[Core] rename`PromptInputs` and `inputs` (#8876)

benchmarks/benchmark_latency.py

@@ -11,7 +11,7 @@ from tqdm import tqdm
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import DEVICE_OPTIONS, EngineArgs
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import FlexibleArgumentParser
 
@@ -61,7 +61,7 @@ def main(args: argparse.Namespace):
     dummy_prompt_token_ids = np.random.randint(10000,
                                                size=(args.batch_size,
                                                      args.input_len))
-    dummy_inputs: List[PromptInputs] = [{
+    dummy_prompts: List[PromptType] = [{
         "prompt_token_ids": batch
     } for batch in dummy_prompt_token_ids.tolist()]
 
@@ -74,13 +74,13 @@ def main(args: argparse.Namespace):
                     ],
                     on_trace_ready=torch.profiler.tensorboard_trace_handler(
                         str(profile_dir))) as p:
-                llm.generate(dummy_inputs,
+                llm.generate(dummy_prompts,
                              sampling_params=sampling_params,
                              use_tqdm=False)
             print(p.key_averages())
         else:
             start_time = time.perf_counter()
-            llm.generate(dummy_inputs,
+            llm.generate(dummy_prompts,
                          sampling_params=sampling_params,
                          use_tqdm=False)
             end_time = time.perf_counter()

docs/source/dev/multimodal/multimodal_index.rst

@@ -8,7 +8,7 @@ Multi-Modality
 vLLM provides experimental support for multi-modal models through the :mod:`vllm.multimodal` package.
 
 Multi-modal inputs can be passed alongside text and token prompts to :ref:`supported models <supported_vlms>`
-via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptInputs`.
+via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptType`.
 
 Currently, vLLM only has built-in support for image data. You can extend vLLM to process additional modalities
 by following :ref:`this guide <adding_multimodal_plugin>`.

docs/source/dev/offline_inference/llm_inputs.rst

 LLM Inputs
 ==========
 
-.. autodata:: vllm.inputs.PromptInputs
+.. autodata:: vllm.inputs.PromptType
 
 .. autoclass:: vllm.inputs.TextPrompt
     :show-inheritance:

docs/source/models/vlm.rst

@@ -27,7 +27,7 @@ The :class:`~vllm.LLM` class can be instantiated in much the same way as languag
     We have removed all vision language related CLI args in the ``0.5.1`` release. **This is a breaking change**, so please update your code to follow
     the above snippet. Specifically, ``image_feature_size`` can no longer be specified as we now calculate that internally for each model.
 
-To pass an image to the model, note the following in :class:`vllm.inputs.PromptInputs`:
+To pass an image to the model, note the following in :class:`vllm.inputs.PromptType`:
 
 * ``prompt``: The prompt should follow the format that is documented on HuggingFace.
 * ``multi_modal_data``: This is a dictionary that follows the schema defined in :class:`vllm.multimodal.MultiModalDataDict`. 

tests/async_engine/test_async_llm_engine.py

@@ -86,17 +86,19 @@ class MockAsyncLLMEngine(AsyncLLMEngine):
 
 @pytest.mark.asyncio
 async def test_new_requests_event():
+    params = SamplingParams()
+
     engine = MockAsyncLLMEngine()
     engine.start_background_loop()
     await asyncio.sleep(0.01)
     assert engine.engine.step_calls == 0
 
-    await engine.add_request("1", "", None)
+    await engine.add_request("1", "", params)
     await asyncio.sleep(0.01)
     assert engine.engine.add_request_calls == 1
     assert engine.engine.step_calls == 1
 
-    await engine.add_request("2", "", None)
+    await engine.add_request("2", "", params)
     engine.engine.generate("2")
     await asyncio.sleep(0)
     await asyncio.sleep(0)
@@ -111,7 +113,7 @@ async def test_new_requests_event():
     await asyncio.sleep(0.001)
     assert engine.engine.step_calls == old_step_calls
 
-    await engine.add_request("3", "", None)
+    await engine.add_request("3", "", params)
     await asyncio.sleep(0.01)
     assert engine.engine.add_request_calls == 3
     assert engine.engine.step_calls == old_step_calls + 1

tests/entrypoints/llm/test_encode.py

@@ -49,21 +49,6 @@ def assert_outputs_equal(o1: List[EmbeddingRequestOutput],
     assert [o.outputs for o in o1] == [o.outputs for o in o2]
 
 
-@pytest.mark.skip_global_cleanup
-@pytest.mark.parametrize('prompt', PROMPTS)
-def test_v1_v2_api_consistency_single_prompt_string(llm: LLM, prompt):
-    pooling_params = PoolingParams()
-
-    with pytest.warns(DeprecationWarning, match="'prompts'"):
-        v1_output = llm.encode(prompts=prompt, pooling_params=pooling_params)
-
-    v2_output = llm.encode(prompt, pooling_params=pooling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-    v2_output = llm.encode({"prompt": prompt}, pooling_params=pooling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize('prompt_token_ids', TOKEN_IDS)
 def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
@@ -79,25 +64,6 @@ def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
     assert_outputs_equal(v1_output, v2_output)
 
