[refactor] Refactor current gpu and cpu block allocation strategy.

bench_offload.py

@@ -40,7 +40,6 @@ def main():
         max_model_len=128 * 1024,
         max_num_batched_tokens=128 * 1024,
         enable_cpu_offload=True,
-        cpu_memory_gb=32.0,
     )
 
     # Warmup

nanovllm/config.py

@@ -19,7 +19,6 @@ class Config:
 
     # CPU Offload configuration
     enable_cpu_offload: bool = False
-    cpu_memory_gb: float = 16.0  # CPU memory limit for KV cache
     offload_policy: str = "lru"  # "lru", "fifo", or full class path
     num_transfer_streams: int = 4  # Number of CUDA streams for async transfers
     num_gpu_blocks: int = -1  # User-specified GPU blocks count, -1 = auto (use max available)

nanovllm/engine/model_runner.py

@@ -10,8 +10,11 @@ from nanovllm.models.qwen3 import Qwen3ForCausalLM
 from nanovllm.layers.sampler import Sampler
 from nanovllm.utils.context import set_context, get_context, reset_context
 from nanovllm.utils.loader import load_model
+from nanovllm.utils.logger import get_logger
 from nanovllm.kvcache import create_kvcache_manager, KVCacheManager
 
+logger = get_logger("model_runner")
+
 
 class ModelRunner:
 
@@ -120,9 +123,11 @@ class ModelRunner:
             num_gpu_blocks = max_gpu_blocks
 
         if config.enable_cpu_offload:
-            # Calculate CPU blocks based on cpu_memory_gb
-            cpu_bytes = int(config.cpu_memory_gb * 1024**3)
-            num_cpu_blocks = cpu_bytes // block_bytes
+            # Ping-Pong设计：CPU是主存储，GPU是工作缓冲区
+            # CPU blocks = 支持max_model_len所需的全部blocks（存储一个最大序列的完整KV）
+            # GPU blocks = Ping-Pong工作缓冲区（用户指定或自动）
+            num_cpu_blocks = (config.max_model_len + self.block_size - 1) // self.block_size
+
             config.num_gpu_kvcache_blocks = num_gpu_blocks
             config.num_cpu_kvcache_blocks = num_cpu_blocks
             # For backward compatibility
@@ -143,6 +148,27 @@ class ModelRunner:
             dtype=hf_config.torch_dtype,
         )
 
+        # Log KV cache allocation info
+        gpu_memory_mb = config.num_gpu_kvcache_blocks * block_bytes / (1024 ** 2)
+        cpu_memory_mb = config.num_cpu_kvcache_blocks * block_bytes / (1024 ** 2)
+        total_memory_mb = gpu_memory_mb + cpu_memory_mb
+
+        if config.enable_cpu_offload:
+            logger.info(
+                f"KV Cache allocated (Ping-Pong mode): "
+                f"GPU={config.num_gpu_kvcache_blocks} blocks ({gpu_memory_mb:.1f}MB), "
+                f"CPU={config.num_cpu_kvcache_blocks} blocks ({cpu_memory_mb:.1f}MB), "
+                f"Total={total_memory_mb:.1f}MB, "
+                f"block_size={self.block_size}, "
+                f"ping_size={config.num_gpu_kvcache_blocks // 2}"
+            )
+        else:
+            logger.info(
+                f"KV Cache allocated: "
+                f"GPU={config.num_gpu_kvcache_blocks} blocks ({gpu_memory_mb:.1f}MB), "
+                f"block_size={self.block_size}"
+            )
+
         # Bind layer caches to attention modules and set layer_id
         layer_id = 0
         for module in self.model.modules():
@@ -328,7 +354,16 @@ class ModelRunner:
             return self.model.compute_logits(graph_vars["outputs"][:bs])
 
     def run(self, seqs: list[Sequence], is_prefill: bool) -> list[int]:
-        # Check if chunked prefill is needed
+        # Check if Ping-Pong mode should be used (all blocks on CPU)
+        if hasattr(self, 'kvcache_manager') and hasattr(self.kvcache_manager, 'get_all_cpu_blocks'):
+            use_pingpong = self._should_use_pingpong(seqs, is_prefill)
+            if use_pingpong:
+                if is_prefill:
+                    return self.run_pingpong_prefill(seqs)
+                else:
+                    return self.run_pingpong_decode(seqs)
+
+        # Check if chunked prefill is needed (legacy path)
         if is_prefill and hasattr(self, 'kvcache_manager'):
             needs_chunked = any(
                 hasattr(self.kvcache_manager, 'needs_chunked_prefill') and
@@ -338,7 +373,7 @@ class ModelRunner:
             if needs_chunked:
                 return self.run_chunked_prefill(seqs)
 
-        # Check if chunked decode is needed
+        # Check if chunked decode is needed (legacy path)
         if not is_prefill and hasattr(self, 'kvcache_manager'):
             needs_chunked = any(
                 hasattr(self.kvcache_manager, 'needs_chunked_decode') and
@@ -355,6 +390,36 @@ class ModelRunner:
         reset_context()
         return token_ids
 
+    def _should_use_pingpong(self, seqs: list[Sequence], is_prefill: bool) -> bool:
+        """
+        Check if Ping-Pong mode should be used.
+
+        Use Ping-Pong when:
+        - CPU offload is enabled
+        - There are blocks on CPU (either allocated there or offloaded)
+        - Sequence exceeds GPU capacity
+        """
+        if not hasattr(self.kvcache_manager, 'offload_engine'):
+            return False
+
+        for seq in seqs:
+            if not seq.block_table:
+                continue  # Skip warmup sequences
+
+            # Check if any blocks are on CPU
+            cpu_blocks, _ = self.kvcache_manager.get_all_cpu_blocks(seq)
+            if cpu_blocks:
+                # Has CPU blocks - use Ping-Pong
+                return True
+
+            # Check if sequence needs more blocks than GPU can hold
+            ping_size = self.kvcache_manager.offload_engine.ping_size
+            if seq.num_blocks > ping_size:
+                # Needs chunked processing
+                return True
+
+        return False
+
     def run_chunked_prefill(self, seqs: list[Sequence]) -> list[int]:
         """
         Run prefill in chunks when sequences exceed GPU capacity.
@@ -543,6 +608,210 @@ class ModelRunner:
 
