update pre-commit config (#18860)

python/sglang/srt/checkpoint_engine/checkpoint_engine_worker.py

@@ -15,6 +15,7 @@
 Checkpoint-engine integration for SGLang.
 This module provides weight update functionality via IPC for checkpoint-engine compatibility.
 """
+
 import logging
 from typing import Callable, Dict, Optional
 

python/sglang/srt/compilation/weak_ref_tensor.py

@@ -13,7 +13,7 @@ else:
 
 
 def weak_ref_tensors(
-    tensors: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]]
+    tensors: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]],
 ) -> Union[torch.Tensor, list[Any], tuple[Any], Any]:
     """
     Convenience function to create weak references to tensors,

python/sglang/srt/configs/exaone.py

@@ -14,7 +14,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" EXAONE model configuration """
+"""EXAONE model configuration"""
+
 from typing import Any, Dict
 
 from transformers.configuration_utils import PretrainedConfig

python/sglang/srt/configs/falcon_h1.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """Falcon-H1 model configuration"""
 
-
 from transformers.configuration_utils import PretrainedConfig
 from transformers.utils import logging
 

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

@@ -970,7 +970,7 @@ class MooncakeKVManager(CommonKVManager):
                     self._handle_aux_data(msg)
                     continue
 
-                (bootstrap_room, status, prefill_rank) = msg
+                bootstrap_room, status, prefill_rank = msg
                 status = int(status.decode("ascii"))
                 bootstrap_room = int(bootstrap_room.decode("ascii"))
                 prefill_rank = int(prefill_rank.decode("ascii"))

python/sglang/srt/distributed/parallel_state.py

@@ -21,6 +21,7 @@ If you only need to use the distributed environment without model/pipeline
  parallelism, you can skip the model parallel initialization and destruction
  steps.
 """
+
 import contextlib
 import gc
 import logging
@@ -79,7 +80,7 @@ class P2PWork:
 
 
 def _split_tensor_dict(
-    tensor_dict: Dict[str, Union[torch.Tensor, Any]]
+    tensor_dict: Dict[str, Union[torch.Tensor, Any]],
 ) -> Tuple[List[Tuple[str, Any]], List[torch.Tensor]]:
     """Split the tensor dictionary into two parts:
     1. A list of (key, value) pairs. If the value is a tensor, it is replaced

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

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Adapted from vLLM's OpenAIServingResponses
 """Handler for /v1/responses requests"""
+
 from __future__ import annotations
 
 import asyncio

python/sglang/srt/function_call/base_format_detector.py

@@ -186,7 +186,7 @@ class BaseFormatDetector(ABC):
                 if start_idx >= len(current_text):
                     return StreamingParseResult()
 
-                (obj, end_idx) = _partial_json_loads(current_text[start_idx:], flags)
+                obj, end_idx = _partial_json_loads(current_text[start_idx:], flags)
 
                 is_current_complete = _is_complete_json(
                     current_text[start_idx : start_idx + end_idx]

python/sglang/srt/hardware_backend/npu/graph_runner/eagle_draft_npu_graph_runner.py

@@ -11,7 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-""" Run the model with npu graph and torch.compile """
+"""Run the model with npu graph and torch.compile"""
 
 from __future__ import annotations
 

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

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
-"""Attention layer with Dual chunk flash attention and sparse attention.
-"""
+"""Attention layer with Dual chunk flash attention and sparse attention."""
+
 import functools
 import logging
 import math

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

@@ -670,9 +670,9 @@ class KimiLinearAttnBackend(MambaAttnBackendBase):
         **kwargs,
     ):
         assert isinstance(mixed_qkv, Tuple)
-        (q_proj_states, k_proj_states, v_proj_states) = mixed_qkv
-        (q_conv_weights, k_conv_weights, v_conv_weights) = layer.conv_weights
-        (q_conv_bias, k_conv_bias, v_conv_bias) = layer.bias
+        q_proj_states, k_proj_states, v_proj_states = mixed_qkv
+        q_conv_weights, k_conv_weights, v_conv_weights = layer.conv_weights
+        q_conv_bias, k_conv_bias, v_conv_bias = layer.bias
 