 
-@pytest.mark.skip_global_cleanup
-def test_v1_v2_api_consistency_multi_prompt_string(llm: LLM):
-    pooling_params = PoolingParams()
-
-    with pytest.warns(DeprecationWarning, match="'prompts'"):
-        v1_output = llm.encode(prompts=PROMPTS, pooling_params=pooling_params)
-
-    v2_output = llm.encode(PROMPTS, pooling_params=pooling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-    v2_output = llm.encode(
-        [{
-            "prompt": p
-        } for p in PROMPTS],
-        pooling_params=pooling_params,
-    )
-    assert_outputs_equal(v1_output, v2_output)
-
-
 @pytest.mark.skip_global_cleanup
 def test_v1_v2_api_consistency_multi_prompt_tokens(llm: LLM):
     pooling_params = PoolingParams()

tests/entrypoints/llm/test_generate.py

@@ -47,23 +47,6 @@ def assert_outputs_equal(o1: List[RequestOutput], o2: List[RequestOutput]):
     assert [o.outputs for o in o1] == [o.outputs for o in o2]
 
 
-@pytest.mark.skip_global_cleanup
-@pytest.mark.parametrize('prompt', PROMPTS)
-def test_v1_v2_api_consistency_single_prompt_string(llm: LLM, prompt):
-    sampling_params = SamplingParams(temperature=0.0, top_p=1.0)
-
-    with pytest.warns(DeprecationWarning, match="'prompts'"):
-        v1_output = llm.generate(prompts=prompt,
-                                 sampling_params=sampling_params)
-
-    v2_output = llm.generate(prompt, sampling_params=sampling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-    v2_output = llm.generate({"prompt": prompt},
-                             sampling_params=sampling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize('prompt_token_ids', TOKEN_IDS)
 def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
@@ -79,26 +62,6 @@ def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
     assert_outputs_equal(v1_output, v2_output)
 
 
-@pytest.mark.skip_global_cleanup
-def test_v1_v2_api_consistency_multi_prompt_string(llm: LLM):
-    sampling_params = SamplingParams(temperature=0.0, top_p=1.0)
-
-    with pytest.warns(DeprecationWarning, match="'prompts'"):
-        v1_output = llm.generate(prompts=PROMPTS,
-                                 sampling_params=sampling_params)
-
-    v2_output = llm.generate(PROMPTS, sampling_params=sampling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-    v2_output = llm.generate(
-        [{
-            "prompt": p
-        } for p in PROMPTS],
-        sampling_params=sampling_params,
-    )
-    assert_outputs_equal(v1_output, v2_output)
-
-
 @pytest.mark.skip_global_cleanup
 def test_v1_v2_api_consistency_multi_prompt_tokens(llm: LLM):
     sampling_params = SamplingParams(temperature=0.0, top_p=1.0)

tests/mq_llm_engine/test_error_handling.py

@@ -61,7 +61,7 @@ async def test_evil_forward(tmp_socket):
 
         # Throws an error in first forward pass.
         with pytest.raises(RAISED_ERROR):
-            async for _ in client.generate(inputs="Hello my name is",
+            async for _ in client.generate(prompt="Hello my name is",
                                            sampling_params=SamplingParams(),
                                            request_id=uuid.uuid4()):
                 pass
@@ -69,7 +69,7 @@ async def test_evil_forward(tmp_socket):
 
         # Engine is errored, should get ENGINE_DEAD_ERROR.
         with pytest.raises(MQEngineDeadError):
-            async for _ in client.generate(inputs="Hello my name is",
+            async for _ in client.generate(prompt="Hello my name is",
                                            sampling_params=SamplingParams(),
                                            request_id=uuid.uuid4()):
                 pass
@@ -118,7 +118,7 @@ async def test_failed_health_check(tmp_socket):
 
         # Generate call should throw ENGINE_DEAD_ERROR
         with pytest.raises(MQEngineDeadError):
-            async for _ in client.generate(inputs="Hello my name is",
+            async for _ in client.generate(prompt="Hello my name is",
                                            sampling_params=SamplingParams(),
                                            request_id=uuid.uuid4()):
                 pass
@@ -160,7 +160,7 @@ async def test_failed_abort(tmp_socket):
         # with reference to the original KeyError("foo")
         with pytest.raises(MQEngineDeadError) as execinfo:
             async for _ in client.generate(
-                    inputs="Hello my name is",
+                    prompt="Hello my name is",
                     sampling_params=SamplingParams(max_tokens=10),
                     request_id=uuid.uuid4()):
                 pass
@@ -183,7 +183,7 @@ async def test_bad_request(tmp_socket):
 
         # Invalid request should fail, but not crash the server.
         with pytest.raises(ValueError):
-            async for _ in client.generate(inputs="Hello my name is",
+            async for _ in client.generate(prompt="Hello my name is",
                                            sampling_params=SamplingParams(),
                                            request_id="abcd-1",
                                            lora_request=LoRARequest(
@@ -192,7 +192,7 @@ async def test_bad_request(tmp_socket):
                 pass
 
