shfit to shift (#2266)

Rin Arakaki

2024-12-24 1367973f9818d8e15c7bf52ad6ffba4ddb6ac2b2

 funasr/models/paraformer/decoder.py

@@ -248,7 +248,7 @@
        concat_after: bool = False,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shfit: int = 0,
        sanm_shift: int = 0,
        lora_list: List[str] = None,
        lora_rank: int = 8,
        lora_alpha: int = 16,
@@ -298,14 +298,14 @@

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        if sanm_shift is None:
            sanm_shift = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=sanm_shift
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads,
@@ -330,7 +330,7 @@
                lambda lnum: DecoderLayerSANM(
                    attention_dim,
                    MultiHeadedAttentionSANMDecoder(
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=0
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
@@ -785,20 +785,20 @@
            for _ in range(cache_num)
        ]
        return (tgt, memory, pre_acoustic_embeds, cache)
    

    def is_optimizable(self):
        return True
    

    def get_input_names(self):
        cache_num = len(self.model.decoders) + len(self.model.decoders2)
        return ['tgt', 'memory', 'pre_acoustic_embeds'] \
               + ['cache_%d' % i for i in range(cache_num)]
    

    def get_output_names(self):
        cache_num = len(self.model.decoders) + len(self.model.decoders2)
        return ['y'] \
               + ['out_cache_%d' % i for i in range(cache_num)]
    

    def get_dynamic_axes(self):
        ret = {
            'tgt': {