[Model] Refactor Ultravox to use merged input processor

examples/offline_inference_audio_language.py

@@ -25,16 +25,16 @@ def run_ultravox(question: str, audio_count: int):
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     messages = [{
-        'role':
-        'user',
-        'content':
-        "<|reserved_special_token_0|>\n" * audio_count + question
+        'role': 'user',
+        'content': "<|audio|>\n" * audio_count + question
     }]
     prompt = tokenizer.apply_chat_template(messages,
                                            tokenize=False,
                                            add_generation_prompt=True)
 
-    llm = LLM(model=model_name, limit_mm_per_prompt={"audio": audio_count})
+    llm = LLM(model=model_name,
+              trust_remote_code=True,
+              limit_mm_per_prompt={"audio": audio_count})
     stop_token_ids = None
     return llm, prompt, stop_token_ids
 

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -16,7 +16,7 @@
 
 AudioTuple = Tuple[np.ndarray, int]
 
-VLLM_PLACEHOLDER = "<|reserved_special_token_0|>"
+VLLM_PLACEHOLDER = "<|audio|>"
 HF_PLACEHOLDER = "<|audio|>"
 
 CHUNKED_PREFILL_KWARGS = {
@@ -46,7 +46,8 @@ def audio(request):
 def server(request, audio_assets):
     args = [
         "--dtype=bfloat16", "--max-model-len=4096", "--enforce-eager",
-        f"--limit-mm-per-prompt=audio={len(audio_assets)}"
+        f"--limit-mm-per-prompt=audio={len(audio_assets)}",
+        "--trust-remote-code"
     ] + [
         f"--{key.replace('_','-')}={value}"
         for key, value in request.param.items()

vllm/entrypoints/chat_utils.py

@@ -418,7 +418,7 @@ def _placeholder_str(self, modality: ModalityStr,
             raise TypeError(f"Unknown {modality} model type: {model_type}")
         elif modality == "audio":
             if model_type == "ultravox":
-                return "<|reserved_special_token_0|>"
+                return "<|audio|>"
             if model_type == "qwen2_audio":
                 return (f"Audio {current_count}: "
                         f"<|audio_bos|><|AUDIO|><|audio_eos|>")

vllm/model_executor/models/ultravox.py

@@ -2,42 +2,43 @@
 """PyTorch Ultravox model."""
 
 import math
+from collections import defaultdict
 from functools import cached_property, lru_cache
-from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
-                    TypedDict, Union, cast)
+from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, Set,
+                    Tuple, TypedDict, Union)
 
 import numpy as np
 import torch
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import functional as F
+from transformers import BatchFeature, ProcessorMixin
 from transformers.models.whisper import WhisperFeatureExtractor
 from transformers.models.whisper.modeling_whisper import WhisperEncoder
 
 from vllm.attention import AttentionMetadata
 from vllm.config import VllmConfig
-from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
-                         InputContext, token_inputs)
+from vllm.inputs import InputContext
 from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.model_loader.loader import DefaultModelLoader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
-                             NestedTensors)
-from vllm.multimodal.utils import (cached_get_tokenizer,
-                                   consecutive_placeholder_ranges,
-                                   repeat_and_pad_placeholder_tokens)
-from vllm.sequence import IntermediateTensors, SequenceData
+from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        MultiModalDataDict,
+                                        MultiModalDataItems, ProcessorInputs,
+                                        PromptReplacement)
+from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
-from vllm.utils import is_list_of
 
 from .interfaces import SupportsMultiModal, SupportsPP
 from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     init_vllm_registered_model, maybe_prefix,
                     merge_multimodal_embeddings_from_map)
 
 _AUDIO_PLACEHOLDER_TOKEN = 128002
+_AUDIO_PLACEHOLDER_STR = "<|reserved_special_token_0|>"
 _AUDIO_TOKENS_PER_SECOND = 6.25
 
 
@@ -72,64 +73,15 @@ def get_ultravox_max_audio_tokens(ctx: InputContext):
     return math.ceil(feature_extractor.chunk_length * _AUDIO_TOKENS_PER_SECOND)
 
