[Fix] Skip HF Hub API calls for cached repos and honor --revision

python/sglang/benchmark/datasets/longbench_v2.py

@@ -1,3 +1,4 @@
+import json
 import random
 from argparse import Namespace
 from dataclasses import dataclass
@@ -8,9 +9,24 @@
 from sglang.benchmark.datasets.common import BaseDataset, DatasetRow
 
 LONGBENCH_V2_REPO_ID = "THUDM/LongBench-v2"
+# Pinned to a commit so a cached copy loads without any Hub API call.
+LONGBENCH_V2_REVISION = "2b48e494f2c7a2f0af81aae178e05c7e1dde0fe9"
 LONGBENCH_V2_DEFAULT_OUTPUT_LEN = 10  # answer letter + short explanation
 
 
+def load_longbench_v2_examples() -> List[dict]:
+    from huggingface_hub import hf_hub_download
+
+    path = hf_hub_download(
+        LONGBENCH_V2_REPO_ID,
+        "data.json",
+        repo_type="dataset",
+        revision=LONGBENCH_V2_REVISION,
+    )
+    with open(path) as f:
+        return json.load(f)
+
+
 def _format_prompt(example: dict) -> str:
     return (
         f"{example['context']}\n\n"
@@ -78,10 +94,7 @@ def sample_longbench_v2_requests(
             with open(dataset_path) as f:
                 examples = [json.loads(line) for line in f if line.strip()]
     else:
-        from datasets import load_dataset
-
-        ds = load_dataset(LONGBENCH_V2_REPO_ID, split="train")
-        examples = list(ds)
+        examples = load_longbench_v2_examples()
 
     random.shuffle(examples)
 

python/sglang/multimodal_gen/runtime/pipelines/hunyuan3d_pipeline.py

@@ -65,6 +65,7 @@
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 from sglang.multimodal_gen.runtime.utils.precision_types import PRECISION_TO_TYPE
+from sglang.srt.utils.common import retry_on_hub_rate_limit
 
 logger = init_logger(__name__)
 
@@ -158,9 +159,11 @@ def _resolve_shape_dir(
                 "Local path %s not found, downloading from HuggingFace Hub",
                 local_path,
             )
-            downloaded = snapshot_download(
-                repo_id=model_path,
-                allow_patterns=[f"{subfolder}/**"],
+            downloaded = retry_on_hub_rate_limit(
+                lambda: snapshot_download(
+                    repo_id=model_path,
+                    allow_patterns=[f"{subfolder}/**"],
+                )
             )
             local_path = os.path.join(downloaded, subfolder)
 
@@ -205,9 +208,11 @@ def _resolve_model_subfolder(
                 "Local path %s not found, downloading from HuggingFace Hub",
                 local_path,
             )
-            downloaded = snapshot_download(
-                repo_id=model_path,
-                allow_patterns=[f"{subfolder}/**"],
+            downloaded = retry_on_hub_rate_limit(
+                lambda: snapshot_download(
+                    repo_id=model_path,
+                    allow_patterns=[f"{subfolder}/**"],
+                )
             )
             local_path = os.path.join(downloaded, subfolder)
 

python/sglang/multimodal_gen/runtime/pipelines/ideogram.py

@@ -33,6 +33,7 @@
     verify_model_config_and_directory,
 )
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+from sglang.srt.utils.common import retry_on_hub_rate_limit
 
 logger = init_logger(__name__)
 
@@ -70,12 +71,14 @@ def _resolve_ideogram4_distilled_components_path() -> str:
     # fal's model cards explicitly reference the NF4 Diffusers repository for
     # these shared components. Download only those components: its conditional
     # and unconditional base transformers are unused by distilled variants.
-    return snapshot_download(
-        repo_id=_IDEOGRAM4_DISTILLED_COMPONENTS_MODEL,
-        revision=_IDEOGRAM4_DISTILLED_COMPONENTS_REVISION,
-        allow_patterns=_IDEOGRAM4_DISTILLED_COMPONENT_PATTERNS,
-        ignore_patterns=["*.onnx", "*.msgpack"],
-        max_workers=8,
+    return retry_on_hub_rate_limit(
+        lambda: snapshot_download(
+            repo_id=_IDEOGRAM4_DISTILLED_COMPONENTS_MODEL,
+            revision=_IDEOGRAM4_DISTILLED_COMPONENTS_REVISION,
+            allow_patterns=_IDEOGRAM4_DISTILLED_COMPONENT_PATTERNS,
+            ignore_patterns=["*.onnx", "*.msgpack"],
+            max_workers=8,
+        )
     )
 
