1194 lines
44 KiB
Python
1194 lines
44 KiB
Python
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#!/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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from typing import List, 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.decoder import DecoderLayer
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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.transformer.attention import MultiHeadedAttention
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.models.transformer.decoder import BaseTransformerDecoder
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from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
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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 DecoderLayerSANM(torch.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 = 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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self.reserve_attn = False
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self.attn_mat = []
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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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if self.reserve_attn:
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x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
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self.attn_mat.append(attn_mat)
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else:
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x_src_attn = self.src_attn(x, memory, memory_mask, ret_attn=False)
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x = residual + self.dropout(x_src_attn)
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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 get_attn_mat(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
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residual = tgt
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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 is not None:
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tgt = self.norm2(tgt)
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x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
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x = residual + x
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residual = x
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x = self.norm3(x)
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x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
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return attn_mat
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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(
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self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0
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):
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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", "ParaformerSANMDecoder")
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class ParaformerSANMDecoder(BaseTransformerDecoder):
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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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wo_input_layer: bool = False,
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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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lora_list: List[str] = None,
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lora_rank: int = 8,
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lora_alpha: int = 16,
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lora_dropout: float = 0.1,
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chunk_multiply_factor: tuple = (1,),
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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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):
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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 wo_input_layer:
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self.embed = None
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else:
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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,
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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,
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attention_dim,
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src_attention_dropout_rate,
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lora_list,
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lora_rank,
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lora_alpha,
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lora_dropout,
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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=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),
|
||
|
|
dropout_rate,
|
||
|
|
normalize_before,
|
||
|
|
concat_after,
|
||
|
|
),
|
||
|
|
)
|
||
|
|
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
|
||
|
|
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
||
|
|
self.chunk_multiply_factor = chunk_multiply_factor
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
chunk_mask: torch.Tensor = None,
|
||
|
|
return_hidden: bool = False,
|
||
|
|
return_both: bool = False,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
"""Forward decoder.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
||
|
|
hlens: (batch)
|
||
|
|
ys_in_pad:
|
||
|
|
input token ids, int64 (batch, maxlen_out)
|
||
|
|
if input_layer == "embed"
|
||
|
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
||
|
|
ys_in_lens: (batch)
|
||
|
|
Returns:
|
||
|
|
(tuple): tuple containing:
|
||
|
|
|
||
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
||
|
|
if use_output_layer is True,
|
||
|
|
olens: (batch, )
|
||
|
|
"""
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
if chunk_mask is not None:
|
||
|
|
memory_mask = memory_mask * chunk_mask
|
||
|
|
if tgt_mask.size(1) != memory_mask.size(1):
|
||
|
|
memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)
|
||
|
|
|
||
|
|
x = tgt
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.decoders(x, tgt_mask, memory, memory_mask)
|
||
|
|
if self.decoders2 is not None:
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.decoders2(x, tgt_mask, memory, memory_mask)
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.decoders3(x, tgt_mask, memory, memory_mask)
|
||
|
|
if self.normalize_before:
|
||
|
|
hidden = self.after_norm(x)
|
||
|
|
|
||
|
|
olens = tgt_mask.sum(1)
|
||
|
|
if self.output_layer is not None and return_hidden is False:
|
||
|
|
x = self.output_layer(hidden)
|
||
|
|
return x, olens
|
||
|
|
if return_both:
|
||
|
|
x = self.output_layer(hidden)
|
||
|
|
return x, hidden, olens
|
||
|
|
return hidden, olens
|
||
|
|
|
||
|
|
def score(self, ys, state, x):
|
||
|
|
"""Score."""
