♻️ refactor: migrate chunked prefill attention to SparsePolicy

Move all chunked prefill attention computation from attention.py to
SparsePolicy.compute_chunked_attention(). This is the v4 architecture
refactoring for sparse attention policies.

Changes:
- Add compute_chunked_attention abstract method to SparsePolicy base
- Add offload_engine parameter to select_blocks for policies needing
  KV access during block selection
- Implement compute_chunked_attention in FullAttentionPolicy with
  complete ring buffer pipeline logic
- Simplify attention.py to delegate all chunked prefill to policy
- Remove redundant _sync_load_previous_chunks and
  _ring_buffer_pipeline_load methods from Attention class

Test: test_needle.py --enable-offload PASSED

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

nanovllm/kvcache/sparse/full_policy.py

@@ -5,12 +5,20 @@ This serves as a baseline and default policy when sparse
 attention is not needed.
 """
 
+import logging
 import torch
-from typing import List, Optional
+from typing import List, Optional, TYPE_CHECKING
 
 from .policy import SparsePolicy, PolicyContext
 from nanovllm.utils.context import get_context
 
+if TYPE_CHECKING:
+    from nanovllm.kvcache.offload_engine import OffloadEngine
+    from nanovllm.kvcache.manager import KVCacheManager
+    from nanovllm.engine.sequence import Sequence
+
+logger = logging.getLogger(__name__)
+
 
 class FullAttentionPolicy(SparsePolicy):
     """
@@ -32,30 +40,34 @@ class FullAttentionPolicy(SparsePolicy):
     def select_blocks(
         self,
         available_blocks: List[int],
+        offload_engine: "OffloadEngine",
         ctx: PolicyContext,
     ) -> List[int]:
         """Return all blocks - no sparsity."""
         return available_blocks
 
-    def compute_prefill_attention(
+    def compute_chunked_attention(
         self,
         q: torch.Tensor,
         k: torch.Tensor,
         v: torch.Tensor,
         layer_id: int,
         softmax_scale: float,
-        offload_engine,
+        offload_engine: "OffloadEngine",
+        kvcache_manager: "KVCacheManager",
         current_chunk_idx: int,
-        seq,
+        seq: "Sequence",
+        num_tokens: int,
     ) -> torch.Tensor:
         """
         Compute full attention for chunked prefill.
 
         This method handles the complete chunked prefill flow:
-        1. Load historical blocks from CPU
+        1. Get historical blocks
-        2. Compute attention to historical chunks
+        2. Select blocks via select_blocks
-        3. Compute attention to current chunk
+        3. Load and compute attention to historical chunks
-        4. Merge all results
+        4. Compute attention to current chunk
+        5. Merge all results
 
         Args:
             q: Query tensor [seq_len, num_heads, head_dim]
@@ -64,22 +76,41 @@ class FullAttentionPolicy(SparsePolicy):
             layer_id: Current layer index
             softmax_scale: Softmax scaling factor
             offload_engine: OffloadEngine for loading blocks
+            kvcache_manager: KVCacheManager for block management
             current_chunk_idx: Current chunk index
-            seq: ChunkedSequence
+            seq: Sequence object
+            num_tokens: Number of tokens in current chunk
 
         Returns:
             Attention output [seq_len, num_heads, head_dim]
         """
         from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
 
+        logger.debug(f"[DEBUG] FullPolicy.compute_chunked_attention called, "
+                     f"layer={layer_id}, chunk={current_chunk_idx}, num_tokens={num_tokens}")
+
         q_batched = q.unsqueeze(0)  # [1, seq_len, num_heads, head_dim]
-        num_tokens = q.shape[0]
         o_acc = None
         lse_acc = None
         compute_stream = offload_engine.compute_stream
 
