python/FunASR-XL.git

copy from funasr/models/paraformer/contextual_decoder.py copy to funasr/models/scama/sanm_decoder.py

File was copied from funasr/models/paraformer/contextual_decoder.py
			@@ -6,17 +6,38 @@
			import numpy as np

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

			from funasr.models.transformer.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
			from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
			from funasr.models.transformer.embedding import PositionalEncoding
			from funasr.models.transformer.layer_norm import LayerNorm
			from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
			from funasr.models.transformer.repeat import repeat
			from funasr.models.decoder.sanm_decoder import DecoderLayerSANM, ParaformerSANMDecoder
			from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
			from funasr.models.transformer.utils.repeat import repeat

			from funasr.utils.register import register_class, registry_tables

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

			Args:
			size (int): Input dimension.
			self_attn (torch.nn.Module): Self-attention module instance.
			`MultiHeadedAttention` instance can be used as the argument.
			src_attn (torch.nn.Module): Self-attention module instance.
			`MultiHeadedAttention` instance can be used as the argument.
			feed_forward (torch.nn.Module): Feed-forward module instance.
			`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
			can be used as the argument.
			dropout_rate (float): Dropout rate.
			normalize_before (bool): Whether to use layer_norm before the first block.
			concat_after (bool): 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)


			class ContextualDecoderLayer(nn.Module):
			"""

			def __init__(
			self,
			size,
			@@ -28,7 +49,7 @@
			concat_after=False,
			):
			"""Construct an DecoderLayer object."""
			super(ContextualDecoderLayer, self).__init__()
			super(DecoderLayerSANM, self).__init__()
			self.size = size
			self.self_attn = self_attn
			self.src_attn = src_attn
			@@ -45,85 +66,161 @@
			self.concat_linear1 = nn.Linear(size + size, size)
			self.concat_linear2 = 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=None, cache=None):
			"""Compute decoded features.

			Args:
			tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
			tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
			memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
			memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
			cache (List[torch.Tensor]): List of cached tensors.
			Each tensor shape should be (#batch, maxlen_out - 1, size).

			Returns:
			torch.Tensor: Output tensor(#batch, maxlen_out, size).
			torch.Tensor: Mask for output tensor (#batch, maxlen_out).
			torch.Tensor: Encoded memory (#batch, maxlen_in, size).
			torch.Tensor: Encoded memory mask (#batch, maxlen_in).

			"""
			# tgt = self.dropout(tgt)
			if isinstance(tgt, Tuple):
			tgt, _ = tgt
			residual = tgt
			if self.normalize_before:
			tgt = self.norm1(tgt)
			tgt = self.feed_forward(tgt)

			x = tgt
			if self.normalize_before:
			tgt = self.norm2(tgt)
			if self.training:
			cache = None
			x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
			x = residual + self.dropout(x)
			x_self_attn = x
			if self.self_attn:
			if self.normalize_before:
			tgt = self.norm2(tgt)
			x, _ = self.self_attn(tgt, tgt_mask)
			x = residual + self.dropout(x)

			residual = x
			if self.normalize_before:
			x = self.norm3(x)
			x = self.src_attn(x, memory, memory_mask)
			x_src_attn = x

			x = residual + self.dropout(x)
			return x, tgt_mask, x_self_attn, x_src_attn


			class ContextualBiasDecoder(nn.Module):
			def __init__(
			self,
			size,
			src_attn,
			dropout_rate,
			normalize_before=True,
			):
			"""Construct an DecoderLayer object."""
			super(ContextualBiasDecoder, self).__init__()
			self.size = size
			self.src_attn = src_attn
			if src_attn is not None:
			self.norm3 = LayerNorm(size)
			self.dropout = nn.Dropout(dropout_rate)
			self.normalize_before = normalize_before

			def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
			x = tgt
			if self.src_attn is not None:
			residual = x
			if self.normalize_before:
			x = self.norm3(x)
			x = self.dropout(self.src_attn(x, memory, memory_mask))

			x = residual + self.dropout(self.src_attn(x, memory, memory_mask))

			return x, tgt_mask, memory, memory_mask, cache

			def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
			"""Compute decoded features.

			class ContextualParaformerDecoder(ParaformerSANMDecoder):
			Args:
			tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
			tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
			memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
			memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
			cache (List[torch.Tensor]): List of cached tensors.
			Each tensor shape should be (#batch, maxlen_out - 1, size).

			Returns:
			torch.Tensor: Output tensor(#batch, maxlen_out, size).
			torch.Tensor: Mask for output tensor (#batch, maxlen_out).
			torch.Tensor: Encoded memory (#batch, maxlen_in, size).
			torch.Tensor: Encoded memory mask (#batch, maxlen_in).