|
||
|
|
ys_mask = myutils.sequence_mask(
|
||
|
|
torch.tensor([len(ys)], dtype=torch.int32), device=x.device
|
||
|
|
)[:, :, None]
|
||
|
|
logp, state = self.forward_one_step(ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state)
|
||
|
|
return logp.squeeze(0), state
|
||
|
|
|
||
|
|
def forward_asf2(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
|
||
|
|
attn_mat = self.model.decoders[1].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
|
||
|
|
return attn_mat
|
||
|
|
|
||
|
|
def forward_asf6(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[1](tgt, tgt_mask, memory, memory_mask)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[2](tgt, tgt_mask, memory, memory_mask)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[3](tgt, tgt_mask, memory, memory_mask)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[4](tgt, tgt_mask, memory, memory_mask)
|
||
|
|
attn_mat = self.decoders[5].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
|
||
|
|
return attn_mat
|
||
|
|
|
||
|
|
def forward_chunk(
|
||
|
|
self,
|
||
|
|
memory: torch.Tensor,
|
||
|
|
tgt: torch.Tensor,
|
||
|
|
cache: dict = None,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
"""Forward decoder.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
||
|
|
hlens: (batch)
|
||
|
|
ys_in_pad:
|
||
|
|
input token ids, int64 (batch, maxlen_out)
|
||
|
|
if input_layer == "embed"
|
||
|
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
||
|
|
ys_in_lens: (batch)
|
||
|
|
Returns:
|
||
|
|
(tuple): tuple containing:
|
||
|
|
|
||
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
||
|
|
if use_output_layer is True,
|
||
|
|
olens: (batch, )
|
||
|
|
"""
|
||
|
|
x = tgt
|
||
|
|
if cache["decode_fsmn"] is None:
|
||
|
|
cache_layer_num = len(self.decoders)
|
||
|
|
if self.decoders2 is not None:
|
||
|
|
cache_layer_num += len(self.decoders2)
|
||
|
|
fsmn_cache = [None] * cache_layer_num
|
||
|
|
else:
|
||
|
|
fsmn_cache = cache["decode_fsmn"]
|
||
|
|
|
||
|
|
if cache["opt"] is None:
|
||
|
|
cache_layer_num = len(self.decoders)
|
||
|
|
opt_cache = [None] * cache_layer_num
|
||
|
|
else:
|
||
|
|
opt_cache = cache["opt"]
|
||
|
|
|
||
|
|
for i in range(self.att_layer_num):
|
||
|
|
decoder = self.decoders[i]
|
||
|
|
x, memory, fsmn_cache[i], opt_cache[i] = decoder.forward_chunk(
|
||
|
|
x,
|
||
|
|
memory,
|
||
|
|
fsmn_cache=fsmn_cache[i],
|
||
|
|
opt_cache=opt_cache[i],
|
||
|
|
chunk_size=cache["chunk_size"],
|
||
|
|
look_back=cache["decoder_chunk_look_back"],
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.num_blocks - self.att_layer_num > 1:
|
||
|
|
for i in range(self.num_blocks - self.att_layer_num):
|
||
|
|
j = i + self.att_layer_num
|
||
|
|
decoder = self.decoders2[i]
|
||
|
|
x, memory, fsmn_cache[j], _ = decoder.forward_chunk(
|
||
|
|
x, memory, fsmn_cache=fsmn_cache[j]
|
||
|
|
)
|
||
|
|
|
||
|
|
for decoder in self.decoders3:
|
||
|
|
x, memory, _, _ = decoder.forward_chunk(x, memory)
|
||
|
|
if self.normalize_before:
|
||
|
|
x = self.after_norm(x)
|
||
|
|
if self.output_layer is not None:
|
||
|
|
x = self.output_layer(x)
|
||
|
|
|
||
|
|
cache["decode_fsmn"] = fsmn_cache
|
||
|
|
if cache["decoder_chunk_look_back"] > 0 or cache["decoder_chunk_look_back"] == -1:
|
||
|
|
cache["opt"] = opt_cache
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward_one_step(
|
||
|
|
self,
|
||
|
|
tgt: torch.Tensor,
|
||
|
|
tgt_mask: torch.Tensor,
|
||
|
|
memory: torch.Tensor,
|
||
|
|
cache: List[torch.Tensor] = None,
|
||
|
|
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
|
||
|
|
"""Forward one step.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tgt: input token ids, int64 (batch, maxlen_out)
|
||
|
|
tgt_mask: input token mask, (batch, maxlen_out)
|
||
|
|
dtype=torch.uint8 in PyTorch 1.2-
|
||
|
|
dtype=torch.bool in PyTorch 1.2+ (include 1.2)
|
||
|
|
memory: encoded memory, float32 (batch, maxlen_in, feat)
|
||
|
|
cache: cached output list of (batch, max_time_out-1, size)
|
||
|
|
Returns:
|
||
|
|
y, cache: NN output value and cache per `self.decoders`.
