[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,
@@ -126,17 +123,18 @@ 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:
-            default_outputs = llm_default.embed(prompts)
+        ) as llm_default,
+    ):
+        default_outputs = llm_default.embed(prompts)
 
     check_embeddings_close(
         embeddings_0_lst=flex_outputs,

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
@@ -40,39 +40,34 @@ 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(
-            model=model, disable_hybrid_kv_cache_manager=disable_hybrid_kv_cache_manager
-        )
-        sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
-
-        prompts, answer, indices = prep_prompts(
-            batch_size, ln_range=test_config.ln_range
-        )
-
-        check_length(prompts, llm, test_config.sliding_window)
-
-        # Fresh generation
-        responses = llm.generate(prompts, sampling_params)
-        check_answers(
-            indices,
-            answer,
-            [response.outputs[0].text for response in responses],
-            accept_rate=1.0,
-        )
-
-        # Re-generate with the same prompts to test prefix caching
-        responses = llm.generate(prompts, sampling_params)
-        check_answers(
-            indices,
-            answer,
-            [response.outputs[0].text for response in responses],
-            accept_rate=1.0,
-        )
+    test_config = model_config[model]
+
+    llm = LLM(
+        model=model, disable_hybrid_kv_cache_manager=disable_hybrid_kv_cache_manager
+    )
+    sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
+
+    prompts, answer, indices = prep_prompts(batch_size, ln_range=test_config.ln_range)
+
+    check_length(prompts, llm, test_config.sliding_window)
+
+    # Fresh generation
+    responses = llm.generate(prompts, sampling_params)
+    check_answers(
+        indices,
+        answer,
+        [response.outputs[0].text for response in responses],
+        accept_rate=1.0,
+    )
+
+    # Re-generate with the same prompts to test prefix caching
+    responses = llm.generate(prompts, sampling_params)
+    check_answers(
+        indices,
+        answer,
+        [response.outputs[0].text for response in responses],
+        accept_rate=1.0,
+    )
 
 
 def check_length(prompts: list[str], llm: LLM, sliding_window: int):

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_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")
 
@@ -592,76 +582,71 @@ 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
-
-        engine_args = EngineArgs(
-            model=MODEL_NAME,
-            enforce_eager=True,
-            enable_prefix_caching=True,
-            block_size=block_size,
-        )
-        engine_args.kv_events_config = publisher_config
+    block_size = 16
+    num_blocks = 2
+
+    engine_args = EngineArgs(
+        model=MODEL_NAME,
+        enforce_eager=True,
+        enable_prefix_caching=True,
+        block_size=block_size,
+    )
+    engine_args.kv_events_config = publisher_config
 
-        vllm_config = engine_args.create_engine_config(UsageContext.UNKNOWN_CONTEXT)
+    vllm_config = engine_args.create_engine_config(UsageContext.UNKNOWN_CONTEXT)
 
-        executor_class = Executor.get_class(vllm_config)
-        with set_default_torch_num_threads(1):
-            client = EngineCoreClient.make_client(
-                multiprocess_mode=multiprocessing_mode,
-                asyncio_mode=False,
-                vllm_config=vllm_config,
-                executor_class=executor_class,
-                log_stats=False,
-            )
-        endpoint = publisher_config.endpoint.replace("*", "127.0.0.1")
-        subscriber = MockSubscriber(
-            endpoint, topic=publisher_config.topic, decode_type=KVEventBatch
+    executor_class = Executor.get_class(vllm_config)
+    with set_default_torch_num_threads(1):
+        client = EngineCoreClient.make_client(
+            multiprocess_mode=multiprocessing_mode,
+            asyncio_mode=False,
+            vllm_config=vllm_config,
+            executor_class=executor_class,
+            log_stats=False,
         )
+    endpoint = publisher_config.endpoint.replace("*", "127.0.0.1")
+    subscriber = MockSubscriber(
+        endpoint, topic=publisher_config.topic, decode_type=KVEventBatch
+    )
 
