[Tests] Add tests for headless internal DP LB (#21450)

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

.buildkite/test-pipeline.yaml

@@ -165,6 +165,7 @@ steps:
   - tests/examples/offline_inference/data_parallel.py
   - tests/v1/test_async_llm_dp.py
   - tests/v1/test_external_lb_dp.py
+  - tests/v1/test_internal_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
   commands:
   # test with tp=2 and external_dp=2
@@ -176,6 +177,7 @@ steps:
   - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
   - 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
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_internal_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

tests/v1/entrypoints/openai/test_multi_api_servers.py

@@ -2,136 +2,19 @@
 # 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
+from tests.v1.test_utils import check_request_balancing
 
 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 [
@@ -217,7 +100,7 @@ async def test_single_completion(client: openai.AsyncOpenAI,
     assert all(completion is not None for completion in results)
 
     # Check request balancing via Prometheus metrics if DP_SIZE > 1
-    check_request_balancing(server)
+    check_request_balancing(server, int(DP_SIZE))
 
 
 @pytest.mark.asyncio
@@ -295,4 +178,4 @@ async def test_completion_streaming(client: openai.AsyncOpenAI,
     assert all(results), "Not all streaming requests completed successfully."
 
     # Check request balancing via Prometheus metrics if DP_SIZE > 1
-    check_request_balancing(server)
+    check_request_balancing(server, int(DP_SIZE))

tests/v1/test_utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import re
+
+import pytest
+import requests
 import torch
 
+from tests.utils import RemoteOpenAIServer
 from vllm.v1.worker.utils import bind_kv_cache
 
 
@@ -61,3 +66,122 @@ def test_bind_kv_cache_non_attention():
 
     assert runner_kv_caches[0] is kv_cache['model.layers.20.attn']
     assert runner_kv_caches[1] is kv_cache['model.layers.28.attn']
+
+
+# Prometheus metrics utilities for testing
+
+
+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, dp_size: int):
+    """Check request balancing via Prometheus metrics if dp_size > 1.
+    
+    Args:
+        server: The RemoteOpenAIServer instance
+        dp_size: Number of data parallel ranks
+    """
+    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}")