Single location to update optional args for all attentions

examples/models/llama/attention.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, Tuple, Type
+from typing import Any, Dict, Optional, Tuple, Type, TypedDict
 
 import torch
 import torch.nn as nn
@@ -8,6 +8,15 @@
 from executorch.examples.models.llama.rope import Rope
 
 
+class ForwardOptions(TypedDict, total=False):
+    """Optional parameters for `Attention.forward` (compative with Python 3.10 and plus)."""
+
+    mask: Optional[torch.Tensor]
+    input_pos: Optional[torch.Tensor]
+    in_cache_state: Optional[Any]
+    out_cache_state: Optional[Any]
+
+
 class Attention(nn.Module, ABC):
     """Abstract base class for attention mechanisms with unified interface."""
 
@@ -17,19 +26,14 @@ def forward(
         x: torch.Tensor,
         freqs_cos: torch.Tensor,
         freqs_sin: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        input_pos: Optional[torch.Tensor] = None,
-        in_cache_state: Optional[Any] = None,
-        out_cache_state: Optional[Any] = None,
+        **kwargs: ForwardOptions,
     ) -> Tuple[torch.Tensor, Optional[Any]]:
         """Forward pass for attention mechanism.
 
         Args:
             x: Input tensor of shape (batch_size, seq_len, dim)
             freqs_cos, freqs_sin: Rotary position embedding frequencies
-            mask: Optional attention mask
-            input_pos: Positions for KV cache updates
-            in_cache_state/out_cache_state: Cache states
+            ForwardOptions: grouped optional args
 
         Returns:
             Tuple of (output tensor, updated cache state)
@@ -209,11 +213,9 @@ def forward(
         x: torch.Tensor,
         freqs_cos: torch.Tensor,
         freqs_sin: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        input_pos: Optional[torch.Tensor] = None,
-        in_cache_state: Optional[Any] = None,
-        out_cache_state: Optional[Any] = None,
+        **kwargs: ForwardOptions,
     ) -> Tuple[torch.Tensor, Optional[Any]]:
+        input_pos = kwargs.get("input_pos")
         bsz, seqlen, _ = x.shape
 
         # QKV