code optimize, model update, scripts

shixian.shi

2024-01-15 97d648c255316ec1fff5d82e46749076faabdd2d

 funasr/models/paraformer/decoder.py

@@ -1,25 +1,26 @@
from typing import List
from typing import Tuple
import logging
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import torch
import torch.nn as nn
import numpy as np
from typing import List, Tuple

from funasr.register import tables
from funasr.models.scama import utils as myutils
from funasr.models.transformer.decoder import BaseTransformerDecoder

from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.models.transformer.layer_norm import LayerNorm
from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
from funasr.models.transformer.utils.repeat import repeat
from funasr.models.transformer.decoder import DecoderLayer
from funasr.models.transformer.attention import MultiHeadedAttention
from funasr.models.transformer.layer_norm import LayerNorm
from funasr.models.transformer.embedding import PositionalEncoding
from funasr.models.transformer.attention import MultiHeadedAttention
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.models.transformer.decoder import BaseTransformerDecoder
from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
from funasr.register import tables
from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt

class DecoderLayerSANM(nn.Module):

class DecoderLayerSANM(torch.nn.Module):
    """Single decoder layer module.

    Args:
@@ -62,12 +63,12 @@
            self.norm2 = LayerNorm(size)
        if src_attn is not None:
            self.norm3 = LayerNorm(size)
        self.dropout = nn.Dropout(dropout_rate)
        self.dropout = torch.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)
            self.concat_linear1 = torch.nn.Linear(size + size, size)
            self.concat_linear2 = torch.nn.Linear(size + size, size)
        self.reserve_attn=False
        self.attn_mat = []