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

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

vllm/model_executor/models/qwen2_5_vl.py

@@ -24,7 +24,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Qwen2.5-VL model compatible with HuggingFace weights."""
-from functools import cached_property, partial
+from functools import partial
 from typing import (Callable, Iterable, List, Literal, Mapping, Optional, Set,
                     Tuple, TypedDict, Union)
 
@@ -51,7 +51,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import (
     GPTQMarlinConfig)
-from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -833,13 +832,6 @@ class Qwen2_5_VLForConditionalGeneration(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 _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
         # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
         # seems to avoid vision encoder sections for some models.
@@ -1127,13 +1119,6 @@ class Qwen2_5_VLForConditionalGeneration(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]: