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funasr_local/models/decoder/transformer_decoder.py

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+# Copyright 2019 Shigeki Karita
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Decoder definition."""
+from typing import Any
+from typing import List
+from typing import Sequence
+from typing import Tuple
+
+import torch
+from torch import nn
+from typeguard import check_argument_types
+
+from funasr_local.models.decoder.abs_decoder import AbsDecoder
+from funasr_local.modules.attention import MultiHeadedAttention
+from funasr_local.modules.dynamic_conv import DynamicConvolution
+from funasr_local.modules.dynamic_conv2d import DynamicConvolution2D
+from funasr_local.modules.embedding import PositionalEncoding
+from funasr_local.modules.layer_norm import LayerNorm
+from funasr_local.modules.lightconv import LightweightConvolution
+from funasr_local.modules.lightconv2d import LightweightConvolution2D
+from funasr_local.modules.mask import subsequent_mask
+from funasr_local.modules.nets_utils import make_pad_mask
+from funasr_local.modules.positionwise_feed_forward import (
+    PositionwiseFeedForward,  # noqa: H301
+)
+from funasr_local.modules.repeat import repeat
+from funasr_local.modules.scorers.scorer_interface import BatchScorerInterface
+
+
+class DecoderLayer(nn.Module):
+    """Single decoder layer module.
+
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` instance can be used as the argument.
+        src_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
+            can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool): Whether to use layer_norm before the first block.
+        concat_after (bool): Whether to concat attention layer's input and output.
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied. i.e. x -> x + att(x)
+
+
+    """
+
+    def __init__(
+            self,
+            size,
+            self_attn,
+            src_attn,
+            feed_forward,
+            dropout_rate,
+            normalize_before=True,
+            concat_after=False,
+    ):
+        """Construct an DecoderLayer object."""
+        super(DecoderLayer, self).__init__()
+        self.size = size
+        self.self_attn = self_attn
+        self.src_attn = src_attn
+        self.feed_forward = feed_forward
+        self.norm1 = LayerNorm(size)
+        self.norm2 = LayerNorm(size)
+        self.norm3 = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.normalize_before = normalize_before
+        self.concat_after = concat_after
+        if self.concat_after:
+            self.concat_linear1 = nn.Linear(size + size, size)
+            self.concat_linear2 = nn.Linear(size + size, size)
+
+    def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None):
+        """Compute decoded features.
+
+        Args:
+            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
+            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
+            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
+            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
+            cache (List[torch.Tensor]): List of cached tensors.
+                Each tensor shape should be (#batch, maxlen_out - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor(#batch, maxlen_out, size).
+            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
+            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
+            torch.Tensor: Encoded memory mask (#batch, maxlen_in).
+
+        """
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm1(tgt)
+
+        if cache is None:
+            tgt_q = tgt
+            tgt_q_mask = tgt_mask
+        else:
+            # compute only the last frame query keeping dim: max_time_out -> 1
+            assert cache.shape == (
+                tgt.shape[0],
+                tgt.shape[1] - 1,
+                self.size,
+            ), f"{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}"
+            tgt_q = tgt[:, -1:, :]
+            residual = residual[:, -1:, :]
+            tgt_q_mask = None
+            if tgt_mask is not None:
+                tgt_q_mask = tgt_mask[:, -1:, :]
+
+        if self.concat_after:
+            tgt_concat = torch.cat(
+                (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), dim=-1
+            )
+            x = residual + self.concat_linear1(tgt_concat)
+        else:
+            x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask))
+        if not self.normalize_before:
+            x = self.norm1(x)
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm2(x)
+        if self.concat_after:
+            x_concat = torch.cat(
+                (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1
+            )
+            x = residual + self.concat_linear2(x_concat)
+        else:
+            x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask))
+        if not self.normalize_before:
+            x = self.norm2(x)
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm3(x)
+        x = residual + self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm3(x)
+
+        if cache is not None:
+            x = torch.cat([cache, x], dim=1)
+
+        return x, tgt_mask, memory, memory_mask
+
+
+class BaseTransformerDecoder(AbsDecoder, BatchScorerInterface):
+    """Base class of Transfomer decoder module.
