[Core] Consolidate prompt arguments to LLM engines

.buildkite/test-pipeline.yaml

@@ -62,9 +62,9 @@ steps:
 - label: Entrypoints Test
   #mirror_hardwares: [amd]
   commands:
-  # these tests have to be separated, because each one will allocate all posible GPU memory
-  - pytest -v -s entrypoints --ignore=entrypoints/test_server_oot_registration.py
-  - pytest -v -s entrypoints/test_server_oot_registration.py
+  - pytest -v -s test_inputs.py
+  - pytest -v -s entrypoints -m llm
+  - pytest -v -s entrypoints -m openai
 
 - label: Examples Test
   working_dir: "/vllm-workspace/examples"
@@ -109,6 +109,9 @@ steps:
   mirror_hardwares: [amd]
   command: pytest -v -s test_logits_processor.py
 
+- label: Utils Test
+  command: pytest -v -s test_utils.py
+
 - label: Worker Test
   mirror_hardwares: [amd]
   command: pytest -v -s worker

benchmarks/benchmark_latency.py

@@ -3,13 +3,14 @@
 import json
 import time
 from pathlib import Path
-from typing import Optional
+from typing import List, Optional
 
 import numpy as np
 import torch
 from tqdm import tqdm
 
 from vllm import LLM, SamplingParams
+from vllm.inputs import PromptStrictInputs
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 
 
@@ -48,7 +49,9 @@ def main(args: argparse.Namespace):
     dummy_prompt_token_ids = np.random.randint(10000,
                                                size=(args.batch_size,
                                                      args.input_len))
-    dummy_prompt_token_ids = dummy_prompt_token_ids.tolist()
+    dummy_inputs: List[PromptStrictInputs] = [{
+        "prompt_token_ids": batch
+    } for batch in dummy_prompt_token_ids.tolist()]
 
     def run_to_completion(profile_dir: Optional[str] = None):
         if profile_dir:
@@ -59,13 +62,13 @@ def run_to_completion(profile_dir: Optional[str] = None):
                     ],
                     on_trace_ready=torch.profiler.tensorboard_trace_handler(
                         str(profile_dir))) as p:
-                llm.generate(prompt_token_ids=dummy_prompt_token_ids,
+                llm.generate(dummy_inputs,
                              sampling_params=sampling_params,
                              use_tqdm=False)
             print(p.key_averages())
         else:
             start_time = time.perf_counter()
-            llm.generate(prompt_token_ids=dummy_prompt_token_ids,
+            llm.generate(dummy_inputs,
                          sampling_params=sampling_params,
                          use_tqdm=False)
             end_time = time.perf_counter()

docs/source/offline_inference/llm.rst → docs/source/dev/offline_inference/llm.rst

@@ -1,5 +1,5 @@
 LLM Class
-==========
+=========
 
 .. autoclass:: vllm.LLM
     :members:

docs/source/dev/offline_inference/llm_inputs.rst

@@ -0,0 +1,14 @@
+LLM Inputs
+==========
+
+.. autodata:: vllm.inputs.PromptStrictInputs
+
+.. autoclass:: vllm.inputs.TextPrompt
+    :show-inheritance:
+    :members:
+    :member-order: bysource
+
+.. autoclass:: vllm.inputs.TokensPrompt
+    :show-inheritance:
+    :members:
+    :member-order: bysource

docs/source/dev/offline_inference/offline_index.rst

@@ -0,0 +1,8 @@
+Offline Inference
+=================================
+
+.. toctree::
+   :maxdepth: 1
+
+   llm
+   llm_inputs

docs/source/index.rst

@@ -68,13 +68,6 @@ Documentation
    getting_started/quickstart
    getting_started/examples/examples_index
 
-.. toctree::
-   :maxdepth: 1
-   :caption: Offline Inference
-
-   offline_inference/llm
-   offline_inference/sampling_params
-
 .. toctree::
    :maxdepth: 1
    :caption: Serving
@@ -108,7 +101,9 @@ Documentation
 .. toctree::
    :maxdepth: 2
    :caption: Developer Documentation
-
+
+   dev/sampling_params
+   dev/offline_inference/offline_index
    dev/engine/engine_index
    dev/kernel/paged_attention
    dev/dockerfile/dockerfile

docs/source/serving/openai_compatible_server.md

@@ -48,7 +48,7 @@ completion = client.chat.completions.create(
 ```
 
 ### Extra Parameters for Chat API
-The following [sampling parameters (click through to see documentation)](../offline_inference/sampling_params.rst) are supported.
+The following [sampling parameters (click through to see documentation)](../dev/sampling_params.rst) are supported.
 
