def __init__(self, special_symbols,
enc_n_units, attn_type, n_heads, n_layers,
d_model, d_ff, ffn_bottleneck_dim,
pe_type, layer_norm_eps, ffn_activation,
vocab, tie_embedding,
dropout, dropout_emb, dropout_att, dropout_layer, dropout_head,
lsm_prob, ctc_weight, ctc_lsm_prob, ctc_fc_list, backward,
global_weight, mtl_per_batch, param_init,
memory_transformer, mem_len,
mocha_chunk_size, mocha_n_heads_mono, mocha_n_heads_chunk,
mocha_init_r, mocha_eps, mocha_std,
mocha_no_denominator, mocha_1dconv,
mocha_quantity_loss_weight, mocha_head_divergence_loss_weight,
latency_metric, latency_loss_weight,
mocha_first_layer, share_chunkwise_attention,
external_lm, lm_fusion):
super(TransformerDecoder, self).__init__()
self.eos = special_symbols['eos']
self.unk = special_symbols['unk']
self.pad = special_symbols['pad']
self.blank = special_symbols['blank']
self.vocab = vocab
self.enc_n_units = enc_n_units
self.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.pe_type = pe_type
self.lsm_prob = lsm_prob
self.att_weight = global_weight - ctc_weight
self.ctc_weight = ctc_weight
self.bwd = backward
self.mtl_per_batch = mtl_per_batch
self.prev_spk = ''
self.lmstate_final = None
# for TransformerXL decoder
self.memory_transformer = memory_transformer
self.mem_len = mem_len
if memory_transformer:
assert pe_type == 'none'
# for attention plot
self.aws_dict = {}
self.data_dict = {}
# for MMA
self.attn_type = attn_type
self.quantity_loss_weight = mocha_quantity_loss_weight
self._quantity_loss_weight = 0 # for curriculum
self.mocha_first_layer = mocha_first_layer
self.headdiv_loss_weight = mocha_head_divergence_loss_weight
self.latency_metric = latency_metric
self.latency_loss_weight = latency_loss_weight
self.ctc_trigger = (self.latency_metric in ['ctc_sync'])
if self.ctc_trigger:
assert 0 < self.ctc_weight < 1
if ctc_weight > 0:
self.ctc = CTC(eos=self.eos,
blank=self.blank,
enc_n_units=enc_n_units,
vocab=self.vocab,
dropout=dropout,
lsm_prob=ctc_lsm_prob,
fc_list=ctc_fc_list,
param_init=0.1,
backward=backward)
if self.att_weight > 0:
# token embedding
self.embed = nn.Embedding(self.vocab, d_model, padding_idx=self.pad)
self.pos_enc = PositionalEncoding(d_model, dropout_emb, pe_type, param_init)
# positional embedding
self.u = None
self.v = None
if memory_transformer:
self.scale = math.sqrt(d_model) # for token embedding
self.dropout_emb = nn.Dropout(p=dropout_emb) # for token embedding
self.pos_emb = XLPositionalEmbedding(d_model, dropout_emb)
if self.mem_len > 0:
self.u = nn.Parameter(torch.Tensor(n_heads, d_model // n_heads))
self.v = nn.Parameter(torch.Tensor(n_heads, d_model // n_heads))
# NOTE: u and v are global parameters
# self-attention
assert mocha_first_layer <= n_layers
self.layers = nn.ModuleList([copy.deepcopy(TransformerDecoderBlock(
d_model, d_ff, attn_type, n_heads, dropout, dropout_att, dropout_layer,
layer_norm_eps, ffn_activation, param_init,
src_tgt_attention=False if lth < mocha_first_layer - 1 else True,
memory_transformer=memory_transformer,
