[MISC] Consolidate cleanup() and refactor offline_inference_with_prefix.py (#9510)

examples/offline_inference_with_prefix.py

@@ -1,4 +1,5 @@
 from vllm import LLM, SamplingParams
+from vllm.distributed import cleanup_dist_env_and_memory
 
 # NOTE: This is just a running example. For benchmarking purpose,
 # please see benchmarks/benchmark_prefix_caching.py
@@ -28,14 +29,9 @@ generating_prompts = [prefix + prompt for prompt in prompts]
 # Create a sampling params object.
 sampling_params = SamplingParams(temperature=0.0)
 
-# Create an LLM.
-regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.3)
+# Create an LLM without prefix caching as a baseline.
+regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.4)
 
-# The second LLM needs to request a higher gpu_memory_utilization because
-# the first LLM has already allocated a full 30% of the gpu memory.
-prefix_cached_llm = LLM(model="facebook/opt-125m",
-                        enable_prefix_caching=True,
-                        gpu_memory_utilization=0.6)
 print("Results without `enable_prefix_caching`")
 
 # Generate texts from the prompts. The output is a list of RequestOutput objects
@@ -52,6 +48,15 @@ for output in outputs:
 
 print("-" * 80)
 
+# Destroy the LLM object and free up the GPU memory.
+del regular_llm
+cleanup_dist_env_and_memory()
+
+# Create an LLM with prefix caching enabled.
+prefix_cached_llm = LLM(model="facebook/opt-125m",
+                        enable_prefix_caching=True,
+                        gpu_memory_utilization=0.4)
+
 # Warmup so that the shared prompt's KV cache is computed.
 prefix_cached_llm.generate(generating_prompts[0], sampling_params)
 

tests/async_engine/test_async_llm_engine.py

@@ -12,11 +12,11 @@ import torch
 
 from vllm import SamplingParams
 from vllm.config import ParallelConfig
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.engine.async_llm_engine import AsyncEngineArgs, AsyncLLMEngine
 from vllm.outputs import RequestOutput as RealRequestOutput
 from vllm.sampling_params import RequestOutputKind
 
-from ..conftest import cleanup
 from ..utils import wait_for_gpu_memory_to_clear
 
 
@@ -157,7 +157,7 @@ async def async_engine():
         engine.shutdown_background_loop()
         del engine
         await asyncio.sleep(0.1)
-        cleanup()
+        cleanup_dist_env_and_memory()
 
 
 @pytest.fixture()

tests/conftest.py

@@ -1,5 +1,3 @@
-import contextlib
-import gc
 import json
 import os
 import sys
@@ -27,8 +25,7 @@ from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
 from vllm.config import TaskOption, TokenizerPoolConfig
 from vllm.connections import global_http_connection
-from vllm.distributed import (destroy_distributed_environment,
-                              destroy_model_parallel,
+from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,
                               initialize_model_parallel)
 from vllm.inputs import (ExplicitEncoderDecoderPrompt, TextPrompt,
@@ -140,17 +137,7 @@ def dist_init():
     )
     initialize_model_parallel(1, 1)
     yield
-    cleanup()
-
-
-def cleanup():
-    destroy_model_parallel()
-    destroy_distributed_environment()
-    with contextlib.suppress(AssertionError):
-        torch.distributed.destroy_process_group()
-    gc.collect()
-    if not is_cpu():
-        torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
 
 
 @pytest.fixture()
@@ -167,7 +154,7 @@ def should_do_global_cleanup_after_test(request) -> bool:
 def cleanup_fixture(should_do_global_cleanup_after_test: bool):
     yield
     if should_do_global_cleanup_after_test:
-        cleanup()
+        cleanup_dist_env_and_memory()
 
 
 @pytest.fixture(autouse=True)
@@ -606,7 +593,7 @@ class HfRunner:
 
     def __exit__(self, exc_type, exc_value, traceback):
         del self.model
-        cleanup()
+        cleanup_dist_env_and_memory()
 
 
 @pytest.fixture(scope="session")
@@ -861,7 +848,7 @@ class VllmRunner:
 
     def __exit__(self, exc_type, exc_value, traceback):
         del self.model
-        cleanup()
+        cleanup_dist_env_and_memory()
 
 
 @pytest.fixture(scope="session")

tests/core/block/e2e/conftest.py

@@ -3,10 +3,9 @@ from typing import Callable, Iterable, Optional
 import pytest
 
 from vllm import LLM
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.model_executor.utils import set_random_seed
 
-from ....conftest import cleanup
-
 
 @pytest.fixture
 def baseline_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
@@ -37,7 +36,7 @@ def create_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
 
         yield llm
         del llm
-        cleanup()
+        cleanup_dist_env_and_memory()
 
     for llm in generator_inner():
         yield llm

tests/entrypoints/llm/test_encode.py

@@ -4,8 +4,7 @@ from typing import List
 import pytest
 
 from vllm import LLM, EmbeddingRequestOutput, PoolingParams
-
-from ...conftest import cleanup
+from vllm.distributed import cleanup_dist_env_and_memory
 
 MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
 
@@ -41,7 +40,7 @@ def llm():
 
         del llm
 
-    cleanup()
+    cleanup_dist_env_and_memory()
 
 
 def assert_outputs_equal(o1: List[EmbeddingRequestOutput],

tests/entrypoints/llm/test_generate.py

@@ -4,8 +4,7 @@ from typing import List
 import pytest
 
 from vllm import LLM, RequestOutput, SamplingParams
-
-from ...conftest import cleanup
+from vllm.distributed import cleanup_dist_env_and_memory
 
 MODEL_NAME = "facebook/opt-125m"
 
@@ -39,7 +38,7 @@ def llm():
 
         del llm
 
-    cleanup()
+    cleanup_dist_env_and_memory()
 
 
 def assert_outputs_equal(o1: List[RequestOutput], o2: List[RequestOutput]):

tests/entrypoints/llm/test_generate_multiple_loras.py

@@ -5,10 +5,9 @@ import pytest
 from huggingface_hub import snapshot_download
 
 from vllm import LLM
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.lora.request import LoRARequest
 
-from ...conftest import cleanup
-
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
 
 PROMPTS = [
@@ -39,7 +38,7 @@ def llm():
 
         del llm
 
-    cleanup()
+    cleanup_dist_env_and_memory()
 
 
 @pytest.fixture(scope="module")

tests/entrypoints/llm/test_guided_generate.py

@@ -5,12 +5,11 @@ import weakref
 import jsonschema
 import pytest
 
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.entrypoints.llm import LLM
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
-from ...conftest import cleanup
-
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
 
 
@@ -23,7 +22,7 @@ def llm():
     with llm.deprecate_legacy_api():
         yield weakref.proxy(llm)
         del llm
-    cleanup()
+    cleanup_dist_env_and_memory()
 
 
 @pytest.mark.skip_global_cleanup

tests/entrypoints/llm/test_lazy_outlines.py

@@ -1,6 +1,7 @@
 import sys
 
 from vllm import LLM, SamplingParams
+from vllm.distributed import cleanup_dist_env_and_memory
 
 
 def test_lazy_outlines(sample_regex):
@@ -14,6 +15,7 @@ def test_lazy_outlines(sample_regex):
     ]
     sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
+    # Create an LLM without guided decoding as a baseline.
     llm = LLM(model="facebook/opt-125m",
               enforce_eager=True,
               gpu_memory_utilization=0.3)
@@ -26,8 +28,11 @@ def test_lazy_outlines(sample_regex):
     # make sure outlines is not imported
     assert 'outlines' not in sys.modules
 
-    # The second LLM needs to request a higher gpu_memory_utilization because
-    # the first LLM has already allocated a full 30% of the gpu memory.
+    # Destroy the LLM object and free up the GPU memory.
+    del llm
+    cleanup_dist_env_and_memory()
+
+    # Create an LLM with guided decoding enabled.
     llm = LLM(model="facebook/opt-125m",
               enforce_eager=True,
               guided_decoding_backend="lm-format-enforcer",

tests/entrypoints/offline_mode/test_offline_mode.py

@@ -6,8 +6,7 @@ import weakref
 import pytest
 
 from vllm import LLM
-
-from ...conftest import cleanup
+from vllm.distributed import cleanup_dist_env_and_memory
 
