Make bare hugging_face imports forbidden

tests/conftest.py

@@ -31,7 +31,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from huggingface_hub import snapshot_download
+from vllm.transformers_utils.repo_utils import hf_api
 from PIL import Image
 from transformers import (
     AutoConfig,
@@ -1496,7 +1496,7 @@ def num_gpus_available():
 def dummy_opt_path():
     json_path = os.path.join(_dummy_opt_path, "config.json")
     if not os.path.exists(_dummy_opt_path):
-        snapshot_download(
+        hf_api().snapshot_download(
             repo_id="facebook/opt-125m",
             local_dir=_dummy_opt_path,
             ignore_patterns=["*.bin", "*.bin.index.json", "*.pt", "*.h5", "*.msgpack"],
@@ -1514,7 +1514,7 @@ def dummy_opt_path():
 def dummy_llava_path():
     json_path = os.path.join(_dummy_llava_path, "config.json")
     if not os.path.exists(_dummy_llava_path):
-        snapshot_download(
+        hf_api().snapshot_download(
             repo_id="llava-hf/llava-1.5-7b-hf",
             local_dir=_dummy_llava_path,
             ignore_patterns=[
@@ -1539,7 +1539,7 @@ def dummy_llava_path():
 def dummy_gemma2_embedding_path():
     json_path = os.path.join(_dummy_gemma2_embedding_path, "config.json")
     if not os.path.exists(_dummy_gemma2_embedding_path):
-        snapshot_download(
+        hf_api().snapshot_download(
             repo_id="BAAI/bge-multilingual-gemma2",
             local_dir=_dummy_gemma2_embedding_path,
             ignore_patterns=[

tests/distributed/test_rocm_quick_reduce.py

@@ -15,12 +15,12 @@
 import pytest
 import torch
 import torch.distributed as dist
-from huggingface_hub import snapshot_download
 
 import vllm.envs as envs
 from vllm import LLM, SamplingParams
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.platforms import current_platform
+from vllm.transformers_utils.repo_utils import hf_api
 from vllm.utils.network_utils import get_open_port
 
 pytestmark = pytest.mark.skipif(
@@ -210,11 +210,11 @@ def _log_prompt_summaries() -> None:
 @lru_cache(maxsize=1)
 def _get_model_path() -> str:
     try:
-        path = snapshot_download(repo_id=MODEL_NAME, local_files_only=True)
+        path = hf_api().snapshot_download(repo_id=MODEL_NAME, local_files_only=True)
         _log(f"using cached model snapshot: {path}")
         return path
     except Exception:
-        path = snapshot_download(repo_id=MODEL_NAME)
+        path = hf_api().snapshot_download(repo_id=MODEL_NAME)
         _log(f"downloaded model snapshot: {path}")
         return path
 

tests/entrypoints/conftest.py

@@ -190,30 +190,32 @@ def sample_sql_statements():
 @pytest.fixture(scope="session")
 def qwen3_lora_files():
     """Download Qwen3 LoRA files once per test session."""
-    from huggingface_hub import snapshot_download
+    from vllm.transformers_utils.repo_utils import hf_api
 
-    return snapshot_download(repo_id="charent/self_cognition_Alice")
+    return hf_api().snapshot_download(repo_id="charent/self_cognition_Alice")
 
 
 @pytest.fixture(scope="session")
 def qwen3_meowing_lora_files():
     """Download Qwen3 LoRA files once per test session."""
-    from huggingface_hub import snapshot_download
+    from vllm.transformers_utils.repo_utils import hf_api
 
-    return snapshot_download(repo_id="Jackmin108/Qwen3-0.6B-Meow-LoRA")
+    return hf_api().snapshot_download(repo_id="Jackmin108/Qwen3-0.6B-Meow-LoRA")
 
 
 @pytest.fixture(scope="session")
 def qwen3_woofing_lora_files():
     """Download Qwen3 LoRA files once per test session."""
-    from huggingface_hub import snapshot_download
+    from vllm.transformers_utils.repo_utils import hf_api
 
-    return snapshot_download(repo_id="Jackmin108/Qwen3-0.6B-Woof-LoRA")
+    return hf_api().snapshot_download(repo_id="Jackmin108/Qwen3-0.6B-Woof-LoRA")
 
 
 @pytest.fixture(scope="session")
 def opt125_lora_files() -> str:
     """Download opt-125m LoRA files once per test session."""
-    from huggingface_hub import snapshot_download
+    from vllm.transformers_utils.repo_utils import hf_api
 