mocha_chunk_size=mocha_chunk_size,
mocha_n_heads_mono=mocha_n_heads_mono,
mocha_n_heads_chunk=mocha_n_heads_chunk,
mocha_init_r=mocha_init_r,
mocha_eps=mocha_eps,
mocha_std=mocha_std,
mocha_no_denominator=mocha_no_denominator,
mocha_1dconv=mocha_1dconv,
dropout_head=dropout_head,
lm_fusion=lm_fusion,
ffn_bottleneck_dim=ffn_bottleneck_dim,
share_chunkwise_attention=share_chunkwise_attention)) for lth in range(n_layers)])
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.output = nn.Linear(d_model, self.vocab)
if tie_embedding:
self.output.weight = self.embed.weight
self.lm = external_lm
if external_lm is not None:
self.lm_output_proj = nn.Linear(external_lm.output_dim, d_model)
self.reset_parameters(param_init)
def __init__(self, input_dim, enc_type, n_heads, n_layers, n_layers_sub1,
n_layers_sub2, d_model, d_ff, ffn_bottleneck_dim,
ffn_activation, pe_type, layer_norm_eps, last_proj_dim,
dropout_in, dropout, dropout_att, dropout_layer, subsample,
subsample_type, n_stacks, n_splices, conv_in_channel,
conv_channels, conv_kernel_sizes, conv_strides, conv_poolings,
conv_batch_norm, conv_layer_norm, conv_bottleneck_dim,
conv_param_init, task_specific_layer, param_init, clamp_len,
lookahead, chunk_size_left, chunk_size_current,
chunk_size_right, streaming_type):
super(TransformerEncoder, self).__init__()
# parse subsample
subsamples = [1] * n_layers
for lth, s in enumerate(list(map(int,
subsample.split('_')[:n_layers]))):
subsamples[lth] = s
# parse lookahead
lookaheads = [0] * n_layers
for lth, s in enumerate(list(map(int,
lookahead.split('_')[:n_layers]))):
lookaheads[lth] = s
if len(subsamples) > 0 and len(subsamples) != n_layers:
raise ValueError(
'subsample must be the same size as n_layers. n_layers: %d, subsample: %s'
% (n_layers, subsamples))
if n_layers_sub1 < 0 or (n_layers_sub1 > 1
and n_layers < n_layers_sub1):
raise Warning(
'Set n_layers_sub1 between 1 to n_layers. n_layers: %d, n_layers_sub1: %d'
% (n_layers, n_layers_sub1))
if n_layers_sub2 < 0 or (n_layers_sub2 > 1
and n_layers_sub1 < n_layers_sub2):
raise Warning(
'Set n_layers_sub2 between 1 to n_layers_sub1. n_layers_sub1: %d, n_layers_sub2: %d'
% (n_layers_sub1, n_layers_sub2))
self.enc_type = enc_type
self.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.pe_type = pe_type
self.scale = math.sqrt(d_model)
# for compatibility
chunk_size_left = str(chunk_size_left)
chunk_size_current = str(chunk_size_current)
chunk_size_right = str(chunk_size_right)
# for streaming encoder
self.unidir = 'uni' in enc_type
self.lookaheads = lookaheads
if sum(lookaheads) > 0:
assert self.unidir
self.chunk_size_left = int(chunk_size_left.split('_')[-1]) // n_stacks
self.chunk_size_current = int(
chunk_size_current.split('_')[-1]) // n_stacks
self.chunk_size_right = int(
chunk_size_right.split('_')[-1]) // n_stacks
self.lc_bidir = self.chunk_size_current > 0 and enc_type != 'conv' and 'uni' not in enc_type
self.cnn_lookahead = self.unidir or enc_type == 'conv'
self.streaming_type = streaming_type if self.lc_bidir else ''
