FunASR/funasr/models/decoder/sanm_decoder.py

from typing import List
from typing import Tuple

import torch
import torch.nn as nn
from funasr.modules.streaming_utils import utils as myutils
from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
from typeguard import check_argument_types

from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.modules.embedding import PositionalEncoding
from funasr.modules.layer_norm import LayerNorm
from funasr.modules.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
from funasr.modules.repeat import repeat


class DecoderLayerSANM(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(DecoderLayerSANM, self).__init__()
        self.size = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)
        if self_attn is not None:
            self.norm2 = LayerNorm(size)
        if src_attn is not None:
            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=None, 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).

        """
        # tgt = self.dropout(tgt)
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)
        tgt = self.feed_forward(tgt)

        x = tgt
        if self.self_attn:
            if self.normalize_before:
                tgt = self.norm2(tgt)
            if self.training:
                cache = None
            x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
            x = residual + self.dropout(x)

        if self.src_attn is not None:
            residual = x
            if self.normalize_before:
                x = self.norm3(x)

            x = residual + self.dropout(self.src_attn(x, memory, memory_mask))


        return x, tgt_mask, memory, memory_mask, cache


class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
    """
    author: Speech Lab, Alibaba Group, China
    SCAMA: Streaming chunk-aware multihead attention for online 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,
            att_layer_num: int = 6,
            kernel_size: int = 21,
            sanm_shfit: int = None,
            concat_embeds: bool = False,
            attention_dim: int = None,
    ):
        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,
        )
        if attention_dim is None:
            attention_dim = encoder_output_size

        if input_layer == "embed":
            self.embed = torch.nn.Sequential(
                torch.nn.Embedding(vocab_size, attention_dim),
            )
        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

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads, attention_dim, src_attention_dropout_rate, encoder_output_size=encoder_output_size
                ),
                PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )
        if num_blocks - att_layer_num <= 0:
            self.decoders2 = None
        else:
            self.decoders2 = repeat(
                num_blocks - att_layer_num,
                lambda lnum: DecoderLayerSANM(
                    attention_dim,
                    MultiHeadedAttentionSANMDecoder(
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                    dropout_rate,
                    normalize_before,
                    concat_after,
                ),
            )

        self.decoders3 = repeat(
            1,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                None,
                None,
                PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )
        if concat_embeds:
            self.embed_concat_ffn = repeat(
                1,
                lambda lnum: DecoderLayerSANM(
                    attention_dim + encoder_output_size,
                    None,
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim + encoder_output_size, linear_units, dropout_rate,
                                                      adim=attention_dim),
                    dropout_rate,
                    normalize_before,
                    concat_after,
                ),
            )
        else:
            self.embed_concat_ffn = None
        self.concat_embeds = concat_embeds

    def forward(
            self,
            hs_pad: torch.Tensor,
            hlens: torch.Tensor,
            ys_in_pad: torch.Tensor,
            ys_in_lens: torch.Tensor,
            chunk_mask: torch.Tensor = None,
            pre_acoustic_embeds: torch.Tensor = None,
    ) -> 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 = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]

        memory = hs_pad
        memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
        if chunk_mask is not None:
            memory_mask = memory_mask * chunk_mask
            if tgt_mask.size(1) != memory_mask.size(1):
                memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)

        x = self.embed(tgt)

        if pre_acoustic_embeds is not None and self.concat_embeds:
            x = torch.cat((x, pre_acoustic_embeds), dim=-1)
            x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)

        x, tgt_mask, memory, memory_mask, _ = self.decoders(
            x, tgt_mask, memory, memory_mask
        )
        if self.decoders2 is not None:
            x, tgt_mask, memory, memory_mask, _ = self.decoders2(
                x, tgt_mask, memory, memory_mask
            )
        x, tgt_mask, memory, memory_mask, _ = self.decoders3(
            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 score(self, ys, state, x, x_mask=None, pre_acoustic_embeds: torch.Tensor = None, ):
        """Score."""
        ys_mask = myutils.sequence_mask(torch.tensor([len(ys)], dtype=torch.int32), device=x.device)[:, :, None]
        logp, state = self.forward_one_step(
            ys.unsqueeze(0), ys_mask, x.unsqueeze(0), memory_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds,
            cache=state
        )
        return logp.squeeze(0), state

