Reduce time it takes to import SGLang

python/sglang/srt/compilation/piecewise_context_manager.py

@@ -1,8 +1,11 @@
+from __future__ import annotations
+
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import Any, List, Optional
+from typing import TYPE_CHECKING, Any, List, Optional
 
-from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+if TYPE_CHECKING:
+    from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 
 @dataclass

python/sglang/srt/configs/__init__.py

@@ -1,24 +1,3 @@
-from sglang.srt.configs.chatglm import ChatGLMConfig
-from sglang.srt.configs.dbrx import DbrxConfig
-from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
-from sglang.srt.configs.dots_ocr import DotsOCRConfig
-from sglang.srt.configs.dots_vlm import DotsVLMConfig
-from sglang.srt.configs.exaone import ExaoneConfig
-from sglang.srt.configs.falcon_h1 import FalconH1Config
-from sglang.srt.configs.janus_pro import MultiModalityConfig
-from sglang.srt.configs.kimi_linear import KimiLinearConfig
-from sglang.srt.configs.kimi_vl import KimiVLConfig
-from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
-from sglang.srt.configs.longcat_flash import LongcatFlashConfig
-from sglang.srt.configs.nemotron_h import NemotronHConfig
-from sglang.srt.configs.olmo3 import Olmo3Config
-from sglang.srt.configs.qwen3_next import Qwen3NextConfig
-from sglang.srt.configs.step3_vl import (
-    Step3TextConfig,
-    Step3VisionEncoderConfig,
-    Step3VLConfig,
-)
-
 __all__ = [
     "ExaoneConfig",
     "ChatGLMConfig",
@@ -39,3 +18,80 @@
     "FalconH1Config",
     "NemotronHConfig",
 ]
+
+
+def __getattr__(name):
+    """Lazily import config classes on first access."""
+    if name == "ChatGLMConfig":
+        from sglang.srt.configs.chatglm import ChatGLMConfig
+
+        return ChatGLMConfig
+    elif name == "DbrxConfig":
+        from sglang.srt.configs.dbrx import DbrxConfig
+
+        return DbrxConfig
+    elif name == "DeepseekVL2Config":
+        from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
+
+        return DeepseekVL2Config
+    elif name == "DotsOCRConfig":
+        from sglang.srt.configs.dots_ocr import DotsOCRConfig
+
+        return DotsOCRConfig
+    elif name == "DotsVLMConfig":
+        from sglang.srt.configs.dots_vlm import DotsVLMConfig
+
+        return DotsVLMConfig
+    elif name == "ExaoneConfig":
+        from sglang.srt.configs.exaone import ExaoneConfig
+
+        return ExaoneConfig
+    elif name == "FalconH1Config":
+        from sglang.srt.configs.falcon_h1 import FalconH1Config
+
+        return FalconH1Config
+    elif name == "MultiModalityConfig":
+        from sglang.srt.configs.janus_pro import MultiModalityConfig
+
+        return MultiModalityConfig
+    elif name == "KimiLinearConfig":
+        from sglang.srt.configs.kimi_linear import KimiLinearConfig
+
+        return KimiLinearConfig
+    elif name == "KimiVLConfig":
+        from sglang.srt.configs.kimi_vl import KimiVLConfig
+
+        return KimiVLConfig
+    elif name == "MoonViTConfig":
+        from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
+
+        return MoonViTConfig
+    elif name == "LongcatFlashConfig":
+        from sglang.srt.configs.longcat_flash import LongcatFlashConfig
+
+        return LongcatFlashConfig
+    elif name == "NemotronHConfig":
+        from sglang.srt.configs.nemotron_h import NemotronHConfig
+
+        return NemotronHConfig
+    elif name == "Olmo3Config":
+        from sglang.srt.configs.olmo3 import Olmo3Config
+
+        return Olmo3Config
+    elif name == "Qwen3NextConfig":
+        from sglang.srt.configs.qwen3_next import Qwen3NextConfig
+
+        return Qwen3NextConfig
+    elif name == "Step3TextConfig":
+        from sglang.srt.configs.step3_vl import Step3TextConfig
+
+        return Step3TextConfig
+    elif name == "Step3VisionEncoderConfig":
+        from sglang.srt.configs.step3_vl import Step3VisionEncoderConfig
+
+        return Step3VisionEncoderConfig
+    elif name == "Step3VLConfig":
+        from sglang.srt.configs.step3_vl import Step3VLConfig
+
+        return Step3VLConfig
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

python/sglang/srt/configs/model_config.py

@@ -12,18 +12,19 @@
 # limitations under the License.
 # ==============================================================================
 
