[Bugfix] Fix unable to load some models (#10312)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/test-pipeline.yaml

@@ -313,14 +313,15 @@ steps:
 
 #####  models test  #####
 
-- label: Basic Models Test # 10min
+- label: Basic Models Test # 30min
   source_file_dependencies:
   - vllm/
   - tests/models
   commands:
     - pip install -e ./plugins/vllm_add_dummy_model
     - pytest -v -s models/test_oot_registration.py # it needs a clean process
-    - pytest -v -s models/*.py --ignore=models/test_oot_registration.py
+    - pytest -v -s models/test_registry.py
+    - pytest -v -s models/test_initialization.py
 
 - label: Decoder-only Language Models Test (Standard) # 18min
   #mirror_hardwares: [amd]

tests/distributed/test_pipeline_parallel.py

@@ -166,14 +166,14 @@ TEXT_GENERATION_MODELS = {
     "mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(tp_base=4),
     "mosaicml/mpt-7b": PPTestSettings.fast(),
     "nvidia/Minitron-8B-Base": PPTestSettings.fast(),
-    "allenai/OLMoE-1B-7B-0924-Instruct": PPTestSettings.fast(),
     "allenai/OLMo-1B-hf": PPTestSettings.fast(),
+    "allenai/OLMoE-1B-7B-0924-Instruct": PPTestSettings.fast(),
     "facebook/opt-iml-max-1.3b": PPTestSettings.fast(),
     "OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(trust_remote_code=True),
+    "adept/persimmon-8b-chat": PPTestSettings.fast(),
     "microsoft/phi-2": PPTestSettings.fast(),
-    "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True, load_format="dummy", hf_overrides='{"num_hidden_layers": 4, "hidden_size": 512, "intermediate_size": 800, "num_attention_heads": 4, "num_key_value_heads": 1}'),  # noqa: E501
     "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(trust_remote_code=True),  # noqa: E501
-    "adept/persimmon-8b-chat": PPTestSettings.fast(),
+    "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True, load_format="dummy", hf_overrides='{"num_hidden_layers": 4, "hidden_size": 512, "intermediate_size": 800, "num_attention_heads": 4, "num_key_value_heads": 1}'),  # noqa: E501
     "Qwen/Qwen-7B-Chat": PPTestSettings.fast(trust_remote_code=True),
     "Qwen/Qwen2-7B-Instruct": PPTestSettings.fast(),
     "Qwen/Qwen1.5-MoE-A2.7B-Chat": PPTestSettings.fast(),

