python/FunASR-XL.git

			@@ -1,8 +1,10 @@
			from typing import List
			from typing import Tuple

			import logging
			import torch
			import torch.nn as nn
			import numpy as np

			from funasr.modules.streaming_utils import utils as myutils
			from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
			from typeguard import check_argument_types
			@@ -92,6 +94,46 @@
			if self.self_attn:
			if self.normalize_before:
			tgt = self.norm2(tgt)
			x, _ = self.self_attn(tgt, tgt_mask)
			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, 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)
			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)
			@@ -106,7 +148,6 @@


			return x, tgt_mask, memory, memory_mask, cache


			class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
			"""
			@@ -136,6 +177,9 @@
			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,
			):
			assert check_argument_types()
			super().__init__(
			@@ -241,6 +285,9 @@
			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,
			@@ -352,7 +399,7 @@
			for i in range(self.att_layer_num):
			decoder = self.decoders[i]
			c = cache[i]
			x, tgt_mask, memory, memory_mask, c_ret = decoder(
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, tgt_mask, memory, memory_mask, cache=c
			)
			new_cache.append(c_ret)
			@@ -362,13 +409,13 @@
			j = i + self.att_layer_num
			decoder = self.decoders2[i]
			c = cache[j]
			x, tgt_mask, memory, memory_mask, c_ret = decoder(
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			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(
			x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
			x, tgt_mask, memory, None, cache=None
			)

			@@ -381,6 +428,387 @@
			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


			class ParaformerSANMDecoder(BaseTransformerDecoder):
			"""
			@@ -407,6 +835,8 @@
			att_layer_num: int = 6,
			kernel_size: int = 21,
			sanm_shfit: int = 0,
			tf2torch_tensor_name_prefix_torch: str = "decoder",
			tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
			):
			assert check_argument_types()
			super().__init__(
			@@ -496,6 +926,8 @@
			concat_after,
			),
			)
			self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
			self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf

			def forward(
			self,
			@@ -554,6 +986,65 @@
			)
			return logp.squeeze(0), state

			def forward_chunk(
			self,
			memory: torch.Tensor,
			tgt: torch.Tensor,
			cache: dict = None,
			) -> Tuple[torch.Tensor, torch.Tensor]:
			"""Forward decoder.

			Args:
			hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
			hlens: (batch)
			ys_in_pad:
			input token ids, int64 (batch, maxlen_out)
			if input_layer == "embed"
			input tensor (batch, maxlen_out, #mels) in the other cases
			ys_in_lens: (batch)
			Returns:
			(tuple): tuple containing:

			x: decoded token score before softmax (batch, maxlen_out, token)
			if use_output_layer is True,
			olens: (batch, )
			"""
			x = tgt
			if cache["decode_fsmn"] is None:
			cache_layer_num = len(self.decoders)
			if self.decoders2 is not None:
			cache_layer_num += len(self.decoders2)
			new_cache = [None] * cache_layer_num
			else:
			new_cache = cache["decode_fsmn"]
			for i in range(self.att_layer_num):
			decoder = self.decoders[i]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, None, memory, None, cache=new_cache[i]
			)
			new_cache[i] = 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]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, None, memory, None, cache=new_cache[j]
			)
			new_cache[j] = c_ret

			for decoder in self.decoders3:

			x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
			x, None, memory, None, cache=None
			)
			if self.normalize_before:
			x = self.after_norm(x)
			if self.output_layer is not None:
			x = self.output_layer(x)
			cache["decode_fsmn"] = new_cache
			return x

			def forward_one_step(
			self,
			tgt: torch.Tensor,
			@@ -585,7 +1076,7 @@
			for i in range(self.att_layer_num):
			decoder = self.decoders[i]
			c = cache[i]
			x, tgt_mask, memory, memory_mask, c_ret = decoder(
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, tgt_mask, memory, None, cache=c
			)
			new_cache.append(c_ret)
			@@ -595,14 +1086,14 @@
			j = i + self.att_layer_num
			decoder = self.decoders2[i]
			c = cache[j]
			x, tgt_mask, memory, memory_mask, c_ret = decoder(
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, tgt_mask, memory, None, cache=c
			)
			new_cache.append(c_ret)

			for decoder in self.decoders3:

			x, tgt_mask, memory, memory_mask, _ = decoder(
			x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
			x, tgt_mask, memory, None, cache=None
			)

			@@ -613,4 +1104,381 @@
			if self.output_layer is not None:
			y = torch.log_softmax(self.output_layer(y), dim=-1)

			return y, new_cache
			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
			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(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)],
			"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