#!/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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from typing import List
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from typing import Tuple
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import logging
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import torch
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import torch.nn as nn
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import numpy as np
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from funasr.models.scama import utils as myutils
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from funasr.models.transformer.decoder import BaseTransformerDecoder
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from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
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from funasr.models.transformer.embedding import PositionalEncoding
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from funasr.models.transformer.layer_norm import LayerNorm
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from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
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from funasr.models.transformer.utils.repeat import repeat
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from funasr.register import tables
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class DecoderLayerSANM(nn.Module):
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"""Single decoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` instance can be used as the argument.
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src_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` instance can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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"""
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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(DecoderLayerSANM, 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 = 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 = nn.Linear(size + size, size)
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self.concat_linear2 = nn.Linear(size + size, size)
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def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
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"""Compute decoded features.
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Args:
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tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
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tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
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memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
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memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
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cache (List[torch.Tensor]): List of cached tensors.
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Each tensor shape should be (#batch, maxlen_out - 1, size).
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Returns:
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torch.Tensor: Output tensor(#batch, maxlen_out, size).
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torch.Tensor: Mask for output tensor (#batch, maxlen_out).
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torch.Tensor: Encoded memory (#batch, maxlen_in, size).
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torch.Tensor: Encoded memory mask (#batch, maxlen_in).
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"""
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# tgt = self.dropout(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.self_attn:
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if self.normalize_before:
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tgt = self.norm2(tgt)
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x, _ = self.self_attn(tgt, tgt_mask)
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x = residual + self.dropout(x)
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if self.src_attn is not None:
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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 = residual + 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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def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
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"""Compute decoded features.
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Args:
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tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
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tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
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memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
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memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
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cache (List[torch.Tensor]): List of cached tensors.
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Each tensor shape should be (#batch, maxlen_out - 1, size).
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Returns:
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torch.Tensor: Output tensor(#batch, maxlen_out, size).
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torch.Tensor: Mask for output tensor (#batch, maxlen_out).
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torch.Tensor: Encoded memory (#batch, maxlen_in, size).
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torch.Tensor: Encoded memory mask (#batch, maxlen_in).
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"""
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# tgt = self.dropout(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.self_attn:
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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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if self.src_attn is not None:
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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 = residual + 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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def forward_chunk(self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0):
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"""Compute decoded features.
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Args:
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tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
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tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
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memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
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memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
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cache (List[torch.Tensor]): List of cached tensors.
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Each tensor shape should be (#batch, maxlen_out - 1, size).
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Returns:
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torch.Tensor: Output tensor(#batch, maxlen_out, size).
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torch.Tensor: Mask for output tensor (#batch, maxlen_out).
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torch.Tensor: Encoded memory (#batch, maxlen_in, size).
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torch.Tensor: Encoded memory mask (#batch, maxlen_in).
