[lint] improve ruff check (#11922)

Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com>

.pre-commit-config.yaml

@@ -27,7 +27,9 @@ repos:
     rev: v0.11.7
     hooks:
       - id: ruff
-        args: [--select=F401,F821, --fixable=F401]
+        args:
+          - --select=F401,F821
+          - --fix
         files: ^(benchmark/|docs/|examples/|python/sglang/)
         exclude: __init__\.py$|\.ipynb$|^python/sglang/srt/grpc/.*_pb2\.py$|^python/sglang/srt/grpc/.*_pb2_grpc\.py$|^python/sglang/srt/grpc/.*_pb2\.pyi$|^python/sglang/srt/grpc/.*_pb2_grpc\.pyi$
   - repo: https://github.com/psf/black

benchmark/kernels/minmax-text-01-lightning_attention/benchmark_lightning_attention_decode.py

@@ -167,6 +167,7 @@ class MiniMaxText01LightningAttention(nn.Module):
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         use_cache: bool = False,
         slope_rate: Optional[torch.Tensor] = None,
+        do_eval: bool = False,
         **kwargs,
     ):
         if (not self.training) and (not do_eval):

benchmark/kernels/minmax-text-01-lightning_attention/benchmark_lightning_attention_prefill.py

 import itertools
+import logging
 import math
 import os
 from typing import Optional, Tuple
@@ -10,6 +11,8 @@ import triton
 import triton.language as tl
 from einops import rearrange
 
+logger = logging.getLogger(__name__)
+
 
 # Adapted from https://github.com/OpenNLPLab/lightning-attention/blob/main/lightning_attn/ops/triton/lightning_attn2.py
 @triton.jit
@@ -302,6 +305,7 @@ class MiniMaxText01LightningAttention(nn.Module):
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         use_cache: bool = False,
         slope_rate: Optional[torch.Tensor] = None,
+        do_eval: bool = False,
         **kwargs,
     ):
         if (not self.training) and (not do_eval):

python/sglang/bench_one_batch_server.py

@@ -16,6 +16,7 @@ import argparse
 import dataclasses
 import itertools
 import json
+import logging
 import multiprocessing
 import os
 import random
@@ -39,6 +40,8 @@ from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import is_blackwell, kill_process_tree
 from sglang.test.test_utils import is_in_ci, write_github_step_summary
 
+logger = logging.getLogger(__name__)
+
 
 class ProfileLinks(BaseModel):
     """Pydantic model for profile trace links."""

python/sglang/srt/disaggregation/common/conn.py

@@ -77,8 +77,8 @@ class CommonKVManager(BaseKVManager):
 
         if self.disaggregation_mode == DisaggregationMode.PREFILL:
             self._register_to_bootstrap()
-            self.transfer_infos: Dict[int, Dict[str, TransferInfo]] = {}
-            self.decode_kv_args_table: Dict[str, KVArgsRegisterInfo] = {}
+            self.transfer_infos = {}
+            self.decode_kv_args_table = {}
             self.pp_group = get_pp_group()
         elif self.disaggregation_mode == DisaggregationMode.DECODE:
             self.connection_pool: Dict[str, Dict[str, Union[str, int]]] = {}

python/sglang/srt/disaggregation/mooncake/conn.py

@@ -9,7 +9,7 @@ import struct
 import threading
 import time
 from collections import defaultdict
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Set, Tuple
 
 import numpy as np
 import numpy.typing as npt

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

@@ -14,6 +14,7 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Optional,
 
 import jinja2
 import openai.types.responses as openai_responses_types
+import orjson
 from fastapi import Request
 from fastapi.responses import ORJSONResponse
 from openai.types.responses import (
@@ -1063,7 +1064,7 @@ class OpenAIServingResponses(OpenAIServingChat):
                 ):
                     function_name = previous_item.recipient[len("browser.") :]
                     action = None
-                    parsed_args = ororjson.loads(previous_item.content[0].text)
+                    parsed_args = orjson.loads(previous_item.content[0].text)
                     if function_name == "search":
                         action = openai_responses_types.response_function_web_search.ActionSearch(
                             type="search",

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -194,7 +194,7 @@ class FlashInferAttnBackend(AttentionBackend):
             )
         if init_new_workspace:
             self.workspace_buffer = torch.empty(
-                global_config.flashinfer_workspace_size,
+                envs.SGLANG_FLASHINFER_WORKSPACE_SIZE.get(),
                 dtype=torch.uint8,
                 device=model_runner.device,
             )

python/sglang/srt/layers/attention/flashinfer_mla_backend.py

@@ -38,6 +38,9 @@ from sglang.srt.utils import (
 )
 
 if TYPE_CHECKING:
+    from sglang.srt.layers.attention.flashinfer_mla_backend import (
+        FlashInferMlaAttnBackend,
+    )
     from sglang.srt.layers.radix_attention import RadixAttention
     from sglang.srt.model_executor.model_runner import ModelRunner
     from sglang.srt.speculative.spec_info import SpecInput
@@ -66,7 +69,7 @@ global_workspace_buffer = None
 
 class FlashInferMhaChunkKVRunner:
     def __init__(
-        self, model_runner: ModelRunner, attn_backend: "FlashInferMlaAttnBackend"
+        self, model_runner: ModelRunner, attn_backend: FlashInferMlaAttnBackend
     ):
         # Parse Constants
         self.num_local_heads = (