         return input_ids, positions
 
+    def run_pingpong_prefill(self, seqs: list[Sequence]) -> list[int]:
+        """
+        Run prefill with Ping-Pong dual buffer (CPU is primary storage).
+
+        Flow:
+        1. All blocks are allocated to CPU (primary storage)
+        2. Process tokens in chunks using Ping/Pong GPU buffers
+        3. After each chunk, offload from GPU to CPU
+        4. Alternate between Ping and Pong buffers
+        """
+        import sys
+
+        assert len(seqs) == 1, "Ping-Pong prefill only supports single sequence"
+        seq = seqs[0]
+
+        offload_engine = self.kvcache_manager.offload_engine
+        ping_size = offload_engine.ping_size
+        tokens_per_chunk = ping_size * self.block_size
+
+        total_tokens = len(seq)
+        print(f"[Ping-Pong Prefill] Starting: {total_tokens} tokens, "
+              f"ping_size={ping_size} blocks, chunk={tokens_per_chunk} tokens",
+              file=sys.stderr)
+
+        current_buffer = "ping"
+        chunk_num = 0
+        logits = None
+        processed_tokens = 0
+
+        # Get CPU block table for offload targets
+        cpu_block_ids, logical_ids = self.kvcache_manager.get_all_cpu_blocks(seq)
+
+        while processed_tokens < total_tokens:
+            chunk_num += 1
+            chunk_start = processed_tokens
+            chunk_end = min(processed_tokens + tokens_per_chunk, total_tokens)
+            chunk_tokens = chunk_end - chunk_start
+
+            # Calculate which CPU blocks this chunk covers
+            start_block_idx = chunk_start // self.block_size
+            end_block_idx = (chunk_end + self.block_size - 1) // self.block_size
+            num_blocks = end_block_idx - start_block_idx
+
+            print(f"[Ping-Pong Prefill] Chunk {chunk_num}: tokens {chunk_start}-{chunk_end}, "
+                  f"blocks {start_block_idx}-{end_block_idx-1}, buffer={current_buffer}",
+                  file=sys.stderr)
+
+            # Get GPU slots for this chunk (Ping or Pong buffer)
+            if current_buffer == "ping":
+                gpu_slots = offload_engine.ping_slots[:num_blocks]
+            else:
+                gpu_slots = offload_engine.pong_slots[:num_blocks]
+
+            # Prepare inputs
+            input_ids, positions = self._prepare_pingpong_chunk(
+                seq, chunk_start, chunk_end, gpu_slots, start_block_idx
+            )
+
+            if input_ids.numel() == 0:
+                break
+
+            # Run model forward
+            logits = self.run_model(input_ids, positions, is_prefill=True)
+            reset_context()
+
+            # Mark blocks as prefilled
+            for i in range(start_block_idx, min(end_block_idx, len(seq.block_table))):
+                logical_id = seq.block_table[i]
+                self.kvcache_manager.prefilled_blocks.add(logical_id)
+
+            # Offload this chunk from GPU to CPU (async)
+            chunk_cpu_blocks = cpu_block_ids[start_block_idx:end_block_idx]
+            offload_engine.offload_buffer_to_cpu(current_buffer, chunk_cpu_blocks)
+
+            # Switch buffer for next chunk
+            if current_buffer == "ping":
+                offload_engine.wait_ping_offload_done()
+                current_buffer = "pong"
+            else:
+                offload_engine.wait_pong_offload_done()
+                current_buffer = "ping"
+
+            processed_tokens = chunk_end
+
+        # Wait for all offloads to complete
+        offload_engine.wait_all_offload_done()
+
+        print(f"[Ping-Pong Prefill] Complete: {chunk_num} chunks", file=sys.stderr)
+
+        # Sample from last logits
+        temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
+        if logits is not None:
+            token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
+        else:
+            token_ids = [0] if self.rank == 0 else None
+
+        return token_ids
+
+    def _prepare_pingpong_chunk(
+        self,
+        seq: Sequence,
+        chunk_start: int,
+        chunk_end: int,
+        gpu_slots: list[int],
+        start_block_idx: int,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Prepare inputs for a Ping-Pong prefill chunk."""
+        # Input tokens for this chunk
+        input_ids = seq[chunk_start:chunk_end]
+        positions = list(range(chunk_start, chunk_end))
+
+        # Create slot mapping pointing to GPU slots
+        slot_mapping = []
+        num_tokens = chunk_end - chunk_start
+
+        token_idx = 0
+        for i, gpu_slot in enumerate(gpu_slots):
+            block_idx = start_block_idx + i
+            block_start = block_idx * self.block_size
+            block_end = min(block_start + self.block_size, len(seq))
+
+            # How many tokens in this block for this chunk
+            overlap_start = max(chunk_start, block_start)
+            overlap_end = min(chunk_end, block_end)
+
+            for pos in range(overlap_start, overlap_end):
+                pos_in_block = pos % self.block_size
+                slot = gpu_slot * self.block_size + pos_in_block
+                slot_mapping.append(slot)
+
+        # Convert to tensors
+        input_ids = torch.tensor(input_ids, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
+        positions = torch.tensor(positions, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
+        slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
+
+        # Set up context for chunked prefill
+        seqlen = num_tokens
+        cu_seqlens_q = torch.tensor([0, seqlen], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
+        cu_seqlens_k = torch.tensor([0, seqlen], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
+
+        set_context(
+            is_prefill=True,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=seqlen,
+            max_seqlen_k=seqlen,
+            slot_mapping=slot_mapping,
+            is_chunked_prefill=True,
+            offload_engine=self.kvcache_manager,
+            chunked_seq=seq,
+        )
+
+        return input_ids, positions
+
+    def run_pingpong_decode(self, seqs: list[Sequence]) -> list[int]:
+        """
+        Run decode with Ping-Pong dual buffer.
+
+        All KV is on CPU. Uses Ping-Pong to load KV chunks and compute attention.
+        New token's KV is written to GPU then offloaded to CPU.
+        """
+        assert len(seqs) == 1, "Ping-Pong decode only supports single sequence"
+        seq = seqs[0]
+
+        # Prepare inputs
+        input_ids = torch.tensor([seq.last_token], dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
+        positions = torch.tensor([len(seq) - 1], dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
+
+        # Get write slot for new KV (will use last slot of the buffer used for final chunk)
+        write_slot = self.kvcache_manager.get_write_slot_for_pingpong(seq)
+
+        # Calculate position in block for slot mapping
+        last_block_idx = seq.num_blocks - 1
+        pos_in_block = (len(seq) - 1) % self.block_size
+        slot = write_slot * self.block_size + pos_in_block
+        slot_mapping = torch.tensor([slot], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
+        context_len = torch.tensor([len(seq)], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
+
+        # Set up context for chunked decode
+        set_context(
+            is_prefill=False,
+            slot_mapping=slot_mapping,
+            context_lens=context_len,
+            is_chunked_prefill=True,  # Use chunked attention path
+            offload_engine=self.kvcache_manager,
+            chunked_seq=seq,
+        )
+
+        # Run model forward pass
+        logits = self.run_model(input_ids, positions, is_prefill=False)
+        reset_context()
+
+        # Offload new KV from write_slot to CPU
+        last_cpu_block = self.kvcache_manager.get_last_cpu_block(seq)
+        if last_cpu_block >= 0:
+            self.kvcache_manager.offload_engine.offload_slot_to_cpu(write_slot, last_cpu_block)
+            self.kvcache_manager.offload_engine.wait_all_offload_done()
+
+        # Sample
+        temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
+        token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
+
+        return token_ids
+
     @torch.inference_mode()
     def capture_cudagraph(self):
         config = self.config