         # This request should be okay.
-        async for _ in client.generate(inputs="Hello my name is",
+        async for _ in client.generate(prompt="Hello my name is",
                                        sampling_params=SamplingParams(),
                                        request_id="abcd-2"):
             pass

tests/mq_llm_engine/utils.py

@@ -20,7 +20,7 @@ async def generate(
     count = 0
     async for out in client.generate(
             request_id=request_id,
-            inputs="Hello my name is Robert and",
+            prompt="Hello my name is Robert and",
             sampling_params=SamplingParams(max_tokens=num_tokens,
                                            temperature=0)):
 

vllm/__init__.py

@@ -5,7 +5,7 @@ from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.engine.llm_engine import LLMEngine
 from vllm.entrypoints.llm import LLM
 from vllm.executor.ray_utils import initialize_ray_cluster
-from vllm.inputs import PromptInputs, TextPrompt, TokensPrompt
+from vllm.inputs import PromptType, TextPrompt, TokensPrompt
 from vllm.model_executor.models import ModelRegistry
 from vllm.outputs import (CompletionOutput, EmbeddingOutput,
                           EmbeddingRequestOutput, RequestOutput)
@@ -19,7 +19,7 @@ __all__ = [
     "__version_tuple__",
     "LLM",
     "ModelRegistry",
-    "PromptInputs",
+    "PromptType",
     "TextPrompt",
     "TokensPrompt",
     "SamplingParams",

vllm/engine/async_llm_engine.py

@@ -2,8 +2,8 @@ import asyncio
 import time
 import weakref
 from functools import partial
-from typing import (Any, AsyncGenerator, Callable, Dict, Iterable, List,
-                    Mapping, Optional, Set, Tuple, Type, Union)
+from typing import (Any, AsyncGenerator, Callable, Coroutine, Dict, Iterable,
+                    List, Mapping, Optional, Set, Tuple, Type, Union, overload)
 from weakref import ReferenceType
 
 import vllm.envs as envs
@@ -17,7 +17,7 @@ from vllm.engine.metrics_types import StatLoggerBase
 from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.gpu_executor import GPUExecutorAsync
 from vllm.executor.ray_utils import initialize_ray_cluster
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
@@ -28,7 +28,7 @@ from vllm.sampling_params import SamplingParams
 from vllm.sequence import ExecuteModelRequest
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import weak_bind
+from vllm.utils import deprecate_kwargs, weak_bind
 
 logger = init_logger(__name__)
 ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
@@ -402,17 +402,54 @@ class _AsyncLLMEngine(LLMEngine):
         """Stop the remote worker execution loop."""
         await self.model_executor.stop_remote_worker_execution_loop_async()
 
+    @overload  # DEPRECATED
     async def add_request_async(
         self,
         request_id: str,
-        inputs: PromptInputs,
+        *,
+        inputs: PromptType,
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> None:
+        ...
+
+    @overload
+    async def add_request_async(
+        self,
+        request_id: str,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        arrival_time: Optional[float] = None,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> None:
+        ...
+
+    @deprecate_kwargs(
+        "inputs",
+        additional_message="Please use the 'prompt' parameter instead.",
+    )
+    async def add_request_async(
+            self,
+            request_id: str,
+            prompt: Optional[PromptType] = None,
+            params: Optional[Union[SamplingParams, PoolingParams]] = None,
+            arrival_time: Optional[float] = None,
+            lora_request: Optional[LoRARequest] = None,
+            trace_headers: Optional[Mapping[str, str]] = None,
+            prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+            *,
+            inputs: Optional[PromptType] = None,  # DEPRECATED
     ) -> None:
         """Async version of :meth:`add_request`."""
+        if inputs is not None:
+            prompt = inputs
+        assert prompt is not None and params is not None
+
         if lora_request is not None and not self.lora_config:
             raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                              "not enabled!")
@@ -420,7 +457,7 @@ class _AsyncLLMEngine(LLMEngine):
             arrival_time = time.time()
 
         preprocessed_inputs = await self.input_preprocessor.preprocess_async(
-            inputs,
+            prompt,
             request_id=request_id,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,
@@ -774,16 +811,55 @@ class AsyncLLMEngine:
 
     # This method does not need to be async, but kept that way
     # for backwards compatibility.
-    async def add_request(
+    @overload  # DEPRECATED
+    def add_request(
         self,
         request_id: str,
-        inputs: PromptInputs,
+        *,
+        inputs: PromptType,
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
-        prompt_adapter_request: Optional[PromptAdapterRequest] = None
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> Coroutine[None, None, AsyncGenerator[Union[
+            RequestOutput, EmbeddingRequestOutput], None]]:
+        ...
+
+    @overload
+    def add_request(
+        self,
+        request_id: str,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        arrival_time: Optional[float] = None,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> Coroutine[None, None, AsyncGenerator[Union[
+            RequestOutput, EmbeddingRequestOutput], None]]:
+        ...
+
+    @deprecate_kwargs(
+        "inputs",
+        additional_message="Please use the 'prompt' parameter instead.",
+    )
+    async def add_request(
+        self,
+        request_id: str,
+        prompt: Optional[PromptType] = None,
+        params: Optional[Union[SamplingParams, PoolingParams]] = None,
+        arrival_time: Optional[float] = None,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        *,
+        inputs: Optional[PromptType] = None,  # DEPRECATED
     ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]:
+        if inputs is not None:
+            prompt = inputs
+        assert prompt is not None and params is not None
+
         if not self.is_running:
             if self.start_engine_loop:
                 self.start_background_loop()
@@ -797,7 +873,7 @@ class AsyncLLMEngine:
         stream = self._request_tracker.add_request(
             request_id,
             verbose=self.log_requests,
-            inputs=inputs,
+            prompt=prompt,
             params=params,
             arrival_time=arrival_time or time.time(),
             lora_request=lora_request,
@@ -808,7 +884,7 @@ class AsyncLLMEngine:
 
     async def generate(
         self,
-        inputs: PromptInputs,
+        prompt: PromptType,
         sampling_params: SamplingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
@@ -822,8 +898,7 @@ class AsyncLLMEngine:
         from the LLMEngine to the caller.
 