 ```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
 :language: python
@@ -65,7 +65,7 @@ The following extra parameters are supported:
 ```
 
 ### Extra Parameters for Completions API
-The following [sampling parameters (click through to see documentation)](../offline_inference/sampling_params.rst) are supported.
+The following [sampling parameters (click through to see documentation)](../dev/sampling_params.rst) are supported.
 
 ```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
 :language: python

examples/llava_example.py

@@ -23,11 +23,15 @@ def run_llava_pixel_values():
         "\nUSER: What is the content of this image?\nASSISTANT:")
 
     # This should be provided by another online or offline component.
-    images = torch.load("images/stop_sign_pixel_values.pt")
+    image = torch.load("images/stop_sign_pixel_values.pt")
+
+    outputs = llm.generate({
+        "prompt":
+        prompt,
+        "multi_modal_data":
+        MultiModalData(type=MultiModalData.Type.IMAGE, data=image),
+    })
 
-    outputs = llm.generate(prompt,
-                           multi_modal_data=MultiModalData(
-                               type=MultiModalData.Type.IMAGE, data=images))
     for o in outputs:
         generated_text = o.outputs[0].text
         print(generated_text)
@@ -46,11 +50,14 @@ def run_llava_image_features():
         "\nUSER: What is the content of this image?\nASSISTANT:")
 
     # This should be provided by another online or offline component.
-    images = torch.load("images/stop_sign_image_features.pt")
-
-    outputs = llm.generate(prompt,
-                           multi_modal_data=MultiModalData(
-                               type=MultiModalData.Type.IMAGE, data=images))
+    image = torch.load("images/stop_sign_image_features.pt")
+
+    outputs = llm.generate({
+        "prompt":
+        prompt,
+        "multi_modal_data":
+        MultiModalData(type=MultiModalData.Type.IMAGE, data=image),
+    })
     for o in outputs:
         generated_text = o.outputs[0].text
         print(generated_text)

pyproject.toml

@@ -65,3 +65,10 @@ skip = "./tests/prompts,./benchmarks/sonnet.txt,./tests/lora/data"
 [tool.isort]
 use_parentheses = true
 skip_gitignore = true
+
+[tool.pytest.ini_options]
+markers = [
+    "skip_global_cleanup",
+    "llm: run tests for vLLM API only",
+    "openai: run tests for OpenAI API only",
+]

tests/async_engine/test_async_llm_engine.py

@@ -25,7 +25,7 @@ async def step_async(self):
         return [RequestOutput(
             request_id=self.request_id)] if self.request_id else []
 
-    async def encode_request_async(self, *args, **kwargs):
+    async def process_model_inputs_async(self, *args, **kwargs):
         pass
 
     def generate(self, request_id):

tests/async_engine/test_openapi_server_ray.py

@@ -29,7 +29,7 @@ def server():
     ray.shutdown()
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture(scope="module")
 def client():
     client = openai.AsyncOpenAI(
         base_url="http://localhost:8000/v1",

tests/conftest.py

@@ -12,6 +12,7 @@
 from vllm import LLM, SamplingParams
 from vllm.config import TokenizerPoolConfig, VisionLanguageConfig
 from vllm.distributed import destroy_model_parallel
+from vllm.inputs import PromptInputs
 from vllm.logger import init_logger
 from vllm.sequence import MultiModalData
 
