add attention-decoder loss option for zipformer recipe

egs/librispeech/ASR/zipformer/attention_decoder.py

@@ -0,0 +1,485 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# The model structure is modified from Daniel Povey's Zipformer
+# https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+
+import math
+from typing import List, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from label_smoothing import LabelSmoothingLoss
+
+from icefall.utils import add_eos, add_sos, make_pad_mask
+
+
+class AttentionDecoderModel(nn.Module):
+    """
+    Args:
+        vocab_size (int): Number of classes.
+        decoder_dim: (int,int): embedding dimension of 2 encoder stacks
+        attention_dim: (int,int): attention dimension of 2 encoder stacks
+        nhead (int, int): number of heads
+        dim_feedforward (int, int): feedforward dimension in 2 encoder stacks
+        num_encoder_layers (int): number of encoder layers
+        dropout (float): dropout rate
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        decoder_dim: int = 512,
+        num_decoder_layers: int = 6,
+        attention_dim: int = 512,
+        nhead: int = 8,
+        feedforward_dim: int = 2048,
+        sos_id: int = 1,
+        eos_id: int = 1,
+        dropout: float = 0.1,
+        ignore_id: int = -1,
+        label_smoothing: float = 0.1,
+    ):
+        super().__init__()
+        self.eos_id = eos_id
+        self.sos_id = sos_id
+        self.ignore_id = ignore_id
+
+        # For the segment of the warmup period, we let the Embedding
+        # layer learn something.  Then we start to warm up the other encoders.
+        self.decoder = TransformerDecoder(
+            vocab_size=vocab_size,
+            d_model=decoder_dim,
+            num_decoder_layers=num_decoder_layers,
+            attention_dim=attention_dim,
+            nhead=nhead,
+            feedforward_dim=feedforward_dim,
+            dropout=dropout,
+        )
+
+        # Used to calculate attention-decoder loss
+        self.loss_fun = LabelSmoothingLoss(
+            ignore_index=ignore_id, label_smoothing=label_smoothing, reduction="sum"
+        )
+
+    def _pre_ys_in_out(self, ys: k2.RaggedTensor, ys_lens: torch.Tensor):
+        """Prepare ys_in_pad and ys_out_pad."""
+        ys_in = add_sos(ys, sos_id=self.sos_id)
+        # [B, S+1], start with SOS
+        ys_in_pad = ys_in.pad(mode="constant", padding_value=self.eos_id)
+        ys_in_lens = ys_lens + 1
+
+        ys_out = add_eos(ys, eos_id=self.eos_id)
+        # [B, S+1], end with EOS
+        ys_out_pad = ys_out.pad(mode="constant", padding_value=self.ignore_id)
+
+        return ys_in_pad.to(torch.int64), ys_in_lens, ys_out_pad.to(torch.int64)
+
+    def calc_att_loss(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        ys: k2.RaggedTensor,
+        ys_lens: torch.Tensor,
+    ) -> torch.Tensor:
+        """Calculate attention-decoder loss.
+        Args:
+          encoder_out: (batch, num_frames, encoder_dim)
+          encoder_out_lens: (batch,)
+          token_ids: A list of token id list.
+
+        Return: The attention-decoder loss.
+        """
+        ys_in_pad, ys_in_lens, ys_out_pad = self._pre_ys_in_out(ys, ys_lens)
+
+        # decoder forward
+        decoder_out = self.decoder(encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens)
+
+        loss = self.loss_fun(x=decoder_out, target=ys_out_pad)
+        return loss
+
+    def nll(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        token_ids: List[List[int]],
+    ) -> torch.Tensor:
+        """Compute negative log likelihood(nll) from attention-decoder.
+        Args:
+          encoder_out: (batch, num_frames, encoder_dim)
+          encoder_out_lens: (batch,)
+          token_ids: A list of token id list.
+
+        Return: A tensor of shape (batch, num_tokens).
+        """
+        ys = k2.RaggedTensor(token_ids).to(device=encoder_out.device)
+        row_splits = ys.shape.row_splits(1)
+        ys_lens = row_splits[1:] - row_splits[:-1]
+
+        ys_in_pad, ys_in_lens, ys_out_pad = self._pre_ys_in_out(ys, ys_lens)
+
+        # decoder forward
+        decoder_out = self.decoder(encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens)
+
+        batch_size, _, num_classes = decoder_out.size()
+        nll = nn.functional.cross_entropy(
+            decoder_out.view(-1, num_classes),
+            ys_out_pad.view(-1),
+            ignore_index=self.ignore_id,
+            reduction="none",
+        )