nanovllm/kvcache/hybrid_manager.py

@@ -81,20 +81,24 @@ class HybridKVCacheManager(KVCacheManager):
         num_cpu_blocks: int,
         block_size: int,
         policy: Optional[EvictionPolicy] = None,
+        cpu_primary: bool = True,
     ):
         """
         Initialize hybrid manager.
 
         Args:
             num_gpu_slots: Number of GPU buffer slots (working set)
-            num_cpu_blocks: Number of CPU pool blocks (overflow)
+            num_cpu_blocks: Number of CPU pool blocks (overflow or primary storage)
             block_size: Tokens per block
             policy: Eviction policy (default: LRU)
+            cpu_primary: If True, use CPU as primary storage with Ping-Pong GPU buffer.
+                        If False, use GPU as primary with CPU as overflow (legacy mode).
         """
         self._block_size = block_size
         self.num_gpu_slots = num_gpu_slots
         self.num_cpu_blocks = num_cpu_blocks
         self.total_blocks = num_gpu_slots + num_cpu_blocks
+        self.cpu_primary = cpu_primary  # Ping-Pong mode flag
 
         # Eviction policy
         self.policy = policy or LRUPolicy()
@@ -321,12 +325,16 @@ class HybridKVCacheManager(KVCacheManager):
         """
         Allocate logical blocks for prefill.
 
-        New blocks are allocated on GPU when possible. If GPU is full and all
-        GPU blocks belong to this sequence (can't evict), remaining blocks
-        are allocated to CPU for chunked prefill.
+        In cpu_primary mode (Ping-Pong): All blocks are allocated to CPU.
+        In legacy mode: Blocks are allocated to GPU when possible, overflow to CPU.
         """
         assert not seq.block_table, "Sequence already has blocks"
 
+        # Ping-Pong模式：所有blocks都分配到CPU
+        if self.cpu_primary:
+            return self.allocate_cpu_only(seq)
+
+        # Legacy模式：GPU为主，CPU为overflow
         h = -1
         cache_miss = False
 
@@ -451,13 +459,22 @@ class HybridKVCacheManager(KVCacheManager):
             block.hash = -1
             block.token_ids = []
 
-            # New decode blocks go to GPU
-            gpu_slot = self._allocate_gpu_slot()
-            block.location = BlockLocation.GPU
-            block.gpu_slot = gpu_slot
-
-            self.gpu_slot_to_logical[gpu_slot] = logical_id
-            self.policy.on_block_allocated(gpu_slot, self.current_step)
+            if self.cpu_primary:
+                # Ping-Pong模式：新block分配到CPU
+                if not self.free_cpu_blocks:
+                    raise RuntimeError("No free CPU blocks for decode")
+                cpu_block_id = self.free_cpu_blocks.popleft()
+                block.location = BlockLocation.CPU
+                block.cpu_block_id = cpu_block_id
+                block.gpu_slot = -1
+                self.cpu_block_to_logical[cpu_block_id] = logical_id
+            else:
+                # Legacy模式：新block分配到GPU
+                gpu_slot = self._allocate_gpu_slot()
+                block.location = BlockLocation.GPU
+                block.gpu_slot = gpu_slot
+                self.gpu_slot_to_logical[gpu_slot] = logical_id
+                self.policy.on_block_allocated(gpu_slot, self.current_step)
 
             block_table.append(logical_id)
 
@@ -993,6 +1010,158 @@ class HybridKVCacheManager(KVCacheManager):
                 break
         return pos
 
+    # ========== Ping-Pong 双缓冲支持 ==========
+
+    def allocate_cpu_only(self, seq: Sequence) -> None:
+        """
+        为序列分配 CPU blocks（用于 Ping-Pong 模式）。
+
+        与 allocate() 不同，这里所有 blocks 都分配到 CPU，
+        GPU 只用作工作缓冲区。
+
+        Args:
+            seq: 要分配的序列
+        """
+        assert not seq.block_table, "Sequence already has blocks"
+
+        for i in range(seq.num_blocks):
+            # 分配 CPU block
+            if not self.free_cpu_blocks:
+                raise RuntimeError(
+                    f"No free CPU blocks. Need {seq.num_blocks}, "
+                    f"available: {len(self.free_cpu_blocks)}"
+                )
+
+            cpu_block_id = self.free_cpu_blocks.popleft()
+
+            # 分配逻辑块
+            logical_id = self.free_logical_ids.popleft()
+            block = self.logical_blocks[logical_id]
+            block.ref_count = 1
+            block.location = BlockLocation.CPU
+            block.cpu_block_id = cpu_block_id
+            block.gpu_slot = -1
+
+            self.cpu_block_to_logical[cpu_block_id] = logical_id
+            seq.block_table.append(logical_id)
+
+    def get_cpu_block_table(self, seq: Sequence) -> List[int]:
+        """
+        获取序列的 CPU block ID 列表。
+
+        Args:
+            seq: 序列
+
+        Returns:
+            CPU block IDs 列表，按序列顺序
+        """
+        cpu_blocks = []
+        for logical_id in seq.block_table:
+            block = self.logical_blocks[logical_id]
+            if block.location == BlockLocation.CPU:
+                cpu_blocks.append(block.cpu_block_id)
+            else:
+                # 如果 block 在 GPU 上，它应该有一个对应的 CPU block
+                # 在 Ping-Pong 模式下，所有数据最终都在 CPU 上
+                raise RuntimeError(
+                    f"Block {logical_id} not on CPU (location={block.location}). "
+                    f"In Ping-Pong mode, all blocks should be on CPU."
+                )
+        return cpu_blocks
+
+    def get_all_cpu_blocks(self, seq: Sequence) -> Tuple[List[int], List[int]]:
+        """
+        获取序列的所有 CPU blocks 及其逻辑 ID。
+
+        Args:
+            seq: 序列
+
+        Returns:
+            (cpu_block_ids, logical_ids)
+        """
+        cpu_block_ids = []
+        logical_ids = []
+        for logical_id in seq.block_table:
+            block = self.logical_blocks[logical_id]
+            if block.location == BlockLocation.CPU:
+                cpu_block_ids.append(block.cpu_block_id)
+                logical_ids.append(logical_id)
+        return cpu_block_ids, logical_ids
+
+    def allocate_next_cpu_block(self, seq: Sequence) -> int:
+        """
+        为序列分配下一个 CPU block（用于 decode 时新 token）。
+
+        Args:
+            seq: 序列
+
+        Returns:
+            新分配的 CPU block ID
+        """
+        if not self.free_cpu_blocks:
+            raise RuntimeError("No free CPU blocks")
+
+        cpu_block_id = self.free_cpu_blocks.popleft()
+        logical_id = self.free_logical_ids.popleft()
+
+        block = self.logical_blocks[logical_id]
+        block.ref_count = 1
+        block.location = BlockLocation.CPU
+        block.cpu_block_id = cpu_block_id
+        block.gpu_slot = -1
+
+        self.cpu_block_to_logical[cpu_block_id] = logical_id
+        seq.block_table.append(logical_id)
+
+        return cpu_block_id
+
+    def get_last_cpu_block(self, seq: Sequence) -> int:
+        """
+        获取序列最后一个 block 的 CPU block ID。
+
+        如果最后一个 block 不在 CPU 上，返回 -1。
+
+        Args:
+            seq: 序列
+
+        Returns:
+            CPU block ID，如果不在 CPU 上则返回 -1
+        """
+        if not seq.block_table:
+            return -1
+
+        last_logical_id = seq.block_table[-1]
+        block = self.logical_blocks[last_logical_id]
+
+        if block.location == BlockLocation.CPU:
+            return block.cpu_block_id
+        return -1
+
+    def get_write_slot_for_pingpong(self, seq: Sequence) -> int:
+        """
+        获取 Ping-Pong decode 时新 KV 写入的 GPU slot。
+
+        策略：使用序列所需 chunks 数决定最后用的是 Ping 还是 Pong buffer，
+        然后使用该 buffer 的最后一个 slot。
+
+        Args:
+            seq: 序列
+
+        Returns:
+            GPU slot ID
+        """
+        cpu_blocks, _ = self.get_all_cpu_blocks(seq)
+        ping_size = self.offload_engine.ping_size
+        num_chunks = (len(cpu_blocks) + ping_size - 1) // ping_size if cpu_blocks else 0
+
+        # 最后一个 chunk 用的是哪个 buffer
+        if num_chunks % 2 == 1 or num_chunks == 0:
+            # 奇数个 chunk（或0个），最后用的是 ping
+            return self.offload_engine.ping_slots[-1]
+        else:
+            # 偶数个 chunk，最后用的是 pong
+            return self.offload_engine.pong_slots[-1]
+
     def __repr__(self) -> str:
         return (
             f"HybridKVCacheManager(\n"

nanovllm/kvcache/offload_engine.py

@@ -64,6 +64,14 @@ class OffloadEngine:
         self.kv_dim = num_kv_heads * head_dim
         self.block_numel = block_size * self.kv_dim
 
+        # ========== Ping-Pong 双缓冲配置 ==========
+        assert num_gpu_blocks >= 2, "Ping-Pong需要至少2个GPU blocks"
+        self.ping_size = num_gpu_blocks // 2
+        self.pong_size = num_gpu_blocks - self.ping_size
+        self.ping_slots = list(range(self.ping_size))  # [0, 1, 2, ...]
+        self.pong_slots = list(range(self.ping_size, num_gpu_blocks))  # [ping_size, ping_size+1, ...]
+        self.num_gpu_slots = num_gpu_blocks  # alias
+
         # ========== Fixed-address GPU KV cache ==========
         # Shape: [num_layers, num_gpu_blocks, block_size, kv_heads, head_dim]
         self.k_cache_gpu = torch.empty(
@@ -103,6 +111,17 @@ class OffloadEngine:
         self.compute_stream = torch.cuda.current_stream()
         self._stream_idx = 0
 
+        # ========== Ping-Pong 专用 stream 和事件 ==========
+        self.pingpong_stream = torch.cuda.Stream()  # 专用于Ping-Pong传输
+
+        # 同步事件 - 加载完成
+        self.ping_ready = torch.cuda.Event()
+        self.pong_ready = torch.cuda.Event()
+
+        # 同步事件 - offload完成
+        self.ping_offload_done = torch.cuda.Event()
+        self.pong_offload_done = torch.cuda.Event()
+
         # ========== Event tracking for async transfers ==========
         self.pending_events: Dict[Tuple[int, int], torch.cuda.Event] = {}
 
