提交

kongdeqiang

2026-03-13 28ccfbfc51068a663a80764e14074df5edf2b5ba

 funasr/models/contextual_paraformer/decoder.py

@@ -15,7 +15,10 @@
from funasr.models.transformer.embedding import PositionalEncoding
from funasr.models.paraformer.decoder import DecoderLayerSANM, ParaformerSANMDecoder
from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.models.sanm.attention import (
    MultiHeadedAttentionSANMDecoder,
    MultiHeadedAttentionCrossAtt,
)


class ContextualDecoderLayer(torch.nn.Module):
@@ -47,7 +50,14 @@
            self.concat_linear1 = torch.nn.Linear(size + size, size)
            self.concat_linear2 = torch.nn.Linear(size + size, size)

    def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None,):
    def forward(
        self,
        tgt,
        tgt_mask,
        memory,
        memory_mask,
        cache=None,
    ):
        # tgt = self.dropout(tgt)
        if isinstance(tgt, Tuple):
            tgt, _ = tgt
@@ -97,8 +107,9 @@
        if self.src_attn is not None:
            if self.normalize_before:
                x = self.norm3(x)
            x =  self.dropout(self.src_attn(x, memory, memory_mask))
            x = self.dropout(self.src_attn(x, memory, memory_mask))
        return x, tgt_mask, memory, memory_mask, cache


@tables.register("decoder_classes", "ContextualParaformerDecoder")
class ContextualParaformerDecoder(ParaformerSANMDecoder):
@@ -107,6 +118,7 @@
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
    https://arxiv.org/abs/2006.01713
    """

    def __init__(
        self,
        vocab_size: int,
@@ -139,7 +151,7 @@
        )

        attention_dim = encoder_output_size
        if input_layer == 'none':
        if input_layer == "none":
            self.embed = None
        if input_layer == "embed":
            self.embed = torch.nn.Sequential(
@@ -194,20 +206,20 @@
            dropout_rate=dropout_rate,
            normalize_before=True,
        )
        self.bias_output = torch.nn.Conv1d(attention_dim*2, attention_dim, 1, bias=False)
        self.bias_output = torch.nn.Conv1d(attention_dim * 2, attention_dim, 1, bias=False)
        self.last_decoder = ContextualDecoderLayer(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads, attention_dim, src_attention_dropout_rate
                ),
                PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
                dropout_rate,
                normalize_before,
                concat_after,
            )
            attention_dim,
            MultiHeadedAttentionSANMDecoder(
                attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
            ),
            MultiHeadedAttentionCrossAtt(
                attention_heads, attention_dim, src_attention_dropout_rate
            ),
            PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
            dropout_rate,
            normalize_before,
            concat_after,
        )
        if num_blocks - att_layer_num <= 0:
            self.decoders2 = None
        else:
@@ -273,31 +285,25 @@
        memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]

        x = tgt
        x, tgt_mask, memory, memory_mask, _ = self.decoders(
            x, tgt_mask, memory, memory_mask
        )
        _, _, x_self_attn, x_src_attn = self.last_decoder(
            x, tgt_mask, memory, memory_mask
        )
        x, tgt_mask, memory, memory_mask, _ = self.decoders(x, tgt_mask, memory, memory_mask)
        _, _, x_self_attn, x_src_attn = self.last_decoder(x, tgt_mask, memory, memory_mask)

        # contextual paraformer related
        contextual_length = torch.Tensor([contextual_info.shape[1]]).int().repeat(hs_pad.shape[0])
        contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
        cx, tgt_mask, _, _, _ = self.bias_decoder(x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask)
        cx, tgt_mask, _, _, _ = self.bias_decoder(
            x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask
        )

        if self.bias_output is not None:
            x = torch.cat([x_src_attn, cx*clas_scale], dim=2)
            x = torch.cat([x_src_attn, cx * clas_scale], dim=2)
            x = self.bias_output(x.transpose(1, 2)).transpose(1, 2)  # 2D -> D
            x = x_self_attn + self.dropout(x)