         Args:
-            inputs: The inputs to the LLM. See
-                :class:`~vllm.inputs.PromptInputs`
+            prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
                 for more details about the format of each input.
             sampling_params: The sampling parameters of the request.
             request_id: The unique id of the request.
@@ -881,7 +956,7 @@ class AsyncLLMEngine:
         """
         async for output in await self.add_request(
                 request_id,
-                inputs,
+                prompt,
                 sampling_params,
                 lora_request=lora_request,
                 trace_headers=trace_headers,
@@ -891,7 +966,7 @@ class AsyncLLMEngine:
 
     async def encode(
         self,
-        inputs: PromptInputs,
+        prompt: PromptType,
         pooling_params: PoolingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
@@ -904,8 +979,7 @@ class AsyncLLMEngine:
         from the LLMEngine to the caller.
 
         Args:
-            inputs: The inputs to the LLM. See
-                :class:`~vllm.inputs.PromptInputs`
+            prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
                 for more details about the format of each input.
             pooling_params: The pooling parameters of the request.
             request_id: The unique id of the request.
@@ -959,7 +1033,7 @@ class AsyncLLMEngine:
         """
         async for output in await self.add_request(
                 request_id,
-                inputs,
+                prompt,
                 pooling_params,
                 lora_request=lora_request,
                 trace_headers=trace_headers,

vllm/engine/llm_engine.py

@@ -6,7 +6,7 @@ from functools import partial
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict,
                     Iterable, List, Mapping, NamedTuple, Optional)
 from typing import Sequence as GenericSequence
-from typing import Set, Type, Union
+from typing import Set, Type, Union, overload
 
 import torch
 from typing_extensions import TypeVar
@@ -29,7 +29,7 @@ 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, PromptInputs)
+                         InputRegistry, LLMInputs, PromptType)
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -51,7 +51,7 @@ from vllm.transformers_utils.tokenizer_group import (
     BaseTokenizerGroup, init_tokenizer_from_configs)
 from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                   usage_message)
-from vllm.utils import Counter, Device, weak_bind
+from vllm.utils import Counter, Device, deprecate_kwargs, weak_bind
 from vllm.version import __version__ as VLLM_VERSION
 
 logger = init_logger(__name__)
@@ -689,16 +689,51 @@ class LLMEngine:
     def stop_remote_worker_execution_loop(self) -> None:
         self.model_executor.stop_remote_worker_execution_loop()
 
+    @overload  # DEPRECATED
     def add_request(
         self,
         request_id: str,
-        inputs: PromptInputs,
+        *,
+        inputs: PromptType,
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
+    ) -> None:
+        ...
+
+    @overload
+    def add_request(
+        self,
+        request_id: str,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        arrival_time: Optional[float] = None,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        priority: int = 0,
+    ) -> None:
+        ...
+
+    @deprecate_kwargs(
+        "inputs",
+        additional_message="Please use the 'prompt' parameter instead.",
+    )
+    def add_request(
+            self,
+            request_id: str,
+            prompt: Optional[PromptType] = None,
+            params: Optional[Union[SamplingParams, PoolingParams]] = None,
+            arrival_time: Optional[float] = None,
+            lora_request: Optional[LoRARequest] = None,
+            trace_headers: Optional[Mapping[str, str]] = None,
+            prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+            priority: int = 0,
+            *,
+            inputs: Optional[PromptType] = None,  # DEPRECATED
     ) -> None:
         """Add a request to the engine's request pool.
 
@@ -708,8 +743,7 @@ class LLMEngine:
 
         Args:
             request_id: The unique ID of the request.
-            inputs: The inputs to the LLM. See
-                :class:`~vllm.inputs.PromptInputs`
+            prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
                 for more details about the format of each input.
             params: Parameters for sampling or pooling.
                 :class:`~vllm.SamplingParams` for text generation.
@@ -744,6 +778,10 @@ class LLMEngine:
             >>> # continue the request processing
             >>> ...
         """
+        if inputs is not None:
+            prompt = inputs
+        assert prompt is not None and params is not None
+
         if lora_request is not None and not self.lora_config:
             raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                              "not enabled!")
@@ -756,7 +794,7 @@ class LLMEngine:
             arrival_time = time.time()
 
         preprocessed_inputs = self.input_preprocessor.preprocess(
-            inputs,
+            prompt,
             request_id=request_id,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,

vllm/engine/multiprocessing/__init__.py

 from dataclasses import dataclass
 from enum import Enum
-from typing import List, Mapping, Optional, Union
+from typing import List, Mapping, Optional, Union, overload
 
 from vllm import PoolingParams
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
+from vllm.utils import deprecate_kwargs
 
 VLLM_RPC_SUCCESS_STR = "SUCCESS"
 
@@ -23,13 +24,67 @@ class MQEngineDeadError(RuntimeError):
 
 @dataclass
 class RPCProcessRequest:
-    inputs: PromptInputs
+    prompt: PromptType
     params: Union[SamplingParams, PoolingParams]
     request_id: str
     lora_request: Optional[LoRARequest] = None
     trace_headers: Optional[Mapping[str, str]] = None
     prompt_adapter_request: Optional[PromptAdapterRequest] = None
 
