[Core] Rename input data types

docs/source/dev/input_processing/model_inputs_index.rst

@@ -25,7 +25,7 @@ Module Contents
 LLM Engine Inputs
 -----------------
 
-.. autoclass:: vllm.inputs.LLMInputs
+.. autoclass:: vllm.inputs.DecoderOnlyInputs
     :members:
     :show-inheritance:
 

tests/models/decoder_only/vision_language/test_phi3v.py

@@ -1,12 +1,12 @@
 import os
 import re
-from typing import Callable, List, Optional, Tuple, Type
+from typing import List, Optional, Tuple, Type
 
 import pytest
 import torch
 from transformers import AutoImageProcessor, AutoTokenizer
 
-from vllm.inputs import InputContext, LLMInputs
+from vllm.inputs import InputContext, token_inputs
 from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID
 from vllm.multimodal import MultiModalRegistry
 from vllm.multimodal.utils import rescale_image_size
@@ -311,7 +311,7 @@ def test_input_mapper_override(model: str, image_assets: _ImageAssets,
     (4, 781),
     (16, 2653),
 ])
-def test_max_tokens_override(get_max_phi3v_image_tokens: Callable, model: str,
+def test_max_tokens_override(get_max_phi3v_image_tokens, model: str,
                              num_crops: int, expected_max_tokens: int):
     """Ensure get_max_phi3v_image_tokens handles num_crops properly."""
     # NOTE: mm_processor_kwargs on the context in this test is unused, since
@@ -343,8 +343,8 @@ def test_max_tokens_override(get_max_phi3v_image_tokens: Callable, model: str,
     (16, 2653, 1),
     (16, 2653, 2),
 ])
-def test_dummy_data_override(dummy_data_for_phi3v: Callable, model: str,
-                             num_crops: int, toks_per_img: int, num_imgs: int):
+def test_dummy_data_override(dummy_data_for_phi3v, model: str, num_crops: int,
+                             toks_per_img: int, num_imgs: int):
     """Ensure dummy_data_for_phi3v 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
@@ -374,7 +374,7 @@ def test_dummy_data_override(dummy_data_for_phi3v: Callable, model: str,
     (16, 1921, 1),
     (16, 1921, 2),
 ])
-def test_input_processor_override(input_processor_for_phi3v: Callable,
+def test_input_processor_override(input_processor_for_phi3v,
                                   image_assets: _ImageAssets, model: str,
                                   num_crops: int, expected_toks_per_img: int,
                                   num_imgs: int):
@@ -393,16 +393,14 @@ def test_input_processor_override(input_processor_for_phi3v: Callable,
     prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n"
     images = [image_assets[0].pil_image] * num_imgs
 
-    llm_inputs = LLMInputs(prompt_token_ids=tokenizer.encode(prompt),
-                           prompt=prompt,
-                           multi_modal_data={"image": images})
+    inputs = token_inputs(prompt_token_ids=tokenizer.encode(prompt),
+                          prompt=prompt,
+                          multi_modal_data={"image": images})
 
-    proc_llm_inputs = input_processor_for_phi3v(
-        ctx=ctx,
-        llm_inputs=llm_inputs,
-        num_crops=num_crops,
-    )
+    processed_inputs = input_processor_for_phi3v(ctx,
+                                                 inputs,
+                                                 num_crops=num_crops)
 
     # Ensure we have the right number of placeholders per num_crops size
-    img_tok_count = proc_llm_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
+    img_tok_count = processed_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
     assert img_tok_count == expected_toks_per_img * num_imgs

tests/models/decoder_only/vision_language/test_qwen.py

@@ -5,7 +5,7 @@
 import torch
 from PIL.Image import Image
 
-from vllm.inputs import InputContext, LLMInputs
+from vllm.inputs import InputContext, token_inputs
 from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.utils import cached_get_tokenizer, rescale_image_size
 