        if self.decoders2 is not None:
            x, tgt_mask, memory, memory_mask, _ = self.decoders2(
                x, tgt_mask, memory, memory_mask
            )
            x, tgt_mask, memory, memory_mask, _ = self.decoders2(x, tgt_mask, memory, memory_mask)

        x, tgt_mask, memory, memory_mask, _ = self.decoders3(
            x, tgt_mask, memory, memory_mask
        )
        x, tgt_mask, memory, memory_mask, _ = self.decoders3(x, tgt_mask, memory, memory_mask)
        if self.normalize_before:
            x = self.after_norm(x)
        olens = tgt_mask.sum(1)
@@ -305,473 +311,135 @@
            x = self.output_layer(x)
        return x, olens

    def gen_tf2torch_map_dict(self):

        tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
        tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
        map_dict_local = {
@tables.register("decoder_classes", "ContextualParaformerDecoderExport")
class ContextualParaformerDecoderExport(torch.nn.Module):
    def __init__(
        self,
        model,
        max_seq_len=512,
        model_name="decoder",
        onnx: bool = True,
        **kwargs,
    ):
        super().__init__()
        from funasr.utils.torch_function import sequence_mask

            ## decoder
            # ffn
            "{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.decoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,1024),(1,1024,256)
        self.model = model
        self.make_pad_mask = sequence_mask(max_seq_len, flip=False)

            # fsmn
            "{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format(
                    tensor_name_prefix_tf),
                    "squeeze": None,
                    "transpose": None,
                },  # (256,),(256,)
            "{}.decoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/beta".format(
                    tensor_name_prefix_tf),
                    "squeeze": None,
                    "transpose": None,
                },  # (256,),(256,)
            "{}.decoders.layeridx.self_attn.fsmn_block.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/depth_conv_w".format(
                    tensor_name_prefix_tf),
                    "squeeze": 0,
                    "transpose": (1, 2, 0),
                },  # (256,1,31),(1,31,256,1)
            # src att
            "{}.decoders.layeridx.norm3.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders.layeridx.norm3.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders.layeridx.src_attn.linear_q.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,256),(1,256,256)
            "{}.decoders.layeridx.src_attn.linear_q.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders.layeridx.src_attn.linear_k_v.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.decoders.layeridx.src_attn.linear_k_v.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders.layeridx.src_attn.linear_out.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,256),(1,256,256)
            "{}.decoders.layeridx.src_attn.linear_out.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            # dnn
            "{}.decoders3.layeridx.norm1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders3.layeridx.norm1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.decoders3.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.decoders3.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders3.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders3.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.decoders3.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_dnn_layer_layeridx/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,1024),(1,1024,256)
        from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoderExport
        from funasr.models.sanm.attention import MultiHeadedAttentionCrossAttExport
        from funasr.models.paraformer.decoder import DecoderLayerSANMExport
        from funasr.models.transformer.positionwise_feed_forward import (
            PositionwiseFeedForwardDecoderSANMExport,
        )

            # embed_concat_ffn
            "{}.embed_concat_ffn.layeridx.norm1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.embed_concat_ffn.layeridx.norm1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.embed_concat_ffn.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.embed_concat_ffn.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.embed_concat_ffn.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.embed_concat_ffn.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/LayerNorm_1/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.embed_concat_ffn.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
                {"name": "{}/cif_concat/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,1024),(1,1024,256)
        for i, d in enumerate(self.model.decoders):
            if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
                d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
            if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
                d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
            if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
                d.src_attn = MultiHeadedAttentionCrossAttExport(d.src_attn)
            self.model.decoders[i] = DecoderLayerSANMExport(d)

            # out norm
            "{}.after_norm.weight".format(tensor_name_prefix_torch):
                {"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.after_norm.bias".format(tensor_name_prefix_torch):
                {"name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
        if self.model.decoders2 is not None:
            for i, d in enumerate(self.model.decoders2):
                if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
                    d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
                if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
                    d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
                self.model.decoders2[i] = DecoderLayerSANMExport(d)

            # in embed
            "{}.embed.0.weight".format(tensor_name_prefix_torch):
                {"name": "{}/w_embs".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (4235,256),(4235,256)
        for i, d in enumerate(self.model.decoders3):
            if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
                d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
            self.model.decoders3[i] = DecoderLayerSANMExport(d)

