[Frontend][Core] Override HF config.json via CLI (#5836)

Signed-off-by: DarkLight1337 <[email protected]>
Co-authored-by: DarkLight1337 <[email protected]>

tests/test_config.py

@@ -200,8 +200,10 @@ def test_rope_customization():
         trust_remote_code=False,
         dtype="float16",
         seed=0,
-        rope_scaling=TEST_ROPE_SCALING,
-        rope_theta=TEST_ROPE_THETA,
+        hf_overrides={
+            "rope_scaling": TEST_ROPE_SCALING,
+            "rope_theta": TEST_ROPE_THETA,
+        },
     )
     assert getattr(llama_model_config.hf_config, "rope_scaling",
                    None) == TEST_ROPE_SCALING
@@ -232,7 +234,9 @@ def test_rope_customization():
         trust_remote_code=False,
         dtype="float16",
         seed=0,
-        rope_scaling=TEST_ROPE_SCALING,
+        hf_overrides={
+            "rope_scaling": TEST_ROPE_SCALING,
+        },
     )
     assert getattr(longchat_model_config.hf_config, "rope_scaling",
                    None) == TEST_ROPE_SCALING

vllm/config.py

@@ -1,5 +1,6 @@
 import enum
 import json
+import warnings
 from dataclasses import dataclass, field
 from typing import (TYPE_CHECKING, Any, ClassVar, Dict, Final, List, Literal,
                     Mapping, Optional, Set, Tuple, Type, Union)
@@ -74,9 +75,6 @@ class ModelConfig:
         code_revision: The specific revision to use for the model code on
             Hugging Face Hub. It can be a branch name, a tag name, or a
             commit id. If unspecified, will use the default version.
-        rope_scaling: Dictionary containing the scaling configuration for the
-            RoPE embeddings. When using this flag, don't update
-            `max_position_embeddings` to the expected new maximum.
         tokenizer_revision: The specific tokenizer version to use. It can be a
             branch name, a tag name, or a commit id. If unspecified, will use
             the default version.
@@ -116,6 +114,7 @@ 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.
         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 
@@ -146,7 +145,7 @@ def __init__(
             allowed_local_media_path: str = "",
             revision: Optional[str] = None,
             code_revision: Optional[str] = None,
-            rope_scaling: Optional[dict] = None,
+            rope_scaling: Optional[Dict[str, Any]] = None,
             rope_theta: Optional[float] = None,
             tokenizer_revision: Optional[str] = None,
             max_model_len: Optional[int] = None,
@@ -164,6 +163,7 @@ def __init__(
             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,
             mm_processor_kwargs: Optional[Dict[str, Any]] = None,
             pooling_type: Optional[str] = None,
             pooling_norm: Optional[bool] = None,
@@ -178,8 +178,22 @@ def __init__(
         self.seed = seed
         self.revision = revision
         self.code_revision = code_revision
-        self.rope_scaling = rope_scaling
-        self.rope_theta = rope_theta
+
+        if hf_overrides is None:
+            hf_overrides = {}
+        if rope_scaling is not None:
+            hf_override: Dict[str, Any] = {"rope_scaling": rope_scaling}
+            hf_overrides.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)
+            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)
+
         # The tokenizer version is consistent with the model version by default.
         if tokenizer_revision is None:
             self.tokenizer_revision = revision
@@ -193,8 +207,8 @@ def __init__(
         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, rope_scaling, rope_theta,
-                                    config_format)
+                                    code_revision, config_format,
+                                    **hf_overrides)
         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

@@ -128,8 +128,9 @@ class EngineArgs:
     disable_log_stats: bool = False
     revision: Optional[str] = None
     code_revision: Optional[str] = None
-    rope_scaling: Optional[dict] = None
+    rope_scaling: Optional[Dict[str, Any]] = None
     rope_theta: Optional[float] = None
+    hf_overrides: Optional[Dict[str, Any]] = None
     tokenizer_revision: Optional[str] = None
     quantization: Optional[str] = None
     enforce_eager: Optional[bool] = None
@@ -140,8 +141,9 @@ class EngineArgs:
     # is intended for expert use only. The API may change without
     # notice.
     tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
-    tokenizer_pool_extra_config: Optional[dict] = None
+    tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None
     limit_mm_per_prompt: Optional[Mapping[str, int]] = None
+    mm_processor_kwargs: Optional[Dict[str, Any]] = None
     enable_lora: bool = False
     max_loras: int = 1
     max_lora_rank: int = 16
@@ -187,7 +189,6 @@ class EngineArgs:
     collect_detailed_traces: Optional[str] = None
     disable_async_output_proc: bool = False
     override_neuron_config: Optional[Dict[str, Any]] = None
-    mm_processor_kwargs: Optional[Dict[str, Any]] = None
     scheduling_policy: Literal["fcfs", "priority"] = "fcfs"
 
