From 0e622e694e6cb4459955f1e5942a7c53349ce640 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 19 十二月 2023 21:58:14 +0800
Subject: [PATCH] funasr2
---
funasr/models/language_model/transformer_encoder.py | 231 ++-------------------------------------------------------
1 files changed, 9 insertions(+), 222 deletions(-)
diff --git a/funasr/models/transformer/transformer_encoder.py b/funasr/models/language_model/transformer_encoder.py
similarity index 66%
rename from funasr/models/transformer/transformer_encoder.py
rename to funasr/models/language_model/transformer_encoder.py
index 1126da0..21f3548 100644
--- a/funasr/models/transformer/transformer_encoder.py
+++ b/funasr/models/language_model/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":
--
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