mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-09 01:35:01 +08:00
[Chore] Rename Processor to InputProcessor (#29682)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
GitHub
parent
d40c854009
commit
8d9338fae4
@ -114,7 +114,7 @@ def mock_serving_setup():
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mock_engine.add_lora.reset_mock()
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mock_engine.model_config = MockModelConfig()
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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models = OpenAIServingModels(
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@ -429,7 +429,7 @@ async def test_serving_chat_returns_correct_model_name():
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = MockModelConfig()
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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serving_chat = _build_serving_chat(mock_engine)
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@ -459,7 +459,7 @@ async def test_serving_chat_should_set_correct_max_tokens():
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = MockModelConfig()
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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serving_chat = _build_serving_chat(mock_engine)
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@ -492,7 +492,7 @@ async def test_serving_chat_should_set_correct_max_tokens():
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = mock_model_config
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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# Initialize the serving chat
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@ -537,7 +537,7 @@ async def test_serving_chat_should_set_correct_max_tokens():
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = mock_model_config
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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# Initialize the serving chat
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@ -583,7 +583,7 @@ async def test_serving_chat_could_load_correct_generation_config():
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = mock_model_config
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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# Initialize the serving chat
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@ -629,7 +629,7 @@ async def test_serving_chat_did_set_correct_cache_salt(model_type):
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = mock_model_config
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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serving_chat = _build_serving_chat(mock_engine)
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@ -662,7 +662,7 @@ async def test_serving_chat_data_parallel_rank_extraction():
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mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
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mock_engine.errored = False
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mock_engine.model_config = MockModelConfig()
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mock_engine.processor = MagicMock()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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# Mock the generate method to return an async generator
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@ -23,7 +23,7 @@ def serving() -> OpenAIServing:
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model_config.max_model_len = 32768
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models = Mock(spec=OpenAIServingModels)
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models.model_config = model_config
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models.processor = Mock()
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models.input_processor = Mock()
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models.io_processor = Mock()
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serving = OpenAIServing(
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@ -30,7 +30,7 @@ async def _async_serving_models_init() -> OpenAIServingModels:
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mock_model_config = MagicMock(spec=ModelConfig)
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mock_model_config.max_model_len = 2048
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mock_engine_client.model_config = mock_model_config
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mock_engine_client.processor = MagicMock()
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mock_engine_client.input_processor = MagicMock()
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mock_engine_client.io_processor = MagicMock()
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serving_models = OpenAIServingModels(
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@ -127,7 +127,7 @@ class TestInitializeToolSessions:
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model_config.get_diff_sampling_param.return_value = {}
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engine_client.model_config = model_config
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engine_client.processor = MagicMock()
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engine_client.input_processor = MagicMock()
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engine_client.io_processor = MagicMock()
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models = MagicMock()
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@ -213,7 +213,7 @@ class TestValidateGeneratorInput:
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model_config.get_diff_sampling_param.return_value = {}
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engine_client.model_config = model_config
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engine_client.processor = MagicMock()
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engine_client.input_processor = MagicMock()
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engine_client.io_processor = MagicMock()
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models = MagicMock()
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@ -7,18 +7,17 @@ from vllm.assets.image import ImageAsset
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from vllm.assets.video import VideoAsset
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from vllm.config import CacheConfig, DeviceConfig, ModelConfig, VllmConfig
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from vllm.sampling_params import SamplingParams
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from vllm.v1.engine import processor as processor_mod
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from vllm.v1.engine.processor import Processor
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from vllm.v1.engine import input_processor as input_processor_mod
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from vllm.v1.engine.input_processor import InputProcessor
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cherry_pil_image = ImageAsset("cherry_blossom").pil_image
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stop_pil_image = ImageAsset("stop_sign").pil_image
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baby_reading_np_ndarrays = VideoAsset("baby_reading").np_ndarrays
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# Mock processor for testing
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def _mk_processor(
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def _mock_input_processor(
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monkeypatch, *, mm_cache_gb: float = 4.0, enable_prefix_caching: bool = True
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) -> Processor:
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) -> InputProcessor:
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"""
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Create a Processor instance with minimal configuration suitable for unit
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tests without accessing external resources.
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@ -36,7 +35,7 @@ def _mk_processor(
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raising=True,
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)
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monkeypatch.setattr(
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processor_mod,
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input_processor_mod,
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"processor_cache_from_config",
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lambda vllm_config, mm_registry: None,
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raising=True,
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@ -65,11 +64,11 @@ def _mk_processor(
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device_config=DeviceConfig(device="cpu"),
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)
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return Processor(vllm_config, tokenizer=None)
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return InputProcessor(vllm_config, tokenizer=None)
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def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
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processor = _mk_processor(monkeypatch)
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input_processor = _mock_input_processor(monkeypatch)
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prompt = {
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"prompt": "USER: <image>\nDescribe\nASSISTANT:",
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@ -79,7 +78,7 @@ def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
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}
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with pytest.raises(ValueError, match="must have same length as data"):
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processor.process_inputs(
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input_processor.process_inputs(
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request_id="req-1",
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prompt=prompt, # type: ignore[arg-type]
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params=SamplingParams(),
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@ -87,7 +86,7 @@ def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
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def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
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processor = _mk_processor(monkeypatch)
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input_processor = _mock_input_processor(monkeypatch)
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prompt = {
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"prompt": "USER: <image><video>\nDescribe\nASSISTANT:",
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@ -101,7 +100,7 @@ def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
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}
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with pytest.raises(ValueError, match="must be provided if multi_modal_data"):
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processor.process_inputs(
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input_processor.process_inputs(
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request_id="req-2",
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prompt=prompt, # type: ignore[arg-type]
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params=SamplingParams(),
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@ -119,7 +118,7 @@ def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
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def test_multi_modal_uuids_accepts_none_and_passes_through(
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monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool
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):
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processor = _mk_processor(
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input_processor = _mock_input_processor(
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monkeypatch,
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mm_cache_gb=mm_cache_gb,
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enable_prefix_caching=enable_prefix_caching,
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@ -137,7 +136,7 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
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# Monkeypatch only the bound preprocess method on this instance
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monkeypatch.setattr(
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processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
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input_processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
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)
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# Use a consistent two-image scenario across all configurations
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@ -151,7 +150,7 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
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"multi_modal_uuids": mm_uuids,
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}
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processor.process_inputs(
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input_processor.process_inputs(
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request_id="req-3",
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prompt=prompt, # type: ignore[arg-type]
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params=SamplingParams(),
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@ -163,7 +162,9 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
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def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
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# When both processor cache is 0 and prefix caching disabled, the
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# processor builds overrides from request id instead of using user UUIDs.
