class TransformerEncoder(EncoderBase):
"""Transformer encoder.
Args:
input_dim (int): dimension of input features (freq * channel)
enc_type (str): type of encoder
attn_type (str): type of attention
n_heads (int): number of heads for multi-head attention
n_layers (int): number of blocks
n_layers_sub1 (int): number of layers in the 1st auxiliary task
n_layers_sub2 (int): number of layers in the 2nd auxiliary task
d_model (int): dimension of MultiheadAttentionMechanism
d_ff (int): dimension of PositionwiseFeedForward
last_proj_dim (int): dimension of the last projection layer
pe_type (str): type of positional encoding
layer_norm_eps (float): epsilon value for layer normalization
ffn_activation (str): nonolinear function for PositionwiseFeedForward
dropout_in (float): dropout probability for input-hidden connection
dropout (float): dropout probabilities for linear layers
dropout_att (float): dropout probabilities for attention distributions
dropout_residual (float): dropout probability for stochastic residual connections
n_stacks (int): number of frames to stack
n_splices (int): frames to splice. Default is 1 frame.
conv_in_channel (int): number of channels of input features
conv_channels (int): number of channles in the CNN blocks
conv_kernel_sizes (list): size of kernels in the CNN blocks
conv_strides (list): number of strides in the CNN blocks
conv_poolings (list): size of poolings in the CNN blocks
conv_batch_norm (bool): apply batch normalization only in the CNN blocks
conv_layer_norm (bool): apply layer normalization only in the CNN blocks
conv_bottleneck_dim (int): dimension of the bottleneck layer between CNN and self-attention layers
conv_param_init (float): only for CNN layers before Transformer layers
chunk_size_left (int): left chunk size for time-restricted Transformer encoder
chunk_size_current (int): current chunk size for time-restricted Transformer encoder
chunk_size_right (int): right chunk size for time-restricted Transformer encoder
task_specific_layer (bool): add a task specific layer for each sub task
param_init (str): parameter initialization method
"""
def __init__(self, input_dim, enc_type, attn_type, n_heads, n_layers,
n_layers_sub1, n_layers_sub2, d_model, d_ff, last_proj_dim,
pe_type, layer_norm_eps, ffn_activation, dropout_in, dropout,
dropout_att, dropout_residual, 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(TransformerEncoder, 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
# for latency-controlled
self.chunk_size_left = chunk_size_left
self.chunk_size_cur = chunk_size_current
self.chunk_size_right = chunk_size_right
# 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 before RNNs
if 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)
self.pos_enc = PositionalEncoding(d_model, dropout_in, pe_type)
self.layers = nn.ModuleList([
copy.deepcopy(
TransformerEncoderBlock(d_model, d_ff, attn_type, n_heads,
dropout, dropout_att,
dropout_residual * (l + 1) / n_layers,
layer_norm_eps, ffn_activation,
param_init)) for l 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, attn_type, n_heads, dropout, dropout_att,
dropout_residual * n_layers_sub1 / n_layers,
layer_norm_eps, ffn_activation, param_init)
self.norm_out_sub1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
if 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, attn_type, n_heads, dropout, dropout_att,
dropout_residual * n_layers_sub2 / n_layers,
layer_norm_eps, ffn_activation, param_init)
self.norm_out_sub2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim != self.output_dim:
self.bridge_sub2 = nn.Linear(self._odim, last_proj_dim)
if last_proj_dim != self.output_dim:
self.bridge = nn.Linear(self._odim, last_proj_dim)
self._odim = last_proj_dim
# calculate subsampling factor
self._factor = 1
if self.conv is not None:
self._factor *= self.conv.subsampling_factor()
if param_init == 'xavier_uniform':
self.reset_parameters()
def reset_parameters(self):
"""Initialize parameters with Xavier uniform distribution."""
logger.info(
'===== Initialize %s with Xavier uniform distribution =====' %
self.__class__.__name__)
if self.conv is None:
nn.init.xavier_uniform_(self.embed.weight)
nn.init.constant_(self.embed.bias, 0.)
if self.bridge is not None:
nn.init.xavier_uniform_(self.bridge.weight)
nn.init.constant_(self.bridge.bias, 0.)
def forward(self, xs, xlens, task, use_cache=False, streaming=False):
"""Forward computation.
