Fix Nightly NV CI (#33564)

Co-authored-by: Brayden Zhong <brayden@radixark.ai>
Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>

python/sglang/srt/arg_groups/overrides.py

@@ -1725,6 +1725,9 @@ def _deepseek_moe_quant_resolution(view: Any) -> dict:
         if (
             view.moe_a2a_backend == "none"
             and view.moe_runner_backend == "auto"
+            # LongCat top-k spans the zero-expert logits, which trtllm-gen's
+            # fused routing cannot see.
+            and not model_arch.startswith("LongcatFlash")
             and (
                 quantization
                 in ["fp8", "modelopt_fp8", "modelopt_fp4", "modelopt_mixed"]

python/sglang/srt/layers/n_gram_embedding.py

@@ -49,7 +49,7 @@ class NgramEmbedding(torch.nn.Module):
                 + int(over_embedding_m + i * 2 + 1)
             )
         self.oe_embeder = VocabParallelEmbedding(
-            num_embeddings=self.exclusive_oe_embedder_size_sums[-1],
+            num_embeddings=int(self.exclusive_oe_embedder_size_sums[-1]),
             embedding_dim=oe_hidden_dim,
             use_attn_tp_group=use_attn_tp_group,
         )

test/registered/8-gpu-models/test_glm52_fp8.py

@@ -15,7 +15,7 @@ COMMON_ARGS = [
     "--trust-remote-code",
     "--reasoning-parser=glm45",
     "--tool-call-parser=glm47",
-    "--mem-fraction-static=0.85",
+    "--mem-fraction-static=0.8",
     "--enable-metrics",
 ]
 

test/registered/8-gpu-models/test_longcat_flash_lite_fp8.py

@@ -6,8 +6,7 @@ from sglang.test.performance_test_runner import PerformanceTestParams
 from sglang.test.run_combined_tests import run_combined_tests
 from sglang.test.test_utils import ModelLaunchSettings
 
-# Runs on both H200 and B200 via the nightly-8-gpu-common suite.
-register_cuda_ci(est_time=1200, suite="nightly-8-gpu-common", nightly=True)
+register_cuda_ci(est_time=1200, suite="nightly-8-gpu-h200", nightly=True)
 
 # LongCat-Flash-Lite-FP8 is the smallest member of the LongCat family
 # (~138 GB FP8 weights, hidden=3072, 14 layers, 256 routed + 128 zero
@@ -20,7 +19,6 @@ register_cuda_ci(est_time=1200, suite="nightly-8-gpu-common", nightly=True)
 # LongcatFlashNgramForCausalLM is remapped to model_type "longcat_flash")
 # with LongCat-Flash-Chat-FP8 and LongCat-2.0-FP8, so it guards the shared
 # code paths that recent LongCat EP fixes touched:
-#   - the scheduler moe-topk gate for --moe-a2a-backend (PR #30975)
 #   - ScMoE dense-branch gather (RoPE) + the MoE-vs-DeepEPMoE double
 #     all_reduce fix (PR #31311)
 #   - the zero-expert (identity) compute path (zero_expert_num=128) and
@@ -41,28 +39,14 @@ class TestLongCatFlashLiteFp8(unittest.TestCase):
 
     Two variants exercise the two MoE all-to-all backends that the LongCat
     EP fixes gate on:
-      - EP8 + deepep : real expert parallelism (the path #30975/#31311 fix)
       - EP8 + none   : EP-over-TP baseline (all_reduce / gather correctness)
+      - EP8 + deepep : real expert parallelism (the path #30975/#31311 fix),
+        skipped until DeepEP raises its low-latency top-k cap to 12
     """
 
-    def test_longcat_flash_lite_fp8(self):
-        variants = [
-            ModelLaunchSettings(
-                LONGCAT_FLASH_LITE_FP8_MODEL_PATH,
-                tp_size=8,
-                extra_args=COMMON_ARGS + ["--ep=8"],
-                variant="TP8+EP8+none",
-            ),
-            ModelLaunchSettings(
-                LONGCAT_FLASH_LITE_FP8_MODEL_PATH,
-                tp_size=8,
-                extra_args=COMMON_ARGS + ["--ep=8", "--moe-a2a-backend=deepep"],
-                variant="TP8+EP8+deepep",
-            ),
-        ]
-
+    def _run(self, variant: ModelLaunchSettings):
         run_combined_tests(
-            models=variants,
+            models=[variant],
             test_name="LongCat-Flash-Lite-FP8",
             # Measured 2026-07-22 on 8xH100-80GB, gsm8k 200q, 5-shot, greedy:
             #   TP8+EP8+none   -> 0.840
@@ -78,6 +62,30 @@ class TestLongCatFlashLiteFp8(unittest.TestCase):
             ),
         )
 
+    def test_longcat_flash_lite_fp8(self):
+        self._run(
+            ModelLaunchSettings(
+                LONGCAT_FLASH_LITE_FP8_MODEL_PATH,
+                tp_size=8,
+                extra_args=COMMON_ARGS + ["--ep=8"],
+                variant="TP8+EP8+none",
+            )
+        )
+
+    @unittest.skip(
+        "Blocked: DeepEP low-latency dispatch asserts num_topk <= kNumMaxTopK "
+        "(11 in internode_ll.cu), LongCat moe_topk is 12."
+    )
+    def test_longcat_flash_lite_fp8_deepep(self):
+        self._run(
+            ModelLaunchSettings(
+                LONGCAT_FLASH_LITE_FP8_MODEL_PATH,
+                tp_size=8,
+                extra_args=COMMON_ARGS + ["--ep=8", "--moe-a2a-backend=deepep"],
+                variant="TP8+EP8+deepep",
+            )
+        )
+
 
 if __name__ == "__main__":
     unittest.main()