tests/models/registry.py

+from dataclasses import dataclass, field
+from typing import AbstractSet, Mapping, Optional
+
+
+@dataclass(frozen=True)
+class _HfExamplesInfo:
+    default: str
+    """The default model to use for testing this architecture."""
+
+    extras: Mapping[str, str] = field(default_factory=dict)
+    """Extra models to use for testing this architecture."""
+
+    tokenizer: Optional[str] = None
+    """Set the tokenizer to load for this architecture."""
+
+    tokenizer_mode: str = "auto"
+    """Set the tokenizer type for this architecture."""
+
+    speculative_model: Optional[str] = None
+    """
+    The default model to use for testing this architecture, which is only used
+    for speculative decoding.
+    """
+
+    is_available_online: bool = True
+    """
+    Set this to ``False`` if the name of this architecture no longer exists on
+    the HF repo. To maintain backwards compatibility, we have not removed them
+    from the main model registry, so without this flag the registry tests will
+    fail.
+    """
+
+    trust_remote_code: bool = False
+    """The ``trust_remote_code`` level required to load the model."""
+
+
+# yapf: disable
+_TEXT_GENERATION_EXAMPLE_MODELS = {
+    # [Decoder-only]
+    "AquilaModel": _HfExamplesInfo("BAAI/AquilaChat-7B",
+                                   trust_remote_code=True),
+    "AquilaForCausalLM": _HfExamplesInfo("BAAI/AquilaChat2-7B",
+                                         trust_remote_code=True),
+    "ArcticForCausalLM": _HfExamplesInfo("Snowflake/snowflake-arctic-instruct",
+                                         trust_remote_code=True),
+    "BaiChuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan-7B",
+                                         trust_remote_code=True),
+    "BaichuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan2-7B-chat",
+                                         trust_remote_code=True),
+    "BloomForCausalLM": _HfExamplesInfo("bigscience/bloomz-1b1"),
+    # ChatGLMModel supports multimodal
+    "CohereForCausalLM": _HfExamplesInfo("CohereForAI/c4ai-command-r-v01",
+                                         trust_remote_code=True),
+    "DbrxForCausalLM": _HfExamplesInfo("databricks/dbrx-instruct"),
+    "DeciLMForCausalLM": _HfExamplesInfo("Deci/DeciLM-7B-instruct",
+                                         trust_remote_code=True),
+    "DeepseekForCausalLM": _HfExamplesInfo("deepseek-ai/deepseek-llm-7b-chat"),
+    "DeepseekV2ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V2-Lite-Chat",  # noqa: E501
+                                         trust_remote_code=True),
+    "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
+    "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
+    "GemmaForCausalLM": _HfExamplesInfo("google/gemma-2b"),
+    "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
+    "GPT2LMHeadModel": _HfExamplesInfo("gpt2"),
+    "GPTBigCodeForCausalLM": _HfExamplesInfo("bigcode/starcoder"),
+    "GPTJForCausalLM": _HfExamplesInfo("EleutherAI/gpt-j-6b"),
+    "GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-160m"),
+    "GraniteForCausalLM": _HfExamplesInfo("ibm/PowerLM-3b"),
+    "GraniteMoeForCausalLM": _HfExamplesInfo("ibm/PowerMoE-3b"),
+    "InternLMForCausalLM": _HfExamplesInfo("internlm/internlm-chat-7b",
+                                           trust_remote_code=True),
+    "InternLM2ForCausalLM": _HfExamplesInfo("internlm/internlm2-chat-7b",
+                                            trust_remote_code=True),
+    "InternLM2VEForCausalLM": _HfExamplesInfo("OpenGVLab/Mono-InternVL-2B",
+                                              trust_remote_code=True),
+    "JAISLMHeadModel": _HfExamplesInfo("inceptionai/jais-13b-chat"),
+    "JambaForCausalLM": _HfExamplesInfo("ai21labs/AI21-Jamba-1.5-Mini"),
+    "LlamaForCausalLM": _HfExamplesInfo("meta-llama/Meta-Llama-3-8B"),
+    "LLaMAForCausalLM": _HfExamplesInfo("decapoda-research/llama-7b-hf",
+                                        is_available_online=False),