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processor = _mk_processor(monkeypatch, mm_cache_gb=0.0, enable_prefix_caching=False)
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input_processor = _mock_input_processor(
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monkeypatch, mm_cache_gb=0.0, enable_prefix_caching=False
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)
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captured: dict[str, object] = {}
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@ -174,7 +175,7 @@ def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
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return {"type": "token", "prompt_token_ids": [1]}
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monkeypatch.setattr(
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processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
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input_processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
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)
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request_id = "req-42"
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@ -188,7 +189,7 @@ def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
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"multi_modal_uuids": mm_uuids,
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}
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processor.process_inputs(
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input_processor.process_inputs(
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request_id=request_id,
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prompt=prompt, # type: ignore[arg-type]
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params=SamplingParams(),
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@ -15,7 +15,7 @@ from vllm.sampling_params import SamplingParams
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from vllm.tasks import SupportedTask
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.processor import Processor
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from vllm.v1.engine.input_processor import InputProcessor
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class EngineClient(ABC):
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@ -23,7 +23,7 @@ class EngineClient(ABC):
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vllm_config: VllmConfig
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model_config: ModelConfig
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processor: Processor
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input_processor: InputProcessor
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io_processor: IOProcessor | None
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@property
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@ -347,7 +347,7 @@ class LLM:
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self.supported_tasks = supported_tasks
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self.model_config = self.llm_engine.model_config
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self.processor = self.llm_engine.processor
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self.input_processor = self.llm_engine.input_processor
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self.io_processor = self.llm_engine.io_processor
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def get_tokenizer(self) -> AnyTokenizer:
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@ -364,7 +364,7 @@ class LLM:
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self.llm_engine.tokenizer = get_cached_tokenizer(tokenizer)
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def reset_mm_cache(self) -> None:
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self.processor.clear_mm_cache()
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self.input_processor.clear_mm_cache()
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self.llm_engine.reset_mm_cache()
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def get_default_sampling_params(self) -> SamplingParams:
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@ -1674,7 +1674,7 @@ class LLM:
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tokenization_kwargs,
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)
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engine_request = self.processor.process_inputs(
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engine_request = self.input_processor.process_inputs(
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request_id,
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engine_prompt,
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params,
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@ -284,7 +284,7 @@ class OpenAIServing:
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self._async_tokenizer_pool: dict[AnyTokenizer, AsyncMicrobatchTokenizer] = {}
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self.log_error_stack = log_error_stack
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self.processor = self.models.processor
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self.input_processor = self.models.input_processor
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self.io_processor = self.models.io_processor
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self.model_config = self.models.model_config
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self.max_model_len = self.model_config.max_model_len
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@ -330,7 +330,7 @@ class OpenAIServing:
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return parser
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async def reset_mm_cache(self) -> None:
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self.processor.clear_mm_cache()
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self.input_processor.clear_mm_cache()
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await self.engine_client.reset_mm_cache()
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async def beam_search(
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@ -348,8 +348,8 @@ class OpenAIServing:
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length_penalty = params.length_penalty
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include_stop_str_in_output = params.include_stop_str_in_output
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processor = self.processor
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tokenizer = processor.tokenizer
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input_processor = self.input_processor
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tokenizer = input_processor.tokenizer
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if tokenizer is None:
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raise ValueError(
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"You cannot use beam search when `skip_tokenizer_init` is True"
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@ -1214,7 +1214,7 @@ class OpenAIServing:
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self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
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)
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engine_request = self.processor.process_inputs(
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engine_request = self.input_processor.process_inputs(
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request_id,
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engine_prompt,
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params,
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@ -69,7 +69,7 @@ class OpenAIServingModels:
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)
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self.lora_resolver_lock: dict[str, Lock] = defaultdict(Lock)
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self.processor = self.engine_client.processor
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self.input_processor = self.engine_client.input_processor
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self.io_processor = self.engine_client.io_processor
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self.model_config = self.engine_client.model_config
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self.max_model_len = self.model_config.max_model_len
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@ -10,6 +10,7 @@ from typing import Any, cast
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import numpy as np
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import torch
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from typing_extensions import deprecated
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import vllm.envs as envs
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from vllm.config import VllmConfig
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@ -35,9 +36,9 @@ from vllm.utils.math_utils import cdiv
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.core_client import EngineCoreClient
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from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
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from vllm.v1.engine.input_processor import InputProcessor
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from vllm.v1.engine.output_processor import OutputProcessor, RequestOutputCollector
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from vllm.v1.engine.parallel_sampling import ParentRequest
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from vllm.v1.engine.processor import Processor
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from vllm.v1.executor import Executor
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from vllm.v1.metrics.loggers import (
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StatLoggerFactory,
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@ -112,7 +113,7 @@ class AsyncLLM(EngineClient):
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else:
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tokenizer = init_tokenizer_from_configs(self.model_config)
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self.processor = Processor(self.vllm_config, tokenizer)
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self.input_processor = InputProcessor(self.vllm_config, tokenizer)
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self.io_processor = get_io_processor(
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self.vllm_config,
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self.model_config.io_processor_plugin,
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@ -193,6 +194,14 @@ class AsyncLLM(EngineClient):
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else:
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self.profiler = None
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@property
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@deprecated(
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"`AsyncLLM.processor` has been renamed to `AsyncLLM.input_processor`. "
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"The old name will be removed in v0.13."
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)
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def processor(self):
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return self.input_processor
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@classmethod
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def from_vllm_config(
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cls,
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@ -293,11 +302,7 @@ class AsyncLLM(EngineClient):
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request = prompt
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else:
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assert prompt_text is None
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logger.warning_once(
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"Processor has been moved under OpenAIServing and will "
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"be removed from AsyncLLM in v0.13."
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)
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request = self.processor.process_inputs(
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request = self.input_processor.process_inputs(
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request_id,
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prompt,
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params,
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@ -481,7 +486,7 @@ class AsyncLLM(EngineClient):
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output_processor = self.output_processor
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log_stats = self.log_stats
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logger_manager = self.logger_manager
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processor = self.processor
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input_processor = self.input_processor
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async def output_handler():
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try:
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@ -532,7 +537,7 @@ class AsyncLLM(EngineClient):
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engine_idx=outputs.engine_index,
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scheduler_stats=outputs.scheduler_stats,
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iteration_stats=iteration_stats,
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mm_cache_stats=processor.stat_mm_cache(),
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mm_cache_stats=input_processor.stat_mm_cache(),
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)
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except Exception as e:
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logger.exception("AsyncLLM output_handler failed.")
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@ -699,11 +704,11 @@ class AsyncLLM(EngineClient):
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@property
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def tokenizer(self) -> AnyTokenizer | None:
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return self.processor.tokenizer
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return self.input_processor.tokenizer
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@tokenizer.setter
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def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
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self.processor.tokenizer = tokenizer
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self.input_processor.tokenizer = tokenizer
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async def get_tokenizer(self) -> AnyTokenizer:
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if self.tokenizer is None:
|
||||
@ -738,7 +743,7 @@ class AsyncLLM(EngineClient):
|
||||
await asyncio.gather(*coros)
|
||||
|
||||
async def reset_mm_cache(self) -> None:
|
||||
self.processor.clear_mm_cache()
|
||||
self.input_processor.clear_mm_cache()
|
||||
await self.engine_core.reset_mm_cache_async()
|
||||
|
||||
async def reset_prefix_cache(self) -> None:
|
||||
|
||||
637
vllm/v1/engine/input_processor.py
Normal file
637
vllm/v1/engine/input_processor.py
Normal file
@ -0,0 +1,637 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import time
|
||||
from collections.abc import Mapping
|
||||
from typing import Any, Literal, cast
|
||||
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
|
||||
from vllm.inputs.parse import split_enc_dec_inputs
|
||||
from vllm.inputs.preprocess import InputPreprocessor
|
||||
from vllm.logger import init_logger
|
||||
from vllm.lora.request import LoRARequest
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
|
||||
from vllm.multimodal.cache import processor_cache_from_config
|
||||
from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
|
||||
from vllm.multimodal.parse import MultiModalDataParser
|
||||
from vllm.multimodal.processing import EncDecMultiModalProcessor
|
||||
from vllm.multimodal.utils import argsort_mm_positions
|
||||
from vllm.pooling_params import PoolingParams
|
||||
from vllm.sampling_params import SamplingParams
|
||||
from vllm.transformers_utils.tokenizer import AnyTokenizer
|
||||
from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
|
||||
from vllm.utils import length_from_prompt_token_ids_or_embeds
|
||||
from vllm.v1.engine import EngineCoreRequest
|
||||
from vllm.v1.metrics.stats import MultiModalCacheStats
|
||||
from vllm.v1.structured_output.backend_guidance import validate_guidance_grammar
|
||||
from vllm.v1.structured_output.backend_lm_format_enforcer import (
|
||||
validate_structured_output_request_lm_format_enforcer,
|
||||
)
|
||||
from vllm.v1.structured_output.backend_outlines import (
|
||||
validate_structured_output_request_outlines,
|
||||
)
|
||||
from vllm.v1.structured_output.backend_xgrammar import validate_xgrammar_grammar
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class InputProcessor:
|
||||
def __init__(
|
||||
self,
|
||||
vllm_config: VllmConfig,
|
||||
tokenizer: AnyTokenizer | None,
|
||||
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
||||
) -> None:
|
||||
self.vllm_config = vllm_config
|
||||
self.model_config = vllm_config.model_config
|
||||
self.cache_config = vllm_config.cache_config
|
||||
self.lora_config = vllm_config.lora_config
|
||||
self.structured_outputs_config = vllm_config.structured_outputs_config
|
||||
|
||||
self.generation_config_fields = self.model_config.try_get_generation_config()
|
||||
|
||||
self.mm_registry = mm_registry
|
||||
self.mm_processor_cache = processor_cache_from_config(vllm_config, mm_registry)
|
||||
|
||||
self.input_preprocessor = InputPreprocessor(
|
||||
self.model_config,
|
||||
tokenizer,
|
||||
mm_registry,
|
||||
mm_processor_cache=self.mm_processor_cache,
|
||||
)
|
||||
|
||||
@property
|
||||
def tokenizer(self) -> AnyTokenizer | None:
|
||||
return self.input_preprocessor.tokenizer
|
||||
|
||||
@tokenizer.setter
|
||||
def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
|
||||
self.input_preprocessor.tokenizer = tokenizer
|
||||
|
||||
def _validate_logprobs(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
max_logprobs = self.model_config.max_logprobs
|
||||
if max_logprobs == -1:
|
||||
max_logprobs = self.model_config.get_vocab_size()
|
||||
|
||||
# Validate sample logprobs.
|
||||
if params.logprobs:
|
||||
num_logprobs = params.logprobs
|
||||
if num_logprobs == -1:
|
||||
num_logprobs = self.model_config.get_vocab_size()
|
||||
if num_logprobs > max_logprobs:
|
||||
raise ValueError(
|
||||
f"Requested sample logprobs of {num_logprobs}, "
|
||||
f"which is greater than max allowed: {max_logprobs}"
|
||||
)
|
||||
|
||||
# Validate prompt logprobs.
|
||||
if params.prompt_logprobs:
|
||||
num_prompt_logprobs = params.prompt_logprobs
|
||||
if num_prompt_logprobs == -1:
|
||||
num_prompt_logprobs = self.model_config.get_vocab_size()
|
||||
if num_prompt_logprobs > max_logprobs:
|
||||
raise ValueError(
|
||||
f"Requested prompt logprobs of {num_prompt_logprobs}, "
|
||||
f"which is greater than max allowed: {max_logprobs}"
|
||||
)
|
||||
|
||||
def _validate_sampling_params(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
self._validate_structured_output(params)
|
||||
self._validate_logit_bias(params)
|
||||
|
||||
if params.allowed_token_ids is None:
|
||||
return
|
||||
if not params.allowed_token_ids:
|
||||
raise ValueError("allowed_token_ids is not None and empty!")
