mirror of
https://github.com/modelscope/FunASR
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1092 lines
42 KiB
Python
1092 lines
42 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Multi-Head Attention layer definition."""
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import math
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import numpy
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import torch
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from torch import nn
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from typing import Optional, Tuple
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import torch.nn.functional as F
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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import funasr.models.lora.layers as lora
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class MultiHeadedAttention(nn.Module):
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"""Multi-Head Attention layer.
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self, n_head, n_feat, dropout_rate):
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"""Construct an MultiHeadedAttention object."""
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super(MultiHeadedAttention, self).__init__()
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assert n_feat % n_head == 0
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# We assume d_v always equals d_k
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self.d_k = n_feat // n_head
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self.h = n_head
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self.linear_q = nn.Linear(n_feat, n_feat)
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self.linear_k = nn.Linear(n_feat, n_feat)
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self.linear_v = nn.Linear(n_feat, n_feat)
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self.linear_out = nn.Linear(n_feat, n_feat)
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self.attn = None
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self.dropout = nn.Dropout(p=dropout_rate)
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def forward_qkv(self, query, key, value):
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"""Transform query, key and value.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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Returns:
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torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
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torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
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torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
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"""
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n_batch = query.size(0)
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q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
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k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
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v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
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q = q.transpose(1, 2) # (batch, head, time1, d_k)
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k = k.transpose(1, 2) # (batch, head, time2, d_k)
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v = v.transpose(1, 2) # (batch, head, time2, d_k)
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return q, k, v
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def forward_attention(self, value, scores, mask):
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"""Compute attention context vector.
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Args:
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value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
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scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
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mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
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Returns:
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torch.Tensor: Transformed value (#batch, time1, d_model)
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weighted by the attention score (#batch, time1, time2).
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"""
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n_batch = value.size(0)
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if mask is not None:
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
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min_value = float(
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numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
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)
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scores = scores.masked_fill(mask, min_value)
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self.attn = torch.softmax(scores, dim=-1).masked_fill(
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mask, 0.0
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) # (batch, head, time1, time2)
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else:
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self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(self.attn)
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x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
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x = (
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x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
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) # (batch, time1, d_model)
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return self.linear_out(x) # (batch, time1, d_model)
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def forward(self, query, key, value, mask):
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"""Compute scaled dot product attention.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2).
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask)
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class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding (old version).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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Paper: https://arxiv.org/abs/1901.02860
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
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"""
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def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
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"""Construct an RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate)
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self.zero_triu = zero_triu
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# linear transformation for positional encoding
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self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
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# these two learnable bias are used in matrix c and matrix d
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
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self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
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torch.nn.init.xavier_uniform_(self.pos_bias_u)
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torch.nn.init.xavier_uniform_(self.pos_bias_v)
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def rel_shift(self, x):
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"""Compute relative positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, head, time1, time2).
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Returns:
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torch.Tensor: Output tensor.
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"""
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zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
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x_padded = torch.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
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x = x_padded[:, :, 1:].view_as(x)
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if self.zero_triu:
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ones = torch.ones((x.size(2), x.size(3)))
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x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
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return x
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def forward(self, query, key, value, pos_emb, mask):
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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pos_emb (torch.Tensor): Positional embedding tensor (#batch, time1, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2).
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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q = q.transpose(1, 2) # (batch, time1, head, d_k)
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n_batch_pos = pos_emb.size(0)
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p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
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p = p.transpose(1, 2) # (batch, head, time1, d_k)
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# (batch, head, time1, d_k)
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q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
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# (batch, head, time1, d_k)
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q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
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# compute attention score
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# first compute matrix a and matrix c
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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# (batch, head, time1, time2)
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matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
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# compute matrix b and matrix d
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# (batch, head, time1, time1)
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matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
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matrix_bd = self.rel_shift(matrix_bd)
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scores = (matrix_ac + matrix_bd) / math.sqrt(
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self.d_k
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) # (batch, head, time1, time2)
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return self.forward_attention(v, scores, mask)
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class RelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding (new implementation).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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Paper: https://arxiv.org/abs/1901.02860
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
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"""
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def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
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"""Construct an RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate)
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self.zero_triu = zero_triu
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# linear transformation for positional encoding
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self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
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# these two learnable bias are used in matrix c and matrix d
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
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self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
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torch.nn.init.xavier_uniform_(self.pos_bias_u)
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torch.nn.init.xavier_uniform_(self.pos_bias_v)
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def rel_shift(self, x):
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"""Compute relative positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
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time1 means the length of query vector.
