461 lines
17 KiB
Python
461 lines
17 KiB
Python
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# Copyright 2022 Kwangyoun Kim (ASAPP inc.)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""E-Branchformer encoder definition.
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Reference:
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Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan,
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Prashant Sridhar, Kyu J. Han, Shinji Watanabe,
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"E-Branchformer: Branchformer with Enhanced merging
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for speech recognition," in SLT 2022.
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"""
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import logging
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from typing import List, Optional, Tuple
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import torch
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import torch.nn as nn
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from funasr.models.ctc.ctc import CTC
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from funasr.models.branchformer.cgmlp import ConvolutionalGatingMLP
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from funasr.models.branchformer.fastformer import FastSelfAttention
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from funasr.models.transformer.utils.nets_utils import get_activation, make_pad_mask
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from funasr.models.transformer.attention import ( # noqa: H301
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LegacyRelPositionMultiHeadedAttention,
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MultiHeadedAttention,
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RelPositionMultiHeadedAttention,
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)
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from funasr.models.transformer.embedding import ( # noqa: H301
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LegacyRelPositionalEncoding,
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PositionalEncoding,
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RelPositionalEncoding,
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ScaledPositionalEncoding,
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)
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from funasr.models.transformer.layer_norm import LayerNorm
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from funasr.models.transformer.positionwise_feed_forward import (
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PositionwiseFeedForward,
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)
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from funasr.models.transformer.utils.repeat import repeat
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from funasr.models.transformer.utils.subsampling import (
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Conv2dSubsampling,
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Conv2dSubsampling2,
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Conv2dSubsampling6,
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Conv2dSubsampling8,
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TooShortUttError,
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check_short_utt,
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)
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from funasr.register import tables
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class EBranchformerEncoderLayer(torch.nn.Module):
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"""E-Branchformer encoder layer module.
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Args:
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size (int): model dimension
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attn: standard self-attention or efficient attention
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cgmlp: ConvolutionalGatingMLP
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feed_forward: feed-forward module, optional
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feed_forward: macaron-style feed-forward module, optional
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dropout_rate (float): dropout probability
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merge_conv_kernel (int): kernel size of the depth-wise conv in merge module
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"""
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def __init__(
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self,
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size: int,
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attn: torch.nn.Module,
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cgmlp: torch.nn.Module,
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feed_forward: Optional[torch.nn.Module],
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feed_forward_macaron: Optional[torch.nn.Module],
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dropout_rate: float,
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merge_conv_kernel: int = 3,
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):
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super().__init__()
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self.size = size
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self.attn = attn
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self.cgmlp = cgmlp
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self.feed_forward = feed_forward
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self.feed_forward_macaron = feed_forward_macaron
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self.ff_scale = 1.0
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if self.feed_forward is not None:
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self.norm_ff = LayerNorm(size)
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if self.feed_forward_macaron is not None:
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self.ff_scale = 0.5
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self.norm_ff_macaron = LayerNorm(size)
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self.norm_mha = LayerNorm(size) # for the MHA module
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self.norm_mlp = LayerNorm(size) # for the MLP module
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self.norm_final = LayerNorm(size) # for the final output of the block
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.depthwise_conv_fusion = torch.nn.Conv1d(
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size + size,
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size + size,
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kernel_size=merge_conv_kernel,
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stride=1,
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padding=(merge_conv_kernel - 1) // 2,
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groups=size + size,
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bias=True,
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)
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self.merge_proj = torch.nn.Linear(size + size, size)
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def forward(self, x_input, mask, cache=None):
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"""Compute encoded features.
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Args:
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x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
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- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
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- w/o pos emb: Tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, 1, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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if cache is not None:
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raise NotImplementedError("cache is not None, which is not tested")
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if isinstance(x_input, tuple):
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x, pos_emb = x_input[0], x_input[1]
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else:
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x, pos_emb = x_input, None
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if self.feed_forward_macaron is not None:
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residual = x
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x = self.norm_ff_macaron(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
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# Two branches
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x1 = x
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x2 = x
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# Branch 1: multi-headed attention module
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x1 = self.norm_mha(x1)
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if isinstance(self.attn, FastSelfAttention):
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x_att = self.attn(x1, mask)
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else:
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if pos_emb is not None:
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x_att = self.attn(x1, x1, x1, pos_emb, mask)
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else:
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x_att = self.attn(x1, x1, x1, mask)
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x1 = self.dropout(x_att)
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# Branch 2: convolutional gating mlp
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x2 = self.norm_mlp(x2)
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if pos_emb is not None:
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x2 = (x2, pos_emb)
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x2 = self.cgmlp(x2, mask)
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if isinstance(x2, tuple):
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x2 = x2[0]
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x2 = self.dropout(x2)
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# Merge two branches
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x_concat = torch.cat([x1, x2], dim=-1)
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x_tmp = x_concat.transpose(1, 2)
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x_tmp = self.depthwise_conv_fusion(x_tmp)
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x_tmp = x_tmp.transpose(1, 2)
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x = x + self.dropout(self.merge_proj(x_concat + x_tmp))
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if self.feed_forward is not None:
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# feed forward module
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residual = x
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x = self.norm_ff(x)
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x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
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x = self.norm_final(x)
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if pos_emb is not None:
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return (x, pos_emb), mask
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return x, mask
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@tables.register("encoder_classes", "EBranchformerEncoder")
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class EBranchformerEncoder(nn.Module):
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"""E-Branchformer encoder module."""