+        nll = nll.view(batch_size, -1)
+        return nll
+
+
+class TransformerDecoder(nn.Module):
+    """Transfomer decoder module.
+    It is modified from https://github.com/espnet/espnet/blob/master/espnet2/asr/decoder/transformer_decoder.py.
+
+    Args:
+        vocab_size: output dim
+        d_model: decoder dimension
+        num_decoder_layers: number of decoder layers
+        attention_dim: total dimension of multi head attention
+        n_head: number of attention heads
+        feedforward_dim: hidden dimension of feed_forward module
+        dropout: dropout rate
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        d_model: int = 512,
+        num_decoder_layers: int = 6,
+        attention_dim: int = 512,
+        nhead: int = 8,
+        feedforward_dim: int = 2048,
+        dropout: float = 0.1,
+    ):
+        super().__init__()
+        self.embed = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
+
+        # Using absolute positional encoding
+        self.pos = PositionalEncoding(d_model, dropout_rate=0.1)
+
+        self.num_layers = num_decoder_layers
+        self.layers = nn.ModuleList(
+            [
+                DecoderLayer(d_model, attention_dim, nhead, feedforward_dim, dropout)
+                for _ in range(num_decoder_layers)
+            ]
+        )
+
+        self.output_layer = nn.Linear(d_model, vocab_size)
+
+    def forward(
+        self,
+        memory: torch.Tensor,
+        memory_lens: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+
+        Args:
+            memory: encoded memory, (batch, maxlen_in, feat)
+            memory_lens: (batch,)
+            ys_in_pad: input token ids, (batch, maxlen_out)
+            ys_in_lens: (batch, )
+
+        Returns:
+            tgt: decoded token score before softmax (batch, maxlen_out, vocab_size)
+        """
+        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_mask = make_pad_mask(memory_lens)[:, None, :].to(memory.device)
+
+        tgt = self.embed(tgt)
+        tgt = self.pos(tgt)
+
+        for i, mod in enumerate(self.layers):
+            tgt = mod(tgt, tgt_mask, memory, memory_mask)
+
+        tgt = self.output_layer(tgt)
+        return tgt
+
+
+class DecoderLayer(nn.Module):
+    """Single decoder layer module.
+
+    Args:
+        d_model: equal to encoder_dim
+        attention_dim: total dimension of multi head attention
+        n_head: number of attention heads
+        feedforward_dim: hidden dimension of feed_forward module
+        dropout: dropout rate
+    """
+
+    def __init__(
+        self,
+        d_model: int = 512,
+        attention_dim: int = 512,
+        nhead: int = 8,
+        feedforward_dim: int = 2048,
+        dropout: float = 0.1,
+    ):
+        """Construct an DecoderLayer object."""
+        super(DecoderLayer, self).__init__()
+
+        self.norm_self_attn = nn.LayerNorm(d_model)
+        self.self_attn = MultiHeadedAttention(d_model, attention_dim, nhead, dropout=0.0)
+
+        self.norm_src_attn = nn.LayerNorm(d_model)
+        self.src_attn = MultiHeadedAttention(d_model, attention_dim, nhead, dropout=0.0)
+
+        self.norm_ff = nn.LayerNorm(d_model)
+        self.feed_forward = nn.Sequential(
+            nn.Linear(d_model, feedforward_dim),
+            Swish(),
+            nn.Dropout(dropout),
+            nn.Linear(feedforward_dim, d_model),
+        )
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, tgt, tgt_mask, memory, memory_mask):
+        """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).
+
+        Returns:
+            torch.Tensor: Output tensor(#batch, maxlen_out, size).
+        """
+        # self-attn module
+        tgt_norm = self.norm_self_attn(tgt)
+        tgt = tgt + self.dropout(self.self_attn(tgt_norm, tgt_norm, tgt_norm, tgt_mask))
+
+        # cross-attn module
+        tgt = tgt + self.dropout(self.src_attn(self.norm_src_attn(tgt), memory, memory, memory_mask))
+
+        # feed-forward module
+        tgt = tgt + self.dropout(self.feed_forward(self.norm_ff(tgt)))
+
+        return tgt
+
+
+class MultiHeadedAttention(nn.Module):
+    """Multi-Head Attention layer.
+
+    Args:
+        embed_dim: total dimension of the model.
+        attention_dim: dimension in the attention module, may be less or more than embed_dim
+            but must be a multiple of num_heads.
+        num_heads: parallel attention heads.
+        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
+    """
+
+    def __init__(
