🔀 merge: integrate tzj/minference-exp (GPU-only sparse attention)

Merge GPU-only sparse attention support from tzj/minference-exp branch:

**GPU-only mode additions:**
- Add compute_prefill/compute_decode methods to SparsePolicy base class
- Add GPU-only attention routing in attention.py
- Add alloc_policy_metadata() for pre-allocating GQA buffers
- Add XAttention + BSA sparse attention for GPU-only prefill
- Add kvcache_manager to set_context() for policy access

**bench.py enhancements:**
- Add --model argument for configurable model path
- Add --policy argument (full, xattn) for sparse policy selection
- Add --enable-policy flag for FullAttentionPolicy routing
- Add --enforce-eager option to disable CUDA graphs
- Add --gpu-util option for GPU memory utilization

**Documentation:**
- Add gpu_only_xattn_guide.md with performance analysis
- Add gpu_only_sparse_integration.md baseline document
- Add gpu-vram-requirement.md rule for GPU-only mode

Both CPU offload and GPU-only paths are preserved and functional.

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

.claude/rules/gpu-vram-requirement.md

@@ -0,0 +1,54 @@
+# GPU VRAM Requirement Rule
+
+## GPU-only 模式显存要求
+
+**强制规则**：执行 GPU-only 代码（不启用 CPU offload）时，**必须**在 40GB 及以上显存的 GPU 上进行测试。
+
+### 检测方法
+
+在运行 GPU-only 测试之前，**必须**先检查 GPU 显存：
+
+```bash
+nvidia-smi --query-gpu=index,name,memory.total --format=csv,noheader
+```
+
+### GPU 分类
+
+| GPU 型号 | 显存 | GPU-only 测试 |
+|----------|------|---------------|
+| A100 40GB | 40GB | ✅ 允许 |
+| A100 80GB | 80GB | ✅ 允许 |
+| H100 80GB | 80GB | ✅ 允许 |
+| A6000 | 48GB | ✅ 允许 |
+| RTX 3090 | 24GB | ❌ **禁止**（仅 offload 模式） |
+| RTX 4090 | 24GB | ❌ **禁止**（仅 offload 模式） |
+
+### 执行流程
+
+1. **检测 GPU 显存**（必须）
+2. **显存 >= 40GB**：继续执行 GPU-only 测试
+3. **显存 < 40GB**：**停止**，提示用户：
+   > "当前 GPU 显存为 XXX GB，不满足 GPU-only 模式的最低 40GB 要求。请使用 `--enable-offload` 参数启用 CPU offload 模式。"
+
+### 代码示例
+
+```python
+# 在运行 GPU-only benchmark 之前
+import subprocess
+result = subprocess.run(
+    ["nvidia-smi", "--query-gpu=memory.total", "--format=csv,noheader,nounits"],
+    capture_output=True, text=True
+)
+vram_mb = int(result.stdout.strip().split('\n')[0])
+if vram_mb < 40000:  # 40GB = 40000MB
+    raise RuntimeError(f"GPU VRAM ({vram_mb}MB) < 40GB. Use --enable-offload for this GPU.")
+```
+
+### 适用范围
+
+| 脚本 | 适用此规则 |
+|------|-----------|
+| `bench.py` | ✅ 必须检查显存 |
+| `bench_offload.py` | ❌ 不适用（始终使用 offload） |
+| `tests/test_*.py --enable-offload` | ❌ 不适用 |
+| `tests/test_*.py` (无 offload) | ✅ 必须检查显存 |

.claude/rules/sparse-policy.md

@@ -1,5 +1,39 @@
 # Sparse Policy 代码规范
 
+## Policy 不能为 None (CRITICAL)
+
+**强制规则**: `sparse_policy` 参数**永远不能为 None**，必须至少为 `FullAttentionPolicy`。
+
+```python
+# ❌ 错误：允许 None
+sparse_policy = getattr(config, 'sparse_policy', None)
+
+# ✅ 正确：显式处理 None，默认使用 FULL
+sparse_policy_type = getattr(config, 'sparse_policy', None)
+if sparse_policy_type is None:
+    sparse_policy_type = SparsePolicyType.FULL
+```
+
+**原因**:
+1. 统一的 API：所有代码路径都通过 policy 进行 attention 计算
+2. 避免空指针：消除 `policy.xxx` 调用时的 None 检查
+3. 简化逻辑：不需要 `if policy is not None` 的分支
+
+**唯一例外：Warmup 阶段**
+
+在 `model_runner.warmup_model()` 期间，kvcache_manager 还未分配。此时 `attention.py` 使用 flash_attn fallback：
+
+```python
+# attention.py 中的 warmup 处理
+if context.kvcache_manager is None:
+    # Warmup phase: use flash_attn directly
+    return flash_attn_varlen_func(...) if context.is_prefill else flash_attn_with_kvcache(...)
+```
+
+这是唯一允许 kvcache_manager 为 None 的情况。正式推理时，policy 必须存在。
+
+---
+
 ## 基类要求 (MANDATORY)
 
 每个 `SparsePolicy` 子类 **必须** 遵守以下要求：

CLAUDE.md

@@ -29,6 +29,7 @@ Nano-vLLM is a lightweight vLLM implementation (~1,200 lines) for fast offline L
 | [`docs/cpu_scheduling_latency_analysis.md`](docs/cpu_scheduling_latency_analysis.md) | ⚡ PERF: CPU 调度延迟分析，kernel 间隙来源，GPU 利用率优化方向 |
 | [`docs/bench_offload_results.md`](docs/bench_offload_results.md) | 📊 BENCH: CPU offload 性能测试结果，Full vs XAttention 对比 (32K/128K) |
 | [`docs/cpu_offload_optimization_strategies.md`](docs/cpu_offload_optimization_strategies.md) | 🚀 OPT: CPU offload 优化策略：chunk size、CUDA Graph、前沿研究(InfiniGen/ShadowKV) |
+| [`docs/gpu_only_xattn_guide.md`](docs/gpu_only_xattn_guide.md) | 🚀 GPU-Only XAttention: 内存预分配、性能分析 (32K +15%, 64K +41%)、CUDA Graph 限制 |
 
 ## Rules Index
 

bench.py

@@ -40,24 +40,49 @@ def bench_prefill(llm, num_seqs, input_len):
 
 def main():
     import argparse
+    from nanovllm.config import SparsePolicyType
+
     parser = argparse.ArgumentParser(description="Benchmark nanovllm GPU performance")
+    parser.add_argument("--model", type=str, default="~/models/Llama-3.1-8B-Instruct",
+                        help="Model path (default: ~/models/Llama-3.1-8B-Instruct)")
     parser.add_argument("--input-len", type=int, default=None, help="Input length in tokens")
     parser.add_argument("--output-len", type=int, default=64, help="Output length for decode benchmark (default: 64)")
     parser.add_argument("--max-len", type=int, default=32*1024, help="Max model length (default: 32K)")
     parser.add_argument("--bench-decode", action="store_true", help="Run decode benchmark (default: prefill only)")
     parser.add_argument("--bench-all", action="store_true", help="Run both prefill and decode benchmarks")
+    # Sparse policy option (GPU-only mode now supports policy routing)
+    parser.add_argument("--policy", type=str, default=None,
+                        choices=["full", "xattn"],
+                        help="Sparse policy: full (FullAttention), xattn (XAttention+BSA)")
+    parser.add_argument("--enable-policy", action="store_true",
+                        help="Enable sparse policy routing (FullAttentionPolicy by default)")
+    parser.add_argument("--gpu-util", type=float, default=0.9,
+                        help="GPU memory utilization (default: 0.9)")
+    parser.add_argument("--enforce-eager", action="store_true",
+                        help="Disable CUDA graphs (default: False)")
     args = parser.parse_args()
 
