525 lines
20 KiB
Python
525 lines
20 KiB
Python
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# Copyright 2022 Yifan Peng (Carnegie Mellon University)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Branchformer encoder definition.
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Reference:
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Yifan Peng, Siddharth Dalmia, Ian Lane, and Shinji Watanabe,
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“Branchformer: Parallel MLP-Attention Architectures to Capture
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Local and Global Context for Speech Recognition and Understanding,”
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in Proceedings of ICML, 2022.
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"""
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import logging
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from typing import List, Optional, Tuple, Union
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import numpy
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import torch
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import torch.nn as nn
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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 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.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 BranchformerEncoderLayer(torch.nn.Module):
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"""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, optional
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cgmlp: ConvolutionalGatingMLP, optional
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dropout_rate (float): dropout probability
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merge_method (str): concat, learned_ave, fixed_ave
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cgmlp_weight (float): weight of the cgmlp branch, between 0 and 1,
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used if merge_method is fixed_ave
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attn_branch_drop_rate (float): probability of dropping the attn branch,
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used if merge_method is learned_ave
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stochastic_depth_rate (float): stochastic depth probability
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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: Optional[torch.nn.Module],
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cgmlp: Optional[torch.nn.Module],
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dropout_rate: float,
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merge_method: str,
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cgmlp_weight: float = 0.5,
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attn_branch_drop_rate: float = 0.0,
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stochastic_depth_rate: float = 0.0,
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):
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super().__init__()
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assert (attn is not None) or (cgmlp is not None), "At least one branch should be valid"
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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.merge_method = merge_method
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self.cgmlp_weight = cgmlp_weight
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self.attn_branch_drop_rate = attn_branch_drop_rate
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self.stochastic_depth_rate = stochastic_depth_rate
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self.use_two_branches = (attn is not None) and (cgmlp is not None)
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if attn is not None:
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self.norm_mha = LayerNorm(size) # for the MHA module
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if cgmlp is not None:
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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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if self.use_two_branches:
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if merge_method == "concat":
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self.merge_proj = torch.nn.Linear(size + size, size)
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elif merge_method == "learned_ave":
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# attention-based pooling for two branches
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self.pooling_proj1 = torch.nn.Linear(size, 1)
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self.pooling_proj2 = torch.nn.Linear(size, 1)
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# linear projections for calculating merging weights
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self.weight_proj1 = torch.nn.Linear(size, 1)
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self.weight_proj2 = torch.nn.Linear(size, 1)
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# linear projection after weighted average
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self.merge_proj = torch.nn.Linear(size, size)
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elif merge_method == "fixed_ave":
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assert 0.0 <= cgmlp_weight <= 1.0, "cgmlp weight should be between 0.0 and 1.0"
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# remove the other branch if only one branch is used
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if cgmlp_weight == 0.0:
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self.use_two_branches = False
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self.cgmlp = None
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self.norm_mlp = None
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elif cgmlp_weight == 1.0:
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self.use_two_branches = False
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self.attn = None
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self.norm_mha = None
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# linear projection after weighted average
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self.merge_proj = torch.nn.Linear(size, size)
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else:
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raise ValueError(f"unknown merge method: {merge_method}")
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else:
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self.merge_proj = torch.nn.Identity()
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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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skip_layer = False
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# with stochastic depth, residual connection `x + f(x)` becomes
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# `x <- x + 1 / (1 - p) * f(x)` at training time.
