Add Ray actor support for scheduler process management (DP=1) (#17684)
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Xinyu Zhang
@@ -0,0 +1,461 @@
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"""Integration tests for RayEngine and Ray HTTP server (requires GPU + Ray).
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Tests the Ray actor scheduler backend:
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- Offline inference via Engine(use_ray=True) inside a Ray actor on a placement group
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- Error paths in RayEngine._launch_scheduler_processes()
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- HTTP server launched via --use-ray flag
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Usage:
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# 1-GPU tests
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python -m pytest test/manual/test_ray_engine.py::TestRayEngineOfflineTP1 -v -s
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python -m pytest test/manual/test_ray_engine.py::TestRayEngineErrors -v -s
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python -m pytest test/manual/test_ray_engine.py::TestRayHTTPServerTP1 -v -s
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# 2-GPU tests
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python -m pytest test/manual/test_ray_engine.py::TestRayEngineOfflineTP2 -v -s
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python -m pytest test/manual/test_ray_engine.py::TestRayEngineOfflinePP2 -v -s
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"""
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from __future__ import annotations
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import os
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import time
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import unittest
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import torch
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from sglang.test.test_utils import DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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# Allow overriding the model via env var for environments without gated access
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_MODEL = os.environ.get("SGLANG_TEST_MODEL", DEFAULT_SMALL_MODEL_NAME_FOR_TEST)
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try:
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import ray
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from ray.runtime_env import RuntimeEnv
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from ray.util.placement_group import placement_group
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from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
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# Prevent Ray from overriding CUDA_VISIBLE_DEVICES so that all GPUs
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# remain visible inside actors regardless of num_gpus allocation.
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_env_vars = {"RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES": "1"}
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if os.environ.get("HF_TOKEN"):
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_env_vars["HF_TOKEN"] = os.environ["HF_TOKEN"]
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_RAY_RUNTIME_ENV = RuntimeEnv(env_vars=_env_vars)
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_has_ray = True
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except ImportError:
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_has_ray = False
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_RAY_RUNTIME_ENV = None
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_NUM_GPUS = torch.cuda.device_count()
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_SAMPLING_PARAMS = {"max_new_tokens": 32, "temperature": 0.0}
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_PROMPTS = [
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"The capital of France is",
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"Explain quantum computing in simple terms:",
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"Write a haiku about programming:",
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"What is 2 + 2?",
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]
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _create_engine_on_pg(tp_size, pp_size=1, model=_MODEL, extra_kwargs=None):
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"""Create an EngineActor on a placement group and wait for it to be ready.
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Returns (engine_actor, placement_group).
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"""
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@ray.remote
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class EngineActor:
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def __init__(self, **kwargs):
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from sglang.srt.ray.engine import RayEngine
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self.engine = RayEngine(**kwargs)
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def is_ready(self):
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return True
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def generate(self, prompt, sampling_params):
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return self.engine.generate(prompt=prompt, sampling_params=sampling_params)
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def shutdown(self):
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if self.engine:
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self.engine.shutdown()
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self.engine = None
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total_gpus = tp_size * pp_size
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pg = placement_group(
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[{"CPU": 1, "GPU": total_gpus}],
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strategy="STRICT_PACK",
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)
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ray.get(pg.ready())
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kwargs = dict(
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model_path=model,
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tp_size=tp_size,
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pp_size=pp_size,
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)
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if extra_kwargs:
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kwargs.update(extra_kwargs)
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actor = EngineActor.options(
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num_cpus=1,
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num_gpus=0,
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scheduling_strategy=PlacementGroupSchedulingStrategy(
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placement_group=pg,
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placement_group_bundle_index=0,
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),
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).remote(**kwargs)
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ray.get(actor.is_ready.remote(), timeout=600)
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return actor, pg
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def _cleanup(actor, pg):
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"""Shutdown engine actor and remove placement group."""
