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"""
The MIT License (MIT)
Copyright (c) Microsoft Corporation
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import math
from typing import Optional, Tuple
import torch
from torch import nn, Tensor
class WavLMSelfAttention(nn.Module):
"""Multi-headed self-attention for WavLM model :cite:`chen2022wavlm`.
Wraps around ``torch.nn.MultiheadAttention``, creating relaive position embeddings and passing them to multi-headed
attention as a mask.
Source: https://github.com/microsoft/unilm/blob/2d8302f09c99bca2b82e6e868d81d4281cceebc8/wavlm/modules.py#L303-L763
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: to ``0.0``)
bias (bool, optional): If ``True``, add bias to input / output projection layers. (Default: ``True``)
has_relative_attention_bias (bool, optional): If ``True``, apply relative position embedding.
Necessary in the first encoder layer, but not in the subsequent ones. (Default: ``False``)
num_buckets (int, optional): Number of buckets for relative position embedding. (Default: ``32``)
max_distance (int, optional): Naximum distance for relative position embedding. (Default: ``128``)
gru_rel_pos (bool, optional): If ``True``, apply gated relative position embedding. (Default: ``False``)
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
bias: bool = True,
has_relative_attention_bias: bool = False,
num_buckets: int = 32,
max_distance: int = 128,
gru_rel_pos: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.has_relative_attention_bias = has_relative_attention_bias
self.num_buckets = num_buckets
self.max_distance = max_distance
if has_relative_attention_bias:
self.rel_attn_embed = nn.Embedding(num_buckets, num_heads)
else:
self.rel_attn_embed = None
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.dropout = dropout
self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True)
self.gru_rel_pos = gru_rel_pos
if self.gru_rel_pos:
self.gru_rel_pos_linear = nn.Linear(self.head_dim, 8)
self.gru_rel_pos_const = nn.Parameter(torch.ones(1, num_heads, 1, 1))
self.has_position_bias = True
def compute_bias(self, query_length: int, key_length: int) -> Tensor:
"""Compute relative position embeddings for WavLM model.
Args:
query_length (int): Query position can take values between 0 and ``query_length - 1``.
key_length (int): Key position can take values between 0 and ``key_length - 1``.
Returns:
Tensor of shape `(num_heads, query_length, key_length)`, relative positions embeddings
"""
context_position = torch.arange(query_length, dtype=torch.long)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
relative_position = memory_position - context_position # Shape (query_length, key_length)
relative_position_bucket = self._relative_positions_bucket(relative_position, bidirectional=True)
relative_position_bucket = relative_position_bucket.to(self.rel_attn_embed.weight.device)
values = self.rel_attn_embed(relative_position_bucket) # Shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1])
return values
def _relative_positions_bucket(self, relative_positions: Tensor, bidirectional: bool = True):
"""Compute relative position buckets for WavLM model. Computation similar to formula (5) in WavLM
paper :cite:`chen2022wavlm`.
Args:
relative_positions (Tensor): Relative offsets between query and key positions,
of shape ``(query_length, key_length)``.
bidirectional (bool): If ``True``, values will be filled both above and below the diagonal in the resulting
matrix. If ``False``, the elements above the diagonal (i.e. with negative relative offsets) will be set
to zero. (Default ``True``)
Returns:
Tensor of shape ``(query_length, key_length)`` filled bucketed values of with relative positions.
"""
num_buckets = self.num_buckets
max_distance = self.max_distance
# Shape (query_length, key_length)
relative_buckets = torch.zeros_like(relative_positions, dtype=torch.long)
if bidirectional:
num_buckets = num_buckets // 2
relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
relative_positions = torch.abs(relative_positions)
else:
relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))
max_exact = num_buckets // 2
is_small = relative_positions < max_exact
relative_postion_if_large = max_exact + (
torch.log(relative_positions.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_postion_if_large = torch.min(
relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
)
relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
return relative_buckets
def forward(
self,
query: Tensor,
key_padding_mask: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
position_bias: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
"""
Args:
query (Tensor): Input of shape ``(batch_size, src_len, embed_dim)``.
key_padding_mask (Tensor or None, optional): Mask to exclude keys that are pads, of shape
`(batch, src_len)`, where padding elements are indicated by 1s. (Default: ``None``)
attn_mask: Needs to be ``None``. The argument exists for compatibility with
``EncoderLayer``. (Default: ``None``)
position_bias (Tensor or None, optional): Position bias of shape
``(batch_size * num_heads, src_len, src_len)``. When used inside WavLM model encoder, will be
generated in the first layer and then passed from each encoder layer to the next one.
(Default: ``None``)
Returns:
attn_output (Tensor): Attention output of shape ``(batch_size, src_len, embed_dim)``.
position_bias (Tensor or None): Position bias of shape ``(batch_size * num_heads, src_len, src_len)``.
"""
bsz, seq_len, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert attention_mask is None
if self.rel_attn_embed is not None and position_bias is None:
position_bias = self.compute_bias(seq_len, seq_len)
position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1)
attn_mask_rel_pos: Optional[Tensor] = None
if position_bias is not None:
attn_mask_rel_pos = position_bias
if self.gru_rel_pos: # Apply gating on relative position bias
query_layer = query.view(bsz, seq_len, self.num_heads, -1)
query_layer = query_layer.permute(0, 2, 1, 3)
gate_a, gate_b = torch.sigmoid(
self.gru_rel_pos_linear(query_layer).view(bsz, self.num_heads, seq_len, 2, 4).sum(-1, keepdim=False)
).chunk(2, dim=-1)
gate_a_1 = gate_a * (gate_b * self.gru_rel_pos_const - 1.0) + 2.0
attn_mask_rel_pos = gate_a_1.view(bsz, self.num_heads, -1, 1) * position_bias
attn_mask_rel_pos = attn_mask_rel_pos.view((bsz, self.num_heads, seq_len, seq_len))
if attn_mask_rel_pos is not None and key_padding_mask is not None:
key_padding_mask = key_padding_mask.view(bsz, 1, 1, seq_len).expand(-1, self.num_heads, -1, -1)
key_padding_mask = torch.nn.functional._canonical_mask(
mask=key_padding_mask,
mask_name="key_padding_mask",
other_type=torch.nn.functional._none_or_dtype(attn_mask_rel_pos),
other_name="",
target_type=query.dtype,
)
if attn_mask_rel_pos is not None and key_padding_mask is not None:
attn_mask_rel_pos = attn_mask_rel_pos + key_padding_mask
query_projected = torch.nn.functional.linear(query, self.attention.in_proj_weight, self.attention.in_proj_bias)
query, key, value = query_projected.chunk(3, -1)
shape = (bsz, seq_len, self.num_heads, self.head_dim)
query = query.view(shape).transpose(2, 1) # (batch, num_heads, seq_len, head_dim)
key = key.view(shape).transpose(2, 1) # (batch, num_heads, seq_len, head_dim)
value = value.view(shape).transpose(2, 1) # (batch, num_heads, seq_len, head_dim)
dropout = self.dropout if self.training else 0.0
attn_output = torch.nn.functional.scaled_dot_product_attention(
query,
key,
value,
attn_mask=attn_mask_rel_pos,
dropout_p=dropout,
is_causal=False,
)
attn_output = attn_output.transpose(1, 2).reshape(bsz, -1, self.num_heads * self.head_dim)
attn_output = self.attention.out_proj(attn_output)
return attn_output, position_bias
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