[V0 deprecation] Remove VLLM_USE_V1 usage in most modules (#27955)

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

docs/usage/v1_guide.md

@@ -6,8 +6,6 @@
 
 V1 is now enabled by default for all supported use cases, and we will gradually enable it for every use case we plan to support. Please share any feedback on [GitHub](https://github.com/vllm-project/vllm) or in the [vLLM Slack](https://inviter.co/vllm-slack).
 
-To disable V1, please set the environment variable as: `VLLM_USE_V1=0`, and send us a GitHub issue sharing the reason!
-
 ## Why vLLM V1?
 
 vLLM V0 successfully supported a wide range of models and hardware, but as new features were developed independently, the system grew increasingly complex. This complexity made it harder to integrate new capabilities and introduced technical debt, revealing the need for a more streamlined and unified design.

tests/conftest.py

@@ -154,26 +154,6 @@ AUDIO_ASSETS = AudioTestAssets()
 """Singleton instance of {class}`AudioTestAssets`."""
 
 
-@pytest.fixture(scope="function", autouse=True)
-def cleanup_VLLM_USE_V1(monkeypatch):
-    """
-    The V1 oracle sets "VLLM_USE_V1" during loading. This means
-    that each invocation of a test change the env variable.
-
-    If we touch "VLLM_USE_V1" with monkeypatch, then any changes
-    made during the test run by vLLM will be cleaned up.
-
-    This fixture is used by every test.
-    """
-
-    # If VLLM_USE_V1 is not set, set then delete. This will
-    # cause monkeypatch to clean up VLLM_USE_V1 upon exit
-    # if VLLM modifies the value of envs.VLLM_USE_V1.
-    if "VLLM_USE_V1" not in os.environ:
-        monkeypatch.setenv("VLLM_USE_V1", "")
-        monkeypatch.delenv("VLLM_USE_V1")
-
-
 @pytest.fixture(autouse=True)
 def init_test_http_connection():
     # pytest_asyncio may use a different event loop per test

tests/v1/engine/test_async_llm.py

@@ -424,15 +424,12 @@ async def test_customize_loggers(monkeypatch):
 
 
 @pytest.mark.asyncio
-async def test_customize_aggregated_loggers(monkeypatch):
+async def test_customize_aggregated_loggers():
     """Test that we can customize the aggregated loggers.
     If a customized logger is provided at the init, it should
     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,

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -868,11 +868,8 @@ def test_structured_output_batched_with_non_structured_outputs_requests(
 
 @pytest.mark.parametrize("guided_decoding_backend", ["xgrammar"])
 def test_structured_output_with_structural_tag(
-    monkeypatch: pytest.MonkeyPatch,
     guided_decoding_backend: str,
 ):
-    monkeypatch.setenv("VLLM_USE_V1", "1")
-
     llm = LLM(
         model="Qwen/Qwen2.5-1.5B-Instruct",
         guided_decoding_backend=guided_decoding_backend,

tests/v1/sample/test_logprobs.py

@@ -530,7 +530,6 @@ def test_logprobs_mode(logprobs_mode: LogprobsMode):
 def test_spec_decode_logprobs(
     logprobs_mode: LogprobsMode,
     model_setup: tuple[str, str, str],
-    monkeypatch: pytest.MonkeyPatch,
 ):
     """Spec decode logprobs should match those of the base model.
 
@@ -541,64 +540,62 @@ def test_spec_decode_logprobs(
     """
     from vllm import LLM
 
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-        prompt = "Hello world"
-        sampling_params = SamplingParams(
-            temperature=0, logprobs=3, max_tokens=10, ignore_eos=False
-        )
-        method, model_name, spec_model_name = model_setup
-        max_model_len = 256
+    prompt = "Hello world"
+    sampling_params = SamplingParams(
+        temperature=0, logprobs=3, max_tokens=10, ignore_eos=False
+    )
+    method, model_name, spec_model_name = model_setup
+    max_model_len = 256
 