+    "MambaForCausalLM": _HfExamplesInfo("state-spaces/mamba-130m-hf"),
+    "FalconMambaForCausalLM": _HfExamplesInfo("tiiuae/falcon-mamba-7b-instruct"),  # noqa: E501
+    "MiniCPMForCausalLM": _HfExamplesInfo("openbmb/MiniCPM-2B-sft-bf16",
+                                         trust_remote_code=True),
+    "MiniCPM3ForCausalLM": _HfExamplesInfo("openbmb/MiniCPM3-4B",
+                                         trust_remote_code=True),
+    "MistralForCausalLM": _HfExamplesInfo("mistralai/Mistral-7B-Instruct-v0.1"),
+    "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1"),  # noqa: E501
+    "QuantMixtralForCausalLM": _HfExamplesInfo("mistral-community/Mixtral-8x22B-v0.1-AWQ"),  # noqa: E501
+    "MptForCausalLM": _HfExamplesInfo("mpt", is_available_online=False),
+    "MPTForCausalLM": _HfExamplesInfo("mosaicml/mpt-7b"),
+    "NemotronForCausalLM": _HfExamplesInfo("nvidia/Minitron-8B-Base"),
+    "OlmoForCausalLM": _HfExamplesInfo("allenai/OLMo-1B-hf"),
+    "OlmoeForCausalLM": _HfExamplesInfo("allenai/OLMoE-1B-7B-0924-Instruct"),
+    "OPTForCausalLM": _HfExamplesInfo("facebook/opt-iml-max-1.3b"),
+    "OrionForCausalLM": _HfExamplesInfo("OrionStarAI/Orion-14B-Chat",
+                                        trust_remote_code=True),
+    "PersimmonForCausalLM": _HfExamplesInfo("adept/persimmon-8b-chat"),
+    "PhiForCausalLM": _HfExamplesInfo("microsoft/phi-2"),
+    "Phi3ForCausalLM": _HfExamplesInfo("microsoft/Phi-3-mini-4k-instruct"),
+    "Phi3SmallForCausalLM": _HfExamplesInfo("microsoft/Phi-3-small-8k-instruct",
+                                            trust_remote_code=True),
+    "PhiMoEForCausalLM": _HfExamplesInfo("microsoft/Phi-3.5-MoE-instruct",
+                                         trust_remote_code=True),
+    # QWenLMHeadModel supports multimodal
+    "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-7B-Instruct"),
+    "Qwen2MoeForCausalLM": _HfExamplesInfo("Qwen/Qwen1.5-MoE-A2.7B-Chat"),
+    "RWForCausalLM": _HfExamplesInfo("tiiuae/falcon-40b",
+                                     is_available_online=False),
+    "StableLMEpochForCausalLM": _HfExamplesInfo("stabilityai/stablelm-zephyr-3b",  # noqa: E501
+                                                is_available_online=False),
+    "StableLmForCausalLM": _HfExamplesInfo("stabilityai/stablelm-3b-4e1t"),
+    "Starcoder2ForCausalLM": _HfExamplesInfo("bigcode/starcoder2-3b"),
+    "SolarForCausalLM": _HfExamplesInfo("upstage/solar-pro-preview-instruct"),
+    "XverseForCausalLM": _HfExamplesInfo("xverse/XVERSE-7B-Chat",
+                                         is_available_online=False,
+                                         trust_remote_code=True),
+    # [Encoder-decoder]
+    "BartModel": _HfExamplesInfo("facebook/bart-base"),
+    "BartForConditionalGeneration": _HfExamplesInfo("facebook/bart-large-cnn"),
+    # Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
+    # Therefore, we borrow the BartTokenizer from the original Bart model
+    "Florence2ForConditionalGeneration": _HfExamplesInfo("microsoft/Florence-2-base",  # noqa: E501
+                                                         tokenizer="facebook/bart-base",
+                                                         trust_remote_code=True),  # noqa: E501
+}
+
+_EMBEDDING_EXAMPLE_MODELS = {
+    # [Text-only]
+    "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
+    "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2"),
+    "MistralModel": _HfExamplesInfo("intfloat/e5-mistral-7b-instruct"),
+    "Qwen2ForRewardModel": _HfExamplesInfo("Qwen/Qwen2.5-Math-RM-72B"),
+    "Qwen2ForSequenceClassification": _HfExamplesInfo("jason9693/Qwen2.5-1.5B-apeach"),  # noqa: E501
+    # [Multimodal]
+    "LlavaNextForConditionalGeneration": _HfExamplesInfo("royokong/e5-v"),
+    "Phi3VForCausalLM": _HfExamplesInfo("TIGER-Lab/VLM2Vec-Full",
+                                         trust_remote_code=True),
+    "Qwen2VLForConditionalGeneration": _HfExamplesInfo("MrLight/dse-qwen2-2b-mrl-v1"), # noqa: E501
+}
+
+_MULTIMODAL_EXAMPLE_MODELS = {
+    # [Decoder-only]
