add `.isort.cfg` (#378)

python/sglang/srt/managers/router/infer_batch.py

@@ -4,6 +4,7 @@ from typing import List
 
 import numpy as np
 import torch
+
 from sglang.srt.managers.router.radix_cache import RadixCache
 from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
 

python/sglang/srt/managers/router/manager.py

@@ -4,6 +4,7 @@ import logging
 import uvloop
 import zmq
 import zmq.asyncio
+
 from sglang.srt.backend_config import GLOBAL_BACKEND_CONFIG
 from sglang.srt.managers.router.model_rpc import ModelRpcClient
 from sglang.srt.server_args import PortArgs, ServerArgs

python/sglang/srt/managers/router/model_rpc.py

@@ -10,6 +10,8 @@ import rpyc
 import torch
 from rpyc.utils.classic import obtain
 from rpyc.utils.server import ThreadedServer
+from vllm.logger import _default_handler as vllm_default_handler
+
 from sglang.srt.constrained.fsm_cache import FSMCache
 from sglang.srt.constrained.jump_forward import JumpForwardCache
 from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
@@ -30,7 +32,6 @@ from sglang.srt.utils import (
     is_multimodal_model,
     set_random_seed,
 )
-from vllm.logger import _default_handler as vllm_default_handler
 
 logger = logging.getLogger("model_rpc")
 

python/sglang/srt/managers/router/model_runner.py

@@ -9,16 +9,17 @@ from typing import List
 
 import numpy as np
 import torch
-from sglang.srt.managers.router.infer_batch import Batch, ForwardMode
-from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
-from sglang.srt.utils import is_multimodal_model
-from sglang.utils import get_available_gpu_memory
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.model_loader import _set_default_torch_dtype
 from vllm.model_executor.parallel_utils.parallel_state import initialize_model_parallel
 
+from sglang.srt.managers.router.infer_batch import Batch, ForwardMode
+from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
+from sglang.srt.utils import is_multimodal_model
+from sglang.utils import get_available_gpu_memory
+
 QUANTIZATION_CONFIG_MAPPING = {
     "awq": AWQConfig,
     "gptq": GPTQConfig,

python/sglang/srt/managers/router/radix_cache.py

 import heapq
 import time
 from collections import defaultdict
-from dataclasses import dataclass
-from typing import Tuple
 
 import torch
 

python/sglang/srt/managers/tokenizer_manager.py

@@ -10,6 +10,7 @@ import transformers
 import uvloop
 import zmq
 import zmq.asyncio
+
 from sglang.srt.hf_transformers_utils import (
     get_config,
     get_context_length,

python/sglang/srt/models/commandr.py

@@ -20,13 +20,10 @@
 
 # This file is based on the LLama model definition file in transformers
 """PyTorch Cohere model."""
-from typing import List, Optional, Tuple
+from typing import Optional, Tuple
 
 import torch
 import torch.utils.checkpoint
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from torch.nn.parameter import Parameter
 from transformers import PretrainedConfig
@@ -49,6 +46,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 
 @torch.compile
 def layer_norm_func(hidden_states, weight, variance_epsilon):

python/sglang/srt/models/dbrx.py

@@ -5,10 +5,6 @@ from typing import Optional
 
 import torch
 import torch.nn as nn
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
-from sglang.srt.models.dbrx_config import DbrxConfig
 from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.linear import (
     LinearMethodBase,
@@ -35,6 +31,11 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+from sglang.srt.models.dbrx_config import DbrxConfig
+
 
 class DbrxRouter(nn.Module):
     """A Router implementation for DBRX that returns logits for each expert

python/sglang/srt/models/gemma.py

@@ -4,9 +4,6 @@
 from typing import Optional, Tuple
 
 import torch
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.config import LoRAConfig
@@ -28,6 +25,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 
 class GemmaMLP(nn.Module):
     def __init__(

python/sglang/srt/models/llama2.py

 # Adapted from
 # https://github.com/vllm-project/vllm/blob/671af2b1c0b3ed6d856d37c21a561cc429a10701/vllm/model_executor/models/llama.py#L1
 """Inference-only LLaMA model compatible with HuggingFace weights."""
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple
 
 import torch
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from transformers import LlamaConfig
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -30,6 +27,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 
 class LlamaMLP(nn.Module):
     def __init__(