@@ -516,7 +535,211 @@ class OffloadEngine:
             f"  kv_heads={self.num_kv_heads},\n"
             f"  head_dim={self.head_dim},\n"
             f"  dtype={self.dtype},\n"
+            f"  ping_size={self.ping_size}, pong_size={self.pong_size},\n"
             f"  gpu_memory={self.gpu_memory_bytes() / 1024**2:.1f}MB,\n"
             f"  cpu_memory={self.cpu_memory_bytes() / 1024**2:.1f}MB\n"
             f")"
-        )
+        )
+
+    # ========== Ping-Pong 双缓冲方法 ==========
+
+    def load_to_ping(self, cpu_block_ids: List[int]) -> None:
+        """
+        异步加载CPU blocks到Ping buffer。
+
+        Args:
+            cpu_block_ids: 要加载的CPU block IDs列表
+        """
+        if not cpu_block_ids:
+            self.ping_ready.record(self.pingpong_stream)
+            return
+
+        num_to_load = min(len(cpu_block_ids), self.ping_size)
+        logger.debug(f"Ping load: CPU{cpu_block_ids[:num_to_load]} -> GPU ping slots {self.ping_slots[:num_to_load]}")
+
+        with torch.cuda.stream(self.pingpong_stream):
+            for i in range(num_to_load):
+                cpu_id = cpu_block_ids[i]
+                gpu_slot = self.ping_slots[i]
+                # 所有层一起复制
+                self.k_cache_gpu[:, gpu_slot].copy_(
+                    self.k_cache_cpu[:, cpu_id], non_blocking=True
+                )
+                self.v_cache_gpu[:, gpu_slot].copy_(
+                    self.v_cache_cpu[:, cpu_id], non_blocking=True
+                )
+            self.ping_ready.record(self.pingpong_stream)
+
+    def load_to_pong(self, cpu_block_ids: List[int]) -> None:
+        """
+        异步加载CPU blocks到Pong buffer。
+
+        Args:
+            cpu_block_ids: 要加载的CPU block IDs列表
+        """
+        if not cpu_block_ids:
+            self.pong_ready.record(self.pingpong_stream)
+            return
+
+        num_to_load = min(len(cpu_block_ids), self.pong_size)
+        logger.debug(f"Pong load: CPU{cpu_block_ids[:num_to_load]} -> GPU pong slots {self.pong_slots[:num_to_load]}")
+
+        with torch.cuda.stream(self.pingpong_stream):
+            for i in range(num_to_load):
+                cpu_id = cpu_block_ids[i]
+                gpu_slot = self.pong_slots[i]
+                self.k_cache_gpu[:, gpu_slot].copy_(
+                    self.k_cache_cpu[:, cpu_id], non_blocking=True
+                )
+                self.v_cache_gpu[:, gpu_slot].copy_(
+                    self.v_cache_cpu[:, cpu_id], non_blocking=True
+                )
+            self.pong_ready.record(self.pingpong_stream)
+
+    def wait_ping(self) -> None:
+        """等待Ping buffer加载完成。"""
+        self.compute_stream.wait_event(self.ping_ready)
+
+    def wait_pong(self) -> None:
+        """等待Pong buffer加载完成。"""
+        self.compute_stream.wait_event(self.pong_ready)
+
+    def offload_buffer_to_cpu(
+        self,
+        buffer: str,
+        cpu_block_ids: List[int],
+    ) -> None:
+        """
+        异步将buffer中的KV offload到CPU。
+
+        Args:
+            buffer: "ping" 或 "pong"
+            cpu_block_ids: 目标CPU block IDs列表
+        """
+        slots = self.ping_slots if buffer == "ping" else self.pong_slots
+        event = self.ping_offload_done if buffer == "ping" else self.pong_offload_done
+
+        if not cpu_block_ids:
+            event.record(self.pingpong_stream)
+            return
+
+        num_to_offload = min(len(cpu_block_ids), len(slots))
+        logger.debug(f"{buffer.capitalize()} offload: GPU {slots[:num_to_offload]} -> CPU{cpu_block_ids[:num_to_offload]}")
+
+        with torch.cuda.stream(self.pingpong_stream):
+            # 等待计算完成
+            self.pingpong_stream.wait_stream(self.compute_stream)
+
+            for i in range(num_to_offload):
+                gpu_slot = slots[i]
+                cpu_id = cpu_block_ids[i]
+                self.k_cache_cpu[:, cpu_id].copy_(
+                    self.k_cache_gpu[:, gpu_slot], non_blocking=True
+                )
+                self.v_cache_cpu[:, cpu_id].copy_(
+                    self.v_cache_gpu[:, gpu_slot], non_blocking=True
+                )
+            event.record(self.pingpong_stream)
+
+    def offload_slot_to_cpu(
+        self,
+        gpu_slot: int,
+        cpu_block_id: int,
+    ) -> None:
+        """
+        异步将单个GPU slot的KV offload到CPU。
+
+        Args:
+            gpu_slot: GPU slot ID
+            cpu_block_id: 目标CPU block ID
+        """
+        logger.debug(f"Slot offload: GPU[{gpu_slot}] -> CPU[{cpu_block_id}]")
+
+        with torch.cuda.stream(self.pingpong_stream):
+            self.pingpong_stream.wait_stream(self.compute_stream)
+            self.k_cache_cpu[:, cpu_block_id].copy_(
+                self.k_cache_gpu[:, gpu_slot], non_blocking=True
+            )
+            self.v_cache_cpu[:, cpu_block_id].copy_(
+                self.v_cache_gpu[:, gpu_slot], non_blocking=True
+            )
+
+    def wait_ping_offload_done(self) -> None:
+        """等待Ping buffer offload完成。"""
+        self.compute_stream.wait_event(self.ping_offload_done)
+
+    def wait_pong_offload_done(self) -> None:
+        """等待Pong buffer offload完成。"""
+        self.compute_stream.wait_event(self.pong_offload_done)
+
+    def wait_all_offload_done(self) -> None:
+        """等待所有offload完成。"""
+        self.pingpong_stream.synchronize()
+
+    def get_kv_for_ping_slots(
+        self,
+        layer_id: int,
+        num_slots: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        获取Ping buffer中指定数量slots的KV。
+
+        Args:
+            layer_id: 层ID
+            num_slots: 需要的slot数量
+
+        Returns:
+            (k_cache, v_cache)，shape: [1, num_slots * block_size, kv_heads, head_dim]
+        """
+        slots = self.ping_slots[:num_slots]
+        k = self.k_cache_gpu[layer_id, slots]  # [num_slots, block_size, heads, dim]
+        v = self.v_cache_gpu[layer_id, slots]
+        # Reshape: [num_slots, block_size, heads, dim] -> [1, num_slots*block_size, heads, dim]
+        k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        return k, v
+
+    def get_kv_for_pong_slots(
+        self,
+        layer_id: int,
+        num_slots: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        获取Pong buffer中指定数量slots的KV。
+
+        Args:
+            layer_id: 层ID
+            num_slots: 需要的slot数量
+
+        Returns:
+            (k_cache, v_cache)，shape: [1, num_slots * block_size, kv_heads, head_dim]
+        """
+        slots = self.pong_slots[:num_slots]
+        k = self.k_cache_gpu[layer_id, slots]
+        v = self.v_cache_gpu[layer_id, slots]
+        k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        return k, v
+
+    def get_kv_for_slots(
+        self,
+        layer_id: int,
+        gpu_slots: List[int],
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        获取指定GPU slots的KV。
+
+        Args:
+            layer_id: 层ID
+            gpu_slots: GPU slot IDs列表
+
+        Returns:
+            (k_cache, v_cache)，shape: [1, len(slots) * block_size, kv_heads, head_dim]
+        """
+        if not gpu_slots:
+            return None, None
+        k = self.k_cache_gpu[layer_id, gpu_slots]
+        v = self.v_cache_gpu[layer_id, gpu_slots]
+        k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        return k, v

nanovllm/layers/attention.py

@@ -97,51 +97,89 @@ class Attention(nn.Module):
         context,
     ) -> torch.Tensor:
         """
-        Compute attention with chunked KV from CPU cache.
+        Compute attention with Ping-Pong dual buffer for chunked prefill.
 