+from __future__ import annotations
+
 import json
 import logging
 import math
 import os
 from enum import Enum, IntEnum, auto
-from typing import Any, List, Optional, Set, Union
+from typing import TYPE_CHECKING, Any, List, Optional, Set, Union
 
 import torch
 from transformers import PretrainedConfig
 
 from sglang.srt.environ import envs
-from sglang.srt.layers.quantization import QUANTIZATION_METHODS
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import is_hip, retry
 from sglang.srt.utils.hf_transformers_utils import (
@@ -37,6 +38,9 @@
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig
+
 
 class AttentionArch(IntEnum):
     MLA = auto()
@@ -623,6 +627,8 @@ def _validate_quantize_and_serve_config(self):
 
     # adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
     def _verify_quantization(self) -> None:
+        from sglang.srt.layers.quantization import QUANTIZATION_METHODS
+
         supported_quantization = [*QUANTIZATION_METHODS]
         rocm_supported_quantization = [
             "awq",

python/sglang/srt/disaggregation/decode.py

@@ -46,8 +46,6 @@
     poll_and_all_reduce,
     prepare_abort,
 )
-from sglang.srt.layers.dp_attention import get_attention_tp_size
-from sglang.srt.managers.schedule_batch import FINISH_ABORT, RequestStage, ScheduleBatch
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.memory_pool import (
@@ -69,7 +67,7 @@
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
-    from sglang.srt.managers.schedule_batch import Req
+    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
     from sglang.srt.managers.scheduler import Scheduler
 
 CLIP_MAX_NEW_TOKEN = get_int_env_var("SGLANG_CLIP_MAX_NEW_TOKENS_ESTIMATION", 4096)
@@ -227,6 +225,8 @@ def __init__(
         self.kv_manager = self._init_kv_manager()
 
     def _init_kv_manager(self) -> BaseKVManager:
+        from sglang.srt.layers.dp_attention import get_attention_tp_size
+
         kv_args_class = get_kv_class(self.transfer_backend, KVClassType.KVARGS)
         kv_args = kv_args_class()
 
@@ -296,6 +296,8 @@ def _init_kv_manager(self) -> BaseKVManager:
 
     def add(self, req: Req, is_retracted: bool = False) -> None:
         """Add a request to the pending queue."""
+        from sglang.srt.managers.schedule_batch import RequestStage
+
         if self._check_if_req_exceed_kv_capacity(req):
             return
 
@@ -410,6 +412,8 @@ def _update_handshake_waiters(self) -> None:
 
     def pop_preallocated(self) -> List[DecodeRequest]:
         """Pop the preallocated requests from the pending queue (FIFO)."""
+        from sglang.srt.managers.schedule_batch import FINISH_ABORT, RequestStage
+
         self._update_handshake_waiters()
 
         preallocated_reqs = []
@@ -697,6 +701,8 @@ def extend(self, decode_reqs: List[DecodeRequest]) -> None:
         self.queue.extend(decode_reqs)
 
     def pop_transferred(self) -> List[Req]:
+        from sglang.srt.managers.schedule_batch import RequestStage
+
         if not self.queue:
             return []
         polls = poll_and_all_reduce(
@@ -824,6 +830,7 @@ class SchedulerDisaggregationDecodeMixin:
     @torch.no_grad()
     def event_loop_normal_disagg_decode(self: Scheduler):
         """A normal scheduler loop for decode worker in disaggregation mode."""
+        from sglang.srt.managers.schedule_batch import RequestStage
 
         while True:
             recv_reqs = self.recv_requests()
@@ -868,6 +875,8 @@ def event_loop_normal_disagg_decode(self: Scheduler):
 
     @torch.no_grad()
     def event_loop_overlap_disagg_decode(self: Scheduler):
+        from sglang.srt.managers.schedule_batch import RequestStage
+
         self.result_queue = deque()
         self.last_batch: Optional[ScheduleBatch] = None
         self.last_batch_in_queue = False  # last batch is modified in-place, so we need another variable to track if it's extend
@@ -982,6 +991,8 @@ def get_next_disagg_decode_batch_to_run(
 
     def get_new_prebuilt_batch(self: Scheduler) -> Optional[ScheduleBatch]:
         """Create a schedulebatch for fake completed prefill"""
+        from sglang.srt.managers.schedule_batch import RequestStage, ScheduleBatch
+
         if self.grammar_queue:
             self.move_ready_grammar_requests()
 

python/sglang/srt/disaggregation/prefill.py

@@ -42,12 +42,6 @@
     poll_and_all_reduce,
     prepare_abort,
 )
-from sglang.srt.managers.schedule_batch import (
-    FINISH_LENGTH,
-    Req,
-    RequestStage,
-    ScheduleBatch,
-)
 from sglang.srt.mem_cache.memory_pool import (
     HybridLinearKVPool,
     NSATokenToKVPool,
@@ -59,6 +53,7 @@
 if TYPE_CHECKING:
     from torch.distributed import ProcessGroup
 