+    "Blip2ForConditionalGeneration": _HfExamplesInfo("Salesforce/blip2-opt-2.7b"),  # noqa: E501
+    "ChameleonForConditionalGeneration": _HfExamplesInfo("facebook/chameleon-7b"),  # noqa: E501
+    "ChatGLMModel": _HfExamplesInfo("THUDM/glm-4v-9b",
+                                    extras={"text_only": "THUDM/chatglm3-6b"},
+                                    trust_remote_code=True),
+    "ChatGLMForConditionalGeneration": _HfExamplesInfo("chatglm2-6b",
+                                                       is_available_online=False),
+    "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
+    "H2OVLChatModel": _HfExamplesInfo("h2oai/h2ovl-mississippi-800m"),
+    "InternVLChatModel": _HfExamplesInfo("OpenGVLab/InternVL2-1B",
+                                         trust_remote_code=True),
+    "Idefics3ForConditionalGeneration": _HfExamplesInfo("HuggingFaceM4/Idefics3-8B-Llama3"),  # noqa: E501
+    "LlavaForConditionalGeneration": _HfExamplesInfo("llava-hf/llava-1.5-7b-hf",
+                                                     extras={"mistral": "mistral-community/pixtral-12b"}),  # noqa: E501
+    "LlavaNextForConditionalGeneration": _HfExamplesInfo("llava-hf/llava-v1.6-mistral-7b-hf"),  # noqa: E501
+    "LlavaNextVideoForConditionalGeneration": _HfExamplesInfo("llava-hf/LLaVA-NeXT-Video-7B-hf"),  # noqa: E501
+    "LlavaOnevisionForConditionalGeneration": _HfExamplesInfo("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"),  # noqa: E501
+    "MiniCPMV": _HfExamplesInfo("openbmb/MiniCPM-Llama3-V-2_5",
+                                trust_remote_code=True),
+    "MolmoForCausalLM": _HfExamplesInfo("allenai/Molmo-7B-D-0924",
+                                        trust_remote_code=True),
+    "NVLM_D": _HfExamplesInfo("nvidia/NVLM-D-72B",
+                              trust_remote_code=True),
+    "PaliGemmaForConditionalGeneration": _HfExamplesInfo("google/paligemma-3b-pt-224"),  # noqa: E501
+    "Phi3VForCausalLM": _HfExamplesInfo("microsoft/Phi-3-vision-128k-instruct",
+                                        trust_remote_code=True),
+    "PixtralForConditionalGeneration": _HfExamplesInfo("mistralai/Pixtral-12B-2409",  # noqa: E501
+                                                       tokenizer_mode="mistral"),
+    "QWenLMHeadModel": _HfExamplesInfo("Qwen/Qwen-VL-Chat",
+                                       extras={"text_only": "Qwen/Qwen-7B-Chat"},  # noqa: E501
+                                       trust_remote_code=True),
+    "Qwen2AudioForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-Audio-7B-Instruct"),  # noqa: E501
+    "Qwen2VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-VL-2B-Instruct"),  # noqa: E501
+    "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_3"),
+    # [Encoder-decoder]
+    "MllamaForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-3.2-11B-Vision-Instruct"),  # noqa: E501
+}
+
+_SPECULATIVE_DECODING_EXAMPLE_MODELS = {
+    "EAGLEModel": _HfExamplesInfo("JackFram/llama-68m",
+                                  speculative_model="abhigoyal/vllm-eagle-llama-68m-random"),  # noqa: E501
+    "MedusaModel": _HfExamplesInfo("JackFram/llama-68m",
+                                   speculative_model="abhigoyal/vllm-medusa-llama-68m-random"),  # noqa: E501
+    "MLPSpeculatorPreTrainedModel": _HfExamplesInfo("JackFram/llama-160m",
+                                                    speculative_model="ibm-fms/llama-160m-accelerator"),  # noqa: E501
+}
+
+_EXAMPLE_MODELS = {
+    **_TEXT_GENERATION_EXAMPLE_MODELS,
+    **_EMBEDDING_EXAMPLE_MODELS,
+    **_MULTIMODAL_EXAMPLE_MODELS,
+    **_SPECULATIVE_DECODING_EXAMPLE_MODELS,
+}
+
+
+class HfExampleModels:
+    def __init__(self, hf_models: Mapping[str, _HfExamplesInfo]) -> None:
+        super().__init__()
+
+        self.hf_models = hf_models
+
+    def get_supported_archs(self) -> AbstractSet[str]:
+        return self.hf_models.keys()
+
+    def get_hf_info(self, model_arch: str) -> _HfExamplesInfo:
+        return self.hf_models[model_arch]
+
+
+HF_EXAMPLE_MODELS = HfExampleModels(_EXAMPLE_MODELS)

tests/models/test_initialization.py

+from unittest.mock import patch
+
+import pytest
+import transformers
+from transformers import PretrainedConfig
+
+from vllm import LLM
+
+from .registry import HF_EXAMPLE_MODELS
+
+
+@pytest.mark.parametrize("model_arch", HF_EXAMPLE_MODELS.get_supported_archs())
+def test_can_initialize(model_arch):
+    if (model_arch == "Idefics3ForConditionalGeneration"
+            and transformers.__version__ < "4.46.0"):
+        pytest.skip(reason="Model introduced in HF >= 4.46.0")
+
+    model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
+    if not model_info.is_available_online:
+        pytest.skip("Model is not available online")
+
+    # Avoid OOM
+    def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
+        if hasattr(hf_config, "text_config"):
+            text_config: PretrainedConfig = hf_config.text_config
+        else:
+            text_config = hf_config
+
+        text_config.update({
+            "num_layers": 1,
+            "num_hidden_layers": 1,
+            "num_experts": 2,
+            "num_experts_per_tok": 2,
+            "num_local_experts": 2,
+        })
+
+        return hf_config
+
+    # Avoid calling model.forward()
+    def _initialize_kv_caches(self) -> None:
+        self.cache_config.num_gpu_blocks = 0
+        self.cache_config.num_cpu_blocks = 0
+
+    with patch.object(LLM.get_engine_class(), "_initialize_kv_caches",
+                      _initialize_kv_caches):
+        LLM(
+            model_info.default,
+            tokenizer=model_info.tokenizer,
+            tokenizer_mode=model_info.tokenizer_mode,
+            speculative_model=model_info.speculative_model,
+            num_speculative_tokens=1 if model_info.speculative_model else None,
+            trust_remote_code=model_info.trust_remote_code,
+            load_format="dummy",
+            hf_overrides=hf_overrides,
+        )

tests/models/test_registry.py

@@ -14,6 +14,7 @@ from vllm.model_executor.models.registry import (_EMBEDDING_MODELS,
 from vllm.platforms import current_platform
 
 from ..utils import fork_new_process_for_each_test
+from .registry import HF_EXAMPLE_MODELS
 
 
 @pytest.mark.parametrize("model_arch", ModelRegistry.get_supported_archs())
@@ -73,3 +74,12 @@ def test_registry_is_pp(model_arch, is_pp, init_cuda):
                 "This model no longer initializes CUDA on import. "
                 "Please test using a different one.",
                 stacklevel=2)
+
+
+def test_hf_registry_coverage():
+    untested_archs = (HF_EXAMPLE_MODELS.get_supported_archs() -
+                      set(ModelRegistry.get_supported_archs()))
+
+    assert not untested_archs, (
+        "Please add the following architectures to "
+        f"`tests/models/registry.py`: {untested_archs}")

vllm/config.py

@@ -3,8 +3,8 @@ import enum
 import json
 import warnings
 from dataclasses import dataclass, field, replace
-from typing import (TYPE_CHECKING, Any, ClassVar, Dict, Final, List, Literal,
-                    Mapping, Optional, Set, Tuple, Type, Union)
+from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Dict, Final, List,
+                    Literal, Mapping, Optional, Set, Tuple, Type, Union)
 
 import torch
 from transformers import PretrainedConfig
@@ -20,7 +20,7 @@ from vllm.transformers_utils.config import (
     get_hf_text_config, get_pooling_config,
     get_sentence_transformer_tokenizer_config, is_encoder_decoder, uses_mrope)
 from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory,
-                        print_warning_once)
+                        identity, print_warning_once)
 
 if TYPE_CHECKING:
     from ray.util.placement_group import PlacementGroup
@@ -44,6 +44,9 @@ TaskOption = Literal["auto", "generate", "embedding"]
 # "draft" is only used internally for speculative decoding
 _Task = Literal["generate", "embedding", "draft"]
 
+HfOverrides = Union[Dict[str, Any], Callable[[PretrainedConfig],
+                                             PretrainedConfig]]
+
 
 class ModelConfig:
     """Configuration for the model.
@@ -115,7 +118,9 @@ class ModelConfig:
             can not be gathered from the vllm arguments.
         config_format: The config format which shall be loaded.
             Defaults to 'auto' which defaults to 'hf'.
-        hf_overrides: Arguments to be forwarded to the HuggingFace config.
+        hf_overrides: If a dictionary, contains arguments to be forwarded to the
+            HuggingFace config. If a callable, it is called to update the
+            HuggingFace config.
         mm_processor_kwargs: Arguments to be forwarded to the model's processor
             for multi-modal data, e.g., image processor.
         pooling_type: Used to configure the pooling method in the embedding 
@@ -164,7 +169,7 @@ class ModelConfig:
             override_neuron_config: Optional[Dict[str, Any]] = None,
             config_format: ConfigFormat = ConfigFormat.AUTO,
             chat_template_text_format: str = "string",
-            hf_overrides: Optional[Dict[str, Any]] = None,
+            hf_overrides: Optional[HfOverrides] = None,
             mm_processor_kwargs: Optional[Dict[str, Any]] = None,
             pooling_type: Optional[str] = None,
             pooling_norm: Optional[bool] = None,
@@ -182,15 +187,23 @@ class ModelConfig:
 
