[Chore] Remove Sampler from Model Code (#17084)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

vllm/model_executor/models/kimi_vl.py

@@ -58,7 +58,6 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead)
 from vllm.model_executor.model_loader.weight_utils import (
@@ -298,7 +297,6 @@ class KimiVLForConditionalGeneration(nn.Module, SupportsMultiModal):
         logit_scale = getattr(config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 config.vocab_size, logit_scale)
-        self.sampler = get_sampler()
         self.media_placeholder: int = self.config.media_placeholder_token_id
         self.tp_rank = get_tensor_model_parallel_rank()
         self.tp_world_size = get_tensor_model_parallel_world_size()
@@ -409,7 +407,7 @@ class KimiVLForConditionalGeneration(nn.Module, SupportsMultiModal):
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
         **kwargs: object,
-    ) -> SamplerOutput:
+    ) -> IntermediateTensors:
         if intermediate_tensors is not None:
             inputs_embeds = None
         # NOTE: In v1, inputs_embeds is always generated at model runner from
@@ -447,14 +445,6 @@ class KimiVLForConditionalGeneration(nn.Module, SupportsMultiModal):
                                        sampling_metadata, **kwargs)
         return logits
 
-    def sample(
-        self,
-        logits: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[SamplerOutput]:
-        next_tokens = self.sampler(logits, sampling_metadata)
-        return next_tokens
-
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         config = self.config.text_config
         _KEYS_TO_MODIFY_MAPPING = {