+    @overload  # DEPRECATED
+    def __init__(
+        self,
+        *,
+        inputs: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        request_id: str,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> None:
+        ...
+
+    @overload
+    def __init__(
+        self,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        request_id: str,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> None:
+        ...
+
+    @deprecate_kwargs(
+        "inputs",
+        additional_message="Please use the 'prompt' parameter instead.",
+    )
+    def __init__(
+            self,
+            prompt: Optional[PromptType] = None,
+            params: Optional[Union[SamplingParams, PoolingParams]] = None,
+            request_id: Optional[str] = None,
+            lora_request: Optional[LoRARequest] = None,
+            trace_headers: Optional[Mapping[str, str]] = None,
+            prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+            *,
+            inputs: Optional[PromptType] = None,  # DEPRECATED
+    ) -> None:
+        if inputs is not None:
+            prompt = inputs
+        assert (prompt is not None and params is not None
+                and request_id is not None)
+
+        super().__init__()
+
+        self.prompt = prompt
+        self.params = params
+        self.request_id = request_id
+        self.lora_request = lora_request
+        self.trace_headers = trace_headers
+        self.prompt_adapter_request = prompt_adapter_request
+
 
 @dataclass
 class RPCError:

vllm/engine/multiprocessing/client.py

@@ -3,7 +3,7 @@ import copy
 import pickle
 from contextlib import contextmanager, suppress
 from typing import (Any, AsyncGenerator, Dict, Iterator, Mapping, Optional,
-                    Union)
+                    Union, overload)
 
 import cloudpickle
 import zmq
@@ -25,13 +25,14 @@ from vllm.engine.multiprocessing import (ENGINE_DEAD_ERROR, IPC_DATA_EXT,
                                          RPCUProfileRequest)
 # yapf: enable
 from vllm.envs import VLLM_RPC_TIMEOUT
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import EmbeddingRequestOutput, RequestOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
+from vllm.utils import deprecate_kwargs
 
 logger = init_logger(__name__)
 
@@ -367,14 +368,45 @@ class MQLLMEngineClient:
     def dead_error(self) -> BaseException:
         return ENGINE_DEAD_ERROR(self._errored_with)
 
+    @overload  # DEPRECATED
     def generate(
         self,
-        inputs: PromptInputs,
+        *,
+        inputs: PromptType,
         sampling_params: SamplingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
-        prompt_adapter_request: Optional[PromptAdapterRequest] = None
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> AsyncGenerator[RequestOutput, None]:
+        ...
+
+    @overload
+    def generate(
+        self,
+        prompt: PromptType,
+        sampling_params: SamplingParams,
+        request_id: str,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> AsyncGenerator[RequestOutput, None]:
+        ...
+
+    @deprecate_kwargs(
+        "inputs",
+        additional_message="Please use the 'prompt' parameter instead.",
+    )
+    def generate(
+        self,
+        prompt: Optional[PromptType] = None,
+        sampling_params: Optional[SamplingParams] = None,
+        request_id: Optional[str] = None,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        *,
+        inputs: Optional[PromptType] = None  # DEPRECATED
     ) -> AsyncGenerator[RequestOutput, None]:
         """Generate outputs for a request.
 
@@ -383,8 +415,7 @@ class MQLLMEngineClient:
         from the LLMEngine to the caller.
 
         Args:
-            inputs: The inputs to the LLM. See
-                :class:`~vllm.inputs.PromptInputs`
+            prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
                 for more details about the format of each input.
             sampling_params: The sampling parameters of the request.
             request_id: The unique id of the request.
@@ -393,17 +424,51 @@ class MQLLMEngineClient:
             prompt_adapter_request: Prompt Adapter request to use
                                             for generation, if any.
         """
-        return self._process_request(inputs, sampling_params, request_id,
+        if inputs is not None:
+            prompt = inputs
+        assert (prompt is not None and sampling_params is not None
+                and request_id is not None)
+
+        return self._process_request(prompt, sampling_params, request_id,
                                      lora_request, trace_headers,
                                      prompt_adapter_request)
 
+    @overload  # DEPRECATED
     def encode(
         self,
-        inputs: PromptInputs,
+        *,
+        inputs: PromptType,
         pooling_params: PoolingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
+    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+        ...
+
+    @overload
+    def encode(
+        self,
+        prompt: PromptType,
+        pooling_params: PoolingParams,
+        request_id: str,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+        ...
+
+    @deprecate_kwargs(
+        "inputs",
+        additional_message="Please use the 'prompt' parameter instead.",
+    )
+    def encode(
+        self,
+        prompt: Optional[PromptType] = None,
+        pooling_params: Optional[PoolingParams] = None,
+        request_id: Optional[str] = None,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        *,
+        inputs: Optional[PromptType] = None  # DEPRECATED
     ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
         """Generate outputs for a request from an embedding model.
 
@@ -412,8 +477,7 @@ class MQLLMEngineClient:
         from the LLMEngine to the caller.
 
         Args:
-            inputs: The inputs to the LLM. See
-                :class:`~vllm.inputs.PromptInputs`
+            prompt: The prompt to the LLM. See :class:`~vllm.inputs.PromptType`
                 for more details about the format of each input.
             pooling_params: The pooling parameters of the request.
             request_id: The unique id of the request.
@@ -424,12 +488,17 @@ class MQLLMEngineClient:
             The output `EmbeddingRequestOutput` objects from the LLMEngine
             for the request.
         """
-        return self._process_request(inputs, pooling_params, request_id,
+        if inputs is not None:
+            prompt = inputs
+        assert (prompt is not None and pooling_params is not None
+                and request_id is not None)
+
+        return self._process_request(prompt, pooling_params, request_id,
                                      lora_request, trace_headers)
 
     async def _process_request(
         self,
-        inputs: PromptInputs,
+        prompt: PromptType,
         params: Union[SamplingParams, PoolingParams],
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
@@ -462,7 +531,7 @@ class MQLLMEngineClient:
 
