Attention.py

# Written by Shigeki Karita, 2019
# Published under Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
# Adapted by Florian Lux, 2021

"""Multi-Head Attention layer definition."""

import math

import numpy
import torch
from torch import nn


from utils import make_non_pad_mask


class MultiHeadedAttention(nn.Module):
    """
    Multi-Head Attention layer.

    Args:
        n_head (int): The number of heads.
        n_feat (int): The number of features.
        dropout_rate (float): Dropout rate.
    """

    def __init__(self, n_head, n_feat, dropout_rate):
        """
        Construct an MultiHeadedAttention object.
        """
        super(MultiHeadedAttention, self).__init__()
        assert n_feat % n_head == 0
        # We assume d_v always equals d_k
        self.d_k = n_feat // n_head
        self.h = n_head
        self.linear_q = nn.Linear(n_feat, n_feat)
        self.linear_k = nn.Linear(n_feat, n_feat)
        self.linear_v = nn.Linear(n_feat, n_feat)
        self.linear_out = nn.Linear(n_feat, n_feat)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward_qkv(self, query, key, value):
        """
        Transform query, key and value.

        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).

        Returns:
            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
        """
        n_batch = query.size(0)
        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
        v = v.transpose(1, 2)  # (batch, head, time2, d_k)

        return q, k, v

    def forward_attention(self, value, scores, mask):
        """
        Compute attention context vector.

        Args:
            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).

        Returns:
            torch.Tensor: Transformed value (#batch, time1, d_model)
                weighted by the attention score (#batch, time1, time2).
        """
        n_batch = value.size(0)
        if mask is not None:
            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
            min_value = float(numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min)
            scores = scores.masked_fill(mask, min_value)
            self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)  # (batch, head, time1, time2)
        else:
            self.attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)

        p_attn = self.dropout(self.attn)
        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
        x = (x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k))  # (batch, time1, d_model)

        return self.linear_out(x)  # (batch, time1, d_model)

    def forward(self, query, key, value, mask):
        """
        Compute scaled dot product attention.

        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).
            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
                (#batch, time1, time2).

        Returns:
            torch.Tensor: Output tensor (#batch, time1, d_model).
        """
        q, k, v = self.forward_qkv(query, key, value)
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
        return self.forward_attention(v, scores, mask)


class RelPositionMultiHeadedAttention(MultiHeadedAttention):
    """
    Multi-Head Attention layer with relative position encoding.
    Details can be found in https://github.com/espnet/espnet/pull/2816.
    Paper: https://arxiv.org/abs/1901.02860
    Args:
        n_head (int): The number of heads.
        n_feat (int): The number of features.
        dropout_rate (float): Dropout rate.
        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
    """

    def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
        """Construct an RelPositionMultiHeadedAttention object."""
        super().__init__(n_head, n_feat, dropout_rate)
        self.zero_triu = zero_triu
        # linear transformation for positional encoding
        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
        # these two learnable bias are used in matrix c and matrix d
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
        torch.nn.init.xavier_uniform_(self.pos_bias_u)
        torch.nn.init.xavier_uniform_(self.pos_bias_v)

    def rel_shift(self, x):
        """
        Compute relative positional encoding.
        Args:
            x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
            time1 means the length of query vector.
        Returns:
            torch.Tensor: Output tensor.
        """
        zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
        x_padded = torch.cat([zero_pad, x], dim=-1)

        x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
        x = x_padded[:, :, 1:].view_as(x)[:, :, :, : x.size(-1) // 2 + 1]  # only keep the positions from 0 to time2

        if self.zero_triu:
            ones = torch.ones((x.size(2), x.size(3)), device=x.device)
            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]

        return x

    def forward(self, query, key, value, pos_emb, mask):
        """
        Compute 'Scaled Dot Product Attention' with rel. positional encoding.
        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).
            pos_emb (torch.Tensor): Positional embedding tensor
                (#batch, 2*time1-1, size).
            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
                (#batch, time1, time2).
        Returns:
            torch.Tensor: Output tensor (#batch, time1, d_model).
        """
        q, k, v = self.forward_qkv(query, key, value)
        q = q.transpose(1, 2)  # (batch, time1, head, d_k)

        n_batch_pos = pos_emb.size(0)
        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
        p = p.transpose(1, 2)  # (batch, head, 2*time1-1, d_k)

        # (batch, head, time1, d_k)
        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
        # (batch, head, time1, d_k)
        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)

        # compute attention score
        # first compute matrix a and matrix c
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        # (batch, head, time1, time2)
        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))

        # compute matrix b and matrix d
        # (batch, head, time1, 2*time1-1)
        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
        matrix_bd = self.rel_shift(matrix_bd)

        scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k)  # (batch, head, time1, time2)

        return self.forward_attention(v, scores, mask)


class GuidedAttentionLoss(torch.nn.Module):
    """
    Guided attention loss function module.