Args:
xs (FloatTensor): `[B, T, input_dim]`
xlens (list): `[B]`
task (str): not supported now
use_cache (bool):
streaming (bool): streaming encoding
Returns:
eouts (dict):
xs (FloatTensor): `[B, T, d_model]`
xlens (list): `[B]`
"""
eouts = {
'ys': {
'xs': None,
'xlens': None
},
'ys_sub1': {
'xs': None,
'xlens': None
},
'ys_sub2': {
'xs': None,
'xlens': None
}
}
if self.conv is None:
xs = self.embed(xs)
else:
# Path through CNN blocks before RNN layers
xs, xlens = self.conv(xs, xlens)
if not self.training:
self.data_dict['elens'] = tensor2np(xlens)
bs, xmax, idim = xs.size()
xs = self.pos_enc(xs)
if self.chunk_size_left > 0:
# Time-restricted self-attention for streaming models
cs_l = self.chunk_size_left
cs_c = self.chunk_size_cur
cs_r = self.chunk_size_right
xs_chunks = []
xx_aws = [[] for l in range(self.n_layers)]
xs_pad = torch.cat([
xs.new_zeros(bs, cs_l, idim), xs,
xs.new_zeros(bs, cs_r, idim)
],
dim=1)
# TODO: remove right padding
for t in range(cs_l, cs_l + xmax, self.chunk_size_cur):
xs_chunk = xs_pad[:, t - cs_l:t + cs_c + cs_r]
for l, layer in enumerate(self.layers):
xs_chunk, xx_aws_chunk = layer(xs_chunk, None) # no mask
xx_aws[l].append(xx_aws_chunk[:, :, cs_l:cs_l + cs_c,
cs_l:cs_l + cs_c])
xs_chunks.append(xs_chunk[:, cs_l:cs_l + cs_c])
xs = torch.cat(xs_chunks, dim=1)[:, :xmax]
if not self.training:
for l in range(self.n_layers):
self.aws_dict['xx_aws_layer%d' % l] = tensor2np(
torch.cat(xx_aws[l], dim=3)[:, :, :xmax, :xmax])
else:
# Create the self-attention mask
xx_mask = make_pad_mask(xlens, self.device_id).unsqueeze(2).repeat(
[1, 1, xmax])
for l, layer in enumerate(self.layers):
xs, xx_aws = layer(xs, xx_mask)
if not self.training:
self.aws_dict['xx_aws_layer%d' % l] = tensor2np(xx_aws)
# Pick up outputs in the sub task before the projection layer
if l == self.n_layers_sub1 - 1:
xs_sub1 = self.layer_sub1(
xs, xx_mask
)[0] if self.task_specific_layer else xs.clone()
xs_sub1 = self.norm_out_sub1(xs_sub1)
if self.bridge_sub1 is not None:
xs_sub1 = self.bridge_sub1(xs_sub1)
if task == 'ys_sub1':
eouts[task]['xs'], eouts[task][
'xlens'] = xs_sub1, xlens
return eouts
if l == self.n_layers_sub2 - 1:
xs_sub2 = self.layer_sub2(
xs, xx_mask
)[0] if self.task_specific_layer else xs.clone()
xs_sub2 = self.norm_out_sub2(xs_sub2)
if self.bridge_sub2 is not None:
xs_sub2 = self.bridge_sub2(xs_sub2)
if task == 'ys_sub2':
eouts[task]['xs'], eouts[task][
'xlens'] = xs_sub2, xlens
return eouts
xs = self.norm_out(xs)
# Bridge layer
if self.bridge is not None:
xs = self.bridge(xs)
if task in ['all', 'ys']:
eouts['ys']['xs'], eouts['ys']['xlens'] = xs, xlens
if self.n_layers_sub1 >= 1 and task == 'all':
eouts['ys_sub1']['xs'], eouts['ys_sub1']['xlens'] = xs_sub1, xlens
if self.n_layers_sub2 >= 1 and task == 'all':
eouts['ys_sub2']['xs'], eouts['ys_sub2']['xlens'] = xs_sub2, xlens
return eouts
def _plot_attention(self, save_path, n_cols=2):
"""Plot attention for each head in all layers."""
