[serve][llm] Data Parallel Attention: Public API and Documentation

.buildkite/llm.rayci.yml

@@ -46,4 +46,18 @@ steps:
     commands:
       - RAYCI_DISABLE_TEST_DB=1 bazel run //ci/ray_ci:test_in_docker -- //python/ray/llm/... //doc/... llm
         --python-version 3.11 --build-name llmgpubuild --only-tags gpu
+        --except-tags multi_gpu_4
+    depends_on: llmgpubuild
+
+  - label: "llm gpu tests (4 GPUs)"
+    key: "llm-gpu-tests-4gpu"
+    tags:
+      - llm
+      - gpu
+    instance_type: gpu-large
+    commands:
+      - RAYCI_DISABLE_TEST_DB=1 bazel run //ci/ray_ci:test_in_docker -- //doc/... llm
+        --python-version 3.11 --build-name llmgpubuild
+        --only-tags multi_gpu_4
+        --gpus 4
     depends_on: llmgpubuild

doc/BUILD.bazel

@@ -342,11 +342,11 @@ filegroup(
     visibility = ["//doc:__subpackages__"],
 )
 
-# GPU Tests
+# GPU Tests (standard GPU tests)
 py_test_run_all_subdirectory(
     size = "large",
     include = ["source/llm/doc_code/serve/**/*.py"],
-    exclude = [],
+    exclude = ["source/llm/doc_code/serve/multi_gpu/**/*.py"],
     extra_srcs = [],
     data = ["source/llm/doc_code/serve/qwen/llm_config_example.yaml"],
     tags = [
@@ -356,6 +356,20 @@ py_test_run_all_subdirectory(
     ],
 )
 
+# Multi-GPU Tests (4+ GPUs)
+py_test_run_all_subdirectory(
+    size = "large",
+    include = ["source/llm/doc_code/serve/multi_gpu/**/*.py"],
+    exclude = [],
+    extra_srcs = [],
+    tags = [
+        "exclusive",
+        "gpu",
+        "multi_gpu_4",
+        "team:llm",
+    ],
+)
+
 # --------------------------------------------------------------------
 # Test all doc/source/data/doc_code/working-with-llms code included in rst/md files.
 # --------------------------------------------------------------------

doc/source/llm/doc_code/serve/multi_gpu/dp_basic_example.py

@@ -0,0 +1,84 @@
+"""
+This file serves as a documentation example and CI test for basic data parallel attention deployment.
+
+Structure:
+1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
+2. Docs example (between __dp_basic_example_start/end__): Embedded in Sphinx docs via literalinclude.
+3. Test validation (deployment status polling + cleanup)
+"""
+
+import time
+from ray import serve
+from ray.serve.schema import ApplicationStatus
+from ray.serve._private.constants import SERVE_DEFAULT_APP_NAME
+from ray.serve import llm
+
+_original_serve_run = serve.run
+_original_build_dp_openai_app = llm.build_dp_openai_app
+
+
+def _non_blocking_serve_run(app, **kwargs):
+    """Forces blocking=False for testing"""
+    kwargs["blocking"] = False
+    return _original_serve_run(app, **kwargs)
+
+
+def _testing_build_dp_openai_app(builder_config, **kwargs):
+    """Removes accelerator requirements for testing"""
+    if "llm_config" in builder_config:
+        config = builder_config["llm_config"]
+        if hasattr(config, "accelerator_type") and config.accelerator_type is not None:
+            config.accelerator_type = None
+    return _original_build_dp_openai_app(builder_config, **kwargs)
+
+
+serve.run = _non_blocking_serve_run
+llm.build_dp_openai_app = _testing_build_dp_openai_app
+
+# __dp_basic_example_start__
+from ray import serve
+from ray.serve.llm import LLMConfig, build_dp_openai_app
+
+# Configure the model with data parallel settings
+config = LLMConfig(
+    model_loading_config={
+        "model_id": "Qwen/Qwen2.5-0.5B-Instruct"
+    },
+    engine_kwargs={
+        "data_parallel_size": 2,  # Number of DP replicas
+        "tensor_parallel_size": 1,  # TP size per replica
+    },
+    experimental_configs={
+        # This is a temporary required config. We will remove this in future versions.
+        "dp_size_per_node": 2,  # DP replicas per node
+    },
+)
+
+app = build_dp_openai_app({
+    "llm_config": config
+})
+
+serve.run(app, blocking=True)
+# __dp_basic_example_end__
+
+status = ApplicationStatus.NOT_STARTED
+timeout_seconds = 300
+start_time = time.time()
+
+while (
+    status != ApplicationStatus.RUNNING and time.time() - start_time < timeout_seconds
+):
+    status = serve.status().applications[SERVE_DEFAULT_APP_NAME].status
+
+    if status in [ApplicationStatus.DEPLOY_FAILED, ApplicationStatus.UNHEALTHY]:
+        raise AssertionError(f"Deployment failed with status: {status}")
+
+    time.sleep(1)
+
+if status != ApplicationStatus.RUNNING:
+    raise AssertionError(
+        f"Deployment failed to reach RUNNING status within {timeout_seconds}s. Current status: {status}"
+    )
+
+serve.shutdown()
+