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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attention_layer_type: str = "rel_selfattn",
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pos_enc_layer_type: str = "rel_pos",
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rel_pos_type: str = "latest",
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cgmlp_linear_units: int = 2048,
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cgmlp_conv_kernel: int = 31,
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use_linear_after_conv: bool = False,
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gate_activation: str = "identity",
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num_blocks: int = 12,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: Optional[str] = "conv2d",
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zero_triu: bool = False,
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padding_idx: int = -1,
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layer_drop_rate: float = 0.0,
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max_pos_emb_len: int = 5000,
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use_ffn: bool = False,
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macaron_ffn: bool = False,
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ffn_activation_type: str = "swish",
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linear_units: int = 2048,
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positionwise_layer_type: str = "linear",
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merge_conv_kernel: int = 3,
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interctc_layer_idx=None,
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interctc_use_conditioning: bool = False,
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):
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super().__init__()
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self._output_size = output_size
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if rel_pos_type == "legacy":
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if pos_enc_layer_type == "rel_pos":
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pos_enc_layer_type = "legacy_rel_pos"
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if attention_layer_type == "rel_selfattn":
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attention_layer_type = "legacy_rel_selfattn"
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elif rel_pos_type == "latest":
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assert attention_layer_type != "legacy_rel_selfattn"
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assert pos_enc_layer_type != "legacy_rel_pos"
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else:
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raise ValueError("unknown rel_pos_type: " + rel_pos_type)
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if pos_enc_layer_type == "abs_pos":
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pos_enc_class = PositionalEncoding
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elif pos_enc_layer_type == "scaled_abs_pos":
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pos_enc_class = ScaledPositionalEncoding
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elif pos_enc_layer_type == "rel_pos":
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assert attention_layer_type == "rel_selfattn"
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pos_enc_class = RelPositionalEncoding
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elif pos_enc_layer_type == "legacy_rel_pos":
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assert attention_layer_type == "legacy_rel_selfattn"
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pos_enc_class = LegacyRelPositionalEncoding
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logging.warning("Using legacy_rel_pos and it will be deprecated in the future.")
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else:
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raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
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if input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(input_size, output_size),
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torch.nn.LayerNorm(output_size),
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torch.nn.Dropout(dropout_rate),
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif input_layer == "conv2d2":
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self.embed = Conv2dSubsampling2(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif isinstance(input_layer, torch.nn.Module):
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self.embed = torch.nn.Sequential(
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input_layer,
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pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
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)
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elif input_layer is None:
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if input_size == output_size:
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self.embed = None
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else:
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self.embed = torch.nn.Linear(input_size, output_size)
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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activation = get_activation(ffn_activation_type)
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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (
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output_size,
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linear_units,
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dropout_rate,
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activation,
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)
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elif positionwise_layer_type is None:
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logging.warning("no macaron ffn")
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else:
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raise ValueError("Support only linear.")
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if attention_layer_type == "selfattn":
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encoder_selfattn_layer = MultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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)
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elif attention_layer_type == "legacy_rel_selfattn":
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assert pos_enc_layer_type == "legacy_rel_pos"
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encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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)
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logging.warning("Using legacy_rel_selfattn and it will be deprecated in the future.")