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try:
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ray.get(actor.shutdown.remote(), timeout=60)
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except Exception:
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pass
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try:
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ray.util.remove_placement_group(pg)
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except Exception:
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pass
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# ---------------------------------------------------------------------------
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# Tests: Offline TP=1
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# ---------------------------------------------------------------------------
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@unittest.skipUnless(_has_ray, "ray is not installed")
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@unittest.skipUnless(_NUM_GPUS >= 1, "requires at least 1 GPU")
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class TestRayEngineOfflineTP1(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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if not ray.is_initialized():
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ray.init(log_to_driver=True, runtime_env=_RAY_RUNTIME_ENV)
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cls.actor, cls.pg = _create_engine_on_pg(tp_size=1)
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@classmethod
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def tearDownClass(cls):
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_cleanup(cls.actor, cls.pg)
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ray.shutdown()
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def test_offline_generate(self):
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result = ray.get(
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self.actor.generate.remote("The capital of France is", _SAMPLING_PARAMS)
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)
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self.assertIn("text", result)
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self.assertGreater(len(result["text"]), 0)
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print(f"Generated: {result['text'][:200]}")
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def test_batch_generate(self):
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for prompt in _PROMPTS:
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result = ray.get(self.actor.generate.remote(prompt, _SAMPLING_PARAMS))
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self.assertIn("text", result)
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self.assertGreater(len(result["text"]), 0, f"Empty output for: {prompt}")
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def test_deterministic(self):
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prompt = "The meaning of life is"
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r1 = ray.get(self.actor.generate.remote(prompt, _SAMPLING_PARAMS))
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r2 = ray.get(self.actor.generate.remote(prompt, _SAMPLING_PARAMS))
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self.assertEqual(r1["text"], r2["text"])
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# ---------------------------------------------------------------------------
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# Tests: Offline TP=2
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# ---------------------------------------------------------------------------
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@unittest.skipUnless(_has_ray, "ray is not installed")
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@unittest.skipUnless(_NUM_GPUS >= 2, "requires at least 2 GPUs")
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class TestRayEngineOfflineTP2(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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if not ray.is_initialized():
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ray.init(log_to_driver=True, runtime_env=_RAY_RUNTIME_ENV)
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cls.actor, cls.pg = _create_engine_on_pg(tp_size=2)
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@classmethod
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def tearDownClass(cls):
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_cleanup(cls.actor, cls.pg)
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ray.shutdown()
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def test_offline_generate_tp2(self):
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result = ray.get(
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self.actor.generate.remote("The capital of France is", _SAMPLING_PARAMS)
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)
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self.assertIn("text", result)
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self.assertGreater(len(result["text"]), 0)
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print(f"Generated (TP=2): {result['text'][:200]}")
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def test_batch_generate_tp2(self):
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for prompt in _PROMPTS:
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result = ray.get(self.actor.generate.remote(prompt, _SAMPLING_PARAMS))
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self.assertIn("text", result)
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self.assertGreater(len(result["text"]), 0, f"Empty output for: {prompt}")
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# ---------------------------------------------------------------------------
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# Tests: Offline PP=2
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# ---------------------------------------------------------------------------
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@unittest.skipUnless(_has_ray, "ray is not installed")
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@unittest.skipUnless(_NUM_GPUS >= 2, "requires at least 2 GPUs")
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class TestRayEngineOfflinePP2(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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if not ray.is_initialized():
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ray.init(log_to_driver=True, runtime_env=_RAY_RUNTIME_ENV)
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cls.actor, cls.pg = _create_engine_on_pg(tp_size=1, pp_size=2)
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@classmethod
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def tearDownClass(cls):
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_cleanup(cls.actor, cls.pg)
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ray.shutdown()
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def test_offline_generate_pp2(self):
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result = ray.get(
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self.actor.generate.remote("The capital of France is", _SAMPLING_PARAMS)
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)
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self.assertIn("text", result)
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self.assertGreater(len(result["text"]), 0)
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print(f"Generated (PP=2): {result['text'][:200]}")
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def test_batch_generate_pp2(self):
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for prompt in _PROMPTS:
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result = ray.get(self.actor.generate.remote(prompt, _SAMPLING_PARAMS))
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self.assertIn("text", result)
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self.assertGreater(len(result["text"]), 0, f"Empty output for: {prompt}")
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# ---------------------------------------------------------------------------
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# Tests: Error paths
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# ---------------------------------------------------------------------------
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@unittest.skipUnless(_has_ray, "ray is not installed")
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@unittest.skipUnless(_NUM_GPUS >= 1, "requires at least 1 GPU")
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class TestRayEngineErrors(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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if not ray.is_initialized():
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ray.init(log_to_driver=True, runtime_env=_RAY_RUNTIME_ENV)
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@classmethod
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def tearDownClass(cls):
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ray.shutdown()
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def test_dp_greater_than_1_raises(self):
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"""RayEngine with dp_size > 1 should raise NotImplementedError."""
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@ray.remote
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class _BadActor:
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def try_create(self):
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from sglang.srt.ray.engine import RayEngine
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try:
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RayEngine(
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model_path=_MODEL,
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tp_size=1,
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dp_size=2,
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use_ray=True,
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)
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return None
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except (NotImplementedError, RuntimeError) as e:
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return str(e)
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pg = placement_group([{"CPU": 1, "GPU": 1}], strategy="STRICT_PACK")
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ray.get(pg.ready())
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actor = _BadActor.options(
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num_cpus=1,
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num_gpus=0,
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scheduling_strategy=PlacementGroupSchedulingStrategy(
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placement_group=pg,
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placement_group_bundle_index=0,
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),
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).remote()
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try:
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error_msg = ray.get(actor.try_create.remote(), timeout=120)
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self.assertIsNotNone(error_msg, "Expected error but RayEngine created OK")
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self.assertIn("dp_size", error_msg.lower())
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finally:
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ray.util.remove_placement_group(pg)
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def test_missing_placement_group_raises(self):
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"""RayEngine without a placement group should raise RuntimeError."""