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stoch_layer_coeff = 1.0
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if self.training and self.stochastic_depth_rate > 0:
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skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
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stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
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if skip_layer:
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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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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# 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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if self.attn is not None:
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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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if self.cgmlp is not None:
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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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if self.use_two_branches:
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if self.merge_method == "concat":
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x = x + stoch_layer_coeff * self.dropout(
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self.merge_proj(torch.cat([x1, x2], dim=-1))
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)
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elif self.merge_method == "learned_ave":
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if (
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self.training
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and self.attn_branch_drop_rate > 0
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and torch.rand(1).item() < self.attn_branch_drop_rate
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):
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# Drop the attn branch
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w1, w2 = 0.0, 1.0
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else:
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# branch1
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score1 = (
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self.pooling_proj1(x1).transpose(1, 2) / self.size**0.5
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) # (batch, 1, time)
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if mask is not None:
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min_value = float(
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numpy.finfo(torch.tensor(0, dtype=score1.dtype).numpy().dtype).min
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)
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score1 = score1.masked_fill(mask.eq(0), min_value)
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score1 = torch.softmax(score1, dim=-1).masked_fill(mask.eq(0), 0.0)
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else:
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score1 = torch.softmax(score1, dim=-1)
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pooled1 = torch.matmul(score1, x1).squeeze(1) # (batch, size)
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weight1 = self.weight_proj1(pooled1) # (batch, 1)
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# branch2
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score2 = (
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self.pooling_proj2(x2).transpose(1, 2) / self.size**0.5
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) # (batch, 1, time)
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if mask is not None:
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min_value = float(
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numpy.finfo(torch.tensor(0, dtype=score2.dtype).numpy().dtype).min
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)
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score2 = score2.masked_fill(mask.eq(0), min_value)
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score2 = torch.softmax(score2, dim=-1).masked_fill(mask.eq(0), 0.0)
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else:
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score2 = torch.softmax(score2, dim=-1)
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pooled2 = torch.matmul(score2, x2).squeeze(1) # (batch, size)
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weight2 = self.weight_proj2(pooled2) # (batch, 1)
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# normalize weights of two branches
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merge_weights = torch.softmax(
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torch.cat([weight1, weight2], dim=-1), dim=-1
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) # (batch, 2)
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merge_weights = merge_weights.unsqueeze(-1).unsqueeze(-1) # (batch, 2, 1, 1)
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w1, w2 = merge_weights[:, 0], merge_weights[:, 1] # (batch, 1, 1)
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x = x + stoch_layer_coeff * self.dropout(self.merge_proj(w1 * x1 + w2 * x2))
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elif self.merge_method == "fixed_ave":
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x = x + stoch_layer_coeff * self.dropout(
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self.merge_proj((1.0 - self.cgmlp_weight) * x1 + self.cgmlp_weight * x2)
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)
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else:
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raise RuntimeError(f"unknown merge method: {self.merge_method}")
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else:
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if self.attn is None:
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x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x2))
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elif self.cgmlp is None:
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x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x1))
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else:
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# This should not happen
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raise RuntimeError("Both branches are not None, which is unexpected.")
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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", "BranchformerEncoder")
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class BranchformerEncoder(nn.Module):
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"""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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use_attn: bool = True,
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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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use_cgmlp: bool = True,
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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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merge_method: str = "concat",
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cgmlp_weight: Union[float, List[float]] = 0.5,
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attn_branch_drop_rate: Union[float, List[float]] = 0.0,
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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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stochastic_depth_rate: Union[float, List[float]] = 0.0,
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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),
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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),
|
||
|
|
)
|
||
|
|
elif input_layer == "conv2d2":
|
||
|
|
self.embed = Conv2dSubsampling2(
|
||
|
|
input_size,
|
||
|
|
output_size,
|
||
|
|
dropout_rate,
|
||
|
|
pos_enc_class(output_size, positional_dropout_rate),
|
||
|
|
)
|
||
|
|
elif input_layer == "conv2d6":
|
||
|
|
self.embed = Conv2dSubsampling6(
|
||
|
|
input_size,
|
||
|
|
output_size,
|
||
|
|
dropout_rate,
|
||
|
|
pos_enc_class(output_size, positional_dropout_rate),
|
||
|
|
)
|
||
|
|
elif input_layer == "conv2d8":
|
||
|
|
self.embed = Conv2dSubsampling8(
|
||
|
|
input_size,
|
||
|
|
output_size,
|
||
|
|
dropout_rate,
|
||
|
|
pos_enc_class(output_size, positional_dropout_rate),
|
||
|
|
)
|
||
|
|
elif input_layer == "embed":
|
||
|
|
self.embed = torch.nn.Sequential(
|
||
|
|
torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
|
||
|
|
pos_enc_class(output_size, positional_dropout_rate),
|
||
|
|
)
|
||
|
|
elif isinstance(input_layer, torch.nn.Module):
|
||
|
|
self.embed = torch.nn.Sequential(
|
||
|
|
input_layer,
|
||
|
|
pos_enc_class(output_size, positional_dropout_rate),
|
||
|
|
)
|
||
|
|
elif input_layer is None:
|
||
|
|
if input_size == output_size:
|
||
|
|
self.embed = None
|
||
|
|
else:
|
||
|
|
self.embed = torch.nn.Linear(input_size, output_size)
|
||
|
|
else:
|
||
|
|
raise ValueError("unknown input_layer: " + input_layer)
|
||
|
|
|
||
|
|
if attention_layer_type == "selfattn":
|
||
|
|
encoder_selfattn_layer = MultiHeadedAttention
|
||
|
|
encoder_selfattn_layer_args = (
|
||
|
|
attention_heads,
|
||
|
|
output_size,
|
||
|
|
attention_dropout_rate,
|
||
|
|
)
|
||
|
|
elif attention_layer_type == "legacy_rel_selfattn":
|
||
|
|
assert pos_enc_layer_type == "legacy_rel_pos"
|
||
|
|
encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
|
||
|
|
encoder_selfattn_layer_args = (
|
||
|
|
attention_heads,
|
||
|
|
output_size,
|
||
|
|
attention_dropout_rate,
|
||
|
|
)
|
||
|
|
logging.warning("Using legacy_rel_selfattn and it will be deprecated in the future.")