    This module calculates the guided attention loss described
    in `Efficiently Trainable Text-to-Speech System Based
    on Deep Convolutional Networks with Guided Attention`_,
    which forces the attention to be diagonal.

    .. _`Efficiently Trainable Text-to-Speech System
        Based on Deep Convolutional Networks with Guided Attention`:
        https://arxiv.org/abs/1710.08969
    """

    def __init__(self, sigma=0.4, alpha=1.0):
        """
        Initialize guided attention loss module.

        Args:
            sigma (float, optional): Standard deviation to control
                how close attention to a diagonal.
            alpha (float, optional): Scaling coefficient (lambda).
            reset_always (bool, optional): Whether to always reset masks.
        """
        super(GuidedAttentionLoss, self).__init__()
        self.sigma = sigma
        self.alpha = alpha
        self.guided_attn_masks = None
        self.masks = None

    def _reset_masks(self):
        self.guided_attn_masks = None
        self.masks = None

    def forward(self, att_ws, ilens, olens):
        """
        Calculate forward propagation.

        Args:
            att_ws (Tensor): Batch of attention weights (B, T_max_out, T_max_in).
            ilens (LongTensor): Batch of input lenghts (B,).
            olens (LongTensor): Batch of output lenghts (B,).

        Returns:
            Tensor: Guided attention loss value.
        """
        self._reset_masks()
        self.guided_attn_masks = self._make_guided_attention_masks(ilens, olens).to(att_ws.device)
        self.masks = self._make_masks(ilens, olens).to(att_ws.device)
        losses = self.guided_attn_masks * att_ws
        loss = torch.mean(losses.masked_select(self.masks))
        self._reset_masks()
        return self.alpha * loss

    def _make_guided_attention_masks(self, ilens, olens):
        n_batches = len(ilens)
        max_ilen = max(ilens)
        max_olen = max(olens)
        guided_attn_masks = torch.zeros((n_batches, max_olen, max_ilen), device=ilens.device)
        for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
            guided_attn_masks[idx, :olen, :ilen] = self._make_guided_attention_mask(ilen, olen, self.sigma)
        return guided_attn_masks

    @staticmethod
    def _make_guided_attention_mask(ilen, olen, sigma):
        """
        Make guided attention mask.
        """
        grid_x, grid_y = torch.meshgrid(torch.arange(olen, device=olen.device).float(), torch.arange(ilen, device=ilen.device).float())
        return 1.0 - torch.exp(-((grid_y / ilen - grid_x / olen) ** 2) / (2 * (sigma ** 2)))

    @staticmethod
    def _make_masks(ilens, olens):
        """
        Make masks indicating non-padded part.

        Args:
            ilens (LongTensor or List): Batch of lengths (B,).
            olens (LongTensor or List): Batch of lengths (B,).

        Returns:
            Tensor: Mask tensor indicating non-padded part.
                    dtype=torch.uint8 in PyTorch 1.2-
                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)
        """
        in_masks = make_non_pad_mask(ilens, device=ilens.device)  # (B, T_in)
        out_masks = make_non_pad_mask(olens, device=olens.device)  # (B, T_out)
        return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2)  # (B, T_out, T_in)


class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
    """
    Guided attention loss function module for multi head attention.

    Args:
        sigma (float, optional): Standard deviation to control
        how close attention to a diagonal.
        alpha (float, optional): Scaling coefficient (lambda).
        reset_always (bool, optional): Whether to always reset masks.
    """

    def forward(self, att_ws, ilens, olens):
        """
        Calculate forward propagation.

        Args:
            att_ws (Tensor):
                Batch of multi head attention weights (B, H, T_max_out, T_max_in).
            ilens (LongTensor): Batch of input lenghts (B,).
            olens (LongTensor): Batch of output lenghts (B,).

        Returns:
            Tensor: Guided attention loss value.
        """
        if self.guided_attn_masks is None:
            self.guided_attn_masks = (self._make_guided_attention_masks(ilens, olens).to(att_ws.device).unsqueeze(1))
        if self.masks is None:
            self.masks = self._make_masks(ilens, olens).to(att_ws.device).unsqueeze(1)
        losses = self.guided_attn_masks * att_ws
        loss = torch.mean(losses.masked_select(self.masks))
        if self.reset_always:
            self._reset_masks()

        return self.alpha * loss