|
||
|
|
y.shape` is (batch, maxlen_out, token)
|
||
|
|
"""
|
||
|
|
x = self.embed(tgt)
|
||
|
|
if cache is None:
|
||
|
|
cache_layer_num = len(self.decoders)
|
||
|
|
if self.decoders2 is not None:
|
||
|
|
cache_layer_num += len(self.decoders2)
|
||
|
|
cache = [None] * cache_layer_num
|
||
|
|
new_cache = []
|
||
|
|
# for c, decoder in zip(cache, self.decoders):
|
||
|
|
for i in range(self.att_layer_num):
|
||
|
|
decoder = self.decoders[i]
|
||
|
|
c = cache[i]
|
||
|
|
x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
|
||
|
|
x, tgt_mask, memory, None, cache=c
|
||
|
|
)
|
||
|
|
new_cache.append(c_ret)
|
||
|
|
|
||
|
|
if self.num_blocks - self.att_layer_num > 1:
|
||
|
|
for i in range(self.num_blocks - self.att_layer_num):
|
||
|
|
j = i + self.att_layer_num
|
||
|
|
decoder = self.decoders2[i]
|
||
|
|
c = cache[j]
|
||
|
|
x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
|
||
|
|
x, tgt_mask, memory, None, cache=c
|
||
|
|
)
|
||
|
|
new_cache.append(c_ret)
|
||
|
|
|
||
|
|
for decoder in self.decoders3:
|
||
|
|
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step(
|
||
|
|
x, tgt_mask, memory, None, cache=None
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.normalize_before:
|
||
|
|
y = self.after_norm(x[:, -1])
|
||
|
|
else:
|
||
|
|
y = x[:, -1]
|
||
|
|
if self.output_layer is not None:
|
||
|
|
y = torch.log_softmax(self.output_layer(y), dim=-1)
|
||
|
|
|
||
|
|
return y, new_cache
|
||
|
|
|
||
|
|
|
||
|
|
class DecoderLayerSANMExport(torch.nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, model):
|
||
|
|
super().__init__()
|
||
|
|
self.self_attn = model.self_attn
|
||
|
|
self.src_attn = model.src_attn
|
||
|
|
self.feed_forward = model.feed_forward
|
||
|
|
self.norm1 = model.norm1
|
||
|
|
self.norm2 = model.norm2 if hasattr(model, "norm2") else None
|
||
|
|
self.norm3 = model.norm3 if hasattr(model, "norm3") else None
|
||
|
|
self.size = model.size
|
||
|
|
|
||
|
|
def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
|
||
|
|
|
||
|
|
residual = tgt
|
||
|
|
tgt = self.norm1(tgt)
|
||
|
|
tgt = self.feed_forward(tgt)
|
||
|
|
|
||
|
|
x = tgt
|
||
|
|
if self.self_attn is not None:
|
||
|
|
tgt = self.norm2(tgt)
|
||
|
|
x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
|
||
|
|
x = residual + x
|
||
|
|
|
||
|
|
if self.src_attn is not None:
|
||
|
|
residual = x
|
||
|
|
x = self.norm3(x)
|
||
|
|
x = residual + self.src_attn(x, memory, memory_mask)
|
||
|
|
|
||
|
|
return x, tgt_mask, memory, memory_mask, cache
|
||
|
|
|
||
|
|
def get_attn_mat(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
|
||
|
|
residual = tgt
|
||
|
|
tgt = self.norm1(tgt)
|
||
|
|
tgt = self.feed_forward(tgt)
|
||
|
|
|
||
|
|
x = tgt
|
||
|
|
if self.self_attn is not None:
|
||
|
|
tgt = self.norm2(tgt)
|
||
|
|
x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
|
||
|
|
x = residual + x
|
||
|
|
|
||
|
|
residual = x
|
||
|
|
x = self.norm3(x)
|
||
|
|
x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
|
||
|
|
return attn_mat
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("decoder_classes", "ParaformerSANMDecoderExport")
|
||
|
|
class ParaformerSANMDecoderExport(torch.nn.Module):
|
||
|
|
def __init__(self, model, max_seq_len=512, model_name="decoder", onnx: bool = True, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
# self.embed = model.embed #Embedding(model.embed, max_seq_len)
|
||
|
|
|
||
|
|
from funasr.utils.torch_function import sequence_mask
|
||
|
|
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
|
||