         For chunked prefill:
-        1. Load previous KV from CPU for this layer
-        2. Compute attention against previous KV (no causal mask)
+        1. Load previous KV from CPU using Ping-Pong (if any previous chunks)
+        2. Compute attention against previous KV chunks (no causal mask)
         3. Compute attention against current chunk's KV (causal)
-        4. Merge results using online softmax
+        4. Merge all results using online softmax
         """
         from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
 
         # q, k, v shape: [total_tokens, num_heads, head_dim]
-        total_tokens = q.shape[0]
-
         # Reshape for flash attention: [batch, seq, heads, dim]
         q_batched = q.unsqueeze(0)  # [1, total_tokens, heads, dim]
         k_batched = k.unsqueeze(0)
         v_batched = v.unsqueeze(0)
 
-        accumulated_o = None
-        accumulated_lse = None
+        o_acc = None
+        lse_acc = None
 
-        # Load previous KV from CPU for this layer
-        if context.offload_engine is not None and self.layer_id >= 0:
-            # Get the kvcache_manager from context
-            kvcache_manager = context.offload_engine
+        # Load previous KV from CPU using Ping-Pong
+        # Note: context.offload_engine is actually HybridKVCacheManager
+        kvcache_manager = context.offload_engine
+        seq = context.chunked_seq if hasattr(context, 'chunked_seq') else None
 
-            # For each sequence in the chunk, load previous KV
-            # Currently assuming single sequence
-            if hasattr(context, 'chunked_seq') and context.chunked_seq is not None:
-                prev_k, prev_v = kvcache_manager.load_prev_kv_for_layer(
-                    context.chunked_seq,
-                    self.layer_id,
-                )
+        if kvcache_manager is not None and seq is not None and self.layer_id >= 0:
+            # Get prefilled CPU blocks (blocks already written in previous chunks)
+            cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
 
-                if prev_k is not None and prev_v is not None:
-                    # Compute attention against previous KV (no causal mask)
+            if cpu_block_table:
+                offload_engine = kvcache_manager.offload_engine
+                ping_size = offload_engine.ping_size
+                num_chunks = (len(cpu_block_table) + ping_size - 1) // ping_size
+                current_buffer = "ping"
+
+                # Prefetch first chunk to Ping buffer
+                first_chunk_end = min(ping_size, len(cpu_block_table))
+                first_chunk_ids = cpu_block_table[:first_chunk_end]
+                offload_engine.load_to_ping(first_chunk_ids)
+
+                for chunk_idx in range(num_chunks):
+                    start = chunk_idx * ping_size
+                    end = min(start + ping_size, len(cpu_block_table))
+                    num_blocks_in_chunk = end - start
+
+                    # Prefetch next chunk to OTHER buffer
+                    if chunk_idx + 1 < num_chunks:
+                        next_start = end
+                        next_end = min(next_start + ping_size, len(cpu_block_table))
+                        next_chunk_ids = cpu_block_table[next_start:next_end]
+                        if current_buffer == "ping":
+                            offload_engine.load_to_pong(next_chunk_ids)
+                        else:
+                            offload_engine.load_to_ping(next_chunk_ids)
+
+                    # Wait for current buffer and get KV
+                    if current_buffer == "ping":
+                        offload_engine.wait_ping()
+                        prev_k, prev_v = offload_engine.get_kv_for_ping_slots(
+                            self.layer_id, num_blocks_in_chunk
+                        )
+                    else:
+                        offload_engine.wait_pong()
+                        prev_k, prev_v = offload_engine.get_kv_for_pong_slots(
+                            self.layer_id, num_blocks_in_chunk
+                        )
+
+                    # Compute attention against this chunk (no causal mask)
                     prev_o, prev_lse = flash_attn_with_lse(
                         q_batched,
                         prev_k,
                         prev_v,
                         softmax_scale=self.scale,
-                        causal=False,  # No causal mask for previous context
+                        causal=False,
                     )
-                    accumulated_o = prev_o
-                    accumulated_lse = prev_lse
+
+                    # Merge with accumulated
+                    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)
+
+                    # Switch buffer
+                    current_buffer = "pong" if current_buffer == "ping" else "ping"
 
         # Compute attention against current chunk's KV (with causal mask)
         current_o, current_lse = flash_attn_with_lse(
@@ -149,17 +187,14 @@ class Attention(nn.Module):
             k_batched,
             v_batched,
             softmax_scale=self.scale,
-            causal=True,  # Causal mask for current chunk
+            causal=True,
         )
 
         # Merge with accumulated
-        if accumulated_o is None:
+        if o_acc is None:
             final_o = current_o
         else:
-            final_o, _ = merge_attention_outputs(
-                accumulated_o, accumulated_lse,
-                current_o, current_lse,
-            )
+            final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
 
         # Remove batch dimension: [1, total_tokens, heads, dim] -> [total_tokens, heads, dim]
         return final_o.squeeze(0)
@@ -172,12 +207,13 @@ class Attention(nn.Module):
         context,
     ) -> torch.Tensor:
         """
-        Compute decode attention with KV spread across CPU and GPU.
+        Compute decode attention with Ping-Pong dual buffer.
 
-        Uses chunked attention similar to chunked prefill:
-        1. Process blocks on GPU first (if any)
-        2. Load CPU blocks in chunks to GPU slots (per-layer)
-        3. Compute attention for each chunk, merge with online softmax
+        All KV is stored on CPU. Uses Ping-Pong buffers on GPU:
+        1. Load first chunk to Ping buffer
+        2. While computing on current buffer, prefetch next chunk to other buffer
+        3. Alternate between Ping and Pong buffers
+        4. Merge attention outputs using online softmax (LSE)
         """
         from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
 
@@ -185,62 +221,73 @@ class Attention(nn.Module):
         # Need: [batch, seqlen, heads, dim]
         q_batched = q.unsqueeze(1)  # [batch, 1, heads, dim]
 
+        # Note: context.offload_engine is actually HybridKVCacheManager
         kvcache_manager = context.offload_engine
         seq = context.chunked_seq
 