-        # Step 1: Get and load historical blocks
+        # Step 1: Get historical blocks
-        cpu_block_table = seq.kvcache_manager.get_prefilled_cpu_blocks(seq)
+        cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
+
+        # Step 2: Apply select_blocks to filter blocks
+        if cpu_block_table:
+            num_chunks = current_chunk_idx + 1
+            policy_ctx = PolicyContext(
+                query_chunk_idx=current_chunk_idx,
+                num_query_chunks=num_chunks,
+                layer_id=layer_id,
+                query=None,  # Prefill typically doesn't use query for selection
+                is_prefill=True,
+                block_size=kvcache_manager.block_size,
+                total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
+            )
+            cpu_block_table = self.select_blocks(cpu_block_table, offload_engine, policy_ctx)
+            logger.debug(f"[DEBUG] select_blocks: output={len(cpu_block_table)} blocks")
 
         if cpu_block_table:
             load_slots = list(range(offload_engine.num_ring_slots))
@@ -139,7 +170,7 @@ class FullAttentionPolicy(SparsePolicy):
                         next_cpu_block_id = cpu_block_table[next_block_idx]
                         offload_engine.load_to_slot_layer(next_slot, layer_id, next_cpu_block_id)
 
-        # Step 2: Compute attention to current chunk (causal mask)
+        # Step 4: Compute attention to current chunk (causal mask)
         with torch.cuda.stream(compute_stream):
             k_curr, v_curr = offload_engine.get_prefill_buffer_slice(layer_id, num_tokens)
             current_o, current_lse = flash_attn_with_lse(
@@ -148,7 +179,7 @@ class FullAttentionPolicy(SparsePolicy):
                 causal=True,
             )
 
-        # Step 3: Merge historical and current attention
+        # Step 5: Merge historical and current attention
         with torch.cuda.stream(compute_stream):
             if o_acc is None:
                 final_o = current_o

nanovllm/kvcache/sparse/policy.py

@@ -7,12 +7,17 @@ from CPU for each query chunk during chunked attention computation.
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import List, Optional, Any
+from typing import List, Optional, Any, TYPE_CHECKING
 import torch
 
 # Import SparsePolicyType from config to avoid circular imports
 from nanovllm.config import SparsePolicyType
 
+if TYPE_CHECKING:
+    from nanovllm.kvcache.offload_engine import OffloadEngine
+    from nanovllm.kvcache.manager import KVCacheManager
+    from nanovllm.engine.sequence import Sequence
+
 
 @dataclass
 class PolicyContext:
@@ -107,6 +112,7 @@ class SparsePolicy(ABC):
     def select_blocks(
         self,
         available_blocks: List[int],
+        offload_engine: "OffloadEngine",
         ctx: PolicyContext,
     ) -> List[int]:
         """
@@ -120,6 +126,8 @@ class SparsePolicy(ABC):
             available_blocks: List of CPU block IDs that contain KV cache
                              from previous chunks. These are ordered by
                              their position in the sequence.
+            offload_engine: OffloadEngine for loading KV (some policies need
+                           to load KV to make selection decisions).
             ctx: PolicyContext with information about the current query
                  chunk, layer, phase (prefill/decode), etc.
 
@@ -183,5 +191,47 @@ class SparsePolicy(ABC):
         """
         pass
 
+    @abstractmethod
+    def compute_chunked_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer_id: int,
+        softmax_scale: float,
+        offload_engine: "OffloadEngine",
+        kvcache_manager: "KVCacheManager",
+        current_chunk_idx: int,
+        seq: "Sequence",
+        num_tokens: int,
+    ) -> torch.Tensor:
+        """
+        Compute chunked prefill attention (complete flow).
+
+        This is the main entry point for prefill attention computation.
+        It defines the complete prefill flow:
+        1. Get historical blocks
+        2. Select blocks (call select_blocks)
+        3. Load and compute historical blocks via offload_engine
+        4. Get current chunk KV from offload_engine, compute attention
+        5. Merge all results
+
+        Args:
+            q: [seq_len, num_heads, head_dim] query for current chunk
+            k: [seq_len, num_kv_heads, head_dim] key for current chunk (in prefill buffer)
+            v: [seq_len, num_kv_heads, head_dim] value for current chunk (in prefill buffer)
+            layer_id: transformer layer index
+            softmax_scale: softmax scaling factor
+            offload_engine: OffloadEngine for loading blocks
+            kvcache_manager: KVCacheManager for block management
+            current_chunk_idx: current chunk index
+            seq: Sequence object
+            num_tokens: number of tokens in current chunk
+
+        Returns:
+            [seq_len, num_heads, head_dim] final attention output
+        """
+        pass
+
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}()"