# -: past context
# *: current context
# +: future context
# reshape) overlapped windowing. additional redundant computation is introduced.
# During inference, caching is not applied. However, considering (N_l+N_c+N_r) is very short
# and independent on layer depth, the overhead is negligible.
# chunk1: |**|++
# chunk2: --|**|++
# chunk3: --|**|++
# chunk4: --|**|++
# chunk5: --|**|++
# mask) chunkwise masking. future context is restricted within the current chunk
# to avoid accumuration of future context depending on the layer depth.
# chunk1: |**|
# chunk2: --|**|
# chunk3: -- --|**|
# chunk4: -- --|**|
# chunk5: -- --|**|
if self.unidir:
assert self.chunk_size_left == self.chunk_size_current == self.chunk_size_right == 0
if self.streaming_type == 'mask':
assert self.chunk_size_right == 0
assert self.chunk_size_left == self.chunk_size_current
# NOTE: this is important to cache CNN output at each chunk
if self.lc_bidir:
assert n_layers_sub1 == 0
assert n_layers_sub2 == 0
assert not self.unidir
# for hierarchical encoder
self.n_layers_sub1 = n_layers_sub1
self.n_layers_sub2 = n_layers_sub2
self.task_specific_layer = task_specific_layer
# for bridge layers
self.bridge = None
self.bridge_sub1 = None
self.bridge_sub2 = None
# for attention plot
self.aws_dict = {}
self.data_dict = {}
# Setting for CNNs
if 'conv' in enc_type:
assert conv_channels
assert n_stacks == 1 and n_splices == 1
self.conv = ConvEncoder(input_dim,
in_channel=conv_in_channel,
channels=conv_channels,
kernel_sizes=conv_kernel_sizes,
strides=conv_strides,
poolings=conv_poolings,
dropout=0.,
batch_norm=conv_batch_norm,
layer_norm=conv_layer_norm,
layer_norm_eps=layer_norm_eps,
residual=False,
bottleneck_dim=d_model,
param_init=conv_param_init)
self._odim = self.conv.output_dim
else:
self.conv = None
self._odim = input_dim * n_splices * n_stacks
self.embed = nn.Linear(self._odim, d_model)
# calculate subsampling factor
self._factor = 1
if self.conv is not None:
self._factor *= self.conv.subsampling_factor
self.subsample = None
if np.prod(subsamples) > 1:
self._factor *= np.prod(subsamples)
if subsample_type == 'max_pool':
self.subsample = nn.ModuleList(
[MaxpoolSubsampler(factor) for factor in subsamples])
elif subsample_type == 'concat':
self.subsample = nn.ModuleList([
ConcatSubsampler(factor, self._odim)
for factor in subsamples
])
elif subsample_type == 'drop':
self.subsample = nn.ModuleList(
[DropSubsampler(factor) for factor in subsamples])
elif subsample_type == '1dconv':
self.subsample = nn.ModuleList([
Conv1dSubsampler(factor, self._odim)
for factor in subsamples
])
elif subsample_type == 'add':
self.subsample = nn.ModuleList(
[AddSubsampler(factor) for factor in subsamples])
if self.chunk_size_left > 0:
assert self.chunk_size_left % self._factor == 0
if self.chunk_size_current > 0:
assert self.chunk_size_current % self._factor == 0
if self.chunk_size_right > 0:
assert self.chunk_size_right % self._factor == 0
self.pos_enc, self.pos_emb = None, None
self.u_bias, self.v_bias = None, None
if pe_type in ['relative', 'relative_xl']:
self.pos_emb = XLPositionalEmbedding(d_model, dropout)
if pe_type == 'relative_xl':
self.u_bias = nn.Parameter(
torch.Tensor(n_heads, d_model // n_heads))
self.v_bias = nn.Parameter(
torch.Tensor(n_heads, d_model // n_heads))
# NOTE: u_bias and v_bias are global parameters shared in the whole model
else:
self.pos_enc = PositionalEncoding(d_model, dropout_in, pe_type,
param_init)
self.layers = nn.ModuleList([
copy.deepcopy(
TransformerEncoderBlock(d_model, d_ff, n_heads, dropout,
dropout_att, dropout_layer,
layer_norm_eps, ffn_activation,
param_init, pe_type, clamp_len,
ffn_bottleneck_dim))
for _ in range(n_layers)
])
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
self._odim = d_model