         layer_cache = self.req_to_token_pool.mamba2_layer_cache(layer.layer_id)
         q_conv_state, k_conv_state, v_conv_state = layer_cache.conv
@@ -744,9 +744,9 @@ class KimiLinearAttnBackend(MambaAttnBackendBase):
         )
 
         assert isinstance(mixed_qkv, Tuple)
-        (q_proj_states, k_proj_states, v_proj_states) = mixed_qkv
-        (q_conv_weights, k_conv_weights, v_conv_weights) = layer.conv_weights
-        (q_conv_bias, k_conv_bias, v_conv_bias) = layer.bias
+        q_proj_states, k_proj_states, v_proj_states = mixed_qkv
+        q_conv_weights, k_conv_weights, v_conv_weights = layer.conv_weights
+        q_conv_bias, k_conv_bias, v_conv_bias = layer.bias
 
         query_start_loc = self.forward_metadata.query_start_loc
         cache_indices = self.forward_metadata.mamba_cache_indices

python/sglang/srt/layers/attention/linear/seg_la.py

@@ -2,6 +2,7 @@
 """
 Copyright (c) Ant Financial Service Group and its affiliates.
 """
+
 # Copied from https://code.alipay.com/pia/PainlessInferenceAcceleration/blob/v0.0.6/flood/flood/ops/seg_la.py
 
 from dataclasses import dataclass

python/sglang/srt/layers/attention/wave_ops/prefill_attention.py

@@ -38,7 +38,7 @@ def prefill_attention_wave(
     output_shape = (shape.total_seq_len, shape.num_query_heads, shape.head_size_kv)
     # Run the wave kernel.
     mfma_variant = (MMAType.F32_16x16x16_F16, MMAType.F32_16x16x16_F16)
-    (prefill, hyperparams) = get_prefill_attention_kernel(
+    prefill, hyperparams = get_prefill_attention_kernel(
         shape,
         mfma_variant,
         q.shape,

python/sglang/srt/layers/logits_processor.py

@@ -376,7 +376,7 @@ class LogitsProcessor(nn.Module):
 
             logprobs_result = self.process_input_logprobs(input_logits, logits_metadata)
         else:
-            (logprobs_result, sampled_logits) = self.process_input_logprobs_by_chunk(
+            logprobs_result, sampled_logits = self.process_input_logprobs_by_chunk(
                 pruned_states,
                 sample_indices,
                 input_logprob_indices,

python/sglang/srt/layers/moe/cutlass_w4a8_moe.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 """Cutlass W4A8 MoE kernel."""
+
 from typing import Optional
 
 import torch

python/sglang/srt/layers/quantization/modelslim/modelslim_moe.py

@@ -63,11 +63,9 @@ class ModelSlimMoEMethod(FusedMoEMethodBase):
             logger.info_once("Using ModelSlimW8A8Int8MoE")
             return ModelSlimW8A8Int8MoE(quant_config)
         else:
-            logger.warning(
-                f"Unsupported FusedMoe modelslim scheme: \
+            logger.warning(f"Unsupported FusedMoe modelslim scheme: \
                     {quant_config.quant_description.get(prefix_in_quant_config.strip())} \
-                    in layer: {prefix}"
-            )
+                    in layer: {prefix}")
             return None
 
 

python/sglang/srt/layers/radix_attention.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """Radix attention."""
+
 from __future__ import annotations
 
 from enum import Enum

python/sglang/srt/layers/radix_linear_attention.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """Radix linear attention."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Optional, Tuple, Union

python/sglang/srt/layers/rotary_embedding.py

@@ -1,5 +1,6 @@
 # Adapted from https://raw.githubusercontent.com/vllm-project/vllm/refs/tags/v0.6.6.post1/vllm/model_executor/layers/rotary_embedding.py
 """Rotary Positional Embeddings."""
+
 from __future__ import annotations
 
 import itertools
@@ -2896,7 +2897,7 @@ class DualChunkRotaryEmbedding(MultiPlatformOp):
         self.local_size = local_size
         self.dtype = dtype
         self.device = torch.device(f"cuda:{torch.cuda.current_device()}")
-        (q_cache, qc_cache, k_cache, qc_no_clamp_cache, q_inter_cache) = (
+        q_cache, qc_cache, k_cache, qc_no_clamp_cache, q_inter_cache = (
             self._compute_cos_sin_cache()
         )
 