         if hf_overrides is None:
             hf_overrides = {}
+
+        if callable(hf_overrides):
+            hf_overrides_kw = {}
+            hf_overrides_fn = hf_overrides
+        else:
+            hf_overrides_kw = hf_overrides
+            hf_overrides_fn = identity
+
         if rope_scaling is not None:
             hf_override: Dict[str, Any] = {"rope_scaling": rope_scaling}
-            hf_overrides.update(hf_override)
+            hf_overrides_kw.update(hf_override)
             msg = ("`--rope-scaling` will be removed in a future release. "
                    f"'Please instead use `--hf-overrides '{hf_override!r}'`")
             warnings.warn(DeprecationWarning(msg), stacklevel=2)
         if rope_theta is not None:
             hf_override = {"rope_theta": rope_theta}
-            hf_overrides.update(hf_override)
+            hf_overrides_kw.update(hf_override)
             msg = ("`--rope-theta` will be removed in a future release. "
                    f"'Please instead use `--hf-overrides '{hf_override!r}'`")
             warnings.warn(DeprecationWarning(msg), stacklevel=2)
@@ -207,9 +220,12 @@ class ModelConfig:
         self.max_logprobs = max_logprobs
         self.disable_sliding_window = disable_sliding_window
         self.skip_tokenizer_init = skip_tokenizer_init
-        self.hf_config = get_config(self.model, trust_remote_code, revision,
-                                    code_revision, config_format,
-                                    **hf_overrides)
+
+        hf_config = get_config(self.model, trust_remote_code, revision,
+                               code_revision, config_format, **hf_overrides_kw)
+        hf_config = hf_overrides_fn(hf_config)
+        self.hf_config = hf_config
+
         self.hf_text_config = get_hf_text_config(self.hf_config)
         self.encoder_config = self._get_encoder_config()
         self.hf_image_processor_config = get_hf_image_processor_config(

vllm/engine/arg_utils.py

@@ -9,9 +9,9 @@ import torch
 
 import vllm.envs as envs
 from vllm.config import (CacheConfig, ConfigFormat, DecodingConfig,
-                         DeviceConfig, LoadConfig, LoadFormat, LoRAConfig,
-                         ModelConfig, ObservabilityConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
+                         DeviceConfig, HfOverrides, LoadConfig, LoadFormat,
+                         LoRAConfig, ModelConfig, ObservabilityConfig,
+                         ParallelConfig, PromptAdapterConfig, SchedulerConfig,
                          SpeculativeConfig, TaskOption, TokenizerPoolConfig,
                          VllmConfig)
 from vllm.executor.executor_base import ExecutorBase
@@ -128,7 +128,7 @@ class EngineArgs:
     code_revision: Optional[str] = None
     rope_scaling: Optional[Dict[str, Any]] = None
     rope_theta: Optional[float] = None
-    hf_overrides: Optional[Dict[str, Any]] = None
+    hf_overrides: Optional[HfOverrides] = None
     tokenizer_revision: Optional[str] = None
     quantization: Optional[str] = None
     enforce_eager: Optional[bool] = None

vllm/entrypoints/llm.py

@@ -9,7 +9,7 @@ from tqdm import tqdm
 from vllm import envs
 from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
                               BeamSearchSequence, get_beam_search_score)
-from vllm.engine.arg_utils import EngineArgs, TaskOption
+from vllm.engine.arg_utils import EngineArgs, HfOverrides, TaskOption
 from vllm.engine.llm_engine import LLMEngine
 from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
                                          apply_hf_chat_template,
@@ -101,7 +101,9 @@ class LLM:
         disable_custom_all_reduce: See :class:`~vllm.config.ParallelConfig`
         disable_async_output_proc: Disable async output processing.
             This may result in lower performance.
-        hf_overrides: Arguments to be forwarded to the HuggingFace config.
+        hf_overrides: If a dictionary, contains arguments to be forwarded to the
+            HuggingFace config. If a callable, it is called to update the
+            HuggingFace config.
         **kwargs: Arguments for :class:`~vllm.EngineArgs`. (See
             :ref:`engine_args`)
 