 
@@ -289,11 +292,13 @@ def _resolve_distilled_transformer_path(self) -> str:
             model_path = (
                 self.model_path
                 if os.path.exists(self.model_path)
-                else snapshot_download(
-                    repo_id=self.model_path,
-                    allow_patterns=["transformer/*"],
-                    ignore_patterns=["*.onnx", "*.msgpack"],
-                    max_workers=8,
+                else retry_on_hub_rate_limit(
+                    lambda: snapshot_download(
+                        repo_id=self.model_path,
+                        allow_patterns=["transformer/*"],
+                        ignore_patterns=["*.onnx", "*.msgpack"],
+                        max_workers=8,
+                    )
                 )
             )
             self._distilled_transformer_path = os.path.join(model_path, "transformer")

python/sglang/multimodal_gen/runtime/utils/model_overlay.py

@@ -20,6 +20,7 @@
 from sglang.multimodal_gen import envs
 from sglang.multimodal_gen.runtime.loader.weight_utils import get_lock
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+from sglang.srt.utils.common import retry_on_hub_rate_limit
 from sglang.utils import load_diffusion_overlay_registry_from_env
 
 logger = init_logger(__name__)
@@ -470,11 +471,13 @@ def download_overlay_metadata(
         overlay_repo_id,
     )
     return str(
-        snapshot_download_fn(
-            repo_id=overlay_repo_id,
-            allow_patterns=MODEL_OVERLAY_METADATA_PATTERNS,
-            revision=revision,
-            max_workers=4,
+        retry_on_hub_rate_limit(
+            lambda: snapshot_download_fn(
+                repo_id=overlay_repo_id,
+                allow_patterns=MODEL_OVERLAY_METADATA_PATTERNS,
+                revision=revision,
+                max_workers=4,
+            )
         )
     )
 

python/sglang/multimodal_gen/runtime/weights/source.py

@@ -14,6 +14,8 @@
 from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.utils import validate_repo_id
 
+from sglang.srt.utils.common import retry_on_hub_rate_limit
+
 WeightSourceKind = Literal["local", "huggingface"]
 
 _WEIGHT_SUFFIXES = (".safetensors", ".gguf", ".bin", ".pt", ".pth", ".ckpt")
@@ -274,9 +276,8 @@ def resolve_weight_inventory(source: WeightSource) -> WeightInventory:
         )
 
     assert source.repo_id is not None
-    model_info = HfApi().model_info(
-        source.repo_id,
-        revision=source.revision,
+    model_info = retry_on_hub_rate_limit(
+        lambda: HfApi().model_info(source.repo_id, revision=source.revision)
     )
     files = tuple(sibling.rfilename for sibling in model_info.siblings)
     return WeightInventory(

python/sglang/srt/arg_groups/model_path_hook.py

@@ -15,7 +15,7 @@
     resolving_view,
     run_post_process_pass,
 )
-from sglang.srt.utils.common import is_remote_url
+from sglang.srt.utils.common import find_local_repo_dir, is_remote_url
 from sglang.srt.utils.hf_transformers_utils import check_gguf_file
 from sglang.srt.utils.runai_utils import ObjectStorageModel, is_runai_obj_uri
 
@@ -347,10 +347,25 @@ def _check_format(has_params, has_consolidated, has_hf_weights) -> bool:
             ),
         )
 
+    snapshot_dir = find_local_repo_dir(cfg.model_path, revision=cfg.revision)
+    if snapshot_dir is not None:
+        # A cache may hold only some files, so trust it only when what it has decides.
+        has_params = os.path.exists(os.path.join(snapshot_dir, "params.json"))
+        has_hf_weights = bool(
+            glob.glob(os.path.join(snapshot_dir, "model*.safetensors"))
+        )
+        if has_params and name_matches:
+            return True
+        if has_hf_weights and not name_matches:
+            return False
+
     try:
         from huggingface_hub import HfApi
 
-        files = {s.rfilename for s in HfApi().model_info(cfg.model_path).siblings}
+        files = {
+            s.rfilename
+            for s in HfApi().model_info(cfg.model_path, revision=cfg.revision).siblings
+        }
         return _check_format(
             has_params="params.json" in files,
             has_consolidated=any(

python/sglang/srt/lora/lora_config.py

@@ -17,7 +17,9 @@
 import os
 from typing import Dict, Optional
 
-from huggingface_hub import snapshot_download
+from huggingface_hub import hf_hub_download
+
+from sglang.srt.utils.common import download_hf_file_if_exists
 
 logger = logging.getLogger(__name__)
 
