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

vllm/v1/spec_decode/eagle.py

@@ -85,7 +85,7 @@ class EagleProposer:
         # Multi-modal data support
         self.mm_registry = MULTIMODAL_REGISTRY
         self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
-            vllm_config.renderer_config
+            vllm_config.model_config
         )
 
         self.attn_metadata_builder: AttentionMetadataBuilder | None = None

vllm/v1/structured_output/__init__.py

@@ -63,7 +63,7 @@ class StructuredOutputManager:
             max_workers = max(1, min(multiprocessing.cpu_count() // 2, 8))
             self.executor_for_fillmask = ThreadPoolExecutor(max_workers=max_workers)
 
-        if not vllm_config.renderer_config.skip_tokenizer_init:
+        if not self.vllm_config.model_config.skip_tokenizer_init:
             # The default max_workers if not specified is the number of
             # CPUs * 5, which is way too high since these tasks are CPU-bound,
             # not I/O bound. We also know we would never dominate CPU usage
@@ -71,15 +71,21 @@ class StructuredOutputManager:
             # of CPUs.
             max_workers = max(1, (multiprocessing.cpu_count() + 1) // 2)
             self.executor = ThreadPoolExecutor(max_workers=max_workers)
-            self.tokenizer = init_tokenizer_from_config(vllm_config.renderer_config)
-            reasoning_parser = vllm_config.structured_outputs_config.reasoning_parser
+            self.tokenizer = init_tokenizer_from_config(
+                model_config=self.vllm_config.model_config
+            )
+            reasoning_parser = (
+                self.vllm_config.structured_outputs_config.reasoning_parser
+            )
             reasoning_parser_plugin = (
-                vllm_config.structured_outputs_config.reasoning_parser_plugin
+                self.vllm_config.structured_outputs_config.reasoning_parser_plugin
             )
             if reasoning_parser_plugin and len(reasoning_parser_plugin) > 3:
                 ReasoningParserManager.import_reasoning_parser(reasoning_parser_plugin)
 
-            reasoning_parser = vllm_config.structured_outputs_config.reasoning_parser
+            reasoning_parser = (
+                self.vllm_config.structured_outputs_config.reasoning_parser
+            )
             if reasoning_parser:
                 reasoner_cls = ReasoningParserManager.get_reasoning_parser(
                     reasoning_parser
@@ -87,7 +93,7 @@ class StructuredOutputManager:
                 self.reasoner = reasoner_cls(tokenizer=self.tokenizer)
 
         self.enable_in_reasoning = (
-            vllm_config.structured_outputs_config.enable_in_reasoning
+            self.vllm_config.structured_outputs_config.enable_in_reasoning
         )
 
     def grammar_init(self, request: Request) -> None:

vllm/v1/worker/gpu_model_runner.py

@@ -271,7 +271,6 @@ class GPUModelRunner(
         device: torch.device,
     ):
         self.vllm_config = vllm_config
-        self.renderer_config = vllm_config.renderer_config
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.compilation_config = vllm_config.compilation_config
@@ -336,7 +335,7 @@ class GPUModelRunner(
         self.uses_mrope = model_config.uses_mrope
         self.uses_xdrope_dim = model_config.uses_xdrope_dim
         self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
-            self.renderer_config
+            model_config
         )
 
         if self.model_config.is_encoder_decoder:
@@ -559,7 +558,7 @@ class GPUModelRunner(
 
         self.mm_budget = (
             MultiModalBudget(
-                self.renderer_config,
+                self.model_config,
                 self.scheduler_config,
                 self.mm_registry,
             )
@@ -3874,7 +3873,7 @@ class GPUModelRunner(
         assert self.mm_budget is not None
 
         dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
-            renderer_config=self.renderer_config,
+            model_config=self.model_config,
             seq_len=self.max_model_len,
             mm_counts={modality: 1},
             cache=self.mm_budget.cache,

vllm/v1/worker/tpu_model_runner.py

@@ -143,7 +143,6 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         original_parallel_config: ParallelConfig | None = None,
     ):
         self.vllm_config = vllm_config
-        self.renderer_config = vllm_config.renderer_config
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.lora_config = vllm_config.lora_config
@@ -223,7 +222,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         self.mm_registry = MULTIMODAL_REGISTRY
         self.uses_mrope = model_config.uses_mrope
         self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
-            self.renderer_config
+            model_config
         )
         # TODO: Support M-RoPE (e.g, Qwen2-VL)
         assert not self.uses_mrope, "TPU does not support M-RoPE yet."
@@ -354,7 +353,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         self.mm_budget = (
             MultiModalBudget(
-                self.renderer_config,
+                self.model_config,
                 self.scheduler_config,
                 self.mm_registry,
             )
@@ -2039,7 +2038,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         assert self.mm_budget is not None
 
         dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
-            renderer_config=self.renderer_config,
+            model_config=self.model_config,
             seq_len=self.max_model_len,
             mm_counts={modality: 1},
             cache=self.mm_budget.cache,

vllm/v1/worker/utils.py

@@ -7,7 +7,7 @@ import torch
 
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.attention.layer import Attention
-from vllm.config import RendererConfig, SchedulerConfig, VllmConfig
+from vllm.config import ModelConfig, SchedulerConfig, VllmConfig
 from vllm.model_executor.models.interfaces import MultiModalEmbeddings
 from vllm.model_executor.models.utils import extract_layer_index
 from vllm.multimodal.cache import processor_only_cache_from_config
@@ -23,29 +23,24 @@ class MultiModalBudget:
 
     def __init__(
         self,
-        renderer_config: RendererConfig,
+        model_config: ModelConfig,
         scheduler_config: SchedulerConfig,
         mm_registry: MultiModalRegistry,
     ) -> None:
         super().__init__()
 
-        self.renderer_config = renderer_config
-        self.model_config = renderer_config.model_config
+        self.model_config = model_config
         self.scheduler_config = scheduler_config
         self.mm_registry = mm_registry
-        self.cache = cache = processor_only_cache_from_config(
-            renderer_config, mm_registry
-        )
+        self.cache = cache = processor_only_cache_from_config(model_config, mm_registry)
 
-        self.max_model_len = self.model_config.max_model_len
+        self.max_model_len = model_config.max_model_len
         self.max_num_reqs = scheduler_config.max_num_seqs
 
-        self.mm_limits = mm_registry.get_mm_limits_per_prompt(
-            renderer_config, cache=cache
-        )
+        self.mm_limits = mm_registry.get_mm_limits_per_prompt(model_config, cache=cache)
 
         max_tokens_by_modality = mm_registry.get_max_tokens_per_item_by_modality(
-            renderer_config,
+            model_config,
             cache=cache,
             profiler_limits=self.mm_limits,
         )