+
+    Args:
+        vocab_size: output dim
+        encoder_output_size: dimension of attention
+        attention_heads: the number of heads of multi head attention
+        linear_units: the number of units of position-wise feed forward
+        num_blocks: the number of decoder blocks
+        dropout_rate: dropout rate
+        self_attention_dropout_rate: dropout rate for attention
+        input_layer: input layer type
+        use_output_layer: whether to use output layer
+        pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
+        normalize_before: whether to use layer_norm before the first block
+        concat_after: whether to concat attention layer's input and output
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied.
+            i.e. x -> x + att(x)
+    """
+
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+    ):
+        assert check_argument_types()
+        super().__init__()
+        attention_dim = encoder_output_size
+
+        if input_layer == "embed":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Embedding(vocab_size, attention_dim),
+                pos_enc_class(attention_dim, positional_dropout_rate),
+            )
+        elif input_layer == "linear":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Linear(vocab_size, attention_dim),
+                torch.nn.LayerNorm(attention_dim),
+                torch.nn.Dropout(dropout_rate),
+                torch.nn.ReLU(),
+                pos_enc_class(attention_dim, positional_dropout_rate),
+            )
+        else:
+            raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
+
+        self.normalize_before = normalize_before
+        if self.normalize_before:
+            self.after_norm = LayerNorm(attention_dim)
+        if use_output_layer:
+            self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
+        else:
+            self.output_layer = None
+
+        # Must set by the inheritance
+        self.decoders = None
+
+    def forward(
+            self,
+            hs_pad: torch.Tensor,
+            hlens: torch.Tensor,
+            ys_in_pad: torch.Tensor,
+            ys_in_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+
+        Args:
+            hs_pad: encoded memory, float32  (batch, maxlen_in, feat)
+            hlens: (batch)
+            ys_in_pad:
+                input token ids, int64 (batch, maxlen_out)
+                if input_layer == "embed"
+                input tensor (batch, maxlen_out, #mels) in the other cases
+            ys_in_lens: (batch)
+        Returns:
+            (tuple): tuple containing:
+
+            x: decoded token score before softmax (batch, maxlen_out, token)
+                if use_output_layer is True,
+            olens: (batch, )
+        """
+        tgt = ys_in_pad
+        # tgt_mask: (B, 1, L)
+        tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
+        # m: (1, L, L)
+        m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0)
+        # tgt_mask: (B, L, L)
+        tgt_mask = tgt_mask & m
+
+        memory = hs_pad
+        memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(
+            memory.device
+        )
+        # Padding for Longformer
+        if memory_mask.shape[-1] != memory.shape[1]:
+            padlen = memory.shape[1] - memory_mask.shape[-1]
+            memory_mask = torch.nn.functional.pad(
+                memory_mask, (0, padlen), "constant", False
+            )
+
+        x = self.embed(tgt)
+        x, tgt_mask, memory, memory_mask = self.decoders(
+            x, tgt_mask, memory, memory_mask
+        )
+        if self.normalize_before:
+            x = self.after_norm(x)
+        if self.output_layer is not None:
+            x = self.output_layer(x)
+
+        olens = tgt_mask.sum(1)
+        return x, olens
+
+    def forward_one_step(
+            self,
+            tgt: torch.Tensor,
+            tgt_mask: torch.Tensor,
+            memory: torch.Tensor,
+            cache: List[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """Forward one step.
+
+        Args:
+            tgt: input token ids, int64 (batch, maxlen_out)
+            tgt_mask: input token mask,  (batch, maxlen_out)
+                      dtype=torch.uint8 in PyTorch 1.2-
+                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            cache: cached output list of (batch, max_time_out-1, size)
+        Returns:
+            y, cache: NN output value and cache per `self.decoders`.
+            y.shape` is (batch, maxlen_out, token)
+        """
+        x = self.embed(tgt)
+        if cache is None:
+            cache = [None] * len(self.decoders)
+        new_cache = []
+        for c, decoder in zip(cache, self.decoders):
+            x, tgt_mask, memory, memory_mask = decoder(
+                x, tgt_mask, memory, None, cache=c
+            )
+            new_cache.append(x)
+
+        if self.normalize_before:
+            y = self.after_norm(x[:, -1])
+        else:
+            y = x[:, -1]
+        if self.output_layer is not None:
+            y = torch.log_softmax(self.output_layer(y), dim=-1)
+
+        return y, new_cache
+
+    def score(self, ys, state, x):
+        """Score."""
+        ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
+        logp, state = self.forward_one_step(
+            ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
+        )
+        return logp.squeeze(0), state
+
+    def batch_score(
+            self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
+    ) -> Tuple[torch.Tensor, List[Any]]:
+        """Score new token batch.
