Revert "[Renderer] Separate out `RendererConfig` from `ModelConfig` (#30145)" (#30199)

tests/models/multimodal/processing/test_h2ovl.py

@@ -162,7 +162,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": len(size_factors)},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     min_num = min_dynamic_patch if dynamic_image_size else 1

tests/models/multimodal/processing/test_idefics3.py

@@ -38,7 +38,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     # Build the image str / prompt based on the number of images we pass

tests/models/multimodal/processing/test_internvl.py

@@ -116,7 +116,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": len(size_factors)},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     min_num = min_dynamic_patch if dynamic_image_size else 1

tests/models/multimodal/processing/test_llama4.py

@@ -30,7 +30,7 @@ def test_processor_override(
         limit_mm_per_prompt={"image": num_imgs},
         mm_processor_cache_gb=mm_processor_cache_gb,
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     config = processor.info.get_hf_config()
     tokenizer = processor.info.get_tokenizer()
     hf_processor = processor.info.get_hf_processor()

tests/models/multimodal/processing/test_llava_next.py

@@ -42,7 +42,7 @@ def test_processor_max_tokens(model_id):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": 1},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     info = processor.info
 
     seen_aspect_ratios = set[float]()
@@ -140,7 +140,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
 
     image_ratios = [
         (171, 152),
@@ -173,7 +173,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
 
     seen_aspect_ratios = set[float]()
     image_sizes = list[ImageSize]()

tests/models/multimodal/processing/test_llava_onevision.py

@@ -42,7 +42,7 @@ def test_processor_max_tokens(model_id):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": 1},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     info = processor.info
 
     seen_aspect_ratios = set[float]()
@@ -138,7 +138,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
 
     image_ratios = [
         (171, 152),
@@ -171,7 +171,7 @@ def test_processor_prompt_replacements_all(model_id, num_imgs):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
 
     seen_aspect_ratios = set[float]()
     image_sizes = list[ImageSize]()

tests/models/multimodal/processing/test_minimax_vl_01.py

@@ -24,7 +24,7 @@ def test_processor_override(
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     prompt = "<image>" * num_imgs
     image = Image.new("RGB", size=(364, 364))
     mm_data = {"image": [image] * num_imgs}
@@ -83,7 +83,7 @@ def test_processor_prompt_replacements_regression(model_id, num_imgs):
         mm_processor_kwargs=None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
 
     image_ratios = [
         (171, 152),

tests/models/multimodal/processing/test_mllama4.py

@@ -25,7 +25,7 @@ def test_profiling(model_id: str, max_model_len: int):
         limit_mm_per_prompt=mm_counts,
     )
 
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     profiler = MultiModalProfiler(processor)
 
     decoder_dummy_data = profiler.get_decoder_dummy_data(

tests/models/multimodal/processing/test_nemotron_vl.py

@@ -118,7 +118,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": len(size_factors)},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     min_num = min_dynamic_patch if dynamic_image_size else 1

tests/models/multimodal/processing/test_phi3v.py

@@ -39,7 +39,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     # Build the image str / prompt based on the number of images we pass

tests/models/multimodal/processing/test_phi4mm.py

@@ -39,7 +39,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     # Build the image str / prompt based on the number of images we pass

tests/models/multimodal/processing/test_qwen2_vl.py

@@ -34,7 +34,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     tokenizer = processor.info.get_tokenizer()
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 

tests/models/multimodal/processing/test_smolvlm.py

@@ -38,7 +38,7 @@ def test_processor_override(
         mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
         limit_mm_per_prompt={"image": num_imgs},
     )
-    processor = MULTIMODAL_REGISTRY.create_processor(ctx.renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
 
     # Build the image str / prompt based on the number of images we pass

tests/models/multimodal/processing/test_tensor_schema.py

@@ -11,7 +11,7 @@ import pytest
 import torch.nn as nn
 from PIL import Image
 
-from vllm.config import ModelConfig, RendererConfig, VllmConfig, set_current_vllm_config
+from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.config.multimodal import (
     AudioDummyOptions,
     BaseDummyOptions,
@@ -31,6 +31,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, BatchedTensorInputs
 from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
 from vllm.multimodal.utils import group_mm_kwargs_by_modality
 from vllm.platforms import current_platform
+from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.utils.collection_utils import is_list_of
 from vllm.utils.torch_utils import set_default_torch_dtype
 
