[Core][VLM] Add precise multi-modal placeholder tracking

examples/offline_inference_audio_language.py

@@ -34,11 +34,7 @@ def run_ultravox(question: str, audio_count: int):
                                            tokenize=False,
                                            add_generation_prompt=True)
 
-    llm = LLM(model=model_name,
-              enforce_eager=True,
-              enable_chunked_prefill=False,
-              max_model_len=8192,
-              limit_mm_per_prompt={"audio": audio_count})
+    llm = LLM(model=model_name, limit_mm_per_prompt={"audio": audio_count})
     stop_token_ids = None
     return llm, prompt, stop_token_ids
 

tests/kernels/utils.py

@@ -869,6 +869,7 @@ def make_test_metadata(
         return attn_backend.make_metadata(
             num_prefills=num_prefills,
             slot_mapping=(None if kv_mmap is None else kv_mmap.slot_mapping),
+            multi_modal_placeholder_index_maps=None,
             num_prefill_tokens=num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,
             seq_lens=seq_lens,
@@ -914,6 +915,7 @@ def make_test_metadata(
         return attn_backend.make_metadata(
             num_prefills=num_prefills,
             slot_mapping=kv_mmap.slot_mapping,
+            multi_modal_placeholder_index_maps=None,
             num_prefill_tokens=num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,
             seq_lens=seq_lens,

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -2,8 +2,10 @@
 
 import numpy as np
 import pytest
+import pytest_asyncio
 from transformers import AutoModel, AutoTokenizer, BatchEncoding
 
+from tests.utils import RemoteOpenAIServer
 from vllm.sequence import SampleLogprobs
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 
@@ -17,6 +19,13 @@
 VLLM_PLACEHOLDER = "<|reserved_special_token_0|>"
 HF_PLACEHOLDER = "<|audio|>"
 
+CHUNKED_PREFILL_KWARGS = {
+    "enable_chunked_prefill": True,
+    "max_num_seqs": 2,
+    # Use a very small limit to exercise chunked prefill.
+    "max_num_batched_tokens": 16
+}
+
 
 @pytest.fixture(scope="session")
 def audio_assets():
@@ -30,6 +39,26 @@ def audio(request):
     return AudioAsset(request.param)
 
 
+@pytest.fixture(params=({}, CHUNKED_PREFILL_KWARGS))
+def server(request, audio_assets):
+    args = [
+        "--dtype=bfloat16", "--max-model-len=4096", "--enforce-eager",
+        f"--limit-mm-per-prompt=audio={len(audio_assets)}"
+    ] + [
+        f"--{key.replace('_','-')}={value}"
+        for key, value in request.param.items()
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
 def _get_prompt(audio_count, question, placeholder):
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     placeholder = f"{placeholder}\n" * audio_count
@@ -68,8 +97,7 @@ def run_test(
     dtype: str,
     max_tokens: int,
     num_logprobs: int,
-    tensor_parallel_size: int,
-    distributed_executor_backend: Optional[str] = None,
+    **kwargs,
 ):
     """Inference result should be the same between hf and vllm."""
     torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
@@ -79,11 +107,8 @@ def run_test(
     # if we run HF first, the cuda initialization will be done and it
     # will hurt multiprocessing backend with fork method (the default method).
 
-    with vllm_runner(model,
-                     dtype=dtype,
-                     tensor_parallel_size=tensor_parallel_size,
-                     distributed_executor_backend=distributed_executor_backend,
-                     enforce_eager=True) as vllm_model:
+    with vllm_runner(model, dtype=dtype, enforce_eager=True,
+                     **kwargs) as vllm_model:
         vllm_outputs_per_audio = [
             vllm_model.generate_greedy_logprobs([vllm_prompt],
                                                 max_tokens,
@@ -135,18 +160,16 @@ def run_multi_audio_test(
     dtype: str,
     max_tokens: int,
     num_logprobs: int,
-    tensor_parallel_size: int,
-    distributed_executor_backend: Optional[str] = None,
+    **kwargs,
 ):
     with vllm_runner(model,
                      dtype=dtype,
-                     tensor_parallel_size=tensor_parallel_size,
-                     distributed_executor_backend=distributed_executor_backend,
                      enforce_eager=True,
                      limit_mm_per_prompt={
                          "audio":
                          max((len(audio) for _, audio in prompts_and_audios))
-                     }) as vllm_model:
+                     },
+                     **kwargs) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy_logprobs(
             [prompt for prompt, _ in prompts_and_audios],
             max_tokens,
@@ -162,8 +185,9 @@ def run_multi_audio_test(
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
+@pytest.mark.parametrize("vllm_kwargs", [{}, CHUNKED_PREFILL_KWARGS])
 def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int,
-                num_logprobs: int) -> None:
+                num_logprobs: int, vllm_kwargs: dict) -> None:
 
     vllm_prompt = _get_prompt(1, "Describe the audio above.", VLLM_PLACEHOLDER)
     hf_prompt = _get_prompt(1, "Describe the audio above.", HF_PLACEHOLDER)
@@ -175,17 +199,18 @@ def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int,
         dtype=dtype,
         max_tokens=max_tokens,
         num_logprobs=num_logprobs,
-        tensor_parallel_size=1,
+        **vllm_kwargs,
     )
 