-        # Run base LLM.
-        ref_llm = LLM(
-            model=model_name,
-            max_logprobs=5,
-            max_model_len=max_model_len,
-            seed=42,
-            logprobs_mode=logprobs_mode,
-            gpu_memory_utilization=0.4,
-        )
-        ref_results = ref_llm.generate([prompt], sampling_params)
-        # Collect logprobs outputs from reference LLM.
-        ref_logprobs = []
-        for output in ref_results[0].outputs:
-            for logprobs in output.logprobs:
-                for token_id in logprobs:
-                    ref_logprobs.append(logprobs[token_id])
-        del ref_llm
-        torch.cuda.empty_cache()
-        cleanup_dist_env_and_memory()
+    # Run base LLM.
+    ref_llm = LLM(
+        model=model_name,
+        max_logprobs=5,
+        max_model_len=max_model_len,
+        seed=42,
+        logprobs_mode=logprobs_mode,
+        gpu_memory_utilization=0.4,
+    )
+    ref_results = ref_llm.generate([prompt], sampling_params)
+    # Collect logprobs outputs from reference LLM.
+    ref_logprobs = []
+    for output in ref_results[0].outputs:
+        for logprobs in output.logprobs:
+            for token_id in logprobs:
+                ref_logprobs.append(logprobs[token_id])
+    del ref_llm
+    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
 
-        # Run spec decode LLM.
-        spec_llm = LLM(
-            model_name,
-            speculative_config={
-                "method": method,
-                "model": spec_model_name,
-                "num_speculative_tokens": 3,
-                "max_model_len": max_model_len,
-            },
-            max_logprobs=5,
-            max_model_len=max_model_len,
-            seed=42,
-            logprobs_mode=logprobs_mode,
-            gpu_memory_utilization=0.4,
-        )
-        spec_results = spec_llm.generate([prompt], sampling_params)
-        # Collect logprobs outputs from spec decode LLM.
-        spec_logprobs = []
-        for output in spec_results[0].outputs:
-            for logprobs in output.logprobs:
-                for token_id in logprobs:
-                    spec_logprobs.append(logprobs[token_id])
-        del spec_llm
-        torch.cuda.empty_cache()
-        cleanup_dist_env_and_memory()
+    # Run spec decode LLM.
+    spec_llm = LLM(
+        model_name,
+        speculative_config={
+            "method": method,
+            "model": spec_model_name,
+            "num_speculative_tokens": 3,
+            "max_model_len": max_model_len,
+        },
+        max_logprobs=5,
+        max_model_len=max_model_len,
+        seed=42,
+        logprobs_mode=logprobs_mode,
+        gpu_memory_utilization=0.4,
+    )
+    spec_results = spec_llm.generate([prompt], sampling_params)
+    # Collect logprobs outputs from spec decode LLM.
+    spec_logprobs = []
+    for output in spec_results[0].outputs:
+        for logprobs in output.logprobs:
+            for token_id in logprobs:
+                spec_logprobs.append(logprobs[token_id])
+    del spec_llm
+    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
 
-        # Per-token logprobs are expected to be the same.
-        assert len(ref_logprobs) == len(spec_logprobs)
-        for ref_logprob, spec_logprob in zip(ref_logprobs, spec_logprobs):
-            assert math.isclose(ref_logprob.logprob, spec_logprob.logprob, abs_tol=1e-3)
-            assert ref_logprob.rank == spec_logprob.rank
-            assert ref_logprob.decoded_token == spec_logprob.decoded_token
+    # Per-token logprobs are expected to be the same.
+    assert len(ref_logprobs) == len(spec_logprobs)
+    for ref_logprob, spec_logprob in zip(ref_logprobs, spec_logprobs):
+        assert math.isclose(ref_logprob.logprob, spec_logprob.logprob, abs_tol=1e-3)
+        assert ref_logprob.rank == spec_logprob.rank
+        assert ref_logprob.decoded_token == spec_logprob.decoded_token

vllm/attention/layers/chunked_local_attention.py

@@ -5,7 +5,6 @@ from typing import ClassVar
 
 import torch
 
-from vllm import envs
 from vllm.attention.backends.abstract import AttentionBackend, AttentionMetadata
 from vllm.attention.selector import get_attn_backend
 from vllm.config import CacheConfig
@@ -78,17 +77,12 @@ class ChunkedLocalAttention(Attention):
             kv_cache_dtype = "auto"
             block_size = 16
 