|
||||
if self.tokenizer is None:
|
||||
# When skip_tokenizer_init=True, we can't validate token IDs
|
||||
# Skip validation and let the model handle invalid tokens
|
||||
return
|
||||
vocab_size = len(self.tokenizer)
|
||||
if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
|
||||
raise ValueError("allowed_token_ids contains out-of-vocab token id!")
|
||||
|
||||
def _validate_logit_bias(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
"""Validate logit_bias token IDs are within vocabulary range."""
|
||||
if not params.logit_bias:
|
||||
return
|
||||
|
||||
vocab_size = self.model_config.get_vocab_size()
|
||||
invalid_token_ids = []
|
||||
|
||||
for token_id in params.logit_bias:
|
||||
if token_id < 0 or token_id >= vocab_size:
|
||||
invalid_token_ids.append(token_id)
|
||||
|
||||
if invalid_token_ids:
|
||||
raise ValueError(
|
||||
f"token_id(s) {invalid_token_ids} in logit_bias contain "
|
||||
f"out-of-vocab token ids. Vocabulary size: {vocab_size}"
|
||||
)
|
||||
|
||||
def _validate_supported_sampling_params(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
# Logits processors not supported.
|
||||
if params.logits_processors:
|
||||
raise ValueError(
|
||||
"vLLM V1 does not support per request user provided logits processors."
|
||||
)
|
||||
# Async scheduling + spec decode currently incompatible with some
|
||||
# sampling parameters.
|
||||
if (
|
||||
self.vllm_config.speculative_config is not None
|
||||
and self.vllm_config.scheduler_config.async_scheduling
|
||||
and (
|
||||
params.frequency_penalty != 0.0
|
||||
or params.presence_penalty != 0.0
|
||||
or params.repetition_penalty != 1.0
|
||||
or params.bad_words_token_ids
|
||||
or params.structured_outputs
|
||||
)
|
||||
):
|
||||
raise ValueError(
|
||||
"async scheduling with spec decoding doesn't yet support "
|
||||
"penalties, bad words or structured outputs in sampling parameters."
|
||||
)
|
||||
|
||||
def _validate_params(
|
||||
self,
|
||||
params: SamplingParams | PoolingParams,
|
||||
):
|
||||
"""
|
||||
Validate supported SamplingParam.
|
||||
Should raise ValueError if unsupported for API Server.
|
||||
"""
|
||||
|
||||
if isinstance(params, PoolingParams):
|
||||
return
|
||||
|
||||
self._validate_logprobs(params)
|
||||
self._validate_sampling_params(params)
|
||||
self._validate_supported_sampling_params(params)
|
||||
|
||||
def _validate_multi_modal_uuids(self, prompt: PromptType) -> None:
|
||||
"""
|
||||
Validate that user-provided multi_modal_uuids align with
|
||||
multi_modal_data in the incoming request prompt(s).
|
||||
Only checks lengths; `None` entries are allowed and will be
|
||||
auto-hashed downstream.
|
||||
"""
|
||||
|
||||
def _validate_single_prompt(single_prompt: dict | str) -> None:
|
||||
if not isinstance(single_prompt, dict):
|
||||
return
|
||||
mm_data = single_prompt.get("multi_modal_data")
|
||||
mm_uuids = single_prompt.get("multi_modal_uuids")
|
||||
if not mm_data or not mm_uuids:
|
||||
return
|
||||
|
||||
for modality, items in mm_data.items():
|
||||
if modality in mm_uuids:
|
||||
data_len = len(items) if isinstance(items, list) else 1
|
||||
uuid_len = (
|
||||
len(mm_uuids[modality])
|
||||
if isinstance(mm_uuids[modality], list)
|
||||
else 1
|
||||
)
|
||||
if uuid_len != data_len:
|
||||
raise ValueError(
|
||||
f"multi_modal_uuids for modality '{modality}' "
|
||||
"must have same length as data: got "
|
||||
f"{uuid_len} uuids vs "
|
||||
f"{data_len} items."
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"multi_modal_uuids for modality '{modality}' must "
|
||||
"be provided if multi_modal_data is provided."
|
||||
)
|
||||
|
||||
# Handle explicit encoder/decoder prompts or singleton prompt
|
||||
if isinstance(prompt, dict) and "encoder_prompt" in prompt:
|
||||
enc = prompt.get("encoder_prompt")
|
||||
dec = prompt.get("decoder_prompt")
|
||||
if enc is not None:
|
||||
_validate_single_prompt(cast(dict | str, enc))
|
||||
if dec is not None:
|
||||
_validate_single_prompt(cast(dict | str, dec))
|
||||
else:
|
||||
_validate_single_prompt(prompt) # type: ignore[arg-type]
|
||||
|
||||
def _validate_lora(self, lora_request: LoRARequest | None) -> None:
|
||||
if lora_request is None:
|
||||
return
|
||||
|
||||
# LoRA request passed in while LoRA is not enabled
|
||||
if not self.lora_config:
|
||||
raise ValueError(
|
||||
f"Got lora_request {lora_request} but LoRA is not enabled!"
|
||||
)
|
||||
|
||||
if self.tokenizer is not None:
|
||||
logger.warning_once(
|
||||
"vLLM has deprecated support for supporting different "
|
||||
"tokenizers for different LoRAs. By default, vLLM uses base "
|
||||
"model's tokenizer. If you are using a LoRA "
|
||||
"with its own tokenizer, consider specifying `--tokenizer "
|
||||
"[lora_path]` to use the LoRA tokenizer."
|
||||
)
|
||||
|
||||
def _validate_structured_output(self, params: SamplingParams) -> None:
|
||||
if not params.structured_outputs or not self.structured_outputs_config:
|
||||
return
|
||||
|
||||
if self.model_config.skip_tokenizer_init and params.structured_outputs:
|
||||
raise ValueError(
|
||||
"Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'" # noqa: E501
|
||||
)
|
||||
|
||||
backend = self.structured_outputs_config.backend
|
||||
if _backend := params.structured_outputs._backend:
|
||||
# Request-level backend selection is not supported.
|
||||
# The values may differ if `params` is reused and was set
|
||||
# to a specific backend based on `auto` behavior in a previous
|
||||
# request. We remember that it was set as a result of `auto`
|
||||
# using the `_backend_was_auto` field set in the params.
|
||||
if backend != _backend and not (
|
||||
backend == "auto" and params.structured_outputs._backend_was_auto
|
||||
):
|
||||
raise ValueError(
|
||||
"Request-level structured output backend selection is not "
|
||||
f"supported. The request specified '{_backend}', but vLLM "
|
||||
f"was initialised with '{backend}'. This error can be "
|
||||
"resolved by removing '_backend' from the request."
|
||||
)
|
||||
else:
|
||||
params.structured_outputs._backend = backend
|
||||
|
||||
# Request content validation
|
||||
if (
|
||||
isinstance(params.structured_outputs.choice, list)
|
||||
and not params.structured_outputs.choice
|
||||
):
|
||||
# It is invalid for choice to be an empty list
|
||||
raise ValueError(
|
||||
f"Choice '{params.structured_outputs.choice}' cannot be an empty list" # noqa: E501
|
||||
)
|
||||
# Reject empty string grammar early to avoid engine-side crashes
|
||||
if (
|
||||
isinstance(params.structured_outputs.grammar, str)
|
||||
and params.structured_outputs.grammar.strip() == ""
|
||||
):
|
||||
raise ValueError("structured_outputs.grammar cannot be an empty string")
|
||||
|
||||
if backend.startswith("xgrammar"):
|
||||
# xgrammar with no fallback
|
||||
validate_xgrammar_grammar(params)
|
||||
elif backend.startswith("guidance"):
|
||||
# TODO: ideally we would have the LLTokenizer here as Lark syntax
|
||||
# allows <|special_token|> and similar, see
|
||||
# https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
|
||||
# Without tokenizer these are disallowed in grammars.
|
||||
if isinstance(self.tokenizer, MistralTokenizer):
|
||||
raise ValueError(
|
||||
"Mistral tokenizer is not supported for the 'guidance' "
|
||||
"structured output backend. Please use ['xgrammar', 'outlines'] "
|
||||
"backends or tokenizer_mode='hf' instead."
|
||||
)
|
||||
validate_guidance_grammar(params, tokenizer=None)
|
||||
elif backend == "outlines":
|
||||
# outlines backend
|
||||
validate_structured_output_request_outlines(params)
|
||||
elif backend == "lm-format-enforcer":
|
||||
# lm format enforcer backend
|
||||
if isinstance(self.tokenizer, MistralTokenizer):
|
||||
raise ValueError(
|
||||
"Mistral tokenizer is not supported for the 'lm-format-enforcer' "
|
||||
"structured output backend. Please use ['xgrammar', 'outlines'] "
|
||||
"backends or tokenizer_mode='hf' instead."