python/sglang/srt/models/llava.py

@@ -4,6 +4,15 @@ from typing import List, Optional
 
 import numpy as np
 import torch
+from torch import nn
+from transformers import CLIPVisionModel, LlavaConfig
+from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
+from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.weight_utils import (
+    default_weight_loader,
+    hf_model_weights_iterator,
+)
+
 from sglang.srt.managers.router.infer_batch import ForwardMode
 from sglang.srt.managers.router.model_runner import InputMetadata
 from sglang.srt.mm_utils import (
@@ -12,14 +21,6 @@ from sglang.srt.mm_utils import (
     unpad_image_shape,
 )
 from sglang.srt.models.llama2 import LlamaForCausalLM
-from torch import nn
-from transformers import CLIPVisionModel, LlamaConfig, LlavaConfig
-from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
-from vllm.model_executor.layers.linear import LinearMethodBase
-from vllm.model_executor.weight_utils import (
-    default_weight_loader,
-    hf_model_weights_iterator,
-)
 
 
 class LlavaLlamaForCausalLM(nn.Module):

python/sglang/srt/models/mixtral.py

 # Adapted from
 # https://github.com/vllm-project/vllm/blob/d0215a58e78572d91dadafe9d832a2db89b09a13/vllm/model_executor/models/mixtral.py#L1
 """Inference-only Mixtral model."""
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import numpy as np
 import torch
 import torch.nn.functional as F
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from transformers import MixtralConfig
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -35,6 +32,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 
 class MixtralMLP(nn.Module):
     def __init__(

python/sglang/srt/models/qwen.py

-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, Optional
 
 import torch
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -27,6 +24,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 
 class QWenMLP(nn.Module):
     def __init__(

python/sglang/srt/models/qwen2.py

 # Adapted from llama2.py
 # Modify details for the adaptation of Qwen2 model.
 """Inference-only Qwen2 model compatible with HuggingFace weights."""
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple
 
 import torch
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -29,6 +26,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 Qwen2Config = None
 
 

python/sglang/srt/models/stablelm.py

@@ -5,9 +5,6 @@ model compatible with HuggingFace weights."""
 from typing import Optional, Tuple
 
 import torch
-from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.managers.router.model_runner import InputMetadata
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -30,6 +27,10 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+
 
 class StablelmMLP(nn.Module):
     def __init__(

python/sglang/srt/models/yivl.py

 """Inference-only Yi-VL model."""
 
-import os
-from typing import List, Optional
+from typing import Optional
 
 import torch
 import torch.nn as nn
-from sglang.srt.models.llava import (
-    LlavaLlamaForCausalLM,
-    clip_vision_embed_forward,
-    monkey_path_clip_vision_embed_forward,
-)
 from transformers import CLIPVisionModel, LlavaConfig
 from vllm.model_executor.weight_utils import (
     default_weight_loader,
     hf_model_weights_iterator,
 )
 
+from sglang.srt.models.llava import (
+    LlavaLlamaForCausalLM,
+    monkey_path_clip_vision_embed_forward,
+)
+
 
 class YiVLForCausalLM(LlavaLlamaForCausalLM):
     def __init__(self, *args, **kwargs):

python/sglang/srt/server.py

@@ -10,9 +10,6 @@ import threading
 import time
 from typing import List, Optional, Union
 
-# Fix a Python bug
-setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
-
 import aiohttp
 import psutil
 import pydantic
@@ -22,6 +19,9 @@ import uvloop
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.responses import Response, StreamingResponse
 from pydantic import BaseModel
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.responses import JSONResponse
+
 from sglang.backend.runtime_endpoint import RuntimeEndpoint
 from sglang.srt.constrained import disable_cache
 from sglang.srt.conversation import (
@@ -54,8 +54,9 @@ from sglang.srt.managers.router.manager import start_router_process
 from sglang.srt.managers.tokenizer_manager import TokenizerManager
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.utils import enable_show_time_cost, handle_port_init
-from starlette.middleware.base import BaseHTTPMiddleware
-from starlette.responses import JSONResponse
+
+# Fix a Python bug
+setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
 
 asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
 
@@ -618,7 +619,7 @@ def launch_server(server_args, pipe_finish_writer):
             try:
                 requests.get(url + "/get_model_info", timeout=5, headers=headers)
                 break
-            except requests.exceptions.RequestException as e:
+            except requests.exceptions.RequestException:
                 pass
         else:
             if pipe_finish_writer is not None:

python/sglang/srt/utils.py

@@ -157,7 +157,6 @@ def get_exception_traceback():
 
 
 def get_int_token_logit_bias(tokenizer, vocab_size):
-    from transformers import LlamaTokenizer, LlamaTokenizerFast
 
     # a bug when model's vocab size > tokenizer.vocab_size
     vocab_size = tokenizer.vocab_size

python/sglang/test/test_utils.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 import requests
+
 from sglang.backend.openai import OpenAI
 from sglang.backend.runtime_endpoint import RuntimeEndpoint
 from sglang.global_config import global_config

test/lang/run_all.py

 import argparse
 import glob
 import multiprocessing
-import os
 import time
 import unittest