nanovllm/layers/attention.py

@@ -174,123 +174,45 @@ class Attention(nn.Module):
         """
         Compute attention with per-layer prefill buffer for async offload.
 
-        Optimized design:
+        Simplified design:
-        - Current chunk's KV is written to per-layer prefill buffer (not GPU slot)
+        - All computation logic is delegated to sparse_policy.compute_chunked_attention()
-        - Previous chunks' KV are loaded from CPU using GPU slots
+        - This method only handles async offload after computation
-        - Each layer offloads from its own buffer - no waiting required!
 
-        For each layer:
+        The policy handles:
-        1. Current chunk's KV is in prefill_buffer[layer_id] (just written by model)
+        1. Loading historical blocks from CPU
-        2. Load previous chunks from CPU using available slots (pipeline)
+        2. Computing attention against historical KV (no causal mask)
-        3. Compute attention against previous KV (no causal mask)
+        3. Computing attention against current KV from prefill buffer (causal)
-        4. Compute attention against current KV from prefill buffer (causal)
+        4. Merging all results
-        5. Merge all results using online softmax
-        6. Async offload prefill buffer to CPU (no waiting!)
         """
-        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
-
         current_chunk_idx = context.current_chunk_idx
         torch.cuda.nvtx.range_push(f"ChunkedPrefill: L{self.layer_id} Chunk{current_chunk_idx}")
 
-        # q shape: [total_tokens, num_heads, head_dim]
-        q_batched = q.unsqueeze(0)  # [1, total_tokens, heads, dim]
         num_tokens = k.shape[0]
 
-        o_acc = None
-        lse_acc = None
-
         kvcache_manager = context.kvcache_manager
         seq = context.chunked_seq if hasattr(context, 'chunked_seq') else None
         offload_engine = kvcache_manager.offload_engine if kvcache_manager is not None else None
 
-        if kvcache_manager is not None and seq is not None and self.layer_id >= 0:
+        # Get sparse policy - required for chunked prefill
-            # Get prefilled CPU blocks (blocks from previous chunks)
+        sparse_policy = kvcache_manager.sparse_policy
-            cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
+        if sparse_policy is None:
+            raise RuntimeError("sparse_policy is required for chunked prefill")
 
-            # Apply sparse policy if enabled
+        # [DEBUG] Verify execution path
-            sparse_policy = kvcache_manager.sparse_policy
+        logger.debug(f"[DEBUG] Calling sparse_policy.compute_chunked_attention, "
+                     f"policy={sparse_policy}, layer={self.layer_id}, chunk={current_chunk_idx}")
 