    def forward_one_step(
            self,
            tgt: torch.Tensor,
            tgt_mask: torch.Tensor,
            memory: torch.Tensor,
            memory_mask: torch.Tensor = None,
            pre_acoustic_embeds: torch.Tensor = None,
            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 = tgt[:, -1:]
        tgt_mask = None
        x = self.embed(x)

        if pre_acoustic_embeds is not None and self.concat_embeds:
            x = torch.cat((x, pre_acoustic_embeds), dim=-1)
            x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)

        if cache is None:
            cache_layer_num = len(self.decoders)
            if self.decoders2 is not None:
                cache_layer_num += len(self.decoders2)
            cache = [None] * cache_layer_num
        new_cache = []
        # for c, decoder in zip(cache, self.decoders):
        for i in range(self.att_layer_num):
            decoder = self.decoders[i]
            c = cache[i]
            x, tgt_mask, memory, memory_mask, c_ret = decoder(
                x, tgt_mask, memory, memory_mask, cache=c
            )
            new_cache.append(c_ret)

        if self.num_blocks - self.att_layer_num >= 1:
            for i in range(self.num_blocks - self.att_layer_num):
                j = i + self.att_layer_num
                decoder = self.decoders2[i]
                c = cache[j]
                x, tgt_mask, memory, memory_mask, c_ret = decoder(
                    x, tgt_mask, memory, memory_mask, cache=c
                )
                new_cache.append(c_ret)

        for decoder in self.decoders3:
            x, tgt_mask, memory, memory_mask, _ = decoder(
                x, tgt_mask, memory, None, cache=None
            )

        if self.normalize_before:
            y = self.after_norm(x[:, -1])
        else:
            y = x[:, -1]
        if self.output_layer is not None:
            y = self.output_layer(y)
            y = torch.log_softmax(y, dim=-1)

        return y, new_cache

class ParaformerSANMDecoder(BaseTransformerDecoder):
    """
    author: Speech Lab, Alibaba Group, China
    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,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shfit: int = 0,
    ):
        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

        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

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads, attention_dim, src_attention_dropout_rate
                ),
                PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )
        if num_blocks - att_layer_num <= 0:
            self.decoders2 = None
        else:
            self.decoders2 = repeat(
                num_blocks - att_layer_num,
                lambda lnum: DecoderLayerSANM(
                    attention_dim,
                    MultiHeadedAttentionSANMDecoder(
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                    dropout_rate,
                    normalize_before,
                    concat_after,
                ),
            )

        self.decoders3 = repeat(
            1,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                None,
                None,
                PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )

    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 = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]

        memory = hs_pad
        memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]

        x = tgt
        x, tgt_mask, memory, memory_mask, _ = self.decoders(
            x, tgt_mask, memory, memory_mask
        )
        if self.decoders2 is not None:
            x, tgt_mask, memory, memory_mask, _ = self.decoders2(
                x, tgt_mask, memory, memory_mask
            )
        x, tgt_mask, memory, memory_mask, _ = self.decoders3(
            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 score(self, ys, state, x):
        """Score."""
        ys_mask = myutils.sequence_mask(torch.tensor([len(ys)], dtype=torch.int32), device=x.device)[:, :, None]
        logp, state = self.forward_one_step(
            ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
        )
        return logp.squeeze(0), state

    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_layer_num = len(self.decoders)
            if self.decoders2 is not None:
                cache_layer_num += len(self.decoders2)
            cache = [None] * cache_layer_num
        new_cache = []
        # for c, decoder in zip(cache, self.decoders):
        for i in range(self.att_layer_num):
            decoder = self.decoders[i]
            c = cache[i]
            x, tgt_mask, memory, memory_mask, c_ret = decoder(
                x, tgt_mask, memory, None, cache=c
            )
            new_cache.append(c_ret)

        if self.num_blocks - self.att_layer_num > 1:
            for i in range(self.num_blocks - self.att_layer_num):
                j = i + self.att_layer_num
                decoder = self.decoders2[i]
                c = cache[j]
                x, tgt_mask, memory, memory_mask, c_ret = decoder(
                    x, tgt_mask, memory, None, cache=c
                )
                new_cache.append(c_ret)

        for decoder in self.decoders3:

            x, tgt_mask, memory, memory_mask, _ = decoder(
                x, tgt_mask, memory, None, cache=None
            )

        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