update asf code

funasr/models/paraformer/decoder.py

@@ -116,6 +116,22 @@ class DecoderLayerSANM(torch.nn.Module):
             # x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
 
         return x, tgt_mask, memory, memory_mask, cache
+    
+    def get_attn_mat(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
+        residual = tgt
+        tgt = self.norm1(tgt)
+        tgt = self.feed_forward(tgt)
+
+        x = tgt
+        if self.self_attn is not None:
+            tgt = self.norm2(tgt)
+            x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
+            x = residual + x
+
+        residual = x
+        x = self.norm3(x)
+        x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
+        return attn_mat
 
     def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
         """Compute decoded features.
@@ -396,6 +412,46 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
             ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
         )
         return logp.squeeze(0), state
+    
+    def forward_asf2(
+        self,
+        hs_pad: torch.Tensor,
+        hlens: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+    ):
+
+        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, :]
+
+        tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
+        attn_mat = self.model.decoders[1].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
+        return attn_mat
+    
+    def forward_asf6(
+        self,
+        hs_pad: torch.Tensor,
+        hlens: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+    ):
+
+        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, :]
+
+        tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
+        tgt, tgt_mask, memory, memory_mask, _ = self.decoders[1](tgt, tgt_mask, memory, memory_mask)
+        tgt, tgt_mask, memory, memory_mask, _ = self.decoders[2](tgt, tgt_mask, memory, memory_mask)
+        tgt, tgt_mask, memory, memory_mask, _ = self.decoders[3](tgt, tgt_mask, memory, memory_mask)
+        tgt, tgt_mask, memory, memory_mask, _ = self.decoders[4](tgt, tgt_mask, memory, memory_mask)
+        attn_mat = self.decoders[5].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
+        return attn_mat
 
     def forward_chunk(
         self,

funasr/models/seaco_paraformer/model.py

@@ -223,12 +223,8 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
             
             # ASF Core
             if nfilter > 0 and nfilter < num_hot_word:
-                for dec in self.seaco_decoder.decoders:
-                    dec.reserve_attn = True
-                # cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
-                dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
-                # cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
-                hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
+                hotword_scores = self.seaco_decoder.forward_asf6(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
+                hotword_scores = hotword_scores[0].sum(0).sum(0)
                 # hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
                 dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
                 add_filter = dec_filter
@@ -239,9 +235,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
                 contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
                 num_hot_word = contextual_info.shape[1]
                 _contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
-                for dec in self.seaco_decoder.decoders:
-                    dec.attn_mat = []
-                    dec.reserve_attn = False
             
             # SeACo Core
             cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)