[Refactor] Move profiling methods to MM budget (#33559)

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

vllm/lora/model_manager.py

@@ -10,7 +10,7 @@ import torch
 from torch import nn
 
 from vllm.config import VllmConfig
-from vllm.config.lora import LoRAConfig, ModelConfig
+from vllm.config.lora import LoRAConfig
 from vllm.logger import init_logger
 from vllm.lora.layers import (
     BaseLayerWithLoRA,
@@ -35,9 +35,9 @@ from vllm.model_executor.models.interfaces import is_pooling_model
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.utils import PPMissingLayer
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.budget import MultiModalBudget
 from vllm.utils.cache import LRUCache
 from vllm.utils.platform_utils import is_pin_memory_available
-from vllm.v1.worker.utils import MultiModalBudget
 
 logger = init_logger(__name__)
 
@@ -142,7 +142,6 @@ class LoRAModelManager:
         vllm_config: VllmConfig,
         max_num_batched_tokens: int,
     ) -> None:
-        model_config: ModelConfig = vllm_config.model_config
         mm_registry = MULTIMODAL_REGISTRY
 
         self.supports_tower_connector_lora = False
@@ -162,7 +161,6 @@ class LoRAModelManager:
         self.punica_wrapper_mapping[lm_prefix] = llm_punica_wrapper
 
         if self.lora_config.enable_tower_connector_lora:
-            self.mm_processor_info = mm_registry.create_processor(model_config).info
             self.supports_tower_connector_lora = self.supports_mm and hasattr(
                 self.model, "get_num_mm_encoder_tokens"
             )
@@ -176,7 +174,7 @@ class LoRAModelManager:
         )
 
         mm_budget = MultiModalBudget(vllm_config, mm_registry)
-        limit_per_prompt = max(self.mm_processor_info.allowed_mm_limits.values())
+        limit_per_prompt = max(mm_budget.mm_max_items_per_prompt.values())
         num_encoder_tokens = self.model.get_num_mm_encoder_tokens(
             mm_budget.get_encoder_budget()
         )