 
 @pytest.mark.core_model
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
+@pytest.mark.parametrize("vllm_kwargs", [{}, CHUNKED_PREFILL_KWARGS])
 def test_models_with_multiple_audios(vllm_runner, audio_assets, dtype: str,
-                                     max_tokens: int,
-                                     num_logprobs: int) -> None:
+                                     max_tokens: int, num_logprobs: int,
+                                     vllm_kwargs: dict) -> None:
 
     vllm_prompt = _get_prompt(len(audio_assets),
                               "Describe each of the audios above.",
@@ -198,5 +223,37 @@ def test_models_with_multiple_audios(vllm_runner, audio_assets, dtype: str,
         dtype=dtype,
         max_tokens=max_tokens,
         num_logprobs=num_logprobs,
-        tensor_parallel_size=1,
+        **vllm_kwargs,
     )
+
+
+@pytest.mark.asyncio
+async def test_online_inference(client, audio_assets):
+    """Exercises online inference with/without chunked prefill enabled."""
+
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            *[{
+                "type": "audio_url",
+                "audio_url": {
+                    "url": audio.url
+                }
+            } for audio in audio_assets],
+            {
+                "type":
+                "text",
+                "text":
+                f"What's happening in these {len(audio_assets)} audio clips?"
+            },
+        ],
+    }]
+
+    chat_completion = await client.chat.completions.create(model=MODEL_NAME,
+                                                           messages=messages,
+                                                           max_tokens=10)
+
+    assert len(chat_completion.choices) == 1
+    choice = chat_completion.choices[0]
+    assert choice.finish_reason == "length"

tests/multimodal/test_processor_kwargs.py

@@ -5,8 +5,8 @@
 import pytest
 import torch
 
-from vllm.inputs import DecoderOnlyInputs, InputContext, token_inputs
-from vllm.inputs.registry import InputRegistry
+from vllm.inputs import (DecoderOnlyInputs, DummyData, InputContext,
+                         InputRegistry, token_inputs)
 from vllm.multimodal import MultiModalRegistry
 from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
 
@@ -56,7 +56,7 @@ def custom_dummy_data_factory(self,
                                   num_crops=DEFAULT_NUM_CROPS):
         seq_data = SequenceData(
             array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * num_crops))
-        return seq_data, None
+        return DummyData(seq_data, None)
 
     with patch(
             "vllm.inputs.registry.InputRegistry._default_dummy_data_factory",
@@ -177,9 +177,9 @@ def test_dummy_data_kwarg_overrides(use_dummy_data_mock, num_crops):
     # NOTE: seq_len is thrown away here since this will leverage the
     # default dummy data factory that we have patched in, whose seq
     # len is solely dependent on the value of the mm_processor_kwargs.
-    seq_data, _ = dummy_registry.dummy_data_for_profiling(
+    dummy_data = dummy_registry.dummy_data_for_profiling(
         ctx.model_config, seq_len=-1, mm_registry=mm_registry)
-    assert len(seq_data.prompt_token_ids) == expected_seq_count
+    assert len(dummy_data.seq_data.prompt_token_ids) == expected_seq_count
 
 
 @pytest.mark.parametrize(
@@ -206,9 +206,9 @@ def test_dummy_data_with_sad_kwarg_overrides(use_dummy_data_mock,
     # NOTE: seq_len is thrown away here since this will leverage the
     # default dummy data factory that we have patched in, whose seq
     # len is solely dependent on the value of the mm_processor_kwargs.
-    seq_data, _ = dummy_registry.dummy_data_for_profiling(
+    dummy_data = dummy_registry.dummy_data_for_profiling(
         ctx.model_config, seq_len=-1, mm_registry=mm_registry)
-    assert len(seq_data.prompt_token_ids) == DEFAULT_NUM_CROPS
+    assert len(dummy_data.seq_data.prompt_token_ids) == DEFAULT_NUM_CROPS
 