-        if envs.VLLM_USE_V1:
-            underlying_attn_backend = get_attn_backend(
-                head_size, dtype, kv_cache_dtype, block_size
-            )
-
-            attn_backend = create_chunked_local_attention_backend(
-                underlying_attn_backend, attention_chunk_size, block_size
-            )
-        else:
-            # in v0 the local attention is handled inside the backends
-            attn_backend = None
+        underlying_attn_backend = get_attn_backend(
+            head_size, dtype, kv_cache_dtype, block_size
+        )
+        attn_backend = create_chunked_local_attention_backend(
+            underlying_attn_backend, attention_chunk_size, block_size
+        )
 
         super().__init__(
             num_heads=num_heads,

vllm/attention/layers/cross_attention.py

@@ -6,7 +6,6 @@ from copy import copy
 import numpy as np
 import torch
 
-from vllm import envs
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionMetadata,
@@ -150,15 +149,10 @@ class CrossAttention(Attention):
             kv_cache_dtype = "auto"
             block_size = 16
 
-        if envs.VLLM_USE_V1:
-            underlying_attn_backend = get_attn_backend(
-                head_size, dtype, kv_cache_dtype, block_size
-            )
-
-            attn_backend = create_cross_attention_backend(underlying_attn_backend)
-        else:
-            # in v0 cross attention is handled inside the backends
-            attn_backend = None
+        underlying_attn_backend = get_attn_backend(
+            head_size, dtype, kv_cache_dtype, block_size
+        )
+        attn_backend = create_cross_attention_backend(underlying_attn_backend)
 
         if attn_type is not None:
             assert attn_type == AttentionType.ENCODER_DECODER, (

vllm/attention/layers/encoder_only_attention.py

@@ -5,7 +5,6 @@ from copy import copy
 
 import torch
 
-from vllm import envs
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionMetadata,
@@ -74,17 +73,11 @@ class EncoderOnlyAttention(Attention):
             kv_cache_dtype = "auto"
             block_size = 16
 
-        if envs.VLLM_USE_V1:
-            underlying_attn_backend = get_attn_backend(
-                head_size, dtype, kv_cache_dtype, block_size
-            )
+        underlying_attn_backend = get_attn_backend(
+            head_size, dtype, kv_cache_dtype, block_size
+        )
 
-            attn_backend = create_encoder_only_attention_backend(
-                underlying_attn_backend
-            )
-        else:
-            # in v0 encoder only attention is handled inside the backends
-            attn_backend = None
+        attn_backend = create_encoder_only_attention_backend(underlying_attn_backend)
 
         if attn_type is not None:
             assert attn_type == AttentionType.ENCODER_ONLY, (

vllm/attention/selector.py

@@ -134,16 +134,11 @@ def get_attn_backend(
     use_sparse: bool = False,
 ) -> type[AttentionBackend]:
     """Selects which attention backend to use and lazily imports it."""
-    # Accessing envs.* behind an @lru_cache decorator can cause the wrong
-    # value to be returned from the cache if the value changes between calls.
-    # To avoid this, we read envs.VLLM_USE_V1 here and pass it explicitly to the
-    # private function.
     return _cached_get_attn_backend(
         head_size=head_size,
         dtype=dtype,
         kv_cache_dtype=kv_cache_dtype,
         block_size=block_size,
-        use_v1=envs.VLLM_USE_V1,
         use_mla=use_mla,
         has_sink=has_sink,
         use_sparse=use_sparse,
@@ -156,7 +151,6 @@ def _cached_get_attn_backend(
     dtype: torch.dtype,
     kv_cache_dtype: str | None,
     block_size: int,
-    use_v1: bool = False,
     use_mla: bool = False,
     has_sink: bool = False,
     use_sparse: bool = False,
@@ -199,7 +193,7 @@ def _cached_get_attn_backend(
         dtype,
         kv_cache_dtype,
         block_size,
-        use_v1,
+        True,
         use_mla,
         has_sink,
         use_sparse,

vllm/distributed/kv_transfer/kv_connector/factory.py

@@ -5,7 +5,6 @@ import importlib
 from collections.abc import Callable
 from typing import TYPE_CHECKING, Optional, cast
 
