[AMD] feat(moe): fold padded-topk_ids fill into fused shared-experts append+remap (#31370)

test/registered/moe/test_fused_append_remap_per_rank_shared_slots.py

@@ -173,6 +173,53 @@ class TestFusedAppendRemapPerRankSharedSlots(CustomTestCase):
         )
         self.assertTrue(torch.all(got_w[:, -s:] == 1.0))
 
+    def test_pad_fold_matches_separate_fill(self):
+        """HAS_PADDING fold == separate padded-fill(0) then append+remap.
+
+        The fusion folds the padded-topk_ids fill into this kernel: rows
+        >= num_token_non_padded get pad_fill_id in every routed slot. With
+        pad_fill_id=0 this is bit-identical to the previous path that filled the
+        padded region with 0 (topk_ids=0 -> remap 0 + 0//nlr = 0) via a separate
+        _fill_padded_rows launch before append+remap ran.
+        """
+        for m, k, npr, ep_size, ep_rank, s in self.CASES:
+            for n_valid in (0, max(m // 2, 1), m):
+                with self.subTest(m=m, k=k, ep_rank=ep_rank, s=s, n_valid=n_valid):
+                    shared_id_base, num_local_routed = self._shared_id_base(
+                        npr, ep_size, ep_rank, s
+                    )
+                    topk_ids, topk_weights = self._make_inputs(m, k, npr)
+
+                    # Baseline: pre-fill padded rows to 0, no fold.
+                    base_ids = topk_ids.clone()
+                    base_ids[n_valid:] = 0
+                    exp_ids, exp_w = fused_append_remap_shared_experts_deepep(
+                        base_ids,
+                        topk_weights.clone(),
+                        s,
+                        1.0,
+                        shared_id_base,
+                        num_local_routed,
+                    )
+
+                    # Fused: fold the fill (no pre-fill), pad_fill_id=0.
+                    ntnp = torch.tensor(
+                        [n_valid], dtype=torch.int32, device=topk_ids.device
+                    )
+                    got_ids, got_w = fused_append_remap_shared_experts_deepep(
+                        topk_ids.clone(),
+                        topk_weights.clone(),
+                        s,
+                        1.0,
+                        shared_id_base,
+                        num_local_routed,
+                        num_token_non_padded=ntnp,
+                        pad_fill_id=0,
+                    )
+
+                    self.assertTrue(torch.equal(got_ids, exp_ids))
+                    self.assertTrue(torch.allclose(got_w, exp_w))
+
     def test_no_shared_experts_is_noop(self):
         """s == 0 returns the inputs untouched (no kernel launch)."""
         topk_ids, topk_weights = self._make_inputs(4, 8, 256)