chore: cleanup garbage code (#29770)

benchmark/bench_linear_attention/bench_gdn_decode.py

@@ -131,7 +131,7 @@ def run_baseline(inp):
 
 def run_packed(inp):
     """Packed path: single fused kernel directly on mixed_qkv."""
-    B, HV, K, V = inp["B"], inp["HV"], inp["K"], inp["V"]
+    B, HV, V = inp["B"], inp["HV"], inp["V"]
     ssm_states = inp["ssm_states"].clone()
     out = inp["mixed_qkv"].new_empty(B, 1, HV, V)
 
@@ -173,7 +173,7 @@ def check_correctness(B, H, HV, K, V, pool_size, device, dtype, seed=42):
     try:
         torch.testing.assert_close(o_packed, o_baseline, atol=atol, rtol=rtol)
         output_ok = True
-    except AssertionError as e:
+    except AssertionError:
         output_ok = False
         out_diff = (o_packed - o_baseline).abs().max().item()
 

benchmark/mmmu/bench_hf.py

@@ -171,7 +171,7 @@ if __name__ == "__main__":
         "--limit",
         type=int,
         default=None,
-        help="If set, only evaluate this many samples (debug / smoke runs).",
+        help="If set, only evaluate this many samples (debug / quick runs).",
     )
     EvalArgs.add_cli_args(parser)
     args = parser.parse_args()

experimental/sgl-router/BENCHMARKS.md

@@ -32,12 +32,12 @@ cargo bench --bench radix_tree_benchmark -- --sample-size 30 --measurement-time
 cargo bench --bench manual_policy_benchmark
 ```
 
-For the quick smoke runs whose numbers are reproduced below: drop
+For the quick runs whose numbers are reproduced below: drop
 `--sample-size` to 10 and `--measurement-time` to 2 (Criterion will
 warn about reduced statistical confidence but the order-of-magnitude
 comparison stands).
 
-## Smoke-run data points (M1 MacBook, release profile)
+## Quick-run Data Points (M1 MacBook, release profile)
 
 These are NOT the real acceptance numbers — they're a sanity check
 that the sgl-router routing primitives are in the same ballpark as the

experimental/sgl-router/src/workers/manager.rs

@@ -680,7 +680,7 @@ mod tests {
         let _ = manager_handle.await;
     }
 
-    /// End-to-end wiring smoke test: spin up a fake worker, run the
+    /// End-to-end wiring check: spin up a fake worker, run the
     /// manager with a real `KvEventIndex` against that worker URL, and
     /// verify both `Added` and `Removed` propagate through to the
     /// index's internal worker map.

experimental/sgl-router/tests/e2e/chat_completions/test_validation.py

@@ -7,7 +7,7 @@ The shape:
   - non-streaming + streaming chat completion
   - assistant message non-empty, role correct, finish_reason set
 
-These are the smoke tests that run first; if they pass, the heavier
+These quick checks run first; if they pass, the heavier
 multi-worker acceptance tests are worth running.
 """
 

experimental/sgl-router/tests/e2e/conftest.py

@@ -2,7 +2,7 @@
 
 Two flavors of fixtures coexist here:
 
-  1. **Session-scoped smoke fixtures** (``sglang_server`` + ``router``) —
+  1. **Session-scoped sanity fixtures** (``sglang_server`` + ``router``) —
      launch ONE SGLang worker + ONE router on fixed ports for the whole
      test session. Used by the lightweight ``test_chat_smoke.py`` /
      ``test_tokenize_smoke.py`` files. These are the cheap "did the
@@ -174,7 +174,7 @@ def build_smoke_router_args(
     tokenizer_path: str,
     sglang_url: str,
 ) -> list[str]:
-    """Build the sgl-router CLI flags the smoke ``router`` fixture launches.
+    """Build the sgl-router CLI flags the single-worker ``router`` fixture launches.
 
     Static single-worker discovery (``--worker-urls``) pointed at the one
     SGLang worker, serving exactly one model.

experimental/sgl-router/tests/e2e/pyproject.toml

@@ -4,7 +4,7 @@
 
 [tool.pytest.ini_options]
 minversion = "8.0"
-# Default discovery: smoke tests (top-level test_*.py) and the
+# Default discovery: top-level test_*.py files and the
 # multi-worker chat_completions suite. k8s_integration is intentionally
 # not in the default set — it requires a kind/k8s cluster and is
 # invoked explicitly.

experimental/sgl-router/tests/e2e/requirements.txt

@@ -9,7 +9,7 @@ pytest-asyncio==0.24.0
 # pin of `huggingface_hub==0.26.2` here got installed AFTER the SGLang
 # deps and downgraded huggingface_hub past `is_offline_mode`'s top-
 # level export, which broke `from sglang.srt.server_args import …`
-# at module import time and turned every smoke test into a 5-minute
+# at module import time and turned every quick check into a 5-minute
 # `/health` timeout with no actionable signal.
 # The e2e suite only uses huggingface_hub's `try_to_load_from_cache`,
 # which is available in every release SGLang would install.

experimental/sgl-router/tests/proxy/pd_pool_isolation.rs

@@ -13,7 +13,7 @@
 //!   (existing code path; pinned here so a future PD wiring change
 //!   doesn't silently swap codes).
 //! * A PD-disagg model with both pools healthy → request flows to the
-//!   prefill worker (smoke; the decode worker MUST NOT be selected for
+//!   prefill worker (sanity check; the decode worker MUST NOT be selected for
 //!   the chat route).