 MODEL_NAME = "facebook/opt-125m"
 
@@ -27,7 +26,7 @@ def llm():
 
         del llm
 
-    cleanup()
+    cleanup_dist_env_and_memory()
 
 
 @pytest.mark.skip_global_cleanup

tests/lora/conftest.py

@@ -1,20 +1,16 @@
-import contextlib
-import gc
 import tempfile
 from collections import OrderedDict
 from typing import Dict, List, TypedDict
 from unittest.mock import MagicMock, patch
 
 import pytest
-import ray
 import torch
 import torch.nn as nn
 from huggingface_hub import snapshot_download
 
 import vllm
 from vllm.config import LoRAConfig
-from vllm.distributed import (destroy_distributed_environment,
-                              destroy_model_parallel,
+from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,
                               initialize_model_parallel)
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -48,16 +44,6 @@ LONG_LORA_INFOS: List[ContextIDInfo] = [{
 }]
 
 
-def cleanup():
-    destroy_model_parallel()
-    destroy_distributed_environment()
-    with contextlib.suppress(AssertionError):
-        torch.distributed.destroy_process_group()
-    gc.collect()
-    torch.cuda.empty_cache()
-    ray.shutdown()
-
-
 @pytest.fixture()
 def should_do_global_cleanup_after_test(request) -> bool:
     """Allow subdirectories to skip global cleanup by overriding this fixture.
@@ -72,7 +58,7 @@ def should_do_global_cleanup_after_test(request) -> bool:
 def cleanup_fixture(should_do_global_cleanup_after_test: bool):
     yield
     if should_do_global_cleanup_after_test:
-        cleanup()
+        cleanup_dist_env_and_memory(shutdown_ray=True)
 
 
 @pytest.fixture
@@ -87,7 +73,7 @@ def dist_init():
     )
     initialize_model_parallel(1, 1)
     yield
-    cleanup()
+    cleanup_dist_env_and_memory(shutdown_ray=True)
 
 
 @pytest.fixture
@@ -238,7 +224,7 @@ def long_context_lora_files_32k():
 def long_context_infos(long_context_lora_files_16k_1,
                        long_context_lora_files_16k_2,
                        long_context_lora_files_32k):
-    cleanup()
+    cleanup_dist_env_and_memory(shutdown_ray=True)
     infos: Dict[int, ContextInfo] = {}
     for lora_checkpoint_info in LONG_LORA_INFOS:
         lora_id = lora_checkpoint_info["lora_id"]
@@ -259,7 +245,7 @@ def long_context_infos(long_context_lora_files_16k_1,
 
 @pytest.fixture
 def llama_2_7b_engine_extra_embeddings():
-    cleanup()
+    cleanup_dist_env_and_memory(shutdown_ray=True)
     get_model_old = get_model
 
     def get_model_patched(*, model_config, device_config, **kwargs):
@@ -272,7 +258,7 @@ def llama_2_7b_engine_extra_embeddings():
         engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False)
     yield engine.llm_engine
     del engine
-    cleanup()
+    cleanup_dist_env_and_memory(shutdown_ray=True)
 
 
 @pytest.fixture

tests/lora/test_baichuan.py

@@ -3,10 +3,9 @@ from typing import List
 import pytest
 
 import vllm
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.lora.request import LoRARequest
 
-from .conftest import cleanup
-
 MODEL_PATH = "baichuan-inc/Baichuan-7B"
 
 PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:"""  # noqa: E501
@@ -80,7 +79,7 @@ def test_baichuan_tensor_parallel_equality(baichuan_lora_files,
     output_tp1 = do_sample(llm_tp1, baichuan_lora_files, lora_id=1)
 
     del llm_tp1
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     llm_tp2 = vllm.LLM(MODEL_PATH,
                        enable_lora=True,
@@ -93,7 +92,7 @@ def test_baichuan_tensor_parallel_equality(baichuan_lora_files,
     output_tp2 = do_sample(llm_tp2, baichuan_lora_files, lora_id=2)
 
     del llm_tp2
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     assert output_tp1 == output_tp2
 