@@ -149,10 +150,7 @@ def initialize_dummy_model(
         backend="nccl",
     )
     initialize_model_parallel(tensor_model_parallel_size=1)
-    vllm_config = VllmConfig(
-        model_config=model_config,
-        renderer_config=RendererConfig(model_config=model_config),
-    )
+    vllm_config = VllmConfig(model_config=model_config)
     with set_current_vllm_config(vllm_config=vllm_config):
         with set_default_torch_dtype(model_config.dtype):
             model = model_cls(vllm_config=vllm_config)
@@ -184,12 +182,19 @@ def test_model_tensor_schema(model_id: str):
     else:
         dtype = model_info.dtype
 
-    renderer_config = model_info.build_renderer_config(
+    model_config = ModelConfig(
         model_id,
+        tokenizer=model_info.tokenizer or model_id,
+        tokenizer_mode=model_info.tokenizer_mode,
+        revision=model_info.revision,
+        trust_remote_code=model_info.trust_remote_code,
         hf_overrides=hf_overrides_fn,
+        skip_tokenizer_init=model_info.require_embed_inputs,
+        enable_prompt_embeds=model_info.require_embed_inputs,
+        enable_mm_embeds=model_info.require_embed_inputs,
+        enforce_eager=model_info.enforce_eager,
         dtype=dtype,
     )
-    model_config = renderer_config.model_config
 
     model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
     assert supports_multimodal(model_cls)
@@ -207,7 +212,10 @@ def test_model_tensor_schema(model_id: str):
     if not any(inputs_parse_methods):
         pytest.skip(f"{model_arch} does not support tensor schema validation.")
 
-    ctx = InputProcessingContext.from_config(renderer_config)
+    ctx = InputProcessingContext(
+        model_config,
+        tokenizer=cached_tokenizer_from_config(model_config),
+    )
     processing_info = factories.info(ctx)
     supported_mm_limits = processing_info.get_supported_mm_limits()
     limit_mm_per_prompt = {

tests/models/multimodal/processing/test_transformers.py

@@ -3,7 +3,7 @@
 import pytest
 
 from vllm.assets.image import ImageAsset
-from vllm.config import ModelConfig, RendererConfig
+from vllm.config import ModelConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
 
 
@@ -13,9 +13,8 @@ def test_multimodal_processor(model_id):
         model=model_id,
         model_impl="transformers",
     )
-    renderer_config = RendererConfig(model_config=model_config)
 
-    mm_processor = MULTIMODAL_REGISTRY.create_processor(renderer_config)
+    mm_processor = MULTIMODAL_REGISTRY.create_processor(model_config)
 
     image_pil = ImageAsset("cherry_blossom").pil_image
     mm_data = {"image": image_pil}

tests/models/multimodal/test_mapping.py

@@ -7,6 +7,7 @@ import torch
 import transformers
 from transformers import AutoConfig, PreTrainedModel
 
+from vllm.config import ModelConfig
 from vllm.model_executor.models.utils import WeightsMapper
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.transformers_utils.config import try_get_safetensors_metadata
@@ -49,11 +50,37 @@ def test_hf_model_weights_mapper(model_arch: str):
     model_info.check_available_online(on_fail="skip")
     model_info.check_transformers_version(on_fail="skip")
 
-    model_config = model_info.build_model_config(config_format="hf")
+    is_mistral_model = model_arch in [
+        "Mistral3ForConditionalGeneration",
+        "PixtralForConditionalGeneration",
+        "VoxtralForConditionalGeneration",
+    ]
+
+    if not is_mistral_model or model_info.tokenizer_mode == "mistral":
+        tokenizer_mode = model_info.tokenizer_mode
+    else:
+        tokenizer_mode = "hf"
+
+    model_id = model_info.default
+
+    model_config = ModelConfig(
+        model_id,
+        tokenizer=model_info.tokenizer or model_id,
+        tokenizer_mode=tokenizer_mode,
+        config_format="hf",
+        revision=model_info.revision,
+        trust_remote_code=model_info.trust_remote_code,
+        hf_overrides=model_info.hf_overrides,
+        skip_tokenizer_init=model_info.require_embed_inputs,
+        enable_prompt_embeds=model_info.require_embed_inputs,
+        enable_mm_embeds=model_info.require_embed_inputs,
+        enforce_eager=model_info.enforce_eager,
+        dtype=model_info.dtype,
+    )
     model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
 