 
-def dummy_seq_data_for_ultravox(
-    ctx: InputContext,
-    seq_len: int,
-    audio_count: int,
-):
-    audio_length = min(get_ultravox_max_audio_tokens(ctx),
-                       seq_len // audio_count)
-
-    return SequenceData.from_prompt_token_counts(
-        (_AUDIO_PLACEHOLDER_TOKEN, audio_length * audio_count),
-        (0, seq_len - audio_length * audio_count)), {
-            "audio":
-            consecutive_placeholder_ranges(num_items=audio_count,
-                                           item_size=audio_length)
-        }
-
-
-def dummy_audio_for_ultravox(
-    ctx: InputContext,
-    audio_count: int,
-):
-    feature_extractor = whisper_feature_extractor(ctx)
-    audio_and_sr = (np.array([0.0] * feature_extractor.chunk_length), 1)
-    return {"audio": [audio_and_sr] * audio_count}
-
-
-def dummy_data_for_ultravox(
-    ctx: InputContext,
-    seq_len: int,
-    mm_counts: Mapping[str, int],
-):
-    audio_count = mm_counts["audio"]
-    seq_data, ranges = dummy_seq_data_for_ultravox(ctx, seq_len, audio_count)
-    mm_dict = dummy_audio_for_ultravox(ctx, audio_count)
-
-    return DummyData(seq_data, mm_dict, ranges)
-
-
-def input_mapper_for_ultravox(ctx: InputContext, data: object):
-    if not isinstance(data, list):
-        data = [data]
-
-    if len(data) == 0:
-        return MultiModalKwargs()
-
-    # If the audio inputs are embeddings, no need for preprocessing
-    if is_list_of(data, torch.Tensor, check="all"):
-        return MultiModalKwargs({"audio_embeds": data})
-
-    audio_features = []
-    for audio_input in data:
-        if not isinstance(audio_input, tuple):
-            raise NotImplementedError(
-                f"Unsupported data type: {type(audio_input)}")
-
-        (audio, sr) = cast(Tuple[np.ndarray, Union[float, int]], audio_input)
-        feature_extractor = whisper_feature_extractor(ctx)
+class UltravoxProcessor(BaseMultiModalProcessor):
 
+    def _resample_audio(
+        self,
+        audio: np.ndarray,
+        sr: int,
+    ) -> Dict[str, Union[np.ndarray, int]]:
+        # resample audio to the model's sampling rate
+        feature_extractor = whisper_feature_extractor(self.ctx)
         if sr != feature_extractor.sampling_rate:
             try:
                 import librosa
@@ -140,78 +92,99 @@ def input_mapper_for_ultravox(ctx: InputContext, data: object):
                                      orig_sr=sr,
                                      target_sr=feature_extractor.sampling_rate)
             sr = feature_extractor.sampling_rate
+        return {"audio": audio, "sampling_rate": sr}
 