-    path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
+    path = os.path.expanduser(args.model)
     max_len = args.max_len
 
-    print(f"\n[nanovllm GPU] max_len={max_len}")
+    # Configure sparse policy
+    if args.policy == "xattn":
+        sparse_policy = SparsePolicyType.XATTN_BSA
+        print(f"\n[nanovllm GPU + XAttention BSA] max_len={max_len}")
+    elif args.policy == "full" or args.enable_policy:
+        sparse_policy = SparsePolicyType.FULL
+        print(f"\n[nanovllm GPU + Policy] sparse_policy=FULL, max_len={max_len}")
+    else:
+        sparse_policy = None
+        print(f"\n[nanovllm GPU] max_len={max_len}")
 
     llm = LLM(
         path,
-        enforce_eager=False,
+        enforce_eager=args.enforce_eager,
         max_model_len=max_len,
         max_num_batched_tokens=max_len,
+        sparse_policy=sparse_policy,
+        gpu_memory_utilization=args.gpu_util,
     )
 
     # Warmup

docs/gpu_only_sparse_integration.md

@@ -0,0 +1,77 @@
+# GPU-only Sparse Policy 整合
+
+本文档记录将 sparse attention 策略整合到 GPU-only 模式的过程和性能对比。
+
+## 背景
+
+当前 sparse policy（Quest、XAttention）仅在 CPU offload 路径中实现。目标是将其扩展到 GPU-only 模式，以提升长上下文场景下的性能。
+
+## 基准性能（优化前）
+
+**测试环境**:
+- GPU: NVIDIA A100-SXM4-80GB
+- 模型: Llama-3.1-8B-Instruct
+- 上下文长度: 32K tokens
+- 日期: 2026-01-27
+
+### Prefill Benchmark (32K context)
+
+| 模式 | Throughput | Time | KV Cache 分配 |
+|------|------------|------|---------------|
+| **GPU-only (Full Attention)** | 4869.67 tok/s | 6.73s | 438 blocks (56GB GPU) |
+| CPU Offload (Full Attention) | 1500.29 tok/s | 21.84s | 4 blocks GPU + 32 blocks CPU |
+
+**性能比**: GPU-only 比 CPU Offload 快 **3.2x**
+
+### 配置详情
+
+**GPU-only 模式**:
+```bash
+CUDA_VISIBLE_DEVICES=0 python bench.py \
+    --model ~/models/Llama-3.1-8B-Instruct \
+    --max-len 32768
+```
+
+**CPU Offload 模式**:
+```bash
+CUDA_VISIBLE_DEVICES=0 python bench_offload.py \
+    --model ~/models/Llama-3.1-8B-Instruct \
+    --max-len 32768
+```
+
+### KV Cache 配置
+
+| 参数 | GPU-only | CPU Offload |
+|------|----------|-------------|
+| block_size | 1024 tokens | 1024 tokens |
+| per-token KV | 128 KB | 128 KB |
+| per-block KV | 128 MB | 128 MB |
+| GPU blocks | 438 | 4 |
+| CPU blocks | 0 | 32 |
+| Total memory | 56 GB | 4.6 GB |
+
+## 目标
+
+将以下 sparse policy 整合到 GPU-only 模式：
+
+| Policy | 阶段 | 描述 |
+|--------|------|------|
+| Quest | Decode | Top-K block selection based on query-key scores |
+| XAttention BSA | Prefill | Block sparse attention with cumulative threshold |
+
+## 实现进度
+
+- [ ] 分析现有 sparse policy 代码结构
+- [ ] 设计 GPU-only sparse policy 接口
+- [ ] 实现 GPU-only Quest decode
+- [ ] 实现 GPU-only XAttention prefill
+- [ ] 性能测试和对比
+
+## 优化后性能
+
+*待测试*
+
+| 模式 | Throughput | Speedup vs Full |
+|------|------------|-----------------|
+| GPU-only + Quest (decode) | TBD | TBD |
+| GPU-only + XAttn (prefill) | TBD | TBD |