vllm/multimodal/budget.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Mapping
+
+from vllm.config import ModelConfig, VllmConfig
+from vllm.multimodal.processing import BaseMultiModalProcessor
+from vllm.multimodal.registry import MultiModalRegistry
+from vllm.utils.torch_utils import set_default_torch_num_threads
+from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
+
+
+def get_mm_max_toks_per_item(
+    model_config: ModelConfig,
+    mm_registry: MultiModalRegistry,
+    processor: BaseMultiModalProcessor,
+    mm_counts: Mapping[str, int],
+) -> Mapping[str, int]:
+    """
+    Get the maximum number of tokens per data item from each modality based
+    on underlying model configuration.
+    """
+    max_tokens_per_item = processor.info.get_mm_max_tokens_per_item(
+        seq_len=model_config.max_model_len,
+        mm_counts=mm_counts,
+    )
+    if max_tokens_per_item is not None:
+        return max_tokens_per_item
+
+    mm_inputs = mm_registry.get_dummy_mm_inputs(
+        model_config,
+        mm_counts=mm_counts,
+        processor=processor,
+    )
+
+    return {
+        modality: sum(item.get_num_embeds for item in placeholders)
+        for modality, placeholders in mm_inputs["mm_placeholders"].items()
+    }
+
+
+class MultiModalBudget:
+    """Helper class to calculate budget information for multi-modal models."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        mm_registry: MultiModalRegistry,
+    ) -> None:
+        super().__init__()
+
+        self.model_config = model_config = vllm_config.model_config
+        self.scheduler_config = scheduler_config = vllm_config.scheduler_config
+
+        self.max_model_len = model_config.max_model_len
+        self.max_num_reqs = scheduler_config.max_num_seqs
+
+        cache = mm_registry.processor_only_cache_from_config(vllm_config)
+        processor = mm_registry.create_processor(model_config, cache=cache)
+
+        self.cache = cache
+        self.mm_limits = mm_limits = processor.info.allowed_mm_limits
+
+        active_modalities = {
+            modality for modality, limit in mm_limits.items() if limit > 0
+        }
+
+        with set_default_torch_num_threads():  # Avoid hang during startup
+            all_mm_max_toks_per_item = get_mm_max_toks_per_item(
+                model_config,
+                mm_registry,
+                processor,
+                mm_counts=dict.fromkeys(active_modalities, 1),
+            )
+
+        mm_max_toks_per_item = {
+            modality: all_mm_max_toks_per_item[modality]
+            for modality in active_modalities
+        }
+
+        encoder_compute_budget, encoder_cache_size = compute_mm_encoder_budget(
+            scheduler_config,
+            mm_max_toks_per_item,
+        )
+
+        self.encoder_compute_budget = encoder_compute_budget
+        self.encoder_cache_size = encoder_cache_size
+
+        mm_max_items_per_prompt = dict[str, int]()
+        mm_max_items_per_batch = dict[str, int]()
+
+        for modality, max_toks_per_item in mm_max_toks_per_item.items():
+            (
+                mm_max_items_per_prompt[modality],
+                mm_max_items_per_batch[modality],
+            ) = self._get_max_items(modality, max_toks_per_item)
+
+        self.mm_max_toks_per_item = mm_max_toks_per_item
+        self.mm_max_items_per_prompt: Mapping[str, int] = mm_max_items_per_prompt
+        self.mm_max_items_per_batch: Mapping[str, int] = mm_max_items_per_batch
+
+    def _get_max_items(
+        self,
+        modality: str,
+        max_tokens_per_item: int,
+    ) -> tuple[int, int]:
+        if max_tokens_per_item == 0:
+            return 0, 0
+
+        # Check how many items of this modality can be supported by
+        # the encoder budget.
+        if (encoder_budget := self.get_encoder_budget()) == 0:
+            return 0, 0
+
+        max_encoder_items_per_batch = encoder_budget // max_tokens_per_item
+
+        # Check how many items of this modality can be supported by
+        # the decoder budget.
+        mm_limit = self.mm_limits[modality]
+
+        max_items_per_prompt = max(
+            1,
+            min(mm_limit, self.max_model_len // max_tokens_per_item),
+        )
+
+        scheduler_config = self.scheduler_config
+        max_num_reqs = self.max_num_reqs
+
+        if not scheduler_config.enable_chunked_prefill:
+            max_num_reqs = min(
+                max_num_reqs,
+                scheduler_config.max_num_batched_tokens // max_tokens_per_item,
+            )
+
+        max_decoder_items_per_batch = max_num_reqs * max_items_per_prompt
+
+        max_items_per_batch = max(
+            1,
+            min(max_encoder_items_per_batch, max_decoder_items_per_batch),
+        )
+
+        return max_items_per_prompt, max_items_per_batch
+
+    def get_modality_with_max_tokens(self) -> str:
+        mm_max_toks_per_item = self.mm_max_toks_per_item
+        modality, _ = max(mm_max_toks_per_item.items(), key=lambda x: x[1])
+
+        return modality
+
+    def get_encoder_budget(self) -> int:
+        return min(self.encoder_compute_budget, self.encoder_cache_size)
+
+    def reset_cache(self) -> None:
+        if self.cache is not None:
+            self.cache.clear_cache()

vllm/multimodal/registry.py

@@ -130,15 +130,13 @@ class MultiModalRegistry:
         if not model_config.is_multimodal_model:
             return False
 
-        info = self._create_processing_info(model_config, tokenizer=None)
-        supported_modalities = info.get_supported_mm_limits()
-
         mm_config = model_config.get_multimodal_config()
+        info = self._create_processing_info(model_config, tokenizer=None)
 
         # Check if all supported modalities have limit == 0
         if all(
             mm_config.get_limit_per_prompt(modality) == 0
-            for modality in supported_modalities
+            for modality in info.supported_mm_limits
         ):
             logger.info_once(
                 "All limits of multimodal modalities supported by the model "
@@ -148,70 +146,6 @@ class MultiModalRegistry:
 
