmax layer
self.attn_dropout = nn.Dropout(p=dropout)
self.w_key = LinearND(key_dim, attn_dim, bias=False)
self.w_value = LinearND(key_dim, attn_dim, bias=False)
self.w_query = LinearND(query_dim, attn_dim, bias=False)
self.w_out = LinearND(attn_dim, key_dim)
def __init__(self,
input_dim,
in_channel,
channels,
kernel_sizes,
strides,
poolings,
dropout,
batch_norm=False,
residual=False,
bottleneck_dim=0,
param_init=0.1):
super(ConvEncoder, self).__init__()
logger = logging.getLogger("training")
self.in_channel = in_channel
assert input_dim % in_channel == 0
self.input_freq = input_dim // in_channel
self.residual = residual
self.bridge = None
assert len(channels) > 0
assert len(channels) == len(kernel_sizes) == len(strides) == len(
poolings)
self.layers = nn.ModuleList()
in_ch = in_channel
in_freq = self.input_freq
for l in range(len(channels)):
block = Conv2LBlock(input_dim=in_freq,
in_channel=in_ch,
out_channel=channels[l],
kernel_size=kernel_sizes[l],
stride=strides[l],
pooling=poolings[l],
dropout=dropout,
batch_norm=batch_norm,
residual=residual)
self.layers += [block]
in_freq = block.input_dim
in_ch = channels[l]
self._output_dim = int(in_ch * in_freq)
if bottleneck_dim > 0:
self.bridge = LinearND(self._output_dim, bottleneck_dim)
self._output_dim = bottleneck_dim
# Initialize parameters
self.reset_parameters(param_init)
def __init__(self,
eos,
blank,
enc_n_units,
vocab,
dropout=0.0,
lsm_prob=0.0,
fc_list=[],
param_init=0.1):
super(CTC, self).__init__()
logger = logging.getLogger('training')
self.eos = eos
self.blank = blank
self.vocab = vocab
self.lsm_prob = lsm_prob
self.space = -1
# TODO(hirofumi): fix layer
# Fully-connected layers before the softmax
if len(fc_list) > 0:
fc_layers = OrderedDict()
for i in range(len(fc_list)):
input_dim = enc_n_units if i == 0 else fc_list[i - 1]
fc_layers['fc' + str(i)] = LinearND(input_dim,
fc_list[i],
dropout=dropout)
fc_layers['fc' + str(len(fc_list))] = LinearND(fc_list[-1],
vocab,
dropout=0)
self.output = nn.Sequential(fc_layers)
else:
self.output = LinearND(enc_n_units, vocab)
import warpctc_pytorch
self.warpctc_loss = warpctc_pytorch.CTCLoss(size_average=True)
def __init__(self,
input_dim,
in_channel,
channels,
kernel_sizes,
dropout,
bottleneck_dim=0):
super(TDSEncoder, self).__init__()
logger = logging.getLogger("training")
self.in_channel = in_channel
assert input_dim % in_channel == 0
self.input_freq = input_dim // in_channel
self.bridge = None
assert len(channels) > 0
assert len(channels) == len(kernel_sizes)
layers = OrderedDict()
in_ch = in_channel
in_freq = self.input_freq
for l in range(len(channels)):
# subsample
if in_ch != channels[l]:
layers['subsample%d' % l] = SubsampelBlock(in_channel=in_ch,
out_channel=channels[l],
in_freq=in_freq,
dropout=dropout)
# Conv
layers['tds%d_block%d' % (channels[l], l)] = TDSBlock(channel=channels[l],
kernel_size=kernel_sizes[l][0],
in_freq=in_freq,
dropout=dropout)
in_ch = channels[l]
self._output_dim = int(in_ch * in_freq)
if bottleneck_dim > 0:
self.bridge = LinearND(self._output_dim, bottleneck_dim)
self._output_dim = bottleneck_dim
self.layers = nn.Sequential(layers)
# Initialize parameters
self.reset_parameters()
def __init__(self,
input_dim,
in_channel,
channels,
kernel_sizes,
dropout,
bottleneck_dim=0,
param_init=0.1):
super(GatedConvEncoder, self).__init__()
logger = logging.getLogger("training")
self.in_channel = in_channel
assert input_dim % in_channel == 0
self.input_freq = input_dim // in_channel
self.bridge = None
assert len(channels) > 0
assert len(channels) == len(kernel_sizes)
layers = OrderedDict()
for l in range(len(channels)):
layers['conv%d' % l] = GLUBlock(kernel_sizes[l][0],
input_dim,
channels[l],
weight_norm=True,
dropout=0.2)
input_dim = channels[l]
# weight normalization + GLU for the last fully-connected layer
self.fc_glu = nn.utils.weight_norm(nn.Linear(input_dim, input_dim * 2),
name='weight',
dim=0)
self._output_dim = int(input_dim)
if bottleneck_dim > 0:
self.bridge = LinearND(self._output_dim, bottleneck_dim)
self._output_dim = bottleneck_dim
self.layers = nn.Sequential(layers)
# Initialize parameters
self.reset_parameters(param_init)
def __init__(self, args):
super(ModelBase, self).__init__()
self.emb_dim = args.emb_dim
self.n_units = args.n_units
self.n_layers = args.n_layers
self.tie_embedding = args.tie_embedding
self.backward = args.backward
self.vocab = args.vocab
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for cache
self.cache_theta = 0.2 # smoothing parameter
self.cache_lambda = 0.2 # cache weight
self.cache_ids = []
self.cache_keys = []
self.cache_attn = []
self.embed = Embedding(vocab=self.vocab,
emb_dim=args.emb_dim,
dropout=args.dropout_emb,
ignore_index=self.pad)
layers = OrderedDict()
model_size = args.lm_type.replace('gated_conv_', '')
if model_size == 'small':
layers['conv1-1'] = GLUBlock(4,
args.emb_dim,
600,
bottlececk_dim=300,
dropout=args.dropout_hidden)
layers['conv2-1'] = GLUBlock(4,
600,
600,
bottlececk_dim=300,
dropout=args.dropout_hidden)
layers['conv3-1'] = GLUBlock(4,
600,
600,
bottlececk_dim=300,
dropout=args.dropout_hidden)
layers['conv4-1'] = GLUBlock(4,
600,
600,
bottlececk_dim=300,
dropout=args.dropout_hidden)
layers['conv5-1'] = GLUBlock(4,
600,
600,
bottlececk_dim=300,
dropout=args.dropout_hidden)
last_dim = 600
elif model_size == '8':
layers['conv1-1'] = GLUBlock(4,
args.emb_dim,
900,
dropout=args.dropout_hidden)
for i in range(1, 8, 1):
layers['conv2-%d' % i] = GLUBlock(4,
900,
900,
dropout=args.dropout_hidden)
last_dim = 900
elif model_size == '8B':
raise NotImplementedError
elif model_size == '9':
raise NotImplementedError
elif model_size == '13':
layers['conv1-1'] = GLUBlock(4,
args.emb_dim,
1268,
dropout=args.dropout_hidden)
for i in range(1, 13, 1):
layers['conv2-%d' % i] = GLUBlock(4,
1268,
1268,
dropout=args.dropout_hidden)
last_dim = 1268
elif model_size == '14':
for i in range(1, 4, 1):
layers['conv1-%d' % i] = GLUBlock(
6,
args.emb_dim if i == 1 else 850,
850,
dropout=args.dropout_hidden)
layers['conv2-1'] = GLUBlock(1,
850,
850,
dropout=args.dropout_hidden)
for i in range(1, 5, 1):
layers['conv3-%d' % i] = GLUBlock(5,
850,
850,
dropout=args.dropout_hidden)
layers['conv4-1'] = GLUBlock(1,
850,
850,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
layers['conv5-%d' % i] = GLUBlock(4,
850,
850,
dropout=args.dropout_hidden)
layers['conv6-1'] = GLUBlock(4,
850,
1024,
dropout=args.dropout_hidden)
layers['conv7-1'] = GLUBlock(4,
1024,
2048,
dropout=args.dropout_hidden)
last_dim = 2048
elif model_size == '14B':
layers['conv1-1'] = GLUBlock(5,
args.emb_dim,
512,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
layers['conv2-%d' % i] = GLUBlock(5,
512,
512,
bottlececk_dim=128,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
layers['conv3-%d' % i] = GLUBlock(5,
512 if i == 1 else 1024,
1024,
bottlececk_dim=512,
dropout=args.dropout_hidden)
for i in range(1, 7, 1):
layers['conv4-%d' % i] = GLUBlock(5,
1024 if i == 1 else 2048,
2048,
bottlececk_dim=1024,
dropout=args.dropout_hidden)
layers['conv5-1'] = GLUBlock(5,
2048,
4096,
bottlececk_dim=1024,
dropout=args.dropout_hidden)
last_dim = 4096
else:
raise NotImplementedError(model_size)
self.layers = nn.Sequential(layers)
if args.adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
last_dim,
self.vocab,
# cutoffs=[self.vocab // 10, 3 * self.vocab // 10],
cutoffs=[self.vocab // 25, self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(last_dim,
self.vocab,
dropout=args.dropout_out)
# NOTE: include bias even when tying weights
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if args.tie_embedding:
if args.n_units != args.emb_dim:
raise ValueError(
'When using the tied flag, n_units must be equal to emb_dim.'
