modules/wenet_extractor/transformer/decoder.py

# This module is from [WeNet](https://github.com/wenet-e2e/wenet).

# ## Citations

# ```bibtex
# @inproceedings{yao2021wenet,
#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
#   booktitle={Proc. Interspeech},
#   year={2021},
#   address={Brno, Czech Republic },
#   organization={IEEE}
# }

# @article{zhang2022wenet,
#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
#   journal={arXiv preprint arXiv:2203.15455},
#   year={2022}
# }
#
"""Decoder definition."""
from typing import Tuple, List, Optional

import torch

from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from modules.wenet_extractor.transformer.decoder_layer import DecoderLayer
from modules.wenet_extractor.transformer.embedding import PositionalEncoding
from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
from modules.wenet_extractor.transformer.positionwise_feed_forward import (
    PositionwiseFeedForward,
)
from modules.wenet_extractor.utils.mask import subsequent_mask, make_pad_mask


class TransformerDecoder(torch.nn.Module):
    """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 hidden units number of position-wise feedforward
        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:
            True: use layer_norm before each sub-block of a layer.
            False: use layer_norm after each sub-block of a layer.
        src_attention: if false, encoder-decoder cross attention is not
                       applied, such as CIF model
    """

    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,
        normalize_before: bool = True,
        src_attention: bool = True,
    ):
        super().__init__()
        attention_dim = encoder_output_size

        if input_layer == "embed":
            self.embed = torch.nn.Sequential(
                torch.nn.Embedding(vocab_size, attention_dim),
                PositionalEncoding(attention_dim, positional_dropout_rate),
            )
        elif input_layer == "none":
            self.embed = NoPositionalEncoding(attention_dim, positional_dropout_rate)
        else:
            raise ValueError(f"only 'embed' is supported: {input_layer}")

        self.normalize_before = normalize_before
        self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5)
        self.use_output_layer = use_output_layer
        self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
        self.num_blocks = num_blocks
        self.decoders = torch.nn.ModuleList(
            [
                DecoderLayer(
                    attention_dim,
                    MultiHeadedAttention(
                        attention_heads, attention_dim, self_attention_dropout_rate
                    ),
                    MultiHeadedAttention(
                        attention_heads, attention_dim, src_attention_dropout_rate
                    )
                    if src_attention
                    else None,
                    PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
                    dropout_rate,
                    normalize_before,
                )
                for _ in range(self.num_blocks)
            ]
        )

    def forward(
        self,
        memory: torch.Tensor,
        memory_mask: torch.Tensor,
        ys_in_pad: torch.Tensor,
        ys_in_lens: torch.Tensor,
        r_ys_in_pad: torch.Tensor = torch.empty(0),
        reverse_weight: float = 0.0,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Forward decoder.
        Args:
            memory: encoded memory, float32  (batch, maxlen_in, feat)
            memory_mask: encoder memory mask, (batch, 1, maxlen_in)
            ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
            ys_in_lens: input lengths of this batch (batch)
            r_ys_in_pad: not used in transformer decoder, in order to unify api
                with bidirectional decoder
            reverse_weight: not used in transformer decoder, in order to unify
                api with bidirectional decode
        Returns:
            (tuple): tuple containing:
                x: decoded token score before softmax (batch, maxlen_out,
                    vocab_size) if use_output_layer is True,
                torch.tensor(0.0), in order to unify api with bidirectional decoder
                olens: (batch, )
        """
        tgt = ys_in_pad
        maxlen = tgt.size(1)
        # tgt_mask: (B, 1, L)
        tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1)
        tgt_mask = tgt_mask.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
        x, _ = self.embed(tgt)
        for layer in self.decoders:
            x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory, memory_mask)
        if self.normalize_before:
            x = self.after_norm(x)
        if self.use_output_layer:
            x = self.output_layer(x)
        olens = tgt_mask.sum(1)
        return x, torch.tensor(0.0), olens