-import vllm.envs as envs
 from vllm.distributed.kv_transfer.kv_connector.base import (
     KVConnectorBase,
     KVConnectorBaseType,
@@ -47,12 +46,6 @@ class KVConnectorFactory:
         role: KVConnectorRole,
         kv_cache_config: Optional["KVCacheConfig"] = None,
     ) -> KVConnectorBase:
-        if not envs.VLLM_USE_V1:
-            raise ValueError(
-                "Attempting to initialize a V1 Connector, "
-                f"but found {envs.VLLM_USE_V1=}"
-            )
-
         kv_transfer_config = config.kv_transfer_config
         if kv_transfer_config is None:
             raise ValueError("kv_transfer_config must be set to create a connector")

vllm/distributed/kv_transfer/kv_transfer_state.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from typing import TYPE_CHECKING, Optional
 
-from vllm import envs
 from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBaseType
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
 from vllm.distributed.kv_transfer.kv_connector.v1 import (
@@ -65,14 +64,11 @@ def ensure_kv_transfer_initialized(
         vllm_config.kv_transfer_config.is_kv_transfer_instance
         and _KV_CONNECTOR_AGENT is None
     ):
-        if envs.VLLM_USE_V1:
-            _KV_CONNECTOR_AGENT = KVConnectorFactory.create_connector(
-                config=vllm_config,
-                role=KVConnectorRole.WORKER,
-                kv_cache_config=kv_cache_config,
-            )
-        else:
-            raise ValueError("V0 is no longer supported")
+        _KV_CONNECTOR_AGENT = KVConnectorFactory.create_connector(
+            config=vllm_config,
+            role=KVConnectorRole.WORKER,
+            kv_cache_config=kv_cache_config,
+        )
 
 
 def ensure_kv_transfer_shutdown() -> None:

vllm/entrypoints/cli/serve.py

@@ -88,9 +88,6 @@ def run_headless(args: argparse.Namespace):
         usage_context=usage_context, headless=True
     )
 
-    if not envs.VLLM_USE_V1:
-        raise ValueError("Headless mode is only supported for V1")
-
     if engine_args.data_parallel_hybrid_lb:
         raise ValueError("data_parallel_hybrid_lb is not applicable in headless mode")
 
@@ -156,15 +153,10 @@ def run_multi_api_server(args: argparse.Namespace):
     usage_context = UsageContext.OPENAI_API_SERVER
     vllm_config = engine_args.create_engine_config(usage_context=usage_context)
 
-    if num_api_servers > 1:
-        if not envs.VLLM_USE_V1:
-            raise ValueError("api_server_count > 1 is only supported for V1")
-
-        if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
-            raise ValueError(
-                "VLLM_ALLOW_RUNTIME_LORA_UPDATING cannot be used "
-                "with api_server_count > 1"
-            )
+    if num_api_servers > 1 and envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
+        raise ValueError(
+            "VLLM_ALLOW_RUNTIME_LORA_UPDATING cannot be used with api_server_count > 1"
+        )
 
     executor_class = Executor.get_class(vllm_config)
     log_stats = not engine_args.disable_log_stats

vllm/entrypoints/openai/api_server.py

@@ -220,14 +220,8 @@ async def build_async_engine_client_from_engine_args(
     # Create the EngineConfig (determines if we can use V1).
     vllm_config = engine_args.create_engine_config(usage_context=usage_context)
 
-    # V1 AsyncLLM.
-    assert envs.VLLM_USE_V1
-
     if disable_frontend_multiprocessing:
-        logger.warning(
-            "V1 is enabled, but got --disable-frontend-multiprocessing. "
-            "To disable frontend multiprocessing, set VLLM_USE_V1=0."
-        )
+        logger.warning("V1 is enabled, but got --disable-frontend-multiprocessing.")
 
     from vllm.v1.engine.async_llm import AsyncLLM
 

vllm/entrypoints/openai/protocol.py

@@ -79,7 +79,6 @@ from pydantic import (
     model_validator,
 )
 
