Add random-ids dataset, round-robin expert simulation, and kill_process_tree logging (#26862)

Co-authored-by: Jaewon <52840625+jaewonlee-fb@users.noreply.github.com>

python/sglang/srt/environ.py

@@ -244,6 +244,7 @@ class Envs:
     SGLANG_SIMULATE_ACC_LEN = EnvFloat(-1)
     SGLANG_SIMULATE_ACC_METHOD = EnvStr("match-expected")
     SGLANG_SIMULATE_UNIFORM_EXPERTS = EnvBool(False)
+    SGLANG_SIMULATE_ROUND_ROBIN_EXPERTS = EnvBool(False)
     SGLANG_TORCH_PROFILER_DIR = EnvStr("/tmp")
     SGLANG_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS = EnvInt(500)
     SGLANG_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE = EnvInt(64)

python/sglang/srt/layers/moe/topk.py

@@ -299,6 +299,25 @@ class BypassedTopKOutput(NamedTuple):
         )
 
 
+def _make_round_robin_expert_ids(
+    num_tokens: int,
+    topk: int,
+    num_experts: int,
+    *,
+    device: torch.device,
+    dtype: torch.dtype,
+    layer_id: Optional[int] = None,
+) -> torch.Tensor:
+    if topk == 0:
+        return torch.empty((num_tokens, 0), device=device, dtype=dtype)
+
+    step = max(num_experts // topk, 1)
+    layer_offset = 0 if layer_id is None else layer_id
+    offsets = torch.arange(num_tokens, device=device, dtype=dtype).unsqueeze(1)
+    steps = torch.arange(topk, device=device, dtype=dtype).unsqueeze(0) * step
+    return (offsets + layer_offset + steps) % num_experts
+
+
 # -------------------------------- TopK ---------------------------------------
 
 
@@ -1533,17 +1552,42 @@ def select_experts(
             renormalize=renormalize,
         )
 
-    if envs.SGLANG_SIMULATE_UNIFORM_EXPERTS.get():
+    simulate_uniform_experts = envs.SGLANG_SIMULATE_UNIFORM_EXPERTS.get()
-        # Benchmark-only: override gating with uniform round-robin expert assignment
+    simulate_round_robin_experts = envs.SGLANG_SIMULATE_ROUND_ROBIN_EXPERTS.get()
+    if simulate_uniform_experts and simulate_round_robin_experts:
+        raise ValueError(
+            "SGLANG_SIMULATE_UNIFORM_EXPERTS and "
+            "SGLANG_SIMULATE_ROUND_ROBIN_EXPERTS are mutually exclusive"
+        )
+
+    if simulate_uniform_experts:
+        # Benchmark-only: override gating with random-offset uniform expert assignment
         # to avoid expert imbalance from dummy/random weights. Do NOT use in production.
         num_tokens, k = topk_ids.shape
         num_experts = router_logits.shape[1]
-        offsets = torch.randint(0, num_experts, (num_tokens, 1), device=topk_ids.device)
+        if k > 0:
-        steps = torch.arange(k, device=topk_ids.device).unsqueeze(0)
+            offsets = torch.randint(
-        topk_ids = ((offsets + steps * (num_experts // k)) % num_experts).to(
+                0, num_experts, (num_tokens, 1), device=topk_ids.device
-            topk_ids.dtype
+            )
+            steps = torch.arange(k, device=topk_ids.device).unsqueeze(0)
+            step = max(num_experts // k, 1)
+            topk_ids = ((offsets + steps * step) % num_experts).to(topk_ids.dtype)
+            topk_weights = torch.ones_like(topk_weights) / k
+    elif simulate_round_robin_experts:
+        # Benchmark-only: override gating with deterministic expert assignment
+        # to avoid routing noise from dummy/random weights. Do NOT use in production.
+        num_tokens, k = topk_ids.shape
+        num_experts = router_logits.shape[1]
+        topk_ids = _make_round_robin_expert_ids(
+            num_tokens,
+            k,
+            num_experts,
+            device=topk_ids.device,
+            dtype=topk_ids.dtype,
+            layer_id=layer_id,
         )
-        topk_weights = torch.ones_like(topk_weights) / k
+        if k > 0:
+            topk_weights = torch.full_like(topk_weights, 1.0 / k)
 
     topk_ids, topk_weights = _post_process_topk_ids(
         topk_ids=topk_ids,

python/sglang/srt/utils/common.py

@@ -1208,6 +1208,11 @@ def kill_process_tree(
     `parent_pid == os.getpid()` branch calls `sys.exit(0)` and cannot wait
     for itself -- use `include_parent=False` if child reap must finish first.
     """
+    logger.info(
+        f"kill_process_tree called: parent_pid={parent_pid}, "
+        f"include_parent={include_parent}, pid={os.getpid()}"
+    )
+
     if parent_pid is None:
         parent_pid = os.getpid()
         include_parent = False

python/sglang/test/bench_one_batch_server_internal.py

@@ -190,7 +190,7 @@ class BenchArgs:
             "--dataset-name",
             type=str,
             default=BenchArgs.dataset_name,
-            choices=["mmmu", "random", "generated-shared-prefix"],
+            choices=["mmmu", "random", "random-ids", "generated-shared-prefix"],
             help="Name of the dataset to benchmark on.",
         )
         parser.add_argument(
@@ -517,7 +517,7 @@ def run_one_case(
         _flush_cache_with_retry(url, "/flush_cache")
 
     # Load input token ids via bench_serving.get_dataset
-    supported_datasets = ("random", "mmmu", "generated-shared-prefix")
+    supported_datasets = ("random", "random-ids", "mmmu", "generated-shared-prefix")
     if dataset_name not in supported_datasets:
         raise ValueError(
             f"Unsupported dataset for batch benchmark: {dataset_name}. "