-        try:
-            custom_tokens = list(range(num_blocks * block_size))
-            sampling_params = SamplingParams(max_tokens=1)
-            request = make_request(sampling_params, custom_tokens)
-            client.add_request(request)
+    try:
+        custom_tokens = list(range(num_blocks * block_size))
+        sampling_params = SamplingParams(max_tokens=1)
+        request = make_request(sampling_params, custom_tokens)
+        client.add_request(request)
 
-            outputs: dict[str, list] = {request.request_id: []}
-            loop_until_done(client, outputs)
+        outputs: dict[str, list] = {request.request_id: []}
+        loop_until_done(client, outputs)
 
-            result = subscriber.receive_one(timeout=1000)
-            assert result is not None, "No message received"
+        result = subscriber.receive_one(timeout=1000)
+        assert result is not None, "No message received"
 
-            seq, received = result
+        seq, received = result
 
-            assert seq == 0, "Sequence number mismatch"
-            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, (
-                "We should have a BlockStored event with 2 block_hashes"
-            )
-            assert event.block_size == block_size, (
-                "Block size should be the same as the block size"
-            )
-            assert event.parent_block_hash is None, "Parent block hash should be None"
-            assert event.lora_id is None, "Lora id should be None"
-            assert len(event.token_ids) == num_blocks * block_size, (
-                "Token ids should be the same as the custom tokens"
-            )
-            assert event.token_ids == custom_tokens, (
-                "Token ids should be the same as the custom tokens"
-            )
-        finally:
-            client.shutdown()
-            subscriber.close()
+        assert seq == 0, "Sequence number mismatch"
+        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, (
+            "We should have a BlockStored event with 2 block_hashes"
+        )
+        assert event.block_size == block_size, (
+            "Block size should be the same as the block size"
+        )
+        assert event.parent_block_hash is None, "Parent block hash should be None"
+        assert event.lora_id is None, "Lora id should be None"
+        assert len(event.token_ids) == num_blocks * block_size, (
+            "Token ids should be the same as the custom tokens"
+        )
+        assert event.token_ids == custom_tokens, (
+            "Token ids should be the same as the custom tokens"
+        )
+    finally:
+        client.shutdown()
+        subscriber.close()
 
 
 @pytest.mark.asyncio
@@ -672,101 +657,96 @@ 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
-        tp_size = 2
-
-        engine_args = EngineArgs(
-            model=MODEL_NAME,
-            enforce_eager=True,
-            enable_prefix_caching=True,
-            data_parallel_size=dp_size,
-            tensor_parallel_size=tp_size,
-            block_size=block_size,
-        )
-        engine_args.kv_events_config = publisher_config
+    block_size = 16
+    num_blocks = 2
+    dp_size = 2
+    tp_size = 2
+
+    engine_args = EngineArgs(
+        model=MODEL_NAME,
+        enforce_eager=True,
+        enable_prefix_caching=True,
+        data_parallel_size=dp_size,
+        tensor_parallel_size=tp_size,
+        block_size=block_size,
+    )
+    engine_args.kv_events_config = publisher_config
 
-        vllm_config = engine_args.create_engine_config(UsageContext.UNKNOWN_CONTEXT)
+    vllm_config = engine_args.create_engine_config(UsageContext.UNKNOWN_CONTEXT)
 
-        executor_class = Executor.get_class(vllm_config)
-        with set_default_torch_num_threads(1):
-            client = EngineCoreClient.make_client(
-                multiprocess_mode=multiprocessing_mode,
-                asyncio_mode=True,
-                vllm_config=vllm_config,
-                executor_class=executor_class,
-                log_stats=False,
-            )
-        await asyncio.sleep(1)
+    executor_class = Executor.get_class(vllm_config)
+    with set_default_torch_num_threads(1):
+        client = EngineCoreClient.make_client(
+            multiprocess_mode=multiprocessing_mode,
+            asyncio_mode=True,
+            vllm_config=vllm_config,
+            executor_class=executor_class,
+            log_stats=False,
+        )
+    await asyncio.sleep(1)
 