         return True
 
-    def get_max_tokens_per_item_by_modality(
-        self,
-        model_config: "ModelConfig",
-        *,
-        cache: BaseMultiModalProcessorCache | None = None,
-        profiler_limits: Mapping[str, int] | None = None,
-        observability_config: ObservabilityConfig | None = None,
-    ) -> Mapping[str, int]:
-        """
-        Get the maximum number of tokens per data item from each modality based
-        on underlying model configuration.
-        """
-        if not model_config.is_multimodal_model:
-            return {}
-
-        processor = self.create_processor(
-            model_config, observability_config, cache=cache
-        )
-
-        if profiler_limits is None:
-            profiler_limits = processor.info.allowed_mm_limits
-
-        mm_counts = {
-            modality: 1 for modality, limit in profiler_limits.items() if limit > 0
-        }
-
-        max_tokens_per_item = processor.info.get_mm_max_tokens_per_item(
-            seq_len=model_config.max_model_len,
-            mm_counts=mm_counts,
-        )
-        if max_tokens_per_item is not None:
-            return {
-                modality: max_tokens
-                for modality, max_tokens in max_tokens_per_item.items()
-                if mm_counts.get(modality, 0) > 0
-            }
-
-        mm_inputs = self.get_dummy_mm_inputs(
-            model_config,
-            mm_counts=mm_counts,
-            processor=processor,
-        )
-
-        return {
-            modality: sum(item.get_num_embeds for item in placeholders)
-            for modality, placeholders in mm_inputs["mm_placeholders"].items()
-        }
-
-    def get_mm_limits_per_prompt(
-        self,
-        model_config: "ModelConfig",
-        *,
-        observability_config: ObservabilityConfig | None = None,
-    ) -> Mapping[str, int]:
-        """
-        Get the maximum number of multi-modal input instances for each modality
-        that are allowed per prompt for a model class.
-        """
-        if not model_config.is_multimodal_model:
-            return {}
-
-        info = self._create_processing_info(model_config, observability_config)
-        return info.allowed_mm_limits
-
     def register_processor(
         self,
         processor: MultiModalProcessorFactory[_I],
@@ -303,10 +237,9 @@ class MultiModalRegistry:
     def get_dummy_mm_inputs(
         self,
         model_config: "ModelConfig",
-        mm_counts: Mapping[str, int] | None = None,
+        mm_counts: Mapping[str, int],
         *,
         cache: BaseMultiModalProcessorCache | None = None,
-        observability_config: ObservabilityConfig | None = None,
         processor: BaseMultiModalProcessor | None = None,
     ) -> MultiModalInputs:
         """
@@ -317,11 +250,7 @@ class MultiModalRegistry:
         seq_len = model_config.max_model_len
 
         if processor is None:
-            processor = self.create_processor(
-                model_config, observability_config, cache=cache
-            )
-        if mm_counts is None:
-            mm_counts = processor.info.allowed_mm_limits
+            processor = self.create_processor(model_config, cache=cache)
 
         processor_inputs = processor.dummy_inputs.get_dummy_processor_inputs(
             seq_len=seq_len,
@@ -342,26 +271,6 @@ class MultiModalRegistry:
 
         return mm_inputs
 
-    def get_encdec_max_encoder_len(self, model_config: "ModelConfig") -> int:
-        """
-        Get the maximum length of the encoder input for encoder-decoder models.
-        """
-        if not model_config.is_encoder_decoder:
-            return 0
-        max_tokens = self.get_max_tokens_per_item_by_modality(model_config)
-        if not max_tokens:
-            # TODO - this function assumes encoder-decoder models are
-            # multimodal. This will need to change when adding support for more
-            # than whisper.
-            return 0
-        assert len(max_tokens) == 1, (
-            "Encoder-decoder models are expected "
-            "to implement the multimodal interface with at most one modality."
-        )
-
-        first_modality = next(iter(max_tokens))
-        return max_tokens[first_modality]
-
     def _get_cache_type(
         self,
         vllm_config: "VllmConfig",

vllm/v1/core/encoder_cache_manager.py

@@ -6,11 +6,10 @@ from collections.abc import Mapping
 from typing import TYPE_CHECKING
 
 from vllm.logger import init_logger
-from vllm.multimodal import MultiModalRegistry
 from vllm.v1.request import Request
 
 if TYPE_CHECKING:
-    from vllm.config import ModelConfig, SchedulerConfig
+    from vllm.config import SchedulerConfig
 
 logger = init_logger(__name__)
 