if n_layers_sub1 > 0:
if task_specific_layer:
self.layer_sub1 = TransformerEncoderBlock(
d_model, d_ff, n_heads, dropout, dropout_att,
dropout_layer, layer_norm_eps, ffn_activation, param_init,
pe_type, clamp_len, ffn_bottleneck_dim)
odim_sub1 = d_model
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub1 = nn.Linear(self._odim, last_proj_dim)
odim_sub1 = last_proj_dim
if n_layers_sub1 == n_layers:
self.norm_out_sub1 = None
else:
self.norm_out_sub1 = nn.LayerNorm(odim_sub1,
eps=layer_norm_eps)
if n_layers_sub2 > 0:
if task_specific_layer:
self.layer_sub2 = TransformerEncoderBlock(
d_model, d_ff, n_heads, dropout, dropout_att,
dropout_layer, layer_norm_eps, ffn_activation, param_init,
pe_type, clamp_len, ffn_bottleneck_dim)
odim_sub2 = d_model
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub2 = nn.Linear(self._odim, last_proj_dim)
odim_sub2 = last_proj_dim
if n_layers_sub2 == n_layers:
self.norm_out_sub2 = None
else:
self.norm_out_sub2 = nn.LayerNorm(odim_sub2,
eps=layer_norm_eps)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge = nn.Linear(self._odim, last_proj_dim)
self._odim = last_proj_dim
self.reset_parameters(param_init)
# for streaming inference
self.reset_cache()
def __init__(self, input_dim, enc_type, n_heads, kernel_size, n_layers,
n_layers_sub1, n_layers_sub2, d_model, d_ff,
ffn_bottleneck_dim, last_proj_dim, pe_type, layer_norm_eps,
ffn_activation, dropout_in, dropout, dropout_att,
dropout_layer, n_stacks, n_splices, conv_in_channel,
conv_channels, conv_kernel_sizes, conv_strides, conv_poolings,
conv_batch_norm, conv_layer_norm, conv_bottleneck_dim,
conv_param_init, task_specific_layer, param_init,
chunk_size_left, chunk_size_current, chunk_size_right):
super(ConformerEncoder, self).__init__()
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.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.pe_type = pe_type
self.scale = math.sqrt(d_model)
# for streaming encoder
self.chunk_size_left = chunk_size_left
self.chunk_size_current = chunk_size_current
self.chunk_size_right = chunk_size_right
self.latency_controlled = chunk_size_left > 0 or chunk_size_current > 0 or chunk_size_right > 0
# for hierarchical encoder
self.n_layers_sub1 = n_layers_sub1
self.n_layers_sub2 = n_layers_sub2
self.task_specific_layer = task_specific_layer
# for bridge layers
self.bridge = None
self.bridge_sub1 = None
self.bridge_sub2 = None
# for attention plot
self.aws_dict = {}
self.data_dict = {}
# Setting for CNNs
if 'conv' in enc_type:
assert conv_channels
assert n_stacks == 1 and n_splices == 1
self.conv = ConvEncoder(input_dim,
in_channel=conv_in_channel,
channels=conv_channels,
kernel_sizes=conv_kernel_sizes,
strides=conv_strides,
poolings=conv_poolings,
dropout=0.,
batch_norm=conv_batch_norm,
layer_norm=conv_layer_norm,
layer_norm_eps=layer_norm_eps,
residual=False,
bottleneck_dim=d_model,
param_init=conv_param_init)
self._odim = self.conv.output_dim
else:
self.conv = None
self._odim = input_dim * n_splices * n_stacks
self.embed = nn.Linear(self._odim, d_model)
# calculate subsampling factor
self._factor = 1
if self.conv is not None:
self._factor *= self.conv.subsampling_factor
if self.chunk_size_left > 0:
assert self.chunk_size_left % self._factor == 0
if self.chunk_size_current > 0:
assert self.chunk_size_current % self._factor == 0
if self.chunk_size_right > 0:
assert self.chunk_size_right % self._factor == 0
self.pos_emb = XLPositionalEmbedding(d_model, dropout)
assert pe_type == 'relative'
# TODO(hirofumi0810): try other positional encodings
self.layers = nn.ModuleList([
copy.deepcopy(
ConformerEncoderBlock(d_model,
d_ff,
n_heads,
kernel_size,
dropout,
dropout_att,
dropout_layer,
layer_norm_eps,
ffn_activation,
param_init,
ffn_bottleneck_dim=ffn_bottleneck_dim))
