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

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

vllm/model_executor/models/llava_next.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from abc import abstractmethod
-from functools import cached_property
 from typing import (Final, Iterable, List, Literal, Mapping, Optional,
                     Protocol, Set, Tuple, TypedDict, TypeVar, Union)
 
@@ -13,7 +12,6 @@ from transformers.models.llava_next.modeling_llava_next import (
 from typing_extensions import NotRequired
 
 from vllm.config import VllmConfig
-from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalFieldConfig
@@ -250,13 +248,6 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
-    @cached_property
-    def sampler(self):
-        if hasattr(self.language_model, "sampler"):
-            return self.language_model.sampler
-
-        return get_sampler()
-
     def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
         expected_dims = (2, )
 
@@ -585,13 +576,6 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         return self.language_model.compute_logits(hidden_states,
                                                   sampling_metadata)
 
-    def sample(
-        self,
-        logits: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[SamplerOutput]:
-        return self.language_model.sample(logits, sampling_metadata)
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         loader = AutoWeightsLoader(self)