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[V0 deprecation] Remove VLLM_USE_V1 usage in config module (#27784)

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Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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wangxiyuan 2025-10-30 17:42:49 +08:00 committed by GitHub
parent e806178d2a
commit af826e0820
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4 changed files with 9 additions and 62 deletions
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5
vllm/config/lora.py
View File

@ -9,7 +9,6 @@ from pydantic import ConfigDict, Field, model_validator
from pydantic.dataclasses import dataclass from pydantic.dataclasses import dataclass
from typing_extensions import Self from typing_extensions import Self
import vllm.envs as envs
from vllm.config.utils import config from vllm.config.utils import config
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.platforms import current_platform from vllm.platforms import current_platform
@ -106,10 +105,6 @@ class LoRAConfig:
return self return self
def verify_with_cache_config(self, cache_config: CacheConfig):
if cache_config.cpu_offload_gb > 0 and not envs.VLLM_USE_V1:
raise ValueError("V0 LoRA does not support CPU offload, please use V1.")
def verify_with_model_config(self, model_config: ModelConfig): def verify_with_model_config(self, model_config: ModelConfig):
if self.lora_dtype in (None, "auto"): if self.lora_dtype in (None, "auto"):
self.lora_dtype = model_config.dtype self.lora_dtype = model_config.dtype

25
vllm/config/model.py
View File

@ -32,7 +32,6 @@ from vllm.transformers_utils.config import (
get_pooling_config, get_pooling_config,
get_sentence_transformer_tokenizer_config, get_sentence_transformer_tokenizer_config,
is_encoder_decoder, is_encoder_decoder,
is_interleaved,
try_get_dense_modules, try_get_dense_modules,
try_get_generation_config, try_get_generation_config,
try_get_safetensors_metadata, try_get_safetensors_metadata,
@ -442,15 +441,12 @@ class ModelConfig:
self.enforce_eager = True self.enforce_eager = True
# Set the default seed to 0 in V1. # Set the default seed to 0 in V1.
# NOTE(woosuk): In V0, we set the default seed to None because the # NOTE(woosuk): In V1, we use separate processes for workers (unless
# driver worker shares the same process as the user process, and thus
# setting a seed affects the user process as well.
# In V1, we use separate processes for workers (unless
# VLLM_ENABLE_V1_MULTIPROCESSING=0), so setting a seed here # VLLM_ENABLE_V1_MULTIPROCESSING=0), so setting a seed here
# doesn't affect the user process. However, without a consistent seed, # doesn't affect the user process. However, without a consistent seed,
# different tensor parallel workers would sample different tokens, # different tensor parallel workers would sample different tokens,
# leading to inconsistent results. # leading to inconsistent results.
if envs.VLLM_USE_V1 and self.seed is None: if self.seed is None:
self.seed = 0 self.seed = 0
if not envs.VLLM_ENABLE_V1_MULTIPROCESSING: if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
logger.warning( logger.warning(
@ -703,23 +699,6 @@ class ModelConfig:
revision=self.revision, revision=self.revision,
) )
# Interleaved attention is not supported by some backends in V0
if (
not self.disable_sliding_window
and is_interleaved(self.hf_text_config)
and not envs.VLLM_USE_V1
and (backend := envs.VLLM_ATTENTION_BACKEND) in ("XFORMERS", "FLASHINFER")
):
logger.warning_once(
"%s has interleaved attention, which is currently not "
"supported by the %s backend. Disabling sliding window and "
"capping the max length to the sliding window size (%d).",
self.hf_text_config.model_type,
backend,
self.hf_text_config.sliding_window,
)
self.disable_sliding_window = True
self.original_max_model_len = self.max_model_len self.original_max_model_len = self.max_model_len
self.max_model_len = self.get_and_verify_max_len(self.max_model_len) self.max_model_len = self.get_and_verify_max_len(self.max_model_len)
# Init multimodal config if needed # Init multimodal config if needed

7
vllm/config/speculative.py
View File

@ -9,7 +9,6 @@ from pydantic import Field, SkipValidation, model_validator
from pydantic.dataclasses import dataclass from pydantic.dataclasses import dataclass
from typing_extensions import Self from typing_extensions import Self
import vllm.envs as envs
from vllm.config.parallel import ParallelConfig from vllm.config.parallel import ParallelConfig
from vllm.config.utils import config from vllm.config.utils import config
from vllm.logger import init_logger from vllm.logger import init_logger
@ -366,12 +365,6 @@ class SpeculativeConfig:
# Replace hf_config for EAGLE draft_model # Replace hf_config for EAGLE draft_model
if self.method in ("eagle", "eagle3"): if self.method in ("eagle", "eagle3"):
if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
raise ValueError(
"Chunked prefill and EAGLE are not compatible "
"when using V0."
)
from vllm.transformers_utils.configs import SpeculatorsConfig from vllm.transformers_utils.configs import SpeculatorsConfig
from vllm.transformers_utils.configs.eagle import EAGLEConfig from vllm.transformers_utils.configs.eagle import EAGLEConfig