     # Pooling configuration.
@@ -512,6 +513,12 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
                             help='RoPE theta. Use with `rope_scaling`. In '
                             'some cases, changing the RoPE theta improves the '
                             'performance of the scaled model.')
+        parser.add_argument('--hf-overrides',
+                            type=json.loads,
+                            default=EngineArgs.hf_overrides,
+                            help='Extra arguments for the HuggingFace config.'
+                            'This should be a JSON string that will be '
+                            'parsed into a dictionary.')
         parser.add_argument('--enforce-eager',
                             action='store_true',
                             help='Always use eager-mode PyTorch. If False, '
@@ -940,6 +947,7 @@ def create_model_config(self) -> ModelConfig:
             code_revision=self.code_revision,
             rope_scaling=self.rope_scaling,
             rope_theta=self.rope_theta,
+            hf_overrides=self.hf_overrides,
             tokenizer_revision=self.tokenizer_revision,
             max_model_len=self.max_model_len,
             quantization=self.quantization,

vllm/engine/llm_engine.py

@@ -248,8 +248,7 @@ def __init__(
             "Initializing an LLM engine (v%s) with config: "
             "model=%r, speculative_config=%r, tokenizer=%r, "
             "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
-            "override_neuron_config=%s, "
-            "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, "
+            "override_neuron_config=%s, tokenizer_revision=%s, "
             "trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
             "download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
             "pipeline_parallel_size=%d, "
@@ -271,8 +270,6 @@ def __init__(
             model_config.tokenizer_mode,
             model_config.revision,
             model_config.override_neuron_config,
-            model_config.rope_scaling,
-            model_config.rope_theta,
             model_config.tokenizer_revision,
             model_config.trust_remote_code,
             model_config.dtype,

vllm/entrypoints/llm.py

@@ -98,7 +98,10 @@ class LLM:
             to eager mode. Additionally for encoder-decoder models, if the
             sequence length of the encoder input is larger than this, we fall
             back to the eager mode.
-        disable_custom_all_reduce: See ParallelConfig
+        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.
         **kwargs: Arguments for :class:`~vllm.EngineArgs`. (See
             :ref:`engine_args`)
 
@@ -153,6 +156,7 @@ def __init__(
         max_seq_len_to_capture: int = 8192,
         disable_custom_all_reduce: bool = False,
         disable_async_output_proc: bool = False,
+        hf_overrides: Optional[dict] = None,
         mm_processor_kwargs: Optional[Dict[str, Any]] = None,
         # After positional args are removed, move this right below `model`
         task: TaskOption = "auto",
@@ -194,6 +198,7 @@ def __init__(
             max_seq_len_to_capture=max_seq_len_to_capture,
             disable_custom_all_reduce=disable_custom_all_reduce,
             disable_async_output_proc=disable_async_output_proc,
+            hf_overrides=hf_overrides,
             mm_processor_kwargs=mm_processor_kwargs,
             pooling_type=pooling_type,
             pooling_norm=pooling_norm,

vllm/transformers_utils/config.py

@@ -146,9 +146,8 @@ def get_config(
     trust_remote_code: bool,
     revision: Optional[str] = None,
     code_revision: Optional[str] = None,
-    rope_scaling: Optional[dict] = None,
-    rope_theta: Optional[float] = None,
     config_format: ConfigFormat = ConfigFormat.AUTO,
+    token: Optional[str] = None,
     **kwargs,
 ) -> PretrainedConfig:
     # Separate model folder from file path for GGUF models
@@ -159,46 +158,51 @@ def get_config(
         model = Path(model).parent
 