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Returns:
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torch.Tensor: Output tensor.
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"""
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zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
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x_padded = torch.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
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x = x_padded[:, :, 1:].view_as(x)[
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:, :, :, : x.size(-1) // 2 + 1
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] # only keep the positions from 0 to time2
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if self.zero_triu:
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ones = torch.ones((x.size(2), x.size(3)), device=x.device)
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x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
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return x
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def forward(self, query, key, value, pos_emb, mask):
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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pos_emb (torch.Tensor): Positional embedding tensor
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(#batch, 2*time1-1, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2).
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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q = q.transpose(1, 2) # (batch, time1, head, d_k)
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n_batch_pos = pos_emb.size(0)
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p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
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p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k)
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# (batch, head, time1, d_k)
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q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
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# (batch, head, time1, d_k)
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q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
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# compute attention score
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# first compute matrix a and matrix c
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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# (batch, head, time1, time2)
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matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
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# compute matrix b and matrix d
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# (batch, head, time1, 2*time1-1)
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matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
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matrix_bd = self.rel_shift(matrix_bd)
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scores = (matrix_ac + matrix_bd) / math.sqrt(
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self.d_k
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) # (batch, head, time1, time2)
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return self.forward_attention(v, scores, mask)
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class MultiHeadedAttentionSANM(nn.Module):
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"""Multi-Head Attention layer.
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self, n_head, in_feat, n_feat, dropout_rate, kernel_size, sanm_shfit=0, lora_list=None, lora_rank=8, lora_alpha=16, lora_dropout=0.1):
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"""Construct an MultiHeadedAttention object."""
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super(MultiHeadedAttentionSANM, self).__init__()
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assert n_feat % n_head == 0
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# We assume d_v always equals d_k
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self.d_k = n_feat // n_head
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self.h = n_head
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# self.linear_q = nn.Linear(n_feat, n_feat)
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# self.linear_k = nn.Linear(n_feat, n_feat)
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# self.linear_v = nn.Linear(n_feat, n_feat)
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if lora_list is not None:
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if "o" in lora_list:
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self.linear_out = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
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else:
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self.linear_out = nn.Linear(n_feat, n_feat)
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lora_qkv_list = ["q" in lora_list, "k" in lora_list, "v" in lora_list]
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if lora_qkv_list == [False, False, False]:
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self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
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else:
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self.linear_q_k_v = lora.MergedLinear(in_feat, n_feat * 3, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_qkv_list)
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else:
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self.linear_out = nn.Linear(n_feat, n_feat)
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self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
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self.attn = None
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self.dropout = nn.Dropout(p=dropout_rate)
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self.fsmn_block = nn.Conv1d(n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False)
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# padding
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left_padding = (kernel_size - 1) // 2
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if sanm_shfit > 0:
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left_padding = left_padding + sanm_shfit
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right_padding = kernel_size - 1 - left_padding
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self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
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def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None):
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b, t, d = inputs.size()
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if mask is not None:
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mask = torch.reshape(mask, (b, -1, 1))
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if mask_shfit_chunk is not None:
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mask = mask * mask_shfit_chunk
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inputs = inputs * mask
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x = inputs.transpose(1, 2)
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x = self.pad_fn(x)
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x = self.fsmn_block(x)
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x = x.transpose(1, 2)
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x += inputs
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x = self.dropout(x)
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if mask is not None:
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x = x * mask
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return x
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def forward_qkv(self, x):
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"""Transform query, key and value.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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Returns:
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torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
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torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
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torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
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"""
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b, t, d = x.size()
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q_k_v = self.linear_q_k_v(x)
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q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1)
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q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time1, d_k)
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k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
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v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
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return q_h, k_h, v_h, v
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def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None):
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"""Compute attention context vector.