@@ -108,6 +107,6 @@ def test_baichuan_tensor_parallel_equality(baichuan_lora_files,
     output_tp4 = do_sample(llm_tp4, baichuan_lora_files, lora_id=2)
 
     del llm_tp4
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     assert output_tp1 == output_tp4

tests/lora/test_llama.py

@@ -4,10 +4,9 @@ import pytest
 import ray
 
 import vllm
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.lora.request import LoRARequest
 
-from .conftest import cleanup
-
 MODEL_PATH = "meta-llama/Llama-2-7b-hf"
 
 
@@ -93,7 +92,7 @@ def test_llama_tensor_parallel_equality(sql_lora_files, num_gpus_available):
     output_tp1 = do_sample(llm_tp1, sql_lora_files, lora_id=1)
 
     del llm_tp1
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     llm_tp2 = vllm.LLM(MODEL_PATH,
                        enable_lora=True,
@@ -103,7 +102,7 @@ def test_llama_tensor_parallel_equality(sql_lora_files, num_gpus_available):
     output_tp2 = do_sample(llm_tp2, sql_lora_files, lora_id=1)
 
     del llm_tp2
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     assert output_tp1 == output_tp2
 
@@ -115,7 +114,7 @@ def test_llama_tensor_parallel_equality(sql_lora_files, num_gpus_available):
     output_tp4 = do_sample(llm_tp4, sql_lora_files, lora_id=1)
 
     del llm_tp4
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     assert output_tp1 == output_tp4
 

tests/lora/test_quant_model.py

@@ -6,11 +6,10 @@ from typing import List
 import pytest
 
 import vllm
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.lora.request import LoRARequest
 from vllm.utils import is_hip
 
-from .conftest import cleanup
-
 
 @dataclass
 class ModelWithQuantization:
@@ -160,7 +159,7 @@ def test_quant_model_lora(tinyllama_lora_files, num_gpus_available, model,
     print("removing lora")
 
     del llm
-    cleanup()
+    cleanup_dist_env_and_memory()
 
 
 @pytest.mark.parametrize("model", MODELS)
@@ -181,7 +180,7 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available,
     output_tp1 = do_sample(llm_tp1, tinyllama_lora_files, lora_id=1)
 
     del llm_tp1
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     llm_tp2 = vllm.LLM(
         model=model.model_path,
@@ -194,6 +193,6 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available,
     output_tp2 = do_sample(llm_tp2, tinyllama_lora_files, lora_id=1)
 
     del llm_tp2
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     assert output_tp1 == output_tp2

tests/metrics/test_metrics.py

@@ -6,13 +6,12 @@ import ray
 from prometheus_client import REGISTRY
 
 from vllm import EngineArgs, LLMEngine
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.engine.metrics import RayPrometheusStatLogger
 from vllm.sampling_params import SamplingParams
 
-from ..conftest import cleanup
-
 MODELS = [
     "facebook/opt-125m",
 ]
@@ -307,7 +306,7 @@ def test_metric_spec_decode_interval(
 
     finally:
         del engine
-        cleanup()
+        cleanup_dist_env_and_memory()
 
 
 def assert_metrics(engine: LLMEngine, disable_log_stats: bool,

tests/models/decoder_only/vision_language/test_intern_vit.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 from huggingface_hub import snapshot_download
 from transformers import AutoConfig, AutoModel, CLIPImageProcessor
 
-from ....conftest import _ImageAssets, cleanup
+from ....conftest import _ImageAssets
 
 # we use snapshot_download to prevent conflicts between
 # dynamic_module and trust_remote_code for hf_runner
@@ -45,12 +45,13 @@ def run_intern_vit_test(
         for pixel_value in pixel_values
     ]
 
