# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # Timing notes (measured in an SGLang-enabled container): # - GPU-1 subset (`-m "gpu_1"`): 92.35s total for 2 tests (+ 1 skipped). # These tests load a real model and can be slow/flaky when GPU resources are contended, # so we set explicit pytest timeouts to fail fast on hangs (see per-test markers below). import logging import os from typing import Any, Dict, Optional import pytest from tests.router.e2e_harness import ( ManagedEngineProcessMixin, run_basic_router_test, run_disagg_router_decisions_test, run_indexers_sync_test, run_router_decisions_test, ) from tests.router.helper import generate_random_suffix from tests.utils.constants import DefaultPort from tests.utils.managed_process import ManagedProcess from tests.utils.port_utils import allocate_ports, deallocate_ports logger = logging.getLogger(__name__) MODEL_NAME = "silence09/DeepSeek-R1-Small-2layers" pytestmark = [ pytest.mark.e2e, pytest.mark.router, pytest.mark.sglang, pytest.mark.model(MODEL_NAME), ] PAGE_SIZE = 16 # SGLang uses "page_size" instead of "block_size" # Shared SGLang configuration for all tests # mem_fraction_static limits actual VRAM allocation (required for multi-worker on same GPU) SGLANG_ARGS: Dict[str, Any] = { "page_size": PAGE_SIZE, "model": MODEL_NAME, "mem_fraction_static": 0.4, # Limit VRAM allocation per worker (equivalent to vLLM's gpu_memory_utilization) "context_length": 1024, # Limit context length to reduce KV cache size (equivalent to vLLM's max_model_len) "disable_cuda_graph": True, # Disable CUDA graphs for faster startup & lower memory (equivalent to vLLM's enforce_eager) } class SGLangProcess(ManagedEngineProcessMixin): """Manages SGLang workers using dynamo.sglang (HTTP API + KV events). This is a drop-in replacement for MockerProcess that uses real SGLang workers. The key difference: dynamo.sglang automatically handles: - HTTP API serving - KV cache event publishing (ZMQ → NATS bridge) - Integration with dynamo.frontend router """ def __init__( self, request, sglang_args: Optional[Dict[str, Any]] = None, num_workers: int = 2, single_gpu: bool = False, data_parallel_size: Optional[int] = None, request_plane: str = "tcp", store_backend: str = "etcd", durable_kv_events: bool = False, namespace: Optional[str] = None, gpu_start_index: int = 0, disaggregation_mode: Optional[str] = None, ): """Initialize SGLang workers with dynamo integration. Args: request: pytest request fixture for log directory sglang_args: Configuration dict with keys: - page_size: KV cache page size (default: 16) - model: Model name/path (default: TinyLlama-1.1B) - mem_fraction_static: Fraction of GPU memory to allocate (optional) - context_length: Maximum sequence length (optional) - disable_cuda_graph: Disable CUDA graphs (default: False) num_workers: Number of SGLang worker processes single_gpu: If True, all workers share GPU 0 data_parallel_size: If set, enables data parallelism with this many ranks (num_workers must equal data_parallel_size) request_plane: Request plane to use ("nats", "tcp", or "http"). Defaults to "tcp". store_backend: Storage backend to use ("etcd" or "file"). Defaults to "etcd". durable_kv_events: If True, use JetStream for durable KV events. Defaults to False (NATS Core mode). """ # Generate unique namespace for isolation namespace_suffix = generate_random_suffix() self.namespace = namespace or f"test-namespace-{namespace_suffix}" self.component_name = ( "prefill" if disaggregation_mode == "prefill" else "backend" ) self.endpoint = f"dyn://{self.namespace}.