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"""
This file contains the implementation of the Transformer Encoder layer.
Source: https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
from typing import Optional, Tuple
import torch
from torch import nn, Tensor
from torch.nn import Module
class SelfAttention(Module):
"""Multihead Self Attention module
Args:
embed_dim (int): Total dimension of the model.
num_heads (int): The number of heads.
dropout (float, optional):
Dropout probability on attn_output_weights. Default: ``0.0``
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
):
super().__init__()
head_dim = embed_dim // num_heads
if head_dim * num_heads != embed_dim:
raise ValueError(
f"`embed_dim ({embed_dim})` is not divisible by `num_heads ({num_heads})`"
)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = torch.nn.Dropout(dropout)
self.head_dim = head_dim
self.scaling = self.head_dim**-0.5
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)
def forward(
self,
x: Tensor,
attention_mask: Optional[Tensor] = None,
position_bias: Optional[Tensor] = None,
key_padding_mask: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
"""
Args:
x (Tensor): shape: ``[batch_size, sequence_length, embed_dim]``.
attention_mask (Tensor or ``None``, optional):
shape: ``[batch_size, 1, sequence_length, sequence_length]``
position_bias: Not used. Only for the compatibility with :py:class:`WavLMSelfAttention`.
key_padding_mask (Tensor or ``None``): Not used. Only for the compatibility with
:py:class:`WavLMSelfAttention`.
Returns:
(Tensor, ``None``): The resulting attention output and ``None`` (necessary for compatibility
with :py:class:`WavLMSelAttention`).
Attention output shape: ``[batch, sequence_length, embed_dim]``.
"""
if x.ndim != 3 or x.shape[2] != self.embed_dim:
raise ValueError(
f"The expected input shape is (batch, sequence, embed_dim=={self.embed_dim}). "
f"Found {x.shape}."
)
batch_size, length, embed_dim = x.size()
if attention_mask is not None:
shape_ = (batch_size, 1, length, length)
if attention_mask.size() != shape_:
raise ValueError(
f"The expected attention mask shape is {shape_}. "
f"Found {attention_mask.size()}."
)
shape = (batch_size, length, self.num_heads, self.head_dim)
q = self.q_proj(x).view(*shape).transpose(2, 1) # B, nH, L, Hd
k = self.k_proj(x).view(*shape).permute(0, 2, 3, 1) # B, nH, Hd, L
v = self.v_proj(x).view(*shape).transpose(2, 1) # B, nH, L, Hd
# scale down q to avoid value overflow.
weights = (self.scaling * q) @ k # B, nH, L, L
if attention_mask is not None:
weights += attention_mask
# subtracting a constant value from the tensor won't change the output of softmax.
# apply the subtraction to avoid value overflow in torch.nn.functional.softmax.
# for more details, please see Equation 7 in https://arxiv.org/abs/2112.08778
weights = weights - weights.max(dim=-1, keepdim=True)[0]
weights = torch.nn.functional.softmax(weights, dim=-1)
weights = self.dropout(weights)
output = weights @ v # B, nH, L, Hd
output = output.transpose(2, 1).reshape(batch_size, length, embed_dim)
output = self.out_proj(output)
return output, None # Necessary for compatibility with WavLMSelAttention
class FeedForward(Module):
"""Layer that follows attention layer in encoder layer."""
def __init__(
self,
io_features: int,
intermediate_features: int,
intermediate_dropout: float,
output_dropout: float,
):
super().__init__()
self.intermediate_dense = nn.Linear(io_features, intermediate_features)
self.intermediate_dropout = nn.Dropout(intermediate_dropout)
self.output_dense = nn.Linear(intermediate_features, io_features)
self.output_dropout = nn.Dropout(output_dropout)
def forward(self, x):
"""
Args:
x (Tensor): shape: `(batch, sequence_length, io_features)`
Returns:
x (Tensor): shape: `(batch, sequence_length, io_features)`
"""
x = self.intermediate_dense(x)
x = torch.nn.functional.gelu(x)
x = self.intermediate_dropout(x)
x = self.output_dense(x)
x = self.output_dropout(x)
return x
class EncoderLayer(Module):
"""A layer unit in encoder. Combines multihead self attention and feed forward."""
def __init__(
self,
d_model: int,
num_heads: int,
layer_norm_first: bool,
feed_forward_dim: int,
dropout: float = 0.1,
):
super().__init__()
self.attention = SelfAttention(
embed_dim=d_model,
num_heads=num_heads,
dropout=dropout,
)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model)
self.layer_norm_first = layer_norm_first
self.feed_forward = FeedForward(d_model, feed_forward_dim, dropout, dropout)
self.final_layer_norm = nn.LayerNorm(d_model)
def forward(
self,
x: Tensor,
attention_mask: Optional[Tensor] = None,
position_bias: Optional[Tensor] = None,
key_padding_mask: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
"""
Args:
x (Tensor): Input of shape ``(batch, sequence_length, embed_dim)``.
attention_mask (Tensor or ``None``, optional): attention mask
of shape ``(batch, 1, sequence_length, sequence_length)``. (Default: ``None``)
position_bias (Tensor or ``None``, optional): position bias of shape
``(batch_size * num_heads, src_len, src_len)``.
Only necessary for WavLM model, ``None`` otherwise. (Default: ``None``)
key_padding_mask (Tensor or ``None``, optional): key padding mask of shape ``(batch_size, src_len)``.
Only used for WavLM model, ignored otherwise. (Default: ``None``)
Returns:
(x, position_bias): Shapes are the same as in the input. Position bias is only relevant for WaLM model,
``None`` otherwise.
"""
residual = x
if self.layer_norm_first:
x = self.layer_norm(x)
x, position_bias = self.attention(
x,
attention_mask=attention_mask,
position_bias=position_bias,
key_padding_mask=key_padding_mask,
)
x = self.dropout(x)
x = residual + x
if self.layer_norm_first:
x = x + self.feed_forward(self.final_layer_norm(x))
else:
x = self.layer_norm(x)
x = self.final_layer_norm(x + self.feed_forward(x))
return x, position_bias
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