@@ -71,12 +71,12 @@ def test_input_processor_valid_mm_data(input_processor_for_qwen,
     """Happy cases for image inputs to Qwen's multimodal input processor."""
     prompt = "".join(
         [f"Picture {num}: <img></img>\n" for num in range(1, num_images + 1)])
-    inputs = LLMInputs(
+    inputs = token_inputs(
         prompt=prompt,
         # When processing multimodal data for a multimodal model, the qwen
         # input processor will overwrite the provided prompt_token_ids with
         # the image prompts
-        prompt_token_ids=None,
+        prompt_token_ids=[],
         multi_modal_data={"image": torch.rand(num_images, TOKS_PER_IMG, 4096)},
     )
     proc_inputs = input_processor_for_qwen(qwen_vl_context, inputs)
@@ -134,9 +134,9 @@ def test_input_processor_invalid_mm_data(input_processor_for_qwen,
                                      trust_remote_code=True)
     prompt = "Picture 1: <img></img>\n"
     prompt_token_ids = tokenizer.encode(prompt)
-    inputs = LLMInputs(prompt=prompt,
-                       prompt_token_ids=prompt_token_ids,
-                       multi_modal_data=mm_data)
+    inputs = token_inputs(prompt=prompt,
+                          prompt_token_ids=prompt_token_ids,
+                          multi_modal_data=mm_data)
     # Should fail since we have too many or too few dimensions for embeddings
     with pytest.raises(ValueError):
         input_processor_for_qwen(qwen_vl_context, inputs)

tests/multimodal/test_processor_kwargs.py

@@ -5,7 +5,7 @@
 import pytest
 import torch
 
-from vllm.inputs import InputContext, LLMInputs
+from vllm.inputs import DecoderOnlyInputs, InputContext, token_inputs
 from vllm.inputs.registry import InputRegistry
 from vllm.multimodal import MultiModalRegistry
 from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
@@ -31,7 +31,7 @@ def use_processor_mock():
     """Patches the internal model input processor with an override callable."""
 
     def custom_processor(ctx: InputContext,
-                         llm_inputs: LLMInputs,
+                         inputs: DecoderOnlyInputs,
                          *,
                          num_crops=DEFAULT_NUM_CROPS):
         # For testing purposes, we don't worry about the llm inputs / return
@@ -84,7 +84,7 @@ def test_default_processor_is_a_noop():
     dummy_registry = InputRegistry()
     ctx = build_model_context(DUMMY_MODEL_ID)
     processor = dummy_registry.create_input_processor(ctx.model_config)
-    proc_inputs = LLMInputs(prompt_token_ids=[], prompt="")
+    proc_inputs = token_inputs(prompt_token_ids=[], prompt="")
     proc_outputs = processor(inputs=proc_inputs)
     assert proc_inputs is proc_outputs
 
@@ -125,9 +125,9 @@ def test_input_processor_kwargs(use_processor_mock, init_num_crops,
     ctx = build_model_context(DUMMY_MODEL_ID, mm_processor_kwargs=init_kwargs)
     processor = dummy_registry.create_input_processor(ctx.model_config)
     num_crops_val = processor(
-        LLMInputs(prompt_token_ids=[],
-                  prompt="",
-                  mm_processor_kwargs=inference_kwargs))
+        token_inputs(prompt_token_ids=[],
+                     prompt="",
+                     mm_processor_kwargs=inference_kwargs))
     assert num_crops_val == expected_seq_count
 
 
@@ -154,9 +154,9 @@ def test_processor_with_sad_kwarg_overrides(use_processor_mock,
     processor = dummy_registry.create_input_processor(ctx.model_config)
     # Should filter out the inference time kwargs
     num_crops_val = processor(
-        LLMInputs(prompt_token_ids=[],
-                  prompt="",
-                  mm_processor_kwargs=mm_processor_kwargs))
+        token_inputs(prompt_token_ids=[],
+                     prompt="",
+                     mm_processor_kwargs=mm_processor_kwargs))
     assert num_crops_val == DEFAULT_NUM_CROPS
 