			"""
			# tgt = self.dropout(tgt)
			residual = tgt
			if self.normalize_before:
			tgt = self.norm1(tgt)
			tgt = self.feed_forward(tgt)

			x = tgt
			if self.self_attn:
			if self.normalize_before:
			tgt = self.norm2(tgt)
			if self.training:
			cache = None
			x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
			x = residual + self.dropout(x)

			if self.src_attn is not None:
			residual = x
			if self.normalize_before:
			x = self.norm3(x)

			x = residual + self.dropout(self.src_attn(x, memory, memory_mask))


			return x, tgt_mask, memory, memory_mask, cache

			def forward_chunk(self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0):
			"""Compute decoded features.

			Args:
			tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
			tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
			memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
			memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
			cache (List[torch.Tensor]): List of cached tensors.
			Each tensor shape should be (#batch, maxlen_out - 1, size).

			Returns:
			torch.Tensor: Output tensor(#batch, maxlen_out, size).
			torch.Tensor: Mask for output tensor (#batch, maxlen_out).
			torch.Tensor: Encoded memory (#batch, maxlen_in, size).
			torch.Tensor: Encoded memory mask (#batch, maxlen_in).

			"""
			residual = tgt
			if self.normalize_before:
			tgt = self.norm1(tgt)
			tgt = self.feed_forward(tgt)

			x = tgt
			if self.self_attn:
			if self.normalize_before:
			tgt = self.norm2(tgt)
			x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache)
			x = residual + self.dropout(x)

			if self.src_attn is not None:
			residual = x
			if self.normalize_before:
			x = self.norm3(x)

			x, opt_cache = self.src_attn.forward_chunk(x, memory, opt_cache, chunk_size, look_back)
			x = residual + x

			return x, memory, fsmn_cache, opt_cache

			@register_class("decoder_classes", "FsmnDecoderSCAMAOpt")
			class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
			"""
			Author: Speech Lab of DAMO Academy, Alibaba Group
			Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
			SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
			https://arxiv.org/abs/2006.01713

			"""
			def __init__(
			self,
			vocab_size: int,
			encoder_output_size: int,
			attention_heads: int = 4,
			linear_units: int = 2048,
			num_blocks: int = 6,
			dropout_rate: float = 0.1,
			positional_dropout_rate: float = 0.1,
			self_attention_dropout_rate: float = 0.0,
			src_attention_dropout_rate: float = 0.0,
			input_layer: str = "embed",
			use_output_layer: bool = True,
			pos_enc_class=PositionalEncoding,
			normalize_before: bool = True,
			concat_after: bool = False,
			att_layer_num: int = 6,
			kernel_size: int = 21,
			sanm_shfit: int = 0,
			self,
			vocab_size: int,
			encoder_output_size: int,
			attention_heads: int = 4,
			linear_units: int = 2048,
			num_blocks: int = 6,
			dropout_rate: float = 0.1,
			positional_dropout_rate: float = 0.1,
			self_attention_dropout_rate: float = 0.0,
			src_attention_dropout_rate: float = 0.0,
			input_layer: str = "embed",
			use_output_layer: bool = True,
			pos_enc_class=PositionalEncoding,
			normalize_before: bool = True,
			concat_after: bool = False,
			att_layer_num: int = 6,
			kernel_size: int = 21,
			sanm_shfit: int = None,
			concat_embeds: bool = False,
			attention_dim: int = None,
			tf2torch_tensor_name_prefix_torch: str = "decoder",
			tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
			embed_tensor_name_prefix_tf: str = None,
			):
			super().__init__(
			vocab_size=vocab_size,
			@@ -135,14 +232,12 @@
			pos_enc_class=pos_enc_class,
			normalize_before=normalize_before,
			)
			if attention_dim is None:
			attention_dim = encoder_output_size

