import math
import torch
from torch import nn
from torch.nn import Parameter, Linear
from modules.commons.layers import LayerNorm, Embedding
from utils.nn.seq_utils import get_incremental_state, set_incremental_state, softmax, make_positions
import torch.nn.functional as F
DEFAULT_MAX_SOURCE_POSITIONS = 2000
DEFAULT_MAX_TARGET_POSITIONS = 2000
class SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length.
Padding symbols are ignored.
"""
def __init__(self, embedding_dim, padding_idx, init_size=1024):
super().__init__()
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.weights = SinusoidalPositionalEmbedding.get_embedding(
init_size,
embedding_dim,
padding_idx,
)
self.register_buffer('_float_tensor', torch.FloatTensor(1))
@staticmethod
def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
"""Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input.shape[:2]
max_pos = self.padding_idx + 1 + seq_len
if self.weights is None or max_pos > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = SinusoidalPositionalEmbedding.get_embedding(
max_pos,
self.embedding_dim,
self.padding_idx,
)
self.weights = self.weights.to(self._float_tensor)
if incremental_state is not None:
# positions is the same for every token when decoding a single step
pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
positions = make_positions(input, self.padding_idx) if positions is None else positions
return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
def max_positions(self):
"""Maximum number of supported positions."""
return int(1e5) # an arbitrary large number
class TransformerFFNLayer(nn.Module):
def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'):
super().__init__()
self.kernel_size = kernel_size
self.dropout = dropout
self.act = act
if padding == 'SAME':
self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2)
elif padding == 'LEFT':
self.ffn_1 = nn.Sequential(
nn.ConstantPad1d((kernel_size - 1, 0), 0.0),
nn.Conv1d(hidden_size, filter_size, kernel_size)
)
self.ffn_2 = Linear(filter_size, hidden_size)
def forward(self, x, incremental_state=None):
# x: T x B x C
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_input' in saved_state:
prev_input = saved_state['prev_input']
x = torch.cat((prev_input, x), dim=0)
x = x[-self.kernel_size:]
saved_state['prev_input'] = x
self._set_input_buffer(incremental_state, saved_state)
x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1)
x = x * self.kernel_size ** -0.5
if incremental_state is not None:
x = x[-1:]
if self.act == 'gelu':
x = F.gelu(x)
if self.act == 'relu':
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training)
x = self.ffn_2(x)
return x
def _get_input_buffer(self, incremental_state):
return get_incremental_state(
self,
incremental_state,
'f',
) or {}
def _set_input_buffer(self, incremental_state, buffer):
set_incremental_state(
self,
incremental_state,
'f',
buffer,
)
def clear_buffer(self, incremental_state):
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_input' in saved_state:
del saved_state['prev_input']
self._set_input_buffer(incremental_state, saved_state)
class MultiheadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
add_bias_kv=False, add_zero_attn=False, self_attention=False,
encoder_decoder_attention=False):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
'value to be of the same size'
if self.qkv_same_dim:
self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
else:
self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
if bias:
self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
if add_bias_kv:
self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self.reset_parameters()
self.enable_torch_version = False
if hasattr(F, "multi_head_attention_forward"):
self.enable_torch_version = True
else:
self.enable_torch_version = False
self.last_attn_probs = None
def reset_parameters(self):
if self.qkv_same_dim:
nn.init.xavier_uniform_(self.in_proj_weight)
else:
nn.init.xavier_uniform_(self.k_proj_weight)
nn.init.xavier_uniform_(self.v_proj_weight)
nn.init.xavier_uniform_(self.q_proj_weight)
nn.init.xavier_uniform_(self.out_proj.weight)
if self.in_proj_bias is not None:
nn.init.constant_(self.in_proj_bias, 0.)
