modules/wenet_extractor/transformer/encoder.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}
# }
#

"""Encoder definition."""
from typing import Tuple

import torch

from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from modules.wenet_extractor.transformer.attention import (
    RelPositionMultiHeadedAttention,
)
from modules.wenet_extractor.transformer.convolution import ConvolutionModule
from modules.wenet_extractor.transformer.embedding import PositionalEncoding
from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
from modules.wenet_extractor.transformer.encoder_layer import TransformerEncoderLayer
from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer
from modules.wenet_extractor.transformer.positionwise_feed_forward import (
    PositionwiseFeedForward,
)
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8
from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling
from modules.wenet_extractor.utils.common import get_activation
from modules.wenet_extractor.utils.mask import make_pad_mask
from modules.wenet_extractor.utils.mask import add_optional_chunk_mask


class BaseEncoder(torch.nn.Module):
    def __init__(
        self,
        input_size: int,
        output_size: int = 256,
        attention_heads: int = 4,
        linear_units: int = 2048,
        num_blocks: int = 6,
        dropout_rate: float = 0.1,
        positional_dropout_rate: float = 0.1,
        attention_dropout_rate: float = 0.0,
        input_layer: str = "conv2d",
        pos_enc_layer_type: str = "abs_pos",
        normalize_before: bool = True,
        static_chunk_size: int = 0,
        use_dynamic_chunk: bool = False,
        global_cmvn: torch.nn.Module = None,
        use_dynamic_left_chunk: bool = False,
    ):
        """
        Args:
            input_size (int): input dim
            output_size (int): dimension of attention
            attention_heads (int): the number of heads of multi head attention
            linear_units (int): the hidden units number of position-wise feed
                forward
            num_blocks (int): the number of decoder blocks
            dropout_rate (float): dropout rate
            attention_dropout_rate (float): dropout rate in attention
            positional_dropout_rate (float): dropout rate after adding
                positional encoding
            input_layer (str): input layer type.
                optional [linear, conv2d, conv2d6, conv2d8]
            pos_enc_layer_type (str): Encoder positional encoding layer type.
                opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
            normalize_before (bool):
                True: use layer_norm before each sub-block of a layer.
                False: use layer_norm after each sub-block of a layer.
            static_chunk_size (int): chunk size for static chunk training and
                decoding
            use_dynamic_chunk (bool): whether use dynamic chunk size for
                training or not, You can only use fixed chunk(chunk_size > 0)
                or dyanmic chunk size(use_dynamic_chunk = True)
            global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
            use_dynamic_left_chunk (bool): whether use dynamic left chunk in
                dynamic chunk training
        """
        super().__init__()
        self._output_size = output_size

        if pos_enc_layer_type == "abs_pos":
            pos_enc_class = PositionalEncoding
        elif pos_enc_layer_type == "rel_pos":
            pos_enc_class = RelPositionalEncoding
        elif pos_enc_layer_type == "no_pos":
            pos_enc_class = NoPositionalEncoding
        else:
            raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)

        if input_layer == "linear":
            subsampling_class = LinearNoSubsampling
        elif input_layer == "conv2d":
            subsampling_class = Conv2dSubsampling4
        elif input_layer == "conv2d6":
            subsampling_class = Conv2dSubsampling6
        elif input_layer == "conv2d8":
            subsampling_class = Conv2dSubsampling8
        else:
            raise ValueError("unknown input_layer: " + input_layer)

        self.global_cmvn = global_cmvn
        self.embed = subsampling_class(
            input_size,
            output_size,
            dropout_rate,
            pos_enc_class(output_size, positional_dropout_rate),
        )

        self.normalize_before = normalize_before
        self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
        self.static_chunk_size = static_chunk_size
        self.use_dynamic_chunk = use_dynamic_chunk
        self.use_dynamic_left_chunk = use_dynamic_left_chunk

    def output_size(self) -> int:
        return self._output_size

    def forward(
        self,
        xs: torch.Tensor,
        xs_lens: torch.Tensor,
        decoding_chunk_size: int = 0,
        num_decoding_left_chunks: int = -1,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Embed positions in tensor.