 
 use axum::body::Body;

python/sglang/multimodal_gen/runtime/layers/custom_op.py

@@ -52,7 +52,6 @@ class CustomOp(nn.Module):
     def forward_tpu(self, *args, **kwargs) -> Any:
         # By default, we assume that TPU ops are compatible with the
         # PyTorch-native implementation.
-        # NOTE(woosuk): This is a placeholder for future extensions.
         return self.forward_native(*args, **kwargs)
 
     def forward_musa(self, *args, **kwargs) -> Any:

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -854,7 +854,7 @@ class OpenAIServingChat(OpenAIServingBase):
                 prompt_ids = self.tokenizer_manager.tokenizer.encode(
                     rendered_prompt, **encode_kwargs
                 )
-            except Exception as e:
+            except Exception:
                 # If the first attempt fails, try with flat function-only format.
                 # Some templates (e.g. Mistral) expect tools without the OpenAI wrapper.
                 tools = (

python/sglang/srt/entrypoints/openai/serving_responses.py

@@ -1689,7 +1689,7 @@ class OpenAIServingResponses(OpenAIServingChat):
                     )
 
         async def empty_async_generator():
-            if False:
+            for _ in ():
                 yield
 
         final_response = await self.responses_full_generator(

python/sglang/srt/eplb/expert_distribution.py

@@ -801,13 +801,9 @@ class _DetailAccumulator(_UtilizationRateAccumulatorMixin):
         self._records = []
 
     def get_single_pass_gatherer_keys(self):
-        if False:  # TODO `server_args.enable_two_batch_overlap`
-            return [_SINGLE_PASS_GATHERER_KEY_PRIMARY, "child_a", "child_b"]
         return super().get_single_pass_gatherer_keys()
 
     def get_single_pass_gatherer_key(self, debug_name: Optional[str]):
-        if False:  # TODO `server_args.enable_two_batch_overlap`
-            return debug_name or _SINGLE_PASS_GATHERER_KEY_PRIMARY
         return super().get_single_pass_gatherer_key(debug_name)
 
     def append(

python/sglang/srt/layers/activation.py

@@ -280,14 +280,8 @@ class XIELU(MultiPlatformOp):
                 )
                 self._xielu_cuda_fn = self._xielu_cuda
             logger.warning_once(msg)
-        except Exception as err:
+        except Exception:
             pass
-            # logger.warning_once(
-            #     "CUDA-fused xIELU not available (%s) –"
-            #     " falling back to a Python version.\n"
-            #     "For CUDA xIELU (experimental), `pip install git+https://github.com/nickjbrowning/XIELU`",
-            #     str(err),
-            # )
 
     def _xielu_python(self, x: torch.Tensor) -> torch.Tensor:
         alpha_p = nn.functional.softplus(self.alpha_p)

python/sglang/srt/layers/attention/dsa/index_buf_accessor.py

@@ -253,135 +253,11 @@ class GetKAndS:
         )
 
 
-class SetK:
-    @classmethod
-    def execute(cls, *args, buf, **kwargs):
-        return cls.torch_fast(*args, **kwargs, buf=buf)
-
-    @classmethod
-    def slow(
-        cls,
-        pool: "DSATokenToKVPool",
-        buf: torch.Tensor,
-        loc: torch.Tensor,
-        index_k: torch.Tensor,
-    ):
-        for i in range(len(loc)):
-            page_index = loc[i] // pool.page_size
-            offset = loc[i] % pool.page_size
-            buf[
-                page_index,
-                offset * pool.index_head_dim : (offset + 1) * pool.index_head_dim,
-            ] = index_k[i].view(torch.uint8)
-
-    @classmethod
-    def torch_fast(
-        cls,
-        pool: "DSATokenToKVPool",
-        buf: torch.Tensor,
-        loc: torch.Tensor,
-        index_k: torch.Tensor,
-    ):
-        (num_tokens_to_write,) = loc.shape
-        buf_numel_per_page = buf.shape[1]
-        num_k_bytes_per_token = pool.index_head_dim
-
-        # loc: (num_tokens_to_write,), int32, element := the token index to write to
-        loc_page_index = loc // pool.page_size
-        loc_token_offset_in_page = loc % pool.page_size
-
-        flat_buf = buf.flatten()
-        flat_indices = (
-            (loc_page_index * buf_numel_per_page)[:, None]
-            + (loc_token_offset_in_page * num_k_bytes_per_token)[:, None]
-            + torch.arange(num_k_bytes_per_token, dtype=torch.int32, device="cuda")[
-                None, :
-            ]
-        )
-        num_k_bytes_total = num_tokens_to_write * num_k_bytes_per_token
-        flat_indices = flat_indices.flatten()[:num_k_bytes_total]
-        flat_buf[flat_indices] = index_k.view(torch.uint8).flatten()
-
-
-class SetS:
-    @classmethod
-    def execute(cls, *args, buf, **kwargs):
-        return cls.torch_fast(*args, **kwargs, buf=buf)
-
-    @classmethod
-    def slow(
-        cls,
-        pool: "DSATokenToKVPool",
-        buf: torch.Tensor,
-        loc: torch.Tensor,
-        index_k_scale: torch.Tensor,
-    ):
-        for i in range(len(loc)):
-            page_index = loc[i] // pool.page_size
-            offset = loc[i] % pool.page_size