python/sglang/srt/managers/tp_worker.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """A tensor parallel worker."""
+
 from __future__ import annotations
 
 import logging

python/sglang/srt/mem_cache/mamba_radix_cache.py

@@ -639,7 +639,7 @@ class MambaRadixCache(BasePrefixCache):
         match_result = self.match_prefix(
             MatchPrefixParams(key=RadixKey(page_aligned_token_ids, req.extra_key))
         )
-        (new_indices, new_last_node) = (
+        new_indices, new_last_node = (
             match_result.device_indices,
             match_result.last_device_node,
         )

python/sglang/srt/mem_cache/memory_pool_host.py

@@ -8,7 +8,9 @@ from typing import Optional
 import psutil
 import torch
 
-from sglang.jit_kernel.hicache import can_use_hicache_jit_kernel
+from sglang.jit_kernel.hicache import (
+    can_use_hicache_jit_kernel,
+)
 from sglang.jit_kernel.hicache import (
     transfer_hicache_all_layer as jit_transfer_hicache_all_layer,
 )

python/sglang/srt/mem_cache/radix_cache.py

@@ -523,7 +523,7 @@ class RadixCache(BasePrefixCache):
 
         # The prefix indices could be updated, reuse it
         match_result = self.match_prefix(MatchPrefixParams(key=radix_key))
-        (new_indices, new_last_node) = (
+        new_indices, new_last_node = (
             match_result.device_indices,
             match_result.last_device_node,
         )

python/sglang/srt/mem_cache/swa_radix_cache.py

@@ -556,7 +556,7 @@ class SWARadixCache(BasePrefixCache):
         match_result = self.match_prefix(
             MatchPrefixParams(key=RadixKey(page_aligned_token_ids, req.extra_key))
         )
-        (new_indices, new_last_node) = (
+        new_indices, new_last_node = (
             match_result.device_indices,
             match_result.last_device_node,
         )

python/sglang/srt/metrics/collector.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """Utilities for Prometheus Metrics Collection."""
+
 import dataclasses
 import logging
 import os

python/sglang/srt/metrics/utils.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """Utilities for Prometheus Metrics."""
+
 import math
 from typing import List
 

python/sglang/srt/model_loader/utils.py

@@ -1,6 +1,7 @@
 # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/model_loader/utils.py
 
 """Utilities for selecting and loading models."""
+
 import concurrent.futures
 import contextlib
 import logging

python/sglang/srt/model_loader/weight_utils.py

@@ -1,6 +1,7 @@
 # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/model_loader/weight_utils.py
 
 """Utilities for downloading and initializing model weights."""
+
 import collections
 import concurrent.futures
 import fnmatch
@@ -140,12 +141,10 @@ def convert_bin_to_safetensor_file(
     sf_size = os.stat(sf_filename).st_size
     pt_size = os.stat(pt_filename).st_size
     if (sf_size - pt_size) / pt_size > 0.01:
-        raise RuntimeError(
-            f"""The file size different is more than 1%:
+        raise RuntimeError(f"""The file size different is more than 1%:
          - {sf_filename}: {sf_size}
          - {pt_filename}: {pt_size}
-         """
-        )
+         """)
 
     # check if the tensors are the same
     reloaded = safetensors.torch.load_file(sf_filename)

python/sglang/srt/models/baichuan.py

@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only BaiChuan model compatible with HuggingFace weights."""
+
 import math
 from typing import Iterable, Optional, Tuple
 

python/sglang/srt/models/bailing_moe.py

@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """SGLang BailingMoE model."""
+
 import logging
 from typing import Iterable, List, Optional, Tuple, Union
 

python/sglang/srt/models/bailing_moe_nextn.py

@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """SGLang BailingMoENextN model."""
+
 import logging
 from typing import Iterable, Optional, Tuple
 

python/sglang/srt/models/deepseek_nextn.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 
 """Inference-only DeepSeek NextN Speculative Decoding."""
+
 import logging
 from typing import Iterable, Optional, Tuple
 

