python/FunASR-XL.git

			@@ -7,7 +7,6 @@

			from funasr.modules.streaming_utils import utils as myutils
			from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
			from typeguard import check_argument_types

			from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
			from funasr.modules.embedding import PositionalEncoding
			@@ -106,7 +105,7 @@

			return x, tgt_mask, memory, memory_mask, cache

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

			Args:
			@@ -149,6 +148,47 @@

			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


			class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
			"""
			Author: Speech Lab of DAMO Academy, Alibaba Group
			@@ -181,7 +221,6 @@
			tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
			embed_tensor_name_prefix_tf: str = None,
			):
			assert check_argument_types()
			super().__init__(
			vocab_size=vocab_size,
			encoder_output_size=encoder_output_size,
			@@ -399,7 +438,7 @@
			for i in range(self.att_layer_num):
			decoder = self.decoders[i]
			c = cache[i]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			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)
			@@ -409,13 +448,13 @@
			j = i + self.att_layer_num
			decoder = self.decoders2[i]
			c = cache[j]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			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_chunk(
			x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step(
			x, tgt_mask, memory, None, cache=None
			)

			@@ -835,10 +874,14 @@
			att_layer_num: int = 6,
			kernel_size: int = 21,
			sanm_shfit: int = 0,
			lora_list: List[str] = None,
			lora_rank: int = 8,
			lora_alpha: int = 16,
			lora_dropout: float = 0.1,
			chunk_multiply_factor: tuple = (1,),
			tf2torch_tensor_name_prefix_torch: str = "decoder",
			tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
			):
			assert check_argument_types()
			super().__init__(
			vocab_size=vocab_size,
			encoder_output_size=encoder_output_size,
			@@ -888,7 +931,7 @@
			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, lora_list, lora_rank, lora_alpha, lora_dropout
			),
			PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
			dropout_rate,
			@@ -928,6 +971,7 @@
			)
			self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
			self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
			self.chunk_multiply_factor = chunk_multiply_factor

			def forward(
			self,
			@@ -956,13 +1000,13 @@
			"""
			tgt = ys_in_pad
			tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]

			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)

			memory = hs_pad
			memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]

			x = tgt
			x, tgt_mask, memory, memory_mask, _ = self.decoders(
			@@ -1019,35 +1063,43 @@
			cache_layer_num = len(self.decoders)
			if self.decoders2 is not None:
			cache_layer_num += len(self.decoders2)
			new_cache = [None] * cache_layer_num
			fsmn_cache = [None] * cache_layer_num
			else:
			new_cache = cache["decode_fsmn"]
			fsmn_cache = cache["decode_fsmn"]

			if cache["opt"] is None:
			cache_layer_num = len(self.decoders)
			opt_cache = [None] * cache_layer_num
			else:
			opt_cache = cache["opt"]

			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]
			x, memory, fsmn_cache[i], opt_cache[i] = decoder.forward_chunk(
			x, memory, fsmn_cache=fsmn_cache[i], opt_cache=opt_cache[i],
			chunk_size=cache["chunk_size"], look_back=cache["decoder_chunk_look_back"]
			)
			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]
			x, memory, fsmn_cache[j], _ = decoder.forward_chunk(
			x, memory, fsmn_cache=fsmn_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
			x, memory, _, _ = decoder.forward_chunk(
			x, memory
			)
			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

			cache["decode_fsmn"] = fsmn_cache
			if cache["decoder_chunk_look_back"] > 0 or cache["decoder_chunk_look_back"] == -1:
			cache["opt"] = opt_cache
			return x

			def forward_one_step(
			@@ -1081,7 +1133,7 @@
			for i in range(self.att_layer_num):
			decoder = self.decoders[i]
			c = cache[i]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
			x, tgt_mask, memory, None, cache=c
			)
			new_cache.append(c_ret)
			@@ -1091,14 +1143,14 @@
			j = i + self.att_layer_num
			decoder = self.decoders2[i]
			c = cache[j]
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
			x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
			x, tgt_mask, memory, None, cache=c
			)
			new_cache.append(c_ret)

			for decoder in self.decoders3:

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