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Args:
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value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
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scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
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mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
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Returns:
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torch.Tensor: Transformed value (#batch, time1, d_model)
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weighted by the attention score (#batch, time1, time2).
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"""
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n_batch = value.size(0)
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if mask is not None:
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if mask_att_chunk_encoder is not None:
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mask = mask * mask_att_chunk_encoder
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
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min_value = float(
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numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
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)
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scores = scores.masked_fill(mask, min_value)
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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, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
|
|
"""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_h, k_h, v_h, v = self.forward_qkv(x)
|
|
fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk)
|
|
q_h = q_h * self.d_k ** (-0.5)
|
|
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
|
|
att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
|
|
return att_outs + fsmn_memory
|
|
|
|
def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
|
|
"""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_h, k_h, v_h, v = self.forward_qkv(x)
|
|
if chunk_size is not None and look_back > 0 or look_back == -1:
|
|
if cache is not None:
|
|
k_h_stride = k_h[:, :, :-(chunk_size[2]), :]
|
|
v_h_stride = v_h[:, :, :-(chunk_size[2]), :]
|
|
k_h = torch.cat((cache["k"], k_h), dim=2)
|
|
v_h = torch.cat((cache["v"], v_h), dim=2)
|
|
|
|
cache["k"] = torch.cat((cache["k"], k_h_stride), dim=2)
|
|
cache["v"] = torch.cat((cache["v"], v_h_stride), dim=2)
|
|
if look_back != -1:
|
|
cache["k"] = cache["k"][:, :, -(look_back * chunk_size[1]):, :]
|
|
cache["v"] = cache["v"][:, :, -(look_back * chunk_size[1]):, :]
|
|
else:
|
|
cache_tmp = {"k": k_h[:, :, :-(chunk_size[2]), :],
|
|
"v": v_h[:, :, :-(chunk_size[2]), :]}
|
|
cache = cache_tmp
|
|
fsmn_memory = self.forward_fsmn(v, None)
|
|
q_h = q_h * self.d_k ** (-0.5)
|
|
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
|
|
att_outs = self.forward_attention(v_h, scores, None)
|
|
return att_outs + fsmn_memory, cache
|
|
|
|
|
|
class MultiHeadedAttentionSANMwithMask(MultiHeadedAttentionSANM):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
|
|
q_h, k_h, v_h, v = self.forward_qkv(x)
|
|
fsmn_memory = self.forward_fsmn(v, mask[0], mask_shfit_chunk)
|
|
q_h = q_h * self.d_k ** (-0.5)
|
|
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
|
|
att_outs = self.forward_attention(v_h, scores, mask[1], mask_att_chunk_encoder)
|
|
return att_outs + fsmn_memory
|
|
|
|
class MultiHeadedAttentionSANMDecoder(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_feat, dropout_rate, kernel_size, sanm_shfit=0):
|
|
"""Construct an MultiHeadedAttention object."""
|
|
super(MultiHeadedAttentionSANMDecoder, self).__init__()
|
|
|
|
self.dropout = nn.Dropout(p=dropout_rate)
|
|
|
|
self.fsmn_block = nn.Conv1d(n_feat, n_feat,
|
|
kernel_size, stride=1, padding=0, groups=n_feat, bias=False)
|
|
# padding
|
|
# padding
|
|
left_padding = (kernel_size - 1) // 2
|
|
if sanm_shfit > 0:
|
|
left_padding = left_padding + sanm_shfit
|
|
right_padding = kernel_size - 1 - left_padding
|
|
self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
|
|
self.kernel_size = kernel_size
|
|
|
|
def forward(self, inputs, mask, cache=None, mask_shfit_chunk=None):
|
|
'''
|
|
:param x: (#batch, time1, size).