+        # Get all CPU blocks for this sequence
+        cpu_block_table, _ = kvcache_manager.get_all_cpu_blocks(seq)
+        if not cpu_block_table:
+            raise RuntimeError("Chunked decode attention failed: no CPU blocks available")
+
+        # Get the actual offload_engine for Ping-Pong operations
+        offload_engine = kvcache_manager.offload_engine
+
+        # Calculate chunk info
+        ping_size = offload_engine.ping_size
+        num_chunks = (len(cpu_block_table) + ping_size - 1) // ping_size
+
         o_acc = None
         lse_acc = None
+        current_buffer = "ping"
 
-        # Step 1: Process blocks already on GPU (if any)
-        gpu_slots, _ = kvcache_manager.get_gpu_blocks_for_decode(seq)
-        if gpu_slots:
-            k_gpu, v_gpu = kvcache_manager.get_kv_for_gpu_slots(self.layer_id, gpu_slots)
-            o_gpu, lse_gpu = flash_attn_with_lse(
-                q_batched, k_gpu, v_gpu,
+        # Prefetch first chunk to Ping buffer (loads all layers at once)
+        first_chunk_end = min(ping_size, len(cpu_block_table))
+        first_chunk_ids = cpu_block_table[:first_chunk_end]
+        offload_engine.load_to_ping(first_chunk_ids)
+
+        for chunk_idx in range(num_chunks):
+            start = chunk_idx * ping_size
+            end = min(start + ping_size, len(cpu_block_table))
+            num_blocks_in_chunk = end - start
+
+            # Prefetch next chunk to OTHER buffer (overlapped with current computation)
+            if chunk_idx + 1 < num_chunks:
+                next_start = end
+                next_end = min(next_start + ping_size, len(cpu_block_table))
+                next_chunk_ids = cpu_block_table[next_start:next_end]
+                if current_buffer == "ping":
+                    offload_engine.load_to_pong(next_chunk_ids)
+                else:
+                    offload_engine.load_to_ping(next_chunk_ids)
+
+            # Wait for current buffer to be ready and get KV
+            if current_buffer == "ping":
+                offload_engine.wait_ping()
+                k_chunk, v_chunk = offload_engine.get_kv_for_ping_slots(
+                    self.layer_id, num_blocks_in_chunk
+                )
+            else:
+                offload_engine.wait_pong()
+                k_chunk, v_chunk = offload_engine.get_kv_for_pong_slots(
+                    self.layer_id, num_blocks_in_chunk
+                )
+
+            # Compute attention for this chunk
+            o_chunk, lse_chunk = flash_attn_with_lse(
+                q_batched, k_chunk, v_chunk,
                 softmax_scale=self.scale,
                 causal=False,
             )
-            o_acc, lse_acc = o_gpu, lse_gpu
 
-        # Step 2: Process CPU blocks in chunks
-        # Get chunk info from kvcache_manager
-        cpu_block_ids, cpu_logical_ids, num_chunks = kvcache_manager.get_decode_chunk_info(seq)
+            # Merge with accumulated
+            if o_acc is None:
+                o_acc, lse_acc = o_chunk, lse_chunk
+            else:
+                o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, o_chunk, lse_chunk)
 
-        if num_chunks > 0:
-            # Use num_gpu_slots - 1 to avoid the reserved slot (used for write block)
-            chunk_size = kvcache_manager.num_gpu_slots - 1
-
-            for chunk_idx in range(num_chunks):
-                start = chunk_idx * chunk_size
-                end = min(start + chunk_size, len(cpu_block_ids))
-                chunk_cpu_ids = cpu_block_ids[start:end]
-
-                # Load this chunk to GPU slots 0, 1, 2, ... for THIS LAYER
-                # (slot num_gpu_slots-1 is reserved for write block)
-                gpu_slots_for_chunk = list(range(len(chunk_cpu_ids)))
-                kvcache_manager.offload_engine.load_cpu_blocks_to_gpu_slots(
-                    self.layer_id,
-                    chunk_cpu_ids,
-                    gpu_slots_for_chunk,
-                )
-
-                # Get KV for this chunk
-                k_chunk, v_chunk = kvcache_manager.get_kv_for_gpu_slots(
-                    self.layer_id, gpu_slots_for_chunk
-                )
-
-                # Compute attention for this chunk
-                o_chunk, lse_chunk = flash_attn_with_lse(
-                    q_batched, k_chunk, v_chunk,
-                    softmax_scale=self.scale,
-                    causal=False,
-                )
-
-                # Merge with accumulated
-                if o_acc is None:
-                    o_acc, lse_acc = o_chunk, lse_chunk
-                else:
-                    o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, o_chunk, lse_chunk)
+            # Switch buffer for next iteration
+            current_buffer = "pong" if current_buffer == "ping" else "ping"
 
         if o_acc is None:
             raise RuntimeError("Chunked decode attention failed: no KV available")

tests/test_chunked_attention.py

@@ -14,63 +14,66 @@ os.environ["NANOVLLM_LOG_LEVEL"] = "DEBUG"
 from nanovllm import LLM, SamplingParams
 
 
-def test_chunked_prefill(num_gpu_blocks=10, input_len=8192, output_len=16):
+def create_long_context_prompt(target_tokens: int) -> str:
+    """
+    Create a meaningful long context prompt with a question at the end.
+    The answer depends on information scattered throughout the context.
+    """
+    # Key facts to embed in the context
+    facts = [
+        "The capital of France is Paris.",
+        "The Eiffel Tower was built in 1889.",
+        "Python was created by Guido van Rossum.",
+        "The speed of light is approximately 299,792 kilometers per second.",
+        "Mount Everest is 8,848 meters tall.",
+    ]
+
+    # Padding text to reach target length
+    padding_paragraph = """
+This is additional context information that helps extend the length of the prompt.
+Machine learning has revolutionized many fields including computer vision, natural language processing, and robotics.
+Deep neural networks can learn complex patterns from large amounts of data.
+The transformer architecture has become the foundation of modern language models.
+Attention mechanisms allow models to focus on relevant parts of the input.
+"""
+
+    # Build the prompt
+    prompt_parts = []
+
+    # Add instruction
+    prompt_parts.append("Please read the following information carefully and answer the question at the end.\n\n")
+
+    # Add facts at different positions
+    current_tokens = 50  # approximate tokens so far
+    tokens_per_padding = 80  # approximate tokens per padding paragraph
+    fact_interval = target_tokens // (len(facts) + 1)
+
+    fact_idx = 0
+    while current_tokens < target_tokens - 100:
+        # Add padding
+        prompt_parts.append(padding_paragraph)
+        current_tokens += tokens_per_padding
+
+        # Add a fact at intervals
+        if fact_idx < len(facts) and current_tokens > fact_interval * (fact_idx + 1):
+            prompt_parts.append(f"\n[Important Fact #{fact_idx + 1}]: {facts[fact_idx]}\n")
+            current_tokens += 20
+            fact_idx += 1
+
+    # Add the question at the end
+    prompt_parts.append("\n\nQuestion: Based on the information above, what is the capital of France and when was the Eiffel Tower built? Please answer briefly.\n\nAnswer:")
+
+    return "".join(prompt_parts)
+
+
+def test_chunked_prefill(num_gpu_blocks=10, input_len=8192, output_len=64):
     """Test chunked prefill with limited GPU blocks."""
     path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
 
-    total_blocks = (input_len + 255) // 256
     print(f"=" * 60)
-    print(f"Chunked Prefill Test")
+    print(f"Chunked Prefill Test (Ping-Pong)")
     print(f"=" * 60)
-    print(f"  input_len: {input_len} tokens")
-    print(f"  total_blocks: {total_blocks}")
-    print(f"  num_gpu_blocks: {num_gpu_blocks}")
-    print(f"  blocks_on_cpu: {max(0, total_blocks - num_gpu_blocks)}")
-    print()
-
-    llm = LLM(
-        path,
-        enforce_eager=False,
-        max_model_len=16 * 1024,  # 16K is enough for 8K test
-        max_num_batched_tokens=16 * 1024,
-        enable_cpu_offload=True,
-        cpu_memory_gb=4.0,
-        num_gpu_blocks=num_gpu_blocks,
-    )
-
-    print(f"LLM initialized:")
-    print(f"  num_gpu_kvcache_blocks: {llm.model_runner.config.num_gpu_kvcache_blocks}")
-    print(f"  num_cpu_kvcache_blocks: {llm.model_runner.config.num_cpu_kvcache_blocks}")
-    print()
-
-    # Create prompt with approximate token count
-    prompt = "Hello " * (input_len // 2)
-
-    print(f"Running generation...")
-    outputs = llm.generate(
-        [prompt],
-        SamplingParams(temperature=0.6, max_tokens=output_len),
-        use_tqdm=True,
-    )
-
-    print()
-    print(f"Output tokens: {len(outputs[0]['token_ids'])}")
-    print(f"Output text (first 100 chars): {outputs[0]['text'][:100]}")
-    print()
-    return outputs
-
-
-def test_chunked_decode(num_gpu_blocks=10, input_len=8192, output_len=64):
-    """Test chunked decode with limited GPU blocks."""
-    path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
-
-    total_blocks = (input_len + 255) // 256
-    print(f"=" * 60)
-    print(f"Chunked Decode Test")
-    print(f"=" * 60)
-    print(f"  input_len: {input_len} tokens")
-    print(f"  output_len: {output_len} tokens")
-    print(f"  total_blocks: {total_blocks}")
+    print(f"  target_input_len: ~{input_len} tokens")
     print(f"  num_gpu_blocks: {num_gpu_blocks}")
     print()
 
@@ -80,27 +83,62 @@ def test_chunked_decode(num_gpu_blocks=10, input_len=8192, output_len=64):
         max_model_len=16 * 1024,
         max_num_batched_tokens=16 * 1024,
         enable_cpu_offload=True,
-        cpu_memory_gb=4.0,
         num_gpu_blocks=num_gpu_blocks,
     )
-
-    print(f"LLM initialized:")
-    print(f"  num_gpu_kvcache_blocks: {llm.model_runner.config.num_gpu_kvcache_blocks}")
-    print(f"  num_cpu_kvcache_blocks: {llm.model_runner.config.num_cpu_kvcache_blocks}")
     print()
 
-    prompt = "Hello " * (input_len // 2)
+    # Create meaningful prompt
+    prompt = create_long_context_prompt(input_len)
 
     print(f"Running generation...")
     outputs = llm.generate(
         [prompt],
-        SamplingParams(temperature=0.6, max_tokens=output_len),
-        use_tqdm=True,
+        SamplingParams(temperature=0.1, max_tokens=output_len),  # low temperature for more deterministic output
+        use_tqdm=False,
     )
 
     print()
     print(f"Output tokens: {len(outputs[0]['token_ids'])}")
-    print(f"Output text (first 100 chars): {outputs[0]['text'][:100]}")
+    print(f"Output text:\n{outputs[0]['text']}")
+    print()
+    return outputs
+
+
+def test_chunked_decode(num_gpu_blocks=10, input_len=8192, output_len=128):
+    """Test chunked decode with limited GPU blocks."""
+    path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
+
+    print(f"=" * 60)
+    print(f"Chunked Decode Test (Ping-Pong)")
+    print(f"=" * 60)
+    print(f"  target_input_len: ~{input_len} tokens")
+    print(f"  output_len: {output_len} tokens")
+    print(f"  num_gpu_blocks: {num_gpu_blocks}")
+    print()
+
+    llm = LLM(
+        path,
+        enforce_eager=False,
+        max_model_len=16 * 1024,
+        max_num_batched_tokens=16 * 1024,
+        enable_cpu_offload=True,
+        num_gpu_blocks=num_gpu_blocks,
+    )
+    print()
+
+    # Create meaningful prompt
+    prompt = create_long_context_prompt(input_len)
+
+    print(f"Running generation...")
+    outputs = llm.generate(
+        [prompt],
+        SamplingParams(temperature=0.1, max_tokens=output_len),
+        use_tqdm=False,
+    )
+
+    print()
+    print(f"Output tokens: {len(outputs[0]['token_ids'])}")
+    print(f"Output text:\n{outputs[0]['text']}")
     print()
     return outputs
 
@@ -108,7 +146,7 @@ def test_chunked_decode(num_gpu_blocks=10, input_len=8192, output_len=64):
 if __name__ == "__main__":
     # Parse arguments
     num_gpu_blocks = int(sys.argv[1]) if len(sys.argv) > 1 else 10
-    input_len = int(sys.argv[2]) if len(sys.argv) > 2 else 8192
-    output_len = int(sys.argv[3]) if len(sys.argv) > 3 else 32
+    input_len = int(sys.argv[2]) if len(sys.argv) > 2 else 2048
+    output_len = int(sys.argv[3]) if len(sys.argv) > 3 else 64
 
     test_chunked_prefill(num_gpu_blocks, input_len, output_len)