wenet/squeezeformer/encoder.py

# Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# 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.
# Modified from Squeezeformer(https://github.com/kssteven418/Squeezeformer)
#               Squeezeformer(https://github.com/upskyy/Squeezeformer)
#               NeMo(https://github.com/NVIDIA/NeMo)

import torch
import torch.nn as nn
from typing import Tuple, Union, Optional, List
from wenet.squeezeformer.subsampling \
    import DepthwiseConv2dSubsampling4, TimeReductionLayer1D, \
    TimeReductionLayer2D, TimeReductionLayerStream
from wenet.squeezeformer.encoder_layer import SqueezeformerEncoderLayer
from wenet.transformer.embedding import RelPositionalEncoding
from wenet.transformer.attention import MultiHeadedAttention
from wenet.squeezeformer.attention import RelPositionMultiHeadedAttention
from wenet.squeezeformer.positionwise_feed_forward \
    import PositionwiseFeedForward
from wenet.squeezeformer.convolution import ConvolutionModule
from wenet.utils.mask import make_pad_mask, add_optional_chunk_mask
from wenet.utils.class_utils import WENET_ACTIVATION_CLASSES


class SqueezeformerEncoder(nn.Module):

    def __init__(self,
                 input_size: int = 80,
                 encoder_dim: int = 256,
                 output_size: int = 256,
                 attention_heads: int = 4,
                 num_blocks: int = 12,
                 reduce_idx: Optional[Union[int, List[int]]] = 5,
                 recover_idx: Optional[Union[int, List[int]]] = 11,
                 feed_forward_expansion_factor: int = 4,
                 dw_stride: bool = False,
                 input_dropout_rate: float = 0.1,
                 pos_enc_layer_type: str = "rel_pos",
                 time_reduction_layer_type: str = "conv1d",
                 do_rel_shift: bool = True,
                 feed_forward_dropout_rate: float = 0.1,
                 attention_dropout_rate: float = 0.1,
                 cnn_module_kernel: int = 31,
                 cnn_norm_type: str = "batch_norm",
                 dropout: float = 0.1,
                 causal: bool = False,
                 adaptive_scale: bool = True,
                 activation_type: str = "swish",
                 init_weights: bool = True,
                 global_cmvn: torch.nn.Module = None,
                 normalize_before: bool = False,
                 use_dynamic_chunk: bool = False,
                 concat_after: bool = False,
                 static_chunk_size: int = 0,
                 use_dynamic_left_chunk: bool = False):
        """Construct SqueezeformerEncoder

        Args:
            input_size to use_dynamic_chunk, see in Transformer BaseEncoder.
            encoder_dim (int): The hidden dimension of encoder layer.
            output_size (int): The output dimension of final projection layer.
            attention_heads (int): Num of attention head in attention module.
            num_blocks (int): Num of encoder layers.
            reduce_idx Optional[Union[int, List[int]]]:
                reduce layer index, from 40ms to 80ms per frame.
            recover_idx Optional[Union[int, List[int]]]:
                recover layer index, from 80ms to 40ms per frame.
            feed_forward_expansion_factor (int): Enlarge coefficient of FFN.
            dw_stride (bool): Whether do depthwise convolution
                              on subsampling module.
            input_dropout_rate (float): Dropout rate of input projection layer.
            pos_enc_layer_type (str): Self attention type.
            time_reduction_layer_type (str): Conv1d or Conv2d reduction layer.
            do_rel_shift (bool): Whether to do relative shift
                                 operation on rel-attention module.
            cnn_module_kernel (int): Kernel size of CNN module.
            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.
            adaptive_scale (bool): Whether to use adaptive scale.
            init_weights (bool): Whether to initialize weights.
            causal (bool): whether to use causal convolution or not.
        """
        super(SqueezeformerEncoder, self).__init__()
        self.global_cmvn = global_cmvn
        self.reduce_idx: Optional[Union[int, List[int]]] = [reduce_idx] \
            if type(reduce_idx) == int else reduce_idx
        self.recover_idx: Optional[Union[int, List[int]]] = [recover_idx] \
            if type(recover_idx) == int else recover_idx
        self.check_ascending_list()
        if reduce_idx is None:
            self.time_reduce = None
        else:
            if recover_idx is None:
                self.time_reduce = 'normal'  # no recovery at the end
            else:
                self.time_reduce = 'recover'  # recovery at the end
                assert len(self.reduce_idx) == len(self.recover_idx)
            self.reduce_stride = 2
        self._output_size = output_size
        self.normalize_before = normalize_before
        self.static_chunk_size = static_chunk_size
        self.use_dynamic_chunk = use_dynamic_chunk
        self.use_dynamic_left_chunk = use_dynamic_left_chunk
        self.pos_enc_layer_type = pos_enc_layer_type
        activation = WENET_ACTIVATION_CLASSES[activation_type]()

