python/FunASR-XL.git

			@@ -13,13 +13,17 @@
			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.sanm.attention import (
			MultiHeadedAttentionSANMDecoder,
			MultiHeadedAttentionCrossAtt,
			)
			from funasr.models.transformer.embedding import PositionalEncoding
			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.register import tables


			class DecoderLayerSANM(nn.Module):
			"""Single decoder layer module.
			@@ -151,10 +155,11 @@

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

			Args:
			@@ -194,6 +199,7 @@

			return x, memory, fsmn_cache, opt_cache


			@tables.register("decoder_classes", "FsmnDecoderSCAMAOpt")
			class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
			"""
			@@ -201,31 +207,31 @@
			SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
			https://arxiv.org/abs/2006.01712
			"""


			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 = 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,
			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,
			@@ -275,7 +281,10 @@
			attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
			),
			MultiHeadedAttentionCrossAtt(
			attention_heads, attention_dim, src_attention_dropout_rate, encoder_output_size=encoder_output_size
			attention_heads,
			attention_dim,
			src_attention_dropout_rate,
			encoder_output_size=encoder_output_size,
			),
			PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
			dropout_rate,
			@@ -291,7 +300,10 @@
			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_shfit=sanm_shfit,
			),
			None,
			PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
			@@ -320,8 +332,12 @@
			attention_dim + encoder_output_size,
			None,
			None,
			PositionwiseFeedForwardDecoderSANM(attention_dim + encoder_output_size, linear_units, dropout_rate,
			adim=attention_dim),
			PositionwiseFeedForwardDecoderSANM(
			attention_dim + encoder_output_size,
			linear_units,
			dropout_rate,
			adim=attention_dim,
			),
			dropout_rate,
			normalize_before,
			concat_after,
			@@ -335,13 +351,13 @@
			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,
			chunk_mask: torch.Tensor = None,
			pre_acoustic_embeds: torch.Tensor = None,
			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.

			@@ -376,16 +392,10 @@
			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, tgt_mask, memory, memory_mask, _ = self.decoders(x, tgt_mask, memory, memory_mask)
			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
			)
			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)
			if self.output_layer is not None:
			@@ -394,23 +404,36 @@
			olens = tgt_mask.sum(1)
			return x, olens

			def score(self, ys, state, x, x_mask=None, pre_acoustic_embeds: torch.Tensor = None, ):
			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]
			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
			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,
			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.

			@@ -473,384 +496,3 @@
			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):
			{"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)

			# 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)

			# 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)

			# 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,)

			# in embed
			"{}.embed.0.weight".format(tensor_name_prefix_torch):
			{"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(embed_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,)

			}
			return map_dict_local

			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] == "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)
			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] == "decoders3":
			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))

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

			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))

			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