[ci][distributed] add pipeline parallel correctness test (#6410)

.buildkite/test-pipeline.yaml

@@ -72,7 +72,7 @@ steps:
   commands:
   - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py
-    - TP_SIZE=2 PP_SIZE=2 EAGER_MODE=1 CHUNKED_PREFILL=0 pytest -v -s distributed/test_pipeline_parallel.py
+    - pytest -v -s distributed/test_pipeline_parallel.py
   - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py
 
@@ -115,12 +115,7 @@ steps:
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   commands:
-  - TP_SIZE=2 PP_SIZE=2 EAGER_MODE=1 CHUNKED_PREFILL=1 pytest -v -s distributed/test_pipeline_parallel.py
-  - TP_SIZE=2 PP_SIZE=2 EAGER_MODE=1 CHUNKED_PREFILL=0 pytest -v -s distributed/test_pipeline_parallel.py
-  - TP_SIZE=1 PP_SIZE=3 EAGER_MODE=1 CHUNKED_PREFILL=0 pytest -v -s distributed/test_pipeline_parallel.py
-  - PP_SIZE=4 EAGER_MODE=1 CHUNKED_PREFILL=1 pytest -v -s distributed/test_pipeline_parallel.py
-  - PP_SIZE=4 EAGER_MODE=1 CHUNKED_PREFILL=0 pytest -v -s distributed/test_pipeline_parallel.py
-
+  - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: Engine Test
   mirror_hardwares: [amd]

tests/distributed/test_pipeline_parallel.py

-import os
-
-import openai  # use the official client for correctness check
 import pytest
 
 from ..utils import RemoteOpenAIServer
 
-# downloading lora to test lora requests
-
-# any model with a chat template should work here
-MODEL_NAME = "meta-llama/Meta-Llama-3-8B"
-EAGER_MODE = bool(int(os.getenv("EAGER_MODE", 0)))
-CHUNKED_PREFILL = bool(int(os.getenv("CHUNKED_PREFILL", 0)))
-TP_SIZE = int(os.getenv("TP_SIZE", 1))
-PP_SIZE = int(os.getenv("PP_SIZE", 1))
-
-pytestmark = pytest.mark.asyncio
-
 