 

vllm/engine/llm_engine.py

@@ -29,8 +29,8 @@
 from vllm.executor.executor_base import ExecutorBase
 from vllm.executor.gpu_executor import GPUExecutor
 from vllm.executor.ray_utils import initialize_ray_cluster
-from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs,
-                         InputRegistry, LLMInputs, PromptType)
+from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs,
+                         EncoderDecoderInputs, InputRegistry, PromptType)
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -635,7 +635,7 @@ def _verify_args(self) -> None:
     def _add_processed_request(
         self,
         request_id: str,
-        processed_inputs: Union[LLMInputs, EncoderDecoderLLMInputs],
+        processed_inputs: Union[DecoderOnlyInputs, EncoderDecoderInputs],
         params: Union[SamplingParams, PoolingParams],
         arrival_time: float,
         lora_request: Optional[LoRARequest],
@@ -1855,8 +1855,8 @@ def is_encoder_decoder_model(self):
     def is_embedding_model(self):
         return self.model_config.is_embedding_model
 
-    def _validate_model_inputs(self, inputs: Union[LLMInputs,
-                                                   EncoderDecoderLLMInputs]):
+    def _validate_model_inputs(self, inputs: Union[DecoderOnlyInputs,
+                                                   EncoderDecoderInputs]):
         if self.model_config.is_multimodal_model:
             # For encoder-decoder multimodal models, the max_prompt_len
             # restricts the decoder prompt length

vllm/inputs/__init__.py

@@ -1,7 +1,8 @@
-from .data import (EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
-                   LLMInputs, PromptType, SingletonPrompt, TextPrompt,
-                   TokensPrompt, build_explicit_enc_dec_prompt,
-                   to_enc_dec_tuple_list, zip_enc_dec_prompts)
+from .data import (DecoderOnlyInputs, EncoderDecoderInputs,
+                   ExplicitEncoderDecoderPrompt, PromptType, SingletonInputs,
+                   SingletonPrompt, TextPrompt, TokenInputs, TokensPrompt,
+                   build_explicit_enc_dec_prompt, to_enc_dec_tuple_list,
+                   token_inputs, zip_enc_dec_prompts)
 from .registry import InputContext, InputRegistry
 
 INPUT_REGISTRY = InputRegistry()
@@ -19,8 +20,11 @@
     "PromptType",
     "SingletonPrompt",
     "ExplicitEncoderDecoderPrompt",
-    "LLMInputs",
-    "EncoderDecoderLLMInputs",
+    "TokenInputs",
+    "token_inputs",
+    "SingletonInputs",
+    "DecoderOnlyInputs",
+    "EncoderDecoderInputs",
     "build_explicit_enc_dec_prompt",
     "to_enc_dec_tuple_list",
     "zip_enc_dec_prompts",
@@ -31,14 +35,31 @@
 
 
 def __getattr__(name: str):
-    if name == "PromptInput":
-        import warnings
+    import warnings
 
+    if name == "PromptInput":
         msg = ("PromptInput has been renamed to PromptType. "
                "The original name will be removed in an upcoming version.")
 
         warnings.warn(DeprecationWarning(msg), stacklevel=2)
 
         return PromptType
 
+    if name == "LLMInputs":
+        msg = ("LLMInputs has been renamed to DecoderOnlyInputs. "
+               "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return DecoderOnlyInputs
+
+    if name == "EncoderDecoderLLMInputs":
+        msg = (
+            "EncoderDecoderLLMInputs has been renamed to EncoderDecoderInputs. "
+            "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return EncoderDecoderInputs
+
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/inputs/data.py

@@ -1,5 +1,5 @@
 from typing import (TYPE_CHECKING, Any, Dict, Generic, Iterable, List,
-                    Optional, Tuple, Union)
+                    Optional, Tuple, Union, cast)
 
 from typing_extensions import NotRequired, TypedDict, TypeVar
 
@@ -51,7 +51,7 @@ class TokensPrompt(TypedDict):
 
