first commit for takway.ai

funasr/models/branchformer/cgmlp.py

@@ -0,0 +1,124 @@
+"""MLP with convolutional gating (cgMLP) definition.
+
+References:
+    https://openreview.net/forum?id=RA-zVvZLYIy
+    https://arxiv.org/abs/2105.08050
+
+"""
+
+import torch
+
+from funasr.models.transformer.utils.nets_utils import get_activation
+from funasr.models.transformer.layer_norm import LayerNorm
+
+
+class ConvolutionalSpatialGatingUnit(torch.nn.Module):
+    """Convolutional Spatial Gating Unit (CSGU)."""
+
+    def __init__(
+        self,
+        size: int,
+        kernel_size: int,
+        dropout_rate: float,
+        use_linear_after_conv: bool,
+        gate_activation: str,
+    ):
+        super().__init__()
+
+        n_channels = size // 2  # split input channels
+        self.norm = LayerNorm(n_channels)
+        self.conv = torch.nn.Conv1d(
+            n_channels,
+            n_channels,
+            kernel_size,
+            1,
+            (kernel_size - 1) // 2,
+            groups=n_channels,
+        )
+        if use_linear_after_conv:
+            self.linear = torch.nn.Linear(n_channels, n_channels)
+        else:
+            self.linear = None
+
+        if gate_activation == "identity":
+            self.act = torch.nn.Identity()
+        else:
+            self.act = get_activation(gate_activation)
+
+        self.dropout = torch.nn.Dropout(dropout_rate)
+
+    def espnet_initialization_fn(self):
+        torch.nn.init.normal_(self.conv.weight, std=1e-6)
+        torch.nn.init.ones_(self.conv.bias)
+        if self.linear is not None:
+            torch.nn.init.normal_(self.linear.weight, std=1e-6)
+            torch.nn.init.ones_(self.linear.bias)
+
+    def forward(self, x, gate_add=None):
+        """Forward method
+
+        Args:
+            x (torch.Tensor): (N, T, D)
+            gate_add (torch.Tensor): (N, T, D/2)
+
+        Returns:
+            out (torch.Tensor): (N, T, D/2)
+        """
+
+        x_r, x_g = x.chunk(2, dim=-1)
+
+        x_g = self.norm(x_g)  # (N, T, D/2)
+        x_g = self.conv(x_g.transpose(1, 2)).transpose(1, 2)  # (N, T, D/2)
+        if self.linear is not None:
+            x_g = self.linear(x_g)
+
+        if gate_add is not None:
+            x_g = x_g + gate_add
+
+        x_g = self.act(x_g)
+        out = x_r * x_g  # (N, T, D/2)
+        out = self.dropout(out)
+        return out
+
+
+class ConvolutionalGatingMLP(torch.nn.Module):
+    """Convolutional Gating MLP (cgMLP)."""
+
+    def __init__(
+        self,
+        size: int,
+        linear_units: int,
+        kernel_size: int,
+        dropout_rate: float,
+        use_linear_after_conv: bool,
+        gate_activation: str,
+    ):
+        super().__init__()
+
+        self.channel_proj1 = torch.nn.Sequential(
+            torch.nn.Linear(size, linear_units), torch.nn.GELU()
+        )
+        self.csgu = ConvolutionalSpatialGatingUnit(
+            size=linear_units,
+            kernel_size=kernel_size,
+            dropout_rate=dropout_rate,
+            use_linear_after_conv=use_linear_after_conv,
+            gate_activation=gate_activation,
+        )
+        self.channel_proj2 = torch.nn.Linear(linear_units // 2, size)
+
+    def forward(self, x, mask):
+        if isinstance(x, tuple):
+            xs_pad, pos_emb = x
+        else:
+            xs_pad, pos_emb = x, None
+
+        xs_pad = self.channel_proj1(xs_pad)  # size -> linear_units
+        xs_pad = self.csgu(xs_pad)  # linear_units -> linear_units/2
+        xs_pad = self.channel_proj2(xs_pad)  # linear_units/2 -> size
+
+        if pos_emb is not None:
+            out = (xs_pad, pos_emb)
+        else:
+            out = xs_pad
+        return out

