♻️ refactor: migrate chunked decode attention to SparsePolicy

Move decode attention computation from attention.py to SparsePolicy:
- Add compute_chunked_decode abstract method to SparsePolicy base class
- Implement compute_chunked_decode in FullAttentionPolicy with:
  - Ring buffer pipeline (_decode_ring_buffer_pipeline)
  - Cross-layer pipeline (_decode_with_layer_pipeline)
  - Decode buffer handling
- Simplify _chunked_decode_attention to only validate and delegate
- Remove _decode_ring_buffer_pipeline and _decode_with_layer_pipeline from attention.py
- Add supports_decode check for policy validation

This completes the SparsePolicy v5 refactoring where both prefill and
decode paths now delegate all computation to the sparse policy.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

nanovllm/kvcache/sparse/policy.py

@@ -233,5 +233,43 @@ class SparsePolicy(ABC):
         """
         pass
 
+    @abstractmethod
+    def compute_chunked_decode(
+        self,
+        q: torch.Tensor,
+        layer_id: int,
+        softmax_scale: float,
+        offload_engine: "OffloadEngine",
+        kvcache_manager: "KVCacheManager",
+        seq: "Sequence",
+    ) -> torch.Tensor:
+        """
+        Compute chunked decode attention (complete flow).
+
+        This is the main entry point for decode attention computation.
+        It defines the complete decode flow:
+        1. Get prefilled blocks from CPU
+        2. Select blocks (call select_blocks)
+        3. Load blocks via pipeline (ring buffer or cross-layer)
+        4. Read accumulated decode tokens from decode buffer
+        5. Merge all results
+
+        The decode position information can be computed internally:
+        - decode_start_pos = kvcache_manager.get_decode_start_pos(seq)
+        - decode_pos_in_block = (len(seq) - 1) % kvcache_manager.block_size
+
+        Args:
+            q: [batch_size, num_heads, head_dim] query for decode token
+            layer_id: transformer layer index
+            softmax_scale: softmax scaling factor
+            offload_engine: OffloadEngine for loading blocks
+            kvcache_manager: KVCacheManager for block management
+            seq: Sequence object
+
+        Returns:
+            [batch_size, 1, num_heads, head_dim] final attention output
+        """
+        pass
+
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}()"