			attention_dim = encoder_output_size
			if input_layer == 'none':
			self.embed = None
			if input_layer == "embed":
			self.embed = torch.nn.Sequential(
			torch.nn.Embedding(vocab_size, attention_dim),
			# pos_enc_class(attention_dim, positional_dropout_rate),
			)
			elif input_layer == "linear":
			self.embed = torch.nn.Sequential(
			@@ -168,14 +263,14 @@
			if sanm_shfit is None:
			sanm_shfit = (kernel_size - 1) // 2
			self.decoders = repeat(
			att_layer_num - 1,
			att_layer_num,
			lambda lnum: DecoderLayerSANM(
			attention_dim,
			MultiHeadedAttentionSANMDecoder(
			attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
			),
			MultiHeadedAttentionCrossAtt(
			attention_heads, attention_dim, src_attention_dropout_rate
			attention_heads, attention_dim, src_attention_dropout_rate, encoder_output_size=encoder_output_size
			),
			PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
			dropout_rate,
			@@ -183,29 +278,6 @@
			concat_after,
			),
			)
			self.dropout = nn.Dropout(dropout_rate)
			self.bias_decoder = ContextualBiasDecoder(
			size=attention_dim,
			src_attn=MultiHeadedAttentionCrossAtt(
			attention_heads, attention_dim, src_attention_dropout_rate
			),
			dropout_rate=dropout_rate,
			normalize_before=True,
			)
			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,
			)
			if num_blocks - att_layer_num <= 0:
			self.decoders2 = None
			else:
			@@ -214,7 +286,7 @@
			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_shfit=sanm_shfit
			),
			None,
			PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
			@@ -236,16 +308,35 @@
			concat_after,
			),
			)
			if concat_embeds:
			self.embed_concat_ffn = repeat(
			1,
			lambda lnum: DecoderLayerSANM(
			attention_dim + encoder_output_size,
			None,
			None,
			PositionwiseFeedForwardDecoderSANM(attention_dim + encoder_output_size, linear_units, dropout_rate,
			adim=attention_dim),
			dropout_rate,
			normalize_before,
			concat_after,
			),
			)
			else:
			self.embed_concat_ffn = None
			self.concat_embeds = concat_embeds
			self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
			self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
			self.embed_tensor_name_prefix_tf = embed_tensor_name_prefix_tf

			def forward(
			self,
			hs_pad: torch.Tensor,
			hlens: torch.Tensor,
			ys_in_pad: torch.Tensor,
			ys_in_lens: torch.Tensor,
			contextual_info: torch.Tensor,
			clas_scale: float = 1.0,
			return_hidden: bool = False,
			self,
			hs_pad: torch.Tensor,
			hlens: torch.Tensor,
			ys_in_pad: torch.Tensor,
			ys_in_lens: torch.Tensor,
			chunk_mask: torch.Tensor = None,
			pre_acoustic_embeds: torch.Tensor = None,
			) -> Tuple[torch.Tensor, torch.Tensor]:
			"""Forward decoder.

			@@ -269,46 +360,122 @@

			memory = hs_pad
			memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
			if chunk_mask is not None:
			memory_mask = memory_mask * chunk_mask
			if tgt_mask.size(1) != memory_mask.size(1):
			memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)

			x = tgt
			x = self.embed(tgt)

			if pre_acoustic_embeds is not None and self.concat_embeds:
			x = torch.cat((x, pre_acoustic_embeds), dim=-1)
			x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)

			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)

			if self.bias_output is not None:
			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.decoders3(
			x, tgt_mask, memory, memory_mask
			)
			if self.normalize_before:
			x = self.after_norm(x)
			olens = tgt_mask.sum(1)
			if self.output_layer is not None and return_hidden is False:
			if self.output_layer is not None:
			x = self.output_layer(x)

			olens = tgt_mask.sum(1)
			return x, olens

			def gen_tf2torch_map_dict(self):
			def score(self, ys, state, x, x_mask=None, pre_acoustic_embeds: torch.Tensor = None, ):
			"""Score."""
			ys_mask = myutils.sequence_mask(torch.tensor([len(ys)], dtype=torch.int32), device=x.device)[:, :, None]
			logp, state = self.forward_one_step(
			ys.unsqueeze(0), ys_mask, x.unsqueeze(0), memory_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds,
			cache=state
			)
			return logp.squeeze(0), state

			def forward_one_step(
			self,
			tgt: torch.Tensor,
			tgt_mask: torch.Tensor,
			memory: torch.Tensor,
			memory_mask: torch.Tensor = None,
			pre_acoustic_embeds: torch.Tensor = None,
			cache: List[torch.Tensor] = None,
			) -> Tuple[torch.Tensor, List[torch.Tensor]]:
			"""Forward one step.

			Args:
			tgt: input token ids, int64 (batch, maxlen_out)
			tgt_mask: input token mask, (batch, maxlen_out)
			dtype=torch.uint8 in PyTorch 1.2-
			dtype=torch.bool in PyTorch 1.2+ (include 1.2)
			memory: encoded memory, float32 (batch, maxlen_in, feat)
			cache: cached output list of (batch, max_time_out-1, size)
			Returns:
			y, cache: NN output value and cache per `self.decoders`.
			y.shape` is (batch, maxlen_out, token)
			"""

			x = tgt[:, -1:]
			tgt_mask = None
			x = self.embed(x)

			if pre_acoustic_embeds is not None and self.concat_embeds:
			x = torch.cat((x, pre_acoustic_embeds), dim=-1)
			x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)

