Fix silently wrong EPLB output with --moe-a2a-backend none (rank-invariant dispatch) (#32962)

python/sglang/srt/eplb/expert_location.py

@@ -560,8 +560,11 @@ def _compute_logical_to_all_physical_map(
                 physical_expert_id
             )
 
-    # Replace by the physical expert on local GPU or node if possible
+    # Replace by the physical expert on local GPU or node if possible. Skipped
-    if moe_ep_rank is not None:
+    # without an a2a backend, where all EP ranks must agree on the pick: this
+    # collapse is per-rank, and the full candidate list is what lets the dispatch
+    # spread a hot expert over its replicas. See ExpertLocationDispatchInfo.
+    if moe_ep_rank is not None and server_args.moe_a2a_backend != "none":
         num_local_gpu_physical_experts = num_physical_experts // ep_size
         prefer_same_node = _prefer_same_node_experts(server_args)
         num_gpus_per_node = (

python/sglang/srt/eplb/expert_location_dispatch.py

@@ -23,7 +23,7 @@ from sglang.srt.runtime_context import get_server_args
 
 @dataclass
 class ExpertLocationDispatchInfo:
-    ep_dispatch_algorithm: Literal["static", "random"]
+    ep_dispatch_algorithm: Literal["static", "dynamic", "fake", "lp"]
     # (num_logical_experts,)
     partial_logical_to_rank_dispatch_physical_map: Optional[torch.Tensor]
     # (num_logical_experts, X)
@@ -31,10 +31,17 @@ class ExpertLocationDispatchInfo:
     # (num_logical_experts,)
     partial_logical_to_all_physical_map_num_valid: torch.Tensor
     num_physical_experts: int
+    # Whether every rank must pick the same physical expert for a token. True
+    # without an a2a backend: all EP ranks then run the MoE over the same tokens
+    # and sum their partial outputs, so a rank-dependent pick counts a replicated
+    # logical expert several times. With one, each rank dispatches only its own
+    # tokens and is free to disagree.
+    rank_invariant: bool = False
 
     @classmethod
     def init_new(cls, layer_id: int):
-        ep_dispatch_algorithm = get_server_args().ep_dispatch_algorithm
+        server_args = get_server_args()
+        ep_dispatch_algorithm = server_args.ep_dispatch_algorithm
         expert_location_metadata = get_global_expert_location_metadata()
         assert expert_location_metadata is not None
 
@@ -43,6 +50,7 @@ class ExpertLocationDispatchInfo:
 
         return cls(
             ep_dispatch_algorithm=ep_dispatch_algorithm,
+            rank_invariant=server_args.moe_a2a_backend == "none",
             partial_logical_to_rank_dispatch_physical_map=(
                 expert_location_metadata.logical_to_rank_dispatch_physical_map[
                     layer_id, :
@@ -107,14 +115,36 @@ def _topk_ids_logical_to_physical_static(
 def _topk_ids_logical_to_physical_dynamic(
     topk_ids: torch.Tensor, info: Optional[ExpertLocationDispatchInfo]
 ) -> torch.Tensor:
+    """Spread each (token, logical expert) over that logical expert's replicas.
+
+    Under ``rank_invariant`` the replica is picked by token row rather than at
+    random: ``torch.randint`` reads the default CUDA generator, so agreement
+    across ranks would rest on their philox offsets staying aligned, which
+    nothing asserts, and it would make greedy requests non-reproducible. Both
+    pick evenly, which is the load split EPLB's placement solver assumes.
+
+    Row indexing leaves replicas unused for a single-row batch, where there is no
+    imbalance to fix anyway.
+    """
     topk_ids_original_shape = topk_ids.shape
     original_dtype = topk_ids.dtype
     device = topk_ids.device
     topk_ids = topk_ids.flatten()
 
+    num_valid = info.partial_logical_to_all_physical_map_num_valid[topk_ids]
+    if info.rank_invariant:
+        slots_per_token = (
+            topk_ids_original_shape[-1] if len(topk_ids_original_shape) > 1 else 1
+        )
+        row_index = (
+            torch.arange(topk_ids.shape[0], dtype=num_valid.dtype, device=device)
+            // slots_per_token
+        )
+        chosen_dispatch_index = row_index % num_valid
+    else:
         chosen_dispatch_index = (
             torch.randint(0, 65536, topk_ids.shape, dtype=torch.int32, device=device)
-        % info.partial_logical_to_all_physical_map_num_valid[topk_ids]
+            % num_valid
         )
     topk_ids = info.partial_logical_to_all_physical_map[topk_ids, chosen_dispatch_index]
     if topk_ids.dtype != original_dtype:

python/sglang/srt/server_args.py

@@ -6710,10 +6710,29 @@ class ServerArgs:
                 "EPLB is enabled. The expert_distribution_recorder_mode is automatically set."
             )
 
