A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 7, 02:45 UTC
[BugFix] Avoid ignored trust_remote_code warnings (#36290)
Signed-off-by: Nick Hill <nickhill123@gmail.com>
Nick Hill committed
Mar 6, 2026 at 17:24 UTC
c7f32e08c2e49665621be72f8e83d6433b2564d1
6 files changed
+29
-9
vllm/transformers_utils/config.py
+8
-5
@@ -24,7 +24,10 @@ from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME
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from vllm import envs
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from vllm.logger import init_logger
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from vllm.transformers_utils.repo_utils import is_mistral_model_repo
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-from vllm.transformers_utils.utils import parse_safetensors_file_metadata
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+from vllm.transformers_utils.utils import (
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+ parse_safetensors_file_metadata,
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+ without_trust_remote_code,
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+)
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from .config_parser_base import ConfigParserBase
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from .gguf_utils import (
@@ -140,11 +143,12 @@ class HFConfigParser(ConfigParserBase):
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**kwargs,
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) -> tuple[dict, PretrainedConfig]:
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kwargs["local_files_only"] = huggingface_hub.constants.HF_HUB_OFFLINE
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+ trust_remote_code |= kwargs.get("trust_remote_code", False)
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+ kwargs = without_trust_remote_code(kwargs)
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config_dict, _ = PretrainedConfig.get_config_dict(
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model,
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revision=revision,
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code_revision=code_revision,
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- trust_remote_code=trust_remote_code,
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**kwargs,
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)
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# Use custom model class if it's in our registry
@@ -225,7 +229,7 @@ class MistralConfigParser(ConfigParserBase):
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model,
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revision=revision,
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code_revision=code_revision,
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- **kwargs,
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+ **without_trust_remote_code(kwargs),
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)
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except OSError: # Not found
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hf_config_dict = {}
@@ -521,8 +525,7 @@ def maybe_override_with_speculators(
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config_dict, _ = PretrainedConfig.get_config_dict(
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model if gguf_model_repo is None else gguf_model_repo,
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revision=revision,
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- trust_remote_code=trust_remote_code,
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- **kwargs,
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+ **without_trust_remote_code(kwargs),
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)
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speculators_config = config_dict.get("speculators_config")
531
vllm/transformers_utils/configs/eagle.py
+3
-1
@@ -5,6 +5,8 @@ import os
5
6
from transformers import AutoConfig, DeepseekV2Config, PretrainedConfig
7
8
+from vllm.transformers_utils.utils import without_trust_remote_code
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+
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class EAGLEConfig(PretrainedConfig):
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model_type = "eagle"
@@ -79,7 +81,7 @@ class EAGLEConfig(PretrainedConfig):
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**kwargs,
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) -> "EAGLEConfig":
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config_dict, kwargs = cls.get_config_dict(
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- pretrained_model_name_or_path, **kwargs
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+ pretrained_model_name_or_path, **without_trust_remote_code(kwargs)
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)
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return cls.from_dict(config_dict, **kwargs)
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vllm/transformers_utils/configs/extract_hidden_states.py
+3
-1
@@ -7,6 +7,8 @@ import os
7
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from transformers import PretrainedConfig
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+from vllm.transformers_utils.utils import without_trust_remote_code
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+
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class ExtractHiddenStatesConfig(PretrainedConfig):
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model_type = "extract_hidden_states"
@@ -42,7 +44,7 @@ class ExtractHiddenStatesConfig(PretrainedConfig):
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**kwargs,
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) -> "ExtractHiddenStatesConfig":
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config_dict, kwargs = cls.get_config_dict(
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- pretrained_model_name_or_path, **kwargs
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+ pretrained_model_name_or_path, **without_trust_remote_code(kwargs)
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)
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return cls.from_dict(config_dict, **kwargs)
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vllm/transformers_utils/configs/medusa.py
+3
-1
@@ -5,6 +5,8 @@ import os
5
6
from transformers import PretrainedConfig
7
8
+from vllm.transformers_utils.utils import without_trust_remote_code
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+
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class MedusaConfig(PretrainedConfig):
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model_type = "medusa"
@@ -42,7 +44,7 @@ class MedusaConfig(PretrainedConfig):
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**kwargs,
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) -> "MedusaConfig":
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config_dict, kwargs = cls.get_config_dict(
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- pretrained_model_name_or_path, **kwargs
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+ pretrained_model_name_or_path, **without_trust_remote_code(kwargs)
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)
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for k in list(config_dict.keys()):
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if "num" in k:
vllm/transformers_utils/configs/speculators/base.py
+5
-1
@@ -11,6 +11,8 @@ from vllm.transformers_utils.configs.speculators.algos import (
11
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__all__ = ["SpeculatorsConfig"]
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+from vllm.transformers_utils.utils import without_trust_remote_code
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+
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class SpeculatorsConfig(PretrainedConfig):
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model_type = "speculators"
@@ -22,7 +24,9 @@ class SpeculatorsConfig(PretrainedConfig):
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**kwargs,
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) -> "SpeculatorsConfig":
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"""Load speculators Eagle config and convert to vLLM format."""
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- config_dict, _ = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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+ config_dict, _ = cls.get_config_dict(
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+ pretrained_model_name_or_path, **without_trust_remote_code(kwargs)
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+ )
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vllm_config = cls.extract_transformers_pre_trained_config(config_dict)
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return cls(**vllm_config)
vllm/transformers_utils/utils.py
+7
@@ -27,6 +27,13 @@ def is_cloud_storage(model_or_path: str) -> bool:
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return is_s3(model_or_path) or is_gcs(model_or_path)
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29
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+def without_trust_remote_code(kwargs: dict[str, Any]) -> dict[str, Any]:
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+ """Return kwargs without trust_remote_code without modifying original dict."""
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+ if "trust_remote_code" not in kwargs:
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+ return kwargs
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+ return {k: v for k, v in kwargs.items() if k != "trust_remote_code"}
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+
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+
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def modelscope_list_repo_files(
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repo_id: str,
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revision: str | None = None,