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kongdeqiang

2026-03-13 28ccfbfc51068a663a80764e14074df5edf2b5ba

 funasr/models/language_model/transformer_encoder.py

@@ -56,14 +56,14 @@
    """

    def __init__(
            self,
            size,
            self_attn,
            feed_forward,
            dropout_rate,
            normalize_before=True,
            concat_after=False,
            stochastic_depth_rate=0.0,
        self,
        size,
        self_attn,
        feed_forward,
        dropout_rate,
        normalize_before=True,
        concat_after=False,
        stochastic_depth_rate=0.0,
    ):
        """Construct an EncoderLayer object."""
        super(EncoderLayer, self).__init__()
@@ -121,9 +121,7 @@
            x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
            x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
        else:
            x = residual + stoch_layer_coeff * self.dropout(
                self.self_attn(x_q, x, x, mask)
            )
            x = residual + stoch_layer_coeff * self.dropout(self.self_attn(x_q, x, x, mask))
        if not self.normalize_before:
            x = self.norm1(x)

@@ -245,9 +243,7 @@
                pos_enc_class(attention_dim, positional_dropout_rate),
            )
        elif input_layer is None:
            self.embed = torch.nn.Sequential(
                pos_enc_class(attention_dim, positional_dropout_rate)
            )
            self.embed = torch.nn.Sequential(pos_enc_class(attention_dim, positional_dropout_rate))
        else:
            raise ValueError("unknown input_layer: " + input_layer)
        self.normalize_before = normalize_before
@@ -288,8 +284,7 @@
            ]
        elif selfattention_layer_type == "lightconv2d":
            logging.info(
                "encoder self-attention layer "
                "type = lightweight convolution 2-dimensional"
                "encoder self-attention layer " "type = lightweight convolution 2-dimensional"
            )
            encoder_selfattn_layer = LightweightConvolution2D
            encoder_selfattn_layer_args = [
@@ -318,9 +313,7 @@
                for lnum in range(num_blocks)
            ]
        elif selfattention_layer_type == "dynamicconv2d":
            logging.info(
                "encoder self-attention layer type = dynamic convolution 2-dimensional"
            )
            logging.info("encoder self-attention layer type = dynamic convolution 2-dimensional")
            encoder_selfattn_layer = DynamicConvolution2D
            encoder_selfattn_layer_args = [
                (
@@ -355,9 +348,7 @@
        self.use_conditioning = True if ctc_softmax is not None else False
        if self.use_conditioning:
            self.ctc_softmax = ctc_softmax
            self.conditioning_layer = torch.nn.Linear(
                conditioning_layer_dim, attention_dim
            )
            self.conditioning_layer = torch.nn.Linear(conditioning_layer_dim, attention_dim)

    def get_positionwise_layer(
        self,
@@ -466,4 +457,3 @@
        if self.normalize_before:
            xs = self.after_norm(xs)
        return xs, masks, new_cache