|
||||
)
|
||||
validate_structured_output_request_lm_format_enforcer(params)
|
||||
else:
|
||||
# NOTE: backend must be "auto" here, because we have
|
||||
# checked supported_backends above.
|
||||
# In this mode, we set opinionated defaults based on what we think
|
||||
# will satisfy the most use cases without having to worry about
|
||||
# this setting. We include fallback behavior here, but not with any
|
||||
# other setting where a specific backend was specified.
|
||||
try:
|
||||
validate_xgrammar_grammar(params)
|
||||
params.structured_outputs._backend = "xgrammar"
|
||||
except ValueError:
|
||||
# The request either failed validation
|
||||
# or includes some jsonschema feature(s) that
|
||||
# are not supported in xgrammar.
|
||||
if isinstance(self.tokenizer, MistralTokenizer):
|
||||
# Fall back to outlines if the tokenizer is Mistral
|
||||
validate_structured_output_request_outlines(params)
|
||||
params.structured_outputs._backend = "outlines"
|
||||
else:
|
||||
# Fall back to guidance by default.
|
||||
validate_guidance_grammar(params, tokenizer=None)
|
||||
params.structured_outputs._backend = "guidance"
|
||||
# Remember that this backend was set automatically
|
||||
params.structured_outputs._backend_was_auto = True
|
||||
|
||||
def _maybe_build_mm_uuids(
|
||||
self,
|
||||
request_id: str,
|
||||
prompt: PromptType,
|
||||
) -> MultiModalUUIDDict | None:
|
||||
"""Build per-item multimodal hash overrides when enabled. In this case,
|
||||
multimodal data items are identified by their request id, modality and
|
||||
index rather than their content.
|
||||
|
||||
Returns a dictionary of modality -> list[str] of overrides, or None if
|
||||
disabled or no multimodal data is present.
|
||||
"""
|
||||
|
||||
def _extract_mm_data(p: PromptType):
|
||||
if isinstance(p, dict) and "encoder_prompt" in p:
|
||||
enc = p.get("encoder_prompt")
|
||||
if isinstance(enc, dict):
|
||||
return enc.get("multi_modal_data")
|
||||
return None
|
||||
if isinstance(p, dict):
|
||||
return p.get("multi_modal_data")
|
||||
return None
|
||||
|
||||
mm_data = _extract_mm_data(prompt)
|
||||
if not mm_data:
|
||||
return None
|
||||
|
||||
mm_uuids: dict[str, list[str | None] | str] = {}
|
||||
for modality, data in mm_data.items():
|
||||
# Hash each item for embedding inputs.
|
||||
n = (
|
||||
len(data)
|
||||
if isinstance(data, list) or MultiModalDataParser.is_embeddings(data)
|
||||
else 1
|
||||
)
|
||||
mm_uuids[modality] = [f"{request_id}-{modality}-{i}" for i in range(n)]
|
||||
return mm_uuids
|
||||
|
||||
def process_inputs(
|
||||
self,
|
||||
request_id: str,
|
||||
prompt: PromptType,
|
||||
params: SamplingParams | PoolingParams,
|
||||
arrival_time: float | None = None,
|
||||
lora_request: LoRARequest | None = None,
|
||||
tokenization_kwargs: dict[str, Any] | None = None,
|
||||
trace_headers: Mapping[str, str] | None = None,
|
||||
priority: int = 0,
|
||||
data_parallel_rank: int | None = None,
|
||||
) -> EngineCoreRequest:
|
||||
self._validate_lora(lora_request)
|
||||
self._validate_params(params)
|
||||
|
||||
data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
|
||||
if data_parallel_rank is not None and not (
|
||||
0 <= data_parallel_rank < data_parallel_size
|
||||
):
|
||||
raise ValueError(
|
||||
f"data_parallel_rank {data_parallel_rank} "
|
||||
f"is out of range [0, {data_parallel_size})."
|
||||
)
|
||||
|
||||
if arrival_time is None:
|
||||
arrival_time = time.time()
|
||||
|
||||
# Optionally generate multimodal hash overrides to avoid hashing
|
||||
# multimodal data items by their content as their identifiers.
|
||||
|
||||
# NOTE: when users explicitly turn off BOTH prefix caching and input
|
||||
# processing caching, no multimodal features or embeddings will be
|
||||
# reused across requests, therefore identifying multimodal data items
|
||||
# by their content is no longer necessary, and we create uuids with
|
||||
# request id-modality-index as multimodal hash overrides.
|
||||
if (
|
||||
self.model_config.multimodal_config
|
||||
and self.model_config.multimodal_config.mm_processor_cache_gb == 0
|
||||
and not self.cache_config.enable_prefix_caching
|
||||
):
|
||||
mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
|
||||
else:
|
||||
# Otherwise, use user-provided uuids as multimodal hash overrides
|
||||
# if provided.
|
||||
self._validate_multi_modal_uuids(prompt)
|
||||
if isinstance(prompt, dict):
|
||||
mm_uuids = cast(
|
||||
MultiModalUUIDDict | None, prompt.get("multi_modal_uuids")
|
||||
)
|
||||
else:
|
||||
mm_uuids = None
|
||||
|
||||
# Process inputs, which includes:
|
||||
# 1. Tokenize text prompt, with LoRA request if one exists.
|
||||
# 2. For multimodal models with a merged preprocessor, preprocess
|
||||
# multimodal data and expand prompt token ids accordingly.
|
||||
processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
|
||||
prompt,
|
||||
tokenization_kwargs=tokenization_kwargs,
|
||||
mm_uuids=mm_uuids,
|
||||
)
|
||||
from vllm.platforms import current_platform
|
||||
|
||||
current_platform.validate_request(
|
||||
prompt=prompt,
|
||||
params=params,
|
||||
processed_inputs=processed_inputs,
|
||||
)
|
||||
|
||||
eos_token_id = self.input_preprocessor.get_eos_token_id()
|
||||
|
||||
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
|
||||
self._validate_model_inputs(encoder_inputs, decoder_inputs)
|
||||
|
||||
# Mypy can be conservative for TypedDict unions; normalize access.
|
||||
if decoder_inputs["type"] == "embeds":
|
||||
prompt_token_ids = None
|
||||
prompt_embeds = decoder_inputs["prompt_embeds"]
|
||||
else:
|
||||
prompt_token_ids = decoder_inputs["prompt_token_ids"]
|
||||
prompt_embeds = None
|
||||
|
||||
sampling_params = None
|
||||
pooling_params = None
|
||||
if isinstance(params, SamplingParams):
|
||||
# TODO: can we avoid cloning here in multiproc case?
|
||||
sampling_params = params.clone()
|
||||
# If unset max tokens, then generate up to the max_model_len.
|
||||
if sampling_params.max_tokens is None:
|
||||
seq_len = length_from_prompt_token_ids_or_embeds(
|
||||
prompt_token_ids, prompt_embeds
|
||||
)
|
||||
sampling_params.max_tokens = self.model_config.max_model_len - seq_len
|
||||
sampling_params.update_from_generation_config(
|
||||
self.generation_config_fields, eos_token_id
|
||||
)
|
||||
if self.tokenizer is not None:
|
||||
sampling_params.update_from_tokenizer(self.tokenizer)
|
||||
else:
|
||||
pooling_params = params.clone()
|
||||
|
||||
# Multimodal related.
|
||||
mm_features: list[MultiModalFeatureSpec] | None = None
|
||||
|
||||
if decoder_inputs["type"] == "multimodal":
|
||||
decoder_mm_inputs = decoder_inputs["mm_kwargs"]
|
||||
decoder_mm_positions = decoder_inputs["mm_placeholders"]
|
||||
decoder_mm_hashes = decoder_inputs["mm_hashes"]
|
||||
|
||||
# Merge and flatten multimodal placeholders, hashes and inputs
|
||||
# from dictionaries to lists, and sort them by each item's position
|
||||
# in the input sequence.
|
||||
sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
|
||||
|
||||
mm_features = []
|
||||
for modality, idx in sorted_mm_idxs:
|
||||
mm_features.append(
|
||||
MultiModalFeatureSpec(
|
||||
data=decoder_mm_inputs[modality][idx],
|
||||
modality=modality,
|
||||
identifier=decoder_mm_hashes[modality][idx],
|
||||
mm_position=decoder_mm_positions[modality][idx],
|
||||
)
|
||||
)
|
||||
|
||||
return EngineCoreRequest(
|
||||
request_id=request_id,
|
||||
prompt_token_ids=prompt_token_ids,
|
||||
prompt_embeds=prompt_embeds,
|
||||
mm_features=mm_features,
|
||||
sampling_params=sampling_params,
|
||||
pooling_params=pooling_params,
|
||||
eos_token_id=eos_token_id,
|
||||
arrival_time=arrival_time,
|
||||
lora_request=lora_request,
|
||||
cache_salt=decoder_inputs.get("cache_salt"),
|
||||
priority=priority,
|
||||
data_parallel_rank=data_parallel_rank,
|
||||
trace_headers=trace_headers,
|
||||
)
|
||||
|
||||
def _validate_model_inputs(
|
||||
self, encoder_inputs: SingletonInputs | None, decoder_inputs: SingletonInputs
|
||||
):
|
||||
if encoder_inputs is not None:
|
||||
self._validate_model_input(encoder_inputs, prompt_type="encoder")
|
||||
|
||||
self._validate_model_input(decoder_inputs, prompt_type="decoder")
|
||||
|
||||
def _validate_model_input(
|
||||
self,
|
||||
prompt_inputs: SingletonInputs,
|
||||
*,
|
||||
prompt_type: Literal["encoder", "decoder"],
|
||||
):
|
||||
model_config = self.model_config
|
||||
|
||||
prompt_ids = (
|
||||
None
|
||||
if prompt_inputs["type"] == "embeds"
|
||||
else prompt_inputs["prompt_token_ids"]
|
||||
)
|
||||
prompt_embeds = (
|
||||
prompt_inputs["prompt_embeds"]
|
||||
if prompt_inputs["type"] == "embeds"
|
||||
else None
|
||||
)
|
||||
prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
|
||||
if not prompt_ids:
|
||||
if prompt_type == "encoder" and model_config.is_multimodal_model:
|
||||
pass # Mllama may have empty encoder inputs for text-only data
|
||||
elif prompt_inputs["type"] == "embeds":
|
||||
pass # Prompt embeds should not have prompt_ids.