 
 ### Test overrides for the max token count per multimodal instance

tests/multimodal/test_utils.py

@@ -92,18 +92,50 @@ def test_repeat_and_pad_placeholder_tokens(model):
     tokenizer = AutoTokenizer.from_pretrained(model)
 
     test_cases = [
-        ("<image>", 2, "<image><image>", [32000, 32000]),
-        ("<image><image>", 2, "<image><image><image>", [32000, 32000, 32000]),
-        ("<image><image>", [3, 2], "<image><image><image><image><image>",
-         [32000, 32000, 32000, 32000, 32000]),
-        ("Image:<image>Image:<image>!", [3, 2],
-         "Image:<image><image><image>Image:<image><image>!",
-         [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918]),
-        ("<image>", [3, 2], "<image><image><image>", [32000, 32000, 32000]),
-    ]
-
-    for prompt, repeat_count, expected_prompt, expected_token_ids in test_cases:
-        new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
+        (
+            "<image>",
+            2,
+            "<image><image>",
+            [32000, 32000],
+            [{ "offset": 0, "length": 2 }],
+        ),
+        (
+            "<image><image>",
+            2,
+            "<image><image><image>",
+            [32000, 32000, 32000],
+            [{ "offset": 0, "length": 2 }]),
+        (
+            "<image><image>",
+            [3, 2],
+            "<image><image><image><image><image>",
+            [32000, 32000, 32000, 32000, 32000],
+            [{ "offset": 0, "length": 3 }, { "offset": 3, "length": 2 }],
+        ),
+        (
+            "Image:<image>Image:<image>!",
+            [3, 2],
+            "Image:<image><image><image>Image:<image><image>!",
+            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
+            [{ "offset": 2, "length": 3 }, { "offset": 7, "length": 2 }],
+        ),
+        (
+            "<image>",
+            [3, 2],
+            "<image><image><image>",
+            [32000, 32000, 32000],
+            [{ "offset": 0, "length": 3 }],
+        ),
+    ]  # yapf: disable
+
+    for (
+            prompt,
+            repeat_count,
+            expected_prompt,
+            expected_token_ids,
+            expected_ranges,
+    ) in test_cases:
+        new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
             tokenizer=tokenizer,
             prompt=prompt,
             prompt_token_ids=tokenizer.encode(prompt,
@@ -113,3 +145,4 @@ def test_repeat_and_pad_placeholder_tokens(model):
         )
         assert new_prompt == expected_prompt
         assert new_token_ids == expected_token_ids
+        assert ranges == expected_ranges

tests/worker/test_model_input.py

@@ -73,6 +73,7 @@ def test_model_runner_input():
         num_prefill_tokens=2,
         num_decode_tokens=3,
         slot_mapping=torch.zeros(1),
+        multi_modal_placeholder_index_maps=None,
     )
     model_input = ModelInputForGPUWithSamplingMetadata(
         input_tokens=torch.ones(10),
@@ -124,6 +125,7 @@ def test_embedding_model_runner_input():
         num_prefill_tokens=2,
         num_decode_tokens=3,
         slot_mapping=torch.zeros(1),
+        multi_modal_placeholder_index_maps=None,
     )
     model_input = ModelInputForGPUWithPoolingMetadata(
         input_tokens=torch.ones(10),
@@ -174,6 +176,7 @@ def test_multi_step_model_runner_input():
         num_prefill_tokens=2,
         num_decode_tokens=3,
         slot_mapping=torch.zeros(1),
+        multi_modal_placeholder_index_maps=None,
     )
     frozen_model_input = ModelInputForGPUWithSamplingMetadata(
         input_tokens=torch.ones(10),

vllm/attention/backends/abstract.py

@@ -7,6 +7,8 @@
 
 import torch
 
+from vllm.multimodal import MultiModalPlaceholderMap
+
 if TYPE_CHECKING:
     from vllm.worker.model_runner_base import (ModelRunnerBase,
                                                ModelRunnerInputBase,
@@ -108,6 +110,15 @@ class AttentionMetadata:
     # in block 0, and 1st slot in block 1, respectively.
     slot_mapping: torch.Tensor
 
+    # The index maps that relate multi-modal embeddings to the corresponding
+    # placeholders.
+    #
+    # N.B. These aren't really related to attention and don't belong on this
+    # type -- this is just a temporary solution to make them available to
+    # `model_executable`.
+    multi_modal_placeholder_index_maps: Optional[Dict[
+        str, MultiModalPlaceholderMap.IndexMap]]
+
     @property
     @abstractmethod
     def prefill_metadata(self) -> Optional["AttentionMetadata"]:

vllm/attention/backends/blocksparse_attn.py

@@ -215,6 +215,8 @@ def prefill_metadata(
             num_prefill_tokens=self.num_prefill_tokens,
             num_decode_tokens=0,
             slot_mapping=self.slot_mapping[:self.num_prefill_tokens],
+            multi_modal_placeholder_index_maps=self.
+            multi_modal_placeholder_index_maps,
             seq_lens=self.seq_lens[:self.num_prefills],
             seq_lens_tensor=self.seq_lens_tensor[:self.num_prefills],
             max_query_len=self.max_query_len,
@@ -243,6 +245,7 @@ def decode_metadata(self) -> Optional["BlocksparseFlashAttentionMetadata"]:
             num_prefill_tokens=0,
             num_decode_tokens=self.num_decode_tokens,
             slot_mapping=self.slot_mapping[self.num_prefill_tokens:],
+            multi_modal_placeholder_index_maps=None,
             seq_lens=None,
             seq_lens_tensor=self.seq_lens_tensor[self.num_prefills:],
             max_query_len=None,