+        self, embed_dim: int, attention_dim: int, num_heads: int, dropout: float = 0.0
+    ):
+        """Construct an MultiHeadedAttention object."""
+        super(MultiHeadedAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.attention_dim = attention_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = attention_dim // num_heads
+        assert self.head_dim * num_heads == attention_dim, (
+            self.head_dim,
+            num_heads,
+            attention_dim,
+        )
+
+        self.linear_q = nn.Linear(embed_dim, attention_dim, bias=True)
+        self.linear_k = nn.Linear(embed_dim, attention_dim, bias=True)
+        self.linear_v = nn.Linear(embed_dim, attention_dim, bias=True)
+        self.scale = math.sqrt(self.head_dim)
+
+        self.out_proj = nn.Linear(attention_dim, embed_dim, bias=True)
+
+    def forward(self, query, key, value, mask):
+        """Compute scaled dot product attention.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+
+        """
+        bsz, tgt_len, _ = query.size()
+        src_len = key.size(1)
+        num_heads = self.num_heads
+        head_dim = self.head_dim
+
+        q = self.linear_q(query)
+        k = self.linear_k(key)
+        v = self.linear_v(value)
+
+        q = q.reshape(bsz, tgt_len, num_heads, head_dim)
+        q = q.transpose(1, 2)  # (batch, head, time1, head_dim)
+        k = k.reshape(bsz, src_len, num_heads, head_dim)
+        k = k.permute(0, 2, 3, 1)  # (batch, head, head_dim, time2)
+        v = v.reshape(bsz, src_len, num_heads, head_dim)
+        v = v.transpose(1, 2).reshape(bsz * num_heads, src_len, head_dim)
+
+        # (batch, head, time1, time2)
+        attn_output_weights = torch.matmul(q, k) / self.scale
+
+        if mask is not None:
+            attn_output_weights = attn_output_weights.masked_fill(
+                mask.unsqueeze(1), float("-inf")
+            )
+
+        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)
+
+        attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1)
+        attn_output_weights = nn.functional.dropout(
+            attn_output_weights, p=self.dropout, training=self.training
+        )
+
+        # (bsz * head, time1, head_dim_v)
+        attn_output = torch.bmm(attn_output_weights, v)
+        assert attn_output.shape == (bsz * num_heads, tgt_len, head_dim)
+        attn_output = (
+            attn_output.reshape(bsz, num_heads, tgt_len, head_dim)
+            .transpose(1, 2)
+            .reshape(bsz, tgt_len, self.attention_dim)
+        )
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class PositionalEncoding(nn.Module):
+    """Positional encoding.
+    Copied from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py#L35.
+
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Construct an PositionalEncoding object."""
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.d_model)
+        position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.d_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
+
+    def forward(self, x: torch.Tensor):
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class Swish(torch.nn.Module):
+    """Construct an Swish object."""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Return Swich activation function."""
+        return x * torch.sigmoid(x)
+
+
+def subsequent_mask(size, device="cpu", dtype=torch.bool):
+    """Create mask for subsequent steps (size, size).
+
+    :param int size: size of mask
+    :param str device: "cpu" or "cuda" or torch.Tensor.device
+    :param torch.dtype dtype: result dtype
+    :rtype: torch.Tensor
+    >>> subsequent_mask(3)
+    [[1, 0, 0],
+     [1, 1, 0],
+     [1, 1, 1]]
+    """
+    ret = torch.ones(size, size, device=device, dtype=dtype)
+    return torch.tril(ret, out=ret)
+
+
+def _test_attention_decoder_model():
+    m = AttentionDecoderModel(
+        vocab_size=500,
+        decoder_dim=512,
+        num_decoder_layers=6,
+        attention_dim=512,
+        nhead=8,
+        feedforward_dim=2048,
+        dropout=0.1,
+        sos_id=1,
+        eos_id=1,
+        ignore_id=-1,
+    )
+
+    num_param = sum([p.numel() for p in m.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+    m.eval()
+    encoder_out = torch.randn(2, 50, 512)
+    encoder_out_lens = torch.full((2,), 50)
+    token_ids = [[1, 2, 3, 4], [2, 3, 10]]
+
+    nll = m.nll(encoder_out, encoder_out_lens, token_ids)
+    print(nll)
+
+
+if __name__ == "__main__":
+    _test_attention_decoder_model()

egs/librispeech/ASR/zipformer/label_smoothing.py

@@ -0,0 +1,109 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+
+class LabelSmoothingLoss(torch.nn.Module):
+    """
+    Implement the LabelSmoothingLoss proposed in the following paper
+    https://arxiv.org/pdf/1512.00567.pdf
+    (Rethinking the Inception Architecture for Computer Vision)
+
+    """
+
+    def __init__(
+        self,
+        ignore_index: int = -1,
+        label_smoothing: float = 0.1,
+        reduction: str = "sum",
+    ) -> None:
+        """
+        Args:
+          ignore_index:
+            ignored class id
+          label_smoothing:
+            smoothing rate (0.0 means the conventional cross entropy loss)
+          reduction:
+            It has the same meaning as the reduction in
+            `torch.nn.CrossEntropyLoss`. It can be one of the following three
+            values: (1) "none": No reduction will be applied. (2) "mean": the
+            mean of the output is taken. (3) "sum": the output will be summed.
+        """
+        super().__init__()
+        assert 0.0 <= label_smoothing < 1.0, f"{label_smoothing}"
+        assert reduction in ("none", "sum", "mean"), reduction
+        self.ignore_index = ignore_index
+        self.label_smoothing = label_smoothing
+        self.reduction = reduction
+
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        """
+        Compute loss between x and target.
+
+        Args:
+          x:
+            prediction of dimension
+            (batch_size, input_length, number_of_classes).
+          target:
+            target masked with self.ignore_index of
+            dimension (batch_size, input_length).
+
+        Returns:
+          A scalar tensor containing the loss without normalization.
+        """
+        assert x.ndim == 3
+        assert target.ndim == 2
+        assert x.shape[:2] == target.shape
+        num_classes = x.size(-1)
+        x = x.reshape(-1, num_classes)
+        # Now x is of shape (N*T, C)
+
+        # We don't want to change target in-place below,
+        # so we make a copy of it here
+        target = target.clone().reshape(-1)
+
+        ignored = target == self.ignore_index
+
+        # See https://github.com/k2-fsa/icefall/issues/240
+        # and https://github.com/k2-fsa/icefall/issues/297
+        # for why we don't use target[ignored] = 0 here
+        target = torch.where(ignored, torch.zeros_like(target), target)
+
+        true_dist = torch.nn.functional.one_hot(target, num_classes=num_classes).to(x)
+
+        true_dist = (
+            true_dist * (1 - self.label_smoothing) + self.label_smoothing / num_classes
+        )
+
+        # Set the value of ignored indexes to 0
+        #
+        # See https://github.com/k2-fsa/icefall/issues/240
+        # and https://github.com/k2-fsa/icefall/issues/297
+        # for why we don't use true_dist[ignored] = 0 here
+        true_dist = torch.where(
+            ignored.unsqueeze(1).repeat(1, true_dist.shape[1]),
+            torch.zeros_like(true_dist),
+            true_dist,
+        )
+
+        loss = -1 * (torch.log_softmax(x, dim=1) * true_dist)
+        if self.reduction == "sum":
+            return loss.sum()
+        elif self.reduction == "mean":
+            return loss.sum() / (~ignored).sum()
+        else:
+            return loss.sum(dim=-1)

egs/librispeech/ASR/zipformer/model.py

@@ -34,11 +34,13 @@ class AsrModel(nn.Module):
         encoder: EncoderInterface,
         decoder: Optional[nn.Module] = None,
         joiner: Optional[nn.Module] = None,
+        attention_decoder: Optional[nn.Module] = None,
         encoder_dim: int = 384,
         decoder_dim: int = 512,
         vocab_size: int = 500,
         use_transducer: bool = True,
         use_ctc: bool = False,
+        use_attention_decoder: bool = False,
     ):
         """A joint CTC & Transducer ASR model.
 