|
||||
else:
|
||||
raise ValueError(f"The {prompt_type} prompt cannot be empty")
|
||||
|
||||
tokenizer = self.tokenizer
|
||||
if tokenizer is not None:
|
||||
max_input_id = max(prompt_ids or [], default=0)
|
||||
|
||||
# NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
|
||||
# self.model_config.get_vocab_size() is the model’s vocab size.
|
||||
# For Qwen3 models, the language model has extra tokens that do
|
||||
# not exist in the tokenizer, and vice versa for multimodal
|
||||
# placeholder tokens in some multimodal models.
|
||||
# See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
|
||||
# and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
|
||||
|
||||
# Here we take the max of the two to determine if a token id is
|
||||
# truly out-of-vocabulary.
|
||||
if max_input_id > max(
|
||||
tokenizer.max_token_id, self.model_config.get_vocab_size() - 1
|
||||
):
|
||||
raise ValueError(f"Token id {max_input_id} is out of vocabulary")
|
||||
|
||||
max_prompt_len = self.model_config.max_model_len
|
||||
if prompt_len > max_prompt_len:
|
||||
if prompt_type == "encoder" and model_config.is_multimodal_model:
|
||||
mm_registry = self.input_preprocessor.mm_registry
|
||||
mm_processor = mm_registry.create_processor(
|
||||
model_config,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
assert isinstance(mm_processor, EncDecMultiModalProcessor)
|
||||
|
||||
if mm_processor.pad_dummy_encoder_prompt:
|
||||
return # Skip encoder length check for Whisper
|
||||
|
||||
if model_config.is_multimodal_model:
|
||||
suggestion = (
|
||||
"Make sure that `max_model_len` is no smaller than the "
|
||||
"number of text tokens plus multimodal tokens. For image "
|
||||
"inputs, the number of image tokens depends on the number "
|
||||
"of images, and possibly their aspect ratios as well."
|
||||
)
|
||||
else:
|
||||
suggestion = (
|
||||
"Make sure that `max_model_len` is no smaller than the "
|
||||
"number of text tokens."
|
||||
)
|
||||
|
||||
raise ValueError(
|
||||
f"The {prompt_type} prompt (length {prompt_len}) is "
|
||||
f"longer than the maximum model length of {max_prompt_len}. "
|
||||
f"{suggestion}"
|
||||
)
|
||||
|
||||
# TODO: Find out how many placeholder tokens are there so we can
|
||||
# check that chunked prefill does not truncate them
|
||||
# max_batch_len = self.scheduler_config.max_num_batched_tokens
|
||||
|
||||
if (
|
||||
prompt_len == max_prompt_len
|
||||
and prompt_type == "decoder"
|
||||
and not model_config.is_multimodal_model
|
||||
and self.model_config.runner_type != "pooling"
|
||||
):
|
||||
suggestion = (
|
||||
"Make sure that `max_model_len` is no smaller than the "
|
||||
"number of text tokens (prompt + requested output tokens)."
|
||||
)
|
||||
raise ValueError(
|
||||
f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
|
||||
f"requested output tokens (at least 1) is longer than the maximum "
|
||||
f"model length of {max_prompt_len}. {suggestion}"
|
||||
)
|
||||
|
||||
def stat_mm_cache(self) -> MultiModalCacheStats | None:
|
||||
return self.input_preprocessor.stat_mm_cache()
|
||||
|
||||
def clear_mm_cache(self) -> None:
|
||||
self.input_preprocessor.clear_mm_cache()
|
||||
@ -7,7 +7,7 @@ from copy import copy
|
||||
from typing import Any, cast
|
||||
|
||||
import torch.nn as nn
|
||||
from typing_extensions import TypeVar
|
||||
from typing_extensions import TypeVar, deprecated
|
||||
|
||||
import vllm.envs as envs
|
||||
from vllm.config import ParallelConfig, VllmConfig
|
||||
@ -28,9 +28,9 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_
|
||||
from vllm.usage.usage_lib import UsageContext
|
||||
from vllm.v1.engine import EngineCoreRequest
|
||||
from vllm.v1.engine.core_client import EngineCoreClient
|
||||
from vllm.v1.engine.input_processor import InputProcessor
|
||||
from vllm.v1.engine.output_processor import OutputProcessor
|
||||
from vllm.v1.engine.parallel_sampling import ParentRequest
|
||||
from vllm.v1.engine.processor import Processor
|
||||
from vllm.v1.executor import Executor
|
||||
from vllm.v1.metrics.loggers import StatLoggerFactory, StatLoggerManager
|
||||
from vllm.v1.metrics.reader import Metric, get_metrics_snapshot
|
||||
@ -88,7 +88,7 @@ class LLMEngine:
|
||||
else:
|
||||
tokenizer = init_tokenizer_from_configs(self.model_config)
|
||||
|
||||
self.processor = Processor(self.vllm_config, tokenizer)
|
||||
self.input_processor = InputProcessor(self.vllm_config, tokenizer)
|
||||
self.io_processor = get_io_processor(
|
||||
self.vllm_config,
|
||||
self.model_config.io_processor_plugin,
|
||||
@ -135,6 +135,14 @@ class LLMEngine:
|
||||
# Don't keep the dummy data in memory
|
||||
self.reset_mm_cache()
|
||||
|
||||
@property
|
||||
@deprecated(
|
||||
"`LLMEngine.processor` has been renamed to `LLMEngine.input_processor`. "
|
||||
"The old name will be removed in v0.13."
|
||||
)
|
||||
def processor(self):
|
||||
return self.input_processor
|
||||
|
||||
@classmethod
|
||||
def from_vllm_config(
|
||||
cls,
|
||||
@ -231,11 +239,7 @@ class LLMEngine:
|
||||
request = prompt
|
||||
else:
|
||||
assert prompt_text is None
|
||||
logger.warning_once(
|
||||
"Processor has been moved under LLM and will "
|
||||
"be removed from LLMEngine in v0.13."