python/sglang/srt/models/deepseek_ocr.py

@@ -16,6 +16,7 @@
 # Adapted from
 # https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/llama.py#L1
 """Inference-only Apertus model compatible with HuggingFace weights."""
+
 import copy
 import logging
 import math

python/sglang/srt/models/deepseek_v2.py

@@ -15,6 +15,7 @@
 # Adapted from:
 # https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
 """Inference-only DeepseekV2 model."""
+
 from __future__ import annotations
 
 import logging

python/sglang/srt/models/ernie4.py

@@ -12,7 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 
-""" Inference-only Ernie4.5 model compatible with baidu/ERNIE-4.5-*-PT weights. """
+"""Inference-only Ernie4.5 model compatible with baidu/ERNIE-4.5-*-PT weights."""
 
 from typing import Iterable, List, Optional, Tuple, Union
 

python/sglang/srt/models/ernie45_moe_vl.py

@@ -12,7 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 
-""" Inference-only Ernie4.5 VL model compatible with baidu/ERNIE-4.5-VL-*-PT weights. """
+"""Inference-only Ernie4.5 VL model compatible with baidu/ERNIE-4.5-VL-*-PT weights."""
 
 import logging
 from itertools import islice

python/sglang/srt/models/ernie45_vl.py

@@ -11,6 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Ernie45-VL model compatible with HuggingFace weights."""
+
 import logging
 from functools import lru_cache, partial
 from typing import Iterable, List, Optional, Tuple, Type

python/sglang/srt/models/ernie4_eagle.py

@@ -12,7 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 
-""" Ernie4.5 MTP model compatible with baidu/ERNIE-4.5-*-PT weights. """
+"""Ernie4.5 MTP model compatible with baidu/ERNIE-4.5-*-PT weights."""
 
 from typing import Iterable, Optional, Tuple
 

python/sglang/srt/models/gpt2.py

@@ -17,6 +17,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only GPT-2 model compatible with HuggingFace weights."""
+
 from typing import Iterable, Optional, Tuple, Type
 
 import torch

python/sglang/srt/models/hunyuan.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only HunYuan model compatible with HuggingFace weights."""
+
 import re
 from typing import Any, Dict, Iterable, Optional, Tuple
 

python/sglang/srt/models/llada2.py

@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """SGLang LLaDA2MoeModelLM model."""
+
 import logging
 from typing import Iterable, Optional, Tuple, Union
 

python/sglang/srt/models/mllama.py

@@ -1,6 +1,7 @@
 # Adapted from:
 # https://github.com/vllm-project/vllm/blob/7193774b1ff8603ad5bf4598e5efba0d9a39b436/vllm/model_executor/models/mllama.py
 """PyTorch Mllama model."""
+
 import math
 from typing import Iterable, List, Optional, Tuple, Union
 

python/sglang/srt/models/nemotron_nas.py

@@ -14,6 +14,7 @@
 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/nemotron_nas.py
 
 """Inference-only deci model compatible with HuggingFace weights."""
+
 from typing import Iterable, Optional, Tuple, Type, Union
 
 import torch

python/sglang/srt/models/olmo.py

@@ -15,6 +15,7 @@
 # Adapted from
 # https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/olmo.py#L1
 """Inference-only OLMo model compatible with HuggingFace weights."""
+
 from typing import Iterable, Optional, Tuple
 
 import torch

python/sglang/srt/models/olmo2.py

@@ -15,6 +15,7 @@
 # Adapted from
 # https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/olmo2.py
 """Inference-only OLMo2 model compatible with HuggingFace weights."""
+
 from functools import partial
 from typing import Iterable, Optional, Tuple
 

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

python/sglang/srt/models/orion.py

@@ -7,6 +7,7 @@
 # LICENSE: https://huggingface.co/OrionStarAI/Orion-14B-Base/blob/main/LICENSE
 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/orion.py
 """Inference-only Orion-14B model compatible with HuggingFace weights."""
+
 from collections.abc import Iterable
 from typing import Any, Optional, Tuple
 

python/sglang/srt/models/pixtral.py

@@ -23,11 +23,15 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from transformers import PixtralVisionConfig, PretrainedConfig
-from transformers.models.pixtral.modeling_pixtral import PixtralRotaryEmbedding
+from transformers.models.pixtral.modeling_pixtral import (
+    PixtralRotaryEmbedding,
+)
 from transformers.models.pixtral.modeling_pixtral import (
     generate_block_attention_mask as _get_pixtral_attention_mask,
 )
-from transformers.models.pixtral.modeling_pixtral import position_ids_in_meshgrid
+from transformers.models.pixtral.modeling_pixtral import (
+    position_ids_in_meshgrid,
+)
 