|
|
:param mask: Mask tensor (#batch, 1, time)
|
|
:return:
|
|
'''
|
|
# print("in fsmn, inputs", inputs.size())
|
|
b, t, d = inputs.size()
|
|
# logging.info(
|
|
# "mask: {}".format(mask.size()))
|
|
if mask is not None:
|
|
mask = torch.reshape(mask, (b ,-1, 1))
|
|
# logging.info("in fsmn, mask: {}, {}".format(mask.size(), mask[0:100:50, :, :]))
|
|
if mask_shfit_chunk is not None:
|
|
# logging.info("in fsmn, mask_fsmn: {}, {}".format(mask_shfit_chunk.size(), mask_shfit_chunk[0:100:50, :, :]))
|
|
mask = mask * mask_shfit_chunk
|
|
# logging.info("in fsmn, mask_after_fsmn: {}, {}".format(mask.size(), mask[0:100:50, :, :]))
|
|
# print("in fsmn, mask", mask.size())
|
|
# print("in fsmn, inputs", inputs.size())
|
|
inputs = inputs * mask
|
|
|
|
x = inputs.transpose(1, 2)
|
|
b, d, t = x.size()
|
|
if cache is None:
|
|
# print("in fsmn, cache is None, x", x.size())
|
|
|
|
x = self.pad_fn(x)
|
|
if not self.training:
|
|
cache = x
|
|
else:
|
|
# print("in fsmn, cache is not None, x", x.size())
|
|
# x = torch.cat((x, cache), dim=2)[:, :, :-1]
|
|
# if t < self.kernel_size:
|
|
# x = self.pad_fn(x)
|
|
x = torch.cat((cache[:, :, 1:], x), dim=2)
|
|
x = x[:, :, -(self.kernel_size+t-1):]
|
|
# print("in fsmn, cache is not None, x_cat", x.size())
|
|
cache = x
|
|
x = self.fsmn_block(x)
|
|
x = x.transpose(1, 2)
|
|
# print("in fsmn, fsmn_out", x.size())
|
|
if x.size(1) != inputs.size(1):
|
|
inputs = inputs[:, -1, :]
|
|
|
|
x = x + inputs
|
|
x = self.dropout(x)
|
|
if mask is not None:
|
|
x = x * mask
|
|
return x, cache
|
|
|
|
class MultiHeadedAttentionCrossAtt(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, lora_list=None, lora_rank=8, lora_alpha=16, lora_dropout=0.1, encoder_output_size=None):
|
|
"""Construct an MultiHeadedAttention object."""
|
|
super(MultiHeadedAttentionCrossAtt, 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
|
|
if lora_list is not None:
|
|
if "q" in lora_list:
|
|
self.linear_q = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
|
|
else:
|
|
self.linear_q = nn.Linear(n_feat, n_feat)
|
|
lora_kv_list = ["k" in lora_list, "v" in lora_list]
|
|
if lora_kv_list == [False, False]:
|
|
self.linear_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)
|
|
else:
|
|
self.linear_k_v = lora.MergedLinear(n_feat if encoder_output_size is None else encoder_output_size, n_feat * 2,
|
|
r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_kv_list)
|
|
if "o" in lora_list:
|
|
self.linear_out = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
|
|
else:
|
|
self.linear_out = nn.Linear(n_feat, n_feat)
|
|
else:
|
|
self.linear_q = nn.Linear(n_feat, n_feat)
|
|
self.linear_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)
|
|
self.linear_out = nn.Linear(n_feat, n_feat)
|
|
self.attn = None
|
|
self.dropout = nn.Dropout(p=dropout_rate)
|
|
|
|
def forward_qkv(self, x, memory):
|
|
"""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).