|
||
|
|
elif attention_layer_type == "rel_selfattn":
|
||
|
|
assert pos_enc_layer_type == "rel_pos"
|
||
|
|
encoder_selfattn_layer = RelPositionMultiHeadedAttention
|
||
|
|
encoder_selfattn_layer_args = (
|
||
|
|
attention_heads,
|
||
|
|
output_size,
|
||
|
|
attention_dropout_rate,
|
||
|
|
zero_triu,
|
||
|
|
)
|
||
|
|
elif attention_layer_type == "fast_selfattn":
|
||
|
|
assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"]
|
||
|
|
encoder_selfattn_layer = FastSelfAttention
|
||
|
|
encoder_selfattn_layer_args = (
|
||
|
|
output_size,
|
||
|
|
attention_heads,
|
||
|
|
attention_dropout_rate,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
raise ValueError("unknown encoder_attn_layer: " + attention_layer_type)
|
||
|
|
|
||
|
|
cgmlp_layer = ConvolutionalGatingMLP
|
||
|
|
cgmlp_layer_args = (
|
||
|
|
output_size,
|
||
|
|
cgmlp_linear_units,
|
||
|
|
cgmlp_conv_kernel,
|
||
|
|
dropout_rate,
|
||
|
|
use_linear_after_conv,
|
||
|
|
gate_activation,
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(stochastic_depth_rate, float):
|
||
|
|
stochastic_depth_rate = [stochastic_depth_rate] * num_blocks
|
||
|
|
if len(stochastic_depth_rate) != num_blocks:
|
||
|
|
raise ValueError(
|
||
|
|
f"Length of stochastic_depth_rate ({len(stochastic_depth_rate)}) "
|
||
|
|
f"should be equal to num_blocks ({num_blocks})"
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(cgmlp_weight, float):
|
||
|
|
cgmlp_weight = [cgmlp_weight] * num_blocks
|
||
|
|
if len(cgmlp_weight) != num_blocks:
|
||
|
|
raise ValueError(
|
||
|
|
f"Length of cgmlp_weight ({len(cgmlp_weight)}) should be equal to "
|
||
|
|
f"num_blocks ({num_blocks})"
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(attn_branch_drop_rate, float):
|
||
|
|
attn_branch_drop_rate = [attn_branch_drop_rate] * num_blocks
|
||
|
|
if len(attn_branch_drop_rate) != num_blocks:
|
||
|
|
raise ValueError(
|
||
|
|
f"Length of attn_branch_drop_rate ({len(attn_branch_drop_rate)}) "
|
||
|
|
f"should be equal to num_blocks ({num_blocks})"
|
||
|
|
)
|
||
|
|
|
||
|
|
self.encoders = repeat(
|
||
|
|
num_blocks,
|
||
|
|
lambda lnum: BranchformerEncoderLayer(
|
||
|
|
output_size,
|
||
|
|
encoder_selfattn_layer(*encoder_selfattn_layer_args) if use_attn else None,
|
||
|
|
cgmlp_layer(*cgmlp_layer_args) if use_cgmlp else None,
|
||
|
|
dropout_rate,
|
||
|
|
merge_method,
|
||
|
|
cgmlp_weight[lnum],
|
||
|
|
attn_branch_drop_rate[lnum],
|
||
|
|
stochastic_depth_rate[lnum],
|
||
|
|
),
|
||
|
|
)
|
||
|
|
self.after_norm = LayerNorm(output_size)
|
||
|
|
|
||
|
|
def output_size(self) -> int:
|
||
|
|
return self._output_size
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
xs_pad: torch.Tensor,
|
||
|
|
ilens: torch.Tensor,
|
||
|
|
prev_states: torch.Tensor = 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.
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
xs_pad, masks = self.encoders(xs_pad, masks)
|
||
|
|
|
||
|
|
if isinstance(xs_pad, tuple):
|
||
|
|
xs_pad = xs_pad[0]
|
||
|
|
|
||
|
|
xs_pad = self.after_norm(xs_pad)
|
||
|
|
olens = masks.squeeze(1).sum(1)
|
||
|
|
return xs_pad, olens, None
|