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"""
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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.self_attn:
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if self.normalize_before:
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tgt = self.norm2(tgt)
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x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache)
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x = residual + self.dropout(x)
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if self.src_attn is not None:
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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, opt_cache = self.src_attn.forward_chunk(x, memory, opt_cache, chunk_size, look_back)
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x = residual + x
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return x, memory, fsmn_cache, opt_cache
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@tables.register("decoder_classes", "FsmnDecoderSCAMAOpt")
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class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
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"""
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Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie
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SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
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https://arxiv.org/abs/2006.01712
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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 = None,
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concat_embeds: bool = False,
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attention_dim: int = None,
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tf2torch_tensor_name_prefix_torch: str = "decoder",
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tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
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embed_tensor_name_prefix_tf: str = None,
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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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if attention_dim is None:
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attention_dim = encoder_output_size
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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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)
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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,
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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, encoder_output_size=encoder_output_size
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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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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=sanm_shfit
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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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if concat_embeds:
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self.embed_concat_ffn = repeat(
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1,
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lambda lnum: DecoderLayerSANM(
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attention_dim + encoder_output_size,
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None,
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None,
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PositionwiseFeedForwardDecoderSANM(attention_dim + encoder_output_size, linear_units, dropout_rate,
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adim=attention_dim),
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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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else:
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self.embed_concat_ffn = None
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self.concat_embeds = concat_embeds
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self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
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self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
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self.embed_tensor_name_prefix_tf = embed_tensor_name_prefix_tf
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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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chunk_mask: torch.Tensor = None,
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pre_acoustic_embeds: torch.Tensor = None,
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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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if chunk_mask is not None:
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memory_mask = memory_mask * chunk_mask
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if tgt_mask.size(1) != memory_mask.size(1):
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memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)
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x = self.embed(tgt)
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if pre_acoustic_embeds is not None and self.concat_embeds:
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x = torch.cat((x, pre_acoustic_embeds), dim=-1)
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x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)
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x, tgt_mask, memory, memory_mask, _ = self.decoders(
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x, tgt_mask, memory, memory_mask
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)
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if self.decoders2 is not None:
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x, tgt_mask, memory, memory_mask, _ = self.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.decoders3(
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x, tgt_mask, memory, memory_mask
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)
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if self.normalize_before:
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x = self.after_norm(x)
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if self.output_layer is not None:
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x = self.output_layer(x)
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olens = tgt_mask.sum(1)
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return x, olens
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def score(self, ys, state, x, x_mask=None, pre_acoustic_embeds: torch.Tensor = None, ):
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"""Score."""
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ys_mask = myutils.sequence_mask(torch.tensor([len(ys)], dtype=torch.int32), device=x.device)[:, :, None]
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logp, state = self.forward_one_step(
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ys.unsqueeze(0), ys_mask, x.unsqueeze(0), memory_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds,
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cache=state
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)
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return logp.squeeze(0), state
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def forward_one_step(
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self,
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tgt: torch.Tensor,
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tgt_mask: torch.Tensor,
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memory: torch.Tensor,
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memory_mask: torch.Tensor = None,
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pre_acoustic_embeds: torch.Tensor = None,
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cache: List[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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"""Forward one step.
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Args:
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tgt: input token ids, int64 (batch, maxlen_out)
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tgt_mask: input token mask, (batch, maxlen_out)
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dtype=torch.uint8 in PyTorch 1.2-
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dtype=torch.bool in PyTorch 1.2+ (include 1.2)
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memory: encoded memory, float32 (batch, maxlen_in, feat)
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cache: cached output list of (batch, max_time_out-1, size)
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Returns:
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y, cache: NN output value and cache per `self.decoders`.
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y.shape` is (batch, maxlen_out, token)
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"""
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x = tgt[:, -1:]
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tgt_mask = None
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x = self.embed(x)
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if pre_acoustic_embeds is not None and self.concat_embeds:
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x = torch.cat((x, pre_acoustic_embeds), dim=-1)
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x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)
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if cache is None:
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cache_layer_num = len(self.decoders)
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if self.decoders2 is not None:
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cache_layer_num += len(self.decoders2)
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cache = [None] * cache_layer_num
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new_cache = []
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# for c, decoder in zip(cache, self.decoders):
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for i in range(self.att_layer_num):
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decoder = self.decoders[i]
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c = cache[i]
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x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
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x, tgt_mask, memory, memory_mask, cache=c
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)
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new_cache.append(c_ret)
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if self.num_blocks - self.att_layer_num >= 1:
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for i in range(self.num_blocks - self.att_layer_num):
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j = i + self.att_layer_num
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decoder = self.decoders2[i]
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c = cache[j]
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x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
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x, tgt_mask, memory, memory_mask, cache=c
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)
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new_cache.append(c_ret)
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for decoder in self.decoders3:
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x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step(
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x, tgt_mask, memory, None, cache=None
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)
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if self.normalize_before:
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y = self.after_norm(x[:, -1])
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else:
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y = x[:, -1]
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if self.output_layer is not None:
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y = self.output_layer(y)
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y = torch.log_softmax(y, dim=-1)
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return y, new_cache
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