funasr2

游雁

2023-12-19 0e622e694e6cb4459955f1e5942a7c53349ce640

 funasr/models/language_model/transformer_encoder.py

File was renamed from funasr/models/transformer/transformer_encoder.py
@@ -11,8 +11,6 @@
from torch import nn
import logging

from funasr.models.ctc import CTC
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.transformer.attention import MultiHeadedAttention
from funasr.models.transformer.embedding import PositionalEncoding
from funasr.models.transformer.layer_norm import LayerNorm
@@ -22,18 +20,17 @@
from funasr.models.transformer.positionwise_feed_forward import (
    PositionwiseFeedForward,  # noqa: H301
)
from funasr.models.transformer.repeat import repeat
from funasr.models.transformer.utils.nets_utils import rename_state_dict
from funasr.models.transformer.utils.repeat import repeat
from funasr.models.transformer.utils.dynamic_conv import DynamicConvolution
from funasr.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D
from funasr.models.transformer.utils.lightconv import LightweightConvolution
from funasr.models.transformer.utils.lightconv2d import LightweightConvolution2D
from funasr.models.transformer.subsampling import Conv2dSubsampling
from funasr.models.transformer.subsampling import Conv2dSubsampling2
from funasr.models.transformer.subsampling import Conv2dSubsampling6
from funasr.models.transformer.subsampling import Conv2dSubsampling8
from funasr.models.transformer.subsampling import TooShortUttError
from funasr.models.transformer.subsampling import check_short_utt
from funasr.models.transformer.utils.subsampling import Conv2dSubsampling
from funasr.models.transformer.utils.subsampling import Conv2dSubsampling2
from funasr.models.transformer.utils.subsampling import Conv2dSubsampling6
from funasr.models.transformer.utils.subsampling import Conv2dSubsampling8
from funasr.models.transformer.utils.subsampling import TooShortUttError
from funasr.models.transformer.utils.subsampling import check_short_utt


class EncoderLayer(nn.Module):
@@ -143,216 +140,7 @@
        return x, mask


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

    Args:
        input_size: input dim
        output_size: dimension of attention
        attention_heads: the number of heads of multi head attention
        linear_units: the number of units of position-wise feed forward
        num_blocks: the number of decoder blocks
        dropout_rate: dropout rate
        attention_dropout_rate: dropout rate in attention
        positional_dropout_rate: dropout rate after adding positional encoding
        input_layer: input layer type
        pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
        normalize_before: whether to use layer_norm before the first block
        concat_after: 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)
        positionwise_layer_type: linear of conv1d
        positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
        padding_idx: padding_idx for input_layer=embed
    """

    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: Optional[str] = "conv2d",
            pos_enc_class=PositionalEncoding,
            normalize_before: bool = True,
            concat_after: bool = False,
            positionwise_layer_type: str = "linear",
            positionwise_conv_kernel_size: int = 1,
            padding_idx: int = -1,
            interctc_layer_idx: List[int] = [],
            interctc_use_conditioning: bool = False,
    ):
        super().__init__()
        self._output_size = output_size

        if input_layer == "linear":
            self.embed = torch.nn.Sequential(
                torch.nn.Linear(input_size, output_size),
                torch.nn.LayerNorm(output_size),
                torch.nn.Dropout(dropout_rate),
                torch.nn.ReLU(),
                pos_enc_class(output_size, positional_dropout_rate),
            )
        elif input_layer == "conv2d":
            self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
        elif input_layer == "conv2d2":
            self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
        elif input_layer == "conv2d6":
            self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
        elif input_layer == "conv2d8":
            self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
        elif input_layer == "embed":
            self.embed = torch.nn.Sequential(
                torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
                pos_enc_class(output_size, positional_dropout_rate),
            )
        elif input_layer is None:
            if input_size == output_size:
                self.embed = None
            else:
                self.embed = torch.nn.Linear(input_size, output_size)
        else:
            raise ValueError("unknown input_layer: " + input_layer)
        self.normalize_before = normalize_before
        if positionwise_layer_type == "linear":
            positionwise_layer = PositionwiseFeedForward
            positionwise_layer_args = (
                output_size,
                linear_units,
                dropout_rate,
            )
        elif positionwise_layer_type == "conv1d":
            positionwise_layer = MultiLayeredConv1d
            positionwise_layer_args = (
                output_size,
                linear_units,
                positionwise_conv_kernel_size,
                dropout_rate,
            )
        elif positionwise_layer_type == "conv1d-linear":
            positionwise_layer = Conv1dLinear
            positionwise_layer_args = (
                output_size,
                linear_units,
                positionwise_conv_kernel_size,
                dropout_rate,
            )
        else:
            raise NotImplementedError("Support only linear or conv1d.")
        self.encoders = repeat(
            num_blocks,
            lambda lnum: EncoderLayer(
                output_size,
                MultiHeadedAttention(
                    attention_heads, output_size, attention_dropout_rate
                ),
                positionwise_layer(*positionwise_layer_args),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )
        if self.normalize_before:
            self.after_norm = LayerNorm(output_size)