+        # Without an a2a backend all EP ranks run the MoE over the same tokens and
+        # sum their partial outputs, so the pick has to agree across ranks.
+        needs_rank_invariant_dispatch = self._resolved().moe_a2a_backend == "none"
+
         if (self.enable_eplb or (self.init_expert_location != "trivial")) and (
             self.ep_dispatch_algorithm is None
         ):
-            self.ep_dispatch_algorithm = "static"
+            self.ep_dispatch_algorithm = (
+                "dynamic" if needs_rank_invariant_dispatch else "static"
+            )
+
+        # `dynamic` / `fake` switch to the row-index pick; `static` reads a
+        # per-rank table and `lp` samples inside its kernel.
+        if needs_rank_invariant_dispatch and self.ep_dispatch_algorithm in (
+            "static",
+            "lp",
+        ):
+            raise ValueError(
+                f"--ep-dispatch-algorithm {self.ep_dispatch_algorithm} picks a "
+                "different physical replica per rank, which only holds up when an "
+                "a2a backend routes each token to a single rank. Use "
+                "--ep-dispatch-algorithm dynamic with --moe-a2a-backend none."
+            )
 
         if self.enable_eplb and self.ep_join_mode != "scale":
             assert self._resolved().ep_size > 1

test/registered/ep/test_eplb_no_a2a.py

@@ -0,0 +1,110 @@
+"""EPLB with redundant experts on the no-a2a MoE path (--moe-a2a-backend none).
+
+There, all EP ranks run the MoE over the same tokens and sum their partial
+outputs, so the logical->physical pick has to be identical on every rank -- a
+rank-dependent one counts a replicated logical expert several times and silently
+degrades output instead of failing.
+
+`test/manual/ep/test_eplb.py` covers EPLB with an a2a backend.
+"""
+
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_MODEL_NAME_FOR_TEST_MLA,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+    try_cached_model,
+)
+
+register_cuda_ci(est_time=420, suite="nightly-eval-text-2-gpu", nightly=True)
+
+# 72 routed experts + 48 replicas = 120 physical, 60 per rank, so two thirds of
+# the routed (token, expert) pairs get double-counted when ranks disagree. At 24
+# replicas the score only fell to 0.575 against the 0.60 threshold.
+NUM_REDUNDANT_EXPERTS = 48
+
+
+class TestEPLBNoA2A(CustomTestCase):
+    """Initial placement, no rebalance during the eval.
+
+    Guards the candidate-map half of the fix: only the initial placement goes
+    through `_compute_logical_to_all_physical_map`, where the rank-local collapse
+    lives, so a regression there is invisible once rebalancing starts.
+    """
+
+    extra_args = []
+    # Never reached by a 200-question eval. Also sizes the expert-distribution
+    # recorder buffer, so it cannot be made arbitrarily large.
+    rebalance_num_iterations = "20000"
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = try_cached_model(DEFAULT_MODEL_NAME_FOR_TEST_MLA)
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--trust-remote-code",
+                "--tp",
+                "2",
+                "--ep-size",
+                "2",
+                "--enable-eplb",
+                "--ep-num-redundant-experts",
+                str(NUM_REDUNDANT_EXPERTS),
+                "--eplb-rebalance-num-iterations",
+                cls.rebalance_num_iterations,
+                "--mem-fraction-static",
+                "0.5",
+                *cls.extra_args,
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        if hasattr(cls, "process") and cls.process:
+            kill_process_tree(cls.process.pid)
+
+    def test_gsm8k(self):
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="gsm8k",
+            api="completion",
+            max_tokens=512,
+            num_examples=200,
+            num_threads=128,
+        )
+        metrics = run_eval(args)
+        print(metrics)
+
+        # Measured over six runs: 0.625-0.66 correct, 0.415-0.435 with the
+        # rank-dependent pick restored. 0.60 (what the other EP tests use for
+        # this model) sits under a sigma of the low end, so leave room.
+        self.assertGreater(metrics["score"], 0.55)
+
+
+class TestEPLBNoA2ADPAttention(TestEPLBNoA2A):
+    """DP attention -- the MoE runs over the DP-gathered global token buffer --
+    plus ~70 real rebalances, which exercise the post-rebalance placements and
+    the expert-weight migration."""
+
+    extra_args = [
+        "--enable-dp-attention",
+        "--dp",
+        "2",
+    ]
+    rebalance_num_iterations = "50"
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/eplb/test_compute_logical_to_rank_dispatch_physical_map.py

@@ -17,9 +17,18 @@ from sglang.srt.eplb.expert_location import (
 from sglang.test.test_utils import CustomTestCase
 
 
-def _make_server_args(ep_size: int, nnodes: int):
+def _make_server_args(ep_size: int, nnodes: int, moe_a2a_backend: str = "deepep"):
-    """Minimal server_args stub for expert placement tests."""
+    """Minimal server_args stub for expert placement tests.
-    return types.SimpleNamespace(ep_size=ep_size, nnodes=nnodes, ep_join_mode=None)
+
+    `moe_a2a_backend` defaults to an a2a backend because these tests cover the
+    rank-local collapse, which is skipped when there is no a2a backend.
+    """
+    return types.SimpleNamespace(
+        ep_size=ep_size,
+        nnodes=nnodes,
+        ep_join_mode=None,
+        moe_a2a_backend=moe_a2a_backend,
+    )
 
 
 def _make_logical_to_all_physical_map(