[V0 Deprecation] Remove `VLLM_USE_V1` from tests (#26341)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/kernels/test_flex_attention.py

@@ -55,7 +55,6 @@ def test_flex_attention_vs_default_backend(vllm_runner, monkeypatch):
 
     # Run with flex attention
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
 
         set_seed(seed)
@@ -72,7 +71,6 @@ def test_flex_attention_vs_default_backend(vllm_runner, monkeypatch):
 
     # Run with default backend
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         set_seed(seed)
         with vllm_runner(
             model_name,
@@ -113,7 +111,6 @@ def test_encoder_flex_attention_vs_default_backend(vllm_runner, monkeypatch):
 
     # Run with flex attention
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
         with vllm_runner(
             model_name,
@@ -126,16 +123,17 @@ def test_encoder_flex_attention_vs_default_backend(vllm_runner, monkeypatch):
             flex_outputs = llm_flex.embed(prompts)
 
     # Run with default backend
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-        with vllm_runner(
+    with (
+        monkeypatch.context() as m,
+        vllm_runner(
             model_name,
             runner="pooling",
             dtype=torch.bfloat16,
             tensor_parallel_size=1,
             max_model_len=100,
             enforce_eager=True,
-        ) as llm_default:
+        ) as llm_default,
+    ):
         default_outputs = llm_default.embed(prompts)
 
     check_embeddings_close(

tests/models/multimodal/generation/test_maverick.py

@@ -613,7 +613,6 @@ def test_dummy_maverick(
     profile: bool = False,
 ) -> None:
     # Disable multiprocessing allows us to access model executor from LLM engine
-    monkeypatch.setenv("VLLM_USE_V1", "1")
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
 
     model_path = create_reduced_maverick_model(

tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_platform.py

@@ -8,7 +8,6 @@ if TYPE_CHECKING:
     from vllm.config import VllmConfig
 else:
     VllmConfig = None
-from vllm import envs
 
 
 class DummyPlatform(Platform):
@@ -19,10 +18,7 @@ class DummyPlatform(Platform):
 
     @classmethod
     def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
-        if envs.VLLM_USE_V1:
-            compilation_config = vllm_config.compilation_config
-            # Activate custom ops for v1.
-            compilation_config.custom_ops = ["all"]
+        vllm_config.compilation_config.custom_ops = ["all"]
 
     def get_attn_backend_cls(
         self,

tests/plugins_tests/test_scheduler_plugins.py

@@ -16,7 +16,6 @@ class DummyV1Scheduler(Scheduler):
 
 def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         # Explicitly turn off engine multiprocessing so
         # that the scheduler runs in this process
         m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")

tests/samplers/test_no_bad_words.py

@@ -8,18 +8,11 @@ Run `pytest tests/samplers/test_no_bad_words.py`.
 
 from typing import Optional
 
-import pytest
 from transformers import AutoTokenizer
 
 from vllm import LLM, SamplingParams
 
 
-@pytest.fixture(autouse=True)
-def v1(monkeypatch):
-    """Only run on vLLM v1."""
-    monkeypatch.setenv("VLLM_USE_V1", "1")
-
-
 def _generate(
     llm: LLM,
     prompt: str,

tests/tpu/lora/test_lora.py

@@ -17,17 +17,6 @@ from vllm.lora.request import LoRARequest
 # 100 training iterations with a training batch size of 100.
 