			if cache is None:
			cache_layer_num = len(self.decoders)
			if self.decoders2 is not None:
			cache_layer_num += len(self.decoders2)
			cache = [None] * cache_layer_num
			new_cache = []
			# for c, decoder in zip(cache, self.decoders):
			for i in range(self.att_layer_num):
			decoder = self.decoders[i]
			c = cache[i]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
			x, tgt_mask, memory, memory_mask, cache=c
			)
			new_cache.append(c_ret)

			if self.num_blocks - self.att_layer_num >= 1:
			for i in range(self.num_blocks - self.att_layer_num):
			j = i + self.att_layer_num
			decoder = self.decoders2[i]
			c = cache[j]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
			x, tgt_mask, memory, memory_mask, cache=c
			)
			new_cache.append(c_ret)

			for decoder in self.decoders3:
			x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step(
			x, tgt_mask, memory, None, cache=None
			)

			if self.normalize_before:
			y = self.after_norm(x[:, -1])
			else:
			y = x[:, -1]
			if self.output_layer is not None:
			y = self.output_layer(y)
			y = torch.log_softmax(y, dim=-1)

			return y, new_cache

			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
			embed_tensor_name_prefix_tf = self.embed_tensor_name_prefix_tf if self.embed_tensor_name_prefix_tf is not None else tensor_name_prefix_tf
			map_dict_local = {


			## decoder
			# ffn
			"{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
			@@ -346,7 +513,7 @@
			"squeeze": 0,
			"transpose": (1, 0),
			}, # (256,1024),(1,1024,256)


			# fsmn
			"{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
			{"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format(
			@@ -443,7 +610,7 @@
			"squeeze": 0,
			"transpose": (1, 0),
			}, # (256,1024),(1,1024,256)


			# embed_concat_ffn
			"{}.embed_concat_ffn.layeridx.norm1.weight".format(tensor_name_prefix_torch):
			{"name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf),
			@@ -480,7 +647,7 @@
			"squeeze": 0,
			"transpose": (1, 0),
			}, # (256,1024),(1,1024,256)


			# out norm
			"{}.after_norm.weight".format(tensor_name_prefix_torch):
			{"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
			@@ -492,17 +659,18 @@
			"squeeze": None,
			"transpose": None,
			}, # (256,),(256,)


			# in embed
			"{}.embed.0.weight".format(tensor_name_prefix_torch):
			{"name": "{}/w_embs".format(tensor_name_prefix_tf),
			{"name": "{}/w_embs".format(embed_tensor_name_prefix_tf),
			"squeeze": None,
			"transpose": None,
			}, # (4235,256),(4235,256)


			# 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)],
			{"name": ["{}/dense/kernel".format(tensor_name_prefix_tf),
			"{}/w_embs".format(embed_tensor_name_prefix_tf)],
			"squeeze": [None, None],
			"transpose": [(1, 0), None],
			}, # (4235,256),(256,4235)
			@@ -512,56 +680,7 @@
			"squeeze": [None, None],
			"transpose": [None, None],
			}, # (4235,),(4235,)

			## 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)


			}
			return map_dict_local

			@@ -569,6 +688,7 @@
			var_dict_tf,
			var_dict_torch,
			):

			map_dict = self.gen_tf2torch_map_dict()
			var_dict_torch_update = dict()
			decoder_layeridx_sets = set()
			@@ -598,37 +718,13 @@
			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))



			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)


			layeridx += layeridx_bias
			if "decoders." in name:
			decoder_layeridx_sets.add(layeridx)
			@@ -649,11 +745,11 @@
			logging.info(
			"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
			var_dict_tf[name_tf].shape))


			elif names[1] == "decoders3":
			layeridx = int(names[2])
			name_q = name.replace(".{}.".format(layeridx), ".layeridx.")


			layeridx_bias = 0
			layeridx += layeridx_bias
			if "decoders." in name:
			@@ -675,29 +771,8 @@
			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

			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))


			elif names[1] == "embed" or names[1] == "output_layer" or names[1] == "bias_output":

			elif names[1] == "embed" or names[1] == "output_layer":
			name_tf = map_dict[name]["name"]
			if isinstance(name_tf, list):
			idx_list = 0
			@@ -720,7 +795,7 @@
			name_tf[idx_list],
			var_dict_tf[name_tf[
			idx_list]].shape))


			else:
			data_tf = var_dict_tf[name_tf]
			if map_dict[name]["squeeze"] is not None:
			@@ -736,7 +811,7 @@
			logging.info(
			"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
			var_dict_tf[name_tf].shape))


			elif names[1] == "after_norm":
			name_tf = map_dict[name]["name"]
			data_tf = var_dict_tf[name_tf]
			@@ -745,11 +820,11 @@
			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:
			@@ -771,5 +846,6 @@
			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