nn.init.constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
nn.init.xavier_normal_(self.bias_k)
if self.bias_v is not None:
nn.init.xavier_normal_(self.bias_v)
def forward(
self,
query, key, value,
key_padding_mask=None,
incremental_state=None,
need_weights=True,
static_kv=False,
attn_mask=None,
before_softmax=False,
need_head_weights=False,
enc_dec_attn_constraint_mask=None,
reset_attn_weight=None
):
"""Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
"""
if need_head_weights:
need_weights = True
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None:
if self.qkv_same_dim:
return F.multi_head_attention_forward(query, key, value,
self.embed_dim, self.num_heads,
self.in_proj_weight,
self.in_proj_bias, self.bias_k, self.bias_v,
self.add_zero_attn, self.dropout,
self.out_proj.weight, self.out_proj.bias,
self.training, key_padding_mask, need_weights,
attn_mask)
else:
return F.multi_head_attention_forward(query, key, value,
self.embed_dim, self.num_heads,
torch.empty([0]),
self.in_proj_bias, self.bias_k, self.bias_v,
self.add_zero_attn, self.dropout,
self.out_proj.weight, self.out_proj.bias,
self.training, key_padding_mask, need_weights,
attn_mask, use_separate_proj_weight=True,
q_proj_weight=self.q_proj_weight,
k_proj_weight=self.k_proj_weight,
v_proj_weight=self.v_proj_weight)
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_key' in saved_state:
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert self.encoder_decoder_attention and not self.self_attention
key = value = None
else:
saved_state = None
if self.self_attention:
# self-attention
q, k, v = self.in_proj_qkv(query)
elif self.encoder_decoder_attention:
# encoder-decoder attention
q = self.in_proj_q(query)
if key is None:
assert value is None
k = v = None
else:
k = self.in_proj_k(key)
v = self.in_proj_v(key)
else:
q = self.in_proj_q(query)
k = self.in_proj_k(key)
v = self.in_proj_v(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if k is not None:
k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if v is not None:
v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if 'prev_key' in saved_state:
prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
k = torch.cat((prev_key, k), dim=1)
if 'prev_value' in saved_state:
prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
v = torch.cat((prev_value, v), dim=1)
if 'prev_key_padding_mask' in saved_state and saved_state['prev_key_padding_mask'] is not None:
prev_key_padding_mask = saved_state['prev_key_padding_mask']
if static_kv:
key_padding_mask = prev_key_padding_mask
else:
key_padding_mask = torch.cat((prev_key_padding_mask, key_padding_mask), dim=1)
saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim)
saved_state['prev_key_padding_mask'] = key_padding_mask
self._set_input_buffer(incremental_state, saved_state)
src_len = k.size(1)
# This is part of a workaround to get around fork/join parallelism
# not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
key_padding_mask = None
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.add_zero_attn:
src_len += 1
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
if len(attn_mask.shape) == 2:
attn_mask = attn_mask.unsqueeze(0)
elif len(attn_mask.shape) == 3:
attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape(
bsz * self.num_heads, tgt_len, src_len)
attn_weights = attn_weights + attn_mask
if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
enc_dec_attn_constraint_mask.unsqueeze(2).bool(),
-1e8,
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
-1e8,
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
if before_softmax:
return attn_weights, v
attn_weights_float = softmax(attn_weights, dim=-1)
attn_weights = attn_weights_float.type_as(attn_weights)
attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
if reset_attn_weight is not None:
if reset_attn_weight:
self.last_attn_probs = attn_probs.detach()
else:
assert self.last_attn_probs is not None
attn_probs = self.last_attn_probs
attn = torch.bmm(attn_probs, v)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
if need_weights:
attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
if not need_head_weights:
# average attention weights over heads
attn_weights = attn_weights.mean(dim=0)
else:
attn_weights = None
return attn, (attn_weights, attn_logits)
def in_proj_qkv(self, query):
return self._in_proj(query).chunk(3, dim=-1)
def in_proj_q(self, query):
if self.qkv_same_dim:
return self._in_proj(query, end=self.embed_dim)
else:
bias = self.in_proj_bias
if bias is not None:
bias = bias[:self.embed_dim]
return F.linear(query, self.q_proj_weight, bias)
def in_proj_k(self, key):
if self.qkv_same_dim:
return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
else:
weight = self.k_proj_weight
bias = self.in_proj_bias
if bias is not None:
bias = bias[self.embed_dim:2 * self.embed_dim]
return F.linear(key, weight, bias)
def in_proj_v(self, value):
if self.qkv_same_dim:
return self._in_proj(value, start=2 * self.embed_dim)
else:
weight = self.v_proj_weight
bias = self.in_proj_bias
if bias is not None:
bias = bias[2 * self.embed_dim:]
return F.linear(value, weight, bias)
def _in_proj(self, input, start=0, end=None):
weight = self.in_proj_weight
bias = self.in_proj_bias
weight = weight[start:end, :]
if bias is not None:
bias = bias[start:end]
return F.linear(input, weight, bias)
def _get_input_buffer(self, incremental_state):
return get_incremental_state(
self,
incremental_state,
'attn_state',
) or {}
def _set_input_buffer(self, incremental_state, buffer):
set_incremental_state(
self,
incremental_state,
'attn_state',
buffer,
)
def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
return attn_weights
def clear_buffer(self, incremental_state=None):
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_key' in saved_state:
del saved_state['prev_key']
if 'prev_value' in saved_state:
del saved_state['prev_value']
self._set_input_buffer(incremental_state, saved_state)
class EncSALayer(nn.Module):
def __init__(self, c, num_heads, dropout, attention_dropout=0.1,
relu_dropout=0.1, kernel_size=9, padding='SAME', act='gelu'):
super().__init__()
self.c = c
self.dropout = dropout
self.num_heads = num_heads
if num_heads > 0:
self.layer_norm1 = LayerNorm(c)
self.self_attn = MultiheadAttention(
self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False)
self.layer_norm2 = LayerNorm(c)
self.ffn = TransformerFFNLayer(
c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act)
def forward(self, x, encoder_padding_mask=None, **kwargs):
layer_norm_training = kwargs.get('layer_norm_training', None)
if layer_norm_training is not None:
self.layer_norm1.training = layer_norm_training
self.layer_norm2.training = layer_norm_training
if self.num_heads > 0:
residual = x
x = self.layer_norm1(x)
x, _, = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask
)
x = F.dropout(x, self.dropout, training=self.training)
x = residual + x
x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
residual = x
x = self.layer_norm2(x)
x = self.ffn(x)
x = F.dropout(x, self.dropout, training=self.training)
x = residual + x
x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
return x
class DecSALayer(nn.Module):
def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1,
kernel_size=9, act='gelu'):
super().__init__()
self.c = c
self.dropout = dropout
self.layer_norm1 = LayerNorm(c)
self.self_attn = MultiheadAttention(
c, num_heads, self_attention=True, dropout=attention_dropout, bias=False
)
self.layer_norm2 = LayerNorm(c)
self.encoder_attn = MultiheadAttention(
c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False,
)
self.layer_norm3 = LayerNorm(c)
self.ffn = TransformerFFNLayer(
c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act)
def forward(
self,
x,
encoder_out=None,
encoder_padding_mask=None,
incremental_state=None,
self_attn_mask=None,
self_attn_padding_mask=None,
attn_out=None,
reset_attn_weight=None,
**kwargs,
):
layer_norm_training = kwargs.get('layer_norm_training', None)
if layer_norm_training is not None:
self.layer_norm1.training = layer_norm_training
self.layer_norm2.training = layer_norm_training
self.layer_norm3.training = layer_norm_training
residual = x
x = self.layer_norm1(x)
x, _ = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
incremental_state=incremental_state,
attn_mask=self_attn_mask
)
x = F.dropout(x, self.dropout, training=self.training)
x = residual + x
attn_logits = None
if encoder_out is not None or attn_out is not None:
residual = x
x = self.layer_norm2(x)
if encoder_out is not None:
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
enc_dec_attn_constraint_mask=get_incremental_state(self, incremental_state,
'enc_dec_attn_constraint_mask'),
reset_attn_weight=reset_attn_weight
)
attn_logits = attn[1]
elif attn_out is not None:
x = self.encoder_attn.in_proj_v(attn_out)
if encoder_out is not None or attn_out is not None:
x = F.dropout(x, self.dropout, training=self.training)
x = residual + x
residual = x
x = self.layer_norm3(x)
x = self.ffn(x, incremental_state=incremental_state)
x = F.dropout(x, self.dropout, training=self.training)
x = residual + x
return x, attn_logits
def clear_buffer(self, input, encoder_out=None, encoder_padding_mask=None, incremental_state=None):
self.encoder_attn.clear_buffer(incremental_state)
self.ffn.clear_buffer(incremental_state)
def set_buffer(self, name, tensor, incremental_state):
return set_incremental_state(self, incremental_state, name, tensor)
class TransformerEncoderLayer(nn.Module):
def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2):
super().__init__()
self.hidden_size = hidden_size
self.dropout = dropout
self.num_heads = num_heads
self.op = EncSALayer(
hidden_size, num_heads, dropout=dropout,
attention_dropout=0.0, relu_dropout=dropout,
kernel_size=kernel_size)
def forward(self, x, **kwargs):
return self.op(x, **kwargs)
class TransformerDecoderLayer(nn.Module):
def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2):
super().__init__()
self.hidden_size = hidden_size
self.dropout = dropout
self.num_heads = num_heads
self.op = DecSALayer(
hidden_size, num_heads, dropout=dropout,
attention_dropout=0.0, relu_dropout=dropout,
kernel_size=kernel_size)
def forward(self, x, **kwargs):
return self.op(x, **kwargs)
def clear_buffer(self, *args):
return self.op.clear_buffer(*args)
def set_buffer(self, *args):
return self.op.set_buffer(*args)
class FFTBlocks(nn.Module):
def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=0.0,
num_heads=2, use_pos_embed=True, use_last_norm=True,
use_pos_embed_alpha=True):
super().__init__()
self.num_layers = num_layers
embed_dim = self.hidden_size = hidden_size
self.dropout = dropout
self.use_pos_embed = use_pos_embed
self.use_last_norm = use_last_norm
if use_pos_embed:
self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS
self.padding_idx = 0
self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1
self.embed_positions = SinusoidalPositionalEmbedding(
embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
)
self.layers = nn.ModuleList([])
self.layers.extend([
TransformerEncoderLayer(self.hidden_size, self.dropout,
kernel_size=ffn_kernel_size, num_heads=num_heads)
for _ in range(self.num_layers)
])
if self.use_last_norm:
self.layer_norm = nn.LayerNorm(embed_dim)
else:
self.layer_norm = None
def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False):
"""
:param x: [B, T, C]
:param padding_mask: [B, T]
:return: [B, T, C] or [L, B, T, C]
"""
padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1]
if self.use_pos_embed:
positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
x = x + positions
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1) * nonpadding_mask_TB
hiddens = []
for layer in self.layers:
x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
hiddens.append(x)
if self.use_last_norm:
x = self.layer_norm(x) * nonpadding_mask_TB
if return_hiddens:
x = torch.stack(hiddens, 0) # [L, T, B, C]
x = x.transpose(1, 2) # [L, B, T, C]
else:
x = x.transpose(0, 1) # [B, T, C]
return x
class FastSpeechEncoder(FFTBlocks):
def __init__(self, dict_size, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2,
dropout=0.0):
super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads,
use_pos_embed=False, dropout=dropout) # use_pos_embed_alpha for compatibility
self.embed_tokens = Embedding(dict_size, hidden_size, 0)
self.embed_scale = math.sqrt(hidden_size)
self.padding_idx = 0
self.embed_positions = SinusoidalPositionalEmbedding(
hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
)
def forward(self, txt_tokens, attn_mask=None):
"""
:param txt_tokens: [B, T]
:return: {
'encoder_out': [B x T x C]
}
"""
encoder_padding_mask = txt_tokens.eq(self.padding_idx).data
x = self.forward_embedding(txt_tokens) # [B, T, H]
if self.num_layers > 0:
x = super(FastSpeechEncoder, self).forward(x, encoder_padding_mask, attn_mask=attn_mask)
return x
def forward_embedding(self, txt_tokens):
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(txt_tokens)
if self.use_pos_embed:
positions = self.embed_positions(txt_tokens)
x = x + positions
x = F.dropout(x, p=self.dropout, training=self.training)
return x
class FastSpeechDecoder(FFTBlocks):
def __init__(self, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2):
super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)