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
_save_path = mkdir_join(save_path, 'enc_att_weights')
# Clean directory
if _save_path is not None and os.path.isdir(_save_path):
shutil.rmtree(_save_path)
os.mkdir(_save_path)
for k, aw in self.aws_dict.items():
elens = self.data_dict['elens']
plt.clf()
n_heads = aw.shape[1]
n_cols_tmp = 1 if n_heads == 1 else n_cols
fig, axes = plt.subplots(max(1, n_heads // n_cols_tmp),
n_cols_tmp,
figsize=(20, 8),
squeeze=False)
for h in range(n_heads):
ax = axes[h // n_cols_tmp, h % n_cols_tmp]
ax.imshow(aw[-1, h, :elens[-1], :elens[-1]], aspect="auto")
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
fig.savefig(os.path.join(_save_path, '%s.png' % k), dvi=500)
plt.close()
class RNNEncoder(EncoderBase):
"""RNN encoder.
Args:
input_dim (int): dimension of input features (freq * channel)
rnn_type (str): type of encoder (including pure CNN layers)
n_units (int): number of units in each layer
n_projs (int): number of units in each projection layer
last_proj_dim (int): dimension of the last projection layer
n_layers (int): number of layers
n_layers_sub1 (int): number of layers in the 1st auxiliary task
n_layers_sub2 (int): number of layers in the 2nd auxiliary task
dropout_in (float): dropout probability for input-hidden connection
dropout (float): dropout probability for hidden-hidden connection
subsample (list): subsample in the corresponding RNN layers
ex.) [False, True, True, False] means that subsample is conducted in the 2nd and 3rd layers.
subsample_type (str): drop/concat/max_pool
n_stacks (int): number of frames to stack
n_splices (int): number of frames to splice
conv_in_channel (int): number of channels of input features
conv_channels (int): number of channles in the CNN blocks
conv_kernel_sizes (list): size of kernels in the CNN blocks
conv_strides (list): number of strides in the CNN blocks
conv_poolings (list): size of poolings in the CNN blocks
conv_batch_norm (bool): apply batch normalization only in the CNN blocks
conv_layer_norm (bool): apply layer normalization only in the CNN blocks
conv_bottleneck_dim (int): dimension of the bottleneck layer between CNN and RNN layers
nin (bool): insert 1*1 conv + batch normalization + ReLU
bidirectional_sum_fwd_bwd (bool):
task_specific_layer (bool):
param_init (float):
lc_chunk_size_left (int): left chunk size for latency-controlled bidirectional encoder
lc_chunk_size_right (int): right chunk size for latency-controlled bidirectional encoder
lc_state_reset_prob (float): probability to reset states for latency-controlled bidirectional encoder
"""
def __init__(self, input_dim, rnn_type, n_units, n_projs, last_proj_dim,
n_layers, n_layers_sub1, n_layers_sub2,
dropout_in, dropout,
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,
nin, bidirectional_sum_fwd_bwd,
task_specific_layer, param_init,
lc_chunk_size_left, lc_chunk_size_right, lc_state_reset_prob):
super(RNNEncoder, self).__init__()
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 ('blstm' in rnn_type or 'bgru' in rnn_type) else False
self.n_units = n_units
self.n_dirs = 2 if self.bidirectional else 1
self.n_layers = n_layers
# for latency-controlled
self.latency_controlled = lc_chunk_size_left > 0 or lc_chunk_size_right > 0
self.lc_chunk_size_left = lc_chunk_size_left
self.lc_chunk_size_right = lc_chunk_size_right
self.lc_state_reset_prob = lc_state_reset_prob
if self.latency_controlled:
assert n_layers_sub1 == 0
assert n_layers_sub2 == 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
# Dropout for input-hidden connection
self.dropout_in = nn.Dropout(p=dropout_in)
if rnn_type == 'tds':
self.conv = TDSEncoder(input_dim=input_dim * n_stacks,
in_channel=conv_in_channel,
channels=conv_channels,
kernel_sizes=conv_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=conv_channels,
kernel_sizes=conv_kernel_sizes,
dropout=dropout,
bottleneck_dim=last_proj_dim,
param_init=param_init)
elif 'conv' in rnn_type:
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,
residual=False,
bottleneck_dim=conv_bottleneck_dim,
param_init=param_init)
else:
self.conv = None
if self.conv is None:
self._odim = input_dim * n_splices * n_stacks
else:
self._odim = self.conv.output_dim
subsample = [1] * self.n_layers
logger.warning('Subsampling is automatically ignored because CNN layers are used before RNN layers.')