funasr/models/branchformer/encoder.py

@@ -0,0 +1,524 @@
+# 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,
+    “Branchformer: Parallel MLP-Attention Architectures to Capture
+    Local and Global Context for Speech Recognition and Understanding,”
+    in Proceedings of ICML, 2022.
+
+"""
+
+import logging
+from typing import List, Optional, Tuple, Union
+
+import numpy
+import torch
+import torch.nn as nn
+
+from funasr.models.branchformer.cgmlp import ConvolutionalGatingMLP
+from funasr.models.branchformer.fastformer import FastSelfAttention
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
+from funasr.models.transformer.attention import (  # noqa: H301
+    LegacyRelPositionMultiHeadedAttention,
+    MultiHeadedAttention,
+    RelPositionMultiHeadedAttention,
+)
+from funasr.models.transformer.embedding import (  # noqa: H301
+    LegacyRelPositionalEncoding,
+    PositionalEncoding,
+    RelPositionalEncoding,
+    ScaledPositionalEncoding,
+)
+from funasr.models.transformer.layer_norm import LayerNorm
+from funasr.models.transformer.utils.repeat import repeat
+from funasr.models.transformer.utils.subsampling import (
+    Conv2dSubsampling,
+    Conv2dSubsampling2,
+    Conv2dSubsampling6,
+    Conv2dSubsampling8,
+    TooShortUttError,
+    check_short_utt,
+)
+
+from funasr.register import tables
+
+
+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
+
+
+@tables.register("encoder_classes", "BranchformerEncoder")
+class BranchformerEncoder(nn.Module):
+    """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

funasr/models/branchformer/fastformer.py

@@ -0,0 +1,139 @@
+"""Fastformer attention definition.
+
+Reference:
+    Wu et al., "Fastformer: Additive Attention Can Be All You Need"
+    https://arxiv.org/abs/2108.09084
+    https://github.com/wuch15/Fastformer
+
+"""
+
+import numpy
+import torch
+
+
+class FastSelfAttention(torch.nn.Module):
+    """Fast self-attention used in Fastformer."""
+
+    def __init__(
+        self,
+        size,
+        attention_heads,
+        dropout_rate,
+    ):
+        super().__init__()
+        if size % attention_heads != 0:
+            raise ValueError(
+                f"Hidden size ({size}) is not an integer multiple "
+                f"of attention heads ({attention_heads})"
+            )
+        self.attention_head_size = size // attention_heads
+        self.num_attention_heads = attention_heads
+
+        self.query = torch.nn.Linear(size, size)
+        self.query_att = torch.nn.Linear(size, attention_heads)
+        self.key = torch.nn.Linear(size, size)
+        self.key_att = torch.nn.Linear(size, attention_heads)
+        self.transform = torch.nn.Linear(size, size)
+        self.dropout = torch.nn.Dropout(dropout_rate)
+
+    def espnet_initialization_fn(self):
+        self.apply(self.init_weights)
+
+    def init_weights(self, module):
+        if isinstance(module, torch.nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+        if isinstance(module, torch.nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    def transpose_for_scores(self, x):
+        """Reshape and transpose to compute scores.
+
+        Args:
+            x: (batch, time, size = n_heads * attn_dim)
+
+        Returns:
+            (batch, n_heads, time, attn_dim)
+        """
+
+        new_x_shape = x.shape[:-1] + (
+            self.num_attention_heads,
+            self.attention_head_size,
+        )
+        return x.reshape(*new_x_shape).transpose(1, 2)
+
+    def forward(self, xs_pad, mask):
+        """Forward method.
+
+        Args:
+            xs_pad: (batch, time, size = n_heads * attn_dim)
+            mask: (batch, 1, time), nonpadding is 1, padding is 0
+
+        Returns:
+            torch.Tensor: (batch, time, size)
+        """
+
+        batch_size, seq_len, _ = xs_pad.shape
+        mixed_query_layer = self.query(xs_pad)  # (batch, time, size)
+        mixed_key_layer = self.key(xs_pad)  # (batch, time, size)
+
+        if mask is not None:
+            mask = mask.eq(0)  # padding is 1, nonpadding is 0
+
+        # (batch, n_heads, time)
+        query_for_score = (
+            self.query_att(mixed_query_layer).transpose(1, 2) / self.attention_head_size**0.5
+        )
+        if mask is not None:
+            min_value = float(
+                numpy.finfo(torch.tensor(0, dtype=query_for_score.dtype).numpy().dtype).min
+            )
+            query_for_score = query_for_score.masked_fill(mask, min_value)
+            query_weight = torch.softmax(query_for_score, dim=-1).masked_fill(mask, 0.0)
+        else:
+            query_weight = torch.softmax(query_for_score, dim=-1)
+
+        query_weight = query_weight.unsqueeze(2)  # (batch, n_heads, 1, time)
+        query_layer = self.transpose_for_scores(
+            mixed_query_layer
+        )  # (batch, n_heads, time, attn_dim)
+
+        pooled_query = (
+            torch.matmul(query_weight, query_layer)
+            .transpose(1, 2)
+            .reshape(-1, 1, self.num_attention_heads * self.attention_head_size)
+        )  # (batch, 1, size = n_heads * attn_dim)
+        pooled_query = self.dropout(pooled_query)
+        pooled_query_repeat = pooled_query.repeat(1, seq_len, 1)  # (batch, time, size)
+
+        mixed_query_key_layer = mixed_key_layer * pooled_query_repeat  # (batch, time, size)
+
+        # (batch, n_heads, time)
+        query_key_score = (
+            self.key_att(mixed_query_key_layer) / self.attention_head_size**0.5
+        ).transpose(1, 2)
+        if mask is not None:
+            min_value = float(
+                numpy.finfo(torch.tensor(0, dtype=query_key_score.dtype).numpy().dtype).min
+            )
+            query_key_score = query_key_score.masked_fill(mask, min_value)
+            query_key_weight = torch.softmax(query_key_score, dim=-1).masked_fill(mask, 0.0)
+        else:
+            query_key_weight = torch.softmax(query_key_score, dim=-1)
+
+        query_key_weight = query_key_weight.unsqueeze(2)  # (batch, n_heads, 1, time)
+        key_layer = self.transpose_for_scores(
+            mixed_query_key_layer
+        )  # (batch, n_heads, time, attn_dim)
+        pooled_key = torch.matmul(query_key_weight, key_layer)  # (batch, n_heads, 1, attn_dim)
+        pooled_key = self.dropout(pooled_key)
+
+        # NOTE: value = query, due to param sharing
+        weighted_value = (pooled_key * query_layer).transpose(
+            1, 2
+        )  # (batch, time, n_heads, attn_dim)
+        weighted_value = weighted_value.reshape(
+            weighted_value.shape[:-2] + (self.num_attention_heads * self.attention_head_size,)
+        )  # (batch, time, size)
+        weighted_value = self.dropout(self.transform(weighted_value)) + mixed_query_layer
+
+        return weighted_value