|
|
|
||
|
|
from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoderExport
|
||
|
|
from funasr.models.sanm.attention import MultiHeadedAttentionCrossAttExport
|
||
|
|
|
||
|
|
for i, d in enumerate(self.model.decoders):
|
||
|
|
if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
|
||
|
|
d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
|
||
|
|
if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
|
||
|
|
d.src_attn = MultiHeadedAttentionCrossAttExport(d.src_attn)
|
||
|
|
self.model.decoders[i] = DecoderLayerSANMExport(d)
|
||
|
|
|
||
|
|
if self.model.decoders2 is not None:
|
||
|
|
for i, d in enumerate(self.model.decoders2):
|
||
|
|
if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
|
||
|
|
d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
|
||
|
|
self.model.decoders2[i] = DecoderLayerSANMExport(d)
|
||
|
|
|
||
|
|
for i, d in enumerate(self.model.decoders3):
|
||
|
|
self.model.decoders3[i] = DecoderLayerSANMExport(d)
|
||
|
|
|
||
|
|
self.output_layer = model.output_layer
|
||
|
|
self.after_norm = model.after_norm
|
||
|
|
self.model_name = model_name
|
||
|
|
|
||
|
|
def prepare_mask(self, mask):
|
||
|
|
mask_3d_btd = mask[:, :, None]
|
||
|
|
if len(mask.shape) == 2:
|
||
|
|
mask_4d_bhlt = 1 - mask[:, None, None, :]
|
||
|
|
elif len(mask.shape) == 3:
|
||
|
|
mask_4d_bhlt = 1 - mask[:, None, :]
|
||
|
|
mask_4d_bhlt = mask_4d_bhlt * -10000.0
|
||
|
|
|
||
|
|
return mask_3d_btd, mask_4d_bhlt
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
return_hidden: bool = False,
|
||
|
|
return_both: bool = False,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = self.make_pad_mask(ys_in_lens)
|
||
|
|
tgt_mask, _ = self.prepare_mask(tgt_mask)
|
||
|
|
# tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = self.make_pad_mask(hlens)
|
||
|
|
_, memory_mask = self.prepare_mask(memory_mask)
|
||
|
|
# memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
|
||
|
|
x = tgt
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders(x, tgt_mask, memory, memory_mask)
|
||
|
|
if self.model.decoders2 is not None:
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders2(
|
||
|
|
x, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders3(x, tgt_mask, memory, memory_mask)
|
||
|
|
hidden = self.after_norm(x)
|
||
|
|
# x = self.output_layer(x)
|
||
|
|
|
||
|
|
if self.output_layer is not None and return_hidden is False:
|
||
|
|
x = self.output_layer(hidden)
|
||
|
|
return x, ys_in_lens
|
||
|
|
if return_both:
|
||
|
|
x = self.output_layer(hidden)
|
||
|
|
return x, hidden, ys_in_lens
|
||
|
|
return hidden, ys_in_lens
|
||
|
|
|
||
|
|
def forward_asf2(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
_, memory_mask = self.prepare_mask(memory_mask)
|
||
|
|
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.model.decoders[0](
|
||
|
|
tgt, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
attn_mat = self.model.decoders[1].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
|
||
|
|
return attn_mat
|
||
|
|
|
||
|
|
def forward_asf6(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
_, memory_mask = self.prepare_mask(memory_mask)
|
||
|
|
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.model.decoders[0](
|
||
|
|
tgt, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.model.decoders[1](
|
||
|
|
tgt, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.model.decoders[2](
|
||
|
|
tgt, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.model.decoders[3](
|
||
|
|
tgt, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.model.decoders[4](
