[V0 Deprecation] Remove V0 sampling metadata (#25345)

Signed-off-by: Woosuk Kwon <woosuk@thinkingmachines.ai>

vllm/model_executor/models/qwen3_next.py

@@ -53,7 +53,6 @@ from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, sharded_weight_loader)
 from vllm.model_executor.models.mamba_cache import MambaCacheParams
 from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
@@ -1208,10 +1207,8 @@ class Qwen3NextForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        return self.logits_processor(self.lm_head, hidden_states,
-                                     sampling_metadata)
+        return self.logits_processor(self.lm_head, hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/qwen3_next_mtp.py

@@ -19,7 +19,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.qwen3_next import (Qwen3NextDecoderLayer,
                                                    Qwen3NextRMSNorm)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs import Qwen3NextConfig
 
@@ -266,11 +265,9 @@ class Qwen3NextMTP(nn.Module, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
         spec_step_idx: int = 0,
     ) -> Optional[torch.Tensor]:
-        return self.logits_processor(self.lm_head, hidden_states,
-                                     sampling_metadata)
+        return self.logits_processor(self.lm_head, hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/qwen3_vl.py

@@ -45,7 +45,6 @@ from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.distributed import get_pp_group
 from vllm.logger import init_logger
-from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
@@ -1493,10 +1492,8 @@ class Qwen3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
+        return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/seed_oss.py

@@ -47,7 +47,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -472,10 +471,8 @@ class SeedOssForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/skyworkr1v.py

@@ -22,7 +22,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.models.intern_vit import (InternVisionModel,
                                                    InternVisionPatchModel)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
@@ -897,10 +896,8 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
+        return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/solar.py

@@ -47,7 +47,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -495,10 +494,8 @@ class SolarForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                                   inputs_embeds)
         return model_output
 
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/stablelm.py

@@ -42,7 +42,6 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
@@ -332,10 +331,8 @@ class StablelmForCausalLM(nn.Module, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/starcoder2.py

@@ -43,7 +43,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
@@ -339,10 +338,8 @@ class Starcoder2ForCausalLM(nn.Module, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/step3_text.py

@@ -29,7 +29,6 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
@@ -405,10 +404,8 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                                    inputs_embeds)
         return hidden_states
 
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/step3_vl.py

@@ -23,7 +23,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargsItems, NestedTensors)
@@ -1055,10 +1054,8 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
+        return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
 

vllm/model_executor/models/tarsier.py

@@ -23,7 +23,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.models.llava import LlavaDummyInputsBuilder
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.cache import BaseMultiModalProcessorCache
 from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargsItems
@@ -638,10 +637,8 @@ class TarsierForConditionalGeneration(nn.Module, SupportsMultiModal,
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
+        return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/transformers.py

@@ -41,7 +41,6 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalInputs, MultiModalUUIDDict,
@@ -798,10 +797,8 @@ class TransformersForCausalLM(TransformersBase):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
 

vllm/model_executor/models/ultravox.py

@@ -18,7 +18,6 @@ from vllm.model_executor.layers.activation import MulAndSilu, get_act_fn
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.model_loader import DefaultModelLoader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargsItems, NestedTensors)
@@ -616,10 +615,8 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
                                              inputs_embeds=inputs_embeds)
         return hidden_states
 
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/voxtral.py

@@ -30,7 +30,6 @@ from vllm.model_executor.models.module_mapping import MultiModelKeys
 # yapf: disable
 from vllm.model_executor.models.whisper import WhisperEncoder
 # yapf: enable
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargsItems, MultiModalUUIDDict,
@@ -454,10 +453,8 @@ class VoxtralForConditionalGeneration(nn.Module, SupportsMultiModal,
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
+        return self.language_model.compute_logits(hidden_states)
 
     @classmethod
     def get_speech_to_text_config(cls, model_config: ModelConfig,

vllm/model_executor/models/whisper.py

@@ -31,7 +31,6 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargsItems)
@@ -936,10 +935,8 @@ class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
 
         return WhisperAudioInputs(input_features=input_features)
 
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.proj_out, hidden_states,
-                                       sampling_metadata)
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        logits = self.logits_processor(self.proj_out, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/zamba2.py

@@ -41,7 +41,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
                                                     MambaCacheParams)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import HasInnerState, IsHybrid
@@ -1036,7 +1035,6 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
         """Compute logits for next token prediction.
         