        Args:
            xs: padded input tensor (B, T, D)
            xs_lens: input length (B)
            decoding_chunk_size: decoding chunk size for dynamic chunk
                0: default for training, use random dynamic chunk.
                <0: for decoding, use full chunk.
                >0: for decoding, use fixed chunk size as set.
            num_decoding_left_chunks: number of left chunks, this is for decoding,
            the chunk size is decoding_chunk_size.
                >=0: use num_decoding_left_chunks
                <0: use all left chunks
        Returns:
            encoder output tensor xs, and subsampled masks
            xs: padded output tensor (B, T' ~= T/subsample_rate, D)
            masks: torch.Tensor batch padding mask after subsample
                (B, 1, T' ~= T/subsample_rate)
        """
        T = xs.size(1)
        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)
        if self.global_cmvn is not None:
            xs = self.global_cmvn(xs)
        xs, pos_emb, masks = self.embed(xs, masks)
        mask_pad = masks  # (B, 1, T/subsample_rate)
        chunk_masks = add_optional_chunk_mask(
            xs,
            masks,
            self.use_dynamic_chunk,
            self.use_dynamic_left_chunk,
            decoding_chunk_size,
            self.static_chunk_size,
            num_decoding_left_chunks,
        )
        for layer in self.encoders:
            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
        if self.normalize_before:
            xs = self.after_norm(xs)
        # Here we assume the mask is not changed in encoder layers, so just
        # return the masks before encoder layers, and the masks will be used
        # for cross attention with decoder later
        return xs, masks

    def forward_chunk(
        self,
        xs: torch.Tensor,
        offset: int,
        required_cache_size: int,
        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
        att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """ Forward just one chunk

        Args:
            xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
                where `time == (chunk_size - 1) * subsample_rate + \
                        subsample.right_context + 1`
            offset (int): current offset in encoder output time stamp
            required_cache_size (int): cache size required for next chunk
                compuation
                >=0: actual cache size
                <0: means all history cache is required
            att_cache (torch.Tensor): cache tensor for KEY & VALUE in
                transformer/conformer attention, with shape
                (elayers, head, cache_t1, d_k * 2), where
                `head * d_k == hidden-dim` and
                `cache_t1 == chunk_size * num_decoding_left_chunks`.
            cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
                (elayers, b=1, hidden-dim, cache_t2), where
                `cache_t2 == cnn.lorder - 1`

        Returns:
            torch.Tensor: output of current input xs,
                with shape (b=1, chunk_size, hidden-dim).
            torch.Tensor: new attention cache required for next chunk, with
                dynamic shape (elayers, head, ?, d_k * 2)
                depending on required_cache_size.
            torch.Tensor: new conformer cnn cache required for next chunk, with
                same shape as the original cnn_cache.

        """
        assert xs.size(0) == 1
        # tmp_masks is just for interface compatibility
        tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
        tmp_masks = tmp_masks.unsqueeze(1)
        if self.global_cmvn is not None:
            xs = self.global_cmvn(xs)
        # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
        xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
        # NOTE(xcsong): After  embed, shape(xs) is (b=1, chunk_size, hidden-dim)
        elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
        chunk_size = xs.size(1)
        attention_key_size = cache_t1 + chunk_size
        pos_emb = self.embed.position_encoding(
            offset=offset - cache_t1, size=attention_key_size
        )
        if required_cache_size < 0:
            next_cache_start = 0
        elif required_cache_size == 0:
            next_cache_start = attention_key_size
        else:
            next_cache_start = max(attention_key_size - required_cache_size, 0)
        r_att_cache = []
        r_cnn_cache = []
        for i, layer in enumerate(self.encoders):
            # NOTE(xcsong): Before layer.forward
            #   shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
            #   shape(cnn_cache[i])       is (b=1, hidden-dim, cache_t2)
            xs, _, new_att_cache, new_cnn_cache = layer(
                xs,
                att_mask,
                pos_emb,
                att_cache=att_cache[i : i + 1] if elayers > 0 else att_cache,
                cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache,
            )
            # NOTE(xcsong): After layer.forward
            #   shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
            #   shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
            r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
            r_cnn_cache.append(new_cnn_cache.unsqueeze(0))
        if self.normalize_before:
            xs = self.after_norm(xs)

        # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
        #   ? may be larger than cache_t1, it depends on required_cache_size
        r_att_cache = torch.cat(r_att_cache, dim=0)
        # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
        r_cnn_cache = torch.cat(r_cnn_cache, dim=0)

        return (xs, r_att_cache, r_cnn_cache)

    def forward_chunk_by_chunk(
        self,
        xs: torch.Tensor,
        decoding_chunk_size: int,
        num_decoding_left_chunks: int = -1,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Forward input chunk by chunk with chunk_size like a streaming
            fashion

        Here we should pay special attention to computation cache in the
        streaming style forward chunk by chunk. Three things should be taken
        into account for computation in the current network:
            1. transformer/conformer encoder layers output cache
            2. convolution in conformer
            3. convolution in subsampling

        However, we don't implement subsampling cache for:
            1. We can control subsampling module to output the right result by
               overlapping input instead of cache left context, even though it
               wastes some computation, but subsampling only takes a very
               small fraction of computation in the whole model.
            2. Typically, there are several covolution layers with subsampling
               in subsampling module, it is tricky and complicated to do cache
               with different convolution layers with different subsampling
               rate.
            3. Currently, nn.Sequential is used to stack all the convolution
               layers in subsampling, we need to rewrite it to make it work
               with cache, which is not prefered.
        Args:
            xs (torch.Tensor): (1, max_len, dim)
            chunk_size (int): decoding chunk size
        """
        assert decoding_chunk_size > 0
        # The model is trained by static or dynamic chunk
        assert self.static_chunk_size > 0 or self.use_dynamic_chunk
        subsampling = self.embed.subsampling_rate
        context = self.embed.right_context + 1  # Add current frame
        stride = subsampling * decoding_chunk_size
        decoding_window = (decoding_chunk_size - 1) * subsampling + context
        num_frames = xs.size(1)
        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
        outputs = []
        offset = 0
        required_cache_size = decoding_chunk_size * num_decoding_left_chunks

        # Feed forward overlap input step by step
        for cur in range(0, num_frames - context + 1, stride):
            end = min(cur + decoding_window, num_frames)
            chunk_xs = xs[:, cur:end, :]
            (y, att_cache, cnn_cache) = self.forward_chunk(
                chunk_xs, offset, required_cache_size, att_cache, cnn_cache
            )
            outputs.append(y)
            offset += y.size(1)
        ys = torch.cat(outputs, 1)
        masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool)
        return ys, masks


class TransformerEncoder(BaseEncoder):
    """Transformer encoder module."""

    def __init__(
        self,
        input_size: int,
        output_size: int = 256,
        attention_heads: int = 4,
        linear_units: int = 2048,
        num_blocks: int = 6,
        dropout_rate: float = 0.1,
        positional_dropout_rate: float = 0.1,
        attention_dropout_rate: float = 0.0,
        input_layer: str = "conv2d",
        pos_enc_layer_type: str = "abs_pos",
        normalize_before: bool = True,
        static_chunk_size: int = 0,
        use_dynamic_chunk: bool = False,
        global_cmvn: torch.nn.Module = None,
        use_dynamic_left_chunk: bool = False,
    ):
        """Construct TransformerEncoder