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elif attention_layer_type == "rel_selfattn":
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assert pos_enc_layer_type == "rel_pos"
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encoder_selfattn_layer = RelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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zero_triu,
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)
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elif attention_layer_type == "fast_selfattn":
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assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"]
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encoder_selfattn_layer = FastSelfAttention
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encoder_selfattn_layer_args = (
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output_size,
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attention_heads,
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attention_dropout_rate,
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)
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else:
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raise ValueError("unknown encoder_attn_layer: " + attention_layer_type)
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cgmlp_layer = ConvolutionalGatingMLP
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cgmlp_layer_args = (
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output_size,
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cgmlp_linear_units,
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cgmlp_conv_kernel,
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dropout_rate,
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use_linear_after_conv,
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gate_activation,
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)
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self.encoders = repeat(
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num_blocks,
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lambda lnum: EBranchformerEncoderLayer(
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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cgmlp_layer(*cgmlp_layer_args),
|
||
|
|
positionwise_layer(*positionwise_layer_args) if use_ffn else None,
|
||
|
|
positionwise_layer(*positionwise_layer_args) if use_ffn and macaron_ffn else None,
|
||
|
|
dropout_rate,
|
||
|
|
merge_conv_kernel,
|
||
|
|
),
|
||
|
|
layer_drop_rate,
|
||
|
|
)
|
||
|
|
self.after_norm = LayerNorm(output_size)
|
||
|
|
|
||
|
|
if interctc_layer_idx is None:
|
||
|
|
interctc_layer_idx = []
|
||
|
|
self.interctc_layer_idx = interctc_layer_idx
|
||
|
|
if len(interctc_layer_idx) > 0:
|
||
|
|
assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
|
||
|
|
self.interctc_use_conditioning = interctc_use_conditioning
|
||
|
|
self.conditioning_layer = None
|
||
|
|
|
||
|
|
def output_size(self) -> int:
|
||
|
|
return self._output_size
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
xs_pad: torch.Tensor,
|
||
|
|
ilens: torch.Tensor,
|
||
|
|
prev_states: torch.Tensor = None,
|
||
|
|
ctc: CTC = None,
|
||
|
|
max_layer: int = None,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
||
|
|
"""Calculate forward propagation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).
|
||
|
|
ilens (torch.Tensor): Input length (#batch).
|
||
|
|
prev_states (torch.Tensor): Not to be used now.
|
||
|
|
ctc (CTC): Intermediate CTC module.
|
||
|
|
max_layer (int): Layer depth below which InterCTC is applied.
|
||
|
|
Returns:
|
||
|
|
torch.Tensor: Output tensor (#batch, L, output_size).
|
||
|
|
torch.Tensor: Output length (#batch).
|
||
|
|
torch.Tensor: Not to be used now.
|
||
|
|
"""
|
||
|
|
|
||
|
|
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
|
||
|
|
|
||
|
|
if (
|
||
|
|
isinstance(self.embed, Conv2dSubsampling)
|
||
|
|
or isinstance(self.embed, Conv2dSubsampling2)
|
||
|
|
or isinstance(self.embed, Conv2dSubsampling6)
|
||
|
|
or isinstance(self.embed, Conv2dSubsampling8)
|
||
|
|
):
|
||
|
|
short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
|
||
|
|
if short_status:
|
||
|
|
raise TooShortUttError(
|
||
|
|
f"has {xs_pad.size(1)} frames and is too short for subsampling "
|
||
|
|
+ f"(it needs more than {limit_size} frames), return empty results",
|
||
|
|
xs_pad.size(1),
|
||
|
|
limit_size,
|
||
|
|
)
|
||
|
|
xs_pad, masks = self.embed(xs_pad, masks)
|
||
|
|
elif self.embed is not None:
|
||
|
|
xs_pad = self.embed(xs_pad)
|
||
|
|
|
||
|
|
intermediate_outs = []
|
||
|
|
if len(self.interctc_layer_idx) == 0:
|
||
|
|
if max_layer is not None and 0 <= max_layer < len(self.encoders):
|
||
|
|
for layer_idx, encoder_layer in enumerate(self.encoders):
|
||
|
|
xs_pad, masks = encoder_layer(xs_pad, masks)
|
||
|
|
if layer_idx >= max_layer:
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
xs_pad, masks = self.encoders(xs_pad, masks)
|
||
|
|
else:
|
||
|
|
for layer_idx, encoder_layer in enumerate(self.encoders):
|
||
|
|
xs_pad, masks = encoder_layer(xs_pad, masks)
|
||
|
|
|
||
|
|
if layer_idx + 1 in self.interctc_layer_idx:
|
||
|
|
encoder_out = xs_pad
|
||
|
|
|
||
|
|
if isinstance(encoder_out, tuple):
|
||
|
|
encoder_out = encoder_out[0]
|
||
|
|
|
||
|
|
intermediate_outs.append((layer_idx + 1, encoder_out))
|
||
|
|
|
||
|
|
if self.interctc_use_conditioning:
|
||
|
|
ctc_out = ctc.softmax(encoder_out)
|
||
|
|
|
||
|
|
if isinstance(xs_pad, tuple):
|
||
|
|
xs_pad = list(xs_pad)
|
||
|
|
xs_pad[0] = xs_pad[0] + self.conditioning_layer(ctc_out)
|
||
|
|
xs_pad = tuple(xs_pad)
|
||
|
|
else:
|
||
|
|
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
|
||
|
|
|
||
|
|
if isinstance(xs_pad, tuple):
|
||
|
|
xs_pad = xs_pad[0]
|
||
|
|
|
||
|
|
xs_pad = self.after_norm(xs_pad)
|
||
|
|
olens = masks.squeeze(1).sum(1)
|
||
|
|
if len(intermediate_outs) > 0:
|
||
|
|
return (xs_pad, intermediate_outs), olens, None
|
||
|
|
return xs_pad, olens, None
|