[Misc] Replace is_encoder_decoder_inputs with split_enc_dec_inputs (#15620)

Signed-off-by: DarkLight1337 <[email protected]>

Author: DarkLight1337

tests/models/multimodal/processing/test_idefics3.py

@@ -29,7 +29,7 @@ def test_processor_override(
     num_imgs: int,
     kwargs_on_init: bool,
 ):
-    """Ensure input_processor_for_idefics3 handles num_crops properly."""
+    """Ensure Idefics3MultiModalProcessor handles num_crops properly."""
     # Same as the previous test - don't initialize mm_processor_kwargs
     # in this test and assume that the kwargs will be correctly expanded by
     # the partial when calling the custom input processor.

tests/models/multimodal/processing/test_phi3v.py

@@ -30,7 +30,7 @@ def test_processor_override(
     num_imgs: int,
     kwargs_on_init: bool,
 ):
-    """Ensure input_processor_for_phi3v handles num_crops properly."""
+    """Ensure Phi3VMultiModalProcessor handles num_crops properly."""
     # Avoid initializing CUDA early
     from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID
 

vllm/engine/arg_utils.py

@@ -665,7 +665,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             type=nullable_kvs,
             default=EngineArgs.limit_mm_per_prompt,
             # The default value is given in
-            # MultiModalRegistry.init_mm_limits_per_prompt
+            # MultiModalConfig.get_limit_per_prompt
             help=('For each multimodal plugin, limit how many '
                   'input instances to allow for each prompt. '
                   'Expects a comma-separated list of items, '

vllm/engine/llm_engine.py

@@ -30,8 +30,8 @@
     get_logits_processors as get_openai_logits_processors)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
-                         PromptType, SingletonInputsAdapter)
-from vllm.inputs.parse import is_encoder_decoder_inputs, is_token_prompt
+                         PromptType)
+from vllm.inputs.parse import is_token_prompt, split_enc_dec_inputs
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.logits_process import get_bad_words_logits_processors
@@ -609,12 +609,7 @@ def _add_processed_request(
         seq_id = next(self.seq_counter)
         eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)
 
-        if is_encoder_decoder_inputs(processed_inputs):
-            decoder_inputs = processed_inputs["decoder"]
-            encoder_inputs = processed_inputs["encoder"]
-        else:
-            decoder_inputs = processed_inputs
-            encoder_inputs = None
+        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
 
         seq = Sequence(seq_id, decoder_inputs, block_size, eos_token_id,
                        lora_request, prompt_adapter_request)
@@ -2031,15 +2026,16 @@ def create_trace_span(self, seq_group: SequenceGroup) -> None:
 
     def _validate_model_inputs(self, inputs: ProcessorInputs,
                                lora_request: Optional[LoRARequest]):
-        if is_encoder_decoder_inputs(inputs):
-            # For encoder-decoder multimodal models, the max_prompt_len
-            # restricts the decoder prompt length
-            prompt_inputs = inputs["decoder" if self.model_config.
-                                   is_multimodal_model else "encoder"]
+        encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)
+
+        # For encoder-decoder multimodal models, the max_prompt_len
+        # restricts the decoder prompt length
+        if self.model_config.is_multimodal_model:
+            prompt_inputs = decoder_inputs
         else:
-            prompt_inputs = inputs
+            prompt_inputs = encoder_inputs or decoder_inputs
 
-        prompt_ids = SingletonInputsAdapter(prompt_inputs).prompt_token_ids
+        prompt_ids = prompt_inputs["prompt_token_ids"]
 
         if prompt_ids is None or len(prompt_ids) == 0:
             raise ValueError("Prompt cannot be empty")

vllm/inputs/parse.py

@@ -1,15 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
-
 from collections.abc import Sequence
-from typing import Literal, TypedDict, Union, cast, overload
+from typing import Literal, Optional, TypedDict, Union, cast, overload
 
 from typing_extensions import TypeIs
 
 from vllm.utils import is_list_of
 
-from .data import (EncoderDecoderInputs, ExplicitEncoderDecoderPrompt,
-                   ProcessorInputs, PromptType, SingletonPrompt, TextPrompt,
-                   TokensPrompt)
+from .data import (ExplicitEncoderDecoderPrompt, ProcessorInputs, PromptType,
+                   SingletonInputs, SingletonPrompt, TextPrompt, TokensPrompt)
 
 
 class ParsedText(TypedDict):
@@ -110,6 +108,14 @@ def is_explicit_encoder_decoder_prompt(
     return isinstance(prompt, dict) and "encoder_prompt" in prompt
 
 
-def is_encoder_decoder_inputs(
-        inputs: ProcessorInputs) -> TypeIs[EncoderDecoderInputs]:
-    return "encoder" in inputs and "decoder" in inputs
+def split_enc_dec_inputs(
+    inputs: ProcessorInputs,
+) -> tuple[Optional[SingletonInputs], SingletonInputs]:
+    if "encoder" in inputs and "decoder" in inputs:
+        # NOTE: This passes pyright but not mypy
+        return (
+            inputs["encoder"],  # type: ignore[typeddict-item]
+            inputs["decoder"],  # type: ignore[typeddict-item]
+        )
+
+    return None, inputs

vllm/inputs/registry.py

@@ -19,7 +19,7 @@
                         resolve_mm_processor_kwargs)
 
 from .data import ProcessorInputs, SingletonInputs
-from .parse import is_encoder_decoder_inputs
+from .parse import split_enc_dec_inputs
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig
@@ -462,13 +462,11 @@ def process_input(self, model_config: "ModelConfig",
             **mm_processor_kwargs,
         )
 
-        if is_encoder_decoder_inputs(processed_inputs):
-            self._ensure_mm_kwargs(processed_inputs["encoder"],
-                                   mm_processor_kwargs)
-            self._ensure_mm_kwargs(processed_inputs["decoder"],
-                                   mm_processor_kwargs)
-        else:
-            self._ensure_mm_kwargs(processed_inputs, mm_processor_kwargs)
+        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
+        if encoder_inputs is not None:
+            self._ensure_mm_kwargs(encoder_inputs, mm_processor_kwargs)
+        if decoder_inputs is not None:
+            self._ensure_mm_kwargs(decoder_inputs, mm_processor_kwargs)
 
         return processed_inputs
 

vllm/model_executor/models/idefics3.py

@@ -232,7 +232,7 @@ def get_dummy_processor_inputs(
         )
 
 
-class Idefics3MultimodalProcessor(
+class Idefics3MultiModalProcessor(
         BaseMultiModalProcessor[Idefics3ProcessingInfo]):
 
     def _call_hf_processor(
@@ -575,7 +575,7 @@ def forward(
 
 
 @MULTIMODAL_REGISTRY.register_processor(
-    Idefics3MultimodalProcessor,
+    Idefics3MultiModalProcessor,
     info=Idefics3ProcessingInfo,
     dummy_inputs=Idefics3DummyInputsBuilder)
 class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,

vllm/v1/engine/processor.py

@@ -7,7 +7,7 @@
 from vllm.config import VllmConfig
 from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
                          PromptType, SingletonInputsAdapter)
-from vllm.inputs.parse import is_encoder_decoder_inputs
+from vllm.inputs.parse import split_enc_dec_inputs
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.lora.request import LoRARequest
 from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
@@ -209,14 +209,8 @@ def process_inputs(
 
         self._validate_model_inputs(processed_inputs, lora_request)
 
-        if is_encoder_decoder_inputs(processed_inputs):
-            decoder_inputs = SingletonInputsAdapter(
-                processed_inputs["decoder"])
-            encoder_inputs = SingletonInputsAdapter(
-                processed_inputs["encoder"])
-        else:
-            decoder_inputs = SingletonInputsAdapter(processed_inputs)
-            encoder_inputs = None
+        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
+        decoder_inputs = SingletonInputsAdapter(decoder_inputs)
 
         # TODO: Impl encoder-decoder
         if encoder_inputs is not None:
@@ -301,15 +295,16 @@ def process_inputs(
     def _validate_model_inputs(self,
                                inputs: ProcessorInputs,
                                lora_request: Optional[LoRARequest] = None):
-        if is_encoder_decoder_inputs(inputs):
-            # For encoder-decoder multimodal models, the max_prompt_len
-            # restricts the decoder prompt length
-            prompt_inputs = inputs["decoder" if self.model_config.
-                                   is_multimodal_model else "encoder"]
+        encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)
+
+        # For encoder-decoder multimodal models, the max_prompt_len
+        # restricts the decoder prompt length
+        if self.model_config.is_multimodal_model:
+            prompt_inputs = decoder_inputs
         else:
-            prompt_inputs = inputs
+            prompt_inputs = encoder_inputs or decoder_inputs
 
-        prompt_ids = SingletonInputsAdapter(prompt_inputs).prompt_token_ids
+        prompt_ids = prompt_inputs["prompt_token_ids"]
 
         if prompt_ids is None or len(prompt_ids) == 0:
             raise ValueError("Prompt cannot be empty")