        # self-attention module definition
        if pos_enc_layer_type != "rel_pos":
            encoder_selfattn_layer = MultiHeadedAttention
            encoder_selfattn_layer_args = (
                attention_heads,
                output_size,
                attention_dropout_rate,
            )
        else:
            encoder_selfattn_layer = RelPositionMultiHeadedAttention
            encoder_selfattn_layer_args = (attention_heads, encoder_dim,
                                           attention_dropout_rate,
                                           do_rel_shift, adaptive_scale,
                                           init_weights)

        # feed-forward module definition
        positionwise_layer = PositionwiseFeedForward
        positionwise_layer_args = (encoder_dim,
                                   encoder_dim * feed_forward_expansion_factor,
                                   feed_forward_dropout_rate, activation,
                                   adaptive_scale, init_weights)

        # convolution module definition
        convolution_layer = ConvolutionModule
        convolution_layer_args = (encoder_dim, cnn_module_kernel, activation,
                                  cnn_norm_type, causal, True, adaptive_scale,
                                  init_weights)

        self.embed = DepthwiseConv2dSubsampling4(
            1, encoder_dim, RelPositionalEncoding(encoder_dim,
                                                  dropout_rate=0.1), dw_stride,
            input_size, input_dropout_rate, init_weights)

        self.preln = nn.LayerNorm(encoder_dim)
        self.encoders = torch.nn.ModuleList([
            SqueezeformerEncoderLayer(
                encoder_dim,
                encoder_selfattn_layer(*encoder_selfattn_layer_args),
                positionwise_layer(*positionwise_layer_args),
                convolution_layer(*convolution_layer_args),
                positionwise_layer(*positionwise_layer_args), normalize_before,
                dropout, concat_after) for _ in range(num_blocks)
        ])
        if time_reduction_layer_type == 'conv1d':
            time_reduction_layer = TimeReductionLayer1D
            time_reduction_layer_args = {
                'channel': encoder_dim,
                'out_dim': encoder_dim,
            }
        elif time_reduction_layer_type == 'stream':
            time_reduction_layer = TimeReductionLayerStream
            time_reduction_layer_args = {
                'channel': encoder_dim,
                'out_dim': encoder_dim,
            }
        else:
            time_reduction_layer = TimeReductionLayer2D
            time_reduction_layer_args = {'encoder_dim': encoder_dim}

        self.time_reduction_layer = time_reduction_layer(
            **time_reduction_layer_args)
        self.time_recover_layer = nn.Linear(encoder_dim, encoder_dim)
        self.final_proj = None
        if output_size != encoder_dim:
            self.final_proj = nn.Linear(encoder_dim, output_size)

    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]:
        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)
        xs_lens = mask_pad.squeeze(1).sum(1)
        xs = self.preln(xs)
        recover_activations: \
            List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]] = []
        index = 0
        for i, layer in enumerate(self.encoders):
            if self.reduce_idx is not None:
                if self.time_reduce is not None and i in self.reduce_idx:
                    recover_activations.append(
                        (xs, chunk_masks, pos_emb, mask_pad))
                    xs, xs_lens, chunk_masks, mask_pad = \
                        self.time_reduction_layer(xs, xs_lens, chunk_masks, mask_pad)
                    pos_emb = pos_emb[:, ::2, :]
                    index += 1

