[Bugfix]: Clean up chunked prefill logging when using whisper (#25075)

Signed-off-by: simondanielsson <simon.danielsson99@hotmail.com>

tests/v1/core/test_scheduler.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import dataclasses
 from typing import Optional
 from unittest.mock import Mock
 
@@ -1900,3 +1901,52 @@ def test_priority_scheduling_preemption_when_out_of_kv():
     assert output.scheduled_cached_reqs.req_ids[0] == request_high.request_id
     assert len(scheduler.waiting) == 1
     assert len(scheduler.running) == 1
+
+
+@pytest.mark.parametrize(
+    ("enable_chunked_prefill", "is_encoder_decoder", "expect_enabled"),
+    [
+        (True, False, True),
+        (False, False, False),
+        # Encoder-decoder models should always have it disabled
+        (False, True, False),
+        (True, True, False),
+    ])
+def test_chunked_prefill_disabled_for_encoder_decoder(
+        enable_chunked_prefill: bool, is_encoder_decoder: bool,
+        expect_enabled: bool) -> None:
+    """Validate that chunked prefill is appropriately disabled for 
+    encoder-decoder models."""
+    scheduler_config = SchedulerConfig(
+        enable_chunked_prefill=enable_chunked_prefill,
+        is_encoder_decoder=is_encoder_decoder,
+    )
+
+    # `is_encoder_decoder` should only be used during construction
+    # of the config, and otherwise stored in the model config.
+    assert "is_encoder_decoder" not in vars(scheduler_config)
+    assert "is_encoder_decoder" not in [
+        f.name for f in dataclasses.fields(scheduler_config)
+    ]
+    _validate_chunked_prefill_settings_for_encoder_decoder(
+        scheduler_config, is_encoder_decoder, expect_enabled)
+
+    # Ensure it is retained in VllmConfig, even after its post-init.
+    vllm_config = VllmConfig(scheduler_config=scheduler_config)
+    _validate_chunked_prefill_settings_for_encoder_decoder(
+        vllm_config.scheduler_config, is_encoder_decoder, expect_enabled)
+
+
+def _validate_chunked_prefill_settings_for_encoder_decoder(
+        scheduler_config: SchedulerConfig, is_encoder_decoder: bool,
+        expect_enabled: bool) -> None:
+    """Validate chunked prefill settings in the scheduler config for 
+    encoder-decoder models."""
+    assert scheduler_config.chunked_prefill_enabled is expect_enabled
+    assert scheduler_config.enable_chunked_prefill is expect_enabled
+    if is_encoder_decoder:
+        # Encoder-decoder models should automatically disable chunked multimodal
+        # inputs as well
+        assert scheduler_config.disable_chunked_mm_input is not expect_enabled
+    if is_encoder_decoder and not expect_enabled:
+        assert scheduler_config.long_prefill_token_threshold == 0

vllm/config/scheduler.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import hashlib
-from dataclasses import field
+from dataclasses import InitVar, field
 from typing import Any, Literal, Union
 
 from pydantic import SkipValidation, model_validator
@@ -84,6 +84,13 @@ class SchedulerConfig:
     is_multimodal_model: bool = False
     """True if the model is multimodal."""
 
+    is_encoder_decoder: InitVar[bool] = False
+    """True if the model is an encoder-decoder model.
+
+    Note: This is stored in the ModelConfig, and is used only here to
+    disable chunked prefill and prefix caching for encoder-decoder models.
+    """
+
     # TODO (ywang96): Make this configurable.
     max_num_encoder_input_tokens: int = field(init=False)
     """Multimodal encoder compute budget, only used in V1.
@@ -161,13 +168,23 @@ class SchedulerConfig:
                                usedforsecurity=False).hexdigest()
         return hash_str
 
-    def __post_init__(self) -> None:
+    def __post_init__(self, is_encoder_decoder: bool) -> None:
         if self.max_model_len is None:
             self.max_model_len = 8192
 
         if self.max_num_seqs is None:
             self.max_num_seqs = 128
 
+        if is_encoder_decoder:
+            # Chunked prefill should be disabled for encoder-decoder models.
+            self.disable_chunked_mm_input = True
+            self.chunked_prefill_enabled = False
+            self.enable_chunked_prefill = False
+            self.long_prefill_token_threshold = 0
+            logger.info(
+                "Encoder-decoder models do not support chunked prefill nor"
+                " prefix caching; disabling both.")
+
         if self.max_num_batched_tokens is None:
             if self.enable_chunked_prefill:
                 self.max_num_batched_tokens = DEFAULT_MAX_NUM_BATCHED_TOKENS

vllm/config/vllm.py

@@ -386,10 +386,6 @@ class VllmConfig:
                     "Encoder-decoder model detected: setting "
                     "`max_num_encoder_input_tokens` to encoder length (%s)",
                     self.scheduler_config.max_num_encoder_input_tokens)
-                self.scheduler_config.disable_chunked_mm_input = True
-                disable_chunked_prefill_reasons.append(
-                    "Encoder-decoder models do not support chunked prefill nor"
-                    " prefix caching; disabling both.")
                 if (self.model_config.architecture
                         == "WhisperForConditionalGeneration"
                         and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD")
@@ -400,7 +396,10 @@ class VllmConfig:
                         "try setting 'VLLM_WORKER_MULTIPROC_METHOD' "
                         "to 'spawn'.")
 
-        if disable_chunked_prefill_reasons:
+        # Disable prefix caching only if chunked prefill is explicitly disabled
+        # (and not merely unset)
+        if (self.scheduler_config.chunked_prefill_enabled is False
+                or disable_chunked_prefill_reasons):
             for reason in disable_chunked_prefill_reasons:
                 logger.info(reason)
             self.scheduler_config.chunked_prefill_enabled = False

vllm/engine/arg_utils.py

@@ -1367,6 +1367,7 @@ class EngineArgs:
             enable_chunked_prefill=self.enable_chunked_prefill,
             disable_chunked_mm_input=self.disable_chunked_mm_input,
             is_multimodal_model=model_config.is_multimodal_model,
+            is_encoder_decoder=model_config.is_encoder_decoder,
             send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                              and parallel_config.use_ray),
             policy=self.scheduling_policy,