@@ -76,27 +78,25 @@ def get_lora_config(self, dummy=False):
         if dummy:
             raise NotImplementedError()
         else:
+            config_name = "adapter_config.json"
             if not os.path.isdir(self.path):
-                weights_dir = snapshot_download(self.path, allow_patterns=["*.json"])
+                config_path = hf_hub_download(repo_id=self.path, filename=config_name)
             else:
-                weights_dir = self.path
-            config_name = "adapter_config.json"
-            with open(os.path.join(weights_dir, config_name), "r") as f:
+                config_path = os.path.join(self.path, config_name)
+            with open(config_path, "r") as f:
                 return json.load(f)
 
     def get_added_tokens_config(self):
         """Load added tokens from the LoRA adapter if the file exists."""
-        # Determine the weights directory
         if not os.path.isdir(self.path):
-            weights_dir = snapshot_download(self.path, allow_patterns=["*.json"])
+            added_tokens_path = download_hf_file_if_exists(
+                repo_id=self.path, filename="added_tokens.json"
+            )
         else:
-            weights_dir = self.path
-
-        # Construct the path to added_tokens.json
-        added_tokens_path = os.path.join(weights_dir, "added_tokens.json")
+            added_tokens_path = os.path.join(self.path, "added_tokens.json")
 
         # Return None if the file doesn't exist (optional for standard LoRA adapters)
-        if not os.path.exists(added_tokens_path):
+        if added_tokens_path is None or not os.path.exists(added_tokens_path):
             return None
 
         # Load and return the added tokens

python/sglang/srt/model_loader/ci_weight_validation.py

@@ -18,7 +18,11 @@
 
 import safetensors
 
-from sglang.srt.utils import log_info_on_rank0
+from sglang.srt.utils import (
+    find_local_repo_dir,
+    log_info_on_rank0,
+    retry_on_hub_rate_limit,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -152,11 +156,20 @@ def validate_cache_lightweight(
                 custom_files = set()
                 for key, value in auto_map.items():
                     if isinstance(value, str) and "." in value:
-                        module_name = value.split(".")[0]
-                        custom_files.add(f"{module_name}.py")
+                        # "owner/repo--module.Class" loads its code from another repo.
+                        code_repo, _, reference = value.rpartition("--")
+                        code_dir = (
+                            find_local_repo_dir(code_repo)
+                            if code_repo
+                            else snapshot_dir
+                        )
+                        if code_dir is None:
+                            return False
+                        module_name = reference.split(".")[0]
+                        custom_files.add(os.path.join(code_dir, f"{module_name}.py"))
 
-                for custom_file in custom_files:
-                    custom_file_path = os.path.join(snapshot_dir, custom_file)
+                for custom_file_path in custom_files:
+                    custom_file = os.path.basename(custom_file_path)
                     if not os.path.exists(custom_file_path):
                         logger.debug(
                             "Custom module file not in snapshot: %s for %s",
@@ -782,19 +795,21 @@ def __init__(self, *args, **kwargs):
         hf_folder = None
         for attempt in range(max_retries):
             try:
-                hf_folder = snapshot_download(
-                    model_name_or_path,
-                    allow_patterns=allow_patterns,
-                    ignore_patterns=ignore_patterns,
-                    cache_dir=cache_dir,
-                    tqdm_class=DisabledTqdm,
-                    revision=revision,
-                    local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
-                    # Force single-threaded downloads to prevent race conditions
-                    # on NFS. HF hub defaults to max_workers=8, which can cause
-                    # .incomplete file conflicts when multiple threads operate
-                    # on the same files
-                    max_workers=1,
+                hf_folder = retry_on_hub_rate_limit(
+                    lambda: snapshot_download(
+                        model_name_or_path,
+                        allow_patterns=allow_patterns,
+                        ignore_patterns=ignore_patterns,
+                        cache_dir=cache_dir,
+                        tqdm_class=DisabledTqdm,
+                        revision=revision,
+                        local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+                        # Force single-threaded downloads to prevent race conditions
+                        # on NFS. HF hub defaults to max_workers=8, which can cause
+                        # .incomplete file conflicts when multiple threads operate
+                        # on the same files
+                        max_workers=1,
+                    )
                 )
             except (FileNotFoundError, OSError) as e:
                 # Race condition: .incomplete file was moved/deleted by another