[Perf] Delete redundant operations in model_runner and forward_context (#3677)

### What this PR does / why we need it?

Remove redundant operations from `model_runner` and `forward_context`.
This optimization can significantly reduce the idle time (bubble) before
decoding when running models with small parameter counts (e.g.,
Qwen/Qwen2.5-0.5B).

Testing on 800I A2, bubble is reduced from 3.8ms to 2.8ms :
Before
<img width="1655" height="696" alt="image"
src="https://github.com/user-attachments/assets/d7608e52-2438-46dd-8fc9-391fd6274495"
/>

After
<img width="1607" height="774" alt="image"
src="https://github.com/user-attachments/assets/56daf081-2dba-4d2e-99d4-e055187d9806"
/>

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.1

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

vllm_ascend/worker/model_runner_v1.py

@@ -136,7 +136,7 @@ from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
 from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
                                AscendSocVersion, ProfileExecuteDuration,
                                enable_sp, get_ascend_soc_version, is_310p,
-                               is_enable_nz, lmhead_tp_enable,
+                               is_enable_nz, is_moe_model, lmhead_tp_enable,
                                prefill_context_parallel_enable,
                                vllm_version_is)
 from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
@@ -515,11 +515,14 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self.in_profile_run = False
 
         self._init_mc2_tokens_capacity()
-        self.reserved_mc2_mask = torch.zeros(
-            self.mc2_tokens_capacity,
-            dtype=torch.bool,
-            device=self.device,
-        )
+        if is_moe_model(vllm_config):
+            self.reserved_mc2_mask = torch.zeros(
+                self.mc2_tokens_capacity,
+                dtype=torch.bool,
+                device=self.device,
+            )
+        else:
+            self.reserved_mc2_mask = None
         self.dynamic_eplb = self.ascend_config.dynamic_eplb or self.ascend_config.expert_map_record_path
         if self.dynamic_eplb:
             EPLBParamUtils.check_dynamic_eplb(self.ascend_config.dynamic_eplb)
@@ -1497,9 +1500,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self.query_lens = torch.from_numpy(num_scheduled_tokens)
 
         # Copy the tensors to the NPU.
-        self.input_ids[:total_num_scheduled_tokens].copy_(
-            self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
-
+        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)
         self.positions_cpu[total_num_scheduled_tokens:num_input_tokens].zero_()
         self.positions[:num_input_tokens].copy_(
             self.positions_cpu[:num_input_tokens], non_blocking=True)
@@ -1521,16 +1522,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self._update_graph_pad_size(with_prefill, maybe_padded_num_tokens)
         attn_metadata: dict[str, Any] = {}
 
-        # Prepare input_ids
-        token_indices = (positions_np +
-                         req_indices * self.input_batch.token_ids_cpu.shape[1])
-        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
-                           0,
-                           torch.from_numpy(token_indices),
-                           out=self.input_ids_cpu[:total_num_scheduled_tokens])
-        # Copy the tensors to the NPU.
-        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)
-
         # _prepare_inputs may reorder the batch, so we must gather
         # multi-modal outputs after that to ensure the correct order
         if self.is_multimodal_model:
@@ -2075,7 +2066,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         )
 
     def _select_moe_comm_method(self, num_tokens: int,
-                                with_prefill: bool) -> MoECommType:
+                                with_prefill: bool) -> Optional[MoECommType]:
         """1. If expert parallel is not enabled, we use all-gather since MC2 and all-to-all
         are designed for expert parallelism.
         2. If expert parallel is enabled, we need to consider the soc version and the
@@ -2098,6 +2089,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         Returns:
             MoECommType: The selected MoE communication method.
         """
+        if not is_moe_model(self.vllm_config):
+            return None
+
         soc_version = get_ascend_soc_version()
         quant_type = getattr(self.vllm_config.model_config.hf_config,
                              'moe_quantize', None)