@@ -156,7 +158,7 @@ class LLM:
         max_seq_len_to_capture: int = 8192,
         disable_custom_all_reduce: bool = False,
         disable_async_output_proc: bool = False,
-        hf_overrides: Optional[dict] = None,
+        hf_overrides: Optional[HfOverrides] = None,
         mm_processor_kwargs: Optional[Dict[str, Any]] = None,
         # After positional args are removed, move this right below `model`
         task: TaskOption = "auto",

vllm/model_executor/models/fuyu.py

@@ -41,7 +41,8 @@ from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
 from vllm.utils import is_list_of
 
 from .interfaces import SupportsMultiModal, SupportsPP
-from .utils import AutoWeightsLoader, flatten_bn, merge_multimodal_embeddings
+from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
+                    merge_multimodal_embeddings)
 
 # Cannot find the following 2 numbers from hf config.
 _IMAGE_TOKEN_ID = 71011
@@ -245,7 +246,9 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
             gather_output=True,
         )
         self.language_model = PersimmonForCausalLM(
-            vllm_config.with_hf_config(config.text_config))
+            vllm_config=vllm_config.with_hf_config(config.text_config),
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 

vllm/model_executor/models/internlm2_ve.py

@@ -161,11 +161,5 @@ class InternLM2VEForCausalLM(InternLM2ForCausalLM):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config, prefix=prefix)
 
-        config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
-        quant_config = vllm_config.quant_config
-
-        self.model = InternLM2VEModel(config,
-                                      cache_config,
-                                      quant_config,
+        self.model = InternLM2VEModel(vllm_config=vllm_config,
                                       prefix=maybe_prefix(prefix, "model"))

vllm/model_executor/models/minicpmv.py

@@ -382,11 +382,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
     instantiated.
     """
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         multimodal_config = vllm_config.model_config.multimodal_config
         quant_config = vllm_config.quant_config
@@ -699,12 +695,8 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
 
 class MiniCPMV2_0(MiniCPMVBaseModel):
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ):
-        super().__init__(vllm_config)
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
         assert self.version == (2, 0)
 
     def init_llm(
@@ -857,12 +849,8 @@ class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
     embedding_modules = {}
     embedding_padding_modules = []
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ):
-        super().__init__(vllm_config)
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
         assert self.version == (2, 5)
 
     def init_llm(
@@ -999,12 +987,8 @@ class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
     embedding_modules = {}
     embedding_padding_modules = []
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ):
-        super().__init__(vllm_config)
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
         assert self.version == (2, 6)
 
     def init_llm(
@@ -1117,7 +1101,7 @@ class MiniCPMV(MiniCPMVBaseModel, SupportsLoRA):
     embedding_modules = {}
     embedding_padding_modules = []
 
-    def __new__(cls, vllm_config: VllmConfig, prefix: str = ""):
+    def __new__(cls, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         if not hasattr(config, "version"):
             if config.hidden_size == 2304 and config.query_num == 64:

vllm/model_executor/models/mlp_speculator.py

@@ -65,7 +65,7 @@ class MLPSpeculator(nn.Module):
     https://huggingface.co/ibm-fms and https://huggingface.co/ibm-granite
     """
 
-    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
         super().__init__()
         config = vllm_config.model_config.hf_config
         self.n_predict = config.n_predict

vllm/model_executor/models/registry.py

+"""
+Whenever you add an architecture to this page, please also update
+`tests/models/registry.py` with example HuggingFace models for it.
+"""
 import importlib
 import os
 import pickle
@@ -58,14 +62,14 @@ _TEXT_GENERATION_MODELS = {
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
     "MambaForCausalLM": ("mamba", "MambaForCausalLM"),
     "FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
+    "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
+    "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
     "MistralForCausalLM": ("llama", "LlamaForCausalLM"),
     "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
     "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
     # transformers's mpt class has lower case
     "MptForCausalLM": ("mpt", "MPTForCausalLM"),
     "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
-    "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
-    "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
     "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
     "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
     "OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"),