From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/modules/attention.py | 299 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 290 insertions(+), 9 deletions(-)
diff --git a/funasr/modules/attention.py b/funasr/modules/attention.py
index 31d5a87..ab59493 100644
--- a/funasr/modules/attention.py
+++ b/funasr/modules/attention.py
@@ -11,7 +11,11 @@
import numpy
import torch
from torch import nn
+from typing import Optional, Tuple
+import torch.nn.functional as F
+from funasr.modules.nets_utils import make_pad_mask
+import funasr.modules.lora.layers as lora
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
@@ -318,7 +322,7 @@
"""
- def __init__(self, n_head, in_feat, n_feat, dropout_rate, kernel_size, sanm_shfit=0):
+ 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):
"""Construct an MultiHeadedAttention object."""
super(MultiHeadedAttentionSANM, self).__init__()
assert n_feat % n_head == 0
@@ -328,8 +332,19 @@
# 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.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
+ if lora_list is not None:
+ 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)
+ lora_qkv_list = ["q" in lora_list, "k" in lora_list, "v" in lora_list]
+ if lora_qkv_list == [False, False, False]:
+ self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
+ else:
+ 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)
+ else:
+ 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)
@@ -540,18 +555,32 @@
"""
- def __init__(self, n_head, n_feat, dropout_rate, encoder_output_size=None):
+ 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
- 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_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)
+ 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)
@@ -741,3 +770,255 @@
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
--
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