[Bugfix] Disable torch.compile() (#370)

### What this PR does / why we need it?
To resolve this
[patch](https://github.com/vllm-project/vllm-ascend/pull/236/files#diff-43b96b39b5a52fe209d86449ad703a7ff5e1349ebaf1aa12ece8d82163ee5b61R24-R49)
, we need to set `torch.compile()` backend to `eager` to disable
compile, using default pytorch way.


---------

Signed-off-by: shen-shanshan <467638484@qq.com>

vllm_ascend/worker/model_runner.py

@@ -89,6 +89,7 @@ class ModelInputForNPU(ModelRunnerInputBase):
     async_callback: Optional[Callable] = None
     seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None
     scheduler_outputs: Optional[SchedulerOutputs] = None
+    previous_hidden_states: Optional[torch.Tensor] = None
 
     def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
         tensor_dict = {
@@ -1082,6 +1083,7 @@ class NPUModelRunner(NPUModelRunnerBase[ModelInputForNPUWithSamplingMetadata]):
         kv_caches: List[torch.Tensor],
         intermediate_tensors: Optional[IntermediateTensors] = None,
         num_steps: int = 1,
+        **kwargs,
     ) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]:
         if num_steps > 1:
             raise ValueError("num_steps > 1 is not supported in ModelRunner")
@@ -1118,6 +1120,11 @@ class NPUModelRunner(NPUModelRunnerBase[ModelInputForNPUWithSamplingMetadata]):
             "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
         } if self.has_inner_state else {}
 
+        previous_hidden_states = kwargs.get("previous_hidden_states")
+        model_kwargs = {}
+        if previous_hidden_states is not None:
+            model_kwargs["previous_hidden_states"] = previous_hidden_states
+
         if (self.observability_config is not None
                 and self.observability_config.collect_model_forward_time):
             model_forward_start = torch_npu.npu.Event(enable_timing=True)
@@ -1135,7 +1142,8 @@ class NPUModelRunner(NPUModelRunnerBase[ModelInputForNPUWithSamplingMetadata]):
                     intermediate_tensors=intermediate_tensors,
                     **MultiModalKwargs.as_kwargs(multi_modal_kwargs,
                                                  device=self.device),
-                    **seqlen_agnostic_kwargs)
+                    **seqlen_agnostic_kwargs,
+                    **model_kwargs)
 
         if (self.observability_config is not None
                 and self.observability_config.collect_model_forward_time):