 
-@pytest.fixture(scope="function", autouse=True)
-def use_v1_only(monkeypatch: pytest.MonkeyPatch):
-    """
-    Since Multi-LoRA is only supported on the v1 TPU backend, set VLLM_USE_V1=1
-    for all tests in this file
-    """
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-        yield
-
-
 def setup_vllm(num_loras: int, tp: int) -> vllm.LLM:
     return vllm.LLM(
         model="Qwen/Qwen2.5-3B-Instruct",

tests/v1/attention/utils.py

@@ -305,7 +305,6 @@ full_cg_backend_configs = {
     "CutlassMLA": BackendConfig(
         name="CutlassMLA",
         env_vars={
-            "VLLM_USE_V1": "1",
             "VLLM_ATTENTION_BACKEND": "CUTLASS_MLA",
             "FORCE_NUM_KV_SPLITS": "1",  # TODO: remove this when hang issue is fixed
         },

tests/v1/core/test_kv_sharing.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import pytest
 import torch
 
 from vllm.v1.kv_cache_interface import FullAttentionSpec, KVCacheGroupSpec
 from vllm.v1.worker.utils import add_kv_sharing_layers_to_kv_cache_groups
 
+pytestmark = pytest.mark.cpu_test
+
 
 def new_kv_cache_spec():
     return FullAttentionSpec(16, 1, 1, torch.float32, False)

tests/v1/core/test_scheduler_e2e.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import os
 
 import pytest
 
 from vllm import LLM
 
-if os.getenv("VLLM_USE_V1", "0") != "1":
-    pytest.skip("Test package requires V1", allow_module_level=True)
-
 MODEL = "meta-llama/Llama-3.2-1B"
 PROMPT = "Hello my name is Robert and I"
 

tests/v1/cudagraph/test_cudagraph_mode.py

@@ -60,7 +60,7 @@ def test_backend_and_cudagraph_mode_combo(backend_name, cudagraph_mode, supporte
     ):
         pytest.skip("Only Hopper GPUs support FA3 and FlashMLA")
 
-    env_vars = {"VLLM_USE_V1": "1", **backend_configs[backend_name].env_vars}
+    env_vars = backend_configs[backend_name].env_vars
 
     with temporary_environ(env_vars), ExitStack() as stack:
         if not supported:
@@ -117,7 +117,7 @@ combo_cases_2 = [
 def test_cudagraph_compilation_combo(combo_case):
     backend_name, cudagraph_mode, compilation_level, supported = combo_case
 
-    env_vars = {"VLLM_USE_V1": "1", **backend_configs[backend_name].env_vars}
+    env_vars = backend_configs[backend_name].env_vars
 
     with temporary_environ(env_vars), ExitStack() as stack:
         if not supported:

tests/v1/e2e/test_cascade_attention.py

@@ -20,7 +20,6 @@ def test_cascade_attention(example_system_message, monkeypatch, attn_backend):
         )
 
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
 
         llm = LLM(model="Qwen/Qwen2-1.5B-Instruct")

tests/v1/e2e/test_correctness_sliding_window.py

@@ -32,7 +32,7 @@ model_config = {
 @pytest.mark.parametrize("seed", [1])
 @pytest.mark.parametrize("disable_hybrid_kv_cache_manager", [True, False])
 def test_sliding_window_retrieval(
-    monkeypatch, model, batch_size, seed, disable_hybrid_kv_cache_manager
+    model, batch_size, seed, disable_hybrid_kv_cache_manager
 ):
     """
     The test does a bunch of assignments "x1 = 10\nx2 = 33\n..." and then
@@ -40,9 +40,6 @@ def test_sliding_window_retrieval(
     If we tell it upfront which we are going to be looking for, then
     it answers correctly (mostly).
     """
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
     test_config = model_config[model]
 
     llm = LLM(
@@ -50,9 +47,7 @@ def test_sliding_window_retrieval(
     )
     sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
 
-        prompts, answer, indices = prep_prompts(
-            batch_size, ln_range=test_config.ln_range
-        )
+    prompts, answer, indices = prep_prompts(batch_size, ln_range=test_config.ln_range)
 
     check_length(prompts, llm, test_config.sliding_window)
 

tests/v1/e2e/test_kv_sharing_fast_prefill.py

@@ -81,8 +81,6 @@ def test_kv_sharing_fast_prefill(
     )
 
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Make scheduling deterministic for reproducibility
         m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
 

tests/v1/e2e/test_min_tokens.py

@@ -13,7 +13,6 @@ Covers:
 5) Multiple stop conditions
 """
 
-import os
 from typing import Optional, Union
 
 import pytest
@@ -161,9 +160,6 @@ MIN_TOKENS_TEST_CASES = [
 @pytest.fixture(scope="module")
 def llm_v1():
     """Create V1 LLM instance for testing"""
-    # Ensure V1 engine is used
-    os.environ["VLLM_USE_V1"] = "1"
-
     llm = LLM(
         model=TEST_MODEL,
         tensor_parallel_size=1,
@@ -503,6 +499,6 @@ if __name__ == "__main__":
     
     Usage:
         cd vllm/
-        VLLM_USE_V1=1 python -m pytest tests/v1/e2e/test_min_tokens.py -v
+        python -m pytest tests/v1/e2e/test_min_tokens.py -v
     """
     pytest.main([__file__, "-v"])

tests/v1/e2e/test_spec_decode.py

@@ -301,7 +301,6 @@ def test_mtp_correctness(
     model_setup: (method, model_name, tp_size)
     """
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         m.setenv("VLLM_MLA_DISABLE", "1")
 
         method, model_name, tp_size = model_setup

tests/v1/engine/test_async_llm.py

@@ -95,17 +95,11 @@ async def generate(
 )
 @pytest.mark.asyncio
 async def test_load(
-    monkeypatch: pytest.MonkeyPatch,
     output_kind: RequestOutputKind,
     engine_args: AsyncEngineArgs,
     prompt: PromptType,
 ):
-    # TODO(rickyx): Remove monkeypatch once we have a better way to test V1
-    # so that in the future when we switch, we don't have to change all the
-    # tests.
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(engine_args)
         after.callback(engine.shutdown)
@@ -149,14 +143,11 @@ async def test_load(
 )
 @pytest.mark.asyncio
 async def test_abort(
-    monkeypatch: pytest.MonkeyPatch,
     output_kind: RequestOutputKind,
     engine_args: AsyncEngineArgs,
     prompt: PromptType,
 ):
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(engine_args)
         after.callback(engine.shutdown)
@@ -222,13 +213,8 @@ async def test_abort(
     "output_kind", [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY]
 )
 @pytest.mark.asyncio
-async def test_multi_abort(
-    monkeypatch: pytest.MonkeyPatch,
-    output_kind: RequestOutputKind,
-):
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+async def test_multi_abort(output_kind: RequestOutputKind):
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(TEXT_ENGINE_ARGS)
         after.callback(engine.shutdown)
@@ -304,14 +290,11 @@ async def test_multi_abort(
 )
 @pytest.mark.asyncio
 async def test_finished_flag(
-    monkeypatch: pytest.MonkeyPatch,
     n: int,
     engine_args: AsyncEngineArgs,
     prompt: PromptType,
 ):
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(engine_args)
         after.callback(engine.shutdown)
@@ -341,12 +324,10 @@ async def test_finished_flag(
 )
 @pytest.mark.asyncio
 async def test_mid_stream_cancellation(
-    monkeypatch: pytest.MonkeyPatch, engine_args: AsyncEngineArgs, prompt: PromptType
+    engine_args: AsyncEngineArgs, prompt: PromptType
 ):
     """Test that requests can be cancelled mid-stream."""
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(engine_args)
         after.callback(engine.shutdown)
@@ -411,9 +392,7 @@ async def test_customize_loggers(monkeypatch):
     be added to the default loggers.
     """
 
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(
                 TEXT_ENGINE_ARGS,
@@ -430,10 +409,8 @@ async def test_customize_loggers(monkeypatch):
 
 
 @pytest.mark.asyncio(scope="module")
-async def test_dp_rank_argument(monkeypatch: pytest.MonkeyPatch):
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+async def test_dp_rank_argument():
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(TEXT_ENGINE_ARGS)
         after.callback(engine.shutdown)
@@ -466,7 +443,7 @@ async def test_dp_rank_argument(monkeypatch: pytest.MonkeyPatch):
 