            # out layer
            "{}.output_layer.weight".format(tensor_name_prefix_torch):
                {"name": ["{}/dense/kernel".format(tensor_name_prefix_tf), "{}/w_embs".format(tensor_name_prefix_tf)],
                 "squeeze": [None, None],
                 "transpose": [(1, 0), None],
                 },  # (4235,256),(256,4235)
            "{}.output_layer.bias".format(tensor_name_prefix_torch):
                {"name": ["{}/dense/bias".format(tensor_name_prefix_tf),
                          "seq2seq/2bias" if tensor_name_prefix_tf == "seq2seq/decoder/inputter_1" else "seq2seq/bias"],
                 "squeeze": [None, None],
                 "transpose": [None, None],
                 },  # (4235,),(4235,)
        self.output_layer = model.output_layer
        self.after_norm = model.after_norm
        self.model_name = model_name

            ## clas decoder
            # src att
            "{}.bias_decoder.norm3.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/LayerNorm/gamma".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.bias_decoder.norm3.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/LayerNorm/beta".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.bias_decoder.src_attn.linear_q.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,256),(1,256,256)
            "{}.bias_decoder.src_attn.linear_q.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            "{}.bias_decoder.src_attn.linear_k_v.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_1/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (1024,256),(1,256,1024)
            "{}.bias_decoder.src_attn.linear_k_v.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_1/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (1024,),(1024,)
            "{}.bias_decoder.src_attn.linear_out.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_2/kernel".format(tensor_name_prefix_tf),
                 "squeeze": 0,
                 "transpose": (1, 0),
                 },  # (256,256),(1,256,256)
            "{}.bias_decoder.src_attn.linear_out.bias".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_2/bias".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": None,
                 },  # (256,),(256,)
            # dnn
            "{}.bias_output.weight".format(tensor_name_prefix_torch):
                {"name": "{}/decoder_fsmn_layer_15/conv1d/kernel".format(tensor_name_prefix_tf),
                 "squeeze": None,
                 "transpose": (2, 1, 0),
                 },  # (1024,256),(1,256,1024)
        # bias decoder
        if isinstance(self.model.bias_decoder.src_attn, MultiHeadedAttentionCrossAtt):
            self.model.bias_decoder.src_attn = MultiHeadedAttentionCrossAttExport(
                self.model.bias_decoder.src_attn
            )
        self.bias_decoder = self.model.bias_decoder

        }
        return map_dict_local
        # last decoder
        if isinstance(self.model.last_decoder.src_attn, MultiHeadedAttentionCrossAtt):
            self.model.last_decoder.src_attn = MultiHeadedAttentionCrossAttExport(
                self.model.last_decoder.src_attn
            )
        if isinstance(self.model.last_decoder.self_attn, MultiHeadedAttentionSANMDecoder):
            self.model.last_decoder.self_attn = MultiHeadedAttentionSANMDecoderExport(
                self.model.last_decoder.self_attn
            )
        if isinstance(self.model.last_decoder.feed_forward, PositionwiseFeedForwardDecoderSANM):
            self.model.last_decoder.feed_forward = PositionwiseFeedForwardDecoderSANMExport(
                self.model.last_decoder.feed_forward
            )
        self.last_decoder = self.model.last_decoder
        self.bias_output = self.model.bias_output
        self.dropout = self.model.dropout

    def convert_tf2torch(self,
                         var_dict_tf,
                         var_dict_torch,
                         ):
        map_dict = self.gen_tf2torch_map_dict()
        var_dict_torch_update = dict()
        decoder_layeridx_sets = set()
        for name in sorted(var_dict_torch.keys(), reverse=False):
            names = name.split('.')
            if names[0] == self.tf2torch_tensor_name_prefix_torch:
                if names[1] == "decoders":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
                elif names[1] == "last_decoder":
                    layeridx = 15
                    name_q = name.replace("last_decoder", "decoders.layeridx")
                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
    def prepare_mask(self, mask):
        mask_3d_btd = mask[:, :, None]
        if len(mask.shape) == 2:
            mask_4d_bhlt = 1 - mask[:, None, None, :]
        elif len(mask.shape) == 3:
            mask_4d_bhlt = 1 - mask[:, None, :]
        mask_4d_bhlt = mask_4d_bhlt * -10000.0