|
||
|
|
tgt, tgt_mask, memory, memory_mask
|
||
|
|
)
|
||
|
|
attn_mat = self.model.decoders[5].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
|
||
|
|
return attn_mat
|
||
|
|
|
||
|
|
"""
|
||
|
|
def get_dummy_inputs(self, enc_size):
|
||
|
|
tgt = torch.LongTensor([0]).unsqueeze(0)
|
||
|
|
memory = torch.randn(1, 100, enc_size)
|
||
|
|
pre_acoustic_embeds = torch.randn(1, 1, enc_size)
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
cache = [
|
||
|
|
torch.zeros((1, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size))
|
||
|
|
for _ in range(cache_num)
|
||
|
|
]
|
||
|
|
return (tgt, memory, pre_acoustic_embeds, cache)
|
||
|
|
|
||
|
|
def is_optimizable(self):
|
||
|
|
return True
|
||
|
|
|
||
|
|
def get_input_names(self):
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
return ['tgt', 'memory', 'pre_acoustic_embeds'] \
|
||
|
|
+ ['cache_%d' % i for i in range(cache_num)]
|
||
|
|
|
||
|
|
def get_output_names(self):
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
return ['y'] \
|
||
|
|
+ ['out_cache_%d' % i for i in range(cache_num)]
|
||
|
|
|
||
|
|
def get_dynamic_axes(self):
|
||
|
|
ret = {
|
||
|
|
'tgt': {
|
||
|
|
0: 'tgt_batch',
|
||
|
|
1: 'tgt_length'
|
||
|
|
},
|
||
|
|
'memory': {
|
||
|
|
0: 'memory_batch',
|
||
|
|
1: 'memory_length'
|
||
|
|
},
|
||
|
|
'pre_acoustic_embeds': {
|
||
|
|
0: 'acoustic_embeds_batch',
|
||
|
|
1: 'acoustic_embeds_length',
|
||
|
|
}
|
||
|
|
}
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
ret.update({
|
||
|
|
'cache_%d' % d: {
|
||
|
|
0: 'cache_%d_batch' % d,
|
||
|
|
2: 'cache_%d_length' % d
|
||
|
|
}
|
||
|
|
for d in range(cache_num)
|
||
|
|
})
|
||
|
|
return ret
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("decoder_classes", "ParaformerSANMDecoderOnlineExport")
|
||
|
|
class ParaformerSANMDecoderOnlineExport(torch.nn.Module):
|
||
|
|
def __init__(self, model, max_seq_len=512, model_name="decoder", onnx: bool = True, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
# self.embed = model.embed #Embedding(model.embed, max_seq_len)
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
from funasr.utils.torch_function import sequence_mask
|
||
|
|
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
|
||
|
|
|
||
|
|
from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoderExport
|
||
|
|
from funasr.models.sanm.attention import MultiHeadedAttentionCrossAttExport
|
||
|
|
|
||
|
|
for i, d in enumerate(self.model.decoders):
|
||
|
|
if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
|
||
|
|
d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
|
||
|
|
if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
|
||
|
|
d.src_attn = MultiHeadedAttentionCrossAttExport(d.src_attn)
|
||
|
|
self.model.decoders[i] = DecoderLayerSANMExport(d)
|
||
|
|
|
||
|
|
if self.model.decoders2 is not None:
|
||
|
|
for i, d in enumerate(self.model.decoders2):
|
||
|
|
if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
|
||
|
|
d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
|
||
|
|
self.model.decoders2[i] = DecoderLayerSANMExport(d)
|
||
|
|
|
||
|
|
for i, d in enumerate(self.model.decoders3):
|
||
|
|
self.model.decoders3[i] = DecoderLayerSANMExport(d)
|
||
|
|
|
||
|
|
self.output_layer = model.output_layer
|
||
|
|
self.after_norm = model.after_norm
|
||
|
|
self.model_name = model_name
|
||
|
|
|
||
|
|
def prepare_mask(self, mask):
|
||
|
|
mask_3d_btd = mask[:, :, None]
|
||
|
|
if len(mask.shape) == 2:
|
||
|
|
mask_4d_bhlt = 1 - mask[:, None, None, :]
|
||