 
 @pytest.mark.asyncio
-async def test_check_health(monkeypatch: pytest.MonkeyPatch):
+async def test_check_health():
     """Test that check_health returns normally for healthy engine
     and raises EngineDeadError when the engine is dead.
     """
@@ -474,9 +451,7 @@ async def test_check_health(monkeypatch: pytest.MonkeyPatch):
 
     from vllm.v1.engine.exceptions import EngineDeadError
 
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(TEXT_ENGINE_ARGS)
         after.callback(engine.shutdown)
@@ -503,15 +478,10 @@ async def test_check_health(monkeypatch: pytest.MonkeyPatch):
     "output_kind", [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY]
 )
 @pytest.mark.asyncio
-async def test_abort_final_output(
-    monkeypatch: pytest.MonkeyPatch,
-    output_kind: RequestOutputKind,
-):
+async def test_abort_final_output(output_kind: RequestOutputKind):
     """Test that abort() returns a final output with correct information."""
 
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
+    with ExitStack() as after:
         with set_default_torch_num_threads(1):
             engine = AsyncLLM.from_engine_args(TEXT_ENGINE_ARGS)
         after.callback(engine.shutdown)

tests/v1/engine/test_engine_args.py

@@ -5,18 +5,11 @@ from argparse import ArgumentError
 
 import pytest
 
-from vllm import envs
 from vllm.config import VllmConfig
 from vllm.engine.arg_utils import EngineArgs
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import FlexibleArgumentParser
 
-if not envs.VLLM_USE_V1:
-    pytest.skip(
-        "Skipping V1 tests. Rerun with `VLLM_USE_V1=1` to test.",
-        allow_module_level=True,
-    )
-
 
 def test_prefix_caching_from_cli():
     parser = EngineArgs.add_cli_args(FlexibleArgumentParser())

tests/v1/engine/test_engine_core.py

@@ -46,9 +46,7 @@ def make_request() -> EngineCoreRequest:
 
 
 @create_new_process_for_each_test()
-def test_engine_core(monkeypatch: pytest.MonkeyPatch):
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
+def test_engine_core():
     """Setup the EngineCore."""
     engine_args = EngineArgs(model=MODEL_NAME)
     vllm_config = engine_args.create_engine_config()
@@ -176,14 +174,12 @@ def test_engine_core(monkeypatch: pytest.MonkeyPatch):
 
 
 @create_new_process_for_each_test()
-def test_engine_core_advanced_sampling(monkeypatch: pytest.MonkeyPatch):
+def test_engine_core_advanced_sampling():
     """
     A basic end-to-end test to verify that the engine functions correctly
     when additional sampling parameters, such as top_p, min_tokens, and
     presence_penalty, are set.
     """
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
     """Setup the EngineCore."""
     engine_args = EngineArgs(model=MODEL_NAME)
     vllm_config = engine_args.create_engine_config()
@@ -227,7 +223,7 @@ def test_engine_core_advanced_sampling(monkeypatch: pytest.MonkeyPatch):
 
 
 @create_new_process_for_each_test()
-def test_engine_core_concurrent_batches(monkeypatch: pytest.MonkeyPatch):
+def test_engine_core_concurrent_batches():
     """
     Test that the engine can handle multiple concurrent batches.
     """
@@ -272,9 +268,6 @@ def test_engine_core_concurrent_batches(monkeypatch: pytest.MonkeyPatch):
             if hasattr(self, "thread_pool"):
                 self.thread_pool.shutdown(wait=False)
 
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
     engine_args = EngineArgs(
         model=MODEL_NAME,
         # To test concurrent batches.
@@ -364,13 +357,11 @@ def test_engine_core_concurrent_batches(monkeypatch: pytest.MonkeyPatch):
 