-    return snapshot_download(repo_id="peft-internal-testing/opt-125m-dummy-lora")
+    return hf_api().snapshot_download(
+        repo_id="peft-internal-testing/opt-125m-dummy-lora"
+    )

tests/entrypoints/multimodal/openai/chat_completion/test_chat_completion_with_mixed_audio_embeds.py

@@ -12,10 +12,10 @@
 import safetensors
 import torch
 import torch.nn as nn
-from huggingface_hub import hf_hub_download
 from transformers import AutoConfig, AutoTokenizer
 
 from tests.utils import RemoteOpenAIServer
+from vllm.transformers_utils.repo_utils import hf_api
 from vllm.utils.serial_utils import tensor2base64
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
@@ -126,11 +126,13 @@ def qwen2audio_aligned_content_and_embeds_b64() -> tuple[str, str]:
     content = "Describe this audio."
     tokenizer = AutoTokenizer.from_pretrained(QWEN2AUDIO_MODEL, trust_remote_code=True)
 
-    index_path = hf_hub_download(QWEN2AUDIO_MODEL, "model.safetensors.index.json")
+    index_path = hf_api().hf_hub_download(
+        QWEN2AUDIO_MODEL, "model.safetensors.index.json"
+    )
     with open(index_path) as f:
         weight_map = json.load(f)["weight_map"]
     embed_key = next(k for k in weight_map if k.endswith("embed_tokens.weight"))
-    shard_path = hf_hub_download(QWEN2AUDIO_MODEL, weight_map[embed_key])
+    shard_path = hf_api().hf_hub_download(QWEN2AUDIO_MODEL, weight_map[embed_key])
     with safetensors.safe_open(shard_path, framework="pt", device="cpu") as f:
         embed_weight = f.get_tensor(embed_key)
     embed_layer = nn.Embedding.from_pretrained(embed_weight.to(QWEN2AUDIO_DTYPE))

tests/entrypoints/multimodal/openai/chat_completion/test_chat_completion_with_mixed_image_embeds.py

@@ -13,12 +13,12 @@
 import safetensors
 import torch
 import torch.nn as nn
-from huggingface_hub import hf_hub_download
 from transformers import AutoTokenizer
 
 from tests.utils import RemoteOpenAIServer
 from vllm.assets.image import ImageAsset
 from vllm.multimodal.utils import encode_image_url
+from vllm.transformers_utils.repo_utils import hf_api
 from vllm.utils.serial_utils import tensor2base64
 
 MODEL_NAME = "Qwen/Qwen2-VL-2B-Instruct"
@@ -84,11 +84,11 @@ def aligned_content_and_embeds_b64() -> tuple[str, str]:
     content = "Describe this image."
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
 
-    index_path = hf_hub_download(MODEL_NAME, "model.safetensors.index.json")
+    index_path = hf_api().hf_hub_download(MODEL_NAME, "model.safetensors.index.json")
     with open(index_path) as f:
         weight_map = json.load(f)["weight_map"]
     embed_key = next(k for k in weight_map if k.endswith("embed_tokens.weight"))
-    shard_path = hf_hub_download(MODEL_NAME, weight_map[embed_key])
+    shard_path = hf_api().hf_hub_download(MODEL_NAME, weight_map[embed_key])
     with safetensors.safe_open(shard_path, framework="pt", device="cpu") as f:
         embed_weight = f.get_tensor(embed_key)
     embed_layer = nn.Embedding.from_pretrained(embed_weight.to(MODEL_DTYPE))

tests/entrypoints/multimodal/openai/chat_completion/test_default_mm_loras.py

@@ -6,17 +6,19 @@
 import openai  # use the official client for correctness check
 import pytest
 import pytest_asyncio
-from huggingface_hub import snapshot_download
 
 from tests.conftest import AudioTestAssets
 from tests.utils import RemoteOpenAIServer
+from vllm.transformers_utils.repo_utils import hf_api
 
 # NOTE - the tests in this module are currently analogous to test_chat, but are
 # separated to avoid OOM killing due to module-scoped servers, since we
 # need a multimodal model for these tests.
 