self.padding = Padding(bidirectional_sum_fwd_bwd=bidirectional_sum_fwd_bwd)
if rnn_type not in ['conv', 'tds', 'gated_conv']:
self.rnn = nn.ModuleList()
if self.latency_controlled:
self.rnn_bwd = nn.ModuleList()
self.dropout = nn.Dropout(p=dropout)
self.proj = None
if n_projs > 0:
self.proj = nn.ModuleList()
# subsample
self.subsample_layer = None
if subsample_type == 'max_pool' and np.prod(subsample) > 1:
self.subsample_layer = nn.ModuleList([MaxpoolSubsampler(subsample[l])
for l in range(n_layers)])
elif subsample_type == 'concat' and np.prod(subsample) > 1:
self.subsample_layer = nn.ModuleList([ConcatSubsampler(subsample[l], n_units * self.n_dirs)
for l in range(n_layers)])
elif subsample_type == 'drop' and np.prod(subsample) > 1:
self.subsample_layer = nn.ModuleList([DropSubsampler(subsample[l])
for l in range(n_layers)])
elif subsample_type == '1dconv' and np.prod(subsample) > 1:
self.subsample_layer = nn.ModuleList([Conv1dSubsampler(subsample[l], n_units * self.n_dirs)
for l in range(n_layers)])
# NiN
self.nin = nn.ModuleList() if nin else None
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/lcb)lstm" or "(conv_)(b/lcb)gru".')
if self.latency_controlled:
self.rnn += [rnn_i(self._odim, n_units, 1, batch_first=True)]
self.rnn_bwd += [rnn_i(self._odim, n_units, 1, batch_first=True)]
else:
self.rnn += [rnn_i(self._odim, n_units, 1, batch_first=True,
bidirectional=self.bidirectional)]
self._odim = n_units if bidirectional_sum_fwd_bwd else n_units * self.n_dirs
self.bidirectional_sum_fwd_bwd = bidirectional_sum_fwd_bwd
# Projection layer
if self.proj is not None:
if l != n_layers - 1:
self.proj += [nn.Linear(n_units * self.n_dirs, n_projs)]
self._odim = n_projs
# Task specific layer
if l == n_layers_sub1 - 1 and task_specific_layer:
self.rnn_sub1 = rnn_i(self._odim, n_units, 1,
batch_first=True,
bidirectional=self.bidirectional)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub1 = nn.Linear(n_units, last_proj_dim)
if l == n_layers_sub2 - 1 and task_specific_layer:
self.rnn_sub2 = rnn_i(self._odim, n_units, 1,
batch_first=True,
bidirectional=self.bidirectional)
if last_proj_dim > 0 and last_proj_dim != self.output_dim:
self.bridge_sub2 = nn.Linear(n_units, last_proj_dim)
# Network in network
if self.nin is not None:
if l != n_layers - 1:
self.nin += [NiN(self._odim)]
# if n_layers_sub1 > 0 or n_layers_sub2 > 0:
# assert task_specific_layer
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
# calculate subsampling factor
self._factor = 1
if self.conv is not None:
self._factor *= self.conv.subsampling_factor()
self._factor *= np.prod(subsample)
self.reset_parameters(param_init)
# for streaming inference
self.reset_cache()
def reset_parameters(self, param_init):
"""Initialize parameters with uniform distribution."""