+
+        Args:
+            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+            states (List[Any]): Scorer states for prefix tokens.
+            xs (torch.Tensor):
+                The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+        Returns:
+            tuple[torch.Tensor, List[Any]]: Tuple of
+                batchfied scores for next token with shape of `(n_batch, n_vocab)`
+                and next state list for ys.
+
+        """
+        # merge states
+        n_batch = len(ys)
+        n_layers = len(self.decoders)
+        if states[0] is None:
+            batch_state = None
+        else:
+            # transpose state of [batch, layer] into [layer, batch]
+            batch_state = [
+                torch.stack([states[b][i] for b in range(n_batch)])
+                for i in range(n_layers)
+            ]
+
+        # batch decoding
+        ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0)
+        logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state)
+
+        # transpose state of [layer, batch] into [batch, layer]
+        state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
+        return logp, state_list
+
+
+class TransformerDecoder(BaseTransformerDecoder):
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            attention_heads: int = 4,
+            linear_units: int = 2048,
+            num_blocks: int = 6,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            self_attention_dropout_rate: float = 0.0,
+            src_attention_dropout_rate: float = 0.0,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+            concat_after: bool = False,
+    ):
+        assert check_argument_types()
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+
+        attention_dim = encoder_output_size
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, self_attention_dropout_rate
+                ),
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, src_attention_dropout_rate
+                ),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+
+
+class ParaformerDecoderSAN(BaseTransformerDecoder):
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
+    https://arxiv.org/abs/2006.01713
+    """
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            attention_heads: int = 4,
+            linear_units: int = 2048,
+            num_blocks: int = 6,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            self_attention_dropout_rate: float = 0.0,
+            src_attention_dropout_rate: float = 0.0,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+            concat_after: bool = False,
+            embeds_id: int = -1,
+    ):
+        assert check_argument_types()
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+
+        attention_dim = encoder_output_size
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, self_attention_dropout_rate
+                ),
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, src_attention_dropout_rate
+                ),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+        self.embeds_id = embeds_id
+        self.attention_dim = attention_dim
+
+    def forward(
+            self,
+            hs_pad: torch.Tensor,
+            hlens: torch.Tensor,
+            ys_in_pad: torch.Tensor,
+            ys_in_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+
+        Args:
+            hs_pad: encoded memory, float32  (batch, maxlen_in, feat)
+            hlens: (batch)
+            ys_in_pad:
+                input token ids, int64 (batch, maxlen_out)
+                if input_layer == "embed"
+                input tensor (batch, maxlen_out, #mels) in the other cases
+            ys_in_lens: (batch)
+        Returns:
+            (tuple): tuple containing:
+
+            x: decoded token score before softmax (batch, maxlen_out, token)
+                if use_output_layer is True,
+            olens: (batch, )
+        """
+        tgt = ys_in_pad
+        tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
+
+        memory = hs_pad
+        memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(
+            memory.device
+        )
+        # Padding for Longformer
+        if memory_mask.shape[-1] != memory.shape[1]:
+            padlen = memory.shape[1] - memory_mask.shape[-1]
+            memory_mask = torch.nn.functional.pad(
+                memory_mask, (0, padlen), "constant", False
+            )
+
+        # x = self.embed(tgt)
+        x = tgt
+        embeds_outputs = None
+        for layer_id, decoder in enumerate(self.decoders):
+            x, tgt_mask, memory, memory_mask = decoder(
+                x, tgt_mask, memory, memory_mask
+            )
+            if layer_id == self.embeds_id:
+                embeds_outputs = x
+        if self.normalize_before:
+            x = self.after_norm(x)
+        if self.output_layer is not None:
+            x = self.output_layer(x)
+
+        olens = tgt_mask.sum(1)
+        if embeds_outputs is not None:
+            return x, olens, embeds_outputs
+        else:
+            return x, olens
+
+
+class LightweightConvolutionTransformerDecoder(BaseTransformerDecoder):
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            attention_heads: int = 4,
+            linear_units: int = 2048,
+            num_blocks: int = 6,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            self_attention_dropout_rate: float = 0.0,
+            src_attention_dropout_rate: float = 0.0,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+            concat_after: bool = False,
+            conv_wshare: int = 4,
+            conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
+            conv_usebias: int = False,
+    ):
+        assert check_argument_types()
+        if len(conv_kernel_length) != num_blocks:
+            raise ValueError(