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

@@ -13,7 +13,8 @@ from triton_kernels.matmul_ogs import (
     PrecisionConfig,
     matmul_ogs,
 )
-from triton_kernels.numerics import InFlexData
+from triton_kernels.numerics import InFlexData, MicroscalingCtx
+from triton_kernels.quantization import downcast_to_mxfp
 from triton_kernels.routing import GatherIndx, RoutingData, ScatterIndx
 from triton_kernels.swiglu import swiglu_fn
 
@@ -119,14 +120,14 @@ def triton_kernel_fused_experts(
     block_shape: Optional[list[int]] = None,
 ) -> torch.Tensor:
 
-    assert use_fp8_w8a8 == False, "use_fp8_w8a8 is not supported"
-    assert per_channel_quant == False, "per_channel_quant is not supported"
-    assert expert_map == None, "expert_map is not supported"
-    assert w1_scale == None, "w1_scale is not supported"
-    assert w2_scale == None, "w2_scale is not supported"
-    assert a1_scale == None, "a1_scale is not supported"
-    assert a2_scale == None, "a2_scale is not supported"
-    assert block_shape == None, "block_shape is not supported"
+    assert use_fp8_w8a8 is False, "use_fp8_w8a8 is not supported"
+    assert per_channel_quant is False, "per_channel_quant is not supported"
+    assert expert_map is None, "expert_map is not supported"
+    assert w1_scale is None, "w1_scale is not supported"
+    assert w2_scale is None, "w2_scale is not supported"
+    assert a1_scale is None, "a1_scale is not supported"
+    assert a2_scale is None, "a2_scale is not supported"
+    assert block_shape is None, "block_shape is not supported"
 
     # type check
     assert hidden_states.dtype == torch.bfloat16, "hidden_states must be bfloat16"
@@ -143,7 +144,7 @@ def triton_kernel_fused_experts(
     ), f"w2 shape[-1] {w2.shape[-1]} must be equal to w1 shape[1] {w1.shape[1]}"
 
     # feature check
-    assert inplace == False, "Inplace is not supported in new triton MoE kernel"
+    assert inplace is False, "Inplace is not supported in new triton MoE kernel"
 
     M, K = hidden_states.shape
     E, _, N = w1.shape
@@ -264,14 +265,14 @@ def triton_kernel_fused_experts_with_bias(
     gemm1_alpha: Optional[float] = None,
     gemm1_clamp_limit: Optional[float] = None,
 ) -> torch.Tensor:
-    assert use_fp8_w8a8 == False, "use_fp8_w8a8 is not supported"
-    assert per_channel_quant == False, "per_channel_quant is not supported"
-    assert expert_map == None, "expert_map is not supported"
-    assert w1_scale == None, "w1_scale is not supported"
-    assert w2_scale == None, "w2_scale is not supported"
-    assert a1_scale == None, "a1_scale is not supported"
-    assert a2_scale == None, "a2_scale is not supported"
-    assert block_shape == None, "block_shape is not supported"
+    assert use_fp8_w8a8 is False, "use_fp8_w8a8 is not supported"
+    assert per_channel_quant is False, "per_channel_quant is not supported"
+    assert expert_map is None, "expert_map is not supported"
+    assert w1_scale is None, "w1_scale is not supported"
+    assert w2_scale is None, "w2_scale is not supported"
+    assert a1_scale is None, "a1_scale is not supported"
+    assert a2_scale is None, "a2_scale is not supported"
+    assert block_shape is None, "block_shape is not supported"
 
     # type check
     assert hidden_states.dtype == torch.bfloat16, "hidden_states must be bfloat16"
@@ -290,7 +291,7 @@ def triton_kernel_fused_experts_with_bias(
     ), f"w2 shape[-1] {w2.shape[-1]} must be equal to w1 shape[1] {w1.shape[1]}"
 
     # feature check
-    assert inplace == False, "Inplace is not supported in new triton MoE kernel"
+    assert inplace is False, "Inplace is not supported in new triton MoE kernel"
 