-        minimum_audio_length = feature_extractor.n_fft // 2 + 1
-        if len(audio) < minimum_audio_length:
-            # Not enough audio; pad it.
-            audio = np.pad(audio, (0, minimum_audio_length - len(audio)))
-
-        single_audio_features = feature_extractor(
-            audio, sampling_rate=sr, padding="longest",
-            return_tensors="pt")["input_features"]
-
-        # Remove the batch dimension because we're wrapping it in a list.
-        audio_features.append(single_audio_features.squeeze(0))
-
-    return MultiModalKwargs({"audio_features": audio_features})
-
-
-def input_processor_for_ultravox(ctx: InputContext, inputs: DecoderOnlyInputs):
-    multi_modal_data = inputs.get("multi_modal_data")
-    if multi_modal_data is None or "audio" not in multi_modal_data:
-        return inputs
-
-    if "multi_modal_placeholders" in inputs and "audio" in inputs[
-            "multi_modal_placeholders"]:
-        # The inputs already have placeholders.
-        return inputs
+    def _apply_hf_processor(
+        self,
+        prompt: str,
+        mm_data: MultiModalDataDict,
+        mm_processor_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        audio_data = mm_data["audio"]
+        if not isinstance(audio_data, list):
+            audio_data = [audio_data]
+
+        # Ultravox processor doesn't support multiple inputs,
+        # therefore we need to input text and audio one by one
+        tokenizer = self._get_tokenizer()
+        hf_inputs = defaultdict(list)
+        for audio, sr in audio_data:
+            data = self._resample_audio(audio, sr)
+            processed_inputs = super()._apply_hf_processor(
+                prompt, data, mm_processor_kwargs)
+            prompt = tokenizer.decode(processed_inputs["input_ids"][0],
+                                      skip_special_tokens=False)
+            hf_inputs["audio_features"].append(
+                processed_inputs["audio_values"].squeeze(0))
+        hf_inputs["input_ids"] = processed_inputs["input_ids"]
+        return hf_inputs
+
+    def _get_hf_processor(
+        self,
+        **mm_processor_kwargs: Mapping[str, object],
+    ) -> ProcessorMixin:
+        # Ultravox processor use eot_token_id as the audio placeholder token,
+        # we replace it with <|reserved_special_token_0|> for convenience.
+        hf_processor = super()._get_hf_processor(**mm_processor_kwargs)
+        hf_processor.audio_token_replacement = _AUDIO_PLACEHOLDER_STR
+        return hf_processor
+
+    def _get_processor_data(
+        self,
+        mm_data: MultiModalDataDict,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        # Ultravox uses "audio" instead of "audios" as calling keyword
+        processor_data, passthrough_data = super()._get_processor_data(mm_data)
+        if "audios" in processor_data:
+            processor_data["audio"] = processor_data.pop("audios")
+        return processor_data, passthrough_data
+
+    def _get_prompt_replacements(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_inputs: BatchFeature,
+        mm_processor_kwargs: Mapping[str, object],
+    ) -> list[PromptReplacement]:
+        hf_processor = self._get_hf_processor()
+        stack_factor = hf_processor.stack_factor
+        encoder_ds_factor = hf_processor.encoder_ds_factor
+
+        def get_replacement_ultravox(item_idx: int):
+            audio_data, sr = mm_items.audio[item_idx]
+            audio_data = self._resample_audio(audio_data, sr)["audio"]
+            audio_len = audio_data.shape[0]
+            nb_encoder_frames = int(round(audio_len / encoder_ds_factor +
+                                          1e-4))
+            audio_token_len = int(np.ceil(nb_encoder_frames / stack_factor))
+            max_audio_token_len = get_ultravox_max_audio_tokens(self.ctx)
+            audio_token_len = min(max(1, audio_token_len), max_audio_token_len)
+            return [_AUDIO_PLACEHOLDER_TOKEN] * audio_token_len
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target="<|audio|>",
+                replacement=get_replacement_ultravox,
+            )
+        ]
 
-    feature_extractor = whisper_feature_extractor(ctx)
-    audios = multi_modal_data["audio"]
-    if not isinstance(audios, list):
-        audios = [audios]
-
-    audio_token_counts = []
-    for audio in audios:
-        if isinstance(audio, torch.Tensor):
-            audio_num_tokens = audio.shape[1]
-            audio_token_counts.append(audio_num_tokens)
-        else:
-            audio_data, sample_rate = audio
-            audio_length = audio_data.shape[0]
-            if sample_rate != feature_extractor.sampling_rate:
-                # Account for resampling.
-                adjustment = feature_extractor.sampling_rate / sample_rate
-                audio_length = math.ceil(adjustment * audio_length)
-
-            feature_extractor_output_length = math.ceil(
-                (audio_length - (feature_extractor.hop_length - 1)) /
-                feature_extractor.hop_length)
-
-            uv_config = ctx.get_hf_config(UltravoxConfig)
-            audio_num_tokens = min(
-                max(
-                    1,
-                    math.ceil(feature_extractor_output_length /
-                              (uv_config.stack_factor * 2))),
-                get_ultravox_max_audio_tokens(ctx))
-            audio_token_counts.append(audio_num_tokens)
-
-    tokenizer = cached_get_tokenizer(ctx.model_config.tokenizer)
-
-    new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
-        tokenizer,
-        inputs.get("prompt"),
-        inputs["prompt_token_ids"],
-        placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN,
-        repeat_count=audio_token_counts,
-    )
-
-    # NOTE: Create a defensive copy of the original inputs
-    return token_inputs(prompt_token_ids=new_token_ids,
-                        prompt=new_prompt,
-                        multi_modal_data=multi_modal_data,
-                        multi_modal_placeholders={"audio": ranges})
+    def _get_dummy_mm_inputs(
+        self,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        feature_extractor = whisper_feature_extractor(self.ctx)
+        sampling_rate = feature_extractor.sampling_rate
+        audio_len = feature_extractor.chunk_length * sampling_rate
+
+        audio_count = mm_counts["audio"]
+        audio = np.array([0.0] * audio_len)