-    original_weights = create_repo_dummy_weights(model_config.model)
-    hf_dummy_model = create_dummy_model(model_config.model, model_arch)
+    original_weights = create_repo_dummy_weights(model_id)
+    hf_dummy_model = create_dummy_model(model_id, model_arch)
     hf_converted_weights = hf_dummy_model.named_parameters()
     hf_converted_buffers = hf_dummy_model.named_buffers()
     mapper: WeightsMapper = model_cls.hf_to_vllm_mapper

tests/models/registry.py

@@ -9,8 +9,7 @@ import pytest
 from packaging.version import Version
 from transformers import __version__ as TRANSFORMERS_VERSION
 
-from vllm.config.model import ModelConfig, ModelDType
-from vllm.config.renderer import RendererConfig, TokenizerMode
+from vllm.config.model import ModelDType, TokenizerMode
 
 
 @dataclass(frozen=True)
@@ -171,36 +170,6 @@ class _HfExamplesInfo:
             else:
                 pytest.skip(msg)
 
-    def build_model_config(self, model: str | None = None, **kwargs) -> ModelConfig:
-        if model is None:
-            model = self.default
-
-        return ModelConfig(
-            **{
-                "model": model,
-                "revision": self.revision,
-                "trust_remote_code": self.trust_remote_code,
-                "hf_overrides": self.hf_overrides,
-                "enable_prompt_embeds": self.require_embed_inputs,
-                "enable_mm_embeds": self.require_embed_inputs,
-                "enforce_eager": self.enforce_eager,
-                "dtype": self.dtype,
-                **kwargs,
-            }
-        )
-
-    def build_renderer_config(
-        self, model: str | None = None, **kwargs
-    ) -> RendererConfig:
-        model_config = self.build_model_config(model, **kwargs)
-
-        return RendererConfig(
-            model_config=model_config,
-            tokenizer=self.tokenizer or model_config.model,
-            tokenizer_mode=self.tokenizer_mode,
-            skip_tokenizer_init=self.require_embed_inputs,
-        )
-
 
 _TEXT_GENERATION_EXAMPLE_MODELS = {
     # [Decoder-only]

tests/models/utils.py

@@ -13,6 +13,7 @@ from transformers import PretrainedConfig
 from vllm.config.model import ModelConfig, ModelDType, RunnerOption
 from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.multimodal.processing import InputProcessingContext
+from vllm.tokenizers import cached_tokenizer_from_config
 
 from .. import ci_envs
 from .registry import HF_EXAMPLE_MODELS
@@ -295,18 +296,30 @@ def build_model_context(
 
     model_config_kwargs = model_config_kwargs or {}
     limit_mm_per_prompt = limit_mm_per_prompt or {}
-    renderer_config = model_info.build_renderer_config(
+    model_config = ModelConfig(
         model_id,
         runner=runner,
+        tokenizer=model_info.tokenizer or model_id,
+        tokenizer_mode=model_info.tokenizer_mode,
+        revision=model_info.revision,
+        trust_remote_code=model_info.trust_remote_code,
         dtype=dtype,
         seed=0,
         mm_processor_kwargs=mm_processor_kwargs,
         limit_mm_per_prompt=limit_mm_per_prompt,
         mm_processor_cache_gb=mm_processor_cache_gb,
+        hf_overrides=model_info.hf_overrides,
+        skip_tokenizer_init=model_info.require_embed_inputs,
+        enable_prompt_embeds=model_info.require_embed_inputs,
+        enable_mm_embeds=model_info.require_embed_inputs,
+        enforce_eager=model_info.enforce_eager,
         **model_config_kwargs,
     )
 
