[Misc] Remove no-op self-assignments across vLLM (#55988)

Signed-off-by: shaolila <shaolila@buaa.edu.cn>
Co-authored-by: shaolila <shaolila@buaa.edu.cn>

vllm/model_executor/layers/fused_moe/routed_experts.py

@@ -615,14 +615,15 @@ class RoutedExperts(PluggableLayer):
         # compressed-tensors checkpoints with packed weights are stored flipped
         # TODO (mgoin): check self.quant_method.quant_config.quant_format
         # against known CompressionFormat enum values that have this quality
-        if quant_method_name in (
-            "CompressedTensorsWNA16MoEMethod",
-            "CompressedTensorsW4A16FlydslMoEMethod",
+        if (
+            quant_method_name
+            in (
+                "CompressedTensorsWNA16MoEMethod",
+                "CompressedTensorsW4A16FlydslMoEMethod",
+            )
+            and is_transposed
         ):
-            if is_transposed:
-                loaded_weight = loaded_weight.t().contiguous()
-            else:
-                loaded_weight = loaded_weight
+            loaded_weight = loaded_weight.t().contiguous()
 
         if shard_id not in ("w1", "w2", "w3"):
             raise ValueError(f"shard_id must be ['w1','w2','w3'] but got {shard_id}.")

vllm/model_executor/models/deepencoder.py

@@ -780,9 +780,7 @@ class DeepCLIPVisionEmbeddings(CLIPVisionEmbeddings):
         self, pixel_values: torch.Tensor, patch_embeds: torch.Tensor | None = None
     ) -> torch.Tensor:
         batch_size = pixel_values.shape[0]
-        if patch_embeds is not None:
-            patch_embeds = patch_embeds
-        else:
+        if patch_embeds is None:
             patch_embeds = self.patch_embedding(pixel_values)
         patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
 

vllm/model_executor/models/mimo_v2_omni.py

@@ -964,7 +964,6 @@ class MiMoV2OmniMultiModalProcessor(BaseMultiModalProcessor[MiMoV2OmniProcessing
                     assert isinstance(va_item, (list, tuple)) and len(va_item) == 2
                     vid, audio_src = va_item
                     va_item = VideoAudioInput(video=vid, audio=audio_src)
-                    vid = vid
                 # Convert video frames to (TCHW, timestamps) if needed
                 if (
                     isinstance(vid, tuple)

vllm/model_executor/models/step3p5.py

@@ -173,7 +173,6 @@ class Step3p5Attention(nn.Module):
                 rope_scaling = None
 
         if sliding_window is not None and enable_sliding_window:
-            sliding_window = sliding_window
             if swa_num_attention_heads is not None:
                 num_heads = swa_num_attention_heads
                 self.total_num_heads = swa_num_attention_heads