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.attention.vision import VisionAttention

python/sglang/srt/models/qwen2.py

@@ -15,6 +15,7 @@
 # Adapted from llama2.py
 # Modify details for the adaptation of Qwen2 model.
 """Inference-only Qwen2 model compatible with HuggingFace weights."""
+
 import logging
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 

python/sglang/srt/models/qwen2_5_vl.py

@@ -22,6 +22,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
+
 import logging
 import re
 from functools import partial

python/sglang/srt/models/qwen2_audio.py

@@ -22,6 +22,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Qwen2-Audio model compatible with HuggingFace weights."""
+
 import logging
 from typing import Any, Iterable, List, Optional, Tuple
 

python/sglang/srt/models/qwen2_vl.py

@@ -22,6 +22,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
+
 import logging
 from functools import lru_cache, partial
 from typing import Iterable, List, Optional, Tuple, Type, TypedDict

python/sglang/srt/models/qwen3_5.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Inference-only Qwen3.5 model and Qwen3.5 MoE model compatible with HuggingFace weights."""
+
 import logging
 from functools import lru_cache
 from typing import Iterable, Optional, Set, Tuple, Union

python/sglang/srt/models/qwen3_5_mtp.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 
 """Inference-only Qwen3_5 MTP model."""
+
 import logging
 from typing import Iterable, Optional, Tuple
 

python/sglang/srt/models/qwen3_next.py

@@ -366,8 +366,8 @@ class Qwen3GatedDeltaNet(nn.Module):
 
         # [b, sq, ng, (hn + hn + np/ng * hn + np/ng + np/ng)]
         # --> [b, sq, ng, hn], [b, sq, ng, hn], [b, sq, ng, np/ng * hn], [b, sq, ng, np/ng * hn], [b, sq, ng, np/ng], [b, sq, ng, np/ng]
-        (query, key, value, z) = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=2)
-        (b, a) = torch.split(mixed_ba, split_arg_list_ba, dim=2)
+        query, key, value, z = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=2)
+        b, a = torch.split(mixed_ba, split_arg_list_ba, dim=2)
 
         # [b, sq, ng, np/ng * hn] -> [b, sq, np, hn]
         value = value.reshape(value.size(0), -1, self.head_v_dim)

python/sglang/srt/models/qwen3_next_mtp.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 
 """Inference-only Qwen3Next MTP Speculative Decoding."""
+
 import logging
 from typing import Iterable, Optional, Tuple
 

python/sglang/srt/models/qwen3_omni_moe.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Inference-only Qwen3-VL model compatible with HuggingFace weights."""
+
 import math
 from typing import Iterable, List, Optional, Tuple
 

python/sglang/srt/models/qwen3_vl.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Inference-only Qwen3-VL model compatible with HuggingFace weights."""
+
 import logging
 import math
 import re

python/sglang/srt/models/qwen3_vl_moe.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Inference-only Qwen3-VL model compatible with HuggingFace weights."""
+
 import logging
 import re
 from functools import lru_cache

python/sglang/srt/models/starcoder2.py

@@ -20,7 +20,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/starcoder2.py
-""" PyTorch Starcoder2 model."""
+"""PyTorch Starcoder2 model."""
+
 from collections.abc import Iterable
 from typing import Optional, Tuple
 

python/sglang/srt/models/transformers.py

@@ -15,6 +15,7 @@
 # Adapted from
 # https://github.com/vllm-project/vllm/blob/a1a2aaadb9122f05667140e39cf67e5736c8b6d6/vllm/model_executor/models/transformers.py
 """Wrapper around `transformers` models"""
+
 import logging
 import re
 from typing import Iterable, Literal, Optional, Tuple, Union

python/sglang/srt/multimodal/internvl_vit_cuda_graph_runner.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 
 """ViT CUDA Graph Runner class."""
+
 from __future__ import annotations
 
 from typing import Dict, Hashable, Tuple

python/sglang/srt/multimodal/mm_utils.py

@@ -27,6 +27,7 @@ LLaVA-NeXT : https://llava-vl.github.io/blog/2024-01-30-llava-next/
 LLaVA-Onevision : https://arxiv.org/pdf/2408.03326
 