docs/gpu_only_xattn_guide.md

@@ -0,0 +1,296 @@
+# GPU-Only XAttention 指南
+
+本文档介绍 GPU-only 模式下 XAttention BSA 的实现、内存优化和性能分析。
+
+## 概述
+
+GPU-only 模式下，所有 KV cache 存储在 GPU 上，无需 CPU offload。XAttention 通过稀疏注意力加速 prefill 阶段。
+
+### 执行路径对比
+
+| 模式 | Prefill 方法 | Decode 方法 | KV 存储 |
+|------|-------------|-------------|---------|
+| GPU-only Full | `compute_prefill()` | `compute_decode()` | GPU |
+| GPU-only XAttn | `compute_prefill()` | `compute_decode()` | GPU |
+| CPU Offload | `compute_chunked_prefill()` | `compute_chunked_decode()` | CPU + GPU |
+
+## 架构设计
+
+### SparsePolicy 接口
+
+```python
+class SparsePolicy:
+    # GPU-only 方法
+    def compute_prefill(self, q, k, v, ...) -> Tensor
+    def compute_decode(self, q, k_cache, v_cache, ...) -> Tensor
+
+    # CPU Offload 方法
+    def compute_chunked_prefill(self, q, k, v, ...) -> Tensor
+    def compute_chunked_decode(self, q, ...) -> Tensor
+
+    # 初始化方法
+    def initialize(self, num_layers, ...) -> None           # CPU offload metadata
+    def alloc_policy_metadata(self, num_heads, ...) -> None  # GPU-only buffers
+```
+
+### XAttentionBSAPolicy 实现
+
+```
+GPU-only Prefill 流程:
+┌─────────────────────────────────────────────────────────────┐
+│  1. GQA 扩展 (使用预分配 buffer)                              │
+│     K: [seq, kv_heads, dim] → K_exp: [1, heads, seq, dim]   │
+│                                                              │
+│  2. XAttention 估计                                          │
+│     flat_group_gemm_fuse_reshape_kernel (Q@K^T)             │
+│     softmax_fuse_block_sum_kernel (block 重要性)             │
+│     → sparse mask                                            │
+│                                                              │
+│  3. BSA 稀疏注意力                                           │
+│     flash_fwd_block_kernel (只计算选中的 blocks)             │
+│     → output                                                 │
+└─────────────────────────────────────────────────────────────┘
+```
+
+## 内存预分配
+
+### 问题背景
+
+XAttention 的 `compute_prefill()` 需要 GQA 扩展：
+
+```python
+# 之前: 动态分配 (~2GB for 64K)
+K_exp = K.repeat_interleave(num_groups, dim=1)  # 分配 1
+k_bsa = k.repeat_interleave(num_groups, dim=1)  # 分配 2 (重复!)
+```
+
+每次 prefill 都动态分配，导致：
+- 内存碎片
+- 分配延迟
+- 可能 OOM
+
+### 解决方案: alloc_policy_metadata()
+
+在框架初始化时预分配 buffer：
+
+```python
+class XAttentionBSAPolicy(SparsePolicy):
+    def alloc_policy_metadata(self, num_heads, num_kv_heads, head_dim,
+                               max_seq_len, dtype, device):
+        # 预分配 GQA 扩展 buffer
+        shape = (1, num_heads, max_seq_len, head_dim)
+        self._k_expanded = torch.empty(shape, dtype=dtype, device=device)
+        self._v_expanded = torch.empty(shape, dtype=dtype, device=device)
+
+    def compute_prefill(self, q, k, v, ...):
+        seq_len = k.shape[0]
+        # 使用预分配 buffer 的 slice
+        K_exp = self._k_expanded[:, :, :seq_len, :]
+        # 原地 GQA 扩展
+        K_exp.view(...).copy_(K.unsqueeze(2).expand(...))
+        # 复用同一 buffer 给 BSA
+        k_bsa = K_exp.squeeze(0).transpose(0, 1)
+```
+
+### 内存使用
+
+| 序列长度 | 预分配大小 | 说明 |
+|---------|-----------|------|
+| 32K | 512 MB | `2 * 32 * 32768 * 128 * 2 bytes` |
+| 64K | 1024 MB | `2 * 32 * 65536 * 128 * 2 bytes` |
+
+优化效果：
+- 之前: ~2GB 动态分配 (xattn_estimate + BSA 各一次)
+- 之后: ~1GB 预分配 (复用同一 buffer)
+
+### 框架集成
+
+```python
+# model_runner.py - allocate_kv_cache()
+def allocate_kv_cache(self):
+    # ... KV cache 分配 ...
+
+    # GPU-only 模式: 预分配 policy buffers
+    if not config.enable_cpu_offload:
+        self.kvcache_manager.sparse_policy.alloc_policy_metadata(
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            head_dim=head_dim,
+            max_seq_len=config.max_model_len,
+            dtype=dtype,
+            device=torch.device("cuda"),
+        )
+```
+
+## 性能分析
+
+### 32K Prefill 性能
+
+| Policy | Throughput | 相对提升 |
+|--------|------------|----------|
+| Baseline | 4880 tok/s | - |
+| Full | 4892 tok/s | +0.2% |
+| **XAttention** | **5602 tok/s** | **+15%** |
+
+### 64K Prefill 性能
+
+| Policy | Throughput | 相对提升 |
+|--------|------------|----------|
+| Baseline | 3386 tok/s | - |
+| Full | 3355 tok/s | -0.9% |
+| **XAttention** | **4775 tok/s** | **+41%** |
+
+### Kernel 时间分解 (32K)
+
+**XAttention:**
+```
+FFN GEMM:           3219 ms  (54%)
+BSA Attention:      1231 ms  (21%)
+XAttn Estimation:    415 ms  (7%)
+Other:              1020 ms  (18%)
+─────────────────────────────
+Total:              5885 ms
+```
+
+**Full:**
+```
+FFN GEMM:           3244 ms  (48%)
+Dense Attention:    2861 ms  (43%)
+Other:               595 ms  (9%)
+─────────────────────────────
+Total:              6700 ms
+```
+
+### 加速来源
+
+```
+Dense Attention:    2861 ms
+BSA Attention:      1231 ms  (节省 1630 ms, -57%)
+XAttn Estimation:    415 ms  (额外开销)
+─────────────────────────────
+净节省:             1215 ms  (42% attention 时间)
+```
+
+## CUDA Graph 限制
+
+### 为什么 Prefill 不能用 CUDA Graph
+
+CUDA Graph 要求所有操作在 capture 时确定：
+
+| 必须固定 | Prefill 的情况 |
+|---------|---------------|
+| Tensor 形状 | seq_len 可变 (1 ~ max_model_len) |
+| Kernel grid | 依赖 seq_len |
+| 内存地址 | 中间 tensor 大小变化 |
+
+```python
+# 不同请求的 seq_len 不同
+request_1: prefill(seq_len=1024)   # grid=(8, 32, 1)
+request_2: prefill(seq_len=32768)  # grid=(256, 32, 1)
+```
+
+### Decode 可以用 CUDA Graph
+
+```python
+# Decode 每次只处理 1 token
+q: [batch_size, 1, heads, dim]  # 形状固定
+```
+
+nanovllm 为每个 batch_size 预先 capture 一个 graph：
+
+```python
+def capture_cudagraph(self):
+    for batch_size in [1, 2, 4, 8, ...]:
+        with torch.cuda.graph(g):
+            self.run_model(dummy_input, is_prefill=False)
+        self.graphs[batch_size] = g
+```
+
+### Nsys Profile 结果
+
+```
+XAttention 32K Prefill:
+  Total kernels: 41,904
+  Non-graph: 41,904 (100%)
+  Graph: 0
+
+Full 32K Prefill:
+  Total kernels: 35,308
+  Non-graph: 35,308 (100%)
+  Graph: 0
+```
+
+**两者都是 100% NON-GRAPH**，这是 prefill 的本质特性。
+
+## Profiling 工具
+
+### 使用 profile.sh
+
+```bash
+# XAttention 32K
+bash scripts/profile.sh --max-len 32768 --policy xattn
+
+# Full 32K
+bash scripts/profile.sh --max-len 32768 --policy full
+
+# 64K (需要降低 gpu-util)
+bash scripts/profile.sh --max-len 65536 --policy xattn --gpu-util 0.7
+```
+
+### 分析 nsys 结果
+
+```bash
+# 查看 kernel 统计
+nsys stats --report cuda_gpu_kern_sum results/nsys/<file>.nsys-rep
+
+# 用 sqlite 查询详细数据
+sqlite3 results/nsys/<file>.sqlite "
+SELECT
+    (SELECT value FROM StringIds WHERE id = shortName) as kernel,
+    COUNT(*) as count,
+    SUM(end-start)/1e6 as total_ms
+FROM CUPTI_ACTIVITY_KIND_KERNEL
+GROUP BY shortName
+ORDER BY total_ms DESC
+LIMIT 10
+"
+```
+
+## 使用指南
+
+### 启用 XAttention GPU-only
+
+```python
+from nanovllm import LLM
+from nanovllm.config import SparsePolicyType
+
+llm = LLM(
+    model_path,
+    max_model_len=32768,
+    sparse_policy=SparsePolicyType.XATTN_BSA,
+    gpu_memory_utilization=0.9,  # 64K 时可能需要降低
+)
+```
+
+### 命令行测试
+
+```bash
+# bench.py
+python bench.py --max-len 32768 --policy xattn
+
+# 64K 需要降低 gpu-util
+python bench.py --max-len 65536 --policy xattn --gpu-util 0.7
+```
+
+### 最佳实践
+
+1. **32K 及以下**: 使用默认 `gpu_memory_utilization=0.9`
+2. **64K**: 降低到 `gpu_memory_utilization=0.7`
+3. **Decode**: XAttention 自动 fallback 到 FullAttentionPolicy
+4. **Paged KV Cache**: 当 `block_tables` 存在时自动 fallback 到 flash_attn
+
+## 相关文档
+
+- [Sparse Policy 架构](sparse_policy_architecture.md)
+- [XAttention 算法详解](xattention_algorithm_guide.md)
+- [BSA 接口文档](block_sparse_attn_interface.md)

nanovllm/engine/model_runner.py

@@ -202,19 +202,36 @@ class ModelRunner:
             dtype=hf_config.torch_dtype,
         )
 