             request_bytes = pickle.dumps(
                 RPCProcessRequest(
-                    inputs=inputs,
+                    prompt=prompt,
                     params=params,
                     request_id=request_id,
                     lora_request=lora_request,

vllm/engine/multiprocessing/engine.py

@@ -278,7 +278,7 @@ class MQLLMEngine:
         try:
             self.engine.add_request(
                 request_id=request_id,
-                inputs=request.inputs,
+                prompt=request.prompt,
                 params=request.params,
                 lora_request=request.lora_request,
                 trace_headers=request.trace_headers,

vllm/engine/protocol.py

@@ -3,7 +3,7 @@ from typing import (AsyncGenerator, List, Mapping, Optional, Protocol,
 
 from vllm.config import DecodingConfig, ModelConfig
 from vllm.core.scheduler import SchedulerOutputs
-from vllm.inputs.data import PromptInputs
+from vllm.inputs.data import PromptType
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.outputs import EmbeddingRequestOutput, RequestOutput
@@ -35,19 +35,19 @@ class EngineClient(Protocol):
 
     def generate(
         self,
-        inputs: PromptInputs,
+        prompt: PromptType,
         sampling_params: SamplingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None
     ) -> AsyncGenerator[RequestOutput, None]:
-        """Generates outputs for a request"""
+        """Generate outputs for a request."""
         ...
 
     def encode(
         self,
-        inputs: PromptInputs,
+        prompt: PromptType,
         pooling_params: PoolingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,

vllm/entrypoints/llm.py

@@ -12,7 +12,7 @@ from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
                                          apply_hf_chat_template,
                                          apply_mistral_chat_template,
                                          parse_chat_messages)
-from vllm.inputs import PromptInputs, TextPrompt, TokensPrompt
+from vllm.inputs import PromptType, TextPrompt, TokensPrompt
 from vllm.inputs.parse import parse_and_batch_prompt
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -293,8 +293,8 @@ class LLM:
     @overload
     def generate(
         self,
-        inputs: Union[PromptInputs, Sequence[PromptInputs]],
-        /,  # We may enable `inputs` keyword after removing the old API
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
         *,
         sampling_params: Optional[Union[SamplingParams,
                                         Sequence[SamplingParams]]] = None,
@@ -304,14 +304,13 @@ class LLM:
         ...
 
     @deprecate_kwargs(
-        "prompts",
         "prompt_token_ids",
         is_deprecated=lambda: LLM.DEPRECATE_LEGACY,
-        additional_message="Please use the 'inputs' parameter instead.",
+        additional_message="Please use the 'prompts' parameter instead.",
     )
     def generate(
         self,
-        prompts: Union[Union[PromptInputs, Sequence[PromptInputs]],
+        prompts: Union[Union[PromptType, Sequence[PromptType]],
                        Optional[Union[str, List[str]]]] = None,
         sampling_params: Optional[Union[SamplingParams,
                                         Sequence[SamplingParams]]] = None,
@@ -330,7 +329,9 @@ class LLM:
         into a single list and pass it to this method.
 
         Args:
-            inputs: A list of inputs to generate completions for.
+            prompts: The prompts to the LLM. You may pass a sequence of prompts
+                for batch inference. See :class:`~vllm.inputs.PromptType`
+                for more details about the format of each prompts.
             sampling_params: The sampling parameters for text generation. If
                 None, we use the default sampling parameters.
                 When it is a single value, it is applied to every prompt.
@@ -358,12 +359,13 @@ class LLM:
                 "models (XForCausalLM, XForConditionalGeneration).")
 
         if prompt_token_ids is not None:
-            inputs = self._convert_v1_inputs(
+            parsed_prompts = self._convert_v1_inputs(
                 prompts=cast(Optional[Union[str, List[str]]], prompts),
                 prompt_token_ids=prompt_token_ids,
             )
         else:
-            inputs = cast(Union[PromptInputs, Sequence[PromptInputs]], prompts)
+            parsed_prompts = cast(Union[PromptType, Sequence[PromptType]],
+                                  prompts)
 
         if isinstance(guided_options_request, dict):
             if len(guided_options_request) > 1:
@@ -378,7 +380,7 @@ class LLM:
             sampling_params = SamplingParams()
 
         self._validate_and_add_requests(
-            inputs=inputs,
+            prompts=parsed_prompts,
             params=sampling_params,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,
@@ -648,8 +650,8 @@ class LLM:
     @overload
     def encode(
         self,
-        inputs: Union[PromptInputs, Sequence[PromptInputs]],
-        /,  # We may enable `inputs` keyword after removing the old API
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
         *,
         pooling_params: Optional[Union[PoolingParams,
                                        Sequence[PoolingParams]]] = None,
@@ -659,14 +661,13 @@ class LLM:
         ...
 
     @deprecate_kwargs(
-        "prompts",
         "prompt_token_ids",
         is_deprecated=lambda: LLM.DEPRECATE_LEGACY,
-        additional_message="Please use the 'inputs' parameter instead.",
+        additional_message="Please use the 'prompts' parameter instead.",
     )
     def encode(
         self,
-        prompts: Union[Union[PromptInputs, Sequence[PromptInputs]],
+        prompts: Union[Union[PromptType, Sequence[PromptType]],
                        Optional[Union[str, List[str]]]] = None,
         pooling_params: Optional[Union[PoolingParams,
                                        Sequence[PoolingParams]]] = None,
@@ -682,9 +683,9 @@ class LLM:
         into a single list and pass it to this method.
 