 """
+
 import ast
 import itertools
 import math
@@ -519,7 +520,7 @@ def run_dp_sharded_mrope_vision_model(
     # image_to_tp_rank = [0, 2, 1, 3]
     # gpu_sample_counts = [1, 3]
     # grouped_pixel_values_len = [1000, 350]
-    (image_to_tp_rank, gpu_sample_counts, grouped_pixel_values_len) = (
+    image_to_tp_rank, gpu_sample_counts, grouped_pixel_values_len = (
         get_dp_encoder_lb_assignment(patches_per_image, tp_size)
     )
 

python/sglang/srt/multimodal/processors/ernie45_vl.py

@@ -15,7 +15,9 @@ from sglang.srt.models.ernie45_vl import Ernie4_5_VLMoeForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
-from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+from sglang.srt.multimodal.processors.base_processor import (
+    MultimodalSpecialTokens,
+)
 from sglang.srt.utils import get_bool_env_var, is_npu, logger
 
 _is_npu = is_npu()

python/sglang/srt/multimodal/processors/glm4v.py

@@ -7,7 +7,9 @@ from sglang.srt.models.glm_ocr import GlmOcrForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
-from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+from sglang.srt.multimodal.processors.base_processor import (
+    MultimodalSpecialTokens,
+)
 
 
 class Glm4vImageProcessor(SGLangBaseProcessor):

python/sglang/srt/multimodal/processors/kimi_k25.py

@@ -8,7 +8,9 @@ from sglang.srt.models.kimi_k25 import KimiK25ForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
-from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+from sglang.srt.multimodal.processors.base_processor import (
+    MultimodalSpecialTokens,
+)
 
 
 # Compatible with KimiVLForConditionalGeneration

python/sglang/srt/multimodal/processors/kimi_vl.py

@@ -5,7 +5,9 @@ from sglang.srt.models.kimi_vl import KimiVLForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
-from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+from sglang.srt.multimodal.processors.base_processor import (
+    MultimodalSpecialTokens,
+)
 
 
 # Compatible with KimiVLForConditionalGeneration

python/sglang/srt/multimodal/processors/qwen_vl.py

@@ -26,7 +26,9 @@ from sglang.srt.models.qwen3_vl_moe import Qwen3VLMoeForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
-from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+from sglang.srt.multimodal.processors.base_processor import (
+    MultimodalSpecialTokens,
+)
 from sglang.utils import logger
 
 IMAGE_FACTOR = 28

python/sglang/srt/multimodal/processors/step3_vl.py

@@ -15,7 +15,9 @@ from sglang.srt.models.step3_vl_10b import StepVLForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
 )
-from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+from sglang.srt.multimodal.processors.base_processor import (
+    MultimodalSpecialTokens,
+)
 
 ImageWithPatches = tuple[Image.Image, list[Image.Image], list[int] | None]
 

python/sglang/srt/multimodal/vit_cuda_graph_runner.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 
 """ViT CUDA Graph Runner class."""
+
 from __future__ import annotations
 
 import inspect

python/sglang/srt/parser/code_completion_parser.py

@@ -13,7 +13,6 @@
 # ==============================================================================
 """Completion templates."""
 
-
 import dataclasses
 import logging
 from enum import auto

python/sglang/srt/utils/common.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """Common utilities."""
+
 from __future__ import annotations
 
 import argparse

python/sglang/srt/utils/weight_checker.py

@@ -117,7 +117,7 @@ def _random_like(t: torch.Tensor):
 
 
 def _postprocess_tensors(
-    raw: Dict[str, torch.Tensor]
+    raw: Dict[str, torch.Tensor],
 ) -> Iterable[Tuple[str, bool, torch.Tensor]]:
     from sglang.srt.debug_utils.dumper import get_tensor_info