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[Core] Get num_encoder_tokens from scheduler config #24989

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Merged
merged 1 commit into from
Sep 17, 2025
+6 −8
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[Core] Get num_encoder_tokens from scheduler config #24989

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5 changes: 2 additions & 3 deletions vllm/v1/core/sched/scheduler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -465,9 +465,8 @@ def schedule(self) -> SchedulerOutput:
in self.vllm_config.model_config.model.lower()), (
"Whisper is the only supported "
"encoder-decoder model.")
num_encoder_tokens = MULTIMODAL_REGISTRY.\
get_encdec_max_encoder_len(
self.vllm_config.model_config)
num_encoder_tokens =\
self.scheduler_config.max_num_encoder_input_tokens
Comment on lines +468 to +469
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critical

This change replaces the call to get_encdec_max_encoder_len with self.scheduler_config.max_num_encoder_input_tokens. However, max_num_encoder_input_tokens is initialized to max_num_batched_tokens in SchedulerConfig, which is not the correct value for the model's maximum encoder length (e.g., a fixed value like 3000 for Whisper). Using a potentially smaller value from max_num_batched_tokens will lead to under-allocation of the cross-attention KV cache, which can cause out-of-bounds memory access. This is a critical issue. The original call was functionally correct, though slow. I recommend reverting this change until the value is cached correctly in the configuration.

Suggested change
num_encoder_tokens =\
self.scheduler_config.max_num_encoder_input_tokens
num_encoder_tokens = MULTIMODAL_REGISTRY.\
get_encdec_max_encoder_len(
self.vllm_config.model_config)

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Here is where it gets set to the correct value that we want:

vllm/vllm/config/__init__.py

Lines 2788 to 2790 in 218454b

elif self.model_config.is_encoder_decoder:
self.scheduler_config.max_num_encoder_input_tokens = \
MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(self.model_config)

else:
num_encoder_tokens = 0

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5 changes: 2 additions & 3 deletions vllm/v1/kv_cache_interface.py
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Expand Up @@ -11,7 +11,6 @@

from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.utils import cdiv, get_dtype_size

logger = init_logger(__name__)
Expand Down Expand Up @@ -230,8 +229,8 @@ class CrossAttentionSpec(AttentionSpec):
def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int:
# For cross-attention, we need to cache encoder states
# Get encoder length (e.g., 1500 for Whisper).
max_encoder_len = MULTIMODAL_REGISTRY.\
get_encdec_max_encoder_len(vllm_config.model_config)
max_encoder_len = vllm_config.scheduler_config.\
max_num_encoder_input_tokens
return cdiv(max_encoder_len, self.block_size) * self.page_size_bytes


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4 changes: 2 additions & 2 deletions vllm/v1/worker/gpu_model_runner.py
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Expand Up @@ -234,8 +234,8 @@ def __init__(
if self.model_config.is_encoder_decoder:
# Maximum length of the encoder input, only for encoder-decoder
# models.
self.max_encoder_len = self.mm_registry.\
get_encdec_max_encoder_len(model_config)
self.max_encoder_len = scheduler_config.\
max_num_encoder_input_tokens
else:
self.max_encoder_len = 0

Expand Down