@@ -111,6 +113,12 @@ class AsrModel(nn.Module):
                 nn.LogSoftmax(dim=-1),
             )
 
+        self.use_attention_decoder = use_attention_decoder
+        if use_attention_decoder:
+            self.attention_decoder = attention_decoder
+        else:
+            assert attention_decoder is None
+
     def forward_encoder(
         self, x: torch.Tensor, x_lens: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -286,7 +294,7 @@ class AsrModel(nn.Module):
         prune_range: int = 5,
         am_scale: float = 0.0,
         lm_scale: float = 0.0,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Args:
           x:
@@ -308,7 +316,7 @@ class AsrModel(nn.Module):
             part
         Returns:
           Return the transducer losses and CTC loss,
-          in form of (simple_loss, pruned_loss, ctc_loss)
+          in form of (simple_loss, pruned_loss, ctc_loss, attention_decoder_loss)
 
         Note:
            Regarding am_scale & lm_scale, it will make the loss-function one of
@@ -322,6 +330,8 @@ class AsrModel(nn.Module):
 
         assert x.size(0) == x_lens.size(0) == y.dim0, (x.shape, x_lens.shape, y.dim0)
 
+        device = x.device
+
         # Compute encoder outputs
         encoder_out, encoder_out_lens = self.forward_encoder(x, x_lens)
 
@@ -333,7 +343,7 @@ class AsrModel(nn.Module):
             simple_loss, pruned_loss = self.forward_transducer(
                 encoder_out=encoder_out,
                 encoder_out_lens=encoder_out_lens,
-                y=y.to(x.device),
+                y=y.to(device),
                 y_lens=y_lens,
                 prune_range=prune_range,
                 am_scale=am_scale,
@@ -355,4 +365,14 @@ class AsrModel(nn.Module):
         else:
             ctc_loss = torch.empty(0)
 
-        return simple_loss, pruned_loss, ctc_loss
+        if self.use_attention_decoder:
+            attention_decoder_loss = self.attention_decoder.calc_att_loss(
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+                ys=y.to(device),
+                ys_lens=y_lens.to(device),
+            )
+        else:
+            attention_decoder_loss = torch.empty(0)
+
+        return simple_loss, pruned_loss, ctc_loss, attention_decoder_loss

egs/librispeech/ASR/zipformer/train.py

@@ -66,6 +66,7 @@ import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
+from attention_decoder import AttentionDecoderModel
 from decoder import Decoder
 from joiner import Joiner
 from lhotse.cut import Cut
@@ -220,6 +221,41 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         """,
     )
 
+    parser.add_argument(
+        "--attention-decoder-dim",
+        type=int,
+        default=512,
+        help="""Dimension used in the attention decoder""",
+    )
+
+    parser.add_argument(
+        "--attention-decoder-num-layers",
+        type=int,
+        default=6,
+        help="""Number of transformer layers used in attention decoder""",
+    )
+
+    parser.add_argument(
+        "--attention-decoder-attention-dim",
+        type=int,
+        default=512,
+        help="""Attention dimension used in attention decoder""",
+    )
+
+    parser.add_argument(
+        "--attention-decoder-num-heads",
+        type=int,
+        default=8,
+        help="""Number of attention heads used in attention decoder""",
+    )
+
+    parser.add_argument(
+        "--attention-decoder-feedforward-dim",
+        type=int,
+        default=2048,
+        help="""Feedforward dimension used in attention decoder""",
+    )
+
     parser.add_argument(
         "--causal",
         type=str2bool,
@@ -258,6 +294,13 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         help="If True, use CTC head.",
     )
 
+    parser.add_argument(
+        "--use-attention-decoder",
+        type=str2bool,
+        default=False,
+        help="If True, use attention-decoder head.",
+    )
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -403,6 +446,21 @@ def get_parser():
         help="Scale for CTC loss.",
     )
 
