[v0.11.0][Perf] Delete redundant operations in model_runner and forward_context (#3775)

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cherry pick #3677

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"
/>
### What this PR does / why we need it?
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### Does this PR introduce _any_ user-facing change?
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No
### How was this patch tested?
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---------

Signed-off-by: realliujiaxu <[email protected]>

Author: realliujiaxu

tests/ut/worker/test_model_runner_v1.py

@@ -68,6 +68,8 @@ def test_select_moe_comm_method(soc_version, enable_expert_parallel,
     with patch('vllm_ascend.worker.model_runner_v1.get_ascend_soc_version',
                return_value=soc_version), \
          patch('vllm_ascend.worker.model_runner_v1.is_global_first_rank',
+               return_value=True), \
+         patch('vllm_ascend.worker.model_runner_v1.is_moe_model',
                return_value=True):
 
         # Bind the real method to the mock object
@@ -102,6 +104,8 @@ def test_select_moe_comm_method_unsupported_soc():
                return_value=unsupported_soc), \
          patch('vllm_ascend.worker.model_runner_v1.is_global_first_rank',
                return_value=True), \
+         patch('vllm_ascend.worker.model_runner_v1.is_moe_model',
+                  return_value=True), \
          pytest.raises(ValueError, match=f"Unsupported soc_version: {unsupported_soc}"):
 
         NPUModelRunner._select_moe_comm_method(mock_runner, 100, False)

vllm_ascend/ascend_forward_context.py

@@ -11,7 +11,8 @@
                                   set_forward_context)
 
 import vllm_ascend.envs as envs_ascend
-from vllm_ascend.utils import enable_sp, is_moe_model, version_check
+from vllm_ascend.utils import (enable_sp, has_layer_idx, is_moe_model,
+                               version_check)
 
 if TYPE_CHECKING:
     from vllm_ascend.ops.weight_prefetch import WeightPrefetchMethod
@@ -136,9 +137,7 @@ def set_ascend_forward_context(
         # set layer_idx to enable optimization features that depend on this information.
         # This is only applicable to models that contain these necessary attributes.
         forward_context.layer_idx = None
-        if model_instance is not None and \
-            hasattr(model_instance, "model") and \
-            hasattr(model_instance.model, "start_layer"):
+        if has_layer_idx(model_instance):
             forward_context.layer_idx = model_instance.model.start_layer
 
         # TODO(rjg-lyh): refactor mlp weight prefetch method

vllm_ascend/ops/moe/moe_comm_method.py

@@ -39,7 +39,7 @@
 
 def get_moe_comm_method(
         moe_comm_type: Optional[MoECommType]) -> Optional[MoECommMethod]:
-    return _MoECommMethods.get(moe_comm_type)
+    return _MoECommMethods.get(moe_comm_type, None)
 
 
 def setup_moe_comm_method(moe_config):

vllm_ascend/utils.py

@@ -58,6 +58,7 @@
 _MIN_DP_BUFFER_SIZE = 50
 _IS_MOE_MODEL = None
 _ENABLE_SP = None
+_HAS_LAYER_IDX = None
 
 
 def is_310p():
@@ -785,3 +786,14 @@ def version_check():
         if full_date >= "20250919":
             return True
     return False
+
+
+def has_layer_idx(model_instance: torch.nn.Module) -> bool:
+    if model_instance is None:
+        return False
+
+    global _HAS_LAYER_IDX
+    if _HAS_LAYER_IDX is None:
+        _HAS_LAYER_IDX = hasattr(model_instance, "model") and \
+            hasattr(model_instance.model, "start_layer")
+    return _HAS_LAYER_IDX

vllm_ascend/worker/model_runner_v1.py

@@ -131,7 +131,7 @@
 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)
 from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
 
 if TYPE_CHECKING:
@@ -470,11 +470,14 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
         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)
@@ -1341,9 +1344,7 @@ def _prepare_inputs(
         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)
@@ -1364,16 +1365,6 @@ def _prepare_inputs(
         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:
@@ -1835,7 +1826,7 @@ def _pool(
         )
 
     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
@@ -1858,6 +1849,9 @@ def _select_moe_comm_method(self, num_tokens: int,
         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)