logger.info('===== Initialize %s =====' % self.__class__.__name__)
for n, p in self.named_parameters():
if 'conv' in n or 'tds' in n or 'gated_conv' in n:
continue # for CNN layers before RNN layers
if p.dim() == 1:
nn.init.constant_(p, 0.) # bias
logger.info('Initialize %s with %s / %.3f' % (n, 'constant', 0.))
elif p.dim() in [2, 4]:
nn.init.uniform_(p, a=-param_init, b=param_init)
logger.info('Initialize %s with %s / %.3f' % (n, 'uniform', param_init))
else:
raise ValueError(n)
def reset_cache(self):
self.fwd_states = [None] * self.n_layers
logger.debug('Reset cache.')
def forward(self, xs, xlens, task, use_cache=False, streaming=False):
"""Forward computation.
Args:
xs (FloatTensor): `[B, T, input_dim]`
xlens (list): A list of length `[B]`
task (str): all or ys or ys_sub1 or ys_sub2
use_cache (bool): use the cached forward encoder state in the previous chunk as the initial state
streaming (bool): streaming encoding
Returns:
eouts (dict):
xs (FloatTensor): `[B, T // prod(subsample), n_units (*2)]`
xlens (IntTensor): `[B]`
xs_sub1 (FloatTensor): `[B, T // prod(subsample), n_units (*2)]`
xlens_sub1 (IntTensor): `[B]`
xs_sub2 (FloatTensor): `[B, T // prod(subsample), n_units (*2)]`
xlens_sub2 (IntTensor): `[B]`
"""
eouts = {'ys': {'xs': None, 'xlens': None},
'ys_sub1': {'xs': None, 'xlens': None},
'ys_sub2': {'xs': None, 'xlens': None}}
# Sort by lenghts in the descending order for pack_padded_sequence
xlens, perm_ids = torch.IntTensor(xlens).sort(0, descending=True)
xs = xs[perm_ids]
_, perm_ids_unsort = perm_ids.sort()
# Dropout for inputs-hidden connection
xs = self.dropout_in(xs)
# Path through CNN blocks before RNN layers
if self.conv is not None:
xs, xlens = self.conv(xs, xlens)
if self.rnn_type in ['conv', 'tds', 'gated_conv']:
eouts['ys']['xs'] = xs
eouts['ys']['xlens'] = xlens
return eouts
if not use_cache:
self.reset_cache()
if self.latency_controlled:
# Flip the layer and time loop
xs, xlens = self._forward_streaming(xs, xlens, streaming)
else:
for l in range(self.n_layers):
self.rnn[l].flatten_parameters() # for multi-GPUs
xs, self.fwd_states[l] = self.padding(xs, xlens, self.rnn[l],
prev_state=self.fwd_states[l])
xs = self.dropout(xs)
# Pick up outputs in the sub task before the projection layer
if l == self.n_layers_sub1 - 1:
xs_sub1, xlens_sub1 = self.sub_module(xs, xlens, perm_ids_unsort, 'sub1')
if task == 'ys_sub1':
eouts[task]['xs'], eouts[task]['xlens'] = xs_sub1, xlens_sub1
return eouts
if l == self.n_layers_sub2 - 1:
xs_sub2, xlens_sub2 = self.sub_module(xs, xlens, perm_ids_unsort, 'sub2')
if task == 'ys_sub2':
eouts[task]['xs'], eouts[task]['xlens'] = xs_sub2, xlens_sub2
return eouts
# NOTE: Exclude the last layer
if l != self.n_layers - 1:
# Projection layer -> Subsampling -> NiN
if self.proj is not None:
xs = torch.tanh(self.proj[l](xs))
if self.subsample_layer is not None:
xs, xlens = self.subsample_layer[l](xs, xlens)
if self.nin is not None:
xs = self.nin[l](xs)
# Bridge layer
if self.bridge is not None:
xs = self.bridge(xs)
# Unsort
xs = xs[perm_ids_unsort]
xlens = xlens[perm_ids_unsort]
if task in ['all', 'ys']:
eouts['ys']['xs'], eouts['ys']['xlens'] = xs, xlens
if self.n_layers_sub1 >= 1 and task == 'all':
eouts['ys_sub1']['xs'], eouts['ys_sub1']['xlens'] = xs_sub1, xlens_sub1
if self.n_layers_sub2 >= 1 and task == 'all':
eouts['ys_sub2']['xs'], eouts['ys_sub2']['xlens'] = xs_sub2, xlens_sub2
return eouts
def _forward_streaming(self, xs, xlens, streaming):
"""Streaming encoding for the latency-controlled bidirectional encoder.
