Reduce startup log noise and fix Dynamo / CUDA-graph edge cases (#33428)

python/sglang/kernels/jit/utils/compile.py

@@ -169,6 +169,12 @@ def _jit_build_dir_name(module_name: str) -> str:
     return f"{module_name}__arch_{arch}__tvmffi_{_tvm_ffi_version()}"
 
 
+# JIT compilation is pure Python/filesystem plumbing (path `.resolve()` calls
+# `os.lstat`, etc.) that Dynamo cannot trace. When a lazily-loaded kernel is
+# first reached from inside a `@torch.compile`d region, tracing into it produces
+# spurious "Dynamo does not know how to trace the builtin `posix.lstat`" graph
+# breaks. The load happens once and is memoized, so keep it out of the graph.
+@torch.compiler.disable
 def load_jit(
     *args: str,
     cpp_files: List[str] | None = None,

python/sglang/srt/arg_groups/overrides.py

@@ -2235,6 +2235,12 @@ def _a2a_backend_overrides(view: Any) -> dict:
 @register_post_process
 def _a2a_ep_size(view: Any) -> dict:
     if view.moe_a2a_backend in _A2A_EP_SPANNING_BACKENDS:
+        if view.ep_size != view.tp_size:
+            logger.info(
+                f"{view.moe_a2a_backend} MoE is enabled. The expert parallel size "
+                f"is adjusted from {view.ep_size} to the tensor parallel size "
+                f"[{view.tp_size}]."
+            )
         return {"ep_size": view.tp_size}
     return {}
 

python/sglang/srt/configs/model_config.py

@@ -1503,20 +1503,6 @@ class ModelConfig:
                         f"({self.quantization})."
                     )
 
-            # Warn if DeepGemm is enabled for a non-ue8m0 checkpoint on Blackwell.
-            # MXFP8 stores E8M0 block scales that DeepGemm consumes losslessly, so skip the warning there.
-            self.use_scale_ue8m0 = quant_cfg.get("scale_fmt", None) == "ue8m0"
-            from sglang.srt.layers import deep_gemm_wrapper
-
-            if (
-                not self.use_scale_ue8m0
-                and deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
-                and self.quantization != "mxfp8"
-            ):
-                logger.warning(
-                    "DeepGemm is enabled but the scale_fmt of checkpoint is not ue8m0. This might cause accuracy degradation on Blackwell."
-                )
-
         if self.quantization is not None:
             if self.quantization not in supported_quantization:
                 raise ValueError(

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -93,6 +93,11 @@ _is_cpu = is_cpu()
 _is_npu = is_npu()
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 
+# Log the deferred-finalize config at most once per process (rank). Different MoE
+# layers can resolve to different quant methods, so print_info_once (keyed on the
+# full message) would otherwise fire once per distinct quant method.
+_deferred_finalize_info_logged = False
+
 
 def _copy_weight_view_before_h2d(loaded_weight: torch.Tensor) -> torch.Tensor:
     """Copy a CPU tensor view into independent contiguous storage."""
@@ -379,12 +384,15 @@ class FusedMoE(torch.nn.Module):
             and get_moe_runner_backend().is_flashinfer_trtllm()
             and isinstance(self.quant_method, ModelOptNvFp4FusedMoEMethod)
         )
-        print_info_once(
-            "FlashInfer TRTLLM MoE deferred finalize is "
-            f"{'enabled' if self.supports_deferred_finalize else 'disabled'} "
-            f"(moe_runner_backend={get_exec().moe.moe_runner_backend}, "
-            f"quant_method={type(self.quant_method).__name__})."
-        )
+        global _deferred_finalize_info_logged
+        if not _deferred_finalize_info_logged:
+            _deferred_finalize_info_logged = True
+            logging.getLogger(__name__).info(
+                "FlashInfer TRTLLM MoE deferred finalize is "
+                f"{'enabled' if self.supports_deferred_finalize else 'disabled'} "
+                f"(moe_runner_backend={get_exec().moe.moe_runner_backend}, "
+                f"quant_method={type(self.quant_method).__name__})."
+            )
 
         self.quant_method.create_weights(
             layer=self,

python/sglang/srt/model_executor/runner_backend/breakable_cuda_graph_backend.py

@@ -119,7 +119,7 @@ class BreakableCudaGraphBackend(DedupedCudaGraphMixin, BaseCudaGraphBackend):
             if post_warmup_hook is not None:
                 post_warmup_hook()
 
-        graph = BreakableCUDAGraph()
+        graph = BreakableCUDAGraph(self.deduped_cuda_graph)
         captured_fn = (
             eager_on_graph(True)(forward_fn) if self._debug_eager else forward_fn
         )

python/sglang/srt/server_args.py

@@ -4785,9 +4785,15 @@ class ServerArgs:
             )
 
