[WIP] Before refactor policies.

nanovllm/kvcache/sparse/policy.py

@@ -7,10 +7,17 @@ from CPU for each query chunk during chunked attention computation.
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from enum import Enum, auto
 from typing import List, Optional, Any
 import torch
 
 
+class SparsePolicyType(Enum):
+    """Built-in sparse attention policy types."""
+    FULL = auto()   # prefill + decode
+    QUEST = auto()  # decode only
+
+
 @dataclass
 class PolicyContext:
     """
@@ -54,8 +61,15 @@ class SparsePolicy(ABC):
     sparse attention patterns. The policy receives context about
     the current query chunk and returns which KV blocks to load.
 
+    Attributes:
+        supports_prefill: Whether this policy can be used for prefill phase.
+        supports_decode: Whether this policy can be used for decode phase.
+
     Example:
         class MySparsePolicy(SparsePolicy):
+            supports_prefill = False  # decode-only policy
+            supports_decode = True
+
             def select_blocks(self, available_blocks, ctx):
                 # Load first block and last 2 blocks
                 if len(available_blocks) <= 3:
@@ -63,6 +77,34 @@ class SparsePolicy(ABC):
                 return [available_blocks[0]] + available_blocks[-2:]
     """
 
+    # Compatibility flags - override in subclasses
+    supports_prefill: bool = True
+    supports_decode: bool = True
+
+    def initialize(
+        self,
+        num_layers: int,
+        num_kv_heads: int,
+        head_dim: int,
+        num_cpu_blocks: int,
+        dtype: torch.dtype,
+    ) -> None:
+        """
+        Initialize policy resources.
+
+        Called by the framework after KV cache is allocated. Override this
+        to create metadata structures (e.g., BlockMetadataManager for Quest).
+        Default implementation does nothing.
+
+        Args:
+            num_layers: Number of transformer layers
+            num_kv_heads: Number of KV attention heads
+            head_dim: Dimension per head
+            num_cpu_blocks: Number of CPU blocks allocated
+            dtype: Data type for tensors
+        """
+        pass
+
     @abstractmethod
     def select_blocks(
         self,