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

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

vllm/model_executor/models/glm4.py

@@ -40,7 +40,6 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -289,10 +288,8 @@ class Glm4ForCausalLM(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/glm4_1v.py

@@ -52,7 +52,6 @@ from vllm.distributed import (get_tensor_model_parallel_world_size,
                               parallel_state)
 from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
-from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                MergedColumnParallelLinear,
@@ -1654,10 +1653,8 @@ class Glm4vForConditionalGeneration(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/glm4_moe.py

@@ -51,7 +51,6 @@ 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
@@ -703,10 +702,8 @@ class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
     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/glm4_moe_mtp.py

@@ -38,7 +38,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 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 .glm4_moe import Glm4MoeDecoderLayer, get_spec_layer_idx_from_weight_name
@@ -155,15 +154,13 @@ class Glm4MoeMultiTokenPredictor(nn.Module):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
         spec_step_idx: int = 0,
     ) -> torch.Tensor:
         current_step_idx = (spec_step_idx % self.num_mtp_layers)
         mtp_layer = self.layers[str(self.mtp_start_layer_idx +
                                     current_step_idx)]
         logits = self.logits_processor(mtp_layer.shared_head.head,
-                                       mtp_layer.shared_head(hidden_states),
-                                       sampling_metadata)
+                                       mtp_layer.shared_head(hidden_states))
         return logits
 
 
@@ -192,11 +189,9 @@ class Glm4MoeMTP(nn.Module, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
         spec_step_idx: int = 0,
     ) -> Optional[torch.Tensor]:
-        return self.model.compute_logits(hidden_states, sampling_metadata,
-                                         spec_step_idx)
+        return self.model.compute_logits(hidden_states, spec_step_idx)
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:

vllm/model_executor/models/gpt2.py

@@ -41,7 +41,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 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 ..layers.pooler import DispatchPooler, Pooler
@@ -307,10 +306,8 @@ class GPT2LMHeadModel(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/gpt_bigcode.py

@@ -41,7 +41,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 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 SupportsLoRA, SupportsPP
@@ -329,10 +328,8 @@ class GPTBigCodeForCausalLM(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/gpt_j.py

@@ -41,7 +41,6 @@ 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 SupportsPP
@@ -329,10 +328,9 @@ class GPTJForCausalLM(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, self.lm_head.bias)
+                                       self.lm_head.bias)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/gpt_neox.py

@@ -40,7 +40,6 @@ 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
@@ -321,10 +320,8 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.embed_out, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.embed_out, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/gpt_oss.py

@@ -24,7 +24,6 @@ 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 vllm.utils import cdiv
 
@@ -670,10 +669,8 @@ class GptOssForCausalLM(nn.Module, SupportsPP):
         return self.model(input_ids, positions, intermediate_tensors,
                           inputs_embeds)
 
-    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/granite.py

@@ -48,7 +48,6 @@ 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
@@ -463,11 +462,9 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                                   inputs_embeds)
         return model_output
 
-    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)
+    def compute_logits(self,
+                       hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def make_empty_intermediate_tensors(

vllm/model_executor/models/granite_speech.py

@@ -37,7 +37,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 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)
@@ -776,12 +775,8 @@ class GraniteSpeechForConditionalGeneration(
     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,

vllm/model_executor/models/granitemoe.py

@@ -48,7 +48,6 @@ 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
@@ -511,11 +510,9 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                                    inputs_embeds)
         return hidden_states
 
-    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)
+    def compute_logits(self,
+                       hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def make_empty_intermediate_tensors(

vllm/model_executor/models/granitemoehybrid.py

@@ -32,7 +32,6 @@ 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 vllm.utils import LayerBlockType
 
@@ -672,10 +671,8 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
     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/granitemoeshared.py

@@ -25,7 +25,6 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .granitemoe import GraniteMoeAttention, GraniteMoeModel, GraniteMoeMoE
@@ -311,11 +310,9 @@ class GraniteMoeSharedForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                                    inputs_embeds)
         return hidden_states
 
-    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)
+    def compute_logits(self,
+                       hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def make_empty_intermediate_tensors(

vllm/model_executor/models/grok1.py

@@ -46,7 +46,6 @@ 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
@@ -528,10 +527,8 @@ class Grok1ForCausalLM(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/hunyuan_v1.py

@@ -54,7 +54,6 @@ 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 MixtureOfExperts, SupportsLoRA, SupportsPP
@@ -1004,10 +1003,8 @@ class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
     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 make_empty_intermediate_tensors(

vllm/model_executor/models/hyperclovax_vision.py

@@ -31,7 +31,6 @@ from transformers.modeling_utils import no_init_weights
 from vllm.config import VllmConfig
 from vllm.inputs import InputProcessingContext
 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.cache import BaseMultiModalProcessorCache
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
@@ -962,10 +961,8 @@ class HCXVisionForCausalLM(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,

vllm/model_executor/models/idefics3.py

@@ -31,7 +31,6 @@ 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
 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)
@@ -738,10 +737,8 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         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/interfaces_base.py

@@ -13,11 +13,9 @@ from vllm.utils import supports_kw
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
     from vllm.model_executor.layers.pooler import Pooler
-    from vllm.model_executor.sampling_metadata import SamplingMetadata
 else:
     VllmConfig = Any
     Pooler = Any
-    SamplingMetadata = Any
 
 logger = init_logger(__name__)
 
@@ -100,7 +98,6 @@ class VllmModelForTextGeneration(VllmModel[T], Protocol[T]):
     def compute_logits(
         self,
         hidden_states: T,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[T]:
         """Return `None` if TP rank > 0."""
         ...

vllm/model_executor/models/internlm2.py

@@ -29,7 +29,6 @@ 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 SupportsLoRA, SupportsPP
@@ -358,10 +357,8 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.output, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.output, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,