chore: vendor sglang v0.5.10 snapshot

third_party/sglang/sgl-model-gateway/e2e_test/router/test_mmlu.py

@@ -0,0 +1,76 @@
+"""MMLU evaluation tests for router functionality.
+
+Tests the router's ability to handle MMLU benchmark evaluations across
+different backend configurations (gRPC and HTTP workers).
+
+Usage:
+    # Run with gRPC backend only
+    pytest e2e_test/router/test_mmlu.py -v
+
+    # Run with specific backend
+    pytest e2e_test/router/test_mmlu.py -v -k "grpc"
+"""
+
+from __future__ import annotations
+
+import logging
+from types import SimpleNamespace
+
+import pytest
+from infra import run_eval
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.e2e
+@pytest.mark.parametrize("setup_backend", ["grpc", "http"], indirect=True)
+class TestMMLU:
+    """MMLU evaluation tests using local workers (gRPC and HTTP)."""
+
+    def test_mmlu_basic(self, setup_backend):
+        """Basic MMLU evaluation with score threshold.
+
+        Runs MMLU evaluation with 64 examples and validates that
+        accuracy meets minimum threshold (>= 0.65).
+
+        Note: setup_backend fixture already waits for workers to be ready.
+        """
+        backend, model, client, *_ = setup_backend
+
+        args = SimpleNamespace(
+            base_url=str(client.base_url),
+            model=model,
+            eval_name="mmlu",
+            num_examples=64,
+            num_threads=32,
+            temperature=0.1,
+        )
+        metrics = run_eval(args)
+
+        assert (
+            metrics["score"] >= 0.65
+        ), f"MMLU score {metrics['score']:.2f} below threshold 0.65"
+        logger.info("MMLU score: %.2f (threshold: 0.65)", metrics["score"])
+
+    def test_mmlu_extended(self, setup_backend):
+        """Extended MMLU evaluation with more examples.
+
+        Runs MMLU with 128 examples for more statistically
+        significant results.
+        """
+        backend, model, client, *_ = setup_backend
+
+        args = SimpleNamespace(
+            base_url=str(client.base_url),
+            model=model,
+            eval_name="mmlu",
+            num_examples=128,
+            num_threads=64,
+            temperature=0.1,
+        )
+        metrics = run_eval(args)
+
+        assert (
+            metrics["score"] >= 0.65
+        ), f"MMLU score {metrics['score']:.2f} below threshold 0.65"
+        logger.info("MMLU extended score: %.2f (threshold: 0.65)", metrics["score"])