+    from vllm.distributed import cleanup_dist_env_and_memory
     from vllm.model_executor.models.intern_vit import InternVisionModel
     vllm_model = InternVisionModel(config)
     vllm_model.load_weights(hf_model.state_dict().items())
 
     del hf_model
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     vllm_model = vllm_model.to("cuda", dtype)
     vllm_outputs_per_image = [
@@ -58,7 +59,7 @@ def run_intern_vit_test(
         for pixel_value in pixel_values
     ]
     del vllm_model
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     cos_similar = nn.CosineSimilarity(dim=-1)
     for vllm_output, hf_output in zip(vllm_outputs_per_image,

tests/prefix_caching/test_disable_sliding_window.py

@@ -4,8 +4,8 @@ Run `pytest tests/prefix_caching/test_prefix_caching.py`.
 """
 import pytest
 
-from tests.conftest import cleanup
 from vllm import LLM
+from vllm.distributed import cleanup_dist_env_and_memory
 
 MODEL_LEN_LEN = [
     # Example models with sliding window.
@@ -31,7 +31,7 @@ def test_disable_sliding_window(model_len_len, ):
         model_config.max_model_len)
 
     del vllm_disabled_model
-    cleanup()
+    cleanup_dist_env_and_memory()
 
     vllm_enabled_model = LLM(model, disable_sliding_window=False)
     vllm_enabled_model.generate("Hi my name is")
@@ -41,4 +41,4 @@ def test_disable_sliding_window(model_len_len, ):
         model_config.max_model_len)
 
     del vllm_enabled_model
-    cleanup()
+    cleanup_dist_env_and_memory()

tests/spec_decode/e2e/conftest.py

@@ -4,10 +4,10 @@ from typing import List, Optional, Sequence, Tuple, Union
 import pytest
 
 from vllm import LLM, SamplingParams
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.model_executor.utils import set_random_seed
 from vllm.sequence import PromptLogprobs, SampleLogprobs
 
-from ...conftest import cleanup
 from ...models.utils import (TokensTextLogprobs,
                              TokensTextLogprobsPromptLogprobs,
                              check_logprobs_close, check_outputs_equal)
@@ -44,7 +44,7 @@ def test_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs,
         yield llm
 
         del llm
-        cleanup()
+        cleanup_dist_env_and_memory()
 
     return generate
 

tests/tensorizer_loader/conftest.py

@@ -1,27 +1,18 @@
-import contextlib
 import functools
 import gc
 from typing import Callable, TypeVar
 
 import pytest
-import ray
 import torch
 from typing_extensions import ParamSpec
 
-from vllm.distributed import (destroy_distributed_environment,
-                              destroy_model_parallel)
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 
 
 @pytest.fixture(autouse=True)
 def cleanup():
-    destroy_model_parallel()
-    destroy_distributed_environment()
-    with contextlib.suppress(AssertionError):
-        torch.distributed.destroy_process_group()
-    ray.shutdown()
-    gc.collect()
-    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory(shutdown_ray=True)
 
 
 _P = ParamSpec("_P")

vllm/distributed/parallel_state.py

@@ -20,6 +20,7 @@ If you only need to use the distributed environment without model/pipeline
  steps.
 """
 import contextlib
+import gc
 import pickle
 import weakref
 from collections import namedtuple
@@ -36,7 +37,7 @@ from torch.distributed import Backend, ProcessGroup
 import vllm.envs as envs
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import supports_custom_op
+from vllm.utils import is_cpu, supports_custom_op
 
 
 @dataclass
@@ -1129,6 +1130,19 @@ def destroy_distributed_environment():
         torch.distributed.destroy_process_group()
 
 
+def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
+    destroy_model_parallel()
+    destroy_distributed_environment()
+    with contextlib.suppress(AssertionError):
+        torch.distributed.destroy_process_group()
+    if shutdown_ray:
+        import ray  # Lazy import Ray
+        ray.shutdown()
+    gc.collect()
+    if not is_cpu():
+        torch.cuda.empty_cache()
+
+
 def in_the_same_node_as(pg: ProcessGroup, source_rank: int = 0) -> List[bool]:
     """
     This is a collective operation that returns if each rank is in the same node