Keep CP compute padding out of sparse MoE

CP shared-KV compute padding creates per-request lane slots, so valid rows are not a simple prefix/suffix mask. DeepEP MoE was still seeing dummy rows and using scalar non-padded semantics, which let padding participate in gate/topk and corrupted cache-hit tiny-extend inference.\n\nThe fix compacts CP-local valid rows before MoE dispatch and restores the compact output back to the compute-padded row layout before downstream layer communication. The local GSM8K investigation ledger is now removed from the tracked tree and ignored so future debug notes stay local.\n\nConstraint: CP shared-KV compute-padding layout must keep downstream communicator shapes stable.\nRejected: Disable bs>1/current reuse/cache-hit fast paths | hides the semantic bug and loses the intended performance path.\nRejected: Use num_token_non_padded for MoE under compute padding | valid rows are interleaved with dummy lane slots, not suffix-padded.\nConfidence: high\nScope-risk: moderate\nDirective: Do not feed compute-padded dummy rows into sparse MoE gate/topk; compact valid rows at the MoE boundary and restore shape afterward.\nTested: python -m py_compile python/sglang/srt/layers/attention/nsa/utils.py python/sglang/srt/models/deepseek_v2.py\nTested: remote focused CP utils tests passed, 4 tests.\nTested: remote GSM8K 50-question smoke accuracy 0.960; 200-question runs accuracy 0.955 and 0.965; full 1319-question run accuracy 0.952.\nNot-tested: Long-running production traffic beyond GSM8K after this commit.

python/sglang/srt/models/deepseek_v2.py

@@ -60,9 +60,11 @@ from sglang.srt.layers.attention.nsa.utils import (
     cp_collect_last_token_hidden,
     cp_split_and_rebuild_data,
     cp_split_and_rebuild_position,
+    restore_cp_local_valid_rows_for_moe,
     is_nsa_enable_prefill_cp,
     nsa_use_prefill_cp,
     prepare_input_dp_with_cp_dsa,
+    select_cp_local_valid_rows_for_cache_write,
 )
 from sglang.srt.layers.communicator import (
     LayerCommunicator,
@@ -755,6 +757,22 @@ class DeepseekV2MoE(nn.Module):
         hidden_states: torch.Tensor,
         forward_batch: ForwardBatch,
     ) -> torch.Tensor:
+        local_compute_hidden_states = None
+        if nsa_use_prefill_cp(forward_batch):
+            plan = getattr(
+                getattr(forward_batch, "nsa_cp_metadata", None),
+                "batch_plan",
+                None,
+            )
+            if plan is not None and bool(
+                getattr(plan, "compute_padding_enabled", False)
+            ):
+                local_compute_hidden_states = hidden_states
+                hidden_states = select_cp_local_valid_rows_for_cache_write(
+                    forward_batch,
+                    hidden_states,
+                )
+
         shared_output = None
         sbo_enabled_flag = self._fuse_shared_experts_inside_sbo and not self.is_nextn
         sbo_overlap_dispatch_flag = (
@@ -959,6 +977,13 @@ class DeepseekV2MoE(nn.Module):
             ):
                 final_hidden_states *= self.routed_scaling_factor
 
+        if local_compute_hidden_states is not None:
+            final_hidden_states = restore_cp_local_valid_rows_for_moe(
+                forward_batch,
+                final_hidden_states,
+                local_compute_hidden_states,
+            )
+
         return final_hidden_states
 
     def _forward_shared_experts(