Args:
xs (FloatTensor): `[B, T, n_units]`
Returns:
xs (FloatTensor): `[B, T, n_units]`
"""
cs_l = self.lc_chunk_size_left // self.subsampling_factor()
cs_r = self.lc_chunk_size_right // self.subsampling_factor()
# full context BPTT
if cs_l < 0:
for l in range(self.n_layers):
self.rnn[l].flatten_parameters() # for multi-GPUs
self.rnn_bwd[l].flatten_parameters() # for multi-GPUs
# bwd
xs_bwd = torch.flip(xs, dims=[1])
xs_bwd, _ = self.rnn_bwd[l](xs_bwd, hx=None)
xs_bwd = torch.flip(xs_bwd, dims=[1])
# fwd
xs_fwd, _ = self.rnn[l](xs, hx=None)
if self.bidirectional_sum_fwd_bwd:
xs = xs_fwd + xs_bwd
else:
xs = torch.cat([xs_fwd, xs_bwd], dim=-1)
xs = self.dropout(xs)
# Projection layer
if self.proj is not None and l != self.n_layers - 1:
xs = torch.tanh(self.proj[l](xs))
return xs, xlens
bs, xmax, input_dim = xs.size()
n_chunks = 1 if streaming else math.ceil(xmax / cs_l)
xlens = torch.IntTensor(bs).fill_(cs_l if streaming else xmax)
xs_chunks = []
for t in range(0, cs_l * n_chunks, cs_l):
xs_chunk = xs[:, t:t + (cs_l + cs_r)]
for l in range(self.n_layers):
self.rnn[l].flatten_parameters() # for multi-GPUs
self.rnn_bwd[l].flatten_parameters() # for multi-GPUs
# bwd
xs_chunk_bwd = torch.flip(xs_chunk, dims=[1])
xs_chunk_bwd, _ = self.rnn_bwd[l](xs_chunk_bwd, hx=None)
xs_chunk_bwd = torch.flip(xs_chunk_bwd, dims=[1]) # `[B, cs_l+cs_r, n_units]`
# fwd
if xs_chunk.size(1) <= cs_l:
xs_chunk_fwd, self.fwd_states[l] = self.rnn[l](xs_chunk, hx=self.fwd_states[l])
if self.training and self.lc_state_reset_prob > 0 and random.random() < self.lc_state_reset_prob:
self.fwd_states[l] = None
else:
xs_chunk_fwd1, self.fwd_states[l] = self.rnn[l](xs_chunk[:, :cs_l], hx=self.fwd_states[l])
if self.training and self.lc_state_reset_prob > 0 and random.random() < self.lc_state_reset_prob:
self.fwd_states[l] = None
xs_chunk_fwd2, _ = self.rnn[l](xs_chunk[:, cs_l:], hx=self.fwd_states[l])
xs_chunk_fwd = torch.cat([xs_chunk_fwd1, xs_chunk_fwd2], dim=1) # `[B, cs_l+cs_r, n_units]`
# NOTE: xs_chunk_fwd2 is for xs_chunk_bwd in the next layer
if self.bidirectional_sum_fwd_bwd:
xs_chunk = xs_chunk_fwd + xs_chunk_bwd
else:
xs_chunk = torch.cat([xs_chunk_fwd, xs_chunk_bwd], dim=-1)
xs_chunk = self.dropout(xs_chunk)
# Projection layer
if self.proj is not None and l != self.n_layers - 1:
xs_chunk = torch.tanh(self.proj[l](xs_chunk))
xs_chunks.append(xs_chunk[:, :cs_l])
xs = torch.cat(xs_chunks, dim=1)
return xs, xlens
def sub_module(self, xs, xlens, perm_ids_unsort, module='sub1'):
if self.task_specific_layer:
getattr(self, 'rnn_' + module).flatten_parameters() # for multi-GPUs
xs_sub, _ = self.padding(xs, xlens, getattr(self, 'rnn_' + module))
xs_sub = self.dropout(xs_sub)
else:
xs_sub = xs.clone()[perm_ids_unsort]
if getattr(self, 'bridge_' + module) is not None:
xs_sub = getattr(self, 'bridge_' + module)(xs_sub)
xlens_sub = xlens[perm_ids_unsort]
return xs_sub, xlens_sub
class TransformerEncoder(EncoderBase):
"""Transformer encoder.
Args:
input_dim (int): dimension of input features (freq * channel)
attn_type (str): type of attention
n_heads (int): number of heads for multi-head attention
n_layers (int): number of blocks
d_model (int): dimension of MultiheadAttentionMechanism
d_ff (int): dimension of PositionwiseFeedForward
last_proj_dim (int): dimension of the last projection layer
pe_type (str): type of positional encoding
layer_norm_eps (float): epsilon value for layer normalization
ffn_activation (str): nonolinear function for PositionwiseFeedForward
dropout_in (float): dropout probability for input-hidden connection
dropout (float): dropout probabilities for linear layers
dropout_att (float): dropout probabilities for attention distributions
n_stacks (int): number of frames to stack
n_splices (int): frames to splice. Default is 1 frame.
conv_in_channel (int): number of channels of input features
conv_channels (int): number of channles in the CNN blocks
conv_kernel_sizes (list): size of kernels in the CNN blocks
conv_strides (list): number of strides in the CNN blocks
conv_poolings (list): size of poolings in the CNN blocks
conv_batch_norm (bool): apply batch normalization only in the CNN blocks
conv_layer_norm (bool): apply layer normalization only in the CNN blocks
conv_bottleneck_dim (int): dimension of the bottleneck layer between CNN and self-attention layers
conv_param_init (float): only for CNN layers before Transformer layers
chunk_size_left (int): left chunk size for time-restricted Transformer encoder
chunk_size_current (int): current chunk size for time-restricted Transformer encoder
chunk_size_right (int): right chunk size for time-restricted Transformer encoder
param_init (str):
"""
def __init__(self, input_dim, attn_type, n_heads, n_layers, d_model, d_ff,
last_proj_dim, pe_type, layer_norm_eps, ffn_activation,
dropout_in, dropout, dropout_att, 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, param_init,
chunk_size_left, chunk_size_current, chunk_size_right):
super(TransformerEncoder, self).__init__()
self.d_model = d_model
self.n_layers = n_layers
self.n_heads = n_heads
self.pe_type = pe_type
self.chunk_size_left = chunk_size_left
self.chunk_size_current = chunk_size_current
self.chunk_size_right = chunk_size_right
# Setting for CNNs before RNNs
if 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)
self.pos_enc = PositionalEncoding(d_model, dropout_in, pe_type)
self.layers = repeat(
TransformerEncoderBlock(d_model, d_ff, attn_type, n_heads, dropout,
dropout_att, layer_norm_eps,
ffn_activation, param_init), n_layers)
self.norm_out = nn.LayerNorm(d_model, eps=layer_norm_eps)
if last_proj_dim != self.output_dim:
self.bridge = nn.Linear(self._odim, last_proj_dim)
self._odim = last_proj_dim
else:
self.bridge = None
self._odim = d_model
# calculate subsampling factor
self._factor = 1
if self.conv is not None:
self._factor *= self.conv.subsampling_factor()
if param_init == 'xavier_uniform':
self.reset_parameters()
def reset_parameters(self):
"""Initialize parameters with Xavier uniform distribution."""
logger.info(
'===== Initialize %s with Xavier uniform distribution =====' %
self.__class__.__name__)
if self.conv is None:
nn.init.xavier_uniform_(self.embed.weight)
nn.init.constant_(self.embed.bias, 0.)
if self.bridge is not None:
nn.init.xavier_uniform_(self.bridge.weight)
nn.init.constant_(self.bridge.bias, 0.)
def forward(self, xs, xlens, task, use_cache=False, streaming=False):
"""Forward computation.