|
|
elif len(mask.shape) == 3:
|
||
|
|
mask_4d_bhlt = 1 - mask[:, None, :]
|
||
|
|
mask_4d_bhlt = mask_4d_bhlt * -10000.0
|
||
|
|
|
||
|
|
return mask_3d_btd, mask_4d_bhlt
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
*args,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = self.make_pad_mask(ys_in_lens)
|
||
|
|
tgt_mask, _ = self.prepare_mask(tgt_mask)
|
||
|
|
# tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = self.make_pad_mask(hlens)
|
||
|
|
_, memory_mask = self.prepare_mask(memory_mask)
|
||
|
|
# memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
|
||
|
|
x = tgt
|
||
|
|
out_caches = list()
|
||
|
|
for i, decoder in enumerate(self.model.decoders):
|
||
|
|
in_cache = args[i]
|
||
|
|
x, tgt_mask, memory, memory_mask, out_cache = decoder(
|
||
|
|
x, tgt_mask, memory, memory_mask, cache=in_cache
|
||
|
|
)
|
||
|
|
out_caches.append(out_cache)
|
||
|
|
if self.model.decoders2 is not None:
|
||
|
|
for i, decoder in enumerate(self.model.decoders2):
|
||
|
|
in_cache = args[i + len(self.model.decoders)]
|
||
|
|
x, tgt_mask, memory, memory_mask, out_cache = decoder(
|
||
|
|
x, tgt_mask, memory, memory_mask, cache=in_cache
|
||
|
|
)
|
||
|
|
out_caches.append(out_cache)
|
||
|
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders3(x, tgt_mask, memory, memory_mask)
|
||
|
|
x = self.after_norm(x)
|
||
|
|
x = self.output_layer(x)
|
||
|
|
|
||
|
|
return x, out_caches
|
||
|
|
|
||
|
|
def get_dummy_inputs(self, enc_size):
|
||
|
|
enc = torch.randn(2, 100, enc_size).type(torch.float32)
|
||
|
|
enc_len = torch.tensor([30, 100], dtype=torch.int32)
|
||
|
|
acoustic_embeds = torch.randn(2, 10, enc_size).type(torch.float32)
|
||
|
|
acoustic_embeds_len = torch.tensor([5, 10], dtype=torch.int32)
|
||
|
|
cache_num = len(self.model.decoders)
|
||
|
|
if hasattr(self.model, "decoders2") and self.model.decoders2 is not None:
|
||
|
|
cache_num += len(self.model.decoders2)
|
||
|
|
cache = [
|
||
|
|
torch.zeros(
|
||
|
|
(2, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size - 1),
|
||
|
|
dtype=torch.float32,
|
||
|
|
)
|
||
|
|
for _ in range(cache_num)
|
||
|
|
]
|
||
|
|
return (enc, enc_len, acoustic_embeds, acoustic_embeds_len, *cache)
|
||
|
|
|
||
|
|
def get_input_names(self):
|
||
|
|
cache_num = len(self.model.decoders)
|
||
|
|
if hasattr(self.model, "decoders2") and self.model.decoders2 is not None:
|
||
|
|
cache_num += len(self.model.decoders2)
|
||
|
|
return ["enc", "enc_len", "acoustic_embeds", "acoustic_embeds_len"] + [
|
||
|
|
"in_cache_%d" % i for i in range(cache_num)
|
||
|
|
]
|
||
|
|
|
||
|
|
def get_output_names(self):
|
||
|
|
cache_num = len(self.model.decoders)
|
||
|
|
if hasattr(self.model, "decoders2") and self.model.decoders2 is not None:
|
||
|
|
cache_num += len(self.model.decoders2)
|
||
|
|
return ["logits", "sample_ids"] + ["out_cache_%d" % i for i in range(cache_num)]
|
||
|
|
|
||
|
|
def get_dynamic_axes(self):
|
||
|
|
ret = {
|
||
|
|
"enc": {0: "batch_size", 1: "enc_length"},
|
||
|
|
"acoustic_embeds": {0: "batch_size", 1: "token_length"},
|
||
|
|
"enc_len": {
|
||
|
|
0: "batch_size",
|
||
|
|
},
|
||
|
|
"acoustic_embeds_len": {
|
||
|
|
0: "batch_size",
|
||
|
|
},
|
||
|
|
}
|
||
|
|
cache_num = len(self.model.decoders)
|
||
|
|
if hasattr(self.model, "decoders2") and self.model.decoders2 is not None:
|
||
|
|
cache_num += len(self.model.decoders2)
|
||
|
|
ret.update(
|
||
|
|
{
|
||
|
|
"in_cache_%d"
|
||
|
|
% d: {
|
||
|
|
0: "batch_size",
|
||
|
|
}
|
||
|
|
for d in range(cache_num)
|
||
|
|
}
|
||
|
|