         Args:
-            inputs: The inputs to the LLM. You may pass a sequence of inputs for
-                batch inference. See :class:`~vllm.inputs.PromptInputs`
-                for more details about the format of each input.
+            prompts: The prompts to the LLM. You may pass a sequence of prompts
+                for batch inference. See :class:`~vllm.inputs.PromptType`
+                for more details about the format of each prompts.
             pooling_params: The pooling parameters for pooling. If None, we
                 use the default pooling parameters.
             use_tqdm: Whether to use tqdm to display the progress bar.
@@ -707,19 +708,20 @@ class LLM:
             )
 
         if prompt_token_ids is not None:
-            inputs = self._convert_v1_inputs(
+            parsed_prompts = self._convert_v1_inputs(
                 prompts=cast(Optional[Union[str, List[str]]], prompts),
                 prompt_token_ids=prompt_token_ids,
             )
         else:
-            inputs = cast(Union[PromptInputs, Sequence[PromptInputs]], prompts)
+            parsed_prompts = cast(Union[PromptType, Sequence[PromptType]],
+                                  prompts)
 
         if pooling_params is None:
             # Use default pooling params.
             pooling_params = PoolingParams()
 
         self._validate_and_add_requests(
-            inputs=inputs,
+            prompts=parsed_prompts,
             params=pooling_params,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,
@@ -763,9 +765,9 @@ class LLM:
             raise ValueError("Either prompts or prompt_token_ids must be "
                              "provided.")
 
-        inputs: List[PromptInputs] = []
+        parsed_prompts: List[PromptType] = []
         for i in range(num_requests):
-            item: PromptInputs
+            item: PromptType
 
             if prompts is not None:
                 item = TextPrompt(prompt=prompts[i])
@@ -774,13 +776,13 @@ class LLM:
             else:
                 raise AssertionError
 
-            inputs.append(item)
+            parsed_prompts.append(item)
 
-        return inputs
+        return parsed_prompts
 
     def _validate_and_add_requests(
         self,
-        inputs: Union[PromptInputs, Sequence[PromptInputs]],
+        prompts: Union[PromptType, Sequence[PromptType]],
         params: Union[SamplingParams, Sequence[SamplingParams], PoolingParams,
                       Sequence[PoolingParams]],
         lora_request: Optional[Union[Sequence[LoRARequest], LoRARequest]],
@@ -788,11 +790,11 @@ class LLM:
         guided_options: Optional[GuidedDecodingRequest] = None,
         priority: Optional[List[int]] = None,
     ) -> None:
-        if isinstance(inputs, (str, dict)):
+        if isinstance(prompts, (str, dict)):
             # Convert a single prompt to a list.
-            inputs = [inputs]
+            prompts = [prompts]
 
-        num_requests = len(inputs)
+        num_requests = len(prompts)
         if isinstance(params, list) and len(params) != num_requests:
             raise ValueError("The lengths of prompts and params "
                              "must be the same.")
@@ -809,9 +811,9 @@ class LLM:
                 sp.output_kind = RequestOutputKind.FINAL_ONLY
 
         # Add requests to the engine.
-        for i, request_inputs in enumerate(inputs):
+        for i, prompt in enumerate(prompts):
             self._add_request(
-                request_inputs,
+                prompt,
                 params[i] if isinstance(params, Sequence) else params,
                 lora_request=lora_request[i] if isinstance(
                     lora_request, Sequence) else lora_request,
@@ -821,7 +823,7 @@ class LLM:
 
     def _add_request(
         self,
-        inputs: PromptInputs,
+        prompt: PromptType,
         params: Union[SamplingParams, PoolingParams],
         lora_request: Optional[LoRARequest] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
@@ -830,7 +832,7 @@ class LLM:
         request_id = str(next(self.request_counter))
         self.llm_engine.add_request(
             request_id,
-            inputs,
+            prompt,
             params,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,

vllm/inputs/__init__.py

 from .data import (EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
-                   LLMInputs, PromptInputs, SingletonPromptInputs, TextPrompt,
+                   LLMInputs, PromptType, SingletonPrompt, TextPrompt,
                    TokensPrompt, build_explicit_enc_dec_prompt,
                    to_enc_dec_tuple_list, zip_enc_dec_prompts)
 from .registry import InputContext, InputRegistry
@@ -16,8 +16,8 @@ See also:
 __all__ = [
     "TextPrompt",
     "TokensPrompt",
-    "PromptInputs",
-    "SingletonPromptInputs",
+    "PromptType",
+    "SingletonPrompt",
     "ExplicitEncoderDecoderPrompt",
     "LLMInputs",
     "EncoderDecoderLLMInputs",
@@ -28,3 +28,17 @@ __all__ = [
     "InputContext",
     "InputRegistry",
 ]
+
+
+def __getattr__(name: str):
+    if name == "PromptInput":
+        import warnings
+
+        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
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/inputs/data.py

@@ -33,7 +33,7 @@ class TokensPrompt(TypedDict):
     """
 
 
-SingletonPromptInputs = Union[str, TextPrompt, TokensPrompt]
+SingletonPrompt = Union[str, TextPrompt, TokensPrompt]
 """
 Set of possible schemas for a single LLM input:
 
@@ -46,7 +46,7 @@ which may be utilized for encoder/decoder models when
 the user desires to express both the encoder & decoder
 prompts explicitly, i.e. :class:`ExplicitEncoderDecoderPrompt`
 