for _ in range(n_layers)
])
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
self._odim = d_model
if n_layers_sub1 > 0:
if task_specific_layer:
self.layer_sub1 = ConformerEncoderBlock(
d_model,
d_ff,
n_heads,
kernel_size,
dropout,
dropout_att,
dropout_layer,
layer_norm_eps,
ffn_activation,
param_init,
ffn_bottleneck_dim=ffn_bottleneck_dim)
self.norm_out_sub1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub1 = nn.Linear(self._odim, last_proj_dim)
if n_layers_sub2 > 0:
if task_specific_layer:
self.layer_sub2 = ConformerEncoderBlock(
d_model,
d_ff,
n_heads,
kernel_size,
dropout,
dropout_att,
dropout_layer,
layer_norm_eps,
ffn_activation,
param_init,
ffn_bottleneck_dim=ffn_bottleneck_dim)
self.norm_out_sub2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub2 = nn.Linear(self._odim, last_proj_dim)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge = nn.Linear(self._odim, last_proj_dim)
self._odim = last_proj_dim
self.reset_parameters(param_init)
def __init__(self, args, save_path=None):
super(LMBase, self).__init__()
logger.info(self.__class__.__name__)
self.lm_type = args.lm_type
self.save_path = save_path
self.d_model = args.transformer_d_model
self.n_layers = args.n_layers
self.n_heads = args.transformer_n_heads
self.lsm_prob = args.lsm_prob
if args.mem_len > 0:
self.mem_len = args.mem_len
else:
self.mem_len = args.bptt
if args.recog_mem_len > 0:
self.mem_len = args.recog_mem_len
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_cache = None
# positional embedding
self.pos_emb = XLPositionalEmbedding(self.d_model, args.dropout_in)
self.u_bias = nn.Parameter(torch.Tensor(self.n_heads, self.d_model // self.n_heads))
self.v_bias = nn.Parameter(torch.Tensor(self.n_heads, self.d_model // self.n_heads))
# NOTE: u_bias and v_bias are global parameters
self.embed = nn.Embedding(self.vocab, self.d_model, padding_idx=self.pad)
self.scale = math.sqrt(self.d_model) # for token embedding
self.dropout_emb = nn.Dropout(p=args.dropout_in) # for token embedding
self.layers = nn.ModuleList([copy.deepcopy(TransformerDecoderBlock(
self.d_model, args.transformer_d_ff, 'scaled_dot',
self.n_heads, args.dropout_hidden, args.dropout_att, args.dropout_layer,
args.transformer_layer_norm_eps, args.transformer_ffn_activation, args.transformer_param_init,
src_tgt_attention=False, memory_transformer=True)) for lth in range(self.n_layers)])
self.norm_out = nn.LayerNorm(self.d_model, eps=args.transformer_layer_norm_eps)
self.adaptive_softmax = None
self.output = None
if args.adaptive_softmax:
self.adaptive_softmax = nn.AdaptiveLogSoftmaxWithLoss(
self.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)
else:
self.output = nn.Linear(self.d_model, self.vocab)
if args.tie_embedding:
self.output.weight = self.embed.weight
self.reset_parameters()
def __init__(self, input_dim, enc_type, n_heads, n_layers, n_layers_sub1,
n_layers_sub2, d_model, d_ff, ffn_bottleneck_dim,
last_proj_dim, pe_type, layer_norm_eps, ffn_activation,
dropout_in, dropout, dropout_att, dropout_layer, subsample,
subsample_type, n_stacks, n_splices, conv_in_channel,
conv_channels, conv_kernel_sizes, conv_strides, conv_poolings,
conv_batch_norm, conv_layer_norm, conv_bottleneck_dim,
conv_param_init, task_specific_layer, param_init,
chunk_size_left, chunk_size_current, chunk_size_right,
latency_control_type):
super(TransformerEncoder, self).__init__()
# parse subsample
subsamples = [1] * n_layers
for lth, s in enumerate(list(map(int,
subsample.split('_')[:n_layers]))):
subsamples[lth] = s
if len(subsamples) > 0 and len(subsamples) != n_layers:
raise ValueError(
'subsample must be the same size as n_layers. n_layers: %d, subsample: %s'
% (n_layers, subsamples))
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.')
