chore: vendor sglang v0.5.10 snapshot

third_party/sglang/sgl-model-gateway/e2e_test/infra/run_eval.py

@@ -0,0 +1,138 @@
+"""MMLU evaluation runner for E2E tests.
+
+Simplified evaluation runner that uses local eval implementations
+with cleaner logging for CI/CD environments.
+
+Usage:
+    from infra.run_eval import run_eval
+    from types import SimpleNamespace
+
+    args = SimpleNamespace(
+        base_url="http://127.0.0.1:30000",
+        model="meta-llama/Llama-3.1-8B-Instruct",
+        eval_name="mmlu",
+        num_examples=64,
+        num_threads=32,
+        temperature=0.1,
+    )
+    metrics = run_eval(args)
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from .simple_eval_common import Eval
+
+from .simple_eval_common import ChatCompletionSampler, set_ulimit
+from .simple_eval_mmlu import MMLU_DATASET_URL
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EvalConfig:
+    """Configuration for running an evaluation."""
+
+    base_url: str
+    model: str | None = None
+    eval_name: str = "mmlu"
+    num_examples: int = 64
+    num_threads: int = 32
+    temperature: float = 0.0
+    max_tokens: int = 2048
+    host: str = "127.0.0.1"
+    port: int = 30000
+
+
+def _get_eval(eval_name: str, num_examples: int, num_threads: int) -> "Eval":
+    """Get the evaluation object by name."""
+    if eval_name == "mmlu":
+        from .simple_eval_mmlu import MMLUEval
+
+        return MMLUEval(MMLU_DATASET_URL, num_examples, num_threads)
+    else:
+        raise ValueError(f"Unknown eval: {eval_name}. Supported: mmlu")
+
+
+def run_eval(args: Any) -> dict:
+    """Run an evaluation and return metrics.
+
+    Args:
+        args: Configuration object with attributes:
+            - base_url: Base URL of the server (e.g., "http://127.0.0.1:30000")
+            - model: Model name/path (optional, will be auto-detected)
+            - eval_name: Evaluation name ("mmlu")
+            - num_examples: Number of examples to evaluate
+            - num_threads: Number of parallel threads
+            - temperature: Sampling temperature
+
+    Returns:
+        Dict with metrics including 'score' key.
+    """
+    set_ulimit()
+
+    if "OPENAI_API_KEY" not in os.environ:
+        os.environ["OPENAI_API_KEY"] = "EMPTY"
+
+    # Build base URL
+    base_url = getattr(args, "base_url", None)
+    if base_url:
+        base_url = base_url.rstrip("/")  # Remove trailing slashes
+        if not base_url.endswith("/v1"):
+            base_url = f"{base_url}/v1"
+    else:
+        host = getattr(args, "host", "127.0.0.1")
+        port = getattr(args, "port", 30000)
+        base_url = f"http://{host}:{port}/v1"
+
+    eval_name = getattr(args, "eval_name", "mmlu")
+    num_examples = getattr(args, "num_examples", 64)
+    num_threads = getattr(args, "num_threads", 32)
+    temperature = getattr(args, "temperature", 0.0)
+    max_tokens = getattr(args, "max_tokens", 2048)
+    model = getattr(args, "model", None)
+
+    logger.info(
+        "Starting %s eval: %d examples, %d threads, temp=%.2f",
+        eval_name,
+        num_examples,
+        num_threads,
+        temperature,
+    )
+
+    # Create sampler
+    sampler = ChatCompletionSampler(
+        model=model,
+        max_tokens=max_tokens,
+        base_url=base_url,
+        temperature=temperature,
+    )
+
+    # Get eval object
+    eval_obj = _get_eval(eval_name, num_examples, num_threads)
+
+    # Run evaluation
+    start_time = time.perf_counter()
+    result = eval_obj(sampler)
+    latency = time.perf_counter() - start_time
+
+    # Build metrics
+    metrics = result.metrics.copy() if result.metrics else {}
+    metrics["score"] = result.score
+    metrics["latency"] = latency
+
+    logger.info(
+        "%s eval complete: score=%.3f, latency=%.1fs, model=%s",
+        eval_name,
+        result.score,
+        latency,
+        sampler.model,
+    )
+
+    return metrics