# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import asyncio import os import threading import time from contextlib import AsyncExitStack import openai # use the official client for correctness check import pytest import pytest_asyncio from tests.utils import RemoteOpenAIServer from tests.v1.test_utils import check_request_balancing from vllm.platforms import current_platform MODEL_NAME = "ibm-research/PowerMoE-3b" # Number of data parallel ranks for hybrid LB testing (4 total) DP_SIZE = int(os.getenv("DP_SIZE", "4")) # Default tensor parallel size to use TP_SIZE = int(os.getenv("TP_SIZE", "1")) # Number of nodes (2 nodes, each with 2 DP ranks) NUM_NODES = 2 DP_SIZE_LOCAL = DP_SIZE // NUM_NODES # 2 ranks per node class HybridLBServerManager: """Manages hybrid data parallel vLLM server instances where each node runs a single logical API server that balances requests only to the DP engines running on that same node.""" def __init__(self, model_name: str, dp_size: int, api_server_count: int, base_server_args: list, dp_size_local: int = DP_SIZE_LOCAL, tp_size: int = TP_SIZE): self.model_name = model_name self.dp_size = dp_size self.dp_size_local = dp_size_local self.tp_size = tp_size self.api_server_count = api_server_count self.base_server_args = base_server_args self.servers: list[tuple[RemoteOpenAIServer, list[str]]] = [] self.server_threads: list[threading.Thread] = [] self.num_nodes = dp_size // dp_size_local def __enter__(self) -> list[tuple[RemoteOpenAIServer, list[str]]]: """Start all server instances for hybrid LB mode.""" for node_id in range(self.num_nodes): # Create server args for this specific node server_args = self.base_server_args.copy() # Calculate start rank for this node start_rank = node_id * self.dp_size_local # Add hybrid LB specific arguments server_args.extend([ "--data-parallel-size", str(self.dp_size), "--data-parallel-size-local", str(self.dp_size_local), "--data-parallel-start-rank", str(start_rank), "--data-parallel-hybrid-lb", # Enable hybrid LB mode "--tensor-parallel-size", str(self.tp_size), "--port", str(8000 + node_id), # Different port for each node "--api-server-count", str(self.api_server_count), "--data-parallel-address", "127.0.0.1", "--data-parallel-rpc-port", "13345", ]) # Use a thread to start each server to allow parallel initialization def start_server(node: int, sargs: list[str]): try: # Calculate GPU devices for this node gpus_per_node = self.dp_size_local * self.tp_size gpu_start = node * gpus_per_node gpu_end = gpu_start + gpus_per_node # Start the server server = RemoteOpenAIServer( self.model_name, sargs, auto_port=False, env_dict={ current_platform.device_control_env_var: ",".join( str( current_platform. device_id_to_physical_device_id(i)) for i in range(gpu_start, gpu_end)) }) server.__enter__() print(f"Hybrid LB node {node} started successfully with " f"{self.dp_size_local} local DP ranks and " f"{self.api_server_count} API servers") self.servers.append((server, sargs)) except Exception as e: print(f"Failed to start hybrid LB node {node}: {e}") raise thread = threading.Thread(target=start_server, args=(node_id, server_args)) thread.start() self.server_threads.append(thread) # Wait for all servers to start for thread in self.server_threads: thread.join() # Give servers additional time to fully initialize and coordinate time.sleep(3) if len(self.servers) != self.num_nodes: raise Exception("Servers failed to start") return self.servers def __exit__(self, exc_type, exc_val, exc_tb): """Stop all server instances.""" while self.servers: try: self.servers.pop()[0].__exit__(exc_type, exc_val, exc_tb) except Exception as e: print(f"Error stopping server: {e}") @pytest.fixture(scope="module") def default_server_args(): return [ # use half precision for speed and memory savings in CI environment "--dtype", "bfloat16", "--max-model-len", "2048", "--max-num-seqs", "128", "--enforce-eager", ] @pytest.fixture(scope="module", params=[1, 4]) def servers(request, default_server_args): api_server_count = request.param with HybridLBServerManager(MODEL_NAME, DP_SIZE, api_server_count, default_server_args, DP_SIZE_LOCAL, TP_SIZE) as server_list: yield server_list @pytest_asyncio.fixture async def clients(servers: list[tuple[RemoteOpenAIServer, list[str]]]): # Create a client for each node (each node has its own API endpoint) async with AsyncExitStack() as stack: yield [ await stack.enter_async_context(server.get_async_client()) for server, _ in servers ] @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME], ) async def test_hybrid_lb_completion(clients: list[openai.AsyncOpenAI], servers: list[tuple[RemoteOpenAIServer, list[str]]], model_name: str) -> None: async def make_request(client: openai.AsyncOpenAI): completion = await client.completions.create( model=model_name, prompt="Hello, my name is", max_tokens=5, temperature=1.0) assert completion.id is not None assert completion.choices is not None and len(completion.choices) == 1 choice = completion.choices[0] # The exact number of tokens can vary slightly with temperature=1.0, # so we check