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"""Encoder definition.""" |
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from typing import Optional |
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from typing import Tuple |
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import torch |
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from typeguard import check_argument_types |
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from espnet.nets.pytorch_backend.nets_utils import make_pad_mask |
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from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention |
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from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding |
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from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer |
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from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm |
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from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear |
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from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d |
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from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import ( |
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PositionwiseFeedForward, |
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) |
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from espnet.nets.pytorch_backend.transformer.repeat import repeat |
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from espnet.nets.pytorch_backend.transformer.subsampling import check_short_utt |
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from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling |
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from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling6 |
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from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling8 |
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from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError |
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from espnet2.asr.encoder.abs_encoder import AbsEncoder |
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class TransformerEncoder(AbsEncoder): |
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"""Transformer encoder module. |
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Args: |
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input_size: input dim |
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output_size: dimension of attention |
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attention_heads: the number of heads of multi head attention |
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linear_units: the number of units of position-wise feed forward |
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num_blocks: the number of decoder blocks |
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dropout_rate: dropout rate |
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attention_dropout_rate: dropout rate in attention |
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positional_dropout_rate: dropout rate after adding positional encoding |
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input_layer: input layer type |
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pos_enc_class: PositionalEncoding or ScaledPositionalEncoding |
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normalize_before: whether to use layer_norm before the first block |
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concat_after: whether to concat attention layer's input and output |
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if True, additional linear will be applied. |
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i.e. x -> x + linear(concat(x, att(x))) |
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if False, no additional linear will be applied. |
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i.e. x -> x + att(x) |
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positionwise_layer_type: linear of conv1d |
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positionwise_conv_kernel_size: kernel size of positionwise conv1d layer |
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padding_idx: padding_idx for input_layer=embed |
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""" |
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def __init__( |
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self, |
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input_size: int, |
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output_size: int = 256, |
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attention_heads: int = 4, |
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linear_units: int = 2048, |
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num_blocks: int = 6, |
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dropout_rate: float = 0.1, |
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positional_dropout_rate: float = 0.1, |
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attention_dropout_rate: float = 0.0, |
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input_layer: Optional[str] = "conv2d", |
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pos_enc_class=PositionalEncoding, |
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normalize_before: bool = True, |
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concat_after: bool = False, |
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positionwise_layer_type: str = "linear", |
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positionwise_conv_kernel_size: int = 1, |
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padding_idx: int = -1, |
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): |
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assert check_argument_types() |
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super().__init__() |
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self._output_size = output_size |
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if input_layer == "linear": |
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self.embed = torch.nn.Sequential( |
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torch.nn.Linear(input_size, output_size), |
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torch.nn.LayerNorm(output_size), |
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torch.nn.Dropout(dropout_rate), |
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torch.nn.ReLU(), |
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pos_enc_class(output_size, positional_dropout_rate), |
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) |
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elif input_layer == "conv2d": |
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self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) |
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elif input_layer == "conv2d6": |
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self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) |
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elif input_layer == "conv2d8": |
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self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) |
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elif input_layer == "embed": |
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self.embed = torch.nn.Sequential( |
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torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), |
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pos_enc_class(output_size, positional_dropout_rate), |
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) |
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elif input_layer is None: |
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self.embed = torch.nn.Sequential( |
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pos_enc_class(output_size, positional_dropout_rate) |
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) |
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else: |
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raise ValueError("unknown input_layer: " + input_layer) |
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self.normalize_before = normalize_before |
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if positionwise_layer_type == "linear": |
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positionwise_layer = PositionwiseFeedForward |
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positionwise_layer_args = ( |
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output_size, |
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linear_units, |
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dropout_rate, |
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) |
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elif positionwise_layer_type == "conv1d": |
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positionwise_layer = MultiLayeredConv1d |
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positionwise_layer_args = ( |
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output_size, |
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linear_units, |
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positionwise_conv_kernel_size, |
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dropout_rate, |
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) |
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elif positionwise_layer_type == "conv1d-linear": |
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positionwise_layer = Conv1dLinear |
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positionwise_layer_args = ( |
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output_size, |
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linear_units, |
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positionwise_conv_kernel_size, |
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dropout_rate, |
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) |
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else: |
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raise NotImplementedError("Support only linear or conv1d.") |
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self.encoders = repeat( |
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num_blocks, |
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lambda lnum: EncoderLayer( |
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output_size, |
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MultiHeadedAttention( |
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attention_heads, output_size, attention_dropout_rate |
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), |
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positionwise_layer(*positionwise_layer_args), |
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dropout_rate, |
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normalize_before, |
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concat_after, |
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), |
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) |
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if self.normalize_before: |
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self.after_norm = LayerNorm(output_size) |
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def output_size(self) -> int: |
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return self._output_size |
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def forward( |
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self, |
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xs_pad: torch.Tensor, |
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ilens: torch.Tensor, |
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prev_states: torch.Tensor = None, |
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) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: |
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"""Embed positions in tensor. |
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Args: |
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xs_pad: input tensor (B, L, D) |
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ilens: input length (B) |
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prev_states: Not to be used now. |
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Returns: |
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position embedded tensor and mask |
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""" |
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masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) |
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if ( |
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isinstance(self.embed, Conv2dSubsampling) |
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or isinstance(self.embed, Conv2dSubsampling6) |
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or isinstance(self.embed, Conv2dSubsampling8) |
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): |
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short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) |
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if short_status: |
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raise TooShortUttError( |
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f"has {xs_pad.size(1)} frames and is too short for subsampling " |
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+ f"(it needs more than {limit_size} frames), return empty results", |
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xs_pad.size(1), |
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limit_size, |
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) |
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xs_pad, masks = self.embed(xs_pad, masks) |
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else: |
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xs_pad = self.embed(xs_pad) |
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xs_pad, masks = self.encoders(xs_pad, masks) |
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if self.normalize_before: |
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xs_pad = self.after_norm(xs_pad) |
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olens = masks.squeeze(1).sum(1) |
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return xs_pad, olens, None |
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