funasr/models/branchformer/model.py

@@ -0,0 +1,17 @@
+import logging
+
+from funasr.models.transformer.model import Transformer
+from funasr.register import tables
+
+
+@tables.register("model_classes", "Branchformer")
+class Branchformer(Transformer):
+    """CTC-attention hybrid Encoder-Decoder model"""
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+
+        super().__init__(*args, **kwargs)

funasr/models/branchformer/template.yaml

@@ -0,0 +1,116 @@
+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: Branchformer
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+
+# encoder
+encoder: BranchformerEncoder
+encoder_conf:
+    output_size: 256
+    use_attn: true
+    attention_heads: 4
+    attention_layer_type: rel_selfattn
+    pos_enc_layer_type: rel_pos
+    rel_pos_type: latest
+    use_cgmlp: true
+    cgmlp_linear_units: 2048
+    cgmlp_conv_kernel: 31
+    use_linear_after_conv: false
+    gate_activation: identity
+    merge_method: concat
+    cgmlp_weight: 0.5               # used only if merge_method is "fixed_ave"
+    attn_branch_drop_rate: 0.0      # used only if merge_method is "learned_ave"
+    num_blocks: 24
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d
+    stochastic_depth_rate: 0.0
+
+# decoder
+decoder: TransformerDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.
+    src_attention_dropout_rate: 0.
+
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    dither: 0.0
+    lfr_m: 1
+    lfr_n: 1
+
+specaug: SpecAug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 40
+    num_time_mask: 2
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 150
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.001
+   weight_decay: 0.000001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 35000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: BatchSampler
+    batch_type: example # example or length
+    batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 500
+    shuffle: True
+    num_workers: 4
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+  split_with_space: true
+
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null