34
vllm/config/vllm.py
View File

@ -130,7 +130,6 @@ class VllmConfig:
from vllm import __version__ from vllm import __version__
vllm_factors.append(__version__) vllm_factors.append(__version__)
vllm_factors.append(envs.VLLM_USE_V1)
if self.model_config: if self.model_config:
vllm_factors.append(self.model_config.compute_hash()) vllm_factors.append(self.model_config.compute_hash())
else: else:
@ -306,7 +305,6 @@ class VllmConfig:
self.cache_config.verify_with_parallel_config(self.parallel_config) self.cache_config.verify_with_parallel_config(self.parallel_config)
if self.lora_config is not None: if self.lora_config is not None:
self.lora_config.verify_with_cache_config(self.cache_config)
self.lora_config.verify_with_model_config(self.model_config) self.lora_config.verify_with_model_config(self.model_config)
if self.quant_config is None and self.model_config is not None: if self.quant_config is None and self.model_config is not None:
@ -332,18 +330,9 @@ class VllmConfig:
# we use the default mode. The default mode depends on other # we use the default mode. The default mode depends on other
# settings (see the below code). # settings (see the below code).
if self.compilation_config.mode is None: if self.compilation_config.mode is None:
if envs.VLLM_USE_V1: if self.model_config is not None and not self.model_config.enforce_eager:
if ( self.compilation_config.mode = CompilationMode.VLLM_COMPILE
self.model_config is not None
and not self.model_config.enforce_eager
):
self.compilation_config.mode = CompilationMode.VLLM_COMPILE
else:
self.compilation_config.mode = CompilationMode.NONE
else: else:
# NB: Passing both --enforce-eager and a compilation mode
# in V0 means the compilation mode wins out.
self.compilation_config.mode = CompilationMode.NONE self.compilation_config.mode = CompilationMode.NONE
else: else:
assert self.compilation_config.mode >= CompilationMode.NONE assert self.compilation_config.mode >= CompilationMode.NONE
@ -371,10 +360,7 @@ class VllmConfig:
# if cudagraph_mode is not explicitly set by users, set default # if cudagraph_mode is not explicitly set by users, set default
# value # value
if self.compilation_config.cudagraph_mode is None: if self.compilation_config.cudagraph_mode is None:
if ( if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
envs.VLLM_USE_V1
and self.compilation_config.mode == CompilationMode.VLLM_COMPILE
):
# default to full and piecewise for most models # default to full and piecewise for most models
self.compilation_config.cudagraph_mode = ( self.compilation_config.cudagraph_mode = (
CUDAGraphMode.FULL_AND_PIECEWISE CUDAGraphMode.FULL_AND_PIECEWISE
@ -428,7 +414,7 @@ class VllmConfig:
# override related settings when enforce eager # override related settings when enforce eager
self.compilation_config.max_cudagraph_capture_size = 0 self.compilation_config.max_cudagraph_capture_size = 0
self.compilation_config.cudagraph_capture_sizes = [] self.compilation_config.cudagraph_capture_sizes = []
elif envs.VLLM_USE_V1: else:
self.compilation_config.cudagraph_num_of_warmups = 1 self.compilation_config.cudagraph_num_of_warmups = 1
self._set_cudagraph_sizes() self._set_cudagraph_sizes()
@ -535,14 +521,11 @@ class VllmConfig:
current_platform.check_and_update_config(self) current_platform.check_and_update_config(self)
# Do this after all the updates to compilation_config.mode # Do this after all the updates to compilation_config.mode
if ( if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
envs.VLLM_USE_V1
and self.compilation_config.mode == CompilationMode.VLLM_COMPILE
):
self.compilation_config.set_splitting_ops_for_v1() self.compilation_config.set_splitting_ops_for_v1()
# final check of cudagraph mode after all possible updates # final check of cudagraph mode after all possible updates
if envs.VLLM_USE_V1 and current_platform.is_cuda_alike(): if current_platform.is_cuda_alike():
if ( if (
self.compilation_config.cudagraph_mode.has_full_cudagraphs() self.compilation_config.cudagraph_mode.has_full_cudagraphs()
and self.model_config is not None and self.model_config is not None
@ -587,10 +570,7 @@ class VllmConfig:
if not self.instance_id: if not self.instance_id:
self.instance_id = random_uuid()[:5] self.instance_id = random_uuid()[:5]
if ( if not self.scheduler_config.disable_hybrid_kv_cache_manager:
envs.VLLM_USE_V1
and not self.scheduler_config.disable_hybrid_kv_cache_manager
):
# logger should only print warning message for hybrid models. As we # logger should only print warning message for hybrid models. As we
# can't know whether the model is hybrid or not now, so we don't log # can't know whether the model is hybrid or not now, so we don't log
# warning message here and will log it later. # warning message here and will log it later.