)
|
||
|
|
ret.update(
|
||
|
|
{
|
||
|
|
"out_cache_%d"
|
||
|
|
% d: {
|
||
|
|
0: "batch_size",
|
||
|
|
}
|
||
|
|
for d in range(cache_num)
|
||
|
|
}
|
||
|
|
)
|
||
|
|
return ret
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("decoder_classes", "ParaformerSANDecoder")
|
||
|
|
class ParaformerSANDecoder(BaseTransformerDecoder):
|
||
|
|
"""
|
||
|
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||
|
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
||
|
|
https://arxiv.org/abs/2006.01713
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_size: int,
|
||
|
|
encoder_output_size: int,
|
||
|
|
attention_heads: int = 4,
|
||
|
|
linear_units: int = 2048,
|
||
|
|
num_blocks: int = 6,
|
||
|
|
dropout_rate: float = 0.1,
|
||
|
|
positional_dropout_rate: float = 0.1,
|
||
|
|
self_attention_dropout_rate: float = 0.0,
|
||
|
|
src_attention_dropout_rate: float = 0.0,
|
||
|
|
input_layer: str = "embed",
|
||
|
|
use_output_layer: bool = True,
|
||
|
|
pos_enc_class=PositionalEncoding,
|
||
|
|
normalize_before: bool = True,
|
||
|
|
concat_after: bool = False,
|
||
|
|
embeds_id: int = -1,
|
||
|
|
):
|
||
|
|
super().__init__(
|
||
|
|
vocab_size=vocab_size,
|
||
|
|
encoder_output_size=encoder_output_size,
|
||
|
|
dropout_rate=dropout_rate,
|
||
|
|
positional_dropout_rate=positional_dropout_rate,
|
||
|
|
input_layer=input_layer,
|
||
|
|
use_output_layer=use_output_layer,
|
||
|
|
pos_enc_class=pos_enc_class,
|
||
|
|
normalize_before=normalize_before,
|
||
|
|
)
|
||
|
|
|
||
|
|
attention_dim = encoder_output_size
|
||
|
|
self.decoders = repeat(
|
||
|
|
num_blocks,
|
||
|
|
lambda lnum: DecoderLayer(
|
||
|
|
attention_dim,
|
||
|
|
MultiHeadedAttention(attention_heads, attention_dim, self_attention_dropout_rate),
|
||
|
|
MultiHeadedAttention(attention_heads, attention_dim, src_attention_dropout_rate),
|
||
|
|
PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
|
||
|
|
dropout_rate,
|
||
|
|
normalize_before,
|
||
|
|
concat_after,
|
||
|
|
),
|
||
|
|
)
|
||
|
|
self.embeds_id = embeds_id
|
||
|
|
self.attention_dim = attention_dim
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
"""Forward decoder.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
||
|
|
hlens: (batch)
|
||
|
|
ys_in_pad:
|
||
|
|
input token ids, int64 (batch, maxlen_out)
|
||
|
|
if input_layer == "embed"
|
||
|
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
||
|
|
ys_in_lens: (batch)
|
||
|
|
Returns:
|
||
|
|
(tuple): tuple containing:
|
||
|
|
|
||
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
||
|
|
if use_output_layer is True,
|
||
|
|
olens: (batch, )
|
||
|
|
"""
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(memory.device)
|
||
|
|
# Padding for Longformer
|
||
|
|
if memory_mask.shape[-1] != memory.shape[1]:
|
||
|
|
padlen = memory.shape[1] - memory_mask.shape[-1]
|
||
|
|
memory_mask = torch.nn.functional.pad(memory_mask, (0, padlen), "constant", False)
|
||
|
|
|
||
|
|
# x = self.embed(tgt)
|
||
|
|
x = tgt
|
||
|
|
embeds_outputs = None
|
||
|
|
for layer_id, decoder in enumerate(self.decoders):
|
||
|
|
x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, memory_mask)
|
||
|
|
if layer_id == self.embeds_id:
|
||
|
|
embeds_outputs = x
|
||
|
|
if self.normalize_before:
|
||
|
|
x = self.after_norm(x)
|
||
|
|
if self.output_layer is not None:
|
||
|
|
x = self.output_layer(x)
|
||
|
|
|
||
|
|
olens = tgt_mask.sum(1)
|
||
|
|
if embeds_outputs is not None:
|
||
|
|
return x, olens, embeds_outputs
|
||
|
|
else:
|
||
|
|
return x, olens