@@ -267,60 +266,16 @@ class EncoderCacheManager:
         return freed
 
 
-def compute_encoder_budget(
-    model_config: "ModelConfig",
-    scheduler_config: "SchedulerConfig",
-    mm_registry: MultiModalRegistry,
-) -> tuple[int, int]:
-    """Compute the encoder cache budget based on the model and scheduler
-    configurations.
-
-    Returns:
-        - Compute budget for encoder execution, measured in number of tokens
-            from the input sequence.
-        - Space budget for encoder cache size, measured in number of tokens
-            from the input sequence.
-    """
-    if mm_registry.supports_multimodal_inputs(model_config):
-        max_tokens_by_modality = mm_registry.get_max_tokens_per_item_by_modality(
-            model_config
-        )
-
-        return compute_mm_encoder_budget(
-            scheduler_config,
-            max_tokens_by_modality,
-        )
-
-    return compute_text_encoder_budget(scheduler_config)
-
-
-def compute_text_encoder_budget(scheduler_config: "SchedulerConfig") -> tuple[int, int]:
-    """Compute the encoder cache budget based on the model and scheduler
-    configurations for a text-only model.
-
-    Args:
-        scheduler_config: Scheduler configuration.
-
-    Returns:
-        - Compute budget for encoder execution, in unit of number of tokens
-            in the input sequence.
-        - Space budget for encoder cache size, in unit of number of tokens
-            in the input sequence.
-    """
-    # Currently text-only encoder-decoder models are not supported
-    return 0, 0
-
-
 def compute_mm_encoder_budget(
     scheduler_config: "SchedulerConfig",
-    max_tokens_by_modality: Mapping[str, int],
+    mm_max_toks_per_item: Mapping[str, int],
 ) -> tuple[int, int]:
     """Compute the encoder cache budget based on the model and scheduler
     configurations for a multimodal model.
 
     Args:
         scheduler_config: Scheduler configuration.
-        max_tokens_by_modality: The maximum number of tokens for each
+        mm_max_toks_per_item: The maximum number of tokens per item for each
             non-text modality.
 
     Returns:
@@ -330,7 +285,7 @@ def compute_mm_encoder_budget(
             from the input sequence.
     """
 
-    if not max_tokens_by_modality:
+    if not mm_max_toks_per_item:
         logger.warning(
             "All non-text modalities supported by the model have been "
             "explicitly disabled via limit_mm_per_prompt. Encoder cache will "
@@ -338,7 +293,7 @@ def compute_mm_encoder_budget(
         )
         return 0, 0
 
-    max_tokens_per_mm_item = max(max_tokens_by_modality.values())
+    max_tokens_per_mm_item = max(mm_max_toks_per_item.values())
 
     if (
         scheduler_config.disable_chunked_mm_input

vllm/v1/core/sched/scheduler.py

@@ -31,10 +31,10 @@ from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
     RoutedExpertsReader,
 )
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
+from vllm.multimodal.budget import MultiModalBudget
 from vllm.v1.core.encoder_cache_manager import (
     EncoderCacheManager,
     EncoderDecoderCacheManager,
-    compute_encoder_budget,
 )
 from vllm.v1.core.kv_cache_manager import KVCacheBlocks, KVCacheManager
 from vllm.v1.core.kv_cache_metrics import KVCacheMetricsCollector
@@ -174,22 +174,22 @@ class Scheduler(SchedulerInterface):
 
         # Encoder-related.
         # Calculate encoder cache size if applicable
-        # NOTE: For now we use the same budget for both compute and space.
-        # This can be changed when we make encoder cache for embedding caching
-        # across requests.
-        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
-            model_config=vllm_config.model_config,
-            scheduler_config=vllm_config.scheduler_config,
-            mm_registry=mm_registry,
+        self.supports_mm_inputs = mm_registry.supports_multimodal_inputs(
+            vllm_config.model_config
+        )
+        self.mm_budget = mm_budget = (
+            MultiModalBudget(vllm_config, mm_registry)
+            if self.supports_mm_inputs
+            else None
         )
 
