move encoder_pos

egs/librispeech/ASR/conv_emformer_transducer/emformer.py

@@ -1231,10 +1231,7 @@ class EmformerEncoder(nn.Module):
         return attention_mask
 
     def forward(
-        self,
-        x: torch.Tensor,
-        lengths: torch.Tensor,
-        pos_emb: torch.Tensor,
+        self, x: torch.Tensor, lengths: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Forward pass for training and non-streaming inference.
 
@@ -1250,9 +1247,6 @@ class EmformerEncoder(nn.Module):
             With shape (B,) and i-th element representing number of valid
             utterance frames for i-th batch element in x, which contains the
             right_context at the end.
-          pos_emb (torch.Tensor):
-            Position encoding embedding, with shape (PE, D).
-            For training mode, P = 2*U-1.
 
         Returns:
           A tuple of 2 tensors:
@@ -1260,8 +1254,11 @@ class EmformerEncoder(nn.Module):
             - output_lengths, with shape (B,), without containing the
               right_context at the end.
         """
+        U = x.size(0) - self.right_context_length
+        x, pos_emb = self.encoder_pos(x, pos_len=U, neg_len=U)
+
         right_context = self._gen_right_context(x)
-        utterance = x[: x.size(0) - self.right_context_length]
+        utterance = x[:U]
         output_lengths = torch.clamp(lengths - self.right_context_length, min=0)
         attention_mask = self._gen_attention_mask(utterance)
         memory = (
@@ -1271,6 +1268,7 @@ class EmformerEncoder(nn.Module):
             if self.use_memory
             else torch.empty(0).to(dtype=x.dtype, device=x.device)
         )
+
         output = utterance
         for layer in self.emformer_layers:
             output, right_context, memory = layer(
@@ -1289,7 +1287,6 @@ class EmformerEncoder(nn.Module):
         self,
         x: torch.Tensor,
         lengths: torch.Tensor,
-        pos_emb: torch.Tensor,
         states: Optional[List[List[torch.Tensor]]] = None,
         conv_caches: Optional[List[torch.Tensor]] = None,
     ) -> Tuple[
@@ -1309,9 +1306,6 @@ class EmformerEncoder(nn.Module):
             With shape (B,) and i-th element representing number of valid
             utterance frames for i-th batch element in x, which contains the
             right_context at the end.
-          pos_emb (torch.Tensor):
-            Position encoding embedding, with shape (PE, D).
-            For infer mode, PE = L+2*U-1.
           states (List[List[torch.Tensor]], optional):
             Cached states from proceeding chunk's computation, where each
             element (List[torch.Tensor]) corresponds to each emformer layer.
@@ -1332,6 +1326,11 @@ class EmformerEncoder(nn.Module):
             f"expected size of {self.chunk_length + self.right_context_length} "
             f"for dimension 1 of x, but got {x.size(1)}."
         )
+
+        pos_len = self.chunk_length + self.left_context_length
+        neg_len = self.chunk_length
+        x, pos_emb = self.encoder_pos(x, pos_len=pos_len, neg_len=neg_len)
+
         right_context_start_idx = x.size(0) - self.right_context_length
         right_context = x[right_context_start_idx:]
         utterance = x[:right_context_start_idx]
@@ -1414,8 +1413,6 @@ class Emformer(EncoderInterface):
         else:
             self.encoder_embed = Conv2dSubsampling(num_features, d_model)
 
-        self.encoder_pos = RelPositionalEncoding(d_model, dropout)
-
         self.encoder = EmformerEncoder(
             chunk_length // 4,
             d_model,
@@ -1463,10 +1460,6 @@ class Emformer(EncoderInterface):
               right_context at the end.
         """
         x = self.encoder_embed(x)
-
-        # TODO: The length computation in the encoder class should be moved here. # noqa
-        U = x.size(1) - self.right_context_length // 4
-        x, pos_emb = self.encoder_pos(x, pos_len=U, neg_len=U)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
         # Caution: We assume the subsampling factor is 4!
@@ -1475,7 +1468,7 @@ class Emformer(EncoderInterface):
             x_lens = ((x_lens - 1) // 2 - 1) // 2
         assert x.size(0) == x_lens.max().item()
 
-        output, output_lengths = self.encoder(x, x_lens, pos_emb)  # (T, N, C)
+        output, output_lengths = self.encoder(x, x_lens)  # (T, N, C)
 
         logits = self.encoder_output_layer(output)
         logits = logits.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
@@ -1521,12 +1514,6 @@ class Emformer(EncoderInterface):
             - updated convolution caches from current chunk.
         """
         x = self.encoder_embed(x)
-
-        # TODO: The length computation in the encoder class should be moved here. # noqa
-        pos_len = self.chunk_length // 4 + self.left_context_length // 4
-        neg_len = self.chunk_length // 4
-        x, pos_emb = self.encoder_pos(x, pos_len=pos_len, neg_len=neg_len)
-
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
         # Caution: We assume the subsampling factor is 4!
@@ -1540,7 +1527,7 @@ class Emformer(EncoderInterface):
             output_lengths,
             output_states,
             output_conv_caches,
-        ) = self.encoder.infer(x, x_lens, pos_emb, states, conv_caches)
+        ) = self.encoder.infer(x, x_lens, states, conv_caches)
 
         logits = self.encoder_output_layer(output)
         logits = logits.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)