doc/source/llm/doc_code/serve/multi_gpu/dp_pd_example.py

@@ -0,0 +1,135 @@
+"""
+This file serves as a documentation example and CI test for data parallel + prefill-decode disaggregation.
+
+Structure:
+1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
+2. Docs example (between __dp_pd_example_start/end__): Embedded in Sphinx docs via literalinclude.
+3. Test validation (deployment status polling + cleanup)
+"""
+
+import time
+from ray import serve
+from ray.serve.schema import ApplicationStatus
+from ray.serve._private.constants import SERVE_DEFAULT_APP_NAME
+from ray.serve import llm
+from ray.serve.llm.deployment import PDProxyServer
+from ray.serve.llm.ingress import OpenAiIngress, make_fastapi_ingress
+
+# Check if NIXL is available (required for NixlConnector)
+try:
+    import nixl  # noqa: F401
+    NIXL_AVAILABLE = True
+except ImportError:
+    NIXL_AVAILABLE = False
+
+if not NIXL_AVAILABLE:
+    raise ImportError(
+        "NIXL is required for this example but is not installed. "
+        "Install it with: pip install nixl or uv pip install nixl"
+    )
+
+_original_serve_run = serve.run
+_original_build_dp_deployment = llm.build_dp_deployment
+
+
+def _non_blocking_serve_run(app, **kwargs):
+    """Forces blocking=False for testing"""
+    kwargs["blocking"] = False
+    return _original_serve_run(app, **kwargs)
+
+
+def _testing_build_dp_deployment(llm_config, **kwargs):
+    """Removes accelerator requirements for testing"""
+    if llm_config.accelerator_type is not None:
+        llm_config.accelerator_type = None
+    return _original_build_dp_deployment(llm_config, **kwargs)
+
+
+serve.run = _non_blocking_serve_run
+llm.build_dp_deployment = _testing_build_dp_deployment
+
+# __dp_pd_example_start__
+from ray import serve
+from ray.serve.llm import LLMConfig, build_dp_deployment
+from ray.serve.llm.deployment import PDProxyServer
+from ray.serve.llm.ingress import OpenAiIngress, make_fastapi_ingress
+
+# Configure prefill with data parallel attention
+prefill_config = LLMConfig(
+    model_loading_config={
+        "model_id": "Qwen/Qwen2.5-0.5B-Instruct"
+    },
+    engine_kwargs={
+        "data_parallel_size": 2,  # 2 DP replicas for prefill
+        "tensor_parallel_size": 1,
+        "kv_transfer_config": {
+            "kv_connector": "NixlConnector",
+            "kv_role": "kv_both",
+        }
+    },
+    experimental_configs={
+        "dp_size_per_node": 2,
+    },
+)
+
+# Configure decode with data parallel attention
+decode_config = LLMConfig(
+    model_loading_config={
+        "model_id": "Qwen/Qwen2.5-0.5B-Instruct"
+    },
+    engine_kwargs={
+        "data_parallel_size": 2,  # 2 DP replicas for decode (adjusted for 4 GPU limit)
+        "tensor_parallel_size": 1,
+        "kv_transfer_config": {
+            "kv_connector": "NixlConnector",
+            "kv_role": "kv_both",
+        }
+    },
+    experimental_configs={
+        "dp_size_per_node": 2,
+    },
+)
+
+# Build prefill and decode deployments with DP
+prefill_deployment = build_dp_deployment(prefill_config, name_prefix="Prefill:")
+decode_deployment = build_dp_deployment(decode_config, name_prefix="Decode:")
+
+# Create PDProxyServer to coordinate between prefill and decode
+proxy_options = PDProxyServer.get_deployment_options(prefill_config, decode_config)
+proxy_deployment = serve.deployment(PDProxyServer).options(**proxy_options).bind(
+    prefill_server=prefill_deployment,
+    decode_server=decode_deployment,
+)
+
+# Create OpenAI-compatible ingress
+ingress_options = OpenAiIngress.get_deployment_options([prefill_config, decode_config])
+ingress_cls = make_fastapi_ingress(OpenAiIngress)
+ingress_deployment = serve.deployment(ingress_cls).options(**ingress_options).bind(
+    llm_deployments=[proxy_deployment]
+)
+
+# Deploy the application
+serve.run(ingress_deployment, blocking=True)
+# __dp_pd_example_end__
+
+status = ApplicationStatus.NOT_STARTED
+timeout_seconds = 300  # Longer timeout for DP+PD setup
+start_time = time.time()
+
+while (
+    status != ApplicationStatus.RUNNING and time.time() - start_time < timeout_seconds
+):
+    status = serve.status().applications[SERVE_DEFAULT_APP_NAME].status
+
+    if status in [ApplicationStatus.DEPLOY_FAILED, ApplicationStatus.UNHEALTHY]:
+        raise AssertionError(f"Deployment failed with status: {status}")
+
+    time.sleep(1)
+
+if status != ApplicationStatus.RUNNING:
+    raise AssertionError(
+        f"Deployment failed to reach RUNNING status within {timeout_seconds}s. Current status: {status}"
+    )
+
+serve.shutdown()
+