)
self.output.fc.weight = self.embed.embed.weight
# Initialize parameters
self.reset_parameters(args.param_init, dist=args.param_init_dist)
# Initialize bias vectors with zero
self.reset_parameters(0, dist='constant', keys=['bias'])
def __init__(self,
eos,
unk,
pad,
blank,
enc_n_units,
attn_type,
attn_n_heads,
n_layers,
d_model,
d_ff,
pe_type,
tie_embedding,
vocab,
dropout=0.0,
dropout_emb=0.0,
dropout_att=0.0,
lsm_prob=0.0,
layer_norm_eps=1e-6,
ctc_weight=0.0,
ctc_fc_list=[],
backward=False,
global_weight=1.0,
mtl_per_batch=False,
adaptive_softmax=False):
super(TransformerDecoder, self).__init__()
self.eos = eos
self.unk = unk
self.pad = pad
self.blank = blank
self.enc_n_units = enc_n_units
self.d_model = d_model
self.n_layers = n_layers
self.pe_type = pe_type
self.lsm_prob = lsm_prob
self.ctc_weight = ctc_weight
self.ctc_fc_list = ctc_fc_list
self.backward = backward
self.global_weight = global_weight
self.mtl_per_batch = mtl_per_batch
if ctc_weight > 0:
# Fully-connected layers for CTC
if len(ctc_fc_list) > 0:
fc_layers = OrderedDict()
for i in range(len(ctc_fc_list)):
input_dim = d_model if i == 0 else ctc_fc_list[i - 1]
fc_layers['fc' + str(i)] = LinearND(input_dim, ctc_fc_list[i], dropout=dropout)
fc_layers['fc' + str(len(ctc_fc_list))] = LinearND(ctc_fc_list[-1], vocab, dropout=0)
self.output_ctc = nn.Sequential(fc_layers)
else:
self.output_ctc = LinearND(d_model, vocab)
self.decode_ctc_greedy = GreedyDecoder(blank=blank)
self.decode_ctc_beam = BeamSearchDecoder(blank=blank)
import warpctc_pytorch
self.warpctc_loss = warpctc_pytorch.CTCLoss(size_average=True)
if ctc_weight < global_weight:
self.layers = nn.ModuleList(
[TransformerDecoderBlock(d_model, d_ff, attn_type, attn_n_heads,
dropout, dropout_att, layer_norm_eps)
for _ in range(n_layers)])
self.embed = Embedding(vocab, d_model,
dropout=0, # NOTE: do not apply dropout here
ignore_index=pad)
if pe_type:
self.pos_emb_out = PositionalEncoding(d_model, dropout_emb, pe_type)
if adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
d_model, vocab,
cutoffs=[round(self.vocab / 15), 3 * round(self.vocab / 15)],
# cutoffs=[self.vocab // 25, 3 * self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(d_model, vocab)
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if tie_embedding:
self.output.fc.weight = self.embed.embed.weight
self.norm_top = nn.LayerNorm(d_model, eps=layer_norm_eps)
# Initialize parameters
self.reset_parameters()
def __init__(self,
input_dim,
rnn_type,
n_units,
n_projs,
n_layers,
dropout_in,
dropout,
subsample,
subsample_type='drop',
n_stacks=1,
n_splices=1,
last_proj_dim=0,
conv_in_channel=1,
conv_channels=0,
conv_kernel_sizes=[],
conv_strides=[],
conv_poolings=[],
conv_batch_norm=False,
conv_residual=False,
conv_bottleneck_dim=0,
residual=False,
n_layers_sub1=0,
n_layers_sub2=0,
nin=False,
task_specific_layer=False,
param_init=0.1):
super(RNNEncoder, self).__init__()
logger = logging.getLogger("training")
if len(subsample) > 0 and len(subsample) != n_layers:
raise ValueError('subsample must be the same size as n_layers.')
if n_layers_sub1 < 0 or (n_layers_sub1 > 1
and n_layers < n_layers_sub1):
raise ValueError('Set n_layers_sub1 between 1 to n_layers.')
if n_layers_sub2 < 0 or (n_layers_sub2 > 1
and n_layers_sub1 < n_layers_sub2):
raise ValueError('Set n_layers_sub2 between 1 to n_layers_sub1.')
self.rnn_type = rnn_type
self.bidirectional = True if rnn_type in [
'blstm', 'bgru', 'conv_blstm', 'conv_bgru'
] else False
self.n_units = n_units
self.n_dirs = 2 if self.bidirectional else 1
self.n_projs = n_projs
self.n_layers = n_layers
# Setting for hierarchical encoder
self.n_layers_sub1 = n_layers_sub1
self.n_layers_sub2 = n_layers_sub2
self.task_specific_layer = task_specific_layer
# Setting for subsampling
self.subsample = subsample
self.subsample_type = subsample_type
# Setting for bridge layers
self.bridge = None
self.bridge_sub1 = None
self.bridge_sub2 = None
# Setting for residual connections
self.residual = residual
if residual:
assert np.prod(subsample) == 1
# Setting for the NiN (Network in Network)
self.nin = nin
# Dropout for input-hidden connection
self.dropout_in = nn.Dropout(p=dropout_in)
# Setting for CNNs before RNNs
if conv_channels and rnn_type not in ['blstm', 'lstm', 'bgru', 'gru']:
channels = [int(c) for c in conv_channels.split('_')
] if len(conv_channels) > 0 else []
kernel_sizes = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_kernel_sizes.split('_')
] if len(conv_kernel_sizes) > 0 else []
if rnn_type in ['tds', 'gated_conv']:
strides = []
poolings = []
else:
strides = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_strides.split('_')
] if len(conv_strides) > 0 else []
poolings = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_poolings.split('_')
] if len(conv_poolings) > 0 else []
if 'conv_' in rnn_type:
self.subsample = [1] * self.n_layers
logger.warning(
'Subsampling is automatically ignored because CNN layers are used before RNN layers.'