-            # === All sparse policies use select_blocks interface ===
+        # Delegate all computation to policy (no flash_attn or merge calls here!)
-            if cpu_block_table and sparse_policy is not None:
+        final_o = sparse_policy.compute_chunked_attention(
-                num_chunks = getattr(context, 'num_chunks', current_chunk_idx + 1)
+            q, k, v,
-                policy_ctx = PolicyContext(
+            self.layer_id,
-                    query_chunk_idx=current_chunk_idx,
+            self.scale,
-                    num_query_chunks=num_chunks,
+            offload_engine,
-                    layer_id=self.layer_id,
+            kvcache_manager,
-                    query=None,  # Prefill typically doesn't use query for selection
+            current_chunk_idx,
-                    is_prefill=True,
+            seq,
-                    block_size=kvcache_manager.block_size,
+            num_tokens,
-                    total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
+        )
-                )
-                cpu_block_table = sparse_policy.select_blocks(
-                    cpu_block_table, policy_ctx
-                )
-
-            if cpu_block_table:
-                # Get available load slots (all slots can be used since we use prefill buffer)
-                load_slots = list(range(offload_engine.num_ring_slots))
-                pipeline_depth = len(load_slots)
-
-                if pipeline_depth == 0:
-                    # Only 1 slot total, cannot pipeline - use sync loading
-                    o_acc, lse_acc = self._sync_load_previous_chunks(
-                        q_batched, cpu_block_table, offload_engine
-                    )
-                else:
-                    # Use ring buffer pipeline
-                    o_acc, lse_acc = self._ring_buffer_pipeline_load(
-                        q_batched, cpu_block_table, load_slots, offload_engine,
-                        current_chunk_idx
-                    )
-
-        # Get compute stream for all attention operations
-        compute_stream = offload_engine.compute_stream if offload_engine is not None else None
-
-        # Compute attention against current chunk's KV from prefill buffer (with causal mask)
-        needs_current_chunk_attention = True
-
-        if needs_current_chunk_attention:
-            if compute_stream is not None:
-                with torch.cuda.stream(compute_stream):
-                    torch.cuda.nvtx.range_push(f"FlashAttn: L{self.layer_id} CurrentChunk (causal)")
-                    # Get KV from per-layer prefill buffer
-                    k_batched, v_batched = offload_engine.get_prefill_buffer_slice(self.layer_id, num_tokens)
-                    current_o, current_lse = flash_attn_with_lse(
-                        q_batched,
-                        k_batched,
-                        v_batched,
-                        softmax_scale=self.scale,
-                        causal=True,
-                    )
-                    torch.cuda.nvtx.range_pop()
-            else:
-                torch.cuda.nvtx.range_push(f"FlashAttn: L{self.layer_id} CurrentChunk (causal)")
-                k_batched = k.unsqueeze(0)
-                v_batched = v.unsqueeze(0)
-                current_o, current_lse = flash_attn_with_lse(
-                    q_batched,
-                    k_batched,
-                    v_batched,
-                    softmax_scale=self.scale,
-                    causal=True,
-                )
-                torch.cuda.nvtx.range_pop()
-
-        # Merge with accumulated (all on compute_stream for consistency)
-        if o_acc is None:
-            # No accumulated attention (no historical chunks processed)
-            final_o = current_o
-        else:
-            # Has accumulated attention (historical chunks processed)
-            if compute_stream is not None:
-                with torch.cuda.stream(compute_stream):
-                    torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
-                    final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
-                    torch.cuda.nvtx.range_pop()
-            else:
-                torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
-                final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
-                torch.cuda.nvtx.range_pop()
 
         torch.cuda.nvtx.range_pop()  # ChunkedPrefill
 
@@ -305,181 +227,7 @@ class Attention(nn.Module):
                     self.layer_id, cpu_block_id, num_tokens
                 )
 
