# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # Timing notes (measured locally): # - GPU-1 subset (`-m "gpu_1 and not gpu_2"`): 130.43s total for 3 tests. # 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 json import logging import os from typing import Any, Dict, Optional import pytest from tests.router.e2e_harness import ( ManagedEngineProcessMixin, run_basic_router_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 = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" pytestmark = [ pytest.mark.e2e, pytest.mark.router, pytest.mark.vllm, pytest.mark.model(MODEL_NAME), ] SPEEDUP_RATIO = 10.0 BLOCK_SIZE = 16 # Shared vLLM configuration for all tests # gpu_memory_utilization limits actual VRAM allocation (required for multi-worker on same GPU) VLLM_ARGS: Dict[str, Any] = { "block_size": BLOCK_SIZE, "model": MODEL_NAME, "gpu_memory_utilization": 0.4, # Limit VRAM allocation per worker "max_model_len": 1024, # Limit context length to reduce KV cache size "enforce_eager": True, # Disable CUDA graphs for faster startup & lower memory } class VLLMProcess(ManagedEngineProcessMixin): """Manages vLLM workers using dynamo.vllm (HTTP API + KV events). This is a drop-in replacement for MockerProcess that uses real vLLM workers. The key difference: dynamo.vllm automatically handles: - HTTP API serving - KV cache event publishing (ZMQ → NATS bridge) - Integration with dynamo.frontend router """ def __init__( self, request, vllm_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, ): """Initialize vLLM workers with dynamo integration. Args: request: pytest request fixture for log directory vllm_args: Configuration dict with keys: - block_size: KV cache block size (default: 16) - model: Model name/path (default: TinyLlama-1.1B) - gpu_memory_utilization: Fraction of GPU memory to allocate (optional) - num_gpu_blocks_override: Cap on number of KV cache blocks (optional) - max_model_len: Maximum sequence length (optional) - enforce_eager: Disable CUDA graphs (default: False) num_workers: Number of vLLM 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 = f"test-namespace-{namespace_suffix}" self.component_name = "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, KV event, and NIXL side-channel # ports (one of each 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) self._nixl_ports = allocate_ports(num_workers, DefaultPort.SYSTEM1.value) request.addfinalizer( lambda: deallocate_ports( self._system_ports + self._kv_event_ports + self._nixl_ports ) ) if vllm_args is None: vllm_args = {} block_size = vllm_args.get("block_size", BLOCK_SIZE) model = vllm_args.get("model", MODEL_NAME) gpu_memory_utilization = vllm_args.get("gpu_memory_utilization") num_gpu_blocks_override = vllm_args.get("num_gpu_blocks_override") max_model_len = vllm_args.get("max_model_len") enforce_eager = vllm_args.get("enforce_eager", False) self.model_name = model # Create vLLM worker processes # Matches test.sh behavior: # - When data_parallel_size is set, launch one process per DP rank # - Each process gets --data-parallel-rank and --data-parallel-size # - Each process runs on its own GPU via CUDA_VISIBLE_DEVICES # - --kv-transfer-config enables KV cache transfer between ranks 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 = "0" elif data_parallel_size is not None: # Worker sees dp_rank GPUs (each DP rank gets its own GPU) worker_start_gpu = 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(worker_idx) command = [ "python3", "-m", "dynamo.vllm", "--model", model, "--block-size", str(block_size), ] # Disable CUDA graphs for faster startup & lower memory if enforce_eager: command.append("--enforce-eager") # Limit VRAM allocation (required for multi-worker on same GPU) if gpu_memory_utilization is not None: command.extend( ["--gpu-memory-utilization", str(gpu_memory_utilization)] ) # Add optional max_model_len if specified if max_model_len is not None: command.extend(["--max-model-len", str(max_model_len)]) # Cap block count for predictable KV cache behavior if num_gpu_blocks_override is not None: command.extend( ["--num-gpu-blocks-override", str(num_gpu_blocks_override)] ) if data_parallel_size is not None: # Add DP configuration for external load balancing # See: https://docs.vllm.ai/en/v0.10.0/serving/data_parallel_deployment.html#external-load-balancing command.extend( [ "--data-parallel-size", str(data_parallel_size), # "--data-parallel-address", "127.0.0.1", # Required for DP coordination # "--data-parallel-rpc-port", "13345", # RPC port for DP coordination # "--kv-transfer-config", '{"kv_connector":"NixlConnector","kv_role":"kv_both"}', # Required for KV transfer between DP ranks ] ) # Use --durable-kv-events to enable JetStream mode (local indexer disabled) if durable_kv_events: command.append("--durable-kv-events") # Ports are dynamically allocated for xdist-safe parallel execution. system_port = self._system_ports[worker_idx] kv_event_port = self._kv_event_ports[worker_idx] nixl_port = self._nixl_ports[worker_idx] # Pass KV events config explicitly via CLI kv_events_cfg = json.dumps( { "publisher": "zmq", "topic": "kv-events", "endpoint": f"tcp://*:{kv_event_port}", "enable_kv_cache_events": True, } ) command.extend(["--kv-events-config", kv_events_cfg]) 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), "VLLM_NIXL_SIDE_CHANNEL_PORT": str(nixl_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"(gpu_mem={gpu_memory_utilization}, system_port={system_port}) " f"with endpoint: {self.endpoint}" ) else: logger.info( f"Created vLLM worker {worker_idx} on GPU {gpu_device} " f"(gpu_mem={gpu_memory_utilization}, system_port={system_port}) " f"with endpoint: {self.endpoint}" ) process_name = "vLLM worker" cleanup_name = "vLLM worker resources" init_delay_reason = "initialize NIXL before starting next worker" @pytest.mark.pre_merge @pytest.mark.gpu_1 @pytest.mark.timeout(150) # ~3x average (~43s/test), rounded up @pytest.mark.parametrize("request_plane", ["tcp"], indirect=True) def test_vllm_kv_router_basic( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): run_basic_router_test( engine_process_cls=VLLMProcess, engine_args_name="vllm_args", engine_args=VLLM_ARGS, num_workers=2, single_gpu=True, request=request, request_plane=request_plane, block_size=BLOCK_SIZE, model_name=MODEL_NAME, ) @pytest.mark.pre_merge @pytest.mark.gpu_1 @pytest.mark.timeout(150) # ~3x average (~43s/test), rounded up @pytest.mark.parametrize("request_plane", ["tcp"], indirect=True) def test_router_decisions_vllm_multiple_workers( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): run_router_decisions_test( engine_process_cls=VLLMProcess, engine_args_name="vllm_args", engine_args=VLLM_ARGS, request=request, request_plane=request_plane, model_name=MODEL_NAME, block_size=BLOCK_SIZE, component_name="backend", num_workers=2, single_gpu=True, test_dp_rank=False, ) @pytest.mark.gpu_2 @pytest.mark.nightly @pytest.mark.parametrize("request_plane", ["tcp"], indirect=True) @pytest.mark.timeout(600) # 10 min max (multi-GPU + DP startup variance) def test_router_decisions_vllm_dp( request, runtime_services_dynamic_ports, predownload_models, set_ucx_tls_no_mm, request_plane, ): """Validate KV cache prefix reuse with vLLM by sending progressive requests with overlapping prefixes. Same flow as test_router_decisions_vllm_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=VLLMProcess, engine_args_name="vllm_args", engine_args=VLLM_ARGS, request=request, request_plane=request_plane, model_name=MODEL_NAME, block_size=BLOCK_SIZE, component_name="backend", num_workers=1, single_gpu=False, test_dp_rank=True, extra_process_kwargs={"data_parallel_size": 2}, ) @pytest.mark.pre_merge @pytest.mark.gpu_1 @pytest.mark.timeout(150) # ~3x average (~43s/test), rounded up @pytest.mark.parametrize( "store_backend,durable_kv_events,request_plane", [ ("etcd", False, "tcp"), ], ids=["nats_core"], indirect=["durable_kv_events", "request_plane"], ) def test_vllm_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=VLLMProcess, engine_args_name="vllm_args", engine_args=VLLM_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=BLOCK_SIZE, model_name=MODEL_NAME, num_workers=2, )