-A prompt of type :class:`SingletonPromptInputs` may be employed
+A prompt of type :class:`SingletonPrompt` may be employed
 as (1) input to a decoder-only model, (2) input to
 the encoder of an encoder/decoder model, in the scenario
 where the decoder-prompt is not specified explicitly, or
@@ -55,33 +55,32 @@ more than one prompt, i.e. :class:`ExplicitEncoderDecoderPrompt`
 """
 
 _T1_co = TypeVar("_T1_co",
-                 bound=SingletonPromptInputs,
-                 default=SingletonPromptInputs,
+                 bound=SingletonPrompt,
+                 default=SingletonPrompt,
                  covariant=True)
 _T2_co = TypeVar("_T2_co",
-                 bound=SingletonPromptInputs,
-                 default=SingletonPromptInputs,
+                 bound=SingletonPrompt,
+                 default=SingletonPrompt,
                  covariant=True)
 
 
 # TODO: Make fields ReadOnly once mypy supports it
 class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
-    """Represents an encoder/decoder model input prompt,
-    comprising an explicit encoder prompt and a 
-    decoder prompt.
+    """
+    Represents an encoder/decoder model input prompt,
+    comprising an explicit encoder prompt and a decoder prompt.
 
-    The encoder and decoder prompts, respectively,
-    may formatted according to any of the
-    :class:`SingletonPromptInputs` schemas, and are not
-    required to have the same schema.
+    The encoder and decoder prompts, respectively, may be formatted
+    according to any of the :class:`SingletonPrompt` schemas,
+    and are not required to have the same schema.
 
     Only the encoder prompt may have multi-modal data.
 
     Note that an :class:`ExplicitEncoderDecoderPrompt` may not
     be used as an input to a decoder-only model,
-    and that the `encoder_prompt` and `decoder_prompt`
+    and that the :code:`encoder_prompt` and :code:`decoder_prompt`
     fields of this data structure themselves must be
-    :class:`SingletonPromptInputs` instances.
+    :class:`SingletonPrompt` instances.
     """
 
     encoder_prompt: _T1_co
@@ -89,7 +88,7 @@ class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
     decoder_prompt: Optional[_T2_co]
 
 
-PromptInputs = Union[SingletonPromptInputs, ExplicitEncoderDecoderPrompt]
+PromptType = Union[SingletonPrompt, ExplicitEncoderDecoderPrompt]
 """
 Set of possible schemas for an LLM input, including
 both decoder-only and encoder/decoder input types:
@@ -146,12 +145,8 @@ class EncoderDecoderLLMInputs(LLMInputs):
     """
 
 
-_T1 = TypeVar("_T1",
-              bound=SingletonPromptInputs,
-              default=SingletonPromptInputs)
-_T2 = TypeVar("_T2",
-              bound=SingletonPromptInputs,
-              default=SingletonPromptInputs)
+_T1 = TypeVar("_T1", bound=SingletonPrompt, default=SingletonPrompt)
+_T2 = TypeVar("_T2", bound=SingletonPrompt, default=SingletonPrompt)
 
 
 def build_explicit_enc_dec_prompt(
@@ -182,3 +177,17 @@ def to_enc_dec_tuple_list(
     return [(enc_dec_prompt["encoder_prompt"],
              enc_dec_prompt["decoder_prompt"])
             for enc_dec_prompt in enc_dec_prompts]
+
+
+def __getattr__(name: str):
+    if name == "PromptInput":
+        import warnings
+
+        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
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/inputs/parse.py

@@ -5,7 +5,7 @@ from typing_extensions import TypeIs
 from vllm.utils import is_list_of
 
 from .data import (EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
-                   LLMInputs, PromptInputs, SingletonPromptInputs, TextPrompt,
+                   LLMInputs, PromptType, SingletonPrompt, TextPrompt,
                    TokensPrompt)
 
 
@@ -81,23 +81,23 @@ class ParsedTokensPrompt(TypedDict):
 
 
 def parse_singleton_prompt(
-    inputs: SingletonPromptInputs,
+    prompt: SingletonPrompt,
 ) -> Union[ParsedStrPrompt, ParsedTextPrompt, ParsedTokensPrompt]:
-    if isinstance(inputs, str):
-        return ParsedStrPrompt(type="str", content=inputs)
-    elif isinstance(inputs, dict):
-        if "prompt_token_ids" in inputs:
+    if isinstance(prompt, str):
+        return ParsedStrPrompt(type="str", content=prompt)
+    elif isinstance(prompt, dict):
+        if "prompt_token_ids" in prompt:
             return ParsedTokensPrompt(type="tokens",
-                                      content=inputs)  # type: ignore
-        elif "prompt" in inputs:
-            return ParsedTextPrompt(type="text", content=inputs)
+                                      content=prompt)  # type: ignore
+        elif "prompt" in prompt:
+            return ParsedTextPrompt(type="text", content=prompt)
 
     raise TypeError("inputs must be a string, TextPrompt, or TokensPrompt")
 
 
 def is_explicit_encoder_decoder_prompt(
-        inputs: PromptInputs) -> TypeIs[ExplicitEncoderDecoderPrompt]:
-    return isinstance(inputs, dict) and "encoder_prompt" in inputs
+        prompt: PromptType) -> TypeIs[ExplicitEncoderDecoderPrompt]:
+    return isinstance(prompt, dict) and "encoder_prompt" in prompt
 
 
 def is_valid_encoder_decoder_llm_inputs(