-        # Sync default stream with compute_stream before returning
+        return final_o
-        # This ensures the result is ready for the rest of the model (layernorm, MLP)
-        if compute_stream is not None:
-            torch.cuda.default_stream().wait_stream(compute_stream)
-
-        # Remove batch dimension: [1, total_tokens, heads, dim] -> [total_tokens, heads, dim]
-        return final_o.squeeze(0)
-
-    def _sync_load_previous_chunks(
-        self,
-        q_batched: torch.Tensor,
-        cpu_block_table: list,
-        offload_engine,
-    ):
-        """Synchronous loading fallback when pipeline_depth=0."""
-        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
-
-        o_acc, lse_acc = None, None
-        compute_stream = offload_engine.compute_stream
-
-        for block_idx, cpu_block_id in enumerate(cpu_block_table):
-            # Load to slot 0 (single slot)
-            offload_engine.load_to_slot_layer(0, self.layer_id, cpu_block_id)
-            offload_engine.wait_slot_layer(0)
-
-            # IMPORTANT: Must use compute_stream to match wait_slot_layer
-            with torch.cuda.stream(compute_stream):
-                prev_k, prev_v = offload_engine.get_kv_for_slot(0)
-
-                prev_o, prev_lse = flash_attn_with_lse(
-                    q_batched, prev_k, prev_v,
-                    softmax_scale=self.scale,
-                    causal=False,
-                )
-
-                if o_acc is None:
-                    o_acc, lse_acc = prev_o, prev_lse
-                else:
-                    o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
-
-        return o_acc, lse_acc
-
-    def _ring_buffer_pipeline_load(
-        self,
-        q_batched: torch.Tensor,
-        cpu_block_table: list,
-        load_slots: list,
-        offload_engine,
-        current_chunk_idx: int = -1,
-    ):
-        """
-        Ring buffer async pipeline loading with double buffering.
-
-        Uses compute_done events to ensure safe buffer reuse:
-        - Before loading to slot X, wait for previous compute on slot X to finish
-        - Before computing on slot X, wait for load to slot X to finish
-
-        Timeline with 2 slots (A, B):
-        ┌──────────────┐
-        │ Load B0→A    │
-        └──────────────┘
-                       ┌──────────────┐ ┌──────────────┐
-                       │ Load B1→B    │ │ Load B2→A    │ ...
-                       └──────────────┘ └──────────────┘
-                                      ↘               ↘
-                        ┌──────────────┐ ┌──────────────┐
-                        │ Compute(A)   │ │ Compute(B)   │ ...
-                        └──────────────┘ └──────────────┘
-
-        The load_to_slot_layer internally waits for compute_done[slot] before
-        starting the transfer, ensuring no data race.
-        """
-        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
-
-        num_blocks = len(cpu_block_table)
-        if num_blocks == 0:
-            return None, None
-
-        pipeline_depth = len(load_slots)
-        if pipeline_depth == 0:
-            return None, None
-
-        o_acc, lse_acc = None, None
-
-        if pipeline_depth == 1:
-            # Only 1 slot available, cannot pipeline - use synchronous mode
-            # IMPORTANT: Must use compute_stream to match synchronization in
-            # load_to_slot_layer (waits for compute_done) and wait_slot_layer
-            slot = load_slots[0]
-            compute_stream = offload_engine.compute_stream
-            for block_idx in range(num_blocks):
-                cpu_block_id = cpu_block_table[block_idx]
-                offload_engine.load_to_slot_layer(slot, self.layer_id, cpu_block_id)
-                offload_engine.wait_slot_layer(slot)
-
-                with torch.cuda.stream(compute_stream):
-                    # Debug: call hooks on compute_stream (synchronized with transfer)
