Apply minor fixes to Emformer implementation (#2252)

Summary:
Noticed some items to clean up in `Emformer`.
- Make `segment_length` a required argument in `_EmformerLayer`.
- Remove unused variables from `_unpack_state` and `_gen_attention_mask`.

These don't affect `Emformer`'s functionality or public API.

Pull Request resolved: #2252

Reviewed By: carolineechen, mthrok

Differential Revision: D34321430

Pulled By: hwangjeff

fbshipit-source-id: 38a5046f633a3e625352c476ef71c78380ccc597

torchaudio/models/emformer.py

@@ -321,11 +321,11 @@ class _EmformerLayer(torch.nn.Module):
         input_dim (int): input dimension.
         num_heads (int): number of attention heads.
         ffn_dim: (int): hidden layer dimension of feedforward network.
+        segment_length (int): length of each input segment.
         dropout (float, optional): dropout probability. (Default: 0.0)
         activation (str, optional): activation function to use in feedforward network.
             Must be one of ("relu", "gelu", "silu"). (Default: "relu")
         left_context_length (int, optional): length of left context. (Default: 0)
-        segment_length (int, optional): length of each input segment. (Default: 128)
         max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
         weight_init_gain (float or None, optional): scale factor to apply when initializing
             attention module parameters. (Default: ``None``)
@@ -338,10 +338,10 @@ def __init__(
         input_dim: int,
         num_heads: int,
         ffn_dim: int,
+        segment_length: int,
         dropout: float = 0.0,
         activation: str = "relu",
         left_context_length: int = 0,
-        segment_length: int = 128,
         max_memory_size: int = 0,
         weight_init_gain: Optional[float] = None,
         tanh_on_mem: bool = False,
@@ -386,9 +386,7 @@ def _init_state(self, batch_size: int, device: Optional[torch.device]) -> List[t
         past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
         return [empty_memory, left_context_key, left_context_val, past_length]
 
-    def _unpack_state(
-        self, utterance: torch.Tensor, mems: torch.Tensor, state: List[torch.Tensor]
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def _unpack_state(self, state: List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         past_length = state[3][0][0].item()
         past_left_context_length = min(self.left_context_length, past_length)
         past_mem_length = min(self.max_memory_size, math.ceil(past_length / self.segment_length))
@@ -474,7 +472,7 @@ def _apply_attention_infer(
     ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
         if state is None:
             state = self._init_state(utterance.size(1), device=utterance.device)
-        pre_mems, lc_key, lc_val = self._unpack_state(utterance, mems, state)
+        pre_mems, lc_key, lc_val = self._unpack_state(state)
         if self.use_mem:
             summary = self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
             summary = summary[:1]
@@ -652,10 +650,10 @@ def __init__(
                     input_dim,
                     num_heads,
                     ffn_dim,
+                    segment_length,
                     dropout=dropout,
                     activation=activation,
                     left_context_length=left_context_length,
-                    segment_length=segment_length,
                     max_memory_size=max_memory_size,
                     weight_init_gain=weight_init_gains[layer_idx],
                     tanh_on_mem=tanh_on_mem,
@@ -718,7 +716,7 @@ def _gen_attention_mask_col_widths(self, seg_idx: int, utterance_length: int) ->
         return col_widths
 
     def _gen_attention_mask(self, input: torch.Tensor) -> torch.Tensor:
-        utterance_length, batch_size, _ = input.shape
+        utterance_length = input.size(0)
         num_segs = math.ceil(utterance_length / self.segment_length)
 
         rc_mask = []