From 17e8f5b889be2ad31608b5203dc5fbc5fd5c0f8a Mon Sep 17 00:00:00 2001
From: nichongjia-2007 <nichongjia@gmail.com>
Date: 星期四, 20 七月 2023 21:26:58 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR
---
funasr/models/encoder/branchformer_encoder.py | 545 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 545 insertions(+), 0 deletions(-)
diff --git a/funasr/models/encoder/branchformer_encoder.py b/funasr/models/encoder/branchformer_encoder.py
new file mode 100644
index 0000000..70bd2c9
--- /dev/null
+++ b/funasr/models/encoder/branchformer_encoder.py
@@ -0,0 +1,545 @@
+# Copyright 2022 Yifan Peng (Carnegie Mellon University)
+# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Branchformer encoder definition.
+
+Reference:
+ Yifan Peng, Siddharth Dalmia, Ian Lane, and Shinji Watanabe,
+ 鈥淏ranchformer: Parallel MLP-Attention Architectures to Capture
+ Local and Global Context for Speech Recognition and Understanding,鈥�
+ in Proceedings of ICML, 2022.
+
+"""
+
+import logging
+from typing import List, Optional, Tuple, Union
+
+import numpy
+import torch
+
+from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.modules.cgmlp import ConvolutionalGatingMLP
+from funasr.modules.fastformer import FastSelfAttention
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.attention import ( # noqa: H301
+ LegacyRelPositionMultiHeadedAttention,
+ MultiHeadedAttention,
+ RelPositionMultiHeadedAttention,
+)
+from funasr.modules.embedding import ( # noqa: H301
+ LegacyRelPositionalEncoding,
+ PositionalEncoding,
+ RelPositionalEncoding,
+ ScaledPositionalEncoding,
+)
+from funasr.modules.layer_norm import LayerNorm
+from funasr.modules.repeat import repeat
+from funasr.modules.subsampling import (
+ Conv2dSubsampling,
+ Conv2dSubsampling2,
+ Conv2dSubsampling6,
+ Conv2dSubsampling8,
+ TooShortUttError,
+ check_short_utt,
+)
+
+
+class BranchformerEncoderLayer(torch.nn.Module):
+ """Branchformer encoder layer module.
+
+ Args:
+ size (int): model dimension
+ attn: standard self-attention or efficient attention, optional
+ cgmlp: ConvolutionalGatingMLP, optional
+ dropout_rate (float): dropout probability
+ merge_method (str): concat, learned_ave, fixed_ave
+ cgmlp_weight (float): weight of the cgmlp branch, between 0 and 1,
+ used if merge_method is fixed_ave
+ attn_branch_drop_rate (float): probability of dropping the attn branch,
+ used if merge_method is learned_ave
+ stochastic_depth_rate (float): stochastic depth probability
+ """
+
+ def __init__(
+ self,
+ size: int,
+ attn: Optional[torch.nn.Module],
+ cgmlp: Optional[torch.nn.Module],
+ dropout_rate: float,
+ merge_method: str,
+ cgmlp_weight: float = 0.5,
+ attn_branch_drop_rate: float = 0.0,
+ stochastic_depth_rate: float = 0.0,
+ ):
+ super().__init__()
+ assert (attn is not None) or (
+ cgmlp is not None
+ ), "At least one branch should be valid"
+
+ self.size = size
+ self.attn = attn
+ self.cgmlp = cgmlp
+ self.merge_method = merge_method
+ self.cgmlp_weight = cgmlp_weight
+ self.attn_branch_drop_rate = attn_branch_drop_rate
+ self.stochastic_depth_rate = stochastic_depth_rate
+ self.use_two_branches = (attn is not None) and (cgmlp is not None)
+
+ if attn is not None:
+ self.norm_mha = LayerNorm(size) # for the MHA module
+ if cgmlp is not None:
+ self.norm_mlp = LayerNorm(size) # for the MLP module
+ self.norm_final = LayerNorm(size) # for the final output of the block
+
+ self.dropout = torch.nn.Dropout(dropout_rate)
+
+ if self.use_two_branches:
+ if merge_method == "concat":
+ self.merge_proj = torch.nn.Linear(size + size, size)
+
+ elif merge_method == "learned_ave":
+ # attention-based pooling for two branches
+ self.pooling_proj1 = torch.nn.Linear(size, 1)
+ self.pooling_proj2 = torch.nn.Linear(size, 1)
+
+ # linear projections for calculating merging weights
+ self.weight_proj1 = torch.nn.Linear(size, 1)
+ self.weight_proj2 = torch.nn.Linear(size, 1)
+
+ # linear projection after weighted average
+ self.merge_proj = torch.nn.Linear(size, size)
+
+ elif merge_method == "fixed_ave":
+ assert (
+ 0.0 <= cgmlp_weight <= 1.0
+ ), "cgmlp weight should be between 0.0 and 1.0"
+
+ # remove the other branch if only one branch is used
+ if cgmlp_weight == 0.0:
+ self.use_two_branches = False
+ self.cgmlp = None
+ self.norm_mlp = None
+ elif cgmlp_weight == 1.0:
+ self.use_two_branches = False
+ self.attn = None
+ self.norm_mha = None
+
+ # linear projection after weighted average
+ self.merge_proj = torch.nn.Linear(size, size)
+
+ else:
+ raise ValueError(f"unknown merge method: {merge_method}")
+
+ else:
+ self.merge_proj = torch.nn.Identity()
+
+ def forward(self, x_input, mask, cache=None):
+ """Compute encoded features.