 
 @multi_gpu_test(num_gpus=2)
-def test_engine_core_tp(monkeypatch: pytest.MonkeyPatch):
+def test_engine_core_tp():
     """
     Test engine can initialize worker in tp properly
     """
 
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
     """Setup the EngineCore."""
     engine_args = EngineArgs(
         model=MODEL_NAME,
@@ -400,11 +391,8 @@ def test_engine_core_tp(monkeypatch: pytest.MonkeyPatch):
 
 
 @create_new_process_for_each_test()
-def test_engine_core_invalid_request_id_type(monkeypatch: pytest.MonkeyPatch):
+def test_engine_core_invalid_request_id_type():
     """Test that engine raises TypeError for non-string request_id."""
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
     engine_args = EngineArgs(model=MODEL_NAME)
     vllm_config = engine_args.create_engine_config()
     executor_class = Executor.get_class(vllm_config)
@@ -432,9 +420,7 @@ def test_engine_core_invalid_request_id_type(monkeypatch: pytest.MonkeyPatch):
     none_request = make_request()
     none_request.request_id = None
 
-        with pytest.raises(
-            TypeError, match="request_id must be a string, got.*NoneType"
-        ):
+    with pytest.raises(TypeError, match="request_id must be a string, got.*NoneType"):
         engine_core.add_request(*engine_core.preprocess_add_request(none_request))
 
     # Verify engine is still functional after errors

tests/v1/engine/test_engine_core_client.py

@@ -130,8 +130,6 @@ def test_engine_core_client(
     monkeypatch: pytest.MonkeyPatch, multiprocessing_mode: bool
 ):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Monkey-patch core engine utility function to test.
         m.setattr(EngineCore, "echo", echo, raising=False)
 
@@ -218,8 +216,6 @@ def test_engine_core_client(
 @pytest.mark.asyncio(loop_scope="function")
 async def test_engine_core_client_asyncio(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Monkey-patch core engine utility function to test.
         m.setattr(EngineCore, "echo", echo, raising=False)
 
@@ -373,8 +369,6 @@ async def test_engine_core_client_util_method_custom_return(
     monkeypatch: pytest.MonkeyPatch,
 ):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Must set insecure serialization to allow returning custom types.
         m.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
 
@@ -422,8 +416,6 @@ async def test_engine_core_client_util_method_custom_dict_return(
     monkeypatch: pytest.MonkeyPatch,
 ):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Must set insecure serialization to allow returning custom types.
         m.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
 
@@ -480,8 +472,6 @@ async def test_engine_core_client_util_method_nested_structures(
     monkeypatch: pytest.MonkeyPatch,
 ):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Must set insecure serialization to allow returning custom types.
         m.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
 
@@ -592,12 +582,9 @@ async def test_engine_core_client_util_method_nested_structures(
     indirect=["publisher_config"],
 )
 def test_kv_cache_events(
-    monkeypatch: pytest.MonkeyPatch,
     multiprocessing_mode: bool,
     publisher_config,
 ):
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
     block_size = 16
     num_blocks = 2
 
@@ -640,9 +627,7 @@ def test_kv_cache_events(
         seq, received = result
 
         assert seq == 0, "Sequence number mismatch"
-            assert len(received.events) == 1, (
-                "We should have exactly one BlockStored event"
-            )
+        assert len(received.events) == 1, "We should have exactly one BlockStored event"
         event = received.events[0]
         assert isinstance(event, BlockStored), "We should have a BlockStored event"
         assert len(event.block_hashes) == num_blocks, (
@@ -672,12 +657,9 @@ def test_kv_cache_events(
 )
 @multi_gpu_test(num_gpus=4)
 async def test_kv_cache_events_dp(
-    monkeypatch: pytest.MonkeyPatch,
     multiprocessing_mode: bool,
     publisher_config,
 ):
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
     block_size = 16
     num_blocks = 2
     dp_size = 2
@@ -765,8 +747,6 @@ async def test_kv_cache_events_dp(
 @pytest.mark.timeout(20)
 def test_startup_failure(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m, pytest.raises(Exception) as e_info:
-        m.setenv("VLLM_USE_V1", "1")
-
         # Monkey-patch to extract core process pid while it's starting.
         core_proc_pid = [None]
         cepm_ctor = CoreEngineProcManager.__init__
@@ -841,7 +821,6 @@ def test_engine_core_proc_instantiation_cuda_empty(monkeypatch: pytest.MonkeyPat
     mock_executor_class.side_effect = create_mock_executor
 