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@ray.remote(num_gpus=1)
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def _try_create_without_pg():
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from sglang.srt.ray.engine import RayEngine
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try:
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RayEngine(
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model_path=_MODEL,
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tp_size=1,
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use_ray=True,
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)
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return None
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except RuntimeError as e:
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return str(e)
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error_msg = ray.get(_try_create_without_pg.remote(), timeout=120)
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self.assertIsNotNone(
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error_msg, "Expected RuntimeError but RayEngine created OK"
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)
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self.assertIn("placement group", error_msg.lower())
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# ---------------------------------------------------------------------------
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# Tests: HTTP server
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# ---------------------------------------------------------------------------
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@unittest.skipUnless(_has_ray, "ray is not installed")
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@unittest.skipUnless(_NUM_GPUS >= 1, "requires at least 1 GPU")
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class TestRayHTTPServerTP1(unittest.TestCase):
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"""Test the Ray HTTP server path (launch_server.py --use-ray).
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Launches the server inside a Ray task on a placement group (mirrors
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examples/anyscale/driver_online.py) and sends HTTP requests to it.
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"""
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@classmethod
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def setUpClass(cls):
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import requests as req_lib
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if not ray.is_initialized():
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ray.init(log_to_driver=True, runtime_env=_RAY_RUNTIME_ENV)
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cls.port = 30100
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cls.pg = placement_group(
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[{"CPU": 1, "GPU": 1}],
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strategy="STRICT_PACK",
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)
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ray.get(cls.pg.ready())
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pg_strategy = PlacementGroupSchedulingStrategy(
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placement_group=cls.pg,
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placement_group_bundle_index=0,
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)
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# Resolve the node IP where the server will run
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@ray.remote(num_cpus=0, num_gpus=0)
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def _get_ip():
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return ray.util.get_node_ip_address()
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cls.node_ip = ray.get(_get_ip.options(scheduling_strategy=pg_strategy).remote())
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cls.base_url = f"http://{cls.node_ip}:{cls.port}"
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# Launch server as a Ray task (blocks until server exits)
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@ray.remote
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def _launch(**kwargs):
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from sglang.srt.ray.http_server import launch_server
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from sglang.srt.server_args import ServerArgs
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launch_server(ServerArgs(**kwargs))
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cls.server_ref = _launch.options(
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num_cpus=1,
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num_gpus=0,
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scheduling_strategy=pg_strategy,
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).remote(
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model_path=_MODEL,
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tp_size=1,
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port=cls.port,
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host="0.0.0.0",
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use_ray=True,
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)
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# Wait for health check
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t0 = time.time()
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timeout = 600
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healthy = False
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while time.time() - t0 < timeout:
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ready, _ = ray.wait([cls.server_ref], timeout=0)
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if ready:
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try:
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ray.get(cls.server_ref)
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except Exception as e:
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raise RuntimeError(f"Server task crashed: {e}") from e
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raise RuntimeError("Server task exited before becoming healthy")
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try:
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if req_lib.get(f"{cls.base_url}/health", timeout=5).status_code == 200:
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healthy = True
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break
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except req_lib.exceptions.RequestException:
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pass
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time.sleep(3)
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if not healthy:
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ray.cancel(cls.server_ref, force=True)
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raise RuntimeError(f"Server did not become healthy within {timeout}s")
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@classmethod
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def tearDownClass(cls):
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try:
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ray.cancel(cls.server_ref, force=True)
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except Exception:
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pass
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try:
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ray.util.remove_placement_group(cls.pg)
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except Exception:
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pass
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ray.shutdown()
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def test_health_endpoint(self):
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import requests
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resp = requests.get(f"{self.base_url}/health", timeout=10)
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self.assertEqual(resp.status_code, 200)
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def test_generate_endpoint(self):
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import requests
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resp = requests.post(
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f"{self.base_url}/generate",
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json={
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"text": "The capital of France is",
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"sampling_params": _SAMPLING_PARAMS,
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},
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timeout=60,
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)
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resp.raise_for_status()
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data = resp.json()
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self.assertIn("text", data)
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self.assertGreater(len(data["text"]), 0)
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print(f"HTTP response: {data['text'][:200]}")
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def test_generate_multiple(self):
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import requests
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for prompt in _PROMPTS:
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resp = requests.post(
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f"{self.base_url}/generate",
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json={
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"text": prompt,
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"sampling_params": _SAMPLING_PARAMS,
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},
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timeout=60,
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)
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resp.raise_for_status()
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data = resp.json()
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self.assertIn("text", data)
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self.assertGreater(len(data["text"]), 0, f"Empty output for: {prompt}")
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if __name__ == "__main__":
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unittest.main()
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