             # Multimodal models need more memory for the image processing,
-            # so we adjust the mem_fraction_static accordingly.
+            # so we adjust the mem_fraction_static accordingly. The VLM encoder
+            # only runs on the prefill stage, so PD decode engines do not need
+            # this headroom; prefill engines and normal (non-PD) engines do.
             model_config = self.get_model_config()
-            if model_config.is_multimodal and not self.language_only:
+            if (
+                model_config.is_multimodal
+                and not self.language_only
+                and self.disaggregation_mode != "decode"
+            ):
                 self.adjust_mem_fraction_for_vlm(model_config)
 
         # If symm mem is enabled and prealloc size is not set, set it to 4GB
@@ -6611,10 +6617,6 @@ class ServerArgs:
         if a2a_backend == "megamoe":
             if not envs.SGLANG_OPT_FIX_MEGA_MOE_MEMORY.is_set():
                 envs.SGLANG_OPT_FIX_MEGA_MOE_MEMORY.set(True)
-            logger.info(
-                f"Mega MoE is enabled. The expert parallel size is adjusted "
-                f"to be the same as the tensor parallel size[{self.tp_size}]."
-            )
 
         if a2a_backend == "deepep":
             if self.moe_runner_backend == "flashinfer_cutedsl":
@@ -6637,19 +6639,6 @@ class ServerArgs:
                 logger.warning("Cuda graph is disabled because deepep_mode=`normal`")
                 self.cuda_graph_config.decode.backend = Backend.DISABLED
                 self.cuda_graph_config.prefill.backend = Backend.DISABLED
-            logger.warning(
-                f"DeepEP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
-
-        if a2a_backend == "mooncake":
-            logger.warning(
-                f"Mooncake MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
-
-        if a2a_backend == "nixl":
-            logger.warning(
-                f"Nixl MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
 
         if (
             self.moe_a2a_backend == "none" and is_npu()
@@ -6657,17 +6646,10 @@ class ServerArgs:
             # FIXME (OrangeRedeng): for some reasons if pass "ascend_tp" accuracy drops to zero
             self.moe_a2a_backend = "none"
 
-        if self.moe_a2a_backend == "ascend_fuseep":
-            logger.warning(
-                f"Ascend fused EP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
         if self.moe_a2a_backend == "flashinfer":
             assert (
                 resolved_view(self).enable_dp_attention and self.dp_size == self.tp_size
             ), "Flashinfer MoE A2A is only supported with dp_size == tp_size and --enable-dp-attention"
-            logger.warning(
-                f"Flashinfer MoE A2A is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
             if self.deepep_mode != "auto":
                 logger.warning("--deepep-mode is ignored for Flashinfer MoE A2A")
             if not envs.SGLANG_MOE_NVFP4_DISPATCH.is_set() and (
@@ -6688,9 +6670,6 @@ class ServerArgs:
             if self.deepep_mode == "auto":
                 self.deepep_mode = "normal"
                 logger.warning("auto set deepep_mode=`normal` for MORI EP")
-            logger.warning(
-                f"MoRI MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
 
             # Check chunked prefill for mori
             # Skip validation if chunked prefill is disabled (i.e., size <= 0).
@@ -6731,9 +6710,6 @@ class ServerArgs:
             if self.moe_runner_backend == "auto":
                 self.moe_runner_backend = "deep_gemm"
                 logger.warning("auto set moe_runner_backend=`deep_gemm` for PPLX EP")
-            logger.warning(
-                f"PPLX MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
-            )
 
             # Check per-rank dispatch tokens for pplx
             # Skip validation if chunked prefill is disabled (i.e., size <= 0)

python/sglang/srt/utils/hf_transformers/common.py

@@ -489,12 +489,13 @@ def get_generation_config(
         return GenerationConfig.from_pretrained(
             model, trust_remote_code=trust_remote_code, revision=revision, **kwargs
         )
-    except FileNotFoundError:
-        return None
-    except OSError as e:
-        logger.warning(
-            "Failed to load generation config for %s: %s. "
-            "Proceeding without generation config.",
+    except (FileNotFoundError, OSError) as e:
+        # A missing generation_config.json is normal for many checkpoints and
+        # is surfaced by HF as a generic OSError (not FileNotFoundError). Treat
+        # it as benign — proceed without a generation config, at DEBUG level so
+        # normal startup logs stay quiet.
+        logger.debug(
+            "No generation config for %s: %s. Proceeding without it.",
             model,
             e,
         )

test/registered/unit/test_model_overrides.py

@@ -2010,7 +2010,9 @@ class TestGoldenModelOverrides(_IsolatedPublish):
 
         self.assertEqual(
             _a2a_ep_size(
-                ResolvedView(SimpleNamespace(moe_a2a_backend="deepep", tp_size=8))
+                ResolvedView(
+                    SimpleNamespace(moe_a2a_backend="deepep", ep_size=1, tp_size=8)
+                )
             ),
             {"ep_size": 8},
         )