-        # Initialize sparse policy if manager has one (CPU offload mode)
+        # Initialize sparse policy if manager has one (works for both CPU offload and GPU-only modes)
         if hasattr(self.kvcache_manager, 'sparse_policy') and self.kvcache_manager.sparse_policy is not None:
+            # Use CPU blocks for offload mode, GPU blocks for GPU-only mode
+            num_blocks_for_init = config.num_cpu_kvcache_blocks if config.enable_cpu_offload else config.num_kvcache_blocks
             self.kvcache_manager.sparse_policy.initialize(
                 num_layers=hf_config.num_hidden_layers,
                 num_kv_heads=num_kv_heads,
                 head_dim=head_dim,
-                num_cpu_blocks=config.num_cpu_kvcache_blocks,
+                num_cpu_blocks=num_blocks_for_init,
                 dtype=hf_config.torch_dtype,
                 device=torch.device("cuda"),
             )
 
+            # GPU-only mode: pre-allocate policy metadata buffers
+            # This avoids dynamic GPU memory allocation during forward pass
+            if not config.enable_cpu_offload:
+                num_heads = hf_config.num_attention_heads // self.world_size
+                self.kvcache_manager.sparse_policy.alloc_policy_metadata(
+                    num_heads=num_heads,
+                    num_kv_heads=num_kv_heads,
+                    head_dim=head_dim,
+                    max_seq_len=config.max_model_len,
+                    dtype=hf_config.torch_dtype,
+                    device=torch.device("cuda"),
+                )
+
+            # Log policy info (handle both enum and None cases)
+            policy_name = config.sparse_policy.name if config.sparse_policy is not None else "FULL"
             logger.info(
-                f"Sparse policy initialized: {config.sparse_policy.name} "
+                f"Sparse policy initialized: {policy_name} "
                 f"(topk={config.sparse_topk_blocks}, threshold={config.sparse_threshold_blocks})"
             )
 
@@ -375,7 +392,16 @@ class ModelRunner:
         cu_seqlens_q = torch.tensor(cu_seqlens_q, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
         cu_seqlens_k = torch.tensor(cu_seqlens_k, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
         slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
-        set_context(True, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, slot_mapping, None, block_tables)
+        set_context(
+            is_prefill=True,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q,
+            max_seqlen_k=max_seqlen_k,
+            slot_mapping=slot_mapping,
+            block_tables=block_tables,
+            kvcache_manager=getattr(self, 'kvcache_manager', None),
+        )
         return input_ids, positions
 
     def prepare_decode(self, seqs: list[Sequence]):
@@ -404,7 +430,13 @@ class ModelRunner:
         context_lens = torch.tensor(context_lens, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
         # Use GPU physical block tables for attention
         block_tables = self._prepare_gpu_block_tables(gpu_block_tables)
-        set_context(False, slot_mapping=slot_mapping, context_lens=context_lens, block_tables=block_tables)
+        set_context(
+            is_prefill=False,
+            slot_mapping=slot_mapping,
+            context_lens=context_lens,
+            block_tables=block_tables,
+            kvcache_manager=self.kvcache_manager,
+        )
         return input_ids, positions
 
     def _prepare_gpu_block_tables(self, gpu_block_tables: list[list[int]]):
@@ -713,7 +745,13 @@ class ModelRunner:
 
         for bs in reversed(self.graph_bs):
             graph = torch.cuda.CUDAGraph()
-            set_context(False, slot_mapping=slot_mapping[:bs], context_lens=context_lens[:bs], block_tables=block_tables[:bs])
+            set_context(
+                is_prefill=False,
+                slot_mapping=slot_mapping[:bs],
+                context_lens=context_lens[:bs],
+                block_tables=block_tables[:bs],
+                kvcache_manager=self.kvcache_manager,
+            )
             outputs[:bs] = self.model(input_ids[:bs], positions[:bs])    # warmup
             with torch.cuda.graph(graph, self.graph_pool):
                 outputs[:bs] = self.model(input_ids[:bs], positions[:bs])    # capture

nanovllm/kvcache/__init__.py

@@ -25,7 +25,7 @@ def create_kvcache_manager(config: "Config") -> KVCacheManager:
     Factory function to create the appropriate KV cache manager.
 
     Decision logic:
-    1. If enable_cpu_offload=False: use GPUOnlyManager
+    1. If enable_cpu_offload=False: use GPUOnlyManager (optionally with sparse policy)
     2. If enable_cpu_offload=True but all blocks fit in GPU: use GPUOnlyManager
     3. If enable_cpu_offload=True and need CPU blocks: use HybridKVCacheManager
 
@@ -37,9 +37,44 @@ def create_kvcache_manager(config: "Config") -> KVCacheManager:
     """
     if not getattr(config, 'enable_cpu_offload', False):
         # Default: pure GPU mode
+        # Check if sparse policy is requested for GPU-only mode
+        from nanovllm.config import SparsePolicyType
+        sparse_policy_type = getattr(config, 'sparse_policy', None)
+        # Handle None case - use FULL as default
+        if sparse_policy_type is None:
+            sparse_policy_type = SparsePolicyType.FULL
+
+        sparse_policy = None
+        if sparse_policy_type != SparsePolicyType.FULL:
+            # Create sparse policy for GPU-only mode
+            from nanovllm.kvcache.sparse import create_sparse_policy
+
+            policy_kwargs = {}
+            if sparse_policy_type == SparsePolicyType.QUEST:
+                policy_kwargs = {
+                    'topk_blocks': getattr(config, 'sparse_topk_blocks', 8),
+                    'threshold_blocks': getattr(config, 'sparse_threshold_blocks', 4),
+                }
+            elif sparse_policy_type == SparsePolicyType.XATTN_BSA:
+                policy_kwargs = {
+                    'block_size': getattr(config, 'sparse_block_size', 128),
+                    'samples_per_chunk': getattr(config, 'sparse_samples_per_chunk', 128),
+                    'threshold': getattr(config, 'sparse_threshold', 0.9),
+                    'use_triton': getattr(config, 'sparse_use_triton', True),
+                    'stride': getattr(config, 'sparse_stride', 8),
+                    'chunk_size': getattr(config, 'sparse_chunk_size', 16384),
+                }
+
+            sparse_policy = create_sparse_policy(sparse_policy_type, **policy_kwargs)
+        else:
+            # FULL policy for GPU-only mode - always create for consistent API
+            from nanovllm.kvcache.sparse import FullAttentionPolicy
+            sparse_policy = FullAttentionPolicy()
+
         return GPUOnlyManager(
             num_blocks=config.num_kvcache_blocks,
             block_size=config.kvcache_block_size,
+            sparse_policy=sparse_policy,
         )
 