)
else:
channels = []
kernel_sizes = []
strides = []
poolings = []
if len(channels) > 0:
if rnn_type == 'tds':
self.conv = TDSEncoder(input_dim=input_dim * n_stacks,
in_channel=conv_in_channel,
channels=channels,
kernel_sizes=kernel_sizes,
dropout=dropout,
bottleneck_dim=last_proj_dim)
elif rnn_type == 'gated_conv':
self.conv = GatedConvEncoder(input_dim=input_dim * n_stacks,
in_channel=conv_in_channel,
channels=channels,
kernel_sizes=kernel_sizes,
dropout=dropout,
bottleneck_dim=last_proj_dim,
param_init=param_init)
else:
assert n_stacks == 1 and n_splices == 1
self.conv = ConvEncoder(input_dim,
in_channel=conv_in_channel,
channels=channels,
kernel_sizes=kernel_sizes,
strides=strides,
poolings=poolings,
dropout=0,
batch_norm=conv_batch_norm,
residual=conv_residual,
bottleneck_dim=conv_bottleneck_dim,
param_init=param_init)
self._output_dim = self.conv.output_dim
else:
self._output_dim = input_dim * n_splices * n_stacks
self.conv = None
if rnn_type not in ['conv', 'tds', 'gated_conv']:
# Fast implementation without processes between each layer
self.fast_impl = False
if np.prod(
self.subsample
) == 1 and self.n_projs == 0 and not residual and n_layers_sub1 == 0 and not nin:
self.fast_impl = True
if 'lstm' in rnn_type:
rnn = nn.LSTM
elif 'gru' in rnn_type:
rnn = nn.GRU
else:
raise ValueError(
'rnn_type must be "(conv_)(b)lstm" or "(conv_)(b)gru".'
)
self.rnn = rnn(self._output_dim,
n_units,
n_layers,
bias=True,
batch_first=True,
dropout=dropout,
bidirectional=self.bidirectional)
# NOTE: pytorch introduces a dropout layer on the outputs of each layer EXCEPT the last layer
self._output_dim = n_units * self.n_dirs
self.dropout_top = nn.Dropout(p=dropout)
else:
self.rnn = nn.ModuleList()
self.dropout = nn.ModuleList()
if self.n_projs > 0:
self.proj = nn.ModuleList()
if subsample_type == 'max_pool' and np.prod(
self.subsample) > 1:
self.max_pool = nn.ModuleList()
for l in range(n_layers):
if self.subsample[l] > 1:
self.max_pool += [
nn.MaxPool2d((1, 1),
stride=(self.subsample[l], 1),
ceil_mode=True)
]
else:
self.max_pool += [None]
if subsample_type == 'concat' and np.prod(self.subsample) > 1:
self.concat_proj = nn.ModuleList()
self.concat_bn = nn.ModuleList()
for l in range(n_layers):
if self.subsample[l] > 1:
self.concat_proj += [
LinearND(
n_units * self.n_dirs * self.subsample[l],
n_units * self.n_dirs)
]
self.concat_bn += [
nn.BatchNorm1d(n_units * self.n_dirs)
]
else:
self.concat_proj += [None]
self.concat_bn += [None]
if nin:
self.nin_conv = nn.ModuleList()
self.nin_bn = nn.ModuleList()
for l in range(n_layers):
if 'lstm' in rnn_type:
rnn_i = nn.LSTM
elif 'gru' in rnn_type:
rnn_i = nn.GRU
else:
raise ValueError(
'rnn_type must be "(conv_)(b)lstm" or "(conv_)(b)gru".'
)
self.rnn += [
rnn_i(self._output_dim,
n_units,
1,
bias=True,
batch_first=True,
dropout=0,
bidirectional=self.bidirectional)
]
self.dropout += [nn.Dropout(p=dropout)]
self._output_dim = n_units * self.n_dirs
# Projection layer
if n_projs > 0 and l != n_layers - 1:
self.proj += [LinearND(n_units * self.n_dirs, n_projs)]
self._output_dim = n_projs
# Task specific layer
if l == n_layers_sub1 - 1 and task_specific_layer:
self.rnn_sub1 = rnn_i(self._output_dim,
n_units,
1,
bias=True,
batch_first=True,
dropout=0,
bidirectional=self.bidirectional)
self.dropout_sub1 = nn.Dropout(p=dropout)
if last_proj_dim != self.output_dim:
self.bridge_sub1 = LinearND(n_units,
last_proj_dim,
dropout=dropout)
if l == n_layers_sub2 - 1 and task_specific_layer:
self.rnn_sub2 = rnn_i(self._output_dim,
n_units,
1,
bias=True,
batch_first=True,
dropout=0,
bidirectional=self.bidirectional)
self.dropout_sub2 = nn.Dropout(p=dropout)
if last_proj_dim != self.output_dim:
self.bridge_sub2 = LinearND(n_units,
last_proj_dim,
dropout=dropout)
# Network in network (1*1 conv + batch normalization + ReLU)
# NOTE: exclude the last layer
if nin and l != n_layers - 1:
self.nin_conv += [
nn.Conv2d(in_channels=self._output_dim,
out_channels=self._output_dim,
kernel_size=1,
stride=1,
padding=0)
]
self.nin_bn += [nn.BatchNorm2d(self._output_dim)]
if n_layers_sub1 > 0 or n_layers_sub2 > 0:
assert task_specific_layer
if last_proj_dim != self.output_dim:
self.bridge = LinearND(self._output_dim,
last_proj_dim,
dropout=dropout)
self._output_dim = last_proj_dim
# Initialize parameters
self.reset_parameters(param_init)
def __init__(self,
eos,
unk,
pad,
blank,
enc_n_units,
rnn_type,
n_units,
n_projs,
n_layers,
residual,
bottleneck_dim,
emb_dim,
vocab,
tie_embedding=False,
dropout=0.0,
dropout_emb=0.0,
lsm_prob=0.0,
ctc_weight=0.0,
ctc_lsm_prob=0.0,
ctc_fc_list=[],
lm_init=None,
lmobj_weight=0.0,
share_lm_softmax=False,
global_weight=1.0,
mtl_per_batch=False,
param_init=0.1,
start_pointing=False,
end_pointing=True):
super(RNNTransducer, self).__init__()
logger = logging.getLogger('training')
self.eos = eos
self.unk = unk
self.pad = pad
self.blank = blank
self.vocab = vocab
self.rnn_type = rnn_type
assert rnn_type in ['lstm_transducer', 'gru_transducer']