-            start = pool.page_size * pool.index_head_dim
-            buf[page_index, start + offset * 4 : start + (offset + 1) * 4] = (
-                index_k_scale[i].view(torch.uint8)
-            )
-
-    @classmethod
-    def torch_fast(
-        cls,
-        pool: "DSATokenToKVPool",
-        buf: torch.Tensor,
-        loc: torch.Tensor,
-        index_k_scale: torch.Tensor,
-    ):
-        (num_tokens_to_write,) = loc.shape
-        buf_numel_per_page = buf.shape[1]
-        num_s_bytes_per_token = 4
-        s_offset_in_page = pool.page_size * pool.index_head_dim
-
-        # loc: (num_tokens_to_write,), int32, element := the token index to write to
-        loc_page_index = loc // pool.page_size
-        loc_token_offset_in_page = loc % pool.page_size
-
-        flat_buf = buf.flatten()
-        flat_indices = (
-            (loc_page_index * buf_numel_per_page)[:, None]
-            + s_offset_in_page
-            + (loc_token_offset_in_page * num_s_bytes_per_token)[:, None]
-            + torch.arange(num_s_bytes_per_token, dtype=torch.int32, device="cuda")[
-                None, :
-            ]
-        )
-        number_s_bytes_total = num_tokens_to_write * num_s_bytes_per_token
-        flat_indices = flat_indices.flatten()[:number_s_bytes_total]
-        flat_buf[flat_indices] = index_k_scale.view(torch.uint8).flatten()
-
-
 class SetKAndS:
     @classmethod
     def execute(cls, *args, buf, **kwargs):
-        if 0:
-            # print("SetK, SetS comparison test")
-            buf_cloned = buf.clone()
-            cls.vanilla(*args, **kwargs, buf=buf)
-            cls.triton(*args, **kwargs, buf=buf_cloned)
-
-            def _clear_token_0(target):
-                target[0, :128] = target[0, 64 * 128 : 64 * 128 + 4] = 0
-
-            _clear_token_0(buf)
-            _clear_token_0(buf_cloned)
-
-            assert torch.all(
-                buf == buf_cloned
-            ), f"{buf=} {buf_cloned=} {kwargs['loc'].to_list()=}"
-            return
-
         cls.triton(*args, **kwargs, buf=buf)
 
-    @classmethod
-    def vanilla(cls, pool, buf, loc, index_k, index_k_scale):
-        SetK.execute(pool=pool, buf=buf, loc=loc, index_k=index_k)
-        SetS.execute(pool=pool, buf=buf, loc=loc, index_k_scale=index_k_scale)
-
     @classmethod
     def triton(cls, pool, buf, loc, index_k, index_k_scale):
         loc = loc.to(torch.int64)

python/sglang/srt/layers/moe/moe_runner/triton.py

@@ -252,8 +252,7 @@ def pre_permute_standard_to_triton(
     running_state: dict,
 ) -> TritonRunnerInput:
 
-    # NOTE: this is dead code as a fused func for standard format is registered.
+    # Registered fallback for format-conversion tests and examples.
-    # This is left here for testing and examples.
 
     from sglang.srt.layers.moe.moe_runner.triton_utils.fused_moe import (
         _prepare_fused_moe_run,
@@ -309,8 +308,7 @@ def post_permute_triton_to_standard(
     running_state: dict,
 ) -> StandardCombineInput:
 
-    # NOTE: this is dead code as a fused func for standard format is registered.
+    # Registered fallback for format-conversion tests and examples.
-    # This is left here for testing and examples.
 
     from sglang.srt.layers.moe.token_dispatcher.standard import StandardCombineInput
 

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

@@ -195,7 +195,7 @@ if _use_aiter:
 if _is_musa:
     try:
         from mate import moe_fused_gate
-    except ImportError as e:
+    except ImportError:
         raise ImportError("mate is required for the biased grouped topk.")
 
     from sglang.srt.hardware_backend.musa.kernels.topk import topk_sigmoid, topk_softmax

python/sglang/srt/layers/multimodal.py

@@ -94,11 +94,7 @@ def hash_tiles32_kernel_blocked(
     h1 ^= nbytes
     h2 ^= nbytes
     h1 = _fmix32(h1, C1=FM_C1, C2=FM_C2)
-    h2 = (
+    h2 = _fmix32(h2, C1=FM_C1, C2=FM_C2)
-        _fmix32(h2, C1=FMIX32_C1, C2=FMIX32_C2)
-        if False
-        else _fmix32(h2, C1=FM_C1, C2=FM_C2)
-    )
 
     out = (h1.to(tl.uint64) << 32) | h2.to(tl.uint64)
     tl.store(out_ptr + pid, out)

python/sglang/srt/layers/quantization/quark/utils.py

@@ -9,7 +9,7 @@ import torch
 
 try:
     from aiter.ops.triton.quant import dynamic_mxfp4_quant
-except ImportError as err:
+except ImportError:
 
     def raise_aiter_import_error(*args, **kwargs):
         raise ImportError(

python/sglang/srt/managers/mm_utils.py

@@ -68,7 +68,7 @@ def init_feature_buffer(device):
             num_elements, dtype=torch.float32, device=device
         )
         logger.info(f"Preallocated {size_mb}MB GPU buffer")
-    except RuntimeError as e:
+    except RuntimeError:
         _GPU_FEATURE_BUFFER = None
 
 
@@ -160,7 +160,7 @@ class TransportProxyTensor(torch.Tensor):
                     "storage_offset": self.storage_offset(),
                 }
                 state["tensor_data"] = None
-            except Exception as e:
+            except Exception:
                 # Failed to get CUDA IPC handle (possibly tp). Falling back to default transport.
                 state["metadata"]["transport_mode"] = "default"
                 state["tensor_data"] = self.as_subclass(torch.Tensor)

python/sglang/srt/model_executor/model_runner.py

@@ -1064,7 +1064,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
     def remote_instance_init_transfer_engine(self):
         try:
             from mooncake.engine import TransferEngine
-        except ImportError as e:
+        except ImportError:
             logger.warning(
                 "Please install mooncake for using remote instance transfer engine: pip install mooncake-transfer-engine"
             )

python/sglang/srt/models/hunyuan.py

@@ -531,22 +531,15 @@ class HunYuanModel(nn.Module):
             hidden_states = self.get_input_embeddings(input_ids)
         residual = None
 
-        prev_kv_states = None
+        for layer in self.layers:
-        for i in range(len(self.layers)):
+            hidden_states, residual, _ = layer(
-            layer = self.layers[i]
-            hidden_states, residual, kv_states = layer(
                 positions,
                 hidden_states,
                 forward_batch,
                 residual,
-                prev_kv_states,
+                None,
             )
 
-            if False:  # (i - self.start_layer) % cla_factor == 0:
-                prev_kv_states = kv_states
-            else:
-                prev_kv_states = None
-
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 

python/sglang/srt/multimodal/mm_utils.py

@@ -213,7 +213,7 @@ def process_anyres_image(image, processor, grid_pinpoints):
     if isinstance(grid_pinpoints, str) and "x" in grid_pinpoints:
         try:
             patch_size = processor.size[0]
-        except Exception as e:
+        except Exception:
             patch_size = processor.size["shortest_edge"]
         assert patch_size in [
             224,

python/sglang/srt/utils/common.py

@@ -1857,7 +1857,7 @@ def get_npu_memory_capacity():
             return envs.SGLANG_ZBAL_LOCAL_MEM_SIZE.get()  # unit: MB
         else:
             return torch.npu.mem_get_info()[1] // 1024 // 1024  # unit: MB
-    except ImportError as e:
+    except ImportError:
         raise ImportError("torch_npu is required when run on npu device.")
 
 
@@ -2210,7 +2210,7 @@ def get_compiler_backend(mode=None) -> str:
             import torchair
             import torchair.ge_concrete_graph.ge_converter.experimental.patch_for_hcom_allreduce
             from torchair.configs.compiler_config import CompilerConfig
-        except ImportError as e:
+        except ImportError:
             raise ImportError(
                 "NPU detected, but torchair package is not installed. "
                 "Please install torchair for torch.compile support on NPU."

python/sglang/srt/utils/cuda_ipc_transport_utils.py

@@ -364,7 +364,7 @@ class CudaIpcTensorTransportProxy:
                 "recons_dtype": info_data.dtype,
             }
             state["tensor_data"] = None
-        except Exception as e:
+        except Exception:
             # Failed to get CUDA IPC handle (possibly tp). Falling back to default transport.
             state["ipc_extra"] = None
             state["tensor_data"] = data

python/sglang/srt/utils/rpd_utils.py

@@ -368,11 +368,11 @@ def rpd_to_chrome_trace(
             if row[0] == "0":  # Frame start
                 if key not in stacks:
                     stacks[key] = []
-                stack = stacks[key].append((row[1], row[4]))
+                stacks[key].append((row[1], row[4]))
                 # print(f"0: new api frame: pid_tid={key} -> stack={stacks}")
 
             elif row[0] == "1":  # Frame end
-                completed = stacks[key].pop()
+                stacks[key].pop()
                 # print(f"1: end api frame: pid_tid={key} -> stack={stacks}")
 
             elif row[0] == "2":  # API + Op
@@ -400,8 +400,6 @@ def rpd_to_chrome_trace(
                                 or abs(gpuFrame.start - row[8]) < 200
                             )
                         ):
-                            # if gpuFrame.id == frame[0] and gpuFrame.name == frame[1]:    # Another op under the same frame -> union them
-                            # if False:   # Turn off frame joining
                             if row[7] < gpuFrame.start:
                                 gpuFrame.start = row[7]
                             if row[8] > gpuFrame.end:

python/sglang/test/ascend/e2e/test_npu_multi_node_utils.py

@@ -779,7 +779,7 @@ def wait_server_ready(url, timeout=LOCAL_TIMEOUT):
                 logger.info(
                     f"Server {url} returned status code: {response.status_code}"
                 )
-        except Exception as e:
+        except Exception:
             # logger.error(f"Server {url} request error: {e}, retrying...")
             pass
 

python/sglang/test/kits/attention_unittest/attention_methods/dsv4_attention.py

@@ -1104,7 +1104,7 @@ def _seed_c4_if_needed(
     fixture: DSV4AttentionFixture, *, num_entries: int = _DSV4_EXTRA_ENTRIES
 ) -> None:
     """For compress_ratio=4, seed the C4 metadata the exercised path consumes
-    (the C4Indexer would normally populate it; the smoke fixture skips the
+    (the C4Indexer would normally populate it; the compact fixture skips the
     indexer): `c4_sparse_page_indices` for the dense extend path,
     `c4_sparse_raw_indices` for sparse prefill. No-op for other compress_ratios.
     """
@@ -1383,7 +1383,7 @@ def _seed_c4_sparse_indices(
 ) -> None:
     """For compress_ratio=4 the production `init_flashmla_related` initializes
     `c4_sparse_page_indices` to all `-1` (the C4Indexer fills it in later).