@@ -1047,8 +1045,7 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
         Returns:
             Logits for next token prediction
         """
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/sampling_metadata.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-
-class SamplingMetadata:
-    # Placeholder until it can be safely removed.
-    pass

vllm/v1/spec_decode/eagle.py

@@ -239,7 +239,7 @@ class EagleProposer:
             else:
                 last_hidden_states, hidden_states = ret_hidden_states
         sample_hidden_states = last_hidden_states[last_token_indices]
-        logits = self.model.compute_logits(sample_hidden_states, None)
+        logits = self.model.compute_logits(sample_hidden_states)
 
         # Early exit if there is only one draft token to be generated.
         if self.num_speculative_tokens == 1:
@@ -367,8 +367,7 @@ class EagleProposer:
                 else:
                     last_hidden_states, hidden_states = ret_hidden_states
             hidden_states = hidden_states[:batch_size]
-            logits = self.model.compute_logits(last_hidden_states[:batch_size],
-                                               None)
+            logits = self.model.compute_logits(last_hidden_states[:batch_size])
             draft_token_ids = logits.argmax(dim=-1)
             draft_token_ids_list.append(draft_token_ids)
 
@@ -678,9 +677,7 @@ class EagleProposer:
             # Get the output logits for the draft tokens.
             logits = self.model.compute_logits(
                 draft_last_hidden_states.reshape(batch_size * level_num_drafts,
-                                                 -1),
-                None,
-            )
+                                                 -1))
 
             # Sample a draft token for each child at the next tree level.
             num_children = self.child_drafts_per_level[level + 1]

vllm/v1/spec_decode/medusa.py

@@ -41,7 +41,7 @@ class MedusaProposer:
     ) -> list[list[int]]:
         # Generate blocks and compute logits
         blocks = self.model(target_hidden_states)
-        logits = self.model.compute_logits(blocks, None)
+        logits = self.model.compute_logits(blocks)
 
         # Get draft tokens and transpose the result
         # TODO(woosuk): OPTIMIZATION: Return GPU tensor without GPU-CPU

vllm/v1/worker/gpu_model_runner.py

@@ -2240,7 +2240,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     return output
 
                 sample_hidden_states = hidden_states[logits_indices]
-                logits = self.model.compute_logits(sample_hidden_states, None)
+                logits = self.model.compute_logits(sample_hidden_states)
             else:
                 # Rare case.
                 assert not self.is_pooling_model
@@ -2258,8 +2258,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     logits = None
                 else:
                     sample_hidden_states = hidden_states[logits_indices]
-                    logits = self.model.compute_logits(sample_hidden_states,
-                                                       None)
+                    logits = self.model.compute_logits(sample_hidden_states)
 
                 model_output_broadcast_data = {}
                 if logits is not None:
@@ -2706,7 +2705,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             req_idx = self.input_batch.req_id_to_index[req_id]
             offset = self.query_start_loc.np[req_idx].item()
             prompt_hidden_states = hidden_states[offset:offset + num_logits]
-            logits = self.model.compute_logits(prompt_hidden_states, None)
+            logits = self.model.compute_logits(prompt_hidden_states)
 
             # Get the "target" tokens for each index. For prompt at index i,
             # the token at prompt index i+1 is the "sampled" token we want
@@ -3105,7 +3104,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # To avoid breaking the sampler, we use a random tensor here instead.
         hidden_states = torch.rand_like(hidden_states)
 
-        logits = self.model.compute_logits(hidden_states, None)
+        logits = self.model.compute_logits(hidden_states)
         num_reqs = logits.size(0)
 
         dummy_tensors = lambda v: torch.full(