     # CPU offload is enabled

nanovllm/kvcache/gpu_manager.py

@@ -7,13 +7,16 @@ the KVCacheManager interface.
 """
 
 from collections import deque
-from typing import List, Tuple, Dict, Optional
+from typing import List, Tuple, Dict, Optional, TYPE_CHECKING
 import torch
 from torch import Tensor
 
 from nanovllm.engine.sequence import Sequence
 from nanovllm.kvcache.base_manager import KVCacheManager
 
+if TYPE_CHECKING:
+    from nanovllm.kvcache.sparse.policy import SparsePolicy
+
 
 class Block:
     """Physical block in GPU memory."""
@@ -50,17 +53,28 @@ class GPUOnlyManager(KVCacheManager):
     all data stays on GPU at fixed addresses.
     """
 
-    def __init__(self, num_blocks: int, block_size: int):
+    def __init__(
+        self,
+        num_blocks: int,
+        block_size: int,
+        sparse_policy: Optional["SparsePolicy"] = None,
+    ):
         """
         Initialize GPU-only manager.
 
         Args:
             num_blocks: Total number of blocks to manage
             block_size: Tokens per block (default 256)
+            sparse_policy: Optional sparse attention policy for GPU-only mode
         """
         self._block_size = block_size
         self._num_blocks = num_blocks
 
+        # Sparse policy for GPU-only mode (optional)
+        self.sparse_policy = sparse_policy
+        # No offload engine in GPU-only mode
+        self.offload_engine = None
+
         # Block metadata
         self.blocks: List[Block] = [Block(i) for i in range(num_blocks)]
 

nanovllm/kvcache/sparse/full_policy.py

@@ -76,6 +76,75 @@ class FullAttentionPolicy(SparsePolicy):
         logger.info(f"[Full Policy] Density Stats: chunks={stats['num_chunks']}, "
                    f"blocks={stats['total_available_blocks']}, density=100.0%")
 
+    # =========================================================================
+    # GPU-only methods (non-chunked)
+    # =========================================================================
+
+    def compute_prefill(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens_q: torch.Tensor,
+        cu_seqlens_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+        softmax_scale: float,
+        layer_id: int,
+        block_tables: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        GPU-only prefill attention using flash_attn_varlen_func.
+
+        This is the simplest implementation - just call flash attention directly.
+        For sparse policies, this method would implement block selection.
+        """
+        from flash_attn import flash_attn_varlen_func
+
+        return flash_attn_varlen_func(
+            q, k, v,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q,
+            max_seqlen_k=max_seqlen_k,
+            softmax_scale=softmax_scale,
+            causal=True,
+            block_table=block_tables,
+        )
+
+    def compute_decode(
+        self,
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        cache_seqlens: torch.Tensor,
+        softmax_scale: float,
+        layer_id: int,
+        block_tables: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        GPU-only decode attention using flash_attn_with_kvcache.
+
+        This is the simplest implementation - just call flash attention directly.
+        For sparse policies, this method would implement block selection.
+        """
+        from flash_attn import flash_attn_with_kvcache
+
+        # q is [batch, num_heads, head_dim], need to add seq dim
+        return flash_attn_with_kvcache(
+            q.unsqueeze(1),  # [batch, 1, heads, dim]
+            k_cache,
+            v_cache,
+            cache_seqlens=cache_seqlens,
+            block_table=block_tables,
+            softmax_scale=softmax_scale,
+            causal=True,
+        )
+
+    # =========================================================================
+    # Chunked offload methods
+    # =========================================================================
+
     def compute_chunked_prefill(
         self,
         q: torch.Tensor,

nanovllm/kvcache/sparse/policy.py

@@ -108,6 +108,34 @@ class SparsePolicy(ABC):
         """
         pass
 
+    def alloc_policy_metadata(
+        self,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        max_seq_len: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> None:
+        """
+        Pre-allocate GPU buffers for policy computation.
+
+        Called by the framework after KV cache allocation, but ONLY for GPU-only
+        mode (not CPU offload mode). Override this to pre-allocate buffers that
+        would otherwise be dynamically allocated during forward pass.
+
+        This is separate from initialize() which is used for CPU offload metadata.
+
+        Args:
+            num_heads: Number of query heads
+            num_kv_heads: Number of KV heads (for GQA)
+            head_dim: Dimension per head
+            max_seq_len: Maximum sequence length (for buffer sizing)
+            dtype: Data type (typically float16/bfloat16)
+            device: Target device (cuda)
+        """
+        pass
+
     @abstractmethod
     def select_blocks(
         self,
@@ -191,6 +219,87 @@ class SparsePolicy(ABC):
         """
         pass
 
+    # =========================================================================
+    # GPU-only methods (non-chunked)
+    # These methods are used when all KV cache is on GPU, no CPU offload needed.
+    # =========================================================================
+
+    def compute_prefill(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens_q: torch.Tensor,
+        cu_seqlens_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+        softmax_scale: float,
+        layer_id: int,
+        block_tables: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Compute GPU-only prefill attention (non-chunked).
+
+        This method is used when all KV cache resides on GPU (no CPU offload).
+        Override this to implement sparse prefill attention for GPU-only mode.
+        Default implementation raises NotImplementedError.
+
+        Args:
+            q: [total_q, num_heads, head_dim] query tensor (packed variable length)
+            k: [total_kv, num_kv_heads, head_dim] key tensor
+            v: [total_kv, num_kv_heads, head_dim] value tensor
+            cu_seqlens_q: [batch+1] cumulative sequence lengths for queries
+            cu_seqlens_k: [batch+1] cumulative sequence lengths for keys
+            max_seqlen_q: maximum query sequence length
+            max_seqlen_k: maximum key sequence length
+            softmax_scale: softmax scaling factor (1/sqrt(head_dim))
+            layer_id: transformer layer index
+            block_tables: [batch, max_blocks] paged attention block tables (optional)
+
+        Returns:
+            [total_q, num_heads, head_dim] attention output
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not implement compute_prefill for GPU-only mode"
+        )
+
+    def compute_decode(
+        self,
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        cache_seqlens: torch.Tensor,
+        softmax_scale: float,
+        layer_id: int,
+        block_tables: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Compute GPU-only decode attention (non-chunked).
+
+        This method is used when all KV cache resides on GPU (no CPU offload).
+        Override this to implement sparse decode attention for GPU-only mode.
+        Default implementation raises NotImplementedError.
+
+        Args:
+            q: [batch, num_heads, head_dim] query tensor (single token per sequence)
+            k_cache: [num_blocks, block_size, num_kv_heads, head_dim] paged key cache
+            v_cache: [num_blocks, block_size, num_kv_heads, head_dim] paged value cache
+            cache_seqlens: [batch] sequence lengths in cache
+            softmax_scale: softmax scaling factor (1/sqrt(head_dim))
+            layer_id: transformer layer index
+            block_tables: [batch, max_blocks] paged attention block tables (optional)
+
+        Returns:
+            [batch, 1, num_heads, head_dim] attention output
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not implement compute_decode for GPU-only mode"
+        )
+
+    # =========================================================================
+    # Chunked offload methods (for CPU offload mode)
+    # =========================================================================
+
     @abstractmethod
     def compute_chunked_prefill(
         self,

nanovllm/kvcache/sparse/xattn_bsa.py

@@ -122,6 +122,271 @@ class XAttentionBSAPolicy(SparsePolicy):
         self._stats_total_selected_blocks = 0
         self._stats_num_chunks = 0
 