-    Since the smoke fixture does not run the indexer, the C4 path attends to
+    Since the compact fixture does not run the indexer, the C4 path attends to
     zero extra entries unless we seed the indices ourselves. Seed each query
     row to point to `[0, 1, ..., num_entries - 1]` so the backend reads the
     same `num_entries` C4 K's that the reference also reads, exercising the
@@ -1614,7 +1614,7 @@ def run_dsv4_compress_attention_case(
     assert case.compress_ratio in (
         4,
         128,
-    ), f"smoke runner requires compress_ratio in (4, 128); got {case.compress_ratio}"
+    ), f"DSV4 compact runner requires compress_ratio in (4, 128); got {case.compress_ratio}"
     if sparse_prefill:
         assert (
             case.forward_mode.is_extend_without_speculative()

python/sglang/test/kits/basic_decode_correctness_kit.py

@@ -2,7 +2,7 @@
 
 Probes that catch the model producing wrong output: weight load
 failure, sampling path bugs, KV / attention corruption, and cuda graph
-edge cases. Single-prompt smoke only -- dataset-driven accuracy gates
+edge cases. Single-prompt coverage only -- dataset-driven accuracy gates
 belong to the consuming test class, not this kit.
 
 Mix into any ``CustomTestCase`` subclass that exposes ``self.base_url``

python/sglang/test/simple_eval_mgsm.py

@@ -175,7 +175,7 @@ class MGSMEval(Eval):
             ]
             try:
                 response_text = sampler(prompt_messages)
-            except Exception as e:
+            except Exception:
                 response_text = ""
 
             answer_prefix = LANG_TO_ANSWER_PREFIX[language]

scripts/ci/amd/amd_ci_install_dependency.sh

@@ -150,8 +150,6 @@ else
   docker cp human-eval ci_sglang:/
   install_with_retry docker exec -w /human-eval ci_sglang pip install --cache-dir=/sgl-data/pip-cache -e .
 
-  docker exec -w / ci_sglang mkdir -p /dummy-grok
-  # Create dummy grok config inline (bypasses Azure blob storage which may have auth issues)
   mkdir -p dummy-grok
   cat > dummy-grok/config.json << 'EOF'
   {
@@ -176,9 +174,8 @@ else
     "torch_dtype": "bfloat16"
   }
 EOF
-  # docker exec -w / ci_sglang mkdir -p /dummy-grok
+  docker exec -w / ci_sglang mkdir -p /dummy-grok
-  # mkdir -p dummy-grok && wget https://sharkpublic.blob.core.windows.net/sharkpublic/sglang/dummy_grok.json -O dummy-grok/config.json
+  docker cp ./dummy-grok/config.json ci_sglang:/dummy-grok/config.json
-  # docker cp ./dummy-grok ci_sglang:/
 
   docker exec ci_sglang pip install --cache-dir=/sgl-data/pip-cache huggingface_hub[hf_xet]
   docker exec ci_sglang pip install --cache-dir=/sgl-data/pip-cache pytest

scripts/ci/slurm/analyze_logs_with_modal.py

@@ -143,7 +143,7 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
-def build_sandbox_image() -> "modal.Image":
+def build_sandbox_image() -> modal.Image:
     if modal is None:
         raise RuntimeError(
             "The 'modal' package is required. Run this script with "
@@ -213,7 +213,7 @@ Do not skip issue filing. The whole point of this system is automated triage.
 """
 
 
-def upload_tree(sandbox: "modal.Sandbox", log_dir: Path) -> None:
+def upload_tree(sandbox: modal.Sandbox, log_dir: Path) -> None:
     log_files = [path for path in log_dir.rglob("*") if path.is_file()]
     logger.info("Uploading %d log files into the sandbox", len(log_files))
     for index, log_file in enumerate(log_files, start=1):
@@ -225,7 +225,7 @@ def upload_tree(sandbox: "modal.Sandbox", log_dir: Path) -> None:
             logger.info("Uploaded %d/%d files", index, len(log_files))
 
 
-def clone_context_repos(sandbox: "modal.Sandbox", repo_urls: list[str]) -> None:
+def clone_context_repos(sandbox: modal.Sandbox, repo_urls: list[str]) -> None:
     if not repo_urls:
         return
 
@@ -244,7 +244,7 @@ def clone_context_repos(sandbox: "modal.Sandbox", repo_urls: list[str]) -> None:
         ).wait()
 
 
-def read_optional_file(sandbox: "modal.Sandbox", path: str) -> str | None:
+def read_optional_file(sandbox: modal.Sandbox, path: str) -> str | None:
     try:
         return sandbox.filesystem.read_text(path)
     except Exception:

scripts/ci/slurm/launch_mi355x.sh

@@ -231,10 +231,10 @@ docker run $DOCKER_COMMON --name mi355x_decode \
   $COMMON_FLAGS --disaggregation-mode decode --disaggregation-bootstrap-port $DBOOT
 EOF
 
-# Smoke-test payload + validator (separate files to avoid quoting inside the
+# Probe payload + validator (separate files to avoid quoting inside the
 # bench.sh `bash -lc '...'` block). One real request exercises the full
 # prefill->decode KV handoff before we commit to the whole sweep.
-cat > "$WORKDIR/smoke.json" <<'JSON'
+cat > "$WORKDIR/probe.json" <<'JSON'
 {"text": "The capital of France is", "sampling_params": {"max_new_tokens": 16, "temperature": 0.0}}
 JSON
 cat > "$WORKDIR/assert_nonempty.py" <<'PY'
@@ -242,9 +242,9 @@ import sys, json
 d = json.load(sys.stdin)
 t = d.get("text", "") if isinstance(d, dict) else ""
 if not (t and t.strip()):
-    print("[smoke] empty/invalid output:", str(d)[:200])
+    print("[probe] empty/invalid output:", str(d)[:200])
     sys.exit(1)
-print("[smoke] ok:", t[:80].replace("\n", " "))
+print("[probe] ok:", t[:80].replace("\n", " "))
 PY
 