for a reasonable minimum length. assert len(choice.text) >= 1 # Finish reason might not always be 'length' if the model finishes early # or due to other reasons, especially with high temperature. # So, we'll accept 'length' or 'stop'. assert choice.finish_reason in ("length", "stop") # Token counts can also vary, so we check they are positive. assert completion.usage.completion_tokens > 0 assert completion.usage.prompt_tokens > 0 assert completion.usage.total_tokens > 0 return completion # Test single request to each node for i, client in enumerate(clients): result = await make_request(client) assert result is not None print( f"Hybrid LB node {i} handled single completion request successfully" ) await asyncio.sleep(0.5) # Send requests to all nodes - each should balance within its local DP ranks num_requests = 200 # Total 200 requests across 2 nodes all_tasks = [] for i in range(num_requests): client = clients[i % len(clients)] all_tasks.append(asyncio.create_task(make_request(client))) await asyncio.sleep(0.01) results = await asyncio.gather(*all_tasks) assert len(results) == num_requests assert all(completion is not None for completion in results) await asyncio.sleep(0.5) # Second burst of requests all_tasks = [] for i in range(num_requests): client = clients[i % len(clients)] all_tasks.append(asyncio.create_task(make_request(client))) await asyncio.sleep(0.01) results = await asyncio.gather(*all_tasks) assert len(results) == num_requests assert all(completion is not None for completion in results) _, server_args = servers[0] api_server_count = ( server_args.count('--api-server-count') and server_args[server_args.index('--api-server-count') + 1] or 1) print( f"Successfully completed hybrid LB test with {len(clients)} nodes " f"({DP_SIZE_LOCAL} DP ranks each, API server count: {api_server_count})" ) # Check request balancing within each node for i, (server, _) in enumerate(servers): print(f"Checking request balancing for node {i}") check_request_balancing(server, DP_SIZE_LOCAL) @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", [MODEL_NAME], ) async def test_hybrid_lb_completion_streaming(clients: list[ openai.AsyncOpenAI], servers: list[tuple[RemoteOpenAIServer, list[str]]], model_name: str) -> None: prompt = "What is an LLM?" async def make_streaming_request(client: openai.AsyncOpenAI): # Perform a non-streaming request to get the expected full output single_completion = await client.completions.create( model=model_name, prompt=prompt, max_tokens=5, temperature=0.0, ) single_output = single_completion.choices[0].text # Perform the streaming request stream = await client.completions.create(model=model_name, prompt=prompt, max_tokens=5, temperature=0.0, stream=True) chunks: list[str] = [] finish_reason_count = 0 last_chunk = None async for chunk in stream: chunks.append(chunk.choices[0].text) if chunk.choices[0].finish_reason is not None: finish_reason_count += 1 last_chunk = chunk # Keep track of the last chunk # finish reason should only return in the last block for OpenAI API assert finish_reason_count == 1, ( "Finish reason should appear exactly once.") assert last_chunk is not None, ( "Stream should have yielded at least one chunk.") assert last_chunk.choices[ 0].finish_reason == "length", "Finish reason should be 'length'." # Check that the combined text matches the non-streamed version. assert "".join( chunks ) == single_output, "Streamed output should match non-streamed output." return True # Indicate success for this request # Test single request to each node for i, client in enumerate(clients): result = await make_streaming_request(client) assert result is not None print( f"Hybrid LB node {i} handled single streaming request successfully" ) await asyncio.sleep(0.5) # Send streaming requests to all nodes num_requests = 200 # Total 200 requests across 2 nodes all_tasks = [] for i in range(num_requests): client = clients[i % len(clients)] all_tasks.append(asyncio.create_task(make_streaming_request(client))) await asyncio.sleep(0.01) results = await asyncio.gather(*all_tasks) assert len(results) == num_requests assert all(results), "Not all streaming requests completed successfully." await asyncio.sleep(0.5) # Second burst of streaming requests all_tasks = [] for i in range(num_requests): client = clients[i % len(clients)] all_tasks.append(asyncio.create_task(make_streaming_request(client))) await asyncio.sleep(0.01) results = await asyncio.gather(*all_tasks) assert len(results) == num_requests assert all(results), "Not all streaming requests completed successfully." _, server_args = servers[0] api_server_count = ( server_args.count('--api-server-count') and server_args[server_args.index('--api-server-count') + 1] or 1) print(f"Successfully completed hybrid LB streaming test with " f"{len(clients)} nodes ({DP_SIZE_LOCAL} DP ranks each, " f"API server count: {api_server_count})") # Check request balancing within each node for i, (server, _) in enumerate(servers): print(f"Checking streaming request balancing for node {i}") check_request_balancing(server, DP_SIZE_LOCAL)