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

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()