 # Bench script runs on the prefill node; \$PIP/\$DIP injected at srun time.
@@ -267,12 +267,12 @@ docker run $DOCKER_COMMON --name mi355x_bench \
       --disable-circuit-breaker &
     for i in \$(seq 1 30); do curl -sf http://127.0.0.1:$LBPORT/health >/dev/null && break; sleep 2; done
     CIDIR=/host_home/.mi355x_ci/${MATRIX_CONFIG_NAME}
-    echo "[smoke] PD end-to-end check via LB"
+    echo "[probe] PD end-to-end check via LB"
     curl -sf -X POST http://127.0.0.1:$LBPORT/generate \
-      -H "content-type: application/json" -d @\$CIDIR/smoke.json > \$CIDIR/smoke_out.json \
+      -H "content-type: application/json" -d @\$CIDIR/probe.json > \$CIDIR/probe_out.json \
-      || { echo "[smoke] request failed -- PD path not serving; aborting before sweep"; exit 1; }
+      || { echo "[probe] request failed -- PD path not serving; aborting before sweep"; exit 1; }
-    python3 \$CIDIR/assert_nonempty.py < \$CIDIR/smoke_out.json \
+    python3 \$CIDIR/assert_nonempty.py < \$CIDIR/probe_out.json \
-      || { echo "[smoke] empty/invalid generation; aborting before sweep"; exit 1; }
+      || { echo "[probe] empty/invalid generation; aborting before sweep"; exit 1; }
     # Correctness gate runs BEFORE the perf sweep: if the model is wrong there
     # is no point spending ~15min measuring how fast it is wrong, so a failure
     # here exits immediately and the sweep never runs.

scripts/ci/utils/compute_partitions.py

@@ -35,7 +35,7 @@ HWBackend = _ci_register.HWBackend
 # pr-test-amd.yml / pr-test-npu.yml have their own dispatch.
 _TARGET_BACKENDS = {HWBackend.CUDA, HWBackend.CPU}
 
-# base-a is the critical-path entry gate; pin its fanout to smoke-coverage
+# base-a is the critical-path entry gate; pin its fanout to sanity-coverage
 # defaults instead of est_time. max_parallel = size (no throttle).
 _BASE_A_OVERRIDES = {
     "base-a-test-cpu": 8,

scripts/playground/router/tree.py

@@ -1,6 +1,6 @@
 import time
 from collections import defaultdict
-from typing import Dict, List
+from typing import Dict
 
 
 class Node:
@@ -111,9 +111,6 @@ class MultiTenantRadixTree:
         curr = self.root
         curr_idx = 0
 
-        ret_text = ""
-        ret_tenant = None
-
         while curr_idx < len(s):
             matched_node = None
             if s[curr_idx] in curr.children:
@@ -228,18 +225,6 @@ class MultiTenantRadixTree:
 
         return used_size_per_tenant
 
-    def remove_tenant(self, tenant_id: str) -> None:
-        """
-        Remove all data associated with a specific tenant from the tree.
-        This operation maintains the integrity of the shared tree structure while
-        removing only the specified tenant's access information.
-
-        Args:
-            tenant_id: The identifier of the tenant whose data should be removed
-        """
-        # TODO: Implementation needed
-        pass
-
     def pretty_print(self) -> str:
         """
         Returns a string representation of the tree showing the structure, tenant ownership,

sgl-kernel/benchmark/bench_es_fp8_blockwise_grouped_gemm.py

@@ -79,9 +79,6 @@ def bench_es(
     b_tensors = []
     a_scales_tensors = []
     b_scales_tensors = []
-    if False:
-        print("Token Distributtion: ", group_ms[0:num_groups])
-        print("Token Count: ", sum(group_ms[0:num_groups]))
     for g in range(num_groups):
         m_g = group_ms[g]
         expert_offsets[g + 1] = expert_offsets[g] + m_g

sgl-kernel/python/sgl_kernel/metal.py

@@ -32,19 +32,19 @@ else:
 
 
 def rope_pool_fused(
-    q: "mx.array",
+    q: mx.array,
-    k: "mx.array",
+    k: mx.array,
-    v: "mx.array",
+    v: mx.array,
-    positions: "mx.array",
+    positions: mx.array,
-    slots: "mx.array",
+    slots: mx.array,
-    k_pool: "mx.array",
+    k_pool: mx.array,
-    v_pool: "mx.array",
+    v_pool: mx.array,
     *,
     head_dim: int,
     num_qo_heads: int,
     num_kv_heads: int,
     rope_base: float,
-) -> tuple["mx.array", "mx.array", "mx.array", "mx.array"]:
+) -> tuple[mx.array, mx.array, mx.array, mx.array]:
     """Apply NeoX RoPE to Q/K and scatter K/V into the MLX KV pool.
 
     Args:

sgl-kernel/tests/test_dsv4_norm_rope.py

@@ -1,7 +1,5 @@
 """Tests for DeepSeek-V4 fused norm + RoPE kernels."""
 
-import math
-
 import pytest
 import sgl_kernel
 import torch
@@ -93,7 +91,7 @@ def test_fused_q_norm_rope_preallocated_output():
 
 @pytest.mark.parametrize("batch_size", [1, 8])
 def test_fused_q_indexer_rope_hadamard_quant_runs(batch_size):
-    """Smoke test: kernel runs without errors and produces finite results."""