    def forward_one_step(
        self,
        memory: torch.Tensor,
        memory_mask: torch.Tensor,
        tgt: torch.Tensor,
        tgt_mask: torch.Tensor,
        cache: Optional[List[torch.Tensor]] = None,
    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
        """Forward one step.
            This is only used for decoding.
        Args:
            memory: encoded memory, float32  (batch, maxlen_in, feat)
            memory_mask: encoded memory mask, (batch, 1, maxlen_in)
            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)
            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)
        new_cache = []
        for i, decoder in enumerate(self.decoders):
            if cache is None:
                c = None
            else:
                c = cache[i]
            x, tgt_mask, memory, memory_mask = decoder(
                x, tgt_mask, memory, memory_mask, cache=c
            )
            new_cache.append(x)
        if self.normalize_before:
            y = self.after_norm(x[:, -1])
        else:
            y = x[:, -1]
        if self.use_output_layer:
            y = torch.log_softmax(self.output_layer(y), dim=-1)
        return y, new_cache


class BiTransformerDecoder(torch.nn.Module):
    """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 hidden units number of position-wise feedforward
        num_blocks: the number of decoder blocks
        r_num_blocks: the number of right to left 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:
            True: use layer_norm before each sub-block of a layer.
            False: use layer_norm after each sub-block of a layer.
    """

    def __init__(
        self,
        vocab_size: int,
        encoder_output_size: int,
        attention_heads: int = 4,
        linear_units: int = 2048,
        num_blocks: int = 6,
        r_num_blocks: int = 0,
        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,
        normalize_before: bool = True,
    ):
        super().__init__()
        self.left_decoder = TransformerDecoder(
            vocab_size,
            encoder_output_size,
            attention_heads,
            linear_units,
            num_blocks,
            dropout_rate,
            positional_dropout_rate,
            self_attention_dropout_rate,
            src_attention_dropout_rate,
            input_layer,
            use_output_layer,
            normalize_before,
        )

        self.right_decoder = TransformerDecoder(
            vocab_size,
            encoder_output_size,
            attention_heads,
            linear_units,
            r_num_blocks,
            dropout_rate,
            positional_dropout_rate,
            self_attention_dropout_rate,
            src_attention_dropout_rate,
            input_layer,
            use_output_layer,
            normalize_before,
        )

    def forward(
        self,
        memory: torch.Tensor,
        memory_mask: torch.Tensor,
        ys_in_pad: torch.Tensor,
        ys_in_lens: torch.Tensor,
        r_ys_in_pad: torch.Tensor,
        reverse_weight: float = 0.0,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Forward decoder.
        Args:
            memory: encoded memory, float32  (batch, maxlen_in, feat)
            memory_mask: encoder memory mask, (batch, 1, maxlen_in)
            ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
            ys_in_lens: input lengths of this batch (batch)
            r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out),
                used for right to left decoder
            reverse_weight: used for right to left decoder
        Returns:
            (tuple): tuple containing:
                x: decoded token score before softmax (batch, maxlen_out,
                    vocab_size) if use_output_layer is True,
                r_x: x: decoded token score (right to left decoder)
                    before softmax (batch, maxlen_out, vocab_size)
                    if use_output_layer is True,
                olens: (batch, )
        """
        l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad, ys_in_lens)
        r_x = torch.tensor(0.0)
        if reverse_weight > 0.0:
            r_x, _, olens = self.right_decoder(
                memory, memory_mask, r_ys_in_pad, ys_in_lens
            )
        return l_x, r_x, olens

    def forward_one_step(
        self,
        memory: torch.Tensor,
        memory_mask: torch.Tensor,
        tgt: torch.Tensor,
        tgt_mask: torch.Tensor,
        cache: Optional[List[torch.Tensor]] = None,
    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
        """Forward one step.
            This is only used for decoding.
        Args:
            memory: encoded memory, float32  (batch, maxlen_in, feat)
            memory_mask: encoded memory mask, (batch, 1, maxlen_in)
            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)
            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)
        """
        return self.left_decoder.forward_one_step(
            memory, memory_mask, tgt, tgt_mask, cache
        )