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
         m.setenv("CUDA_VISIBLE_DEVICES", "")  # No CUDA devices
 
         from vllm.v1.engine.utils import EngineZmqAddresses

tests/v1/engine/test_llm_engine.py

@@ -21,12 +21,10 @@ DTYPE = "half"
 def _vllm_model(
     apc: bool,
     vllm_runner: type[VllmRunner],
-    monkeypatch: pytest.MonkeyPatch,
     *,
     skip_tokenizer_init: bool = False,
 ):
     """Set up VllmRunner instance."""
-    monkeypatch.setenv("VLLM_USE_V1", "1")
     return vllm_runner(
         MODEL,
         dtype=DTYPE,
@@ -45,16 +43,16 @@ def _vllm_model(
     # Prefix caching
     params=[False, True],
 )
-def vllm_model(vllm_runner, request, monkeypatch):
+def vllm_model(vllm_runner, request):
     """VllmRunner test fixture parameterized by APC True/False."""
-    with _vllm_model(request.param, vllm_runner, monkeypatch) as vllm_model:
+    with _vllm_model(request.param, vllm_runner) as vllm_model:
         yield vllm_model
 
 
 @pytest.fixture(scope="function")
-def vllm_model_apc(vllm_runner, monkeypatch):
+def vllm_model_apc(vllm_runner):
     """VllmRunner test fixture with APC."""
-    with _vllm_model(True, vllm_runner, monkeypatch) as vllm_model:
+    with _vllm_model(True, vllm_runner) as vllm_model:
         yield vllm_model
 
 
@@ -65,12 +63,11 @@ def vllm_model_apc(vllm_runner, monkeypatch):
     # Prefix caching
     params=[False, True],
 )
-def vllm_model_skip_tokenizer_init(vllm_runner, request, monkeypatch):
+def vllm_model_skip_tokenizer_init(vllm_runner, request):
     """VllmRunner test fixture with APC."""
     with _vllm_model(
         request.param,
         vllm_runner,
-        monkeypatch,
         skip_tokenizer_init=True,
     ) as vllm_model:
         yield vllm_model
@@ -152,7 +149,7 @@ def test_parallel_sampling(vllm_model, example_prompts) -> None:
             )
 
 
-def test_engine_metrics(vllm_runner, monkeypatch, example_prompts):
+def test_engine_metrics(vllm_runner, example_prompts):
     max_tokens = 100
     # Use spec decoding to test num_accepted_tokens_per_pos
     speculative_config = {
@@ -161,7 +158,7 @@ def test_engine_metrics(vllm_runner, monkeypatch, example_prompts):
         "prompt_lookup_min": 3,
         "num_speculative_tokens": 5,
     }
-    monkeypatch.setenv("VLLM_USE_V1", "1")
+
     with vllm_runner(
         MODEL,
         speculative_config=speculative_config,
@@ -216,8 +213,7 @@ def test_engine_metrics(vllm_runner, monkeypatch, example_prompts):
 
 
 @pytest.mark.parametrize("model", ["meta-llama/Llama-3.2-1B-Instruct"])
-def test_skip_tokenizer_initialization(model: str, monkeypatch: pytest.MonkeyPatch):
-    monkeypatch.setenv("VLLM_USE_V1", "1")
+def test_skip_tokenizer_initialization(model: str):
     # This test checks if the flag skip_tokenizer_init skips the initialization
     # of tokenizer and detokenizer. The generated output is expected to contain
     # token ids.