[Tests] Update online DP tests to verify that requests are balanced (#20157)

Signed-off-by: Nick Hill <nhill@redhat.com>

tests/v1/entrypoints/openai/test_multi_api_servers.py

@@ -2,10 +2,12 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
 import os
+import re
 
 import openai  # use the official client for correctness check
 import pytest
 import pytest_asyncio
+import requests
 
 from tests.utils import RemoteOpenAIServer
 
@@ -14,6 +16,122 @@ MODEL_NAME = "ibm-research/PowerMoE-3b"
 DP_SIZE = os.getenv("DP_SIZE", "1")
 
 
+def get_prometheus_metrics(
+        server: RemoteOpenAIServer) -> dict[str, dict[str, float]]:
+    """Fetch and parse Prometheus metrics from the /metrics endpoint.
+    
+    Returns:
+        Dict mapping metric names to their values grouped by labels.
+        For example: {"vllm:request_success": {
+            "engine=0": 5.0, "engine=1": 3.0}
+        }
+    """
+    try:
+        response = requests.get(server.url_for("metrics"), timeout=10)
+        response.raise_for_status()
+
+        metrics: dict[str, dict[str, float]] = {}
+
+        # Regex patterns for Prometheus metrics
+        metric_with_labels = re.compile(
+            r'^([a-zA-Z_:][a-zA-Z0-9_:]*)\{([^}]*)\}\s+([\d\.\-\+e]+)$')
+        metric_simple = re.compile(
+            r'^([a-zA-Z_:][a-zA-Z0-9_:]*)\s+([\d\.\-\+e]+)$')
+
+        for line in response.text.split('\n'):
+            line = line.strip()
+            # Skip comments and empty lines
+            if not line or line.startswith('#'):
+                continue
+
+            # Try to match metric with labels first
+            match = metric_with_labels.match(line)
+            if match:
+                metric_name, labels_part, value_str = match.groups()
+                try:
+                    value = float(value_str)
+                    if metric_name not in metrics:
+                        metrics[metric_name] = {}
+                    metrics[metric_name][f'{{{labels_part}}}'] = value
+                except ValueError:
+                    continue
+            else:
+                # Try simple metric without labels
+                match = metric_simple.match(line)
+                if match:
+                    metric_name, value_str = match.groups()
+                    try:
+                        value = float(value_str)
+                        if metric_name not in metrics:
+                            metrics[metric_name] = {}
+                        metrics[metric_name][''] = value
+                    except ValueError:
+                        continue
+
+        return metrics
+    except Exception as e:
+        pytest.fail(f"Failed to fetch Prometheus metrics: {e}")
+        return {}
+
+
+def get_engine_request_counts(
+        metrics: dict[str, dict[str, float]]) -> dict[str, float]:
+    """Extract request counts per engine from Prometheus metrics.
+    
+    Returns:
+        Dict mapping engine indices to request counts.
+        For example: {"0": 15.0, "1": 12.0}
+    """
+    engine_counts = {}
+
+    # Look for request success metrics with engine labels
+    success_metrics = metrics.get("vllm:request_success_total", {})
+    engine_pattern = re.compile(r'engine="([^"]*)"')
+
+    for labels, count in success_metrics.items():
+        # Extract engine ID from labels using regex
+        match = engine_pattern.search(labels)
+        if match:
+            engine_id = match.group(1)
+            if engine_id not in engine_counts:
+                engine_counts[engine_id] = 0.0
+            engine_counts[engine_id] += count
+
+    return engine_counts
+
+
+def check_request_balancing(server: RemoteOpenAIServer):
+    """Check request balancing via Prometheus metrics if DP_SIZE > 1.
+    
+    Args:
+        server: The RemoteOpenAIServer instance
+    """
+    dp_size = int(DP_SIZE)
+    if dp_size <= 1:
+        return
+
+    # Get metrics after all requests are completed
+    metrics = get_prometheus_metrics(server)
+    engine_counts = get_engine_request_counts(metrics)
+
+    # Check that multiple engines received requests
+    engines_with_requests = [
+        engine for engine, count in engine_counts.items() if count > 0
+    ]
+    assert len(engines_with_requests) == dp_size, (
+        f"Expected requests to be distributed across multiple engines,"
+        f" but only engine(s) {engines_with_requests} received "
+        f"requests. Engine counts: {engine_counts}")
+
+    # Verify that the load is reasonably balanced
+    # (no engine should handle all requests)
+    total_requests = sum(engine_counts.values())
+
+    for count in engine_counts.values():
+        assert count > total_requests // (dp_size + 1), (
+            f"requests are imbalanced: {engine_counts}")
+
+
 @pytest.fixture(scope="module")
 def default_server_args():
     return [
@@ -50,6 +168,7 @@ async def client(server):
     [MODEL_NAME],
 )
 async def test_single_completion(client: openai.AsyncOpenAI,
+                                 server: RemoteOpenAIServer,
                                  model_name: str) -> None:
 
     async def make_request():
@@ -97,6 +216,9 @@ async def test_single_completion(client: openai.AsyncOpenAI,
     assert len(results) == num_requests
     assert all(completion is not None for completion in results)
 
+    # Check request balancing via Prometheus metrics if DP_SIZE > 1
+    check_request_balancing(server)
+
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
@@ -104,6 +226,7 @@ async def test_single_completion(client: openai.AsyncOpenAI,
     [MODEL_NAME],
 )
 async def test_completion_streaming(client: openai.AsyncOpenAI,
+                                    server: RemoteOpenAIServer,
                                     model_name: str) -> None:
     prompt = "What is an LLM?"
 
@@ -170,3 +293,6 @@ async def test_completion_streaming(client: openai.AsyncOpenAI,
         results
     ) == num_requests, f"Expected {num_requests} results, got {len(results)}"
     assert all(results), "Not all streaming requests completed successfully."
+
+    # Check request balancing via Prometheus metrics if DP_SIZE > 1
+    check_request_balancing(server)