-                    if offload_engine.debug_mode:
-                        offload_engine._call_debug_hooks(slot, self.layer_id, cpu_block_id)
-
-                    prev_k, prev_v = offload_engine.get_kv_for_slot(slot)
-
-                    prev_o, prev_lse = flash_attn_with_lse(
-                        q_batched, prev_k, prev_v,
-                        softmax_scale=self.scale,
-                        causal=False,
-                    )
-                    # Record compute done so next load can safely reuse this slot
-                    offload_engine.record_slot_compute_done(slot)
-                    if o_acc is None:
-                        o_acc, lse_acc = prev_o, prev_lse
-                    else:
-                        o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
-            return o_acc, lse_acc
-
-        # N-way pipeline: use ALL available slots for maximum overlap
-        # Pipeline depth = num_slots - 1 (num_slots blocks in flight)
-        num_slots = len(load_slots)
-
-        # Phase 1: Pre-load up to num_slots blocks to fill the pipeline
-        # This starts all transfers in parallel, utilizing full PCIe bandwidth
-        num_preload = min(num_slots, num_blocks)
-        for i in range(num_preload):
-            offload_engine.load_to_slot_layer(load_slots[i], self.layer_id, cpu_block_table[i])
-
-        # Phase 2: Main loop - compute and immediately reuse slot for next transfer
-        # Use dedicated compute_stream (not default stream) to enable overlap with transfers
-        compute_stream = offload_engine.compute_stream
-
-        for block_idx in range(num_blocks):
-            torch.cuda.nvtx.range_push(f"PipelineBlock: L{self.layer_id} B{block_idx}")
-
-            # Cycle through slots: slot[block_idx % num_slots]
-            current_slot = load_slots[block_idx % num_slots]
-            cpu_block_id = cpu_block_table[block_idx]
-
-            # Wait for current slot's transfer to complete (on compute_stream)
-            offload_engine.wait_slot_layer(current_slot)
-
-            # Compute attention on current slot's data
-            # IMPORTANT: Use dedicated compute_stream to avoid implicit sync with default stream
-            with torch.cuda.stream(compute_stream):
-                # Debug: call hooks on compute_stream (synchronized with transfer)
-                if offload_engine.debug_mode:
-                    offload_engine._call_debug_hooks(current_slot, self.layer_id, cpu_block_id)
-
-                torch.cuda.nvtx.range_push(f"FlashAttn: L{self.layer_id} PrevBlock{block_idx}")
-                prev_k, prev_v = offload_engine.get_kv_for_slot(current_slot)
-
-                prev_o, prev_lse = flash_attn_with_lse(
-                    q_batched, prev_k, prev_v,
-                    softmax_scale=self.scale,
-                    causal=False,
-                )
-                torch.cuda.nvtx.range_pop()
-
-                # Record compute done - this allows the next transfer to safely overwrite this slot
-                offload_engine.record_slot_compute_done(current_slot)
-
-            # Immediately start loading the NEXT block into this slot (if more blocks remain)
-            # Key insight: reuse current_slot immediately after compute is done!
-            next_block_idx = block_idx + num_slots
-            if next_block_idx < num_blocks:
-                offload_engine.load_to_slot_layer(current_slot, self.layer_id, cpu_block_table[next_block_idx])
-
-            # Merge with accumulated (also on compute_stream for consistency)
-            with torch.cuda.stream(compute_stream):
-                if o_acc is None:
-                    o_acc, lse_acc = prev_o, prev_lse
-                else:
-                    o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
-
-            torch.cuda.nvtx.range_pop()  # PipelineBlock
-
-        return o_acc, lse_acc
 