        self.interctc_layer_idx = interctc_layer_idx
        if len(interctc_layer_idx) > 0:
            assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
        self.interctc_use_conditioning = interctc_use_conditioning
        self.conditioning_layer = None

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

    def forward(
            self,
            xs_pad: torch.Tensor,
            ilens: torch.Tensor,
            prev_states: torch.Tensor = None,
            ctc: CTC = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
        """Embed positions in tensor.

        Args:
            xs_pad: input tensor (B, L, D)
            ilens: input length (B)
            prev_states: Not to be used now.
        Returns:
            position embedded tensor and mask
        """
        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)

        if self.embed is None:
            xs_pad = xs_pad
        elif (
                isinstance(self.embed, Conv2dSubsampling)
                or isinstance(self.embed, Conv2dSubsampling2)
                or isinstance(self.embed, Conv2dSubsampling6)
                or isinstance(self.embed, Conv2dSubsampling8)
        ):
            short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
            if short_status:
                raise TooShortUttError(
                    f"has {xs_pad.size(1)} frames and is too short for subsampling "
                    + f"(it needs more than {limit_size} frames), return empty results",
                    xs_pad.size(1),
                    limit_size,
                )
            xs_pad, masks = self.embed(xs_pad, masks)
        else:
            xs_pad = self.embed(xs_pad)

        intermediate_outs = []
        if len(self.interctc_layer_idx) == 0:
            xs_pad, masks = self.encoders(xs_pad, masks)
        else:
            for layer_idx, encoder_layer in enumerate(self.encoders):
                xs_pad, masks = encoder_layer(xs_pad, masks)

                if layer_idx + 1 in self.interctc_layer_idx:
                    encoder_out = xs_pad

                    # intermediate outputs are also normalized
                    if self.normalize_before:
                        encoder_out = self.after_norm(encoder_out)

                    intermediate_outs.append((layer_idx + 1, encoder_out))

                    if self.interctc_use_conditioning:
                        ctc_out = ctc.softmax(encoder_out)
                        xs_pad = xs_pad + self.conditioning_layer(ctc_out)

        if self.normalize_before:
            xs_pad = self.after_norm(xs_pad)

        olens = masks.squeeze(1).sum(1)
        if len(intermediate_outs) > 0:
            return (xs_pad, intermediate_outs), olens, None
        return xs_pad, olens, None


def _pre_hook(
    state_dict,
    prefix,
    local_metadata,
    strict,
    missing_keys,
    unexpected_keys,
    error_msgs,
):
    # https://github.com/espnet/espnet/commit/21d70286c354c66c0350e65dc098d2ee236faccc#diff-bffb1396f038b317b2b64dd96e6d3563
    rename_state_dict(prefix + "input_layer.", prefix + "embed.", state_dict)
    # https://github.com/espnet/espnet/commit/3d422f6de8d4f03673b89e1caef698745ec749ea#diff-bffb1396f038b317b2b64dd96e6d3563
    rename_state_dict(prefix + "norm.", prefix + "after_norm.", state_dict)


class TransformerEncoder_s0(torch.nn.Module):
class TransformerEncoder_lm(nn.Module):
    """Transformer encoder module.

    Args:
@@ -418,8 +206,7 @@
        conditioning_layer_dim=None,
    ):
        """Construct an Encoder object."""
        super(TransformerEncoder_s0, self).__init__()
        self._register_load_state_dict_pre_hook(_pre_hook)
        super().__init__()

        self.conv_subsampling_factor = 1
        if input_layer == "linear":