     if config_format == ConfigFormat.AUTO:
-        if is_gguf or file_or_path_exists(model,
-                                          HF_CONFIG_NAME,
-                                          revision=revision,
-                                          token=kwargs.get("token")):
+        if is_gguf or file_or_path_exists(
+                model, HF_CONFIG_NAME, revision=revision, token=token):
             config_format = ConfigFormat.HF
         elif file_or_path_exists(model,
                                  MISTRAL_CONFIG_NAME,
                                  revision=revision,
-                                 token=kwargs.get("token")):
+                                 token=token):
             config_format = ConfigFormat.MISTRAL
         else:
             # If we're in offline mode and found no valid config format, then
             # raise an offline mode error to indicate to the user that they
             # don't have files cached and may need to go online.
             # This is conveniently triggered by calling file_exists().
-            file_exists(model,
-                        HF_CONFIG_NAME,
-                        revision=revision,
-                        token=kwargs.get("token"))
+            file_exists(model, HF_CONFIG_NAME, revision=revision, token=token)
 
             raise ValueError(f"No supported config format found in {model}")
 
     if config_format == ConfigFormat.HF:
         config_dict, _ = PretrainedConfig.get_config_dict(
-            model, revision=revision, code_revision=code_revision, **kwargs)
+            model,
+            revision=revision,
+            code_revision=code_revision,
+            token=token,
+            **kwargs,
+        )
 
         # Use custom model class if it's in our registry
         model_type = config_dict.get("model_type")
         if model_type in _CONFIG_REGISTRY:
             config_class = _CONFIG_REGISTRY[model_type]
-            config = config_class.from_pretrained(model,
-                                                  revision=revision,
-                                                  code_revision=code_revision)
+            config = config_class.from_pretrained(
+                model,
+                revision=revision,
+                code_revision=code_revision,
+                token=token,
+                **kwargs,
+            )
         else:
             try:
                 config = AutoConfig.from_pretrained(
                     model,
                     trust_remote_code=trust_remote_code,
                     revision=revision,
                     code_revision=code_revision,
+                    token=token,
                     **kwargs,
                 )
             except ValueError as e:
@@ -216,7 +220,7 @@ def get_config(
                     raise e
 
     elif config_format == ConfigFormat.MISTRAL:
-        config = load_params_config(model, revision, token=kwargs.get("token"))
+        config = load_params_config(model, revision, token=token, **kwargs)
     else:
         raise ValueError(f"Unsupported config format: {config_format}")
 
@@ -228,19 +232,6 @@ def get_config(
         model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
         config.update({"architectures": [model_type]})
 
-    for key, value in [
-        ("rope_scaling", rope_scaling),
-        ("rope_theta", rope_theta),
-    ]:
-        if value is not None:
-            logger.info(
-                "Updating %s from %r to %r",
-                key,
-                getattr(config, key, None),
-                value,
-            )
-            config.update({key: value})
-
     patch_rope_scaling(config)
 
     return config
@@ -462,13 +453,15 @@ def _reduce_modelconfig(mc: ModelConfig):
 
 def load_params_config(model: Union[str, Path],
                        revision: Optional[str],
-                       token: Optional[str] = None) -> PretrainedConfig:
+                       token: Optional[str] = None,
+                       **kwargs) -> PretrainedConfig:
     # This function loads a params.json config which
     # should be used when loading models in mistral format
 
     config_file_name = "params.json"
 
     config_dict = get_hf_file_to_dict(config_file_name, model, revision, token)
+    assert isinstance(config_dict, dict)
 
     config_mapping = {
         "dim": "hidden_size",
@@ -512,6 +505,8 @@ def recurse_elems(elem: Any):
         config_dict["architectures"] = ["PixtralForConditionalGeneration"]
         config_dict["model_type"] = "pixtral"
 
+    config_dict.update(kwargs)
+
     config = recurse_elems(config_dict)
     return config
 

vllm/v1/engine/llm_engine.py

@@ -74,8 +74,7 @@ def __init__(
             "Initializing an LLM engine (v%s) with config: "
             "model=%r, speculative_config=%r, tokenizer=%r, "
             "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
-            "override_neuron_config=%s, "
-            "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, "
+            "override_neuron_config=%s, tokenizer_revision=%s, "
             "trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
             "download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
             "pipeline_parallel_size=%d, "
@@ -94,8 +93,6 @@ def __init__(
             model_config.tokenizer_mode,
             model_config.revision,
             model_config.override_neuron_config,
-            model_config.rope_scaling,
-            model_config.rope_theta,
             model_config.tokenizer_revision,
             model_config.trust_remote_code,
             model_config.dtype,