{self.component_name}.generate" self.num_workers = num_workers self.data_parallel_size = data_parallel_size self.worker_processes = [] self.store_backend = store_backend # Dynamically allocate unique system and KV event ports (one per worker) # to avoid conflicts in parallel test runs. self._system_ports = allocate_ports(num_workers, DefaultPort.SYSTEM1.value) self._kv_event_ports = allocate_ports(num_workers, DefaultPort.SYSTEM1.value) request.addfinalizer( lambda: deallocate_ports(self._system_ports + self._kv_event_ports) ) if sglang_args is None: sglang_args = {} page_size = sglang_args.get("page_size", PAGE_SIZE) model = sglang_args.get("model", MODEL_NAME) mem_fraction_static = sglang_args.get("mem_fraction_static") context_length = sglang_args.get("context_length") disable_cuda_graph = sglang_args.get("disable_cuda_graph", False) self.model_name = model for worker_idx in range(num_workers): # Calculate GPU device for this process if single_gpu: # Force all processes to GPU 0 (for single-GPU testing) gpu_device = str(gpu_start_index) elif data_parallel_size is not None: # Worker sees dp_rank GPUs (each DP rank gets its own GPU) worker_start_gpu = gpu_start_index + worker_idx * data_parallel_size gpu_device = ",".join( str(i) for i in range( worker_start_gpu, worker_start_gpu + data_parallel_size ) ) else: # No DP; worker sees one GPU gpu_device = str(gpu_start_index + worker_idx) command = [ "python3", "-m", "dynamo.sglang", "--model-path", model, "--page-size", str(page_size), ] # Disable CUDA graphs for faster startup & lower memory if disable_cuda_graph: command.append("--disable-cuda-graph") # Limit VRAM allocation (required for multi-worker on same GPU) if mem_fraction_static is not None: command.extend(["--mem-fraction-static", str(mem_fraction_static)]) # Add optional context_length if specified if context_length is not None: command.extend(["--context-length", str(context_length)]) if disaggregation_mode is not None: command.extend(["--disaggregation-mode", disaggregation_mode]) command.extend(["--disaggregation-transfer-backend", "nixl"]) if data_parallel_size is not None: # Add DP configuration command.extend( [ "--dp-size", str(data_parallel_size), "--tp-size", str(data_parallel_size), "--enable-dp-attention", ] ) # Add per-worker KV events config for ZMQ publishing # Ports are dynamically allocated for xdist-safe parallel execution. kv_events_port = self._kv_event_ports[worker_idx] kv_events_config = f'{{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:{kv_events_port}"}}' command.extend(["--kv-events-config", kv_events_config]) # Use --durable-kv-events to enable JetStream mode (local indexer disabled) if durable_kv_events: command.append("--durable-kv-events") # Each SGLang worker needs a unique DYN_SYSTEM_PORT to avoid conflicts. # Ports are dynamically allocated for xdist-safe parallel execution. system_port = self._system_ports[worker_idx] env = os.environ.copy() # Copy parent environment env_vars = { "CUDA_VISIBLE_DEVICES": gpu_device, "DYN_NAMESPACE": self.namespace, "DYN_REQUEST_PLANE": request_plane, "DYN_SYSTEM_PORT": str(system_port), "PYTHONHASHSEED": "0", # for deterministic event id's } # Add DYN_FILE_KV if using file storage backend if self.store_backend == "file" and "DYN_FILE_KV" in os.environ: env_vars["DYN_FILE_KV"] = os.environ["DYN_FILE_KV"] env.update(env_vars) # Create managed process for the worker process = ManagedProcess( command=command, env=env, timeout=120, # Allow time for model loading display_output=True, health_check_ports=[], health_check_urls=[], log_dir=request.node.name, terminate_all_matching_process_names=False, ) self.worker_processes.append(process) if data_parallel_size is not None: logger.info( f"Created {data_parallel_size} DP ranks per worker on GPU(s) {gpu_device} " f"(mem_frac={mem_fraction_static}, system_port={system_port}, kv_port={kv_events_port}) " f"with endpoint: {self.endpoint}" ) else: logger.info( f"Created SGLang worker {worker_idx} on GPU {gpu_device} " f"(mem_frac={mem_fraction_static}, system_port={system_port}, kv_port={kv_events_port}) " f"with endpoint: {self.endpoint}" ) process_name = "SGLang worker" cleanup_name = "SGLang worker