-        # NOTE(woosuk): Here, "encoder" includes the vision encoder (and
-        # projector if needed) for MM models as well as encoder-decoder
-        # transformers.
-        self.max_num_encoder_input_tokens = encoder_compute_budget
-        # NOTE: For the models without encoder (e.g., text-only models),
-        # the encoder cache will not be initialized because cache size is 0
-        # for these models.
+        # NOTE: Text-only encoder-decoder models are implemented as
+        # multi-modal models for convenience
+        # Example: https://github.com/neuralmagic/bart-plugin
+        self.max_num_encoder_input_tokens = (
+            mm_budget.encoder_compute_budget if mm_budget else 0
+        )
+        encoder_cache_size = mm_budget.encoder_cache_size if mm_budget else 0
         self.encoder_cache_manager = (
             EncoderDecoderCacheManager(cache_size=encoder_cache_size)
             if self.is_encoder_decoder
@@ -199,7 +199,9 @@ class Scheduler(SchedulerInterface):
         # Attn blocks, as for Whisper its input is always padded to the maximum length.
         # TODO (NickLucche): Generalize to models with variable-length encoder inputs.
         self._num_encoder_max_input_tokens = (
-            MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(vllm_config.model_config)
+            mm_budget.mm_max_toks_per_item[mm_budget.get_modality_with_max_tokens()]
+            if mm_budget
+            else 0
         )
 
         speculative_config = vllm_config.speculative_config

vllm/v1/engine/input_processor.py

@@ -19,6 +19,7 @@ from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
+from vllm.multimodal.budget import MultiModalBudget
 from vllm.multimodal.inputs import (
     MultiModalDataDict,
     MultiModalFeatureSpec,
@@ -34,7 +35,6 @@ from vllm.tokenizers import TokenizerLike
 from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.utils import length_from_prompt_token_ids_or_embeds, random_uuid
 from vllm.utils.torch_utils import set_default_torch_num_threads
-from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.metrics.stats import MultiModalCacheStats
 from vllm.v1.structured_output.backend_guidance import (
@@ -59,32 +59,30 @@ class InputProcessor:
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
     ) -> None:
         self.vllm_config = vllm_config
-        self.model_config = vllm_config.model_config
+        self.model_config = model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.lora_config = vllm_config.lora_config
+        self.scheduler_config = vllm_config.scheduler_config
         self.structured_outputs_config = vllm_config.structured_outputs_config
+        self.observability_config = vllm_config.observability_config
 
-        self.generation_config_fields = self.model_config.try_get_generation_config()
+        self.generation_config_fields = model_config.try_get_generation_config()
 
         self.mm_registry = mm_registry
         self.mm_processor_cache = mm_registry.processor_cache_from_config(vllm_config)
-        self.mm_encoder_cache_size = None
+
+        self.mm_encoder_cache_size: int | None = None
         if (
-            self.mm_registry.supports_multimodal_inputs(self.model_config)
-            and not self.model_config.skip_tokenizer_init
+            mm_registry.supports_multimodal_inputs(model_config)
+            and not model_config.skip_tokenizer_init
         ):
-            with set_default_torch_num_threads():
-                max_tokens_by_modality = (
-                    mm_registry.get_max_tokens_per_item_by_modality(self.model_config)
-                )
-
-            _, self.mm_encoder_cache_size = compute_mm_encoder_budget(
-                self.vllm_config.scheduler_config, max_tokens_by_modality
-            )
+            mm_budget = MultiModalBudget(vllm_config, mm_registry)
+            self.mm_encoder_cache_size = mm_budget.encoder_cache_size
+            mm_budget.reset_cache()  # Not used anymore
 
         self.input_preprocessor = InputPreprocessor(
-            self.model_config,
-            vllm_config.observability_config,
+            model_config,
+            self.observability_config,
             mm_registry,
             mm_processor_cache=self.mm_processor_cache,
         )

vllm/v1/worker/gpu_model_runner.py

@@ -82,6 +82,7 @@ from vllm.model_executor.models.interfaces_base import (
     is_text_generation_model,
 )
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.budget import MultiModalBudget
 from vllm.multimodal.inputs import (
     BatchedTensorInputs,
     MultiModalKwargsItem,
@@ -180,7 +181,6 @@ from vllm.v1.worker.workspace import lock_workspace
 
 from .utils import (
     AttentionGroup,
-    MultiModalBudget,
     add_kv_sharing_layers_to_kv_cache_groups,
     bind_kv_cache,
     sanity_check_mm_encoder_outputs,
@@ -5104,7 +5104,7 @@ class GPUModelRunner(
                     # modality with the max possible input tokens even when
                     # it supports multiple.
                     dummy_modality = mm_budget.get_modality_with_max_tokens()
-                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
+                    max_mm_items_per_batch = mm_budget.mm_max_items_per_batch[
                         dummy_modality
                     ]
 

vllm/v1/worker/utils.py

@@ -11,125 +11,14 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.models.interfaces import MultiModalEmbeddings
 from vllm.model_executor.models.utils import extract_layer_index
-from vllm.multimodal.registry import MultiModalRegistry
 from vllm.platforms import current_platform
 from vllm.utils.mem_utils import MemorySnapshot, format_gib
 from vllm.v1.attention.backend import AttentionBackend, AttentionMetadataBuilder
-from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
 from vllm.v1.kv_cache_interface import KVCacheGroupSpec, KVCacheSpec
 
 logger = init_logger(__name__)
 