+
+ Args:
+ x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
+ - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
+ - w/o pos emb: Tensor (#batch, time, size).
+ mask (torch.Tensor): Mask tensor for the input (#batch, 1, time).
+ cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time).
+ """
+
+ if cache is not None:
+ raise NotImplementedError("cache is not None, which is not tested")
+
+ if isinstance(x_input, tuple):
+ x, pos_emb = x_input[0], x_input[1]
+ else:
+ x, pos_emb = x_input, None
+
+ skip_layer = False
+ # with stochastic depth, residual connection `x + f(x)` becomes
+ # `x <- x + 1 / (1 - p) * f(x)` at training time.
+ stoch_layer_coeff = 1.0
+ if self.training and self.stochastic_depth_rate > 0:
+ skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
+ stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
+
+ if skip_layer:
+ if cache is not None:
+ x = torch.cat([cache, x], dim=1)
+ if pos_emb is not None:
+ return (x, pos_emb), mask
+ return x, mask
+
+ # Two branches
+ x1 = x
+ x2 = x
+
+ # Branch 1: multi-headed attention module
+ if self.attn is not None:
+ x1 = self.norm_mha(x1)
+
+ if isinstance(self.attn, FastSelfAttention):
+ x_att = self.attn(x1, mask)
+ else:
+ if pos_emb is not None:
+ x_att = self.attn(x1, x1, x1, pos_emb, mask)
+ else:
+ x_att = self.attn(x1, x1, x1, mask)
+
+ x1 = self.dropout(x_att)
+
+ # Branch 2: convolutional gating mlp
+ if self.cgmlp is not None:
+ x2 = self.norm_mlp(x2)
+
+ if pos_emb is not None:
+ x2 = (x2, pos_emb)
+ x2 = self.cgmlp(x2, mask)
+ if isinstance(x2, tuple):
+ x2 = x2[0]
+
+ x2 = self.dropout(x2)
+
+ # Merge two branches
+ if self.use_two_branches:
+ if self.merge_method == "concat":
+ x = x + stoch_layer_coeff * self.dropout(
+ self.merge_proj(torch.cat([x1, x2], dim=-1))
+ )
+ elif self.merge_method == "learned_ave":
+ if (
+ self.training
+ and self.attn_branch_drop_rate > 0
+ and torch.rand(1).item() < self.attn_branch_drop_rate
+ ):
+ # Drop the attn branch
+ w1, w2 = 0.0, 1.0
+ else:
+ # branch1
+ score1 = (
+ self.pooling_proj1(x1).transpose(1, 2) / self.size**0.5
+ ) # (batch, 1, time)
+ if mask is not None:
+ min_value = float(
+ numpy.finfo(
+ torch.tensor(0, dtype=score1.dtype).numpy().dtype
+ ).min
+ )
+ score1 = score1.masked_fill(mask.eq(0), min_value)
+ score1 = torch.softmax(score1, dim=-1).masked_fill(
+ mask.eq(0), 0.0
+ )
+ else:
+ score1 = torch.softmax(score1, dim=-1)
+ pooled1 = torch.matmul(score1, x1).squeeze(1) # (batch, size)
+ weight1 = self.weight_proj1(pooled1) # (batch, 1)
+
+ # branch2
+ score2 = (
+ self.pooling_proj2(x2).transpose(1, 2) / self.size**0.5
+ ) # (batch, 1, time)
+ if mask is not None:
+ min_value = float(
+ numpy.finfo(
+ torch.tensor(0, dtype=score2.dtype).numpy().dtype
+ ).min
+ )
+ score2 = score2.masked_fill(mask.eq(0), min_value)
+ score2 = torch.softmax(score2, dim=-1).masked_fill(
+ mask.eq(0), 0.0
+ )
+ else:
+ score2 = torch.softmax(score2, dim=-1)
+ pooled2 = torch.matmul(score2, x2).squeeze(1) # (batch, size)
+ weight2 = self.weight_proj2(pooled2) # (batch, 1)
+
+ # normalize weights of two branches
+ merge_weights = torch.softmax(
+ torch.cat([weight1, weight2], dim=-1), dim=-1
+ ) # (batch, 2)