resources" @pytest.mark.pre_merge @pytest.mark.gpu_1 @pytest.mark.parametrize("request_plane", ["tcp"], indirect=True) @pytest.mark.timeout(150) # ~3x average (~46s/test), rounded up def test_sglang_kv_router_basic( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): run_basic_router_test( engine_process_cls=SGLangProcess, engine_args_name="sglang_args", engine_args=SGLANG_ARGS, num_workers=2, single_gpu=True, request=request, request_plane=request_plane, block_size=PAGE_SIZE, model_name=MODEL_NAME, ) @pytest.mark.pre_merge @pytest.mark.gpu_1 @pytest.mark.parametrize("request_plane", ["tcp"], indirect=True) def test_router_decisions_sglang_multiple_workers( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): run_router_decisions_test( engine_process_cls=SGLangProcess, engine_args_name="sglang_args", engine_args=SGLANG_ARGS, request=request, request_plane=request_plane, model_name=MODEL_NAME, block_size=PAGE_SIZE, component_name="backend", num_workers=2, single_gpu=True, test_dp_rank=False, ) @pytest.mark.gpu_2 @pytest.mark.pre_merge @pytest.mark.parametrize("request_plane", ["tcp"], indirect=True) @pytest.mark.timeout(600) # 10 min max (multi-GPU + DP startup variance) @pytest.mark.skip( reason="DYN-2265" ) # Currently fails probably due to SGLang startup issues when multiple workers on same GPU; re-enable when fixed def test_router_decisions_sglang_dp( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): """Validate KV cache prefix reuse with SGLang by sending progressive requests with overlapping prefixes. Same flow as test_router_decisions_sglang_multiple_workers; force first request to (worker_id, dp_rank=1). Dump events from router and verify: * All but one (worker_id, dp_rank) should have no events (due to prefix reuse) * The (worker_id, dp_rank) with events should have exactly 4 events (one per request) * All events should be on the forced (worker_id, dp_rank=1) (verifying forced routing and prefix reuse) """ run_router_decisions_test( engine_process_cls=SGLangProcess, engine_args_name="sglang_args", engine_args=SGLANG_ARGS, request=request, request_plane=request_plane, model_name=MODEL_NAME, block_size=PAGE_SIZE, component_name="backend", num_workers=1, single_gpu=False, test_dp_rank=True, extra_process_kwargs={"data_parallel_size": 2}, ) @pytest.mark.skip(reason="Nightly CI failure: https://linear.app/nvidia/issue/DYN-2603") @pytest.mark.gpu_2 @pytest.mark.nightly @pytest.mark.parametrize("request_plane", ["nats"], indirect=True) @pytest.mark.timeout(600) def test_router_decisions_sglang_disagg( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): run_disagg_router_decisions_test( engine_process_cls=SGLangProcess, engine_args_name="sglang_args", engine_args=SGLANG_ARGS, request=request, request_plane=request_plane, model_name=MODEL_NAME, block_size=PAGE_SIZE, num_prefill_workers=2, num_decode_workers=1, prefill_process_kwargs={ "single_gpu": True, "gpu_start_index": 0, "disaggregation_mode": "prefill", }, decode_process_kwargs={ "single_gpu": True, "gpu_start_index": 1, "disaggregation_mode": "decode", }, ) @pytest.mark.pre_merge @pytest.mark.gpu_1 @pytest.mark.parametrize( "store_backend,durable_kv_events,request_plane", [ ("etcd", False, "tcp"), ], ids=["nats_core"], indirect=["durable_kv_events", "request_plane"], ) @pytest.mark.timeout(150) # ~3x average (~46s/test), rounded up def test_sglang_indexers_sync( request, runtime_services_dynamic_ports, predownload_models, file_storage_backend, set_ucx_tls_no_mm, store_backend, durable_kv_events, request_plane, ): run_indexers_sync_test( engine_process_cls=SGLangProcess, engine_args_name="sglang_args", engine_args=SGLANG_ARGS, request=request, runtime_services_dynamic_ports=runtime_services_dynamic_ports, store_backend=store_backend, durable_kv_events=durable_kv_events, request_plane=request_plane, block_size=PAGE_SIZE, model_name=MODEL_NAME, num_workers=2, )