|
||||
)
|
||||
request = self.processor.process_inputs(
|
||||
request = self.input_processor.process_inputs(
|
||||
request_id,
|
||||
prompt,
|
||||
params,
|
||||
@ -307,7 +311,7 @@ class LLMEngine:
|
||||
self.logger_manager.record(
|
||||
scheduler_stats=outputs.scheduler_stats,
|
||||
iteration_stats=iteration_stats,
|
||||
mm_cache_stats=self.processor.stat_mm_cache(),
|
||||
mm_cache_stats=self.input_processor.stat_mm_cache(),
|
||||
)
|
||||
self.do_log_stats_with_interval()
|
||||
|
||||
@ -320,7 +324,7 @@ class LLMEngine:
|
||||
self.engine_core.profile(False)
|
||||
|
||||
def reset_mm_cache(self):
|
||||
self.processor.clear_mm_cache()
|
||||
self.input_processor.clear_mm_cache()
|
||||
self.engine_core.reset_mm_cache()
|
||||
|
||||
def reset_prefix_cache(self):
|
||||
@ -347,11 +351,11 @@ class LLMEngine:
|
||||
|
||||
@property
|
||||
def tokenizer(self) -> AnyTokenizer | None:
|
||||
return self.processor.tokenizer
|
||||
return self.input_processor.tokenizer
|
||||
|
||||
@tokenizer.setter
|
||||
def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
|
||||
self.processor.tokenizer = tokenizer
|
||||
self.input_processor.tokenizer = tokenizer
|
||||
|
||||
def get_tokenizer(self) -> AnyTokenizer:
|
||||
if self.tokenizer is None:
|
||||
|
||||
@ -1,637 +1,20 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import time
|
||||
from collections.abc import Mapping
|
||||
from typing import Any, Literal, cast
|
||||
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
|
||||
from vllm.inputs.parse import split_enc_dec_inputs
|
||||
from vllm.inputs.preprocess import InputPreprocessor
|
||||
from vllm.logger import init_logger
|
||||
from vllm.lora.request import LoRARequest
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
|
||||
from vllm.multimodal.cache import processor_cache_from_config
|
||||
from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
|
||||
from vllm.multimodal.parse import MultiModalDataParser
|
||||
from vllm.multimodal.processing import EncDecMultiModalProcessor
|
||||
from vllm.multimodal.utils import argsort_mm_positions
|
||||
from vllm.pooling_params import PoolingParams
|
||||
from vllm.sampling_params import SamplingParams
|
||||
from vllm.transformers_utils.tokenizer import AnyTokenizer
|
||||
from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
|
||||
from vllm.utils import length_from_prompt_token_ids_or_embeds
|
||||
from vllm.v1.engine import EngineCoreRequest
|
||||
from vllm.v1.metrics.stats import MultiModalCacheStats
|
||||
from vllm.v1.structured_output.backend_guidance import validate_guidance_grammar
|
||||
from vllm.v1.structured_output.backend_lm_format_enforcer import (
|
||||
validate_structured_output_request_lm_format_enforcer,
|
||||
)
|
||||
from vllm.v1.structured_output.backend_outlines import (
|
||||
validate_structured_output_request_outlines,
|
||||
)
|
||||
from vllm.v1.structured_output.backend_xgrammar import validate_xgrammar_grammar
|
||||
|
||||
logger = init_logger(__name__)
|
||||
import warnings
|
||||
|
||||
|
||||
class Processor:
|
||||
def __init__(
|
||||
self,
|
||||
vllm_config: VllmConfig,
|
||||
tokenizer: AnyTokenizer | None,
|
||||
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
||||
) -> None:
|
||||
self.vllm_config = vllm_config
|
||||
self.model_config = vllm_config.model_config
|
||||
self.cache_config = vllm_config.cache_config
|
||||
self.lora_config = vllm_config.lora_config
|
||||
self.structured_outputs_config = vllm_config.structured_outputs_config
|
||||
def __getattr__(name: str):
|
||||
if name == "Processor":
|
||||
from .input_processor import InputProcessor
|
||||
|
||||
self.generation_config_fields = self.model_config.try_get_generation_config()
|
||||
|
||||
self.mm_registry = mm_registry
|
||||
self.mm_processor_cache = processor_cache_from_config(vllm_config, mm_registry)
|
||||
|
||||
self.input_preprocessor = InputPreprocessor(
|
||||
self.model_config,
|
||||
tokenizer,
|
||||
mm_registry,
|
||||
mm_processor_cache=self.mm_processor_cache,
|
||||
warnings.warn(
|
||||
"`vllm.v1.engine.processor.Processor` has been moved to "
|
||||
"`vllm.v1.engine.input_processor.InputProcessor`. "
|
||||
"The old name will be removed in v0.13.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
@property
|
||||
def tokenizer(self) -> AnyTokenizer | None:
|
||||
return self.input_preprocessor.tokenizer
|
||||
return InputProcessor
|
||||
|
||||
@tokenizer.setter
|
||||
def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
|
||||
self.input_preprocessor.tokenizer = tokenizer
|
||||
|
||||
def _validate_logprobs(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
max_logprobs = self.model_config.max_logprobs
|
||||
if max_logprobs == -1:
|
||||
max_logprobs = self.model_config.get_vocab_size()
|
||||
|
||||
# Validate sample logprobs.
|
||||
if params.logprobs:
|
||||
num_logprobs = params.logprobs
|
||||
if num_logprobs == -1:
|
||||
num_logprobs = self.model_config.get_vocab_size()
|
||||
if num_logprobs > max_logprobs:
|
||||
raise ValueError(
|
||||
f"Requested sample logprobs of {num_logprobs}, "
|
||||
f"which is greater than max allowed: {max_logprobs}"
|
||||
)
|
||||
|
||||
# Validate prompt logprobs.
|
||||
if params.prompt_logprobs:
|
||||
num_prompt_logprobs = params.prompt_logprobs
|
||||
if num_prompt_logprobs == -1:
|
||||
num_prompt_logprobs = self.model_config.get_vocab_size()
|
||||
if num_prompt_logprobs > max_logprobs:
|
||||
raise ValueError(
|
||||
f"Requested prompt logprobs of {num_prompt_logprobs}, "
|
||||
f"which is greater than max allowed: {max_logprobs}"
|
||||
)
|
||||
|
||||
def _validate_sampling_params(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
self._validate_structured_output(params)
|
||||
self._validate_logit_bias(params)
|
||||
|
||||
if params.allowed_token_ids is None:
|
||||
return
|
||||
if not params.allowed_token_ids:
|
||||
raise ValueError("allowed_token_ids is not None and empty!")
|
||||
if self.tokenizer is None:
|
||||
# When skip_tokenizer_init=True, we can't validate token IDs
|
||||
# Skip validation and let the model handle invalid tokens
|
||||
return
|
||||
vocab_size = len(self.tokenizer)
|
||||
if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
|
||||
raise ValueError("allowed_token_ids contains out-of-vocab token id!")
|
||||
|
||||
def _validate_logit_bias(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
"""Validate logit_bias token IDs are within vocabulary range."""
|
||||
if not params.logit_bias:
|
||||
return
|
||||
|
||||
vocab_size = self.model_config.get_vocab_size()
|
||||
invalid_token_ids = []
|
||||
|
||||
for token_id in params.logit_bias:
|
||||
if token_id < 0 or token_id >= vocab_size:
|
||||
invalid_token_ids.append(token_id)
|
||||
|
||||
if invalid_token_ids:
|
||||
raise ValueError(
|
||||
f"token_id(s) {invalid_token_ids} in logit_bias contain "
|
||||
f"out-of-vocab token ids. Vocabulary size: {vocab_size}"
|
||||
)
|
||||
|
||||
def _validate_supported_sampling_params(
|
||||
self,
|
||||
params: SamplingParams,
|
||||
) -> None:
|
||||
# Logits processors not supported.
|
||||
if params.logits_processors:
|
||||
raise ValueError(
|
||||
"vLLM V1 does not support per request user provided logits processors."
|
||||
)
|
||||
# Async scheduling + spec decode currently incompatible with some
|
||||
# sampling parameters.
|
||||
if (
|
||||
self.vllm_config.speculative_config is not None
|
||||
and self.vllm_config.scheduler_config.async_scheduling
|
||||
and (
|
||||
params.frequency_penalty != 0.0
|
||||
or params.presence_penalty != 0.0
|
||||
or params.repetition_penalty != 1.0
|
||||
or params.bad_words_token_ids
|
||||
or params.structured_outputs
|
||||
)
|
||||
):
|
||||
raise ValueError(
|
||||
"async scheduling with spec decoding doesn't yet support "
|
||||
"penalties, bad words or structured outputs in sampling parameters."
|
||||
)
|
||||
|
||||
def _validate_params(
|
||||
self,
|
||||
params: SamplingParams | PoolingParams,
|
||||
):
|
||||
"""
|
||||
Validate supported SamplingParam.
|
||||
Should raise ValueError if unsupported for API Server.
|
||||
"""
|
||||
|
||||
if isinstance(params, PoolingParams):
|
||||
return
|
||||
|
||||
self._validate_logprobs(params)
|
||||
self._validate_sampling_params(params)
|
||||
self._validate_supported_sampling_params(params)
|
||||
|
||||
def _validate_multi_modal_uuids(self, prompt: PromptType) -> None:
|
||||
"""
|
||||
Validate that user-provided multi_modal_uuids align with
|
||||
multi_modal_data in the incoming request prompt(s).
|
||||
Only checks lengths; `None` entries are allowed and will be
|
||||
auto-hashed downstream.
|
||||
"""
|
||||
|
||||
def _validate_single_prompt(single_prompt: dict | str) -> None:
|
||||
if not isinstance(single_prompt, dict):
|
||||
return
|
||||
mm_data = single_prompt.get("multi_modal_data")
|
||||
mm_uuids = single_prompt.get("multi_modal_uuids")
|
||||
if not mm_data or not mm_uuids:
|
||||
return
|
||||
|
||||
for modality, items in mm_data.items():
|
||||
if modality in mm_uuids:
|
||||
data_len = len(items) if isinstance(items, list) else 1
|
||||
uuid_len = (
|
||||
len(mm_uuids[modality])
|
||||
if isinstance(mm_uuids[modality], list)
|
||||
else 1
|
||||
)
|
||||
if uuid_len != data_len:
|
||||
raise ValueError(
|
||||
f"multi_modal_uuids for modality '{modality}' "
|
||||
"must have same length as data: got "
|
||||
f"{uuid_len} uuids vs "
|
||||
f"{data_len} items."