+        # Pre-allocated GQA expansion buffers (GPU-only mode)
+        # Set by alloc_policy_metadata(), None if not pre-allocated
+        self._k_expanded: torch.Tensor | None = None
+        self._v_expanded: torch.Tensor | None = None
+        self._max_seq_len: int = 0
+
+    def alloc_policy_metadata(
+        self,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        max_seq_len: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> None:
+        """
+        Pre-allocate GQA expansion buffers for GPU-only mode.
+
+        These buffers are used by compute_prefill() to avoid dynamic allocation
+        during forward pass. The buffers are sized for max_seq_len and sliced
+        to actual seq_len during use.
+
+        Memory usage: 2 * num_heads * max_seq_len * head_dim * dtype_size
+        For 64K seq, 32 heads, 128 dim, fp16: 2 * 32 * 65536 * 128 * 2 = 1 GB
+
+        Args:
+            num_heads: Number of query heads
+            num_kv_heads: Number of KV heads (for GQA)
+            head_dim: Dimension per head
+            max_seq_len: Maximum sequence length
+            dtype: Data type
+            device: Target device
+        """
+        # Only allocate if GQA (num_heads != num_kv_heads)
+        if num_heads == num_kv_heads:
+            logger.info(f"[XAttn] No GQA expansion needed (num_heads == num_kv_heads = {num_heads})")
+            return
+
+        # Shape: [1, num_heads, max_seq_len, head_dim] for xattn_estimate format
+        # Also used for BSA which expects [seq_len, num_heads, head_dim]
+        shape = (1, num_heads, max_seq_len, head_dim)
+        self._k_expanded = torch.empty(shape, dtype=dtype, device=device)
+        self._v_expanded = torch.empty(shape, dtype=dtype, device=device)
+        self._max_seq_len = max_seq_len
+
+        memory_mb = 2 * num_heads * max_seq_len * head_dim * dtype.itemsize / (1024 * 1024)
+        logger.info(f"[XAttn] Pre-allocated GQA buffers: shape={shape}, memory={memory_mb:.1f} MB")
+
+    # =========================================================================
+    # GPU-only methods (non-chunked)
+    # =========================================================================
+
+    def compute_prefill(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens_q: torch.Tensor,
+        cu_seqlens_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+        softmax_scale: float,
+        layer_id: int,
+        block_tables: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """
+        GPU-only prefill attention using XAttention + BSA.
+
+        This method implements sparse attention for GPU-only mode:
+        1. Estimate block importance using xattn_estimate
+        2. Compute sparse attention using block_sparse_attn_func
+
+        Args:
+            q: Query tensor [total_q, num_heads, head_dim] (varlen packed)
+            k: Key tensor [total_kv, num_kv_heads, head_dim] (varlen packed)
+            v: Value tensor [total_kv, num_kv_heads, head_dim] (varlen packed)
+            cu_seqlens_q: Cumulative sequence lengths for Q [batch+1]
+            cu_seqlens_k: Cumulative sequence lengths for K [batch+1]
+            max_seqlen_q: Maximum Q sequence length
+            max_seqlen_k: Maximum K sequence length
+            softmax_scale: Softmax scaling factor
+            layer_id: Transformer layer index
+            block_tables: Paged attention block tables (not used for XAttention)
+
+        Returns:
+            Attention output [total_q, num_heads, head_dim]
+        """
+        # When block_tables is provided (paged KV cache / prefix cache),
+        # fallback to flash_attn as XAttention expects contiguous K, V
+        if block_tables is not None:
+            from flash_attn import flash_attn_varlen_func
+            return flash_attn_varlen_func(
+                q, k, v,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                softmax_scale=softmax_scale,
+                causal=True,
+                block_table=block_tables,
+            )
+
+        if not BSA_AVAILABLE:
+            # Fallback to flash attention if BSA not available
+            from flash_attn import flash_attn_varlen_func
+            return flash_attn_varlen_func(
+                q, k, v,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                softmax_scale=softmax_scale,
+                causal=True,
+            )
+
+        if not XATTN_AVAILABLE:
+            # Fallback to flash attention if xattn not available
+            from flash_attn import flash_attn_varlen_func
+            return flash_attn_varlen_func(
+                q, k, v,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                softmax_scale=softmax_scale,
+                causal=True,
+            )
+
+        from nanovllm.ops.xattn import xattn_estimate
+
+        # Get dimensions
+        total_q, num_heads, head_dim = q.shape
+        total_kv, num_kv_heads, _ = k.shape
+
+        # For now, assume batch_size = 1 (single sequence)
+        # TODO: Support batched varlen format
+        batch_size = cu_seqlens_q.shape[0] - 1
+        if batch_size != 1:
+            # Fallback to flash attention for batched input
+            from flash_attn import flash_attn_varlen_func
+            logger.warning(f"[XAttn] batch_size={batch_size} > 1, falling back to flash attention")
+            return flash_attn_varlen_func(
+                q, k, v,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_q,
+                max_seqlen_k=max_seqlen_k,
+                softmax_scale=softmax_scale,
+                causal=True,
+            )
+
+        q_len = max_seqlen_q
+        k_len = max_seqlen_k
+
+        # Convert from varlen format [total, heads, dim] to [batch, heads, seq, dim]
+        # q: [q_len, num_heads, head_dim] -> [1, num_heads, q_len, head_dim]
+        Q = q.unsqueeze(0).transpose(1, 2)  # [1, num_heads, q_len, head_dim]
+        K = k.unsqueeze(0).transpose(1, 2)  # [1, num_kv_heads, k_len, head_dim]
+        V = v.unsqueeze(0).transpose(1, 2)  # [1, num_kv_heads, k_len, head_dim]
+
+        # Expand KV for GQA - use pre-allocated buffers if available
+        if num_heads != num_kv_heads:
+            num_groups = num_heads // num_kv_heads
+            if self._k_expanded is not None and k_len <= self._max_seq_len:
+                # Use pre-allocated buffers with in-place expansion
+                K_exp = self._k_expanded[:, :, :k_len, :]
+                V_exp = self._v_expanded[:, :, :k_len, :]
+                # In-place GQA expansion: [1, num_kv_heads, k_len, head_dim] -> [1, num_heads, k_len, head_dim]
+                # Reshape K to [1, num_kv_heads, 1, k_len, head_dim] and broadcast to [1, num_kv_heads, num_groups, k_len, head_dim]
+                K_exp.view(1, num_kv_heads, num_groups, k_len, head_dim).copy_(
+                    K.unsqueeze(2).expand(-1, -1, num_groups, -1, -1)
+                )
+                V_exp.view(1, num_kv_heads, num_groups, k_len, head_dim).copy_(
+                    V.unsqueeze(2).expand(-1, -1, num_groups, -1, -1)
+                )
+            else:
+                # Fallback: dynamic allocation (when buffers not pre-allocated or seq too long)
+                K_exp, V_exp = expand_kv_for_gqa(K, V, num_heads)
+        else:
+            K_exp, V_exp = K, V
+
+        # Estimate block importance and get sparse mask
+        _, mask = xattn_estimate(
+            Q, K_exp,
+            chunk_size=self.chunk_size,
+            block_size=self.BSA_BLOCK_SIZE,
+            threshold=self.threshold,
+            use_triton=self.use_triton,
+            causal=True,
+        )
+
+        # Compute block counts
+        q_block_num = (q_len + self.BSA_BLOCK_SIZE - 1) // self.BSA_BLOCK_SIZE
+        k_block_num = (k_len + self.BSA_BLOCK_SIZE - 1) // self.BSA_BLOCK_SIZE
+
+        # Prepare tensors for BSA
+        # q, k, v need to be [seq_len, num_heads, head_dim]
+        q_bsa = q  # Already [q_len, num_heads, head_dim]
+
+        # For GQA with BSA, reuse the expanded K_exp, V_exp (convert to BSA format)
+        # K_exp: [1, num_heads, k_len, head_dim] -> [k_len, num_heads, head_dim]
+        if num_heads != num_kv_heads:
+            k_bsa = K_exp.squeeze(0).transpose(0, 1)  # [k_len, num_heads, head_dim]
+            v_bsa = V_exp.squeeze(0).transpose(0, 1)  # [k_len, num_heads, head_dim]
+        else:
+            k_bsa = k
+            v_bsa = v
+
+        # Prepare BSA inputs
+        cu_seqlens_q_bsa = torch.tensor([0, q_len], dtype=torch.int32, device=q.device)
+        cu_seqlens_k_bsa = torch.tensor([0, k_len], dtype=torch.int32, device=k.device)
+        head_groups = torch.ones(num_heads, dtype=torch.int32, device=q.device)
+
+        # Trim mask to actual block counts
+        mask_trimmed = mask[:, :, :q_block_num, :k_block_num].contiguous()
+
+        # Compute sparse attention using BSA
+        output = block_sparse_attn_func(
+            q_bsa, k_bsa, v_bsa,
+            cu_seqlens_q_bsa,
+            cu_seqlens_k_bsa,
+            head_groups,
+            None,  # key_padding_mask
+            mask_trimmed,
+            q_len, k_len,
+            p_dropout=0.0,
+            deterministic=True,
+            is_causal=True,
+        )
+
+        # Update statistics (layer 0 only to avoid overcounting)
+        if layer_id == 0:
+            selected_blocks = mask_trimmed.sum().item()
+            total_blocks = q_block_num * k_block_num * num_heads
+            density = selected_blocks / total_blocks if total_blocks > 0 else 1.0
+            logger.debug(f"[XAttn GPU-only] layer={layer_id}, q_blocks={q_block_num}, "
+                        f"k_blocks={k_block_num}, density={density:.1%}")
+
+        return output
+
+    def compute_decode(
+        self,
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        cache_seqlens: torch.Tensor,
+        softmax_scale: float,
+        layer_id: int,
+        block_tables: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """
+        GPU-only decode attention - delegates to FullAttentionPolicy.
+
+        XAttention is designed for long prefill sequences. For decode (single token),
+        we use FullAttentionPolicy which calls flash_attn_with_kvcache.
+        """
+        from nanovllm.kvcache.sparse.full_policy import FullAttentionPolicy
+        return FullAttentionPolicy().compute_decode(
+            q, k_cache, v_cache, cache_seqlens, softmax_scale, layer_id, block_tables
+        )
+
+    # =========================================================================
+    # Chunked offload methods
+    # =========================================================================
+
     def select_blocks(
         self,
         available_blocks: List[int],

nanovllm/layers/attention.py

@@ -124,24 +124,47 @@ class Attention(nn.Module):
             if k_cache.numel() and v_cache.numel():
                 store_kvcache(k, v, k_cache, v_cache, context.slot_mapping)
 
+        # Get sparse_policy from kvcache_manager (required, never None after warmup)
+        # During warmup, kvcache_manager is not yet allocated
+        if context.kvcache_manager is None:
+            # Warmup phase: use flash_attn directly
+            if context.is_prefill:
+                return flash_attn_varlen_func(
+                    q, k, v,
+                    max_seqlen_q=context.max_seqlen_q, cu_seqlens_q=context.cu_seqlens_q,
+                    max_seqlen_k=context.max_seqlen_k, cu_seqlens_k=context.cu_seqlens_k,
+                    softmax_scale=self.scale, causal=True,
+                )
+            else:
+                return flash_attn_with_kvcache(
+                    q.unsqueeze(1), k_cache, v_cache,
+                    cache_seqlens=context.context_lens, block_table=context.block_tables,
+                    softmax_scale=self.scale, causal=True,
+                )
+        sparse_policy = context.kvcache_manager.sparse_policy
+        assert sparse_policy is not None, "sparse_policy must not be None"
+
         if context.is_prefill:
             if context.is_chunked_prefill:
-                # Chunked prefill: merge attention from previous KV
+                # Chunked prefill: merge attention from previous KV (CPU offload mode)
                 o = self._chunked_prefill_attention(q, k, v, context)
-            elif context.block_tables is not None:    # prefix cache
-                k, v = k_cache, v_cache
-                o = flash_attn_varlen_func(q, k, v,
-                                           max_seqlen_q=context.max_seqlen_q, cu_seqlens_q=context.cu_seqlens_q,
-                                           max_seqlen_k=context.max_seqlen_k, cu_seqlens_k=context.cu_seqlens_k,
-                                           softmax_scale=self.scale, causal=True, block_table=context.block_tables)
             else:
-                o = flash_attn_varlen_func(q, k, v,
-                                           max_seqlen_q=context.max_seqlen_q, cu_seqlens_q=context.cu_seqlens_q,
-                                           max_seqlen_k=context.max_seqlen_k, cu_seqlens_k=context.cu_seqlens_k,
-                                           softmax_scale=self.scale, causal=True, block_table=context.block_tables)
+                # GPU-only mode: use policy for attention
+                # Use paged attention if block_tables provided, else use k, v directly
+                if context.block_tables is not None:
+                    k_for_attn, v_for_attn = k_cache, v_cache
+                else:
+                    k_for_attn, v_for_attn = k, v
+                o = sparse_policy.compute_prefill(
+                    q, k_for_attn, v_for_attn,
+                    context.cu_seqlens_q, context.cu_seqlens_k,
+                    context.max_seqlen_q, context.max_seqlen_k,
+                    self.scale, self.layer_id,
+                    context.block_tables,
+                )
         else:    # decode
             if context.is_chunked_prefill:
-                # Chunked decode: need to load all KV from CPU+GPU
+                # Chunked decode: need to load all KV from CPU+GPU (CPU offload mode)
                 # Store current decode token to per-layer decode buffer
                 # This is needed because GPU cache has no layer dimension,
                 # so all layers would overwrite each other in decode_slot.
@@ -152,9 +175,12 @@ class Attention(nn.Module):
                 offload_engine.write_to_decode_buffer(self.layer_id, pos_in_block, k.squeeze(0), v.squeeze(0))
                 o = self._chunked_decode_attention(q, k, v, context)
             else:
-                o = flash_attn_with_kvcache(q.unsqueeze(1), k_cache, v_cache,
-                                            cache_seqlens=context.context_lens, block_table=context.block_tables,
-                                            softmax_scale=self.scale, causal=True)
+                # GPU-only mode: use policy for attention
+                o = sparse_policy.compute_decode(
+                    q, k_cache, v_cache,
+                    context.context_lens, self.scale, self.layer_id,
+                    context.block_tables,
+                )
         return o
 