+                "conv_kernel_length must have equal number of values to num_blocks: "
+                f"{len(conv_kernel_length)} != {num_blocks}"
+            )
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+
+        attention_dim = encoder_output_size
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                LightweightConvolution(
+                    wshare=conv_wshare,
+                    n_feat=attention_dim,
+                    dropout_rate=self_attention_dropout_rate,
+                    kernel_size=conv_kernel_length[lnum],
+                    use_kernel_mask=True,
+                    use_bias=conv_usebias,
+                ),
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, src_attention_dropout_rate
+                ),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+
+
+class LightweightConvolution2DTransformerDecoder(BaseTransformerDecoder):
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            attention_heads: int = 4,
+            linear_units: int = 2048,
+            num_blocks: int = 6,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            self_attention_dropout_rate: float = 0.0,
+            src_attention_dropout_rate: float = 0.0,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+            concat_after: bool = False,
+            conv_wshare: int = 4,
+            conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
+            conv_usebias: int = False,
+    ):
+        assert check_argument_types()
+        if len(conv_kernel_length) != num_blocks:
+            raise ValueError(
+                "conv_kernel_length must have equal number of values to num_blocks: "
+                f"{len(conv_kernel_length)} != {num_blocks}"
+            )
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+
+        attention_dim = encoder_output_size
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                LightweightConvolution2D(
+                    wshare=conv_wshare,
+                    n_feat=attention_dim,
+                    dropout_rate=self_attention_dropout_rate,
+                    kernel_size=conv_kernel_length[lnum],
+                    use_kernel_mask=True,
+                    use_bias=conv_usebias,
+                ),
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, src_attention_dropout_rate
+                ),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+
+
+class DynamicConvolutionTransformerDecoder(BaseTransformerDecoder):
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            attention_heads: int = 4,
+            linear_units: int = 2048,
+            num_blocks: int = 6,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            self_attention_dropout_rate: float = 0.0,
+            src_attention_dropout_rate: float = 0.0,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+            concat_after: bool = False,
+            conv_wshare: int = 4,
+            conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
+            conv_usebias: int = False,
+    ):
+        assert check_argument_types()
+        if len(conv_kernel_length) != num_blocks:
+            raise ValueError(
+                "conv_kernel_length must have equal number of values to num_blocks: "
+                f"{len(conv_kernel_length)} != {num_blocks}"
+            )
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+        attention_dim = encoder_output_size
+
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                DynamicConvolution(
+                    wshare=conv_wshare,
+                    n_feat=attention_dim,
+                    dropout_rate=self_attention_dropout_rate,
+                    kernel_size=conv_kernel_length[lnum],
+                    use_kernel_mask=True,
+                    use_bias=conv_usebias,
+                ),
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, src_attention_dropout_rate
+                ),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )
+
+
+class DynamicConvolution2DTransformerDecoder(BaseTransformerDecoder):
+    def __init__(
+            self,
+            vocab_size: int,
+            encoder_output_size: int,
+            attention_heads: int = 4,
+            linear_units: int = 2048,
+            num_blocks: int = 6,
+            dropout_rate: float = 0.1,
+            positional_dropout_rate: float = 0.1,
+            self_attention_dropout_rate: float = 0.0,
+            src_attention_dropout_rate: float = 0.0,
+            input_layer: str = "embed",
+            use_output_layer: bool = True,
+            pos_enc_class=PositionalEncoding,
+            normalize_before: bool = True,
+            concat_after: bool = False,
+            conv_wshare: int = 4,
+            conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11),
+            conv_usebias: int = False,
+    ):
+        assert check_argument_types()
+        if len(conv_kernel_length) != num_blocks:
+            raise ValueError(
+                "conv_kernel_length must have equal number of values to num_blocks: "
+                f"{len(conv_kernel_length)} != {num_blocks}"
+            )
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+        attention_dim = encoder_output_size
+
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                DynamicConvolution2D(
+                    wshare=conv_wshare,
+                    n_feat=attention_dim,
+                    dropout_rate=self_attention_dropout_rate,
+                    kernel_size=conv_kernel_length[lnum],
+                    use_kernel_mask=True,
+                    use_bias=conv_usebias,
+                ),
+                MultiHeadedAttention(
+                    attention_heads, attention_dim, src_attention_dropout_rate
+                ),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+            ),
+        )