⚙️ config: update test_ruler.py defaults

- max_new_tokens: 128 → 16 (sufficient for NIAH answers)
- block_size: 1024 → 4096 (better performance)

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

tests/test_ruler.py

@@ -108,7 +108,7 @@ DEFAULT_MODEL = os.path.expanduser("~/models/Llama-3.1-8B-Instruct")
 # Note: max_model_len must be > max_input_len to leave room for output tokens
 # 64k benchmark has inputs up to 65536 tokens, so we need 65536 + 128 = 65664
 DEFAULT_MAX_MODEL_LEN = 65664
-DEFAULT_MAX_NEW_TOKENS = 128  # Larger for multi-value tasks
+DEFAULT_MAX_NEW_TOKENS = 16  # Sufficient for NIAH single-value answers
 
 # Task categories for evaluation
 NIAH_TASKS = ["niah_single_1", "niah_single_2", "niah_single_3",
@@ -323,7 +323,7 @@ def run_ruler_benchmark(
     max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
     enable_cpu_offload: bool = False,
     num_gpu_blocks: int = 4,
-    block_size: int = 1024,
+    block_size: int = 4096,
     num_kv_buffers: int = 4,
     gpu_utilization: float = 0.9,
     enforce_eager: bool = True,
@@ -335,6 +335,7 @@ def run_ruler_benchmark(
     sparse_samples: int = 128,
     sparse_block_size: int = 128,
     sparse_stride: int = 8,
+    dtype: Optional[str] = None,
 ) -> Dict:
     """
     Run RULER benchmark on multiple tasks.
@@ -389,6 +390,8 @@ def run_ruler_benchmark(
         "kvcache_block_size": block_size,
         "enable_cpu_offload": enable_cpu_offload,
     }
+    if dtype:
+        llm_kwargs["dtype"] = dtype
     if enable_cpu_offload:
         llm_kwargs["num_gpu_blocks"] = num_gpu_blocks
         llm_kwargs["num_kv_buffers"] = num_kv_buffers
@@ -525,8 +528,8 @@ if __name__ == "__main__":
                         help="Enable CPU offload mode")
     parser.add_argument("--num-gpu-blocks", type=int, default=4,
                         help="Number of GPU blocks for CPU offload (default: 4)")
-    parser.add_argument("--block-size", type=int, default=1024,
+    parser.add_argument("--block-size", type=int, default=4096,
-                        help="KV cache block size (default: 1024)")
+                        help="KV cache block size (default: 4096)")
     parser.add_argument("--num-kv-buffers", type=int, default=4,
                         help="Number of KV buffers for ring buffer (default: 4)")
     parser.add_argument("--gpu-utilization", type=float, default=0.9,
@@ -550,6 +553,8 @@ if __name__ == "__main__":
                         help="XAttention BSA: block size for estimation")
     parser.add_argument("--sparse-stride", type=int, default=8,
                         help="XAttention BSA: stride for Q/K downsampling")
+    parser.add_argument("--dtype", type=str, default=None,
+                        help="Model dtype (bfloat16, float16). Required for models with float32 default.")
 
     args = parser.parse_args()
 
@@ -587,6 +592,7 @@ if __name__ == "__main__":
         sparse_samples=args.sparse_samples,
         sparse_block_size=args.sparse_block_size,
         sparse_stride=args.sparse_stride,
+        dtype=args.dtype,
     )
 
     # Exit code (skip for json output mode)