 SingletonPrompt = Union[str, TextPrompt, TokensPrompt]
 """
-Set of possible schemas for a single LLM input:
+Set of possible schemas for a single prompt:
 
 - A text prompt (:class:`str` or :class:`TextPrompt`)
 - A tokenized prompt (:class:`TokensPrompt`)
@@ -120,13 +120,8 @@ class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
 """
 
 
-class LLMInputs(TypedDict):
-    """
-    The inputs in :class:`~vllm.LLMEngine` before they are
-    passed to the model executor.
-
-    This specifies the data required for decoder-only models.
-    """
+class TokenInputs(TypedDict):
+    """Represents token-based inputs."""
     prompt_token_ids: List[int]
     """The token IDs of the prompt."""
 
@@ -150,7 +145,40 @@ class LLMInputs(TypedDict):
     """
 
 
-class EncoderDecoderLLMInputs(LLMInputs):
+def token_inputs(
+    prompt_token_ids: List[int],
+    prompt: Optional[str] = None,
+    multi_modal_data: Optional["MultiModalDataDict"] = None,
+    mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+) -> TokenInputs:
+    """Construct :class:`TokenInputs` from optional values."""
+    inputs = TokenInputs(prompt_token_ids=prompt_token_ids)
+
+    if prompt is not None:
+        inputs["prompt"] = prompt
+    if multi_modal_data is not None:
+        inputs["multi_modal_data"] = multi_modal_data
+    if mm_processor_kwargs is not None:
+        inputs["mm_processor_kwargs"] = mm_processor_kwargs
+
+    return inputs
+
+
+SingletonInputs = TokenInputs
+"""
+A processed :class:`SingletonPrompt` which can be passed to
+:class:`vllm.sequence.Sequence`.
+"""
+
+DecoderOnlyInputs = TokenInputs
+"""
+The inputs in :class:`~vllm.LLMEngine` before they are
+passed to the model executor.
+This specifies the data required for decoder-only models.
+"""
+
+
+class EncoderDecoderInputs(TokenInputs):
     """
     The inputs in :class:`~vllm.LLMEngine` before they are
     passed to the model executor.
@@ -204,11 +232,12 @@ def zip_enc_dec_prompts(
     be zipped with the encoder/decoder prompts.
     """
     if mm_processor_kwargs is None:
-        mm_processor_kwargs = {}
-    if isinstance(mm_processor_kwargs, Dict):
+        mm_processor_kwargs = cast(Dict[str, Any], {})
+    if isinstance(mm_processor_kwargs, dict):
         return [
-            build_explicit_enc_dec_prompt(encoder_prompt, decoder_prompt,
-                                          mm_processor_kwargs)
+            build_explicit_enc_dec_prompt(
+                encoder_prompt, decoder_prompt,
+                cast(Dict[str, Any], mm_processor_kwargs))
             for (encoder_prompt,
                  decoder_prompt) in zip(enc_prompts, dec_prompts)
         ]
@@ -229,14 +258,31 @@ def to_enc_dec_tuple_list(
 
 
 def __getattr__(name: str):
-    if name == "PromptInput":
-        import warnings
+    import warnings
 
+    if name == "PromptInput":
         msg = ("PromptInput has been renamed to PromptType. "
                "The original name will be removed in an upcoming version.")
 
         warnings.warn(DeprecationWarning(msg), stacklevel=2)
 
         return PromptType
 
+    if name == "LLMInputs":
+        msg = ("LLMInputs has been renamed to DecoderOnlyInputs. "
+               "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return DecoderOnlyInputs
+
+    if name == "EncoderDecoderLLMInputs":
+        msg = (
+            "EncoderDecoderLLMInputs has been renamed to EncoderDecoderInputs. "
+            "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return EncoderDecoderInputs
+
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")