+    parser.add_argument(
+        "--attention-decoder-loss-scale",
+        type=float,
+        default=0.8,
+        help="Scale for attention-decoder loss.",
+    )
+
+    parser.add_argument(
+        "--label-smoothing",
+        type=float,
+        default=0.1,
+        help="""Label smoothing rate used in attention decoder,
+        (0.0 means the conventional cross entropy loss)""",
+    )
+
     parser.add_argument(
         "--seed",
         type=int,
@@ -531,6 +589,8 @@ def get_params() -> AttributeDict:
             # parameters for zipformer
             "feature_dim": 80,
             "subsampling_factor": 4,  # not passed in, this is fixed.
+            # parameters for attention-decoder
+            "ignore_id": -1,
             "warm_step": 2000,
             "env_info": get_env_info(),
         }
@@ -603,6 +663,25 @@ def get_joiner_model(params: AttributeDict) -> nn.Module:
     return joiner
 
 
+def get_attention_decoder_model(params: AttributeDict) -> nn.Module:
+    encoder_dim = max(_to_int_tuple(params.encoder_dim))
+    assert params.attention_decoder_dim == encoder_dim, (params.attention_decoder_dim, encoder_dim)
+
+    decoder = AttentionDecoderModel(
+        vocab_size=params.vocab_size,
+        decoder_dim=params.attention_decoder_dim,
+        num_decoder_layers=params.attention_decoder_num_layers,
+        attention_dim=params.attention_decoder_attention_dim,
+        nhead=params.attention_decoder_num_heads,
+        feedforward_dim=params.attention_decoder_feedforward_dim,
+        sos_id=params.sos_id,
+        eos_id=params.eos_id,
+        ignore_id=params.ignore_id,
+        label_smoothing=params.label_smoothing,
+    )
+    return decoder
+
+
 def get_model(params: AttributeDict) -> nn.Module:
     assert params.use_transducer or params.use_ctc, (
         f"At least one of them should be True, "
@@ -620,16 +699,23 @@ def get_model(params: AttributeDict) -> nn.Module:
         decoder = None
         joiner = None
 
+    if params.use_attention_decoder:
+        attention_decoder = get_attention_decoder_model(params)
+    else:
+        attention_decoder = None
+
     model = AsrModel(
         encoder_embed=encoder_embed,
         encoder=encoder,
         decoder=decoder,
         joiner=joiner,
+        attention_decoder=attention_decoder,
         encoder_dim=max(_to_int_tuple(params.encoder_dim)),
         decoder_dim=params.decoder_dim,
         vocab_size=params.vocab_size,
         use_transducer=params.use_transducer,
         use_ctc=params.use_ctc,
+        use_attention_decoder=params.use_attention_decoder,
     )
     return model
 
@@ -792,7 +878,7 @@ def compute_loss(
     y = k2.RaggedTensor(y)
 
     with torch.set_grad_enabled(is_training):
-        simple_loss, pruned_loss, ctc_loss = model(
+        simple_loss, pruned_loss, ctc_loss, attention_decoder_loss = model(
             x=feature,
             x_lens=feature_lens,
             y=y,
@@ -822,6 +908,9 @@ def compute_loss(
         if params.use_ctc:
             loss += params.ctc_loss_scale * ctc_loss
 
+        if params.use_attention_decoder:
+            loss += params.attention_decoder_loss_scale * attention_decoder_loss
+
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
@@ -836,6 +925,8 @@ def compute_loss(
         info["pruned_loss"] = pruned_loss.detach().cpu().item()
     if params.use_ctc:
         info["ctc_loss"] = ctc_loss.detach().cpu().item()
+    if params.use_attention_decoder:
+        info["attn_deocder_loss"] = attention_decoder_loss.detach().cpu().item()
 
     return loss, info
 
@@ -1114,10 +1205,17 @@ def run(rank, world_size, args):
 
     # <blk> is defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
+    params.eos_id = sp.piece_to_id("<sos/eos>")
+    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     if not params.use_transducer:
+        if not params.use_attention_decoder:
             params.ctc_loss_scale = 1.0
+        else:
+            assert params.ctc_loss_scale + params.attention_decoder_loss_scale == 1.0, (
+                params.ctc_loss_scale, params.attention_decoder_loss_scale
+            )
 
     logging.info(params)