update

嘉渊

2023-04-24 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1

 funasr/models/encoder/sanm_encoder.py

@@ -3,14 +3,14 @@
from typing import Sequence
from typing import Tuple
from typing import Union

import logging
import torch
import torch.nn as nn
from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
from typeguard import check_argument_types

import numpy as np
from funasr.modules.nets_utils import make_pad_mask
from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM
from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
from funasr.modules.embedding import SinusoidalPositionEncoder
from funasr.modules.layer_norm import LayerNorm
from funasr.modules.multi_layer_conv import Conv1dLinear
@@ -26,7 +26,7 @@
from funasr.modules.subsampling import TooShortUttError
from funasr.modules.subsampling import check_short_utt
from funasr.models.ctc import CTC
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.mask import subsequent_mask, vad_mask

class EncoderLayerSANM(nn.Module):
    def __init__(
@@ -114,7 +114,7 @@

        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder

class SANMEncoder(AbsEncoder):
class SANMEncoder(torch.nn.Module):
    """
    author: Speech Lab, Alibaba Group, China
    San-m: Memory equipped self-attention for end-to-end speech recognition
@@ -144,6 +144,8 @@
        kernel_size : int = 11,
        sanm_shfit : int = 0,
        selfattention_layer_type: str = "sanm",
        tf2torch_tensor_name_prefix_torch: str = "encoder",
        tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
    ):
        assert check_argument_types()
        super().__init__()
@@ -168,7 +170,7 @@
        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),
                SinusoidalPositionEncoder(),
            )
        elif input_layer is None:
            if input_size == output_size:
@@ -267,6 +269,8 @@
        self.interctc_use_conditioning = interctc_use_conditioning
        self.conditioning_layer = None
        self.dropout = nn.Dropout(dropout_rate)
        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf

    def output_size(self) -> int:
        return self._output_size
@@ -288,7 +292,7 @@
            position embedded tensor and mask
        """
        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
        xs_pad *= self.output_size()**0.5
        xs_pad = xs_pad * self.output_size()**0.5
        if self.embed is None:
            xs_pad = xs_pad
        elif (
@@ -342,8 +346,207 @@
            return (xs_pad, intermediate_outs), olens, None
        return xs_pad, olens, None

    def forward_chunk(self,
                      xs_pad: torch.Tensor,
                      ilens: torch.Tensor,
                      cache: dict = None,
                      ctc: CTC = None,
                      ):
        xs_pad *= self.output_size() ** 0.5
        if self.embed is None:
            xs_pad = xs_pad
        else:
            xs_pad = self.embed.forward_chunk(xs_pad, cache)

class SANMEncoderChunkOpt(AbsEncoder):
        encoder_outs = self.encoders0(xs_pad, None, None, None, None)
        xs_pad, masks = encoder_outs[0], encoder_outs[1]
        intermediate_outs = []
        if len(self.interctc_layer_idx) == 0:
            encoder_outs = self.encoders(xs_pad, None, None, None, None)
            xs_pad, masks = encoder_outs[0], encoder_outs[1]
        else:
            for layer_idx, encoder_layer in enumerate(self.encoders):
                encoder_outs = encoder_layer(xs_pad, None, None, None, None)
                xs_pad, masks = encoder_outs[0], encoder_outs[1]
                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)

        if len(intermediate_outs) > 0:
            return (xs_pad, intermediate_outs), None, None
        return xs_pad, ilens, None

    def gen_tf2torch_map_dict(self):
        tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
        tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
        map_dict_local = {
            ## encoder
            # cicd
            "{}.encoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.self_attn.linear_q_k_v.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (768,256),(1,256,768)
            "{}.encoders.layeridx.self_attn.linear_q_k_v.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (768,),(768,)
            "{}.encoders.layeridx.self_attn.fsmn_block.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/depth_conv_w".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 2, 0),
                 },  # (256,1,31),(1,31,256,1)
            "{}.encoders.layeridx.self_attn.linear_out.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,256),(1,256,256)
            "{}.encoders.layeridx.self_attn.linear_out.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            # ffn
            "{}.encoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.encoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.encoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,1024),(1,1024,256)
            "{}.encoders.layeridx.feed_forward.w_2.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d_1/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            # out norm
            "{}.after_norm.weight".format(tensor_name_prefix_torch):
                {"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.after_norm.bias".format(tensor_name_prefix_torch):
                {"name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
        
        }
    
        return map_dict_local

    def convert_tf2torch(self,
                         var_dict_tf,
                         var_dict_torch,
                         ):
        
        map_dict = self.gen_tf2torch_map_dict()
    
        var_dict_torch_update = dict()
        for name in sorted(var_dict_torch.keys(), reverse=False):
            names = name.split('.')
            if names[0] == self.tf2torch_tensor_name_prefix_torch:
                if names[1] == "encoders0":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                
                    name_q = name_q.replace("encoders0", "encoders")
                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
                elif names[1] == "encoders":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                    layeridx_bias = 1
                    layeridx += layeridx_bias
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
            
                elif names[1] == "after_norm":
                    name_tf = map_dict[name]["name"]
                    data_tf = var_dict_tf[name_tf]
                    data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                    var_dict_torch_update[name] = data_tf
                    logging.info(
                        "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
                                                                                      var_dict_tf[name_tf].shape))
    
        return var_dict_torch_update


class SANMEncoderChunkOpt(torch.nn.Module):
    """
    author: Speech Lab, Alibaba Group, China
    SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
@@ -378,6 +581,8 @@
            pad_left: Union[int, Sequence[int]] = (0,),
            encoder_att_look_back_factor: Union[int, Sequence[int]] = (1,),
            decoder_att_look_back_factor: Union[int, Sequence[int]] = (1,),
            tf2torch_tensor_name_prefix_torch: str = "encoder",
            tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
    ):
        assert check_argument_types()
        super().__init__()
@@ -508,6 +713,8 @@
            encoder_att_look_back_factor=encoder_att_look_back_factor,
            decoder_att_look_back_factor=decoder_att_look_back_factor,
        )
        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf

    def output_size(self) -> int:
        return self._output_size
@@ -593,3 +800,388 @@
        if len(intermediate_outs) > 0:
            return (xs_pad, intermediate_outs), olens, None
        return xs_pad, olens, None

    def gen_tf2torch_map_dict(self):
        tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
        tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
        map_dict_local = {
            ## encoder
            # cicd
            "{}.encoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.self_attn.linear_q_k_v.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (768,256),(1,256,768)
            "{}.encoders.layeridx.self_attn.linear_q_k_v.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (768,),(768,)
            "{}.encoders.layeridx.self_attn.fsmn_block.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/depth_conv_w".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 2, 0),
                 },  # (256,1,31),(1,31,256,1)
            "{}.encoders.layeridx.self_attn.linear_out.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,256),(1,256,256)
            "{}.encoders.layeridx.self_attn.linear_out.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            # ffn
            "{}.encoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.encoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.encoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.encoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,1024),(1,1024,256)
            "{}.encoders.layeridx.feed_forward.w_2.bias".format(tensor_name_prefix_torch):
                {"name": "{}/layer_layeridx/ffn/conv1d_1/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            # out norm
            "{}.after_norm.weight".format(tensor_name_prefix_torch):
                {"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.after_norm.bias".format(tensor_name_prefix_torch):
                {"name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
        
        }
    
        return map_dict_local

    def convert_tf2torch(self,
                         var_dict_tf,
                         var_dict_torch,
                         ):
    
        map_dict = self.gen_tf2torch_map_dict()
    
        var_dict_torch_update = dict()
        for name in sorted(var_dict_torch.keys(), reverse=False):
            names = name.split('.')
            if names[0] == self.tf2torch_tensor_name_prefix_torch:
                if names[1] == "encoders0":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                
                    name_q = name_q.replace("encoders0", "encoders")
                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
                elif names[1] == "encoders":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                    layeridx_bias = 1
                    layeridx += layeridx_bias
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
            
                elif names[1] == "after_norm":
                    name_tf = map_dict[name]["name"]
                    data_tf = var_dict_tf[name_tf]
                    data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                    var_dict_torch_update[name] = data_tf
                    logging.info(
                        "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
                                                                                      var_dict_tf[name_tf].shape))
    
        return var_dict_torch_update


class SANMVadEncoder(torch.nn.Module):
    """
    author: Speech Lab, Alibaba Group, China

    """

    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=SinusoidalPositionEncoder,
        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,
        kernel_size : int = 11,
        sanm_shfit : int = 0,
        selfattention_layer_type: str = "sanm",
    ):
        assert check_argument_types()
        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),
                SinusoidalPositionEncoder(),
            )
        elif input_layer is None:
            if input_size == output_size:
                self.embed = None
            else:
                self.embed = torch.nn.Linear(input_size, output_size)
        elif input_layer == "pe":
            self.embed = SinusoidalPositionEncoder()
        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.")

        if selfattention_layer_type == "selfattn":
            encoder_selfattn_layer = MultiHeadedAttention
            encoder_selfattn_layer_args = (
                attention_heads,
                output_size,
                attention_dropout_rate,
            )

        elif selfattention_layer_type == "sanm":
            self.encoder_selfattn_layer = MultiHeadedAttentionSANMwithMask
            encoder_selfattn_layer_args0 = (
                attention_heads,
                input_size,
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shfit,
            )

            encoder_selfattn_layer_args = (
                attention_heads,
                output_size,
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shfit,
            )

        self.encoders0 = repeat(
            1,
            lambda lnum: EncoderLayerSANM(
                input_size,
                output_size,
                self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
                positionwise_layer(*positionwise_layer_args),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )

        self.encoders = repeat(
            num_blocks-1,
            lambda lnum: EncoderLayerSANM(
                output_size,
                output_size,
                self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
                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
        self.dropout = nn.Dropout(dropout_rate)

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

    def forward(
        self,
        xs_pad: torch.Tensor,
        ilens: torch.Tensor,
        vad_indexes: 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)
        sub_masks = subsequent_mask(masks.size(-1), device=xs_pad.device).unsqueeze(0)
        no_future_masks = masks & sub_masks
        xs_pad *= self.output_size()**0.5
        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)

        # xs_pad = self.dropout(xs_pad)
        mask_tup0 = [masks, no_future_masks]
        encoder_outs = self.encoders0(xs_pad, mask_tup0)
        xs_pad, _ = encoder_outs[0], encoder_outs[1]
        intermediate_outs = []


        for layer_idx, encoder_layer in enumerate(self.encoders):
                if layer_idx + 1 == len(self.encoders):
                    # This is last layer.
                    coner_mask = torch.ones(masks.size(0),
                                            masks.size(-1),
                                            masks.size(-1),
                                            device=xs_pad.device,
                                            dtype=torch.bool)
                    for word_index, length in enumerate(ilens):
                        coner_mask[word_index, :, :] = vad_mask(masks.size(-1),
                                                                vad_indexes[word_index],
                                                                device=xs_pad.device)
                    layer_mask = masks & coner_mask
                else:
                    layer_mask = no_future_masks
                mask_tup1 = [masks, layer_mask]
                encoder_outs = encoder_layer(xs_pad, mask_tup1)
                xs_pad, layer_mask = encoder_outs[0], encoder_outs[1]

        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