MLPY/Lib/site-packages/torchaudio/models/wav2vec2/wavlm_attention.py

"""
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
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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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