#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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import logging
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import numpy as np
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from typing import Tuple
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from funasr.register import tables
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from funasr.models.scama import utils as myutils
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from funasr.models.transformer.utils.repeat import repeat
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from funasr.models.transformer.layer_norm import LayerNorm
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from funasr.models.transformer.embedding import PositionalEncoding
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from funasr.models.paraformer.decoder import DecoderLayerSANM, ParaformerSANMDecoder
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from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
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from funasr.models.sanm.attention import (
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MultiHeadedAttentionSANMDecoder,
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MultiHeadedAttentionCrossAtt,
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)
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class ContextualDecoderLayer(torch.nn.Module):
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def __init__(
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self,
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size,
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self_attn,
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src_attn,
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feed_forward,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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):
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"""Construct an DecoderLayer object."""
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super(ContextualDecoderLayer, self).__init__()
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self.size = size
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self.self_attn = self_attn
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self.src_attn = src_attn
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self.feed_forward = feed_forward
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self.norm1 = LayerNorm(size)
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if self_attn is not None:
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self.norm2 = LayerNorm(size)
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if src_attn is not None:
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self.norm3 = LayerNorm(size)
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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if self.concat_after:
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self.concat_linear1 = torch.nn.Linear(size + size, size)
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self.concat_linear2 = torch.nn.Linear(size + size, size)
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def forward(
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self,
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tgt,
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tgt_mask,
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memory,
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memory_mask,
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cache=None,
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):
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# tgt = self.dropout(tgt)
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if isinstance(tgt, Tuple):
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tgt, _ = tgt
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residual = tgt
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if self.normalize_before:
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tgt = self.norm1(tgt)
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tgt = self.feed_forward(tgt)
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x = tgt
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if self.normalize_before:
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tgt = self.norm2(tgt)
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if self.training:
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cache = None
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x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
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x = residual + self.dropout(x)
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x_self_attn = x
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residual = x
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if self.normalize_before:
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x = self.norm3(x)
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x = self.src_attn(x, memory, memory_mask)
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x_src_attn = x
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x = residual + self.dropout(x)
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return x, tgt_mask, x_self_attn, x_src_attn
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class ContextualBiasDecoder(torch.nn.Module):
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def __init__(
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self,
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size,
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src_attn,
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dropout_rate,
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normalize_before=True,
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):
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"""Construct an DecoderLayer object."""
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super(ContextualBiasDecoder, self).__init__()
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self.size = size
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self.src_attn = src_attn
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if src_attn is not None:
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self.norm3 = LayerNorm(size)
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.normalize_before = normalize_before
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def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
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x = tgt
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if self.src_attn is not None:
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if self.normalize_before:
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x = self.norm3(x)
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x = self.dropout(self.src_attn(x, memory, memory_mask))
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return x, tgt_mask, memory, memory_mask, cache
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@tables.register("decoder_classes", "ContextualParaformerDecoder")
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class ContextualParaformerDecoder(ParaformerSANMDecoder):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2006.01713
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"""
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def __init__(
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self,
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vocab_size: int,
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encoder_output_size: int,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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self_attention_dropout_rate: float = 0.0,
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src_attention_dropout_rate: float = 0.0,
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input_layer: str = "embed",
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use_output_layer: bool = True,
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pos_enc_class=PositionalEncoding,
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normalize_before: bool = True,
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concat_after: bool = False,
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att_layer_num: int = 6,
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kernel_size: int = 21,
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sanm_shfit: int = 0,
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):
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super().__init__(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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dropout_rate=dropout_rate,
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positional_dropout_rate=positional_dropout_rate,
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input_layer=input_layer,
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use_output_layer=use_output_layer,
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pos_enc_class=pos_enc_class,
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normalize_before=normalize_before,
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)
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attention_dim = encoder_output_size
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if input_layer == "none":
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self.embed = None
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if input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(vocab_size, attention_dim),
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# pos_enc_class(attention_dim, positional_dropout_rate),