-        # Build endpoints for all DP ranks
-        base_endpoint = publisher_config.endpoint.replace("*", "127.0.0.1")
-        endpoints = []
-        for i in range(dp_size):
-            offset_endpoint = ZmqEventPublisher.offset_endpoint_port(base_endpoint, i)
-            endpoints.append(offset_endpoint)
+    # Build endpoints for all DP ranks
+    base_endpoint = publisher_config.endpoint.replace("*", "127.0.0.1")
+    endpoints = []
+    for i in range(dp_size):
+        offset_endpoint = ZmqEventPublisher.offset_endpoint_port(base_endpoint, i)
+        endpoints.append(offset_endpoint)
 
-        subscriber = MockSubscriber(
-            endpoints, topic=publisher_config.topic, decode_type=KVEventBatch
-        )
+    subscriber = MockSubscriber(
+        endpoints, topic=publisher_config.topic, decode_type=KVEventBatch
+    )
 
-        try:
-            custom_tokens = list(range(num_blocks * block_size))
-            sampling_params = SamplingParams(max_tokens=1)
-            all_request_ids = []
+    try:
+        custom_tokens = list(range(num_blocks * block_size))
+        sampling_params = SamplingParams(max_tokens=1)
+        all_request_ids = []
 
-            # Create and add 25 requests
-            # NOTE: attempts to force routing to both dp groups but can be flaky
-            for i in range(25):
-                await asyncio.sleep(0.01)
-                request = make_request(sampling_params, custom_tokens)
-                await client.add_request_async(request)
-                all_request_ids.append(request.request_id)
+        # Create and add 25 requests
+        # NOTE: attempts to force routing to both dp groups but can be flaky
+        for i in range(25):
+            await asyncio.sleep(0.01)
+            request = make_request(sampling_params, custom_tokens)
+            await client.add_request_async(request)
+            all_request_ids.append(request.request_id)
 
-            await asyncio.sleep(0.1)
+        await asyncio.sleep(0.1)
 
-            # Initialize outputs dict for all requests
-            outputs: dict[str, list] = {req_id: [] for req_id in all_request_ids}
+        # Initialize outputs dict for all requests
+        outputs: dict[str, list] = {req_id: [] for req_id in all_request_ids}
 
-            print("processing requests...")
-            await asyncio.wait_for(
-                loop_until_fully_done_async(client, outputs), timeout=20.0
-            )
+        print("processing requests...")
+        await asyncio.wait_for(
+            loop_until_fully_done_async(client, outputs), timeout=20.0
+        )
 
-            # Receive from subscriber until no more messages
-            print("collecting results...")
-            results = []
-            while True:
-                result = subscriber.receive_one(timeout=1)
-                print(result)
-                if result is None:
-                    break
-                results.append(result)
-
-            # Collect all events and data_parallel_ranks from all results
-            all_dp_ranks = [received.data_parallel_rank for (_, received) in results]
-            unique_dps = set(all_dp_ranks)
-            assert len(unique_dps) == 2, (
-                f"Expected 2 unique data_parallel_ranks, got {len(unique_dps)}"
-            )
+        # Receive from subscriber until no more messages
+        print("collecting results...")
+        results = []
+        while True:
+            result = subscriber.receive_one(timeout=1)
+            print(result)
+            if result is None:
+                break
+            results.append(result)
+
+        # Collect all events and data_parallel_ranks from all results
+        all_dp_ranks = [received.data_parallel_rank for (_, received) in results]
+        unique_dps = set(all_dp_ranks)
+        assert len(unique_dps) == 2, (
+            f"Expected 2 unique data_parallel_ranks, got {len(unique_dps)}"
+        )
 
-        finally:
-            client.shutdown()
-            subscriber.close()
+    finally:
+        client.shutdown()
+        subscriber.close()
 
 
 @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.