     def _chunked_prefill_attention(

scripts/profile.sh

@@ -0,0 +1,158 @@
+#!/bin/bash
+
+# Profile bench.py using NVIDIA Nsight Systems (GPU-only mode)
+#
+# Usage:
+#   bash scripts/profile.sh [options]
+#
+# Options:
+#   --max-len LENGTH     Max sequence length (default: 32768)
+#   --policy POLICY      Sparse policy: full, xattn (default: xattn)
+#   --gpu GPU_ID         GPU to use (default: 0)
+#   --gpu-util UTIL      GPU memory utilization (default: 0.9)
+#   --input-len LENGTH   Input length (default: max-len - 1)
+#   --bench-decode       Run decode benchmark instead of prefill
+#
+# Output:
+#   results/nsys/bench_<policy>_<max_len>_<timestamp>.nsys-rep
+#
+# Examples:
+#   bash scripts/profile.sh
+#   bash scripts/profile.sh --max-len 65536 --gpu-util 0.7
+#   bash scripts/profile.sh --policy full --max-len 32768
+#   bash scripts/profile.sh --bench-decode
+
+set -e
+
+# Default configuration
+MAX_LEN="32768"
+POLICY="xattn"
+GPU_ID="0"
+GPU_UTIL="0.9"
+INPUT_LEN=""
+BENCH_MODE="prefill"
+
+# Parse arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --max-len)
+            MAX_LEN="$2"
+            shift 2
+            ;;
+        --policy)
+            POLICY="$2"
+            shift 2
+            ;;
+        --gpu)
+            GPU_ID="$2"
+            shift 2
+            ;;
+        --gpu-util)
+            GPU_UTIL="$2"
+            shift 2
+            ;;
+        --input-len)
+            INPUT_LEN="$2"
+            shift 2
+            ;;
+        --bench-decode)
+            BENCH_MODE="decode"
+            shift
+            ;;
+        -h|--help)
+            echo "Usage: $0 [options]"
+            echo ""
+            echo "Options:"
+            echo "  --max-len LENGTH     Max sequence length (default: 32768)"
+            echo "  --policy POLICY      Sparse policy: full, xattn (default: xattn)"
+            echo "  --gpu GPU_ID         GPU to use (default: 0)"
+            echo "  --gpu-util UTIL      GPU memory utilization (default: 0.9)"
+            echo "  --input-len LENGTH   Input length (default: max-len - 1)"
+            echo "  --bench-decode       Run decode benchmark instead of prefill"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            exit 1
+            ;;
+    esac
+done
+
+# Path configuration
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
+OUTPUT_DIR="$PROJECT_ROOT/results/nsys"
+BENCH_SCRIPT="$PROJECT_ROOT/bench.py"
+
+# Create output directory if needed
+mkdir -p "$OUTPUT_DIR"
+
+# Generate timestamp for unique filename
+TIMESTAMP=$(date +%Y%m%d_%H%M%S)
+
+# Convert max_len to human-readable format (e.g., 32768 -> 32k)
+if [ "$MAX_LEN" -ge 1024 ]; then
+    MAX_LEN_SUFFIX="$((MAX_LEN / 1024))k"
+else
+    MAX_LEN_SUFFIX="${MAX_LEN}"
+fi
+
+OUTPUT_FILE="$OUTPUT_DIR/bench_${POLICY}_${MAX_LEN_SUFFIX}_${BENCH_MODE}_${TIMESTAMP}"
+
+# Build bench.py arguments
+BENCH_ARGS="--max-len $MAX_LEN --gpu-util $GPU_UTIL"
+
+if [ -n "$POLICY" ]; then
+    BENCH_ARGS="$BENCH_ARGS --policy $POLICY"
+fi
+
+if [ -n "$INPUT_LEN" ]; then
+    BENCH_ARGS="$BENCH_ARGS --input-len $INPUT_LEN"
+fi
+
+if [ "$BENCH_MODE" = "decode" ]; then
+    BENCH_ARGS="$BENCH_ARGS --bench-decode"
+fi
+
+echo "============================================================"
+echo "NVIDIA Nsight Systems Profiling (GPU-only)"
+echo "============================================================"
+echo "Bench script: $BENCH_SCRIPT"
+echo "Policy:       $POLICY"
+echo "Max length:   $MAX_LEN"
+echo "GPU:          $GPU_ID"
+echo "GPU util:     $GPU_UTIL"
+echo "Bench mode:   $BENCH_MODE"
+echo "Output file:  $OUTPUT_FILE.nsys-rep"
+echo ""
+
+# nsys profile options:
+# --trace=cuda,nvtx : Trace CUDA API and NVTX markers
+# --force-overwrite=true : Overwrite existing output file
+# --output=<path> : Output file path (without .nsys-rep extension)
+
+echo "Running nsys profile..."
+echo "Command: python bench.py $BENCH_ARGS"
+echo ""
+
+CUDA_VISIBLE_DEVICES=$GPU_ID PYTHONPATH="$PROJECT_ROOT:$PYTHONPATH" \
+nsys profile \
+    --trace=cuda,nvtx \
+    --force-overwrite=true \
+    --output="$OUTPUT_FILE" \
+    python "$BENCH_SCRIPT" $BENCH_ARGS
+
+echo ""
+echo "============================================================"
+echo "Profiling completed successfully!"
+echo "============================================================"
+echo "Output file: $OUTPUT_FILE.nsys-rep"
+echo ""
+echo "To view results in GUI:"
+echo "  nsight-sys $OUTPUT_FILE.nsys-rep"
+echo ""
+echo "To export statistics:"
+echo "  nsys stats --report cuda_api_sum $OUTPUT_FILE.nsys-rep"
+echo "  nsys stats --report cuda_gpu_kern_sum $OUTPUT_FILE.nsys-rep"
+echo "  nsys stats --report cuda_gpu_mem_size_sum $OUTPUT_FILE.nsys-rep"
+echo "============================================================"