[CPU][Qwen3 MoE] Enable fused_topk CPU fusion and enhance FP8 TP padding (#7838)

python/sglang/srt/layers/moe/topk.py

@@ -83,13 +83,18 @@ def fused_topk_cpu(
     gating_output: torch.Tensor,
     topk: int,
     renormalize: bool,
+    num_token_non_padded: Optional[torch.Tensor] = None,
+    expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
 ):
-    return torch.ops.sgl_kernel.topk_softmax_cpu(
+    topk_weights, topk_ids = torch.ops.sgl_kernel.topk_softmax_cpu(
         hidden_states=hidden_states,
         gating_output=gating_output,
         topk=topk,
         renormalize=renormalize,
     )
+    topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
+    _mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
+    return topk_weights, topk_ids
 
 
 def fused_topk(
@@ -411,6 +416,7 @@ if _is_cpu and _is_cpu_amx_available:
     biased_grouped_topk = biased_grouped_topk_cpu
     grouped_topk = grouped_topk_cpu
     fused_topk_native = fused_topk_cpu
+    fused_topk = fused_topk_cpu
 else:
     biased_grouped_topk = biased_grouped_topk_gpu
     grouped_topk = grouped_topk_gpu

python/sglang/srt/layers/parameter.py

@@ -187,11 +187,27 @@ class _ColumnvLLMParameter(BasevLLMParameter):
         param_data = self.data
         shard_id = tp_rank if shard_id == "q" else tp_rank // num_heads
         param_data = param_data.narrow(self.output_dim, shard_offset, shard_size)
-        if not use_presharded_weights:
-            loaded_weight = loaded_weight.narrow(
-                self.output_dim, shard_id * shard_size, shard_size
+
+        if _is_cpu:
+            from sglang.srt.model_loader.weight_utils import (
+                narrow_padded_param_and_loaded_weight,
             )
 
+            param_data, loaded_weight = narrow_padded_param_and_loaded_weight(
+                param_data,
+                loaded_weight,
+                0,  # param_data_start
+                shard_id * shard_size,
+                self.output_dim,
+                shard_size,
+                not use_presharded_weights,
+            )
+        else:
+            if not use_presharded_weights:
+                loaded_weight = loaded_weight.narrow(
+                    self.output_dim, shard_id * shard_size, shard_size
+                )
+
         assert (
             param_data.shape == loaded_weight.shape
         ), f"{param_data.shape=}, {loaded_weight.shape=}"