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

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

vllm/config/lora.py

@@ -9,7 +9,6 @@ from pydantic import ConfigDict, Field, model_validator
 from pydantic.dataclasses import dataclass
 from typing_extensions import Self
 
-import vllm.envs as envs
 from vllm.config.utils import config
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
@@ -106,10 +105,6 @@ class LoRAConfig:
 
         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):
         if self.lora_dtype in (None, "auto"):
             self.lora_dtype = model_config.dtype

vllm/config/model.py

@@ -32,7 +32,6 @@ from vllm.transformers_utils.config import (
     get_pooling_config,
     get_sentence_transformer_tokenizer_config,
     is_encoder_decoder,
-    is_interleaved,
     try_get_dense_modules,
     try_get_generation_config,
     try_get_safetensors_metadata,
@@ -442,15 +441,12 @@ class ModelConfig:
             self.enforce_eager = True
 
         # Set the default seed to 0 in V1.
-        # NOTE(woosuk): In V0, we set the default seed to None because the
-        # 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
+        # NOTE(woosuk): In V1, we use separate processes for workers (unless
         # VLLM_ENABLE_V1_MULTIPROCESSING=0), so setting a seed here
         # doesn't affect the user process. However, without a consistent seed,
         # different tensor parallel workers would sample different tokens,
         # leading to inconsistent results.
-        if envs.VLLM_USE_V1 and self.seed is None:
+        if self.seed is None:
             self.seed = 0
             if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
                 logger.warning(
@@ -703,23 +699,6 @@ class ModelConfig:
             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.max_model_len = self.get_and_verify_max_len(self.max_model_len)
         # Init multimodal config if needed

vllm/config/speculative.py

@@ -9,7 +9,6 @@ from pydantic import Field, SkipValidation, model_validator
 from pydantic.dataclasses import dataclass
 from typing_extensions import Self
 
-import vllm.envs as envs
 from vllm.config.parallel import ParallelConfig
 from vllm.config.utils import config
 from vllm.logger import init_logger
@@ -366,12 +365,6 @@ class SpeculativeConfig:
 
                 # Replace hf_config for EAGLE draft_model
                 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.eagle import EAGLEConfig
 

vllm/config/vllm.py

@@ -130,7 +130,6 @@ class VllmConfig:
         from vllm import __version__
 
         vllm_factors.append(__version__)
-        vllm_factors.append(envs.VLLM_USE_V1)
         if self.model_config:
             vllm_factors.append(self.model_config.compute_hash())
         else:
@@ -306,7 +305,6 @@ class VllmConfig:
         self.cache_config.verify_with_parallel_config(self.parallel_config)
 
         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)
 
         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
         # settings (see the below code).
         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
-                ):
-                    self.compilation_config.mode = CompilationMode.VLLM_COMPILE
-                else:
-                    self.compilation_config.mode = CompilationMode.NONE
-
+            if self.model_config is not None and not self.model_config.enforce_eager:
+                self.compilation_config.mode = CompilationMode.VLLM_COMPILE
             else:
-                # NB: Passing both --enforce-eager and a compilation mode
-                # in V0 means the compilation mode wins out.
                 self.compilation_config.mode = CompilationMode.NONE
         else:
             assert self.compilation_config.mode >= CompilationMode.NONE
@@ -371,10 +360,7 @@ class VllmConfig:
             # if cudagraph_mode is not explicitly set by users, set default
             # value
             if self.compilation_config.cudagraph_mode is None:
-                if (
-                    envs.VLLM_USE_V1
-                    and self.compilation_config.mode == CompilationMode.VLLM_COMPILE
-                ):
+                if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
                     # default to full and piecewise for most models
                     self.compilation_config.cudagraph_mode = (
                         CUDAGraphMode.FULL_AND_PIECEWISE
@@ -428,7 +414,7 @@ class VllmConfig:
                 # override related settings when enforce eager
                 self.compilation_config.max_cudagraph_capture_size = 0
                 self.compilation_config.cudagraph_capture_sizes = []
-            elif envs.VLLM_USE_V1:
+            else:
                 self.compilation_config.cudagraph_num_of_warmups = 1
 
             self._set_cudagraph_sizes()
@@ -535,14 +521,11 @@ class VllmConfig:
         current_platform.check_and_update_config(self)
 
         # Do this after all the updates to compilation_config.mode
-        if (
-            envs.VLLM_USE_V1
-            and self.compilation_config.mode == CompilationMode.VLLM_COMPILE
-        ):
+        if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
             self.compilation_config.set_splitting_ops_for_v1()
 
         # 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 (
                 self.compilation_config.cudagraph_mode.has_full_cudagraphs()
                 and self.model_config is not None
@@ -587,10 +570,7 @@ class VllmConfig:
         if not self.instance_id:
             self.instance_id = random_uuid()[:5]
 
-        if (
-            envs.VLLM_USE_V1
-            and not self.scheduler_config.disable_hybrid_kv_cache_manager
-        ):
+        if not self.scheduler_config.disable_hybrid_kv_cache_manager:
             # 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
             # warning message here and will log it later.