Args:
xs (FloatTensor): `[B, T, input_dim]`
xlens (list): `[B]`
task (str): not supported now
use_cache (bool):
streaming (bool): streaming encoding
Returns:
eouts (dict):
xs (FloatTensor): `[B, T, d_model]`
xlens (list): `[B]`
"""
eouts = {
'ys': {
'xs': None,
'xlens': None
},
'ys_sub1': {
'xs': None,
'xlens': None
},
'ys_sub2': {
'xs': None,
'xlens': None
}
}
if self.conv is None:
xs = self.embed(xs)
else:
# Path through CNN blocks before RNN layers
xs, xlens = self.conv(xs, xlens)
bs, xmax, idim = xs.size()
xs = self.pos_enc(xs)
if self.chunk_size_left > 0:
# Time-restricted self-attention for streaming models
cs_l = self.chunk_size_left
cs_c = self.chunk_size_current
cs_r = self.chunk_size_right
hop_size = self.chunk_size_current
xs_chunks = []
xx_aws = [[] for l in range(self.n_layers)]
xs_pad = torch.cat([
xs.new_zeros(bs, cs_l, idim), xs,
xs.new_zeros(bs, cs_r, idim)
],
dim=1)
# TODO: remove right padding
for t in range(cs_l, cs_l + xmax, hop_size):
xs_chunk = xs_pad[:, t - cs_l:t + cs_c + cs_r]
for l in range(self.n_layers):
xs_chunk, xx_aws_chunk = self.layers[l](xs_chunk,
None) # no mask
xx_aws[l].append(xx_aws_chunk[:, :, cs_l:cs_l + cs_c,
cs_l:cs_l + cs_c])
xs_chunks.append(xs_chunk[:, cs_l:cs_l + cs_c])
xs = torch.cat(xs_chunks, dim=1)[:, :xmax]
if not self.training:
for l in range(self.n_layers):
setattr(
self, 'xx_aws_layer%d' % l,
tensor2np(
torch.cat(xx_aws[l], dim=3)[:, :, :xmax, :xmax]))
else:
# Create the self-attention mask
xx_mask = make_pad_mask(xlens, self.device_id).unsqueeze(2).repeat(
[1, 1, xmax])
for l in range(self.n_layers):
xs, xx_aws = self.layers[l](xs, xx_mask)
if not self.training:
setattr(self, 'xx_aws_layer%d' % l, tensor2np(xx_aws))
xs = self.norm_out(xs)
# Bridge layer
if self.bridge is not None:
xs = self.bridge(xs)
eouts['ys']['xs'] = xs
eouts['ys']['xlens'] = xlens
return eouts
def _plot_attention(self, save_path, n_cols=2):
"""Plot attention for each head in all layers."""
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
save_path = mkdir_join(save_path, 'enc_xx_att_weights')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
for l in range(self.n_layers):
if not hasattr(self, 'xx_aws_layer%d' % l):
continue
xx_aws = getattr(self, 'xx_aws_layer%d' % l)
plt.clf()
fig, axes = plt.subplots(self.n_heads // n_cols,
n_cols,
figsize=(20, 8))
for h in range(self.n_heads):
if self.n_heads > n_cols:
ax = axes[h // n_cols, h % n_cols]
else:
ax = axes[h]
ax.imshow(xx_aws[-1, h, :, :], aspect="auto")
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
fig.savefig(os.path.join(save_path, 'layer%d.png' % (l)), dvi=500)
plt.close()