        return mask_3d_btd, mask_4d_bhlt

                elif names[1] == "decoders2":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                    name_q = name_q.replace("decoders2", "decoders")
                    layeridx_bias = len(decoder_layeridx_sets)
    def forward(
        self,
        hs_pad: torch.Tensor,
        hlens: torch.Tensor,
        ys_in_pad: torch.Tensor,
        ys_in_lens: torch.Tensor,
        bias_embed: torch.Tensor,
    ):

                    layeridx += layeridx_bias
                    if "decoders." in name:
                        decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
        tgt = ys_in_pad
        tgt_mask = self.make_pad_mask(ys_in_lens)
        tgt_mask, _ = self.prepare_mask(tgt_mask)
        # tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]

                elif names[1] == "decoders3":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
        memory = hs_pad
        memory_mask = self.make_pad_mask(hlens)
        _, memory_mask = self.prepare_mask(memory_mask)
        # memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]

                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    if "decoders." in name:
                        decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
                elif names[1] == "bias_decoder":
                    name_q = name
        x = tgt
        x, tgt_mask, memory, memory_mask, _ = self.model.decoders(x, tgt_mask, memory, memory_mask)

                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))
        _, _, x_self_attn, x_src_attn = self.last_decoder(x, tgt_mask, memory, memory_mask)

        # contextual paraformer related
        contextual_length = torch.Tensor([bias_embed.shape[1]]).int().repeat(hs_pad.shape[0])
        # contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
        contextual_mask = self.make_pad_mask(contextual_length)
        contextual_mask, _ = self.prepare_mask(contextual_mask)
        contextual_mask = contextual_mask.transpose(2, 1).unsqueeze(1)
        cx, tgt_mask, _, _, _ = self.bias_decoder(
            x_self_attn, tgt_mask, bias_embed, memory_mask=contextual_mask
        )

                elif names[1] == "embed" or names[1] == "output_layer" or names[1] == "bias_output":
                    name_tf = map_dict[name]["name"]
                    if isinstance(name_tf, list):
                        idx_list = 0
                        if name_tf[idx_list] in var_dict_tf.keys():
                            pass
                        else:
                            idx_list = 1
                        data_tf = var_dict_tf[name_tf[idx_list]]
                        if map_dict[name]["squeeze"][idx_list] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"][idx_list])
                        if map_dict[name]["transpose"][idx_list] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name]["transpose"][idx_list])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(),
                                                                                                   name_tf[idx_list],
                                                                                                   var_dict_tf[name_tf[
                                                                                                       idx_list]].shape))
        if self.bias_output is not None:
            x = torch.cat([x_src_attn, cx], dim=2)
            x = self.bias_output(x.transpose(1, 2)).transpose(1, 2)  # 2D -> D
            x = x_self_attn + self.dropout(x)

                    else:
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
                        if map_dict[name]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
                                                                                          var_dict_tf[name_tf].shape))
        if self.model.decoders2 is not None:
            x, tgt_mask, memory, memory_mask, _ = self.model.decoders2(
                x, tgt_mask, memory, memory_mask
            )
        x, tgt_mask, memory, memory_mask, _ = self.model.decoders3(x, tgt_mask, memory, memory_mask)
        x = self.after_norm(x)
        x = self.output_layer(x)

                elif names[1] == "after_norm":
                    name_tf = map_dict[name]["name"]
                    data_tf = var_dict_tf[name_tf]
                    data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                    var_dict_torch_update[name] = data_tf
                    logging.info(
                        "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
                                                                                      var_dict_tf[name_tf].shape))

                elif names[1] == "embed_concat_ffn":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")

                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    if "decoders." in name:
                        decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
                                                                                                        var_dict_torch[
                                                                                                            name].size(),
                                                                                                        data_tf.size())
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
                                                                                          var_dict_tf[name_tf].shape))

        return var_dict_torch_update
        return x, ys_in_lens