            if self.recover_idx is not None:
                if self.time_reduce == 'recover' and i in self.recover_idx:
                    index -= 1
                    (recover_tensor, recover_chunk_masks,
                     recover_pos_emb, recover_mask_pad) \
                        = recover_activations[index]
                    # recover output length for ctc decode
                    xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2)
                    xs = self.time_recover_layer(xs)
                    recoverd_t = recover_tensor.size(1)
                    xs = recover_tensor + xs[:, :recoverd_t, :].contiguous()
                    chunk_masks = recover_chunk_masks
                    pos_emb = recover_pos_emb
                    mask_pad = recover_mask_pad
                    xs = xs.masked_fill(~mask_pad[:, 0, :].unsqueeze(-1), 0.0)

            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)

        if self.final_proj is not None:
            xs = self.final_proj(xs)
        return xs, masks

    def check_ascending_list(self):
        if self.reduce_idx is not None:
            assert self.reduce_idx == sorted(self.reduce_idx), \
                "reduce_idx should be int or ascending list"
        if self.recover_idx is not None:
            assert self.recover_idx == sorted(self.recover_idx), \
                "recover_idx should be int or ascending list"

    def calculate_downsampling_factor(self, i: int) -> int:
        if self.reduce_idx is None:
            return 1
        else:
            reduce_exp, recover_exp = 0, 0
            for exp, rd_idx in enumerate(self.reduce_idx):
                if i >= rd_idx:
                    reduce_exp = exp + 1
            if self.recover_idx is not None:
                for exp, rc_idx in enumerate(self.recover_idx):
                    if i >= rc_idx:
                        recover_exp = exp + 1
            return int(2**(reduce_exp - recover_exp))

    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 = []

        mask_pad = torch.ones(1,
                              xs.size(1),
                              device=xs.device,
                              dtype=torch.bool)
        mask_pad = mask_pad.unsqueeze(1)
        max_att_len: int = 0
        recover_activations: \
            List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]] = []
        index = 0
        xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int)
        xs = self.preln(xs)
        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)
            if self.reduce_idx is not None:
                if self.time_reduce is not None and i in self.reduce_idx:
                    recover_activations.append(
                        (xs, att_mask, pos_emb, mask_pad))
                    xs, xs_lens, att_mask, mask_pad = \
                        self.time_reduction_layer(xs, xs_lens, att_mask, mask_pad)
                    pos_emb = pos_emb[:, ::2, :]
                    index += 1

            if self.recover_idx is not None:
                if self.time_reduce == 'recover' and i in self.recover_idx:
                    index -= 1
                    (recover_tensor, recover_att_mask,
                     recover_pos_emb, recover_mask_pad) \
                        = recover_activations[index]
                    # recover output length for ctc decode
                    xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2)
                    xs = self.time_recover_layer(xs)
                    recoverd_t = recover_tensor.size(1)
                    xs = recover_tensor + xs[:, :recoverd_t, :].contiguous()
                    att_mask = recover_att_mask
                    pos_emb = recover_pos_emb
                    mask_pad = recover_mask_pad
                    if att_mask.size(1) != 0:
                        xs = xs.masked_fill(~att_mask[:, 0, :].unsqueeze(-1),
                                            0.0)

            factor = self.calculate_downsampling_factor(i)

            xs, _, new_att_cache, new_cnn_cache = layer(
                xs,
                att_mask,
                pos_emb,
                att_cache=att_cache[i:i + 1][:, :, ::factor, :]
                [:, :, :pos_emb.size(1) - xs.size(1), :]
                if elayers > 0 else att_cache[:, :, ::factor, :],
                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)
            cached_att \
                = new_att_cache[:, :, next_cache_start // factor:, :]
            cached_cnn = new_cnn_cache.unsqueeze(0)
            cached_att = cached_att.unsqueeze(3).\
                repeat(1, 1, 1, factor, 1).flatten(2, 3)
            if i == 0:
                # record length for the first block as max length
                max_att_len = cached_att.size(2)
            r_att_cache.append(cached_att[:, :, :max_att_len, :])
            r_cnn_cache.append(cached_cnn)
        # 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)

        if self.final_proj is not None:
            xs = self.final_proj(xs)
        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