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)
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elif input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(vocab_size, attention_dim),
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torch.nn.LayerNorm(attention_dim),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate),
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)
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else:
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raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
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self.normalize_before = normalize_before
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if self.normalize_before:
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self.after_norm = LayerNorm(attention_dim)
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if use_output_layer:
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self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
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else:
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self.output_layer = None
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self.att_layer_num = att_layer_num
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self.num_blocks = num_blocks
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if sanm_shfit is None:
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sanm_shfit = (kernel_size - 1) // 2
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self.decoders = repeat(
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att_layer_num - 1,
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lambda lnum: DecoderLayerSANM(
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attention_dim,
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MultiHeadedAttentionSANMDecoder(
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attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
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),
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MultiHeadedAttentionCrossAtt(
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attention_heads, attention_dim, src_attention_dropout_rate
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),
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.bias_decoder = ContextualBiasDecoder(
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size=attention_dim,
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src_attn=MultiHeadedAttentionCrossAtt(
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attention_heads, attention_dim, src_attention_dropout_rate
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),
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dropout_rate=dropout_rate,
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normalize_before=True,
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)
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self.bias_output = torch.nn.Conv1d(attention_dim * 2, attention_dim, 1, bias=False)
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self.last_decoder = ContextualDecoderLayer(
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attention_dim,
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MultiHeadedAttentionSANMDecoder(
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attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
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),
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MultiHeadedAttentionCrossAtt(
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attention_heads, attention_dim, src_attention_dropout_rate
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),
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
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)
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if num_blocks - att_layer_num <= 0:
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self.decoders2 = None
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else:
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self.decoders2 = repeat(
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num_blocks - att_layer_num,
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lambda lnum: DecoderLayerSANM(
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attention_dim,
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MultiHeadedAttentionSANMDecoder(
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attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
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),
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None,
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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self.decoders3 = repeat(
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1,
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lambda lnum: DecoderLayerSANM(
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attention_dim,
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None,
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None,
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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def forward(
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self,
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hs_pad: torch.Tensor,
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hlens: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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contextual_info: torch.Tensor,
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clas_scale: float = 1.0,
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return_hidden: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward decoder.
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Args:
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hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
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hlens: (batch)
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ys_in_pad:
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input token ids, int64 (batch, maxlen_out)
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if input_layer == "embed"
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input tensor (batch, maxlen_out, #mels) in the other cases
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ys_in_lens: (batch)
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Returns:
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(tuple): tuple containing:
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x: decoded token score before softmax (batch, maxlen_out, token)
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if use_output_layer is True,
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olens: (batch, )
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"""
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tgt = ys_in_pad
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tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
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memory = hs_pad
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memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
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x = tgt
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x, tgt_mask, memory, memory_mask, _ = self.decoders(x, tgt_mask, memory, memory_mask)
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_, _, x_self_attn, x_src_attn = self.last_decoder(x, tgt_mask, memory, memory_mask)
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# contextual paraformer related
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contextual_length = torch.Tensor([contextual_info.shape[1]]).int().repeat(hs_pad.shape[0])
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contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
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cx, tgt_mask, _, _, _ = self.bias_decoder(
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x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask
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)
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if self.bias_output is not None:
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x = torch.cat([x_src_attn, cx * clas_scale], dim=2)
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x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D
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x = x_self_attn + self.dropout(x)
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if self.decoders2 is not None:
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x, tgt_mask, memory, memory_mask, _ = self.decoders2(x, tgt_mask, memory, memory_mask)
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x, tgt_mask, memory, memory_mask, _ = self.decoders3(x, tgt_mask, memory, memory_mask)
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if self.normalize_before:
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x = self.after_norm(x)
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olens = tgt_mask.sum(1)
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if self.output_layer is not None and return_hidden is False:
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x = self.output_layer(x)
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return x, olens
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@tables.register("decoder_classes", "ContextualParaformerDecoderExport")
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class ContextualParaformerDecoderExport(torch.nn.Module):
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def __init__(
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self,
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model,