+    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
     from sglang.srt.managers.scheduler import GenerationBatchResult, Scheduler
     from sglang.srt.mem_cache.memory_pool import KVCache
 
@@ -179,6 +174,8 @@ def _init_kv_manager(self) -> BaseKVManager:
         return kv_manager
 
     def add(self, req: Req, num_kv_heads: int) -> None:
+        from sglang.srt.managers.schedule_batch import RequestStage
+
         if self._check_if_req_exceed_kv_capacity(req):
             return
 
@@ -231,6 +228,7 @@ def pop_bootstrapped(
         return_failed_reqs: For PP, on rank 0, also return the failed reqs to notify the next rank
         rids_to_check: For PP, on rank > 0, check the rids from the previous rank has consensus with the current rank.
         """
+        from sglang.srt.managers.schedule_batch import RequestStage
 
         bootstrapped_reqs = []
         failed_reqs = []
@@ -396,6 +394,8 @@ def process_batch_result_disagg_prefill(
         Transfer kv for prefill completed requests and add it into disagg_prefill_inflight_queue
         Adapted from process_batch_result_prefill
         """
+        from sglang.srt.managers.schedule_batch import RequestStage
+
         (
             logits_output,
             next_token_ids,
@@ -513,6 +513,8 @@ def process_disagg_prefill_inflight_queue(
         Poll the requests in the middle of transfer. If done, return the request.
         rids_to_check: For PP, on rank > 0, check the rids from the previous rank has consensus with the current rank.
         """
+        from sglang.srt.managers.schedule_batch import FINISH_LENGTH, RequestStage
+
         if len(self.disagg_prefill_inflight_queue) == 0:
             return []
 

python/sglang/srt/eplb/expert_distribution.py

@@ -29,14 +29,14 @@
 
 from sglang.srt.environ import envs
 from sglang.srt.metrics.collector import ExpertDispatchCollector
-from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import Withable, is_npu
 
 _is_npu = is_npu()
 
 if TYPE_CHECKING:
     from sglang.srt.eplb.expert_location import ExpertLocationMetadata
+    from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 logger = logging.getLogger(__name__)
 

python/sglang/srt/layers/vocab_parallel_embedding.py

@@ -1,8 +1,10 @@
 # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.3.post1/vllm/model_executor/layers/vocab_parallel_embedding.py
 
+from __future__ import annotations
+
 import logging
 from dataclasses import dataclass
-from typing import List, Optional, Sequence, Tuple
+from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple
 
 import torch
 from torch.nn.parameter import Parameter, UninitializedParameter
@@ -20,19 +22,19 @@
 from sglang.srt.layers.amx_utils import PackWeightMethod
 from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
 from sglang.srt.layers.parameter import BasevLLMParameter
-from sglang.srt.layers.quantization.base_config import (
-    QuantizationConfig,
-    QuantizeMethodBase,
-    method_has_implemented_embedding,
-)
-from sglang.srt.layers.quantization.unquant import UnquantizedEmbeddingMethod
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_compiler_backend,
     is_cpu,
     set_weight_attrs,
 )
 
+if TYPE_CHECKING:
+    from sglang.srt.layers.quantization.base_config import (
+        QuantizationConfig,
+        QuantizeMethodBase,
+    )
+
 DEFAULT_VOCAB_PADDING_SIZE = 64
 
 _is_cpu_amx_available = cpu_has_amx_support()
@@ -255,6 +257,11 @@ def __init__(
         )
         self.embedding_dim = embedding_dim
 
+        from sglang.srt.layers.quantization.base_config import (
+            method_has_implemented_embedding,
+        )
+        from sglang.srt.layers.quantization.unquant import UnquantizedEmbeddingMethod
+
         quant_method = None
         if quant_config is not None:
             quant_method = quant_config.get_quant_method(self, prefix=prefix)