     def _chunked_decode_attention(
         self,
@@ -524,6 +272,8 @@ class Attention(nn.Module):
         if last_block_valid_tokens == 0 and total_prefill_tokens > 0:
             last_block_valid_tokens = block_size  # Last block was exactly full
 
+        offload_engine = kvcache_manager.offload_engine
+
         # Apply sparse policy if enabled (Quest does Top-K selection for decode)
         sparse_policy = kvcache_manager.sparse_policy
         if sparse_policy is not None:
@@ -537,11 +287,9 @@ class Attention(nn.Module):
                 total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
             )
             cpu_block_table = sparse_policy.select_blocks(
-                cpu_block_table, policy_ctx
+                cpu_block_table, offload_engine, policy_ctx
             )
 
-        offload_engine = kvcache_manager.offload_engine
-
         # Use cross-layer pipeline if active (initialized in model_runner)
         if offload_engine.is_pipeline_active():
             o_acc, lse_acc = self._decode_with_layer_pipeline(

test_report_sparse_policy_refactor.md

@@ -0,0 +1,114 @@
+# SparsePolicy 重构测试报告
+
+## 任务概述
+
+根据 task_plan.md 的要求，对 nanovllm 的 SparsePolicy 架构进行重构（v4 版本），将 chunked prefill attention 计算逻辑从 attention.py 完全迁移到 SparsePolicy。
+
+## 修改范围
+
+仅针对 FullPolicy，不涉及 QuestPolicy 或 XAttentionBSAPolicy，不修改 decode 阶段逻辑。
+
+## 完成的修改
+
+### 1. policy.py (SparsePolicy 基类)
+
+- 添加 TYPE_CHECKING imports: `OffloadEngine`, `KVCacheManager`, `Sequence`
+- 修改 `select_blocks` 签名：添加 `offload_engine` 参数
+- 添加 `compute_chunked_attention` 抽象方法，参数包括：
+  - `q, k, v`: 张量
+  - `layer_id`: 层索引
+  - `softmax_scale`: softmax 缩放因子
+  - `offload_engine`: OffloadEngine 实例
+  - `kvcache_manager`: KVCacheManager 实例
+  - `current_chunk_idx`: 当前 chunk 索引
+  - `seq`: Sequence 对象
+  - `num_tokens`: 当前 chunk 的 token 数
+
+### 2. full_policy.py (FullAttentionPolicy)
+
+- 更新 TYPE_CHECKING imports
+- `select_blocks` 方法签名添加 `offload_engine` 参数
+- 重命名 `compute_prefill_attention` → `compute_chunked_attention`
+- 添加 `kvcache_manager` 参数，替换所有 `seq.kvcache_manager` 引用
+- 添加 debug 日志输出
+
+### 3. attention.py
+
+- 简化 `_chunked_prefill_attention` 方法：
+  - 删除所有 `flash_attn_*` 调用
+  - 删除所有 `merge_attention_outputs` 调用
+  - 仅保留委托调用 `sparse_policy.compute_chunked_attention()`
+- 删除冗余方法：`_sync_load_previous_chunks`, `_ring_buffer_pipeline_load`
+- decode 路径的 `select_blocks` 调用添加 `offload_engine` 参数
+
+## 验收标准检查
+
+| 标准 | 状态 | 说明 |
+|------|------|------|
+| test_needle.py --enable-offload 通过 | ✅ | 测试输出 PASSED |
+| attention.py chunked prefill path 无 flash_attn_* 调用 | ✅ | `_chunked_prefill_attention` 方法（169-230行）内无直接 flash_attn 调用 |
+| attention.py chunked prefill path 无 merge_attention_outputs 调用 | ✅ | 同上 |
+| 所有 KV 通信通过 offload_engine 方法 | ✅ | 全部通过 `offload_engine.load_to_slot_layer`, `get_kv_for_slot`, `get_prefill_buffer_slice` |
+
+## 测试结果
+
+```
+============================================================
+Needle-in-Haystack Test
+============================================================
+Model: /home/zijie/models/Llama-3.1-8B-Instruct
+Max model len: 131072
+Input length: 8192
+Block size: 1024
+Needle position: 50%
+Needle value: 7492
+CPU offload: True
+Sparse policy: FULL
+============================================================
+
+[NeedleTest] Target: 8192, Actual: 8213 tokens (diff=21)
+Expected: 7492
+Output: 7492<|eot_id|>...
+Status: PASSED
+============================================================
+
+test_needle: PASSED
+```
+
+## 性能指标
+
+- Prefill: 3527 tok/s
+- Decode: 11 tok/s
+- TTFT: 2329.29 ms
+- TPOT: 655.38 ms
+
+## 架构变更总结
+
+**重构前**:
+```
+attention.py::_chunked_prefill_attention()
+  ├── 获取 cpu_block_table
+  ├── 调用 sparse_policy.select_blocks()
+  ├── 直接调用 flash_attn_with_lse + merge_attention_outputs
+  └── 返回结果
+```
+
+**重构后**:
+```
+attention.py::_chunked_prefill_attention()
+  ├── 获取 context 信息
+  ├── 调用 sparse_policy.compute_chunked_attention()  # 委托全部计算
+  └── 返回结果
+
+sparse_policy.compute_chunked_attention()  # 在 FullPolicy 中
+  ├── 获取 cpu_block_table
+  ├── 调用 self.select_blocks()
+  ├── 加载并计算历史 KV attention
+  ├── 计算当前 chunk attention (causal)
+  ├── 合并所有结果
+  └── 返回最终输出
+```
+
+## 结论
+
+SparsePolicy 架构 v4 重构成功完成。所有验收标准均已满足，测试通过。