 # Contains a modality specific lora alongside the base model
-MULTIMODAL_MODEL_NAME = snapshot_download("microsoft/Phi-4-multimodal-instruct")
+MULTIMODAL_MODEL_NAME = hf_api().snapshot_download(
+    "microsoft/Phi-4-multimodal-instruct"
+)
 AUDIO_LORA_PATH = os.path.join(MULTIMODAL_MODEL_NAME, "speech-lora")
 
 ACTIVE_MM_LORA_RESPONSE = "Spoken text: The first words I spoke in the original chronograph, a little piece of practical poetry. Mary had a little lamb, it slept with quite a snow, and everywhere that Mary went, the lamb was sure to go."  # noqa: E501

tests/entrypoints/openai/chat_completion/test_chat.py

@@ -27,9 +27,9 @@
 @pytest.fixture(scope="module")
 def zephyr_lora_files():
     """Download zephyr LoRA files once per test session."""
-    from huggingface_hub import snapshot_download
+    from vllm.transformers_utils.repo_utils import hf_api
 
-    return snapshot_download(repo_id="typeof/zephyr-7b-beta-lora")
+    return hf_api().snapshot_download(repo_id="typeof/zephyr-7b-beta-lora")
 
 
 @pytest.fixture(scope="module")

tests/entrypoints/pooling/scoring/util.py

@@ -6,7 +6,6 @@
 import pybase64 as base64
 import torch
 import torch.nn.functional as F
-from huggingface_hub import hf_hub_download
 from PIL import Image
 from safetensors.torch import load_file
 from transformers import AutoModel, AutoTokenizer
@@ -18,6 +17,7 @@
 )
 from vllm.entrypoints.pooling.scoring.typing import ScoreMultiModalParam
 from vllm.entrypoints.pooling.scoring.utils import compute_maxsim_score
+from vllm.transformers_utils.repo_utils import hf_api
 
 
 class ColBERTScoringHfRunner(torch.nn.Module):
@@ -38,7 +38,7 @@ def __init__(self, model_name, linear_weights_key):
         ).to(self.device)
         self.model.eval()
 
-        path = hf_hub_download(model_name, filename="model.safetensors")
+        path = hf_api().hf_hub_download(model_name, filename="model.safetensors")
         weights = load_file(path)
 
         self.linear_weight = weights[linear_weights_key].to(self.device).float()

tests/entrypoints/serve/sagemaker/conftest.py

@@ -21,9 +21,9 @@
 @pytest.fixture(scope="session")
 def smollm2_lora_files():
     """Download LoRA files once per test session."""
-    from huggingface_hub import snapshot_download
+    from vllm.transformers_utils.repo_utils import hf_api
 
-    return snapshot_download(repo_id=LORA_ADAPTER_NAME_SMOLLM)
+    return hf_api().snapshot_download(repo_id=LORA_ADAPTER_NAME_SMOLLM)
 
 
 @pytest.fixture(scope="module")

tests/entrypoints/tool_parsers/test_hermes_tool_parser.py

@@ -6,13 +6,13 @@
 import openai
 import pytest
 import pytest_asyncio
-from huggingface_hub import snapshot_download
 from typing_extensions import TypedDict
 
 from tests.utils import RemoteOpenAIServer
 from vllm.tool_parsers.abstract_tool_parser import ToolParser
 from vllm.tool_parsers.granite4_tool_parser import Granite4ToolParser
 from vllm.tool_parsers.hermes_tool_parser import Hermes2ProToolParser
+from vllm.transformers_utils.repo_utils import hf_api
 
 LORA_MODEL = "minpeter/LoRA-Llama-3.2-1B-tool-vllm-ci"
 
@@ -91,7 +91,7 @@ def server_config(request):
     config = CONFIGS[request.param]
 
     # download model and tokenizer using transformers
-    snapshot_download(config["model"])
+    hf_api().snapshot_download(config["model"])
     yield CONFIGS[request.param]
 
 

tests/kernels/quantization/test_nvfp4_emulation.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from typing import cast
 
-import huggingface_hub
 import pytest
 import torch
 from safetensors import safe_open
@@ -33,6 +32,7 @@
     kNvfp4Static,
 )
 from vllm.platforms import current_platform
+from vllm.transformers_utils.repo_utils import hf_api
 from vllm.triton_utils import triton
 
 if current_platform.is_rocm():
@@ -56,7 +56,7 @@ def on_gfx950() -> bool:
 @pytest.fixture(scope="module")
 def loaded_model_files():
     return {
-        model_id: huggingface_hub.snapshot_download(
+        model_id: hf_api().snapshot_download(
             repo_id=model_id, allow_patterns=config["shards"]
         )
         for model_id, config in MOE_MODEL_CONFIGS.items()