[Refactor] Simplify code for MM budget (#23310)

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

vllm/v1/core/encoder_cache_manager.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
+from collections.abc import Mapping
 from typing import TYPE_CHECKING
 
 from vllm.logger import init_logger
@@ -188,35 +188,47 @@ def compute_encoder_budget(
         - Space budget for encoder cache size, in unit of number of tokens 
             in the input sequence.
     """
+    if mm_registry.supports_multimodal_inputs(model_config):
+        max_tokens_by_modality = mm_registry \
+            .get_max_tokens_per_item_by_nonzero_modality(model_config)
 
-    if not mm_registry.supports_multimodal_inputs(model_config):
-        return 0, 0
-
-    # TODO: handle encoder-decoder models once we support them.
-    (
-        encoder_compute_budget,
-        encoder_cache_size,
-    ) = _compute_encoder_budget_multimodal(
-        model_config,
+        return compute_mm_encoder_budget(
             scheduler_config,
-        mm_registry,
+            max_tokens_by_modality,
         )
 
-    return encoder_compute_budget, encoder_cache_size
+    return compute_text_encoder_budget(scheduler_config)
 
 
-def _compute_encoder_budget_multimodal(
-    model_config: "ModelConfig",
+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",
-    mm_registry: MultiModalRegistry,
+    max_tokens_by_modality: Mapping[str, int],
 ) -> tuple[int, int]:
     """Compute the encoder cache budget based on the model and scheduler 
     configurations for a multimodal model.
 
     Args:
-        model_config: Model configuration.
         scheduler_config: Scheduler configuration.
-        mm_registry: Provides information about the token cost.
+        max_tokens_by_modality: The maximum number of tokens for each
+            non-text modality.
 
     Returns:
         - Compute budget for encoder execution, in unit of number of tokens 
@@ -225,18 +237,14 @@ def _compute_encoder_budget_multimodal(
             in the input sequence.
     """
 
-    max_tokens_by_modality_dict = mm_registry \
-        .get_max_tokens_per_item_by_nonzero_modality(model_config)
-
-    if not max_tokens_by_modality_dict:
+    if not max_tokens_by_modality:
         logger.warning(
             "All non-text modalities supported by the model have been "
             "explicitly disabled via limit_mm_per_prompt. Encoder cache will "
             "not be initialized.")
         return 0, 0
 
-    _, max_tokens_per_mm_item = max(max_tokens_by_modality_dict.items(),
-                                    key=lambda item: item[1])
+    max_tokens_per_mm_item = max(max_tokens_by_modality.values())
 
     if (scheduler_config.disable_chunked_mm_input and max_tokens_per_mm_item
             > scheduler_config.max_num_batched_tokens):

vllm/v1/worker/gpu_model_runner.py

@@ -341,10 +341,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             self.model_config,
             self.scheduler_config,
             self.mm_registry,
-            max_model_len=self.max_model_len,
-            max_num_reqs=self.max_num_reqs,
-        ) if self.supports_mm_inputs \
-            else None)
+        ) if self.supports_mm_inputs else None)
 
         self.reorder_batch_threshold: Optional[int] = None
 
@@ -669,7 +666,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             mm_budget = self.mm_budget
             assert mm_budget is not None
 
-            dummy_modality, _ = mm_budget.get_modality_with_max_tokens()
+            dummy_modality = mm_budget.get_modality_with_max_tokens()
 
             return self._get_mm_dummy_batch(dummy_modality, num_seqs)
 