|
|
|
|
"""
|
|
|
|
# print("in forward_qkv, x", x.size())
|
|
b = x.size(0)
|
|
q = self.linear_q(x)
|
|
q_h = torch.reshape(q, (b, -1, self.h, self.d_k)).transpose(1, 2) # (batch, head, time1, d_k)
|
|
|
|
k_v = self.linear_k_v(memory)
|
|
k, v = torch.split(k_v, int(self.h*self.d_k), dim=-1)
|
|
k_h = torch.reshape(k, (b, -1, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
|
|
v_h = torch.reshape(v, (b, -1, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
|
|
|
|
|
|
return q_h, k_h, v_h
|
|
|
|
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
|
|
)
|
|
# logging.info(
|
|
# "scores: {}, mask_size: {}".format(scores.size(), mask.size()))
|
|
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, x, memory, memory_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_h, k_h, v_h = self.forward_qkv(x, memory)
|
|
q_h = q_h * self.d_k ** (-0.5)
|
|
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
|
|
return self.forward_attention(v_h, scores, memory_mask)
|
|
|
|
def forward_chunk(self, x, memory, cache=None, chunk_size=None, look_back=0):
|
|
"""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_h, k_h, v_h = self.forward_qkv(x, memory)
|
|
if chunk_size is not None and look_back > 0:
|
|
if cache is not None:
|
|
k_h = torch.cat((cache["k"], k_h), dim=2)
|
|
v_h = torch.cat((cache["v"], v_h), dim=2)
|
|
cache["k"] = k_h[:, :, -(look_back * chunk_size[1]):, :]
|
|
cache["v"] = v_h[:, :, -(look_back * chunk_size[1]):, :]
|
|
else:
|
|
cache_tmp = {"k": k_h[:, :, -(look_back * chunk_size[1]):, :],
|
|
"v": v_h[:, :, -(look_back * chunk_size[1]):, :]}
|
|
cache = cache_tmp
|
|
q_h = q_h * self.d_k ** (-0.5)
|
|
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
|
|
return self.forward_attention(v_h, scores, None), cache
|
|
|
|
|
|
class MultiHeadSelfAttention(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, in_feat, n_feat, dropout_rate):
|
|
"""Construct an MultiHeadedAttention object."""
|
|
super(MultiHeadSelfAttention, 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_out = nn.Linear(n_feat, n_feat)
|
|
self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
|
|
self.attn = None
|
|
self.dropout = nn.Dropout(p=dropout_rate)
|
|
|
|
def forward_qkv(self, x):
|
|
"""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).
|
|
|
|
"""
|
|
b, t, d = x.size()
|
|
q_k_v = self.linear_q_k_v(x)
|
|
q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1)
|
|
q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time1, d_k)
|
|
k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
|
|
v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
|
|
|
|
return q_h, k_h, v_h, v
|
|
|
|
def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None):
|
|
"""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:
|
|
if mask_att_chunk_encoder is not None:
|
|
mask = mask * mask_att_chunk_encoder
|
|
|
|
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, x, mask, mask_att_chunk_encoder=None):
|
|
"""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_h, k_h, v_h, v = self.forward_qkv(x)
|
|
q_h = q_h * self.d_k ** (-0.5)
|
|
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
|
|
att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
|
|
return att_outs
|
|
|
|
class RelPositionMultiHeadedAttentionChunk(torch.nn.Module):
|
|
"""RelPositionMultiHeadedAttention definition.
|
|
Args:
|
|
num_heads: Number of attention heads.
|
|
embed_size: Embedding size.
|
|
dropout_rate: Dropout rate.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
num_heads: int,
|
|
embed_size: int,
|
|
dropout_rate: float = 0.0,
|
|
simplified_attention_score: bool = False,
|
|
) -> None:
|
|
"""Construct an MultiHeadedAttention object."""
|
|
super().__init__()
|
|
|
|
self.d_k = embed_size // num_heads
|
|
self.num_heads = num_heads
|
|
|
|
assert self.d_k * num_heads == embed_size, (
|
|
"embed_size (%d) must be divisible by num_heads (%d)",
|
|
(embed_size, num_heads),
|
|
)
|
|
|
|
self.linear_q = torch.nn.Linear(embed_size, embed_size)
|
|
self.linear_k = torch.nn.Linear(embed_size, embed_size)
|
|
self.linear_v = torch.nn.Linear(embed_size, embed_size)
|
|
|
|
self.linear_out = torch.nn.Linear(embed_size, embed_size)
|
|
|
|
if simplified_attention_score:
|
|
self.linear_pos = torch.nn.Linear(embed_size, num_heads)
|
|
|
|
self.compute_att_score = self.compute_simplified_attention_score
|
|
else:
|
|
self.linear_pos = torch.nn.Linear(embed_size, embed_size, bias=False)
|
|
|
|
self.pos_bias_u = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
|
|
self.pos_bias_v = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
|
|
torch.nn.init.xavier_uniform_(self.pos_bias_u)
|
|
torch.nn.init.xavier_uniform_(self.pos_bias_v)
|
|
|
|
self.compute_att_score = self.compute_attention_score
|
|
|
|
self.dropout = torch.nn.Dropout(p=dropout_rate)
|
|
self.attn = None
|
|
|
|
def rel_shift(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor:
|
|
"""Compute relative positional encoding.
|
|
Args:
|
|
x: Input sequence. (B, H, T_1, 2 * T_1 - 1)
|
|
left_context: Number of frames in left context.
|
|
Returns:
|
|
x: Output sequence. (B, H, T_1, T_2)
|
|
"""
|
|
batch_size, n_heads, time1, n = x.shape
|
|
time2 = time1 + left_context
|
|
|
|
batch_stride, n_heads_stride, time1_stride, n_stride = x.stride()
|
|
|
|
return x.as_strided(
|
|
(batch_size, n_heads, time1, time2),
|
|
(batch_stride, n_heads_stride, time1_stride - n_stride, n_stride),
|
|
storage_offset=(n_stride * (time1 - 1)),
|
|
)
|
|
|
|
def compute_simplified_attention_score(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
pos_enc: torch.Tensor,
|
|
left_context: int = 0,
|
|
) -> torch.Tensor:
|
|
"""Simplified attention score computation.
|
|
Reference: https://github.com/k2-fsa/icefall/pull/458
|
|
Args:
|
|
query: Transformed query tensor. (B, H, T_1, d_k)
|
|
key: Transformed key tensor. (B, H, T_2, d_k)
|
|
pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
|
|
left_context: Number of frames in left context.
|
|
Returns:
|
|
: Attention score. (B, H, T_1, T_2)
|
|
"""
|
|
pos_enc = self.linear_pos(pos_enc)
|
|
|
|
matrix_ac = torch.matmul(query, key.transpose(2, 3))
|
|
|
|
matrix_bd = self.rel_shift(
|
|
pos_enc.transpose(1, 2).unsqueeze(2).repeat(1, 1, query.size(2), 1),
|
|
left_context=left_context,
|
|
)
|
|
|
|
return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
|
|
|
|
def compute_attention_score(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
pos_enc: torch.Tensor,
|
|
left_context: int = 0,
|
|
) -> torch.Tensor:
|
|
"""Attention score computation.
|
|
Args:
|
|
query: Transformed query tensor. (B, H, T_1, d_k)
|
|
key: Transformed key tensor. (B, H, T_2, d_k)
|
|
pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
|
|
left_context: Number of frames in left context.
|
|
Returns:
|
|
: Attention score. (B, H, T_1, T_2)
|
|
"""
|
|
p = self.linear_pos(pos_enc).view(pos_enc.size(0), -1, self.num_heads, self.d_k)
|
|
|
|
query = query.transpose(1, 2)
|
|
q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
|
|
q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
|
|
|
|
matrix_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
|
|
|
|
matrix_bd = torch.matmul(q_with_bias_v, p.permute(0, 2, 3, 1))
|
|
matrix_bd = self.rel_shift(matrix_bd, left_context=left_context)
|
|
|
|
return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
|
|
|
|
def forward_qkv(
|
|
self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
"""Transform query, key and value.
|
|
Args:
|
|
query: Query tensor. (B, T_1, size)
|
|
key: Key tensor. (B, T_2, size)
|
|
v: Value tensor. (B, T_2, size)
|
|
Returns:
|
|
q: Transformed query tensor. (B, H, T_1, d_k)
|
|
k: Transformed key tensor. (B, H, T_2, d_k)
|
|
v: Transformed value tensor. (B, H, T_2, d_k)
|
|
"""
|
|
n_batch = query.size(0)
|
|
|
|
q = (
|
|
self.linear_q(query)
|
|
.view(n_batch, -1, self.num_heads, self.d_k)
|
|
.transpose(1, 2)
|
|
)
|
|
k = (
|
|
self.linear_k(key)
|
|
.view(n_batch, -1, self.num_heads, self.d_k)
|
|
.transpose(1, 2)
|
|
)
|
|
v = (
|
|
self.linear_v(value)
|
|
.view(n_batch, -1, self.num_heads, self.d_k)
|
|
.transpose(1, 2)
|
|
)
|
|
|
|
return q, k, v
|
|
|
|
def forward_attention(
|
|
self,
|
|
value: torch.Tensor,
|
|
scores: torch.Tensor,
|
|
mask: torch.Tensor,
|
|
chunk_mask: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""Compute attention context vector.
|
|
Args:
|
|
value: Transformed value. (B, H, T_2, d_k)
|
|
scores: Attention score. (B, H, T_1, T_2)
|
|
mask: Source mask. (B, T_2)
|
|
chunk_mask: Chunk mask. (T_1, T_1)
|
|
Returns:
|
|
attn_output: Transformed value weighted by attention score. (B, T_1, H * d_k)
|
|
"""
|
|
batch_size = scores.size(0)
|
|
mask = mask.unsqueeze(1).unsqueeze(2)
|
|
if chunk_mask is not None:
|
|
mask = chunk_mask.unsqueeze(0).unsqueeze(1) | mask
|
|
scores = scores.masked_fill(mask, float("-inf"))
|
|
self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
|
|
|
|
attn_output = self.dropout(self.attn)
|
|
attn_output = torch.matmul(attn_output, value)
|
|
|
|
attn_output = self.linear_out(
|
|
attn_output.transpose(1, 2)
|
|
.contiguous()
|
|
.view(batch_size, -1, self.num_heads * self.d_k)
|
|
)
|
|
|
|
return attn_output
|
|
|
|
def forward(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
pos_enc: torch.Tensor,
|
|
mask: torch.Tensor,
|
|
chunk_mask: Optional[torch.Tensor] = None,
|
|
left_context: int = 0,
|
|
) -> torch.Tensor:
|
|
"""Compute scaled dot product attention with rel. positional encoding.
|
|
Args:
|
|
query: Query tensor. (B, T_1, size)
|
|
key: Key tensor. (B, T_2, size)
|
|
value: Value tensor. (B, T_2, size)
|
|
pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
|
|
mask: Source mask. (B, T_2)
|
|
chunk_mask: Chunk mask. (T_1, T_1)
|
|
left_context: Number of frames in left context.
|
|
Returns:
|
|
: Output tensor. (B, T_1, H * d_k)
|
|
"""
|
|
q, k, v = self.forward_qkv(query, key, value)
|
|
scores = self.compute_att_score(q, k, pos_enc, left_context=left_context)
|
|
return self.forward_attention(v, scores, mask, chunk_mask=chunk_mask)
|
|
|
|
|
|
class CosineDistanceAttention(nn.Module):
|
|
""" Compute Cosine Distance between spk decoder output and speaker profile
|
|
Args:
|
|
profile_path: speaker profile file path (.npy file)
|
|
"""
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.softmax = nn.Softmax(dim=-1)
|
|
|
|
def forward(self, spk_decoder_out, profile, profile_lens=None):
|
|
"""
|
|
Args:
|
|
spk_decoder_out(torch.Tensor):(B, L, D)
|
|
spk_profiles(torch.Tensor):(B, N, D)
|
|
"""
|
|
x = spk_decoder_out.unsqueeze(2) # (B, L, 1, D)
|
|
if profile_lens is not None:
|
|
|
|
mask = (make_pad_mask(profile_lens)[:, None, :]).to(profile.device)
|
|
min_value = float(
|
|
numpy.finfo(torch.tensor(0, dtype=x.dtype).numpy().dtype).min
|
|
)
|
|
weights_not_softmax=F.cosine_similarity(x, profile.unsqueeze(1), dim=-1).masked_fill(mask, min_value)
|
|
weights = self.softmax(weights_not_softmax).masked_fill(mask, 0.0) # (B, L, N)
|
|
else:
|
|
x = x[:, -1:, :, :]
|
|
weights_not_softmax=F.cosine_similarity(x, profile.unsqueeze(1).to(x.device), dim=-1)
|
|
weights = self.softmax(weights_not_softmax) # (B, 1, N)
|
|
spk_embedding = torch.matmul(weights, profile.to(weights.device)) # (B, L, D)
|
|
|
|
return spk_embedding, weights
|