add CI test

Signed-off-by: Nick Hill <[email protected]>

Author: njhill

.buildkite/test-pipeline.yaml

@@ -145,6 +145,7 @@ steps:
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
   - tests/v1/test_async_llm_dp.py
+  - tests/v1/test_external_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
   commands:
   # test with tp=2 and external_dp=2
@@ -155,6 +156,7 @@ steps:
   # test with internal dp
   - python3 ../examples/offline_inference/data_parallel.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
   - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
@@ -644,10 +646,12 @@ steps:
   - vllm/worker/model_runner.py
   - entrypoints/llm/test_collective_rpc.py
   - tests/v1/test_async_llm_dp.py
+  - tests/v1/test_external_lb_dp.py
   - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - vllm/v1/engine/
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
   - pytest -v -s ./compile/test_basic_correctness.py

tests/v1/test_external_lb_dp.py

@@ -0,0 +1,311 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import asyncio
+import os
+import threading
+import time
+
+import openai  # use the official client for correctness check
+import pytest
+import pytest_asyncio
+
+from tests.utils import RemoteOpenAIServer
+
+MODEL_NAME = "ibm-research/PowerMoE-3b"
+
+# Number of data parallel ranks for external LB testing
+DP_SIZE = int(os.getenv("DP_SIZE", "2"))
+# Default tensor parallell size to use
+TP_SIZE = int(os.getenv("TP_SIZE", "1"))
+
+
+class ExternalLBServerManager:
+    """Manages data parallel vLLM server instances for external
+    load balancer testing."""
+
+    def __init__(self, model_name: str, dp_size: int, api_server_count: int,
+                 base_server_args: list, tp_size: int = TP_SIZE):
+        self.model_name = model_name
+        self.dp_size = dp_size
+        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 = []
+
+    def __enter__(self) -> list[tuple[RemoteOpenAIServer, list[str]]]:
+        """Start all server instances for external LB mode."""
+        for rank in range(self.dp_size):
+            # Create server args for this specific rank
+            server_args = self.base_server_args.copy()
+
+            # Add external LB specific arguments
+            server_args.extend([
+                "--data-parallel-size",
+                str(self.dp_size),
+                "--data-parallel-rank",
+                str(rank),
+                "--data-parallel-size-local",
+                "1",
+                "--tensor-parallel-size",
+                str(self.tp_size),
+                "--port",
+                str(8000 + rank),  # Different port for each rank
+                "--api-server-count",
+                str(self.api_server_count),
+            ])
+
+            # Use a thread to start each server to allow parallel initialization
+            def start_server(r, sargs):
+                try:
+                    # Start the server
+                    server = RemoteOpenAIServer(
+                        self.model_name, sargs, auto_port=False,
+                        env_dict={"CUDA_VISIBLE_DEVICES": str(r)})
+                    server.__enter__()
+                    print(
+                        f"Server rank {r} started successfully with "
+                        f"{self.api_server_count} API servers"
+                    )
+                    self.servers.append((server, sargs))
+                except Exception as e:
+                    print(f"Failed to start server rank {r}: {e}")
+                    raise
+
+            thread = threading.Thread(target=start_server,
+                                      args=(rank, 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(2)
+
+        if len(self.servers) != self.dp_size:
+            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 ExternalLBServerManager(MODEL_NAME, DP_SIZE, api_server_count,
+                                 default_server_args) as server_list:
+        yield server_list
+
+
+@pytest_asyncio.fixture
+async def clients(servers: list[tuple[RemoteOpenAIServer, list[str]]]):
+    # Create a client for each server
+    async_clients = []
+    for server, _ in servers:
+        client = await server.get_async_client().__aenter__()
+        async_clients.append(client)
+
+    try:
+        yield async_clients
+    finally:
+        # Clean up all clients
+        for client in async_clients:
+            try:
+                await client.__aexit__(None, None, None)
+            except Exception as e:
+                print(f"Error closing client: {e}")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME],
+)
+async def test_external_lb_single_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=10,
+            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 server
+    for i, client in enumerate(clients):
+        result = await make_request(client)
+        assert result is not None
+        print(f"Server {i} handled single completion request successfully")
+
+    await asyncio.sleep(0.5)
+
+    # Send requests to all servers in round-robin fashion
+    num_requests_per_server = 25  # Total 50 requests across 2 servers
+    all_tasks = []
+
+    for i, client in enumerate(clients):
+        tasks = [make_request(client) for _ in range(num_requests_per_server)]
+        all_tasks.extend(tasks)
+
+    results = await asyncio.gather(*all_tasks)
+    assert len(results) == num_requests_per_server * len(clients)
+    assert all(completion is not None for completion in results)
+
+    await asyncio.sleep(0.5)
+
+    # Second burst of requests
+    all_tasks = []
+    for i, client in enumerate(clients):
+        tasks = [make_request(client) for _ in range(num_requests_per_server)]
+        all_tasks.extend(tasks)
+
+    results = await asyncio.gather(*all_tasks)
+    assert len(results) == num_requests_per_server * len(clients)
+    assert all(completion is not None for completion in results)
+
+    _, server_args = servers[0]
+    print(
+        f"Successfully completed external LB test with {len(clients)} servers "
+        f"(API server count: {server_args.count('--api-server-count') 
+            and server_args[server_args.index(
+            '--api-server-count') + 1] or '1'})"
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME],
+)
+async def test_external_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 server
+    for i, client in enumerate(clients):
+        result = await make_streaming_request(client)
+        assert result is not None
+        print(f"Server {i} handled single streaming request successfully")
+
+    await asyncio.sleep(0.5)
+
+    # Send streaming requests to all servers in round-robin fashion
+    num_requests_per_server = 25  # Total 50 requests across 2 servers
+    all_tasks = []
+
+    for i, client in enumerate(clients):
+        tasks = [
+            make_streaming_request(client)
+            for _ in range(num_requests_per_server)
+        ]
+        all_tasks.extend(tasks)
+
+    results = await asyncio.gather(*all_tasks)
+    assert len(results) == num_requests_per_server * len(clients)
+    assert all(results), "Not all streaming requests completed successfully."
+
+    await asyncio.sleep(0.5)
+
+    # Second burst of streaming requests
+    all_tasks = []
+    for i, client in enumerate(clients):
+        tasks = [
+            make_streaming_request(client)
+            for _ in range(num_requests_per_server)
+        ]
+        all_tasks.extend(tasks)
+
+    results = await asyncio.gather(*all_tasks)
+    assert len(results) == num_requests_per_server * len(clients)
+    assert all(results), "Not all streaming requests completed successfully."
+
+    _, server_args = servers[0]
+    print(
+        f"Successfully completed external LB streaming test with "
+        f"(API server count: {server_args.count('--api-server-count')
+                              and server_args[server_args.index(
+            '--api-server-count') + 1] or '1'})"
+    )