@@ -2595,14 +2592,9 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     # NOTE: Currently model is profiled with a single non-text
                     # modality with the max possible input tokens even when
                     # it supports multiple.
-                    (
-                        dummy_modality,
-                        max_tokens,
-                    ) = mm_budget.get_modality_with_max_tokens()
-                    (
-                        max_mm_items_per_prompt,
-                        max_mm_items_per_batch,
-                    ) = mm_budget.get_max_items(dummy_modality, max_tokens)
+                    dummy_modality = mm_budget.get_modality_with_max_tokens()
+                    max_mm_items_per_batch = mm_budget \
+                        .max_items_per_batch_by_modality[dummy_modality]
 
                     logger.info(
                         "Encoder cache will be initialized with a budget of "

vllm/v1/worker/tpu_model_runner.py

@@ -292,8 +292,6 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             self.model_config,
             self.scheduler_config,
             self.mm_registry,
-            max_model_len=self.max_model_len,
-            max_num_reqs=self.max_num_reqs,
         ) if self.supports_mm_inputs else None)
 
         if not self.use_spmd:
@@ -1545,14 +1543,9 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     # NOTE: Currently model is profiled with a single non-text
                     # modality with the max possible input tokens even when
                     # it supports multiple.
-                    (
-                        dummy_modality,
-                        max_tokens,
-                    ) = mm_budget.get_modality_with_max_tokens()
-                    (
-                        max_mm_items_per_prompt,
-                        max_mm_items_per_batch,
-                    ) = mm_budget.get_max_items(dummy_modality, max_tokens)
+                    dummy_modality = mm_budget.get_modality_with_max_tokens()
+                    max_mm_items_per_batch = mm_budget \
+                        .max_items_per_batch_by_modality[dummy_modality]
 
                     logger.info(
                         "Encoder cache will be initialized with a budget of "

vllm/v1/worker/utils.py

@@ -12,7 +12,7 @@ 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.v1.attention.backends.utils import AttentionMetadataBuilder
-from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
+from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
 from vllm.v1.kv_cache_interface import KVCacheGroupSpec
 
 if TYPE_CHECKING:
@@ -27,9 +27,6 @@ class MultiModalBudget:
         model_config: ModelConfig,
         scheduler_config: SchedulerConfig,
         mm_registry: MultiModalRegistry,
-        *,
-        max_model_len: int,
-        max_num_reqs: int,
     ) -> None:
         super().__init__()
 
@@ -37,25 +34,25 @@ class MultiModalBudget:
         self.scheduler_config = scheduler_config
         self.mm_registry = mm_registry
 
-        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
-            model_config=model_config,
-            scheduler_config=scheduler_config,
-            mm_registry=mm_registry,
+        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_nonzero_modality(model_config)
+
+        encoder_compute_budget, encoder_cache_size = compute_mm_encoder_budget(
+            scheduler_config,
+            max_tokens_by_modality,
         )
 
-        self.max_num_encoder_input_tokens = encoder_compute_budget
+        self.encoder_compute_budget = encoder_compute_budget
         self.encoder_cache_size = encoder_cache_size
-        self.max_model_len = max_model_len
-        self.max_num_reqs = max_num_reqs
-
-        self.mm_limits = mm_registry.get_mm_limits_per_prompt(model_config)
 
         max_items_per_prompt_by_modality = dict[str, int]()
         max_items_per_batch_by_modality = dict[str, int]()
 
-        max_tokens_by_modality = mm_registry \
-            .get_max_tokens_per_item_by_nonzero_modality(model_config)
-
         for modality, max_tokens in max_tokens_by_modality.items():
             (
                 max_items_per_prompt,
@@ -69,15 +66,14 @@ class MultiModalBudget:
         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) -> tuple[str, int]:
+    def get_modality_with_max_tokens(self) -> str:
         max_tokens_by_modality = self.max_tokens_by_modality
-        modality, max_tokens = max(max_tokens_by_modality.items(),
-                                   key=lambda item: item[1])
+        modality, _ = max(max_tokens_by_modality.items(), key=lambda x: x[1])
 
-        return modality, max_tokens
+        return modality
 
     def get_encoder_budget(self) -> int:
-        return min(self.max_num_encoder_input_tokens, self.encoder_cache_size)
+        return min(self.encoder_compute_budget, self.encoder_cache_size)
 
     def get_max_items(
         self,