+ merge_weights = merge_weights.unsqueeze(-1).unsqueeze(
+ -1
+ ) # (batch, 2, 1, 1)
+ w1, w2 = merge_weights[:, 0], merge_weights[:, 1] # (batch, 1, 1)
+
+ x = x + stoch_layer_coeff * self.dropout(
+ self.merge_proj(w1 * x1 + w2 * x2)
+ )
+ elif self.merge_method == "fixed_ave":
+ x = x + stoch_layer_coeff * self.dropout(
+ self.merge_proj(
+ (1.0 - self.cgmlp_weight) * x1 + self.cgmlp_weight * x2
+ )
+ )
+ else:
+ raise RuntimeError(f"unknown merge method: {self.merge_method}")
+ else:
+ if self.attn is None:
+ x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x2))
+ elif self.cgmlp is None:
+ x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x1))
+ else:
+ # This should not happen
+ raise RuntimeError("Both branches are not None, which is unexpected.")
+
+ x = self.norm_final(x)
+
+ if pos_emb is not None:
+ return (x, pos_emb), mask
+
+ return x, mask
+
+
+class BranchformerEncoder(AbsEncoder):
+ """Branchformer encoder module."""
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ use_attn: bool = True,
+ attention_heads: int = 4,
+ attention_layer_type: str = "rel_selfattn",
+ pos_enc_layer_type: str = "rel_pos",
+ rel_pos_type: str = "latest",
+ use_cgmlp: bool = True,
+ cgmlp_linear_units: int = 2048,
+ cgmlp_conv_kernel: int = 31,
+ use_linear_after_conv: bool = False,
+ gate_activation: str = "identity",
+ merge_method: str = "concat",
+ cgmlp_weight: Union[float, List[float]] = 0.5,
+ attn_branch_drop_rate: Union[float, List[float]] = 0.0,
+ num_blocks: int = 12,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: Optional[str] = "conv2d",
+ zero_triu: bool = False,
+ padding_idx: int = -1,
+ stochastic_depth_rate: Union[float, List[float]] = 0.0,
+ ):
+ super().__init__()
+ self._output_size = output_size
+
+ if rel_pos_type == "legacy":
+ if pos_enc_layer_type == "rel_pos":
+ pos_enc_layer_type = "legacy_rel_pos"
+ if attention_layer_type == "rel_selfattn":
+ attention_layer_type = "legacy_rel_selfattn"
+ elif rel_pos_type == "latest":
+ assert attention_layer_type != "legacy_rel_selfattn"
+ assert pos_enc_layer_type != "legacy_rel_pos"
+ else:
+ raise ValueError("unknown rel_pos_type: " + rel_pos_type)
+
+ if pos_enc_layer_type == "abs_pos":
+ pos_enc_class = PositionalEncoding
+ elif pos_enc_layer_type == "scaled_abs_pos":
+ pos_enc_class = ScaledPositionalEncoding
+ elif pos_enc_layer_type == "rel_pos":
+ assert attention_layer_type == "rel_selfattn"
+ pos_enc_class = RelPositionalEncoding
+ elif pos_enc_layer_type == "legacy_rel_pos":
+ assert attention_layer_type == "legacy_rel_selfattn"
+ pos_enc_class = LegacyRelPositionalEncoding
+ logging.warning(
+ "Using legacy_rel_pos and it will be deprecated in the future."
+ )
+ else:
+ raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
+
+ if input_layer == "linear":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Linear(input_size, output_size),
+ torch.nn.LayerNorm(output_size),
+ torch.nn.Dropout(dropout_rate),
+ pos_enc_class(output_size, positional_dropout_rate),
+ )
+ elif input_layer == "conv2d":
+ self.embed = Conv2dSubsampling(
+ input_size,
+ output_size,
+ dropout_rate,
+ 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
--
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