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"multi_modal_uuids for modality '{modality}' must "
|
||||
"be provided if multi_modal_data is provided."
|
||||
)
|
||||
|
||||
# Handle explicit encoder/decoder prompts or singleton prompt
|
||||
if isinstance(prompt, dict) and "encoder_prompt" in prompt:
|
||||
enc = prompt.get("encoder_prompt")
|
||||
dec = prompt.get("decoder_prompt")
|
||||
if enc is not None:
|
||||
_validate_single_prompt(cast(dict | str, enc))
|
||||
if dec is not None:
|
||||
_validate_single_prompt(cast(dict | str, dec))
|
||||
else:
|
||||
_validate_single_prompt(prompt) # type: ignore[arg-type]
|
||||
|
||||
def _validate_lora(self, lora_request: LoRARequest | None) -> None:
|
||||
if lora_request is None:
|
||||
return
|
||||
|
||||
# LoRA request passed in while LoRA is not enabled
|
||||
if not self.lora_config:
|
||||
raise ValueError(
|
||||
f"Got lora_request {lora_request} but LoRA is not enabled!"
|
||||
)
|
||||
|
||||
if self.tokenizer is not None:
|
||||
logger.warning_once(
|
||||
"vLLM has deprecated support for supporting different "
|
||||
"tokenizers for different LoRAs. By default, vLLM uses base "
|
||||
"model's tokenizer. If you are using a LoRA "
|
||||
"with its own tokenizer, consider specifying `--tokenizer "
|
||||
"[lora_path]` to use the LoRA tokenizer."
|
||||
)
|
||||
|
||||
def _validate_structured_output(self, params: SamplingParams) -> None:
|
||||
if not params.structured_outputs or not self.structured_outputs_config:
|
||||
return
|
||||
|
||||
if self.model_config.skip_tokenizer_init and params.structured_outputs:
|
||||
raise ValueError(
|
||||
"Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'" # noqa: E501
|
||||
)
|
||||
|
||||
backend = self.structured_outputs_config.backend
|
||||
if _backend := params.structured_outputs._backend:
|
||||
# Request-level backend selection is not supported.
|
||||
# The values may differ if `params` is reused and was set
|
||||
# to a specific backend based on `auto` behavior in a previous
|
||||
# request. We remember that it was set as a result of `auto`
|
||||
# using the `_backend_was_auto` field set in the params.
|
||||
if backend != _backend and not (
|
||||
backend == "auto" and params.structured_outputs._backend_was_auto
|
||||
):
|
||||
raise ValueError(
|
||||
"Request-level structured output backend selection is not "
|
||||
f"supported. The request specified '{_backend}', but vLLM "
|
||||
f"was initialised with '{backend}'. This error can be "
|
||||
"resolved by removing '_backend' from the request."
|
||||
)
|
||||
else:
|
||||
params.structured_outputs._backend = backend
|
||||
|
||||
# Request content validation
|
||||
if (
|
||||
isinstance(params.structured_outputs.choice, list)
|
||||
and not params.structured_outputs.choice
|
||||
):
|
||||
# It is invalid for choice to be an empty list
|
||||
raise ValueError(
|
||||
f"Choice '{params.structured_outputs.choice}' cannot be an empty list" # noqa: E501
|
||||
)
|
||||
# Reject empty string grammar early to avoid engine-side crashes
|
||||
if (
|
||||
isinstance(params.structured_outputs.grammar, str)
|
||||
and params.structured_outputs.grammar.strip() == ""
|
||||
):
|
||||
raise ValueError("structured_outputs.grammar cannot be an empty string")
|
||||
|
||||
if backend.startswith("xgrammar"):
|
||||
# xgrammar with no fallback
|
||||
validate_xgrammar_grammar(params)
|
||||
elif backend.startswith("guidance"):
|
||||
# TODO: ideally we would have the LLTokenizer here as Lark syntax
|
||||
# allows <|special_token|> and similar, see
|
||||
# https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
|
||||
# Without tokenizer these are disallowed in grammars.
|
||||
if isinstance(self.tokenizer, MistralTokenizer):
|
||||
raise ValueError(
|
||||
"Mistral tokenizer is not supported for the 'guidance' "
|
||||
"structured output backend. Please use ['xgrammar', 'outlines'] "
|
||||
"backends or tokenizer_mode='hf' instead."
|
||||
)
|
||||
validate_guidance_grammar(params, tokenizer=None)
|
||||
elif backend == "outlines":
|
||||
# outlines backend
|
||||
validate_structured_output_request_outlines(params)
|
||||
elif backend == "lm-format-enforcer":
|
||||
# lm format enforcer backend
|
||||
if isinstance(self.tokenizer, MistralTokenizer):
|
||||
raise ValueError(
|
||||
"Mistral tokenizer is not supported for the 'lm-format-enforcer' "
|
||||
"structured output backend. Please use ['xgrammar', 'outlines'] "
|
||||
"backends or tokenizer_mode='hf' instead."
|
||||
)
|
||||
validate_structured_output_request_lm_format_enforcer(params)
|
||||
else:
|
||||
# NOTE: backend must be "auto" here, because we have
|
||||
# checked supported_backends above.
|
||||
# In this mode, we set opinionated defaults based on what we think
|
||||
# will satisfy the most use cases without having to worry about
|
||||
# this setting. We include fallback behavior here, but not with any
|
||||
# other setting where a specific backend was specified.
|
||||
try:
|
||||
validate_xgrammar_grammar(params)
|
||||
params.structured_outputs._backend = "xgrammar"
|
||||
except ValueError:
|
||||
# The request either failed validation
|
||||
# or includes some jsonschema feature(s) that
|
||||
# are not supported in xgrammar.
|
||||
if isinstance(self.tokenizer, MistralTokenizer):
|
||||
# Fall back to outlines if the tokenizer is Mistral
|
||||
validate_structured_output_request_outlines(params)
|
||||
params.structured_outputs._backend = "outlines"
|
||||
else:
|
||||
# Fall back to guidance by default.
|
||||
validate_guidance_grammar(params, tokenizer=None)
|
||||
params.structured_outputs._backend = "guidance"
|
||||
# Remember that this backend was set automatically
|
||||
params.structured_outputs._backend_was_auto = True
|
||||
|
||||
def _maybe_build_mm_uuids(
|
||||
self,
|
||||
request_id: str,
|
||||
prompt: PromptType,
|
||||
) -> MultiModalUUIDDict | None:
|
||||
"""Build per-item multimodal hash overrides when enabled. In this case,
|
||||
multimodal data items are identified by their request id, modality and
|
||||
index rather than their content.
|
||||
|
||||
Returns a dictionary of modality -> list[str] of overrides, or None if
|
||||
disabled or no multimodal data is present.
|
||||
"""
|
||||
|
||||
def _extract_mm_data(p: PromptType):
|
||||
if isinstance(p, dict) and "encoder_prompt" in p:
|
||||
enc = p.get("encoder_prompt")
|
||||
if isinstance(enc, dict):
|
||||
return enc.get("multi_modal_data")
|
||||
return None
|
||||
if isinstance(p, dict):
|
||||
return p.get("multi_modal_data")
|
||||
return None
|
||||
|
||||
mm_data = _extract_mm_data(prompt)
|
||||
if not mm_data:
|
||||
return None
|
||||
|
||||
mm_uuids: dict[str, list[str | None] | str] = {}
|
||||
for modality, data in mm_data.items():
|
||||
# Hash each item for embedding inputs.
|
||||
n = (
|
||||
len(data)
|
||||
if isinstance(data, list) or MultiModalDataParser.is_embeddings(data)
|
||||
else 1
|
||||
)
|
||||
mm_uuids[modality] = [f"{request_id}-{modality}-{i}" for i in range(n)]
|
||||
return mm_uuids
|
||||
|
||||
def process_inputs(
|
||||
self,
|
||||
request_id: str,
|
||||
prompt: PromptType,
|
||||
params: SamplingParams | PoolingParams,
|
||||
arrival_time: float | None = None,
|
||||
lora_request: LoRARequest | None = None,
|
||||
tokenization_kwargs: dict[str, Any] | None = None,
|
||||
trace_headers: Mapping[str, str] | None = None,
|
||||
priority: int = 0,
|
||||
data_parallel_rank: int | None = None,
|
||||
) -> EngineCoreRequest:
|
||||
self._validate_lora(lora_request)
|
||||
self._validate_params(params)
|
||||
|
||||
data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
|
||||
if data_parallel_rank is not None and not (
|
||||
0 <= data_parallel_rank < data_parallel_size
|
||||
):
|
||||
raise ValueError(
|
||||
f"data_parallel_rank {data_parallel_rank} "
|
||||
f"is out of range [0, {data_parallel_size})."