        See Encoder for the meaning of each parameter.
        """
        super().__init__(
            input_size,
            output_size,
            attention_heads,
            linear_units,
            num_blocks,
            dropout_rate,
            positional_dropout_rate,
            attention_dropout_rate,
            input_layer,
            pos_enc_layer_type,
            normalize_before,
            static_chunk_size,
            use_dynamic_chunk,
            global_cmvn,
            use_dynamic_left_chunk,
        )
        self.encoders = torch.nn.ModuleList(
            [
                TransformerEncoderLayer(
                    output_size,
                    MultiHeadedAttention(
                        attention_heads, output_size, attention_dropout_rate
                    ),
                    PositionwiseFeedForward(output_size, linear_units, dropout_rate),
                    dropout_rate,
                    normalize_before,
                )
                for _ in range(num_blocks)
            ]
        )


class ConformerEncoder(BaseEncoder):
    """Conformer encoder module."""

    def __init__(
        self,
        input_size: int,
        output_size: int = 256,
        attention_heads: int = 4,
        linear_units: int = 2048,
        num_blocks: int = 6,
        dropout_rate: float = 0.1,
        positional_dropout_rate: float = 0.1,
        attention_dropout_rate: float = 0.0,
        input_layer: str = "conv2d",
        pos_enc_layer_type: str = "rel_pos",
        normalize_before: bool = True,
        static_chunk_size: int = 0,
        use_dynamic_chunk: bool = False,
        global_cmvn: torch.nn.Module = None,
        use_dynamic_left_chunk: bool = False,
        positionwise_conv_kernel_size: int = 1,
        macaron_style: bool = True,
        selfattention_layer_type: str = "rel_selfattn",
        activation_type: str = "swish",
        use_cnn_module: bool = True,
        cnn_module_kernel: int = 15,
        causal: bool = False,
        cnn_module_norm: str = "batch_norm",
    ):
        """Construct ConformerEncoder

        Args:
            input_size to use_dynamic_chunk, see in BaseEncoder
            positionwise_conv_kernel_size (int): Kernel size of positionwise
                conv1d layer.
            macaron_style (bool): Whether to use macaron style for
                positionwise layer.
            selfattention_layer_type (str): Encoder attention layer type,
                the parameter has no effect now, it's just for configure
                compatibility.
            activation_type (str): Encoder activation function type.
            use_cnn_module (bool): Whether to use convolution module.
            cnn_module_kernel (int): Kernel size of convolution module.
            causal (bool): whether to use causal convolution or not.
        """
        super().__init__(
            input_size,
            output_size,
            attention_heads,
            linear_units,
            num_blocks,
            dropout_rate,
            positional_dropout_rate,
            attention_dropout_rate,
            input_layer,
            pos_enc_layer_type,
            normalize_before,
            static_chunk_size,
            use_dynamic_chunk,
            global_cmvn,
            use_dynamic_left_chunk,
        )
        activation = get_activation(activation_type)

        # self-attention module definition
        if pos_enc_layer_type != "rel_pos":
            encoder_selfattn_layer = MultiHeadedAttention
        else:
            encoder_selfattn_layer = RelPositionMultiHeadedAttention
        encoder_selfattn_layer_args = (
            attention_heads,
            output_size,
            attention_dropout_rate,
        )
        # feed-forward module definition
        positionwise_layer = PositionwiseFeedForward
        positionwise_layer_args = (
            output_size,
            linear_units,
            dropout_rate,
            activation,
        )
        # convolution module definition
        convolution_layer = ConvolutionModule
        convolution_layer_args = (
            output_size,
            cnn_module_kernel,
            activation,
            cnn_module_norm,
            causal,
        )

        self.encoders = torch.nn.ModuleList(
            [
                ConformerEncoderLayer(
                    output_size,
                    encoder_selfattn_layer(*encoder_selfattn_layer_args),
                    positionwise_layer(*positionwise_layer_args),
                    (
                        positionwise_layer(*positionwise_layer_args)
                        if macaron_style
                        else None
                    ),
                    (
                        convolution_layer(*convolution_layer_args)
                        if use_cnn_module
                        else None
                    ),
                    dropout_rate,
                    normalize_before,
                )
                for _ in range(num_blocks)
            ]
        )