self.enc_n_units = enc_n_units
self.dec_n_units = n_units
self.n_projs = n_projs
self.n_layers = n_layers
self.residual = residual
self.lsm_prob = lsm_prob
self.ctc_weight = ctc_weight
self.lmobj_weight = lmobj_weight
self.share_lm_softmax = share_lm_softmax
self.global_weight = global_weight
self.mtl_per_batch = mtl_per_batch
# VAD
self.start_pointing = start_pointing
self.end_pointing = end_pointing
# for cache
self.prev_spk = ''
self.lmstate_final = None
self.state_cache = OrderedDict()
if ctc_weight > 0:
self.ctc = CTC(eos=eos,
blank=blank,
enc_n_units=enc_n_units,
vocab=vocab,
dropout=dropout,
lsm_prob=ctc_lsm_prob,
fc_list=ctc_fc_list,
param_init=param_init)
if ctc_weight < global_weight:
import warprnnt_pytorch
self.warprnnt_loss = warprnnt_pytorch.RNNTLoss()
# for MTL with LM objective
if lmobj_weight > 0:
if share_lm_softmax:
self.output_lmobj = self.output # share paramters
else:
self.output_lmobj = LinearND(n_units, vocab)
# Prediction network
self.fast_impl = False
rnn = nn.LSTM if rnn_type == 'lstm_transducer' else nn.GRU
if n_projs == 0 and not residual:
self.fast_impl = True
self.rnn = rnn(emb_dim,
n_units,
n_layers,
bias=True,
batch_first=True,
dropout=dropout,
bidirectional=False)
# NOTE: pytorch introduces a dropout layer on the outputs of each layer EXCEPT the last layer
dec_idim = n_units
self.dropout_top = nn.Dropout(p=dropout)
else:
self.rnn = nn.ModuleList()
self.dropout = nn.ModuleList(
[nn.Dropout(p=dropout) for _ in range(n_layers)])
if n_projs > 0:
self.proj = nn.ModuleList(
[LinearND(dec_idim, n_projs) for _ in range(n_layers)])
dec_idim = emb_dim
for l in range(n_layers):
self.rnn += [
rnn(dec_idim,
n_units,
1,
bias=True,
batch_first=True,
dropout=0,
bidirectional=False)
]
dec_idim = n_projs if n_projs > 0 else n_units
self.embed = Embedding(vocab,
emb_dim,
dropout=dropout_emb,
ignore_index=pad)
self.w_enc = LinearND(enc_n_units, bottleneck_dim, bias=True)
self.w_dec = LinearND(dec_idim, bottleneck_dim, bias=False)
self.output = LinearND(bottleneck_dim, vocab)
# Initialize parameters
self.reset_parameters(param_init)
# prediction network initialization with pre-trained LM
if lm_init is not None:
assert lm_init.vocab == vocab
assert lm_init.n_units == n_units
assert lm_init.n_projs == n_projs
assert lm_init.n_layers == n_layers
assert lm_init.residual == residual
param_dict = dict(lm_init.named_parameters())
for n, p in self.named_parameters():
if n in param_dict.keys() and p.size() == param_dict[n].size():
if 'output' in n:
continue
p.data = param_dict[n].data
logger.info('Overwrite %s' % n)
def __init__(self, args, save_path=None):
super(LMBase, self).__init__()
logger = logging.getLogger('training')
logger.info(self.__class__.__name__)
self.save_path = save_path
self.emb_dim = args.emb_dim
self.rnn_type = args.lm_type
assert args.lm_type in ['lstm', 'gru']
self.n_units = args.n_units
self.n_projs = args.n_projs
self.n_layers = args.n_layers
self.residual = args.residual
self.use_glu = args.use_glu
self.n_units_cv = args.n_units_null_context
self.vocab = args.vocab
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for cache
self.cache_theta = 0.2 # smoothing parameter
self.cache_lambda = 0.2 # cache weight
self.cache_ids = []
self.cache_keys = []
self.cache_attn = []
self.embed = Embedding(vocab=self.vocab,
emb_dim=args.emb_dim,
dropout=args.dropout_emb,
ignore_index=self.pad)
self.fast_impl = False
rnn = nn.LSTM if args.lm_type == 'lstm' else nn.GRU
if args.n_projs == 0 and not args.residual:
self.fast_impl = True
self.rnn = rnn(args.emb_dim + args.n_units_null_context,
args.n_units,
args.n_layers,
bias=True,
batch_first=True,
dropout=args.dropout_hidden,
bidirectional=False)
# NOTE: pytorch introduces a dropout layer on the outputs of each layer EXCEPT the last layer
rnn_idim = args.n_units
self.dropout_top = nn.Dropout(p=args.dropout_hidden)
else:
self.rnn = nn.ModuleList()
self.dropout = nn.ModuleList([
nn.Dropout(p=args.dropout_hidden) for _ in range(args.n_layers)
])
if args.n_projs > 0:
self.proj = nn.ModuleList([
LinearND(args.n_units, args.n_projs)
for _ in range(args.n_layers)
])
rnn_idim = args.emb_dim + args.n_units_null_context
for l in range(args.n_layers):
self.rnn += [
rnn(rnn_idim,
args.n_units,
1,
bias=True,
batch_first=True,
dropout=0,
bidirectional=False)
]
rnn_idim = args.n_units
if args.n_projs > 0:
rnn_idim = args.n_projs
if self.use_glu:
self.fc_glu = LinearND(rnn_idim,
rnn_idim * 2,
dropout=args.dropout_hidden)
if args.adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
rnn_idim,
self.vocab,
# cutoffs=[self.vocab // 10, 3 * self.vocab // 10],
cutoffs=[self.vocab // 25, self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(rnn_idim,
self.vocab,
dropout=args.dropout_out)
# NOTE: include bias even when tying weights
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if args.tie_embedding:
if args.n_units != args.emb_dim:
raise ValueError(
'When using the tied flag, n_units must be equal to emb_dim.'