-    return InputProcessingContext.from_config(renderer_config)
+    return InputProcessingContext(
+        model_config,
+        tokenizer=cached_tokenizer_from_config(model_config),
+    )
 
 
 def check_embeddings_close(

tests/multimodal/test_cache.py

@@ -6,7 +6,7 @@ import numpy as np
 import pytest
 import torch
 
-from vllm.config import ModelConfig, ParallelConfig, RendererConfig, VllmConfig
+from vllm.config import ModelConfig, ParallelConfig, VllmConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.cache import (
     BaseMultiModalProcessorCache,
@@ -110,14 +110,11 @@ def _create_vllm_config(
     mm_processor_cache_gb: float,
     enable_ipc: bool,
 ):
-    model_config = ModelConfig(
-        model="llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
-        mm_processor_cache_gb=mm_processor_cache_gb,
-    )
-
     return VllmConfig(
-        model_config=model_config,
-        renderer_config=RendererConfig(model_config=model_config),
+        model_config=ModelConfig(
+            model="llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            mm_processor_cache_gb=mm_processor_cache_gb,
+        ),
         parallel_config=ParallelConfig(data_parallel_size=1 if enable_ipc else 2),
     )
 
@@ -509,15 +506,13 @@ def _run_test_cache_eviction_shm(
 
 
 def test_cache_eviction_shm_cache():
-    model_config = ModelConfig(
-        model="llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
-        mm_processor_cache_type="shm",
-        mm_shm_cache_max_object_size_mb=6,
-        mm_processor_cache_gb=15.2 * MiB_bytes / GiB_bytes,
-    )
     vllm_config = VllmConfig(
-        model_config=model_config,
-        renderer_config=RendererConfig(model_config=model_config),
+        model_config=ModelConfig(
+            model="llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            mm_processor_cache_type="shm",
+            mm_shm_cache_max_object_size_mb=6,
+            mm_processor_cache_gb=15.2 * MiB_bytes / GiB_bytes,
+        ),
     )
     sender_cache = ShmObjectStoreSenderCache(vllm_config)
     receiver_cache = ShmObjectStoreReceiverCache(vllm_config, mp.Lock())

tests/multimodal/test_processing.py

@@ -7,7 +7,7 @@ from contextlib import nullcontext
 import numpy as np
 import pytest
 
-from vllm.config import ModelConfig, RendererConfig
+from vllm.config import ModelConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.processing import (
     InputProcessingContext,
@@ -920,9 +920,8 @@ def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
         model=model_id,
         limit_mm_per_prompt=limit_mm_per_prompt,
     )
-    renderer_config = RendererConfig(model_config=model_config)
 
-    processor = MULTIMODAL_REGISTRY.create_processor(renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(model_config)
     processor._supported_mm_limits = {"image": num_supported}
 
     profiler = MultiModalProfiler(processor)
@@ -956,9 +955,8 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
         model=model_id,
         limit_mm_per_prompt=limit_mm_per_prompt,
     )
-    renderer_config = RendererConfig(model_config=model_config)
 
-    processor = MULTIMODAL_REGISTRY.create_processor(renderer_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(model_config)
 
     rng = np.random.RandomState(0)
     image = random_image(rng, min_wh=128, max_wh=256)
@@ -1014,13 +1012,11 @@ def test_hf_processor_init_kwargs(
     inference_kwargs,
     expected_kwargs,
 ):
-    model_config = ModelConfig(model_id, mm_processor_kwargs=config_kwargs)
-    renderer_config = RendererConfig(
-        model_config=model_config,
-        tokenizer=model_id,
+    ctx = InputProcessingContext(
+        model_config=ModelConfig(model_id, mm_processor_kwargs=config_kwargs),
+        tokenizer=None,
     )
 
-    ctx = InputProcessingContext.from_config(renderer_config)
     processor = ctx.get_hf_processor(
         DummyProcessor,  # type: ignore[arg-type]
         **inference_kwargs,
@@ -1049,13 +1045,11 @@ def test_hf_processor_call_kwargs(
     inference_kwargs,
     expected_kwargs,
 ):
-    model_config = ModelConfig(model_id, mm_processor_kwargs=config_kwargs)
-    renderer_config = RendererConfig(
-        model_config=model_config,
-        tokenizer=model_id,
+    ctx = InputProcessingContext(
+        model_config=ModelConfig(model_id, mm_processor_kwargs=config_kwargs),
+        tokenizer=None,
     )
 
-    ctx = InputProcessingContext.from_config(renderer_config)
     processor = ctx.get_hf_processor(DummyProcessor)  # type: ignore[arg-type]
 
     result = ctx.call_hf_processor(processor, {}, inference_kwargs)