@@ -402,12 +403,22 @@ def generate(
     ) -> List[Tuple[List[int], str]]:
         if images is not None:
             assert len(prompts) == images.shape[0]
-        req_outputs = self.model.generate(
-            prompts,
-            sampling_params=sampling_params,
-            multi_modal_data=MultiModalData(type=MultiModalData.Type.IMAGE,
-                                            data=images)
-            if images is not None else None)
+
+        prompt_inputs: List[PromptInputs] = []
+        for i, prompt in enumerate(prompts):
+            image = None if images is None else images[i:i + 1]
+            mm_data = None if image is None else MultiModalData(
+                type=MultiModalData.Type.IMAGE,
+                data=image,
+            )
+
+            prompt_inputs.append({
+                "prompt": prompt,
+                "multi_modal_data": mm_data,
+            })
+
+        req_outputs = self.model.generate(prompt_inputs,
+                                          sampling_params=sampling_params)
         outputs = []
         for req_output in req_outputs:
             prompt_str = req_output.prompt

tests/core/test_block_manager.py

@@ -133,8 +133,11 @@ def test_append_slot_cow():
 
     # Allocate prompt to gpu block. There is one slot left in the block.
     prompt = Sequence(seq_id=1,
-                      prompt="one two three",
-                      prompt_token_ids=[1, 2, 3],
+                      inputs={
+                          "prompt": "one two three",
+                          "prompt_token_ids": [1, 2, 3],
+                          "multi_modal_data": None
+                      },
                       block_size=block_size)
 
     # Fork the sequence, such that a COW will be required when we append a new
@@ -304,7 +307,13 @@ def test_sliding_window_multi_seq():
 
     assert block_manager.get_num_free_gpu_blocks() == num_gpu_blocks
 
-    parent = Sequence(1, "one two three", [0, 1, 2], block_size)
+    parent = Sequence(seq_id=1,
+                      inputs={
+                          "prompt": "one two three",
+                          "prompt_token_ids": [0, 1, 2],
+                          "multi_modal_data": None
+                      },
+                      block_size=block_size)
     seq_group = SequenceGroup(request_id="1",
                               seqs=[parent],
                               arrival_time=time.time(),

tests/core/utils.py

@@ -21,7 +21,13 @@ def create_dummy_prompt(
     # and prompt "0 ... block_size".
     prompt_tokens = list(range(prompt_length))
     prompt_str = " ".join([str(t) for t in prompt_tokens])
-    prompt = Sequence(int(request_id), prompt_str, prompt_tokens, block_size)
+    prompt = Sequence(int(request_id),
+                      inputs={
+                          "prompt": prompt_str,
+                          "prompt_token_ids": prompt_tokens,
+                          "multi_modal_data": None,
+                      },
+                      block_size=block_size)
     seq_group = SequenceGroup(request_id=request_id,
                               seqs=[prompt],
                               arrival_time=time.time(),
@@ -51,8 +57,11 @@ def create_seq_group(
     for seq_id_offset, output_len in enumerate(seq_output_lens):
         seq = Sequence(
             seq_id=seq_id_start + seq_id_offset,
-            prompt="",
-            prompt_token_ids=prompt_token_ids,
+            inputs={
+                "prompt": "",
+                "prompt_token_ids": prompt_token_ids,
+                "multi_modal_data": None,
+            },
             block_size=16,
         )
 

tests/engine/test_skip_tokenizer_init.py

@@ -14,7 +14,7 @@ def test_skip_tokenizer_initialization(model: str):
     with pytest.raises(ValueError) as err:
         llm.generate("abc", sampling_params)
     assert "prompts must be None if" in str(err.value)
-    outputs = llm.generate(prompt_token_ids=[[1, 2, 3]],
+    outputs = llm.generate({"prompt_token_ids": [1, 2, 3]},
                            sampling_params=sampling_params)
     assert len(outputs) > 0
     completions = outputs[0].outputs

tests/entrypoints/openai/test_serving_chat.py

@@ -1,11 +1,15 @@
 import asyncio
 from dataclasses import dataclass
 
+import pytest
+
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 
 MODEL_NAME = "openai-community/gpt2"
 CHAT_TEMPLATE = "Dummy chat template for testing {}"
 
+pytestmark = pytest.mark.openai
+
 
 @dataclass
 class MockModelConfig:

tests/entrypoints/test_guided_processors.py

@@ -52,6 +52,8 @@
 TEST_REGEX = (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
               r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
 
+pytestmark = pytest.mark.openai
+
 
 def test_guided_logits_processors():
     """Basic unit test for RegexLogitsProcessor and JSONLogitsProcessor."""