|
||||
)
|
||||
|
||||
if arrival_time is None:
|
||||
arrival_time = time.time()
|
||||
|
||||
# Optionally generate multimodal hash overrides to avoid hashing
|
||||
# multimodal data items by their content as their identifiers.
|
||||
|
||||
# NOTE: when users explicitly turn off BOTH prefix caching and input
|
||||
# processing caching, no multimodal features or embeddings will be
|
||||
# reused across requests, therefore identifying multimodal data items
|
||||
# by their content is no longer necessary, and we create uuids with
|
||||
# request id-modality-index as multimodal hash overrides.
|
||||
if (
|
||||
self.model_config.multimodal_config
|
||||
and self.model_config.multimodal_config.mm_processor_cache_gb == 0
|
||||
and not self.cache_config.enable_prefix_caching
|
||||
):
|
||||
mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
|
||||
else:
|
||||
# Otherwise, use user-provided uuids as multimodal hash overrides
|
||||
# if provided.
|
||||
self._validate_multi_modal_uuids(prompt)
|
||||
if isinstance(prompt, dict):
|
||||
mm_uuids = cast(
|
||||
MultiModalUUIDDict | None, prompt.get("multi_modal_uuids")
|
||||
)
|
||||
else:
|
||||
mm_uuids = None
|
||||
|
||||
# Process inputs, which includes:
|
||||
# 1. Tokenize text prompt, with LoRA request if one exists.
|
||||
# 2. For multimodal models with a merged preprocessor, preprocess
|
||||
# multimodal data and expand prompt token ids accordingly.
|
||||
processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
|
||||
prompt,
|
||||
tokenization_kwargs=tokenization_kwargs,
|
||||
mm_uuids=mm_uuids,
|
||||
)
|
||||
from vllm.platforms import current_platform
|
||||
|
||||
current_platform.validate_request(
|
||||
prompt=prompt,
|
||||
params=params,
|
||||
processed_inputs=processed_inputs,
|
||||
)
|
||||
|
||||
eos_token_id = self.input_preprocessor.get_eos_token_id()
|
||||
|
||||
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
|
||||
self._validate_model_inputs(encoder_inputs, decoder_inputs)
|
||||
|
||||
# Mypy can be conservative for TypedDict unions; normalize access.
|
||||
if decoder_inputs["type"] == "embeds":
|
||||
prompt_token_ids = None
|
||||
prompt_embeds = decoder_inputs["prompt_embeds"]
|
||||
else:
|
||||
prompt_token_ids = decoder_inputs["prompt_token_ids"]
|
||||
prompt_embeds = None
|
||||
|
||||
sampling_params = None
|
||||
pooling_params = None
|
||||
if isinstance(params, SamplingParams):
|
||||
# TODO: can we avoid cloning here in multiproc case?
|
||||
sampling_params = params.clone()
|
||||
# If unset max tokens, then generate up to the max_model_len.
|
||||
if sampling_params.max_tokens is None:
|
||||
seq_len = length_from_prompt_token_ids_or_embeds(
|
||||
prompt_token_ids, prompt_embeds
|
||||
)
|
||||
sampling_params.max_tokens = self.model_config.max_model_len - seq_len
|
||||
sampling_params.update_from_generation_config(
|
||||
self.generation_config_fields, eos_token_id
|
||||
)
|
||||
if self.tokenizer is not None:
|
||||
sampling_params.update_from_tokenizer(self.tokenizer)
|
||||
else:
|
||||
pooling_params = params.clone()
|
||||
|
||||
# Multimodal related.
|
||||
mm_features: list[MultiModalFeatureSpec] | None = None
|
||||
|
||||
if decoder_inputs["type"] == "multimodal":
|
||||
decoder_mm_inputs = decoder_inputs["mm_kwargs"]
|
||||
decoder_mm_positions = decoder_inputs["mm_placeholders"]
|
||||
decoder_mm_hashes = decoder_inputs["mm_hashes"]
|
||||
|
||||
# Merge and flatten multimodal placeholders, hashes and inputs
|
||||
# from dictionaries to lists, and sort them by each item's position
|
||||
# in the input sequence.
|
||||
sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
|
||||
|
||||
mm_features = []
|
||||
for modality, idx in sorted_mm_idxs:
|
||||
mm_features.append(
|
||||
MultiModalFeatureSpec(
|
||||
data=decoder_mm_inputs[modality][idx],
|
||||
modality=modality,
|
||||
identifier=decoder_mm_hashes[modality][idx],
|
||||
mm_position=decoder_mm_positions[modality][idx],
|
||||
)
|
||||
)
|
||||
|
||||
return EngineCoreRequest(
|
||||
request_id=request_id,
|
||||
prompt_token_ids=prompt_token_ids,
|
||||
prompt_embeds=prompt_embeds,
|
||||
mm_features=mm_features,
|
||||
sampling_params=sampling_params,
|
||||
pooling_params=pooling_params,
|
||||
eos_token_id=eos_token_id,
|
||||
arrival_time=arrival_time,
|
||||
lora_request=lora_request,
|
||||
cache_salt=decoder_inputs.get("cache_salt"),
|
||||
priority=priority,
|
||||
data_parallel_rank=data_parallel_rank,
|
||||
trace_headers=trace_headers,
|
||||
)
|
||||
|
||||
def _validate_model_inputs(
|
||||
self, encoder_inputs: SingletonInputs | None, decoder_inputs: SingletonInputs
|
||||
):
|
||||
if encoder_inputs is not None:
|
||||
self._validate_model_input(encoder_inputs, prompt_type="encoder")
|
||||
|
||||
self._validate_model_input(decoder_inputs, prompt_type="decoder")
|
||||
|
||||
def _validate_model_input(
|
||||
self,
|
||||
prompt_inputs: SingletonInputs,
|
||||
*,
|
||||
prompt_type: Literal["encoder", "decoder"],
|
||||
):
|
||||
model_config = self.model_config
|
||||
|
||||
prompt_ids = (
|
||||
None
|
||||
if prompt_inputs["type"] == "embeds"
|
||||
else prompt_inputs["prompt_token_ids"]
|
||||
)
|
||||
prompt_embeds = (
|
||||
prompt_inputs["prompt_embeds"]
|
||||
if prompt_inputs["type"] == "embeds"
|
||||
else None
|
||||
)
|
||||
prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
|
||||
if not prompt_ids:
|
||||
if prompt_type == "encoder" and model_config.is_multimodal_model:
|
||||
pass # Mllama may have empty encoder inputs for text-only data
|
||||
elif prompt_inputs["type"] == "embeds":
|
||||
pass # Prompt embeds should not have prompt_ids.
|
||||
else:
|
||||
raise ValueError(f"The {prompt_type} prompt cannot be empty")
|
||||
|
||||
tokenizer = self.tokenizer
|
||||
if tokenizer is not None:
|
||||
max_input_id = max(prompt_ids or [], default=0)
|
||||
|
||||
# NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
|
||||
# self.model_config.get_vocab_size() is the model’s vocab size.
|
||||
# For Qwen3 models, the language model has extra tokens that do
|
||||
# not exist in the tokenizer, and vice versa for multimodal
|
||||
# placeholder tokens in some multimodal models.
|
||||
# See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
|
||||
# and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
|
||||
|
||||
# Here we take the max of the two to determine if a token id is
|
||||
# truly out-of-vocabulary.
|
||||
if max_input_id > max(
|
||||
tokenizer.max_token_id, self.model_config.get_vocab_size() - 1
|
||||
):
|
||||
raise ValueError(f"Token id {max_input_id} is out of vocabulary")
|
||||
|
||||
max_prompt_len = self.model_config.max_model_len
|
||||
if prompt_len > max_prompt_len:
|
||||
if prompt_type == "encoder" and model_config.is_multimodal_model:
|
||||
mm_registry = self.input_preprocessor.mm_registry
|
||||
mm_processor = mm_registry.create_processor(
|
||||
model_config,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
assert isinstance(mm_processor, EncDecMultiModalProcessor)
|
||||
|
||||
if mm_processor.pad_dummy_encoder_prompt:
|
||||
return # Skip encoder length check for Whisper
|
||||
|
||||
if model_config.is_multimodal_model:
|
||||
suggestion = (
|
||||
"Make sure that `max_model_len` is no smaller than the "
|
||||
"number of text tokens plus multimodal tokens. For image "
|
||||
"inputs, the number of image tokens depends on the number "
|
||||
"of images, and possibly their aspect ratios as well."
|
||||
)
|
||||
else:
|
||||
suggestion = (
|
||||
"Make sure that `max_model_len` is no smaller than the "
|
||||
"number of text tokens."
|
||||
)
|
||||
|
||||
raise ValueError(
|
||||
f"The {prompt_type} prompt (length {prompt_len}) is "
|
||||
f"longer than the maximum model length of {max_prompt_len}. "
|
||||
f"{suggestion}"
|
||||
)
|
||||
|
||||
# TODO: Find out how many placeholder tokens are there so we can
|
||||
# check that chunked prefill does not truncate them
|
||||
# max_batch_len = self.scheduler_config.max_num_batched_tokens
|
||||
|
||||
if (
|
||||
prompt_len == max_prompt_len
|
||||
and prompt_type == "decoder"
|
||||
and not model_config.is_multimodal_model
|
||||
and self.model_config.runner_type != "pooling"
|
||||
):
|
||||
suggestion = (
|
||||
"Make sure that `max_model_len` is no smaller than the "
|
||||
"number of text tokens (prompt + requested output tokens)."
|
||||
)
|
||||
raise ValueError(
|
||||
f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
|
||||
f"requested output tokens (at least 1) is longer than the maximum "
|
||||
f"model length of {max_prompt_len}. {suggestion}"
|
||||
)
|
||||
|
||||
def stat_mm_cache(self) -> MultiModalCacheStats | None:
|
||||
return self.input_preprocessor.stat_mm_cache()
|
||||
|
||||
def clear_mm_cache(self) -> None:
|
||||
self.input_preprocessor.clear_mm_cache()
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user