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("decoder_classes", "ParaformerDecoderSANExport")
|
||
|
|
class ParaformerDecoderSANExport(torch.nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
model,
|
||
|
|
max_seq_len=512,
|
||
|
|
model_name="decoder",
|
||
|
|
onnx: bool = True,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
# self.embed = model.embed #Embedding(model.embed, max_seq_len)
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
from funasr.utils.torch_function import sequence_mask
|
||
|
|
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
|
||
|
|
|
||
|
|
from funasr.models.transformer.decoder import DecoderLayerExport
|
||
|
|
from funasr.models.transformer.attention import MultiHeadedAttentionExport
|
||
|
|
|
||
|
|
for i, d in enumerate(self.model.decoders):
|
||
|
|
if isinstance(d.src_attn, MultiHeadedAttention):
|
||
|
|
d.src_attn = MultiHeadedAttentionExport(d.src_attn)
|
||
|
|
self.model.decoders[i] = DecoderLayerExport(d)
|
||
|
|
|
||
|
|
self.output_layer = model.output_layer
|
||
|
|
self.after_norm = model.after_norm
|
||
|
|
self.model_name = model_name
|
||
|
|
|
||
|
|
def prepare_mask(self, mask):
|
||
|
|
mask_3d_btd = mask[:, :, None]
|
||
|
|
if len(mask.shape) == 2:
|
||
|
|
mask_4d_bhlt = 1 - mask[:, None, None, :]
|
||
|
|
elif len(mask.shape) == 3:
|
||
|
|
mask_4d_bhlt = 1 - mask[:, None, :]
|
||
|
|
mask_4d_bhlt = mask_4d_bhlt * -10000.0
|
||
|
|
|
||
|
|
return mask_3d_btd, mask_4d_bhlt
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
):
|
||
|
|
|
||
|
|
tgt = ys_in_pad
|
||
|
|
tgt_mask = self.make_pad_mask(ys_in_lens)
|
||
|
|
tgt_mask, _ = self.prepare_mask(tgt_mask)
|
||
|
|
# tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = self.make_pad_mask(hlens)
|
||
|
|
_, memory_mask = self.prepare_mask(memory_mask)
|
||
|
|
# memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
||
|
|
|
||
|
|
x = tgt
|
||
|
|
x, tgt_mask, memory, memory_mask = self.model.decoders(x, tgt_mask, memory, memory_mask)
|
||
|
|
x = self.after_norm(x)
|
||
|
|
x = self.output_layer(x)
|
||
|
|
|
||
|
|
return x, ys_in_lens
|
||
|
|
|
||
|
|
def get_dummy_inputs(self, enc_size):
|
||
|
|
tgt = torch.LongTensor([0]).unsqueeze(0)
|
||
|
|
memory = torch.randn(1, 100, enc_size)
|
||
|
|
pre_acoustic_embeds = torch.randn(1, 1, enc_size)
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
cache = [
|
||
|
|
torch.zeros(
|
||
|
|
(1, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size)
|
||
|
|
)
|
||
|
|
for _ in range(cache_num)
|
||
|
|
]
|
||
|
|
return (tgt, memory, pre_acoustic_embeds, cache)
|
||
|
|
|
||
|
|
def is_optimizable(self):
|
||
|
|
return True
|
||
|
|
|
||
|
|
def get_input_names(self):
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
return ["tgt", "memory", "pre_acoustic_embeds"] + ["cache_%d" % i for i in range(cache_num)]
|
||
|
|
|
||
|
|
def get_output_names(self):
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
return ["y"] + ["out_cache_%d" % i for i in range(cache_num)]
|
||
|
|
|
||
|
|
def get_dynamic_axes(self):
|
||
|
|
ret = {
|
||
|
|
"tgt": {0: "tgt_batch", 1: "tgt_length"},
|
||
|
|
"memory": {0: "memory_batch", 1: "memory_length"},
|
||
|
|
"pre_acoustic_embeds": {
|
||
|
|
0: "acoustic_embeds_batch",
|
||
|
|
1: "acoustic_embeds_length",
|
||
|
|
},
|
||
|
|
}
|
||
|
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
||
|
|
ret.update(
|
||
|
|
{
|
||
|
|
"cache_%d" % d: {0: "cache_%d_batch" % d, 2: "cache_%d_length" % d}
|
||
|
|
for d in range(cache_num)
|
||
|
|
}
|
||
|
|
)
|
||
|
|
return ret
|