-@pytest.fixture(scope="module")
-def server():
-    args = [
+@pytest.mark.parametrize(
+    "TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME", [
+        (2, 2, 0, 1, "meta-llama/Meta-Llama-3-8B"),
+        (2, 2, 1, 0, "meta-llama/Meta-Llama-3-8B"),
+        (1, 3, 0, 0, "meta-llama/Meta-Llama-3-8B"),
+        (1, 4, 0, 1, "meta-llama/Meta-Llama-3-8B"),
+        (1, 4, 1, 0, "meta-llama/Meta-Llama-3-8B"),
+    ])
+def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME):
+    pp_args = [
         "--model",
         MODEL_NAME,
         # use half precision for speed and memory savings in CI environment
@@ -32,109 +25,107 @@ def server():
         "--distributed-executor-backend",
         "ray",
     ]
+
+    # compare without pipeline parallelism
+    # NOTE: use mp backend for TP
+    # PP tests might involve multiple nodes, and ray might
+    #  schedule all workers in a node other than the head node,
+    #  which can cause the test to fail.
+    tp_args = [
+        "--model",
+        MODEL_NAME,
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--tensor-parallel-size",
+        str(max(TP_SIZE, 2)),  # use at least TP_SIZE=2 to hold the model
+        "--distributed-executor-backend",
+        "mp",
+    ]
     if CHUNKED_PREFILL:
-        args += [
-            "--enable-chunked-prefill",
-        ]
+        pp_args.append("--enable-chunked-prefill")
+        tp_args.append("--enable-chunked-prefill")
     if EAGER_MODE:
-        args += [
-            "--enforce-eager",
-        ]
-    with RemoteOpenAIServer(args) as remote_server:
-        yield remote_server
-
-
-@pytest.fixture(scope="module")
-def client(server):
-    return server.get_async_client()
-
-
-async def test_check_models(server, client: openai.AsyncOpenAI):
-    models = await client.models.list()
-    models = models.data
-    served_model = models[0]
-    assert served_model.id == MODEL_NAME
-    assert all(model.root == MODEL_NAME for model in models)
-
-
-@pytest.mark.parametrize(
-    "model_name",
-    [MODEL_NAME],
-)
-async def test_single_completion(server, client: openai.AsyncOpenAI,
-                                 model_name: str):
-    completion = await client.completions.create(model=model_name,
-                                                 prompt="Hello, my name is",
-                                                 max_tokens=5,
-                                                 temperature=0.0)
-
-    assert completion.id is not None
-    assert completion.choices is not None and len(completion.choices) == 1
-    assert completion.choices[0].text is not None and len(
-        completion.choices[0].text) >= 5
-    assert completion.choices[0].finish_reason == "length"
-    assert completion.usage == openai.types.CompletionUsage(
-        completion_tokens=5, prompt_tokens=6, total_tokens=11)
-
-    # test using token IDs
-    completion = await client.completions.create(
-        model=MODEL_NAME,
-        prompt=[0, 0, 0, 0, 0],
-        max_tokens=5,
-        temperature=0.0,
-    )
-    assert completion.choices[0].text is not None and len(
-        completion.choices[0].text) >= 5
-
-
-@pytest.mark.parametrize(
-    # just test 1 lora hereafter
-    "model_name",
-    [MODEL_NAME],
-)
-async def test_batch_completions(server, client: openai.AsyncOpenAI,
-                                 model_name: str):
-    # test simple list
-    batch = await client.completions.create(
-        model=model_name,
-        prompt=["Hello, my name is", "Hello, my name is"],
-        max_tokens=5,
-        temperature=0.0,
-    )
-    assert len(batch.choices) == 2
-    assert batch.choices[0].text == batch.choices[1].text
-
-    # test n = 2
-    batch = await client.completions.create(
-        model=model_name,
-        prompt=["Hello, my name is", "Hello, my name is"],
-        n=2,
-        max_tokens=5,
-        temperature=0.0,
-        extra_body=dict(
-            # NOTE: this has to be true for n > 1 in vLLM, but not necessary
-            # for official client.
-            use_beam_search=True),
-    )
-    assert len(batch.choices) == 4
-    assert batch.choices[0].text != batch.choices[
-        1].text, "beam search should be different"
-    assert batch.choices[0].text == batch.choices[
-        2].text, "two copies of the same prompt should be the same"
-    assert batch.choices[1].text == batch.choices[
-        3].text, "two copies of the same prompt should be the same"
-
-    # test streaming
-    batch = await client.completions.create(
-        model=model_name,
-        prompt=["Hello, my name is", "Hello, my name is"],
-        max_tokens=5,
-        temperature=0.0,
-        stream=True,
-    )
-    texts = [""] * 2
-    async for chunk in batch:
-        assert len(chunk.choices) == 1
-        choice = chunk.choices[0]
-        texts[choice.index] += choice.text
-    assert texts[0] == texts[1]
+        pp_args.append("--enforce-eager")
+        tp_args.append("--enforce-eager")
+
+    results = []
+    for args in [pp_args, tp_args]:
+        with RemoteOpenAIServer(args) as server:
+            client = server.get_client()
+
+            # test models list
+            models = client.models.list()
+            models = models.data
+            served_model = models[0]
+            results.append({
+                "test": "models_list",
+                "id": served_model.id,
+                "root": served_model.root,
+            })
+
+            # test with text prompt
+            completion = client.completions.create(model=MODEL_NAME,
+                                                   prompt="Hello, my name is",
+                                                   max_tokens=5,
+                                                   temperature=0.0)
+
+            results.append({
+                "test": "single_completion",
+                "text": completion.choices[0].text,
+                "finish_reason": completion.choices[0].finish_reason,
+                "usage": completion.usage,
+            })
+
+            # test using token IDs
+            completion = client.completions.create(
+                model=MODEL_NAME,
+                prompt=[0, 0, 0, 0, 0],
+                max_tokens=5,
+                temperature=0.0,
+            )
+
+            results.append({
+                "test": "token_ids",
+                "text": completion.choices[0].text,
+                "finish_reason": completion.choices[0].finish_reason,
+                "usage": completion.usage,
+            })
+
+            # test simple list
+            batch = client.completions.create(
+                model=MODEL_NAME,
+                prompt=["Hello, my name is", "Hello, my name is"],
+                max_tokens=5,
+                temperature=0.0,
+            )
+
+            results.append({
+                "test": "simple_list",
+                "text0": batch.choices[0].text,
+                "text1": batch.choices[1].text,
+            })
+
+            # test streaming
+            batch = client.completions.create(
+                model=MODEL_NAME,
+                prompt=["Hello, my name is", "Hello, my name is"],
+                max_tokens=5,
+                temperature=0.0,
+                stream=True,
+            )
+            texts = [""] * 2
+            for chunk in batch:
+                assert len(chunk.choices) == 1
+                choice = chunk.choices[0]
+                texts[choice.index] += choice.text
+            results.append({
+                "test": "streaming",
+                "texts": texts,
+            })
+
+    n = len(results) // 2
+    pp_results = results[:n]
+    tp_results = results[n:]
+    for pp, tp in zip(pp_results, tp_results):
+        assert pp == tp

vllm/executor/multiproc_gpu_executor.py

 import asyncio
 import os
+import signal
+import weakref
 from functools import partial
 from typing import Any, List, Optional
 
@@ -78,6 +80,19 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
             result_handler.start()
             self.worker_monitor.start()
 
+        # Set up signal handlers to shutdown the executor cleanly
+        # sometimes gc does not work well
+
+        # Use weakref to avoid holding a reference to self
+        ref = weakref.ref(self)
+
+        def shutdown(signum, frame):
+            if executor := ref():
+                executor.shutdown()
+
+        signal.signal(signal.SIGINT, shutdown)
+        signal.signal(signal.SIGTERM, shutdown)
+
         self.driver_worker = self._create_worker(
             distributed_init_method=distributed_init_method)
         self._run_workers("init_device")