     E, _, _ = w1.shape
 

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -44,6 +44,13 @@ from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
 from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 
 try:
+    from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_24 import (
+        CompressedTensors24,
+    )
+    from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w4a16_sparse24 import (
+        W4A16SPARSE24_SUPPORTED_BITS,
+        CompressedTensorsW4A16Sparse24,
+    )
     from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_wNa16 import (
         WNA16_SUPPORTED_BITS,
         CompressedTensorsWNA16,

python/sglang/srt/layers/quantization/fpgemm_fp8.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Optional
+from typing import Any, List, Optional
 
 import torch
 from torch.nn import Module

python/sglang/srt/mem_cache/memory_pool.py

@@ -47,6 +47,8 @@ from sglang.srt.utils import get_bool_env_var, is_cuda, is_npu, next_power_of_2
 
 if TYPE_CHECKING:
     from sglang.srt.managers.cache_controller import LayerDoneCounter
+    from sglang.srt.managers.schedule_batch import Req
+
 
 logger = logging.getLogger(__name__)
 
@@ -341,7 +343,7 @@ class HybridReqToTokenPool(ReqToTokenPool):
     # For chunk prefill req, we do not need to allocate mamba cache,
     # We could use allocated mamba cache instead.
     def alloc(
-        self, need_size: int, reqs: Optional[List["Req"]] = None
+        self, need_size: int, reqs: Optional[List[Req]] = None
     ) -> Optional[List[int]]:
         select_index = super().alloc(need_size)
         if select_index == None:

python/sglang/srt/model_loader/weight_utils.py

@@ -110,6 +110,9 @@ def convert_bin_to_safetensor_file(
 
     dirname = os.path.dirname(sf_filename)
     os.makedirs(dirname, exist_ok=True)
+
+    from safetensors.torch import save_file
+
     save_file(loaded, sf_filename, metadata={"format": "pt"})
 
     # check file size

python/sglang/srt/models/deepseek_v2.py

@@ -25,6 +25,7 @@ from typing import Any, Dict, Iterable, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
+import tqdm
 from torch import nn
 from transformers import PretrainedConfig
 
@@ -3499,7 +3500,7 @@ class DeepseekV2ForCausalLM(nn.Module):
         # temporarily only support DeepSeek V3/R1
         weight_block_size = [128, 128]
 
-        for layer_id in trange(
+        for layer_id in tqdm.trange(
             self.config.num_hidden_layers + int(is_nextn),
             desc="quant attn to fp8 ue8m0",
         ):

python/sglang/srt/models/glm4v.py

@@ -9,6 +9,7 @@ from transformers.models.glm4v.configuration_glm4v import Glm4vConfig, Glm4vVisi
 
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.attention import vision_utils
+from sglang.srt.layers.dp_attention import get_attention_tp_size
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
     ColumnParallelLinear,

python/sglang/srt/models/opt.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 
 """Inference-only OPT model compatible with HuggingFace weights."""
+import logging
 from collections.abc import Iterable
 from typing import Optional, Union
 
@@ -46,6 +47,9 @@ from sglang.srt.model_loader.weight_utils import (
     kv_cache_scales_loader,
 )
 from sglang.srt.utils import add_prefix, make_layers
+from sglang.utils import get_exception_traceback
+
+logger = logging.getLogger(__name__)
 
 
 def get_activation(name="relu"):

python/sglang/srt/utils/common.py

@@ -42,6 +42,7 @@ import tempfile
 import threading
 import time
 import traceback
+import types
 import uuid
 import warnings
 from collections import OrderedDict, defaultdict
@@ -55,6 +56,7 @@ from json import JSONDecodeError
 from multiprocessing.reduction import ForkingPickler
 from pathlib import Path
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     Dict,
@@ -62,6 +64,7 @@ from typing import (
     List,
     Optional,
     Protocol,
+    Sequence,
     Set,
     Tuple,
     TypeVar,
@@ -91,6 +94,9 @@ from typing_extensions import Literal
 from sglang.srt.environ import envs
 from sglang.srt.metrics.func_timer import enable_func_timer
 
+if TYPE_CHECKING:
+    from sglang.srt.layers.quantization.base_config import QuantizeMethodBase
+
 logger = logging.getLogger(__name__)
 
 show_time_cost = False
@@ -1076,7 +1082,7 @@ def monkey_patch_vllm_gguf_config():
 
     def get_quant_method_with_embedding_replaced(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> Optional[QuantizeMethodBase]:
         if isinstance(layer, LinearBase):
             return GGUFLinearMethod(self)
         elif isinstance(layer, VocabParallelEmbedding):
@@ -1956,7 +1962,9 @@ def direct_register_custom_op(
         if fake_impl is not None:
             my_lib._register_fake(op_name, fake_impl)
     except RuntimeError as error:
-        if "Tried to register an operator" in str(e) and "multiple times" in str(e):
+        if "Tried to register an operator" in str(error) and "multiple times" in str(
+            error
+        ):
             # Silently ignore duplicate registration errors
             # This can happen in multi-engine scenarios
             pass

python/sglang/test/few_shot_gsm8k_engine.py

@@ -3,6 +3,7 @@ import ast
 import asyncio
 import re
 import time
+from typing import Optional
 
 import numpy as np