)
self.output.fc.weight = self.embed.embed.weight
# Initialize parameters
self.reset_parameters(args.param_init)
# Recurrent weights are orthogonalized
if args.rec_weight_orthogonal:
self.reset_parameters(args.param_init,
dist='orthogonal',
keys=['rnn', 'weight'])
def __init__(self,
input_dim,
attn_type,
attn_n_heads,
n_layers,
d_model,
d_ff,
pe_type,
dropout_in=0,
dropout=0,
dropout_att=0,
layer_norm_eps=1e-6,
n_stacks=1,
n_splices=1,
conv_in_channel=1,
conv_channels=0,
conv_kernel_sizes=[],
conv_strides=[],
conv_poolings=[],
conv_batch_norm=False,
conv_residual=False,
conv_bottleneck_dim=0):
super(TransformerEncoder, self).__init__()
self.d_model = d_model
self.pe_type = pe_type
# Setting for CNNs before RNNs
if conv_channels:
channels = [int(c) for c in conv_channels.split('_')
] if len(conv_channels) > 0 else []
kernel_sizes = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_kernel_sizes.split('_')
] if len(conv_kernel_sizes) > 0 else []
strides = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_strides.split('_')
] if len(conv_strides) > 0 else []
poolings = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_poolings.split('_')
] if len(conv_poolings) > 0 else []
else:
channels = []
kernel_sizes = []
strides = []
poolings = []
if len(channels) > 0:
assert n_stacks == 1 and n_splices == 1
self.conv = ConvEncoder(input_dim * n_stacks,
in_channel=conv_in_channel,
channels=channels,
kernel_sizes=kernel_sizes,
strides=strides,
poolings=poolings,
dropout=0,
batch_norm=conv_batch_norm,
residual=conv_residual,
bottleneck_dim=d_model)
self._output_dim = self.conv.output_dim
else:
self._output_dim = input_dim * n_splices * n_stacks
self.conv = None
self.embed_in = LinearND(
self._output_dim, d_model,
dropout=0) # NOTE: do not apply dropout here
if pe_type:
self.pos_emb_in = PositionalEncoding(d_model, dropout_in, pe_type)
self.layer_norm_in = nn.LayerNorm(d_model, eps=layer_norm_eps)
# Self-attention layers
self.layers = nn.ModuleList([
TransformerEncoderBlock(d_model, d_ff, attn_type, attn_n_heads,
dropout, dropout_att, layer_norm_eps)
for l in range(n_layers)
])
self.layer_norm_top = nn.LayerNorm(d_model, eps=layer_norm_eps)
self._output_dim = d_model
def __init__(self,
key_dim,
query_dim,
attn_type,
attn_dim,
sharpening_factor=1,
sigmoid_smoothing=False,
conv_out_channels=10,
conv_kernel_size=100,
dropout=0):
super(AttentionMechanism, self).__init__()
self.attn_type = attn_type
self.attn_dim = attn_dim
self.sharpening_factor = sharpening_factor
self.sigmoid_smoothing = sigmoid_smoothing
self.n_heads = 1
self.key = None
self.mask = None
# attention dropout applied AFTER the softmax layer
self.attn_dropout = nn.Dropout(p=dropout)
if attn_type == 'no':
pass
# NOTE: sequence-to-sequence without attetnion (use the last state as a context vector)
elif attn_type == 'add':
self.w_key = LinearND(key_dim, attn_dim, bias=True)
self.w_query = LinearND(query_dim, attn_dim, bias=False)
self.v = LinearND(attn_dim, 1, bias=False)
elif attn_type == 'location':
self.w_key = LinearND(key_dim, attn_dim, bias=True)
self.w_query = LinearND(query_dim, attn_dim, bias=False)
self.w_conv = LinearND(conv_out_channels, attn_dim, bias=False)
self.conv = nn.Conv2d(in_channels=1,
out_channels=conv_out_channels,
kernel_size=(1, conv_kernel_size * 2 + 1),
stride=1,
padding=(0, conv_kernel_size),
bias=False)
self.v = LinearND(attn_dim, 1, bias=False)
elif attn_type == 'dot':
self.w_key = LinearND(key_dim, attn_dim, bias=False)
self.w_query = LinearND(query_dim, attn_dim, bias=False)
elif attn_type == 'luong_dot':
pass
# NOTE: no additional parameters
elif attn_type == 'luong_general':
self.w_key = LinearND(key_dim, query_dim, bias=False)
elif attn_type == 'luong_concat':
self.w = LinearND(key_dim + query_dim, attn_dim, bias=False)
self.v = LinearND(attn_dim, 1, bias=False)
else:
raise ValueError(attn_type)
def __init__(self,
eos,
unk,
pad,
blank,
enc_n_units,
attn_type,
attn_n_heads,
n_layers,
d_model,
d_ff,
vocab,
tie_embedding=False,
pe_type='add',
layer_norm_eps=1e-6,
dropout=0.0,
dropout_emb=0.0,
dropout_att=0.0,
lsm_prob=0.0,
focal_loss_weight=0.0,
focal_loss_gamma=2.0,
ctc_weight=0.0,
ctc_lsm_prob=0.0,
ctc_fc_list=[],
backward=False,
global_weight=1.0,
mtl_per_batch=False,
adaptive_softmax=False):
super(TransformerDecoder, self).__init__()
logger = logging.getLogger('training')
self.eos = eos
self.unk = unk
self.pad = pad
self.blank = blank
self.enc_n_units = enc_n_units
self.d_model = d_model
self.n_layers = n_layers
self.attn_n_heads = attn_n_heads
self.pe_type = pe_type
self.lsm_prob = lsm_prob
self.focal_loss_weight = focal_loss_weight
self.focal_loss_gamma = focal_loss_gamma
self.ctc_weight = ctc_weight
self.bwd = backward
self.global_weight = global_weight
self.mtl_per_batch = mtl_per_batch
if ctc_weight > 0:
self.ctc = CTC(eos=eos,
blank=blank,
enc_n_units=enc_n_units,
vocab=vocab,
dropout=dropout,
lsm_prob=ctc_lsm_prob,
fc_list=ctc_fc_list,
param_init=0.1)
if ctc_weight < global_weight:
self.embed = Embedding(
vocab,
d_model,
dropout=0, # NOTE: do not apply dropout here
ignore_index=pad)
self.pos_enc = PositionalEncoding(d_model, dropout_emb, pe_type)
self.layers = nn.ModuleList([
TransformerDecoderBlock(d_model, d_ff, attn_type, attn_n_heads,
dropout, dropout_att, layer_norm_eps)
for _ in range(n_layers)
])
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
if adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
d_model,
vocab,
cutoffs=[
round(self.vocab / 15), 3 * round(self.vocab / 15)
],
# cutoffs=[self.vocab // 25, 3 * self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(d_model, vocab)
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if tie_embedding:
self.output.fc.weight = self.embed.embed.weight
# Initialize parameters
self.reset_parameters()
def __init__(self, args):
super(ModelBase, self).__init__()
self.emb_dim = args.emb_dim
self.rnn_type = args.lm_type
assert args.lm_type in ['lstm', 'gru']
self.n_units = args.n_units
self.n_layers = args.n_layers
self.residual = args.residual
self.use_glu = args.use_glu
self.vocab = args.vocab
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for cache
self.cache_theta = 0.2 # smoothing parameter
self.cache_lambda = 0.2 # cache weight
self.cache_ids = []
self.cache_keys = []
self.cache_attn = []
self.embed = Embedding(vocab=self.vocab,
emb_dim=args.emb_dim,
dropout=args.dropout_emb,
ignore_index=self.pad)
self.fast_impl = False
if args.n_projs == 0 and not args.residual:
self.fast_impl = True
if 'lstm' in args.lm_type:
rnn = nn.LSTM
elif 'gru' in args.lm_type:
rnn = nn.GRU
else:
raise ValueError('rnn_type must be "(b)lstm" or "(b)gru".')
self.rnn = rnn(args.emb_dim,
args.n_units,
args.n_layers,
bias=True,
batch_first=True,
dropout=args.dropout_hidden,
bidirectional=False)
# NOTE: pytorch introduces a dropout layer on the outputs of each layer EXCEPT the last layer
rnn_idim = args.n_units
self.dropout_top = nn.Dropout(p=args.dropout_hidden)
else:
self.rnn = torch.nn.ModuleList()
self.dropout = torch.nn.ModuleList()
if args.n_projs > 0:
self.proj = torch.nn.ModuleList()
rnn_idim = args.emb_dim
for l in range(args.n_layers):
self.rnn += [
getattr(nn, args.lm_type.upper())(rnn_idim,
args.n_units,
1,
bias=True,
batch_first=True,
dropout=0,
bidirectional=False)
]
self.dropout += [nn.Dropout(p=args.dropout_hidden)]
rnn_idim = args.n_units
if l != self.n_layers - 1 and args.n_projs > 0:
self.proj += [LinearND(rnn_idim, args.n_projs)]
rnn_idim = args.n_projs
if self.use_glu:
self.fc_glu = LinearND(rnn_idim,
rnn_idim * 2,
dropout=args.dropout_hidden)
if args.adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
rnn_idim,
self.vocab,
# cutoffs=[self.vocab // 10, 3 * self.vocab // 10],
cutoffs=[self.vocab // 25, self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(rnn_idim,
self.vocab,
dropout=args.dropout_out)
# NOTE: include bias even when tying weights
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if args.tie_embedding:
if args.n_units != args.emb_dim:
raise ValueError(
'When using the tied flag, n_units must be equal to emb_dim.'