assert enc_type in ['transformer', 'conv_transformer']
self.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.pe_type = pe_type
self.scale = math.sqrt(d_model)
# for streaming encoder
self.chunk_size_left = chunk_size_left
self.chunk_size_current = chunk_size_current
self.chunk_size_right = chunk_size_right
self.latency_controlled = chunk_size_left > 0 or chunk_size_current > 0 or chunk_size_right > 0
self.lc_type = latency_control_type
# reshape) not lookahead frames in CNN layers, but requires some additional computations
# mask) there are some lookahead frames in CNN layers, no additional computations
# TransformerXL like streaming encoder
self.memory_transformer = ('transformer_xl' in enc_type)
self.mem_len = chunk_size_left
if self.memory_transformer:
assert pe_type == 'relative'
assert chunk_size_left > 0
assert chunk_size_current > 0
# for hierarchical encoder
self.n_layers_sub1 = n_layers_sub1
self.n_layers_sub2 = n_layers_sub2
self.task_specific_layer = task_specific_layer
# for bridge layers
self.bridge = None
self.bridge_sub1 = None
self.bridge_sub2 = None
# for attention plot
self.aws_dict = {}
self.data_dict = {}
# Setting for CNNs
if 'conv' in enc_type:
assert conv_channels
assert n_stacks == 1 and n_splices == 1
self.conv = ConvEncoder(input_dim,
in_channel=conv_in_channel,
channels=conv_channels,
kernel_sizes=conv_kernel_sizes,
strides=conv_strides,
poolings=conv_poolings,
dropout=0.,
batch_norm=conv_batch_norm,
layer_norm=conv_layer_norm,
layer_norm_eps=layer_norm_eps,
residual=False,
bottleneck_dim=d_model,
param_init=conv_param_init)
self._odim = self.conv.output_dim
else:
self.conv = None
self._odim = input_dim * n_splices * n_stacks
self.embed = nn.Linear(self._odim, d_model)
# calculate subsampling factor
self._factor = 1
if self.conv is not None:
self._factor *= self.conv.subsampling_factor
self.subsample = None
if np.prod(subsamples) > 1:
self._factor *= np.prod(subsamples)
if subsample_type == 'max_pool':
self.subsample = nn.ModuleList(
[MaxpoolSubsampler(factor) for factor in subsamples])
elif subsample_type == 'concat':
self.subsample = nn.ModuleList([
ConcatSubsampler(factor, self._odim)
for factor in subsamples
])
elif subsample_type == 'drop':
self.subsample = nn.ModuleList(
[DropSubsampler(factor) for factor in subsamples])
elif subsample_type == '1dconv':
self.subsample = nn.ModuleList([
Conv1dSubsampler(factor, self._odim)
for factor in subsamples
])
if self.chunk_size_left > 0:
assert self.chunk_size_left % self._factor == 0
if self.chunk_size_current > 0:
assert self.chunk_size_current % self._factor == 0
if self.chunk_size_right > 0:
assert self.chunk_size_right % self._factor == 0
self.pos_emb = None
self.u = None
self.v = None
if self.memory_transformer:
self.pos_emb = XLPositionalEmbedding(d_model, dropout)
self.u = nn.Parameter(torch.Tensor(n_heads, d_model // n_heads))
self.v = nn.Parameter(torch.Tensor(n_heads, d_model // n_heads))
# NOTE: u and v are global parameters
elif pe_type == 'relative':
self.pos_emb = XLPositionalEmbedding(d_model, dropout)
else:
self.pos_enc = PositionalEncoding(d_model, dropout_in, pe_type,
param_init)
self.layers = nn.ModuleList([
copy.deepcopy(
TransformerEncoderBlock(d_model,
d_ff,
n_heads,
dropout,
dropout_att,
dropout_layer,
layer_norm_eps,
ffn_activation,
param_init,
relative_attention=self.pos_emb
is not None,
ffn_bottleneck_dim=ffn_bottleneck_dim))
for _ in range(n_layers)
])
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
self._odim = d_model
if n_layers_sub1 > 0:
if task_specific_layer:
self.layer_sub1 = TransformerEncoderBlock(
d_model,
d_ff,
n_heads,
dropout,
dropout_att,
dropout_layer,
layer_norm_eps,
ffn_activation,
param_init,
ffn_bottleneck_dim=ffn_bottleneck_dim)
self.norm_out_sub1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub1 = nn.Linear(self._odim, last_proj_dim)
if n_layers_sub2 > 0:
if task_specific_layer:
self.layer_sub2 = TransformerEncoderBlock(
d_model,
d_ff,
n_heads,
dropout,
dropout_att,
dropout_layer,
layer_norm_eps,
ffn_activation,
param_init,
ffn_bottleneck_dim=ffn_bottleneck_dim)
self.norm_out_sub2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub2 = nn.Linear(self._odim, last_proj_dim)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge = nn.Linear(self._odim, last_proj_dim)
self._odim = last_proj_dim
self.reset_parameters(param_init)