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max_seq_len=512,
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model_name="decoder",
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onnx: bool = True,
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**kwargs,
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):
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super().__init__()
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from funasr.utils.torch_function import sequence_mask
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self.model = model
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoderExport
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from funasr.models.sanm.attention import MultiHeadedAttentionCrossAttExport
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from funasr.models.paraformer.decoder import DecoderLayerSANMExport
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from funasr.models.transformer.positionwise_feed_forward import (
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PositionwiseFeedForwardDecoderSANMExport,
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)
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for i, d in enumerate(self.model.decoders):
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if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
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d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
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if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
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d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
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if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
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d.src_attn = MultiHeadedAttentionCrossAttExport(d.src_attn)
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self.model.decoders[i] = DecoderLayerSANMExport(d)
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if self.model.decoders2 is not None:
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for i, d in enumerate(self.model.decoders2):
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if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
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d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
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if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
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d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
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self.model.decoders2[i] = DecoderLayerSANMExport(d)
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for i, d in enumerate(self.model.decoders3):
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if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
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d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
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self.model.decoders3[i] = DecoderLayerSANMExport(d)
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self.output_layer = model.output_layer
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self.after_norm = model.after_norm
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self.model_name = model_name
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# bias decoder
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if isinstance(self.model.bias_decoder.src_attn, MultiHeadedAttentionCrossAtt):
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self.model.bias_decoder.src_attn = MultiHeadedAttentionCrossAttExport(
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self.model.bias_decoder.src_attn
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)
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self.bias_decoder = self.model.bias_decoder
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# last decoder
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if isinstance(self.model.last_decoder.src_attn, MultiHeadedAttentionCrossAtt):
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self.model.last_decoder.src_attn = MultiHeadedAttentionCrossAttExport(
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self.model.last_decoder.src_attn
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)
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if isinstance(self.model.last_decoder.self_attn, MultiHeadedAttentionSANMDecoder):
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self.model.last_decoder.self_attn = MultiHeadedAttentionSANMDecoderExport(
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self.model.last_decoder.self_attn
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)
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if isinstance(self.model.last_decoder.feed_forward, PositionwiseFeedForwardDecoderSANM):
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self.model.last_decoder.feed_forward = PositionwiseFeedForwardDecoderSANMExport(
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self.model.last_decoder.feed_forward
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)
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self.last_decoder = self.model.last_decoder
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self.bias_output = self.model.bias_output
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self.dropout = self.model.dropout
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def prepare_mask(self, mask):
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mask_3d_btd = mask[:, :, None]
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if len(mask.shape) == 2:
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mask_4d_bhlt = 1 - mask[:, None, None, :]
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elif len(mask.shape) == 3:
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mask_4d_bhlt = 1 - mask[:, None, :]
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mask_4d_bhlt = mask_4d_bhlt * -10000.0
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return mask_3d_btd, mask_4d_bhlt
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def forward(
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self,
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hs_pad: torch.Tensor,
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hlens: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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bias_embed: torch.Tensor,
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):
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tgt = ys_in_pad
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tgt_mask = self.make_pad_mask(ys_in_lens)
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tgt_mask, _ = self.prepare_mask(tgt_mask)
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# tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
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memory = hs_pad
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memory_mask = self.make_pad_mask(hlens)
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_, memory_mask = self.prepare_mask(memory_mask)
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# memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
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x = tgt
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x, tgt_mask, memory, memory_mask, _ = self.model.decoders(x, tgt_mask, memory, memory_mask)
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_, _, x_self_attn, x_src_attn = self.last_decoder(x, tgt_mask, memory, memory_mask)
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# contextual paraformer related
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contextual_length = torch.Tensor([bias_embed.shape[1]]).int().repeat(hs_pad.shape[0])
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# contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
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contextual_mask = self.make_pad_mask(contextual_length)
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contextual_mask, _ = self.prepare_mask(contextual_mask)
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contextual_mask = contextual_mask.transpose(2, 1).unsqueeze(1)
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cx, tgt_mask, _, _, _ = self.bias_decoder(
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x_self_attn, tgt_mask, bias_embed, memory_mask=contextual_mask
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)
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if self.bias_output is not None:
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x = torch.cat([x_src_attn, cx], dim=2)
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x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D
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x = x_self_attn + self.dropout(x)
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if self.model.decoders2 is not None:
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x, tgt_mask, memory, memory_mask, _ = self.model.decoders2(
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x, tgt_mask, memory, memory_mask
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)
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x, tgt_mask, memory, memory_mask, _ = self.model.decoders3(x, tgt_mask, memory, memory_mask)
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x = self.after_norm(x)
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x = self.output_layer(x)
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return x, ys_in_lens
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