-from vllm import envs
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam, make_tool_call_id
 from vllm.entrypoints.score_utils import ScoreContentPartParam, ScoreMultiModalParam
 from vllm.logger import init_logger
@@ -475,16 +474,12 @@ class ResponsesRequest(OpenAIBaseModel):
 
     @model_validator(mode="before")
     def check_cache_salt_support(cls, data):
-        if data.get("cache_salt") is not None:
-            if not envs.VLLM_USE_V1:
-                raise ValueError(
-                    "Parameter 'cache_salt' is not supported with "
-                    "this instance of vLLM, which uses engine V0."
-                )
-            if not isinstance(data["cache_salt"], str) or not data["cache_salt"]:
-                raise ValueError(
-                    "Parameter 'cache_salt' must be a non-empty string if provided."
-                )
+        if data.get("cache_salt") is not None and (
+            not isinstance(data["cache_salt"], str) or not data["cache_salt"]
+        ):
+            raise ValueError(
+                "Parameter 'cache_salt' must be a non-empty string if provided."
+            )
         return data
 
     @model_validator(mode="before")
@@ -946,10 +941,6 @@ class ChatCompletionRequest(OpenAIBaseModel):
 
             if prompt_logprobs < 0 and prompt_logprobs != -1:
                 raise ValueError("`prompt_logprobs` must be a positive value or -1.")
-            if prompt_logprobs == -1 and not envs.VLLM_USE_V1:
-                raise ValueError(
-                    "`prompt_logprobs=-1` is only supported with vLLM engine V1."
-                )
         if (top_logprobs := data.get("top_logprobs")) is not None:
             if top_logprobs < 0 and top_logprobs != -1:
                 raise ValueError("`top_logprobs` must be a positive value or -1.")
@@ -1083,16 +1074,12 @@ class ChatCompletionRequest(OpenAIBaseModel):
     @model_validator(mode="before")
     @classmethod
     def check_cache_salt_support(cls, data):
-        if data.get("cache_salt") is not None:
-            if not envs.VLLM_USE_V1:
-                raise ValueError(
-                    "Parameter 'cache_salt' is not supported with "
-                    "this instance of vLLM, which uses engine V0."
-                )
-            if not isinstance(data["cache_salt"], str) or not data["cache_salt"]:
-                raise ValueError(
-                    "Parameter 'cache_salt' must be a non-empty string if provided."
-                )
+        if data.get("cache_salt") is not None and (
+            not isinstance(data["cache_salt"], str) or not data["cache_salt"]
+        ):
+            raise ValueError(
+                "Parameter 'cache_salt' must be a non-empty string if provided."
+            )
         return data
 
 
@@ -1449,10 +1436,6 @@ class CompletionRequest(OpenAIBaseModel):
 
             if prompt_logprobs < 0 and prompt_logprobs != -1:
                 raise ValueError("`prompt_logprobs` must be a positive value or -1.")
-            if prompt_logprobs == -1 and not envs.VLLM_USE_V1:
-                raise ValueError(
-                    "`prompt_logprobs=-1` is only supported with vLLM engine V1."
-                )
         if (logprobs := data.get("logprobs")) is not None and logprobs < 0:
             raise ValueError("`logprobs` must be a positive value.")
 
@@ -1487,16 +1470,12 @@ class CompletionRequest(OpenAIBaseModel):
     @model_validator(mode="before")
     @classmethod
     def check_cache_salt_support(cls, data):
-        if data.get("cache_salt") is not None:
-            if not envs.VLLM_USE_V1:
-                raise ValueError(
-                    "Parameter 'cache_salt' is not supported with "
-                    "this instance of vLLM, which uses engine V0."
-                )
-            if not isinstance(data["cache_salt"], str) or not data["cache_salt"]:
-                raise ValueError(
-                    "Parameter 'cache_salt' must be a non-empty string if provided."
-                )
+        if data.get("cache_salt") is not None and (
+            not isinstance(data["cache_salt"], str) or not data["cache_salt"]
+        ):
+            raise ValueError(
+                "Parameter 'cache_salt' must be a non-empty string if provided."
+            )
         return data
 
 

vllm/model_executor/model_loader/tensorizer.py

@@ -726,8 +726,6 @@ def tensorize_vllm_model(
         ) as stream:
             stream.write(encryption_params.key)
 
-    assert envs.VLLM_USE_V1
-
     from vllm.v1.engine.llm_engine import LLMEngine
 
     engine = LLMEngine.from_vllm_config(engine_config)

vllm/model_executor/models/config.py

@@ -285,10 +285,6 @@ class MambaModelConfig(VerifyAndUpdateConfig):
         Args:
             vllm_config: vLLM Config
         """
-
-        if not envs.VLLM_USE_V1:
-            return
-
         model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
 
@@ -329,10 +325,6 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
         Args:
             vllm_config: vLLM Config
         """
-
-        if not envs.VLLM_USE_V1:
-            return
-
         # Save the user input before it gets modified by MambaModelConfig
         mamba_block_size = vllm_config.cache_config.mamba_block_size
         # Enable FULL_AND_PIECEWISE by default

vllm/model_executor/models/gemma3_mm.py

@@ -9,7 +9,6 @@ from torch import nn
 from transformers import BatchFeature, Gemma3Config, Gemma3Processor
 from transformers.models.gemma3.processing_gemma3 import Gemma3ProcessorKwargs
 
-import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.logger import init_logger
@@ -137,11 +136,10 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
         if not do_pan_and_scan:
             return 0
 
-        if envs.VLLM_USE_V1:
-            logger.warning_once(
-                "`do_pan_and_scan=True` has suboptimal results on V1 "
-                "because of the simplified attention pattern being used."
-            )
+        logger.warning_once(
+            "`do_pan_and_scan=True` has suboptimal results on V1 "
+            "because of the simplified attention pattern being used."
+        )
 
         # Based on Gemma3ImageProcessor.pan_and_scan
         if image_width >= image_height:

vllm/model_executor/models/utils.py

@@ -12,7 +12,6 @@ from torch.func import functional_call
 from transformers import PretrainedConfig
 from typing_extensions import deprecated
 
-import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
@@ -576,11 +575,8 @@ def maybe_offload_to_cpu(module: torch.nn.Module) -> torch.nn.Module:
     pin_memory = is_pin_memory_available()
     uva_available = is_uva_available()
 
-    if envs.VLLM_USE_V1:
-        assert uva_available, "V1 CPU offloading requires uva (pin memory) support"
-        uva_offloading = True
-    else:
-        uva_offloading = False
+    assert uva_available, "V1 CPU offloading requires uva (pin memory) support"
+    uva_offloading = True
 
     # offload parameters to CPU
     # use pin_memory if possible, which helps cudagraph capture speed

vllm/multimodal/profiling.py

@@ -9,7 +9,6 @@ import numpy as np
 import numpy.typing as npt
 from PIL import Image
 
-import vllm.envs as envs
 from vllm.config.multimodal import (
     AudioDummyOptions,
     BaseDummyOptions,
@@ -306,18 +305,6 @@ class MultiModalProfiler(Generic[_I]):
         if processor.pad_dummy_encoder_prompt:
             num_tokens_to_pad = max(total_len, seq_len) - total_len
             encoder_prompt_token_ids.extend([0] * num_tokens_to_pad)
-        # NOTE: Whisper allows total_len > seq_len.
-        elif total_len > seq_len and not envs.VLLM_USE_V1:
-            # `max_num_batched_tokens` is defined by `SchedulerConfig`
-            logger.warning_once(
-                "The encoder sequence length used for profiling (max_num_batched_tokens / max_num_seqs = %d) "  # noqa: E501
-                "is too short to hold the multi-modal embeddings in the worst case (%d tokens in total, out of which %s are reserved for multi-modal embeddings). "  # noqa: E501
-                "This may cause certain multi-modal inputs to fail during inference, even when the input text is short. "  # noqa: E501
-                "To avoid this, you should increase `max_model_len`, reduce `max_num_seqs`, and/or reduce `mm_counts`.",  # noqa: E501
-                seq_len,
-                total_len,
-                str(self._get_mm_num_tokens(mm_inputs)),
-            )
 
         return DummyEncoderData(encoder_prompt_token_ids)