+    """Basic launch coverage with finite output checks."""
     torch.manual_seed(42)
     num_heads = 4
     head_dim = 128

sgl-model-gateway/bindings/golang/src/utils.rs

@@ -18,7 +18,7 @@ pub fn generate_tool_call_id(
     }
 }
 
-/// Generate tool constraints (placeholder implementation)
+/// Return an explicit error for tool-constraint generation through this FFI.
 ///
 /// # Arguments
 /// * `tools_json` - JSON array of tools
@@ -37,8 +37,6 @@ pub unsafe extern "C" fn sgl_generate_tool_constraints(
     _constraint_schema_out: *mut *mut std::os::raw::c_char,
     error_out: *mut *mut std::os::raw::c_char,
 ) -> super::error::SglErrorCode {
-    // Implementation would parse JSON and call generate_tool_constraints
-    // This is a placeholder
     super::error::set_error_message(error_out, "Tool constraint generation not yet implemented in FFI");
     super::error::SglErrorCode::UnknownError
 }

sgl-model-gateway/bindings/python/src/sglang_router/mini_lb.py

@@ -336,7 +336,7 @@ async def _get_model_info_impl():
                 model_info_json = await response.json()
                 return ORJSONResponse(content=model_info_json)
 
-        except aiohttp.ClientError as e:
+        except aiohttp.ClientError:
             raise HTTPException(
                 status_code=HTTPStatus.SERVICE_UNAVAILABLE,
                 detail=f"Failed to get model info from backend",

test/manual/chunked_prefill/test_scripted_regression.py

@@ -112,7 +112,7 @@ class TestRegressionBasic(ScriptedTestCase):
         "dbdcdde245 mamba_pool_idx cleanup-skip is mamba-architecture-specific. "
         "The shared test fixture does not configure a mamba model; running this "
         "regression against the default transformer model would not exercise the "
-        "mamba NO_TOKEN cleanup path, so the body would be a pure smoke test "
+        "mamba NO_TOKEN cleanup path, so the body would be a check "
         "with no real protection. Re-enable when a mamba fixture is wired in."
     )
     def test_mamba_chunked_resume_no_token(self):

test/manual/dsv4/_common.py

@@ -8,7 +8,7 @@ and contains one ``CustomTestCase`` subclass per recipe
 Each subclass launches the server with the cookbook's exact flags and
 runs two sgl-eval evaluations (https://github.com/sgl-project/sgl-eval):
 - ``test_smoke_gsm8k`` — short, cheap GSM8K pass to verify the server
-  can produce coherent math answers at all (smoke gate).
+  can produce coherent math answers at all (sanity gate).
 - ``test_aime25`` — full AIME25 accuracy run (heavy; 16 repeats default).
 
 Cookbook reference:
@@ -19,7 +19,7 @@ These are MANUAL tests (not CI). ``sgl-eval`` must be on PATH.
 Per-variant defaults (set on the Flash/Pro intermediate base classes):
     Flash recipes -> AIME25 score threshold 0.93
     Pro   recipes -> AIME25 score threshold 0.95
-GSM8K smoke threshold (0.93) is shared across Flash and Pro.
+GSM8K sanity threshold (0.93) is shared across Flash and Pro.
 
 AIME25 knobs (env vars):
     DSV4_AIME25_NUM_REPEATS       (default 16    -> --n-repeats)
@@ -30,7 +30,7 @@ AIME25 knobs (env vars):
     DSV4_AIME25_SCORE_METRIC      (default "score"; sgl-eval JSON key under "aggregate")
     DSV4_AIME25_SCORE_THRESHOLD   (default 0; >0 overrides per-variant default)
 
-GSM8K smoke knobs (env vars):
+GSM8K sanity knobs (env vars):
     DSV4_GSM8K_NUM_EXAMPLES       (default 50    -> --num-examples)
     DSV4_GSM8K_N_REPEATS          (default 1     -> --n-repeats)
     DSV4_GSM8K_TEMPERATURE        (default 0.6   -> --temperature)

test/registered/8-gpu-models/test_ling_2_6_flash.py

@@ -20,7 +20,7 @@ class TestLing26Flash(GSM8KMixin, DefaultServerBase):
 
     # Native 128K context (no YaRN) — avoids the
     # SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN env-var dance and keeps the
-    # smoke test focused on the dispatcher / hybrid-attention path.
+    # coverage focused on the dispatcher / hybrid-attention path.
     other_args = [
         "--tp-size",
         "4",

test/registered/amd/disaggregation/test_mori_transfer_engine_e2e.py

@@ -39,7 +39,7 @@ class MoriTransferEngineBase(PDDisaggregationServerBase):
                 raise unittest.SkipTest("torch.cuda is not available.")
             if torch.cuda.device_count() < cls.required_gpus:
                 raise unittest.SkipTest(
-                    f"MORI PD smoke test requires >= {cls.required_gpus} visible GPUs."
+                    f"MORI PD check requires >= {cls.required_gpus} visible GPUs."
                 )
         except Exception as e:
             raise unittest.SkipTest(f"torch is not available/usable: {e}")

test/registered/amd/disaggregation/test_nixl_transfer_engine_e2e.py

@@ -46,7 +46,7 @@ class NixlTransferEngineBase(PDDisaggregationServerBase):
                 raise unittest.SkipTest("torch.cuda is not available.")
             if torch.cuda.device_count() < cls.required_gpus:
                 raise unittest.SkipTest(
-                    f"NIXL PD smoke test requires >= {cls.required_gpus} visible GPUs."
