feat: add XAttention sparse policy integration

Integrate COMPASS XAttention algorithm into nano-vllm's CPU offload
execution path. Uses FlashAttention with native GQA support for
offload mode.

New files:
- nanovllm/kvcache/sparse/utils.py: find_blocks_chunked() utility
- nanovllm/kvcache/sparse/kernels.py: Triton kernels for XAttention
- nanovllm/kvcache/sparse/xattn.py: XAttentionPolicy implementation

Modified:
- nanovllm/config.py: Add XATTN configuration parameters
- nanovllm/engine/model_runner.py: Support XATTN policy
- nanovllm/kvcache/sparse/__init__.py: Register XAttentionPolicy
- tests/test_ruler.py: Add --sparse-policy parameter

Test results (32k ruler):
- NIAH tasks: 12/12 (100%)
- QA/Recall tasks: 11/15 (73%)
- Overall: 23/27 (85%)

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

tests/test_ruler.py

@@ -226,6 +226,7 @@ def run_ruler_benchmark(
     gpu_utilization: float = 0.9,
     enforce_eager: bool = True,
     verbose: bool = True,
+    sparse_policy: Optional[str] = None,
 ) -> Dict:
     """
     Run RULER benchmark on multiple tasks.
@@ -236,6 +237,7 @@ def run_ruler_benchmark(
         datasets: List of task names to test (None = all)
         num_samples: Number of samples per task (None = all)
         ...other LLM config params...
+        sparse_policy: Sparse attention policy (FULL, QUEST, MINFERENCE, XATTN)
 
     Returns:
         Dict with overall results and per-task results
@@ -272,6 +274,10 @@ def run_ruler_benchmark(
     if enable_cpu_offload:
         llm_kwargs["num_gpu_blocks"] = num_gpu_blocks
         llm_kwargs["num_kv_buffers"] = num_kv_buffers
+    if sparse_policy:
+        from nanovllm.config import SparsePolicyType
+        sparse_policy_type = SparsePolicyType[sparse_policy]
+        llm_kwargs["sparse_policy"] = sparse_policy_type
 
     llm = LLM(model_path, **llm_kwargs)
 
@@ -366,6 +372,8 @@ if __name__ == "__main__":
                         help="Enable CUDA graph")
     parser.add_argument("--quiet", "-q", action="store_true",
                         help="Quiet mode")
+    parser.add_argument("--sparse-policy", type=str, default="",
+                        help="Sparse attention policy (FULL, QUEST, MINFERENCE, XATTN)")
 
     args = parser.parse_args()
 
@@ -373,6 +381,9 @@ if __name__ == "__main__":
     datasets = args.datasets.split(",") if args.datasets else None
     num_samples = args.num_samples if args.num_samples > 0 else None
 
+    # Parse sparse policy
+    sparse_policy_str = args.sparse_policy.upper() if args.sparse_policy else None
+
     results = run_ruler_benchmark(
         model_path=os.path.expanduser(args.model),
         data_dir=Path(args.data_dir),
@@ -387,6 +398,7 @@ if __name__ == "__main__":
         gpu_utilization=args.gpu_utilization,
         enforce_eager=not args.use_cuda_graph,
         verbose=not args.quiet,
+        sparse_policy=sparse_policy_str,
     )
 
     # Exit code