 
-class MultiModalBudget:
-    """Helper class to calculate budget information for multi-modal models."""
-
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        mm_registry: MultiModalRegistry,
-    ) -> None:
-        super().__init__()
-
-        self.model_config = model_config = vllm_config.model_config
-        self.scheduler_config = scheduler_config = vllm_config.scheduler_config
-        self.mm_registry = mm_registry
-        self.cache = cache = mm_registry.processor_only_cache_from_config(vllm_config)
-
-        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(model_config)
-
-        max_tokens_by_modality = mm_registry.get_max_tokens_per_item_by_modality(
-            model_config,
-            cache=cache,
-            profiler_limits=self.mm_limits,
-        )
-
-        encoder_compute_budget, encoder_cache_size = compute_mm_encoder_budget(
-            scheduler_config,
-            max_tokens_by_modality,
-        )
-
-        self.encoder_compute_budget = encoder_compute_budget
-        self.encoder_cache_size = encoder_cache_size
-
-        max_items_per_prompt_by_modality = dict[str, int]()
-        max_items_per_batch_by_modality = dict[str, int]()
-
-        for modality, max_tokens in max_tokens_by_modality.items():
-            (
-                max_items_per_prompt,
-                max_items_per_batch,
-            ) = self.get_max_items(modality, max_tokens)
-
-            max_items_per_prompt_by_modality[modality] = max_items_per_prompt
-            max_items_per_batch_by_modality[modality] = max_items_per_batch
-
-        self.max_tokens_by_modality = max_tokens_by_modality
-        self.max_items_per_prompt_by_modality = max_items_per_prompt_by_modality
-        self.max_items_per_batch_by_modality = max_items_per_batch_by_modality
-
-    def get_modality_with_max_tokens(self) -> str:
-        max_tokens_by_modality = self.max_tokens_by_modality
-        modality, _ = max(max_tokens_by_modality.items(), key=lambda x: x[1])
-
-        return modality
-
-    def get_encoder_budget(self) -> int:
-        return min(self.encoder_compute_budget, self.encoder_cache_size)
-
-    def get_max_items(
-        self,
-        modality: str,
-        max_tokens_per_item: int,
-    ) -> tuple[int, int]:
-        if max_tokens_per_item == 0:
-            return 0, 0
-
-        # Check how many items of this modality can be supported by
-        # the encoder budget.
-        encoder_budget = self.get_encoder_budget()
-
-        # TODO: handle encoder-decoder models once we support them.
-        if encoder_budget == 0:
-            return 0, 0
-
-        max_encoder_items_per_batch = encoder_budget // max_tokens_per_item
-
-        # Check how many items of this modality can be supported by
-        # the decoder budget.
-        mm_limit = self.mm_limits[modality]
-
-        max_items_per_prompt = max(
-            1,
-            min(mm_limit, self.max_model_len // max_tokens_per_item),
-        )
-
-        scheduler_config = self.scheduler_config
-        max_num_reqs = self.max_num_reqs
-
-        if not scheduler_config.enable_chunked_prefill:
-            max_num_reqs = min(
-                max_num_reqs,
-                scheduler_config.max_num_batched_tokens // max_tokens_per_item,
-            )
-
-        max_decoder_items_per_batch = max_num_reqs * max_items_per_prompt
-
-        max_items_per_batch = max(
-            1,
-            min(max_encoder_items_per_batch, max_decoder_items_per_batch),
-        )
-
-        return max_items_per_prompt, max_items_per_batch
-
-    def reset_cache(self) -> None:
-        if self.cache is not None:
-            self.cache.clear_cache()
-
-
 @dataclass
 class AttentionGroup:
     backend: type[AttentionBackend]