+                    f"NIXL PD check requires >= {cls.required_gpus} visible GPUs."
                 )
         except unittest.SkipTest:
             raise

test/registered/core/test_basic_sanity.py

@@ -1,4 +1,4 @@
-"""Stage-a basic sanity: small-but-broad server smoke that downstream
+"""Stage-a basic sanity: small-but-broad server coverage that downstream
 stages depend on. Multiple sanity-kit mixins driving one shared server,
 covering protocol, decode correctness, scheduler stress, occupancy, and
 hellaswag accuracy."""

test/registered/model_loading/test_load_weights_from_remote_instance.py

@@ -302,7 +302,7 @@ def test_load_weights_from_remote_instance(
         try:
             key, value = param_queue.get(timeout=5)
             results[key] = value
-        except Exception as e:
+        except Exception:
             if all(not p.is_alive() for p in context.processes):
                 break
 

test/registered/model_loading/test_load_weights_from_remote_instance_npu.py

@@ -310,7 +310,7 @@ def test_load_weights_from_remote_instance(
         try:
             key, value = param_queue.get(timeout=5)
             results[key] = value
-        except Exception as e:
+        except Exception:
             if all(not p.is_alive() for p in context.processes):
                 break
 

test/registered/musa/test_llm_server_smoke_musa.py

@@ -18,7 +18,7 @@ _REQUEST_TIMEOUT = 60
     "MUSA device not available",
 )
 class TestMusaDeepSeekV2LiteChatServerSmoke(DefaultServerBase):
-    """MUSA LLM server smoke test: launch, health check, and non-empty generation."""
+    """MUSA LLM server sanity check: launch, health check, and non-empty generation."""
 
     model = os.getenv("SGLANG_MUSA_LLM_MODEL", "deepseek-ai/DeepSeek-V2-Lite-Chat")
     served_model_name = "deepseek-v2-lite-chat"

test/registered/radix_cache/unified_radix_tree/test_unified_radix_cache_kl_dsv4.py

@@ -112,7 +112,7 @@ class TestUnifiedDeepSeekV4FlashHiCache(UnifiedRadixTreeTestMixin, CustomTestCas
 class TestUnifiedDeepSeekV4FlashHiCachePageFirstDirect(
     TestUnifiedDeepSeekV4FlashHiCache
 ):
-    """DeepSeek V4 Flash HiCache layout smoke: page_first_direct + direct."""
+    """DeepSeek V4 Flash HiCache layout check: page_first_direct + direct."""
 
     hicache_io_backend = "kernel"
     hicache_mem_layout = "layer_first"

test/registered/rl/test_update_weights_from_distributed.py

@@ -396,7 +396,8 @@ def init_process_sgl(
             return response.json()
 
         with ThreadPoolExecutor(32) as executor:
-            futures = [executor.submit(run_decode, 1000) for _ in range(32)]
+            for _ in range(32):
+                executor.submit(run_decode, 1000)
             time.sleep(2)
 
     # The last parameter is lm_head.weight, which is tied
@@ -573,7 +574,7 @@ def test_update_weights_from_distributed(
         try:
             key, value = param_queue.get(timeout=5)
             results[key] = value
-        except Exception as e:
+        except Exception:
             if all(not p.is_alive() for p in context.processes):
                 break
 

test/registered/scripted_runtime/test_scripted_runtime_core.py

@@ -736,9 +736,7 @@ class TestScriptedRuntimeCore(ScriptedTestCase):
 
     @staticmethod
     def _script_empty_return(t: ScriptedContext):
-        if False:
+        yield from ()
-            yield
-        return
 
     def test_failing_script_surfaces_and_session_survives(self):
         with self.assertRaises(AssertionError) as ctx:
@@ -753,7 +751,7 @@ class TestScriptedRuntimeCore(ScriptedTestCase):
 
     @staticmethod
     def _script_minimal_ok(t: ScriptedContext):
-        r = t.start_req(prompt_len=_SHORT_PROMPT_LEN, max_new_tokens=2)
+        t.start_req(prompt_len=_SHORT_PROMPT_LEN, max_new_tokens=2)
         yield
         yield
 

test/registered/unit/entrypoints/openai/test_serving_responses.py

@@ -280,7 +280,7 @@ class FullResponseUsageTestCase(unittest.TestCase):
         metadata = RequestResponseMetadata(request_id=request.request_id)
 
         async def empty_generator():
-            if False:
+            for _ in ():
                 yield None
 
         response = asyncio.run(

test/registered/unit/hardware_backend/mlx/test_quantization.py

@@ -3,7 +3,7 @@
 Covers:
   - mlx_q4 / mlx_q8 quantize fp16 weights to QuantizedLinear in-place
   - active-memory drops after quantization
-  - smoke /generate still works post-quantize
+  - /generate still works post-quantize
   - pre-quantized HF repos still load (regression guard for mlx_lm passthrough)
   - mlx_q4 flag on an already-quantized model is a no-op (skip + log)
 

test/registered/unit/models/test_locate_anything.py

@@ -265,7 +265,7 @@ def _image_item(feature, grid_hws):
 
 
 class TestGetImageFeatureWiring(CustomTestCase):
-    """Forward-shape smoke test for get_image_feature.
+    """Forward-shape coverage for get_image_feature.
 
     Guards the production path (pixel concat -> vision tower -> projector) and
     the precomputed-embedding passthrough so a future change to the wiring or