)
self.output.fc.weight = self.embed.embed.weight
# Initialize parameters
self.reset_parameters(args.param_init, dist=args.param_init_dist)
# Initialize bias vectors with zero
self.reset_parameters(0, dist='constant', keys=['bias'])
# Recurrent weights are orthogonalized
if args.rec_weight_orthogonal:
self.reset_parameters(args.param_init,
dist='orthogonal',
keys=['rnn', 'weight'])
def __init__(self, args, save_path=None):
super(LMBase, self).__init__()
logger = logging.getLogger('training')
logger.info(self.__class__.__name__)
self.save_path = save_path
self.d_model = args.d_model
self.d_ff = args.d_ff
self.pe_type = args.pe_type
self.n_layers = args.n_layers
self.n_heads = args.attn_n_heads
self.tie_embedding = args.tie_embedding
self.vocab = args.vocab
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# self.lsm_prob = lsm_prob
# for cache
self.cache_theta = 0.2 # smoothing parameter
self.cache_lambda = 0.2 # cache weight
self.cache_ids = []
self.cache_keys = []
self.cache_attn = []
self.embed = Embedding(
vocab=self.vocab,
emb_dim=self.d_model,
dropout=0, # NOTE: do not apply dropout here
ignore_index=self.pad)
self.pos_enc = PositionalEncoding(args.d_model, args.dropout_emb,
args.pe_type)
self.layers = nn.ModuleList([
TransformerDecoderBlock(args.d_model,
args.d_ff,
args.attn_type,
args.attn_n_heads,
args.dropout_hidden,
args.dropout_att,
args.layer_norm_eps,
src_attention=False)
for _ in range(self.n_layers)
])
self.norm_out = nn.LayerNorm(args.d_model, eps=args.layer_norm_eps)
if args.adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
args.d_model,
self.vocab,
cutoffs=[round(self.vocab / 15), 3 * round(self.vocab / 15)],
# cutoffs=[self.vocab // 25, 3 * self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(self.d_model, self.vocab)
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if args.tie_embedding:
self.output.fc.weight = self.embed.embed.weight
# Initialize parameters
self.reset_parameters()
def __init__(self, args):
super(ModelBase, self).__init__()
# for encoder
self.input_type = args.input_type
self.input_dim = args.input_dim
self.n_stacks = args.n_stacks
self.n_skips = args.n_skips
self.n_splices = args.n_splices
self.enc_type = args.enc_type
self.enc_n_units = args.enc_n_units
if args.enc_type in ['blstm', 'bgru']:
self.enc_n_units *= 2
self.bridge_layer = args.bridge_layer
# for OOV resolution
self.enc_n_layers = args.enc_n_layers
self.enc_n_layers_sub1 = args.enc_n_layers_sub1
self.subsample = [int(s) for s in args.subsample.split('_')]
# for attention layer
self.attn_n_heads = args.attn_n_heads
# for decoder
self.vocab = args.vocab
self.vocab_sub1 = args.vocab_sub1
self.vocab_sub2 = args.vocab_sub2
self.blank = 0
self.unk = 1
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for the sub tasks
self.main_weight = 1 - args.sub1_weight - args.sub2_weight
self.sub1_weight = args.sub1_weight
self.sub2_weight = args.sub2_weight
self.mtl_per_batch = args.mtl_per_batch
self.task_specific_layer = args.task_specific_layer
# for CTC
self.ctc_weight = min(args.ctc_weight, self.main_weight)
self.ctc_weight_sub1 = min(args.ctc_weight_sub1, self.sub1_weight)
self.ctc_weight_sub2 = min(args.ctc_weight_sub2, self.sub2_weight)
# for backward decoder
self.bwd_weight = min(args.bwd_weight, self.main_weight)
self.fwd_weight = self.main_weight - self.bwd_weight - self.ctc_weight
self.fwd_weight_sub1 = self.sub1_weight - self.ctc_weight_sub1
self.fwd_weight_sub2 = self.sub2_weight - self.ctc_weight_sub2
# Feature extraction
self.ssn = None
if args.sequence_summary_network:
assert args.input_type == 'speech'
self.ssn = SequenceSummaryNetwork(args.input_dim,
n_units=512,
n_layers=3,
bottleneck_dim=100,
dropout=0)
# Encoder
if args.enc_type == 'transformer':
self.enc = TransformerEncoder(
input_dim=args.input_dim
if args.input_type == 'speech' else args.emb_dim,
attn_type=args.transformer_attn_type,
attn_n_heads=args.transformer_attn_n_heads,
n_layers=args.transformer_enc_n_layers,
d_model=args.d_model,
d_ff=args.d_ff,
# pe_type=args.pe_type,
pe_type=False,
dropout_in=args.dropout_in,
dropout=args.dropout_enc,
dropout_att=args.dropout_att,
layer_norm_eps=args.layer_norm_eps,
n_stacks=args.n_stacks,
n_splices=args.n_splices,
conv_in_channel=args.conv_in_channel,
conv_channels=args.conv_channels,
conv_kernel_sizes=args.conv_kernel_sizes,
conv_strides=args.conv_strides,
conv_poolings=args.conv_poolings,
conv_batch_norm=args.conv_batch_norm,
conv_residual=args.conv_residual,
conv_bottleneck_dim=args.conv_bottleneck_dim)
else:
self.enc = RNNEncoder(
input_dim=args.input_dim
if args.input_type == 'speech' else args.emb_dim,
rnn_type=args.enc_type,
n_units=args.enc_n_units,
n_projs=args.enc_n_projs,
n_layers=args.enc_n_layers,
n_layers_sub1=args.enc_n_layers_sub1,
n_layers_sub2=args.enc_n_layers_sub2,
dropout_in=args.dropout_in,
dropout=args.dropout_enc,
subsample=list(map(int, args.subsample.split('_'))) + [1] *
(args.enc_n_layers - len(args.subsample.split('_'))),
subsample_type=args.subsample_type,
n_stacks=args.n_stacks,
n_splices=args.n_splices,
conv_in_channel=args.conv_in_channel,
conv_channels=args.conv_channels,
conv_kernel_sizes=args.conv_kernel_sizes,
conv_strides=args.conv_strides,
conv_poolings=args.conv_poolings,
conv_batch_norm=args.conv_batch_norm,
conv_residual=args.conv_residual,
conv_bottleneck_dim=args.conv_bottleneck_dim,
residual=args.enc_residual,
nin=args.enc_nin,
task_specific_layer=args.task_specific_layer)
# NOTE: pure CNN/TDS encoders are also included
if args.freeze_encoder:
for p in self.enc.parameters():
p.requires_grad = False
# Bridge layer between the encoder and decoder
self.is_bridge = False
if (args.enc_type in ['conv', 'tds', 'gated_conv', 'transformer']
and args.ctc_weight < 1
) or args.dec_type == 'transformer' or args.bridge_layer:
self.bridge = LinearND(self.enc.output_dim,
args.d_model if args.dec_type
== 'transformer' else args.dec_n_units,
dropout=args.dropout_enc)
self.is_bridge = True
if self.sub1_weight > 0:
self.bridge_sub1 = LinearND(self.enc.output_dim,
args.dec_n_units,
dropout=args.dropout_enc)
if self.sub2_weight > 0:
self.bridge_sub2 = LinearND(self.enc.output_dim,
args.dec_n_units,
dropout=args.dropout_enc)
self.enc_n_units = args.dec_n_units
# main task
directions = []
if self.fwd_weight > 0 or self.ctc_weight > 0:
directions.append('fwd')
if self.bwd_weight > 0:
directions.append('bwd')
for dir in directions:
# Cold fusion
if args.lm_fusion and dir == 'fwd':
lm = RNNLM(args.lm_conf)
lm, _ = load_checkpoint(lm, args.lm_fusion)
else:
args.lm_conf = False
lm = None
# TODO(hirofumi): cold fusion for backward RNNLM
# Decoder
if args.dec_type == 'transformer':
dec = TransformerDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
attn_type=args.transformer_attn_type,
attn_n_heads=args.transformer_attn_n_heads,
n_layers=args.transformer_dec_n_layers,
d_model=args.d_model,
d_ff=args.d_ff,
pe_type=args.pe_type,
tie_embedding=args.tie_embedding,
vocab=self.vocab,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
lsm_prob=args.lsm_prob,
layer_norm_eps=args.layer_norm_eps,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
backward=(dir == 'bwd'),
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch)
else:
dec = RNNDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
attn_type=args.attn_type,
attn_dim=args.attn_dim,
attn_sharpening_factor=args.attn_sharpening,
attn_sigmoid_smoothing=args.attn_sigmoid,
attn_conv_out_channels=args.attn_conv_n_channels,
attn_conv_kernel_size=args.attn_conv_width,
attn_n_heads=args.attn_n_heads,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
loop_type=args.dec_loop_type,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
tie_embedding=args.tie_embedding,
vocab=self.vocab,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
ss_prob=args.ss_prob,
ss_type=args.ss_type,
lsm_prob=args.lsm_prob,
fl_weight=args.focal_loss_weight,
fl_gamma=args.focal_loss_gamma,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
input_feeding=args.input_feeding,
backward=(dir == 'bwd'),
# lm=args.lm_conf,
lm=lm, # TODO(hirofumi): load RNNLM in the model init.
lm_fusion_type=args.lm_fusion_type,
contextualize=args.contextualize,
lm_init=args.lm_init,
lmobj_weight=args.lmobj_weight,
share_lm_softmax=args.share_lm_softmax,
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch,
adaptive_softmax=args.adaptive_softmax)
setattr(self, 'dec_' + dir, dec)
# sub task
for sub in ['sub1', 'sub2']:
if getattr(self, sub + '_weight') > 0:
if args.dec_type == 'transformer':
raise NotImplementedError
else:
dec_sub = RNNDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc_n_units,
attn_type=args.attn_type,
attn_dim=args.attn_dim,
attn_sharpening_factor=args.attn_sharpening,
attn_sigmoid_smoothing=args.attn_sigmoid,
attn_conv_out_channels=args.attn_conv_n_channels,
attn_conv_kernel_size=args.attn_conv_width,
attn_n_heads=1,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
loop_type=args.dec_loop_type,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
tie_embedding=args.tie_embedding,
vocab=getattr(self, 'vocab_' + sub),
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
ss_prob=args.ss_prob,
ss_type=args.ss_type,
lsm_prob=args.lsm_prob,
fl_weight=args.focal_loss_weight,
fl_gamma=args.focal_loss_gamma,
ctc_weight=getattr(self, 'ctc_weight_' + sub),
ctc_fc_list=[
int(fc) for fc in getattr(args, 'ctc_fc_list_' +
sub).split('_')
] if getattr(args, 'ctc_fc_list_' + sub) is not None
and len(getattr(args, 'ctc_fc_list_' + sub)) > 0 else
[],
input_feeding=args.input_feeding,
global_weight=getattr(self, sub + '_weight'),
mtl_per_batch=args.mtl_per_batch)
setattr(self, 'dec_fwd_' + sub, dec_sub)
if args.input_type == 'text':
if args.vocab == args.vocab_sub1:
# Share the embedding layer between input and output
self.embed_in = dec.embed
else:
self.embed_in = Embedding(vocab=args.vocab_sub1,
emb_dim=args.emb_dim,
dropout=args.dropout_emb,
ignore_index=self.pad)
# Initialize parameters in CNN layers
self.reset_parameters(
args.param_init,
# dist='xavier_uniform',
# dist='kaiming_uniform',
dist='lecun',
keys=['conv'],
ignore_keys=['score'])
# Initialize parameters in the encoder
if args.enc_type == 'transformer':
self.reset_parameters(args.param_init,
dist='xavier_uniform',
keys=['enc'],
ignore_keys=['embed_in'])
self.reset_parameters(args.d_model**-0.5,
dist='normal',
keys=['embed_in'])
else:
self.reset_parameters(args.param_init,
dist=args.param_init_dist,
keys=['enc'],
ignore_keys=['conv'])
# Initialize parameters in the decoder
if args.dec_type == 'transformer':
self.reset_parameters(args.param_init,
dist='xavier_uniform',
keys=['dec'],
ignore_keys=['embed'])
self.reset_parameters(args.d_model**-0.5,
dist='normal',
keys=['embed'])
else:
self.reset_parameters(args.param_init,
dist=args.param_init_dist,
keys=['dec'])
# Initialize bias vectors with zero
self.reset_parameters(0, dist='constant', keys=['bias'])
# Recurrent weights are orthogonalized
if args.rec_weight_orthogonal:
self.reset_parameters(args.param_init,
dist='orthogonal',
keys=['rnn', 'weight'])
# Initialize bias in forget gate with 1
# self.init_forget_gate_bias_with_one()
# Initialize bias in gating with -1 for cold fusion
if args.lm_fusion:
self.reset_parameters(-1,
dist='constant',
keys=['linear_lm_gate.fc.bias'])
if args.lm_fusion_type == 'deep' and args.lm_fusion:
for n, p in self.named_parameters():
if 'output' in n or 'output_bn' in n or 'linear' in n:
p.requires_grad = True
else:
p.requires_grad = False
def __init__(self, args, save_path=None):
super(LMBase, self).__init__()
logger = logging.getLogger('training')
logger.info(self.__class__.__name__)
self.save_path = save_path
self.emb_dim = args.emb_dim
self.n_units = args.n_units
self.n_layers = args.n_layers
self.lsm_prob = args.lsm_prob
self.vocab = args.vocab
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for cache
self.cache_theta = 0.2 # smoothing parameter
self.cache_lambda = 0.2 # cache weight
self.cache_ids = []
self.cache_keys = []
self.cache_attn = []
self.embed = Embedding(vocab=self.vocab,
emb_dim=args.emb_dim,
dropout=args.dropout_in,
ignore_index=self.pad)
model_size = args.lm_type.replace('gated_conv_', '')
blocks = OrderedDict()
if model_size == 'custom':
blocks['conv1'] = GLUBlock(args.kernel_size,
args.emb_dim,
args.n_units,
bottlececk_dim=args.n_projs,
dropout=args.dropout_hidden)
for l in range(args.n_layers - 1):
blocks['conv%d' % (l + 2)] = GLUBlock(
args.kernel_size,
args.n_units,
args.n_units,
bottlececk_dim=args.n_projs,
dropout=args.dropout_hidden)
last_dim = args.n_units
elif model_size == '8':
blocks['conv1'] = GLUBlock(4,
args.emb_dim,
900,
dropout=args.dropout_hidden)
for i in range(1, 8, 1):
blocks['conv2-%d' % i] = GLUBlock(4,
900,
900,
dropout=args.dropout_hidden)
last_dim = 900
elif model_size == '8B':
blocks['conv1'] = GLUBlock(1,
args.emb_dim,
512,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
blocks['conv2-%d' % i] = GLUBlock(5,
512,
512,
bottlececk_dim=128,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
blocks['conv3-%d' % i] = GLUBlock(5,
512,
512,
bottlececk_dim=256,
dropout=args.dropout_hidden)
blocks['conv4'] = GLUBlock(1,
512,
2048,
bottlececk_dim=1024,
dropout=args.dropout_hidden)
last_dim = 2048
elif model_size == '9':
blocks['conv1'] = GLUBlock(4,
args.emb_dim,
807,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
blocks['conv2-%d-1' % i] = GLUBlock(
4, 807, 807, dropout=args.dropout_hidden)
blocks['conv2-%d-2' % i] = GLUBlock(
4, 807, 807, dropout=args.dropout_hidden)
last_dim = 807
elif model_size == '13':
blocks['conv1'] = GLUBlock(4,
args.emb_dim,
1268,
dropout=args.dropout_hidden)
for i in range(1, 13, 1):
blocks['conv2-%d' % i] = GLUBlock(4,
1268,
1268,
dropout=args.dropout_hidden)
last_dim = 1268
elif model_size == '14':
for i in range(1, 4, 1):
blocks['conv1-%d' % i] = GLUBlock(
6,
args.emb_dim if i == 1 else 850,
850,
dropout=args.dropout_hidden)
blocks['conv2'] = GLUBlock(1,
850,
850,
dropout=args.dropout_hidden)
for i in range(1, 5, 1):
blocks['conv3-%d' % i] = GLUBlock(5,
850,
850,
dropout=args.dropout_hidden)
blocks['conv4'] = GLUBlock(1,
850,
850,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
blocks['conv5-%d' % i] = GLUBlock(4,
850,
850,
dropout=args.dropout_hidden)
blocks['conv6'] = GLUBlock(4,
850,
1024,
dropout=args.dropout_hidden)
blocks['conv7'] = GLUBlock(4,
1024,
2048,
dropout=args.dropout_hidden)
last_dim = 2048
elif model_size == '14B':
blocks['conv1'] = GLUBlock(5,
args.emb_dim,
512,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
blocks['conv2-%d' % i] = GLUBlock(5,
512,
512,
bottlececk_dim=128,
dropout=args.dropout_hidden)
for i in range(1, 4, 1):
blocks['conv3-%d' % i] = GLUBlock(5,
512 if i == 1 else 1024,
1024,
bottlececk_dim=512,
dropout=args.dropout_hidden)
for i in range(1, 7, 1):
blocks['conv4-%d' % i] = GLUBlock(5,
1024 if i == 1 else 2048,
2048,
bottlececk_dim=1024,
dropout=args.dropout_hidden)
blocks['conv5'] = GLUBlock(5,
2048,
4096,
bottlececk_dim=1024,
dropout=args.dropout_hidden)
last_dim = 4096
else:
raise NotImplementedError(model_size)
self.blocks = nn.Sequential(blocks)
if args.adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
last_dim,
self.vocab,
# cutoffs=[self.vocab // 10, 3 * self.vocab // 10],
cutoffs=[self.vocab // 25, self.vocab // 5],
div_value=4.0)
self.output = None
else:
self.adaptive_softmax = None
self.output = LinearND(last_dim,
self.vocab,
dropout=args.dropout_out)
# NOTE: include bias even when tying weights
# Optionally tie weights as in:
# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
# https://arxiv.org/abs/1608.05859
# and
# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
# https://arxiv.org/abs/1611.01462
if args.tie_embedding:
if args.n_units != args.emb_dim:
raise ValueError(
'When using the tied flag, n_units must be equal to emb_dim.'
)
self.output.fc.weight = self.embed.embed.weight
# Initialize parameters
self.reset_parameters(args.param_init)
def __init__(self,
input_dim,
attn_type,
attn_n_heads,
n_layers,
d_model,
d_ff,
pe_type='add',
layer_norm_eps=1e-6,
dropout_in=0,
dropout=0,
dropout_att=0,
last_proj_dim=0,
n_stacks=1,
n_splices=1,
conv_in_channel=1,
conv_channels=0,
conv_kernel_sizes=[],
conv_strides=[],
conv_poolings=[],
conv_batch_norm=False,
conv_residual=False,
conv_bottleneck_dim=0,
param_init=0.1):
super(TransformerEncoder, self).__init__()
logger = logging.getLogger("training")
self.d_model = d_model
self.n_layers = n_layers
self.attn_n_heads = attn_n_heads
self.pe_type = pe_type
# Setting for CNNs before RNNs
if conv_channels:
channels = [int(c) for c in conv_channels.split('_')
] if len(conv_channels) > 0 else []
kernel_sizes = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_kernel_sizes.split('_')
] if len(conv_kernel_sizes) > 0 else []
strides = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_strides.split('_')
] if len(conv_strides) > 0 else []
poolings = [[
int(c.split(',')[0].replace('(', '')),
int(c.split(',')[1].replace(')', ''))
] for c in conv_poolings.split('_')
] if len(conv_poolings) > 0 else []
else:
channels = []
kernel_sizes = []
strides = []
poolings = []
logger.warning(
'Subsampling is automatically ignored because CNN layers are used before RNN layers.'
)
if len(channels) > 0:
assert n_stacks == 1 and n_splices == 1
self.conv = ConvEncoder(input_dim,
in_channel=conv_in_channel,
channels=channels,
kernel_sizes=kernel_sizes,
strides=strides,
poolings=poolings,
dropout=0,
batch_norm=conv_batch_norm,
residual=conv_residual,
bottleneck_dim=d_model,
param_init=param_init)
self._output_dim = self.conv.output_dim
else:
self._output_dim = input_dim * n_splices * n_stacks
self.conv = None
self.embed = LinearND(self._output_dim, d_model,
dropout=0) # NOTE: do not apply dropout here
self.pos_enc = PositionalEncoding(d_model, dropout_in, pe_type)
self.layers = nn.ModuleList([
TransformerEncoderBlock(d_model, d_ff, attn_type, attn_n_heads,
dropout, dropout_att, layer_norm_eps)
for l in range(n_layers)
])
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim != self.output_dim:
self.bridge = LinearND(self._output_dim,
last_proj_dim,
dropout=dropout)
self._output_dim = last_proj_dim
else:
self.bridge = None
self._output_dim = d_model
# Initialize parameters
self.reset_parameters()