"""Multi-Head attention layers with relative positional encoding."""
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import math
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from typing import Optional, Tuple
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import torch
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class RelPositionMultiHeadedAttention(torch.nn.Module):
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"""RelPositionMultiHeadedAttention definition.
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Args:
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num_heads: Number of attention heads.
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embed_size: Embedding size.
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dropout_rate: Dropout rate.
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"""
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def __init__(
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self,
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num_heads: int,
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embed_size: int,
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dropout_rate: float = 0.0,
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simplified_attention_score: bool = False,
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) -> None:
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"""Construct an MultiHeadedAttention object."""
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super().__init__()
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self.d_k = embed_size // num_heads
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self.num_heads = num_heads
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assert self.d_k * num_heads == embed_size, (
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"embed_size (%d) must be divisible by num_heads (%d)",
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(embed_size, num_heads),
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)
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self.linear_q = torch.nn.Linear(embed_size, embed_size)
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self.linear_k = torch.nn.Linear(embed_size, embed_size)
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self.linear_v = torch.nn.Linear(embed_size, embed_size)
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self.linear_out = torch.nn.Linear(embed_size, embed_size)
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if simplified_attention_score:
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self.linear_pos = torch.nn.Linear(embed_size, num_heads)
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self.compute_att_score = self.compute_simplified_attention_score
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else:
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self.linear_pos = torch.nn.Linear(embed_size, embed_size, bias=False)
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self.pos_bias_u = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
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self.pos_bias_v = torch.nn.Parameter(torch.Tensor(num_heads, 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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self.compute_att_score = self.compute_attention_score
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self.dropout = torch.nn.Dropout(p=dropout_rate)
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self.attn = None
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def rel_shift(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor:
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"""Compute relative positional encoding.
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Args:
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x: Input sequence. (B, H, T_1, 2 * T_1 - 1)
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left_context: Number of frames in left context.
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Returns:
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x: Output sequence. (B, H, T_1, T_2)
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"""
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batch_size, n_heads, time1, n = x.shape
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time2 = time1 + left_context
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batch_stride, n_heads_stride, time1_stride, n_stride = x.stride()
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return x.as_strided(
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(batch_size, n_heads, time1, time2),
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(batch_stride, n_heads_stride, time1_stride - n_stride, n_stride),
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storage_offset=(n_stride * (time1 - 1)),
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)
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def compute_simplified_attention_score(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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pos_enc: torch.Tensor,
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left_context: int = 0,
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) -> torch.Tensor:
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"""Simplified attention score computation.
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Reference: https://github.com/k2-fsa/icefall/pull/458
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Args:
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query: Transformed query tensor. (B, H, T_1, d_k)
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key: Transformed key tensor. (B, H, T_2, d_k)
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pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
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left_context: Number of frames in left context.
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Returns:
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: Attention score. (B, H, T_1, T_2)
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"""
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pos_enc = self.linear_pos(pos_enc)
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matrix_ac = torch.matmul(query, key.transpose(2, 3))
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matrix_bd = self.rel_shift(
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pos_enc.transpose(1, 2).unsqueeze(2).repeat(1, 1, query.size(2), 1),
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left_context=left_context,
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)
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return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
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def compute_attention_score(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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pos_enc: torch.Tensor,
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left_context: int = 0,
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) -> torch.Tensor:
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"""Attention score computation.
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Args:
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query: Transformed query tensor. (B, H, T_1, d_k)
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key: Transformed key tensor. (B, H, T_2, d_k)
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pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
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left_context: Number of frames in left context.
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Returns:
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: Attention score. (B, H, T_1, T_2)
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"""
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p = self.linear_pos(pos_enc).view(pos_enc.size(0), -1, self.num_heads, self.d_k)
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query = query.transpose(1, 2)
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q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
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q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
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matrix_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
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matrix_bd = torch.matmul(q_with_bias_v, p.permute(0, 2, 3, 1))
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matrix_bd = self.rel_shift(matrix_bd, left_context=left_context)
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return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
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def forward_qkv(
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self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Transform query, key and value.
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Args:
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query: Query tensor. (B, T_1, size)
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key: Key tensor. (B, T_2, size)
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v: Value tensor. (B, T_2, size)
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Returns:
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q: Transformed query tensor. (B, H, T_1, d_k)
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k: Transformed key tensor. (B, H, T_2, d_k)
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v: Transformed value tensor. (B, H, T_2, d_k)
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"""
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n_batch = query.size(0)
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q = (
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self.linear_q(query)
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.view(n_batch, -1, self.num_heads, self.d_k)
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.transpose(1, 2)
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)
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k = (
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self.linear_k(key)
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.view(n_batch, -1, self.num_heads, self.d_k)
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.transpose(1, 2)
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)
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v = (
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self.linear_v(value)
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.view(n_batch, -1, self.num_heads, self.d_k)
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.transpose(1, 2)
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)
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return q, k, v
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def forward_attention(
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self,
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value: torch.Tensor,
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scores: torch.Tensor,
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mask: torch.Tensor,
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chunk_mask: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Compute attention context vector.
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Args:
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value: Transformed value. (B, H, T_2, d_k)
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scores: Attention score. (B, H, T_1, T_2)
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mask: Source mask. (B, T_2)
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chunk_mask: Chunk mask. (T_1, T_1)
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Returns:
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attn_output: Transformed value weighted by attention score. (B, T_1, H * d_k)
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"""
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batch_size = scores.size(0)
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mask = mask.unsqueeze(1).unsqueeze(2)
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if chunk_mask is not None:
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mask = chunk_mask.unsqueeze(0).unsqueeze(1) | mask
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scores = scores.masked_fill(mask, float("-inf"))
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self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
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attn_output = self.dropout(self.attn)
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attn_output = torch.matmul(attn_output, value)
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attn_output = self.linear_out(
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attn_output.transpose(1, 2)
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.contiguous()
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.view(batch_size, -1, self.num_heads * self.d_k)
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)
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return attn_output
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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pos_enc: torch.Tensor,
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mask: torch.Tensor,
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chunk_mask: Optional[torch.Tensor] = None,
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left_context: int = 0,
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) -> torch.Tensor:
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"""Compute scaled dot product attention with rel. positional encoding.
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Args:
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query: Query tensor. (B, T_1, size)
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key: Key tensor. (B, T_2, size)
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value: Value tensor. (B, T_2, size)
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pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
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mask: Source mask. (B, T_2)
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chunk_mask: Chunk mask. (T_1, T_1)
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left_context: Number of frames in left context.
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Returns:
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: Output tensor. (B, T_1, H * d_k)
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"""
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q, k, v = self.forward_qkv(query, key, value)
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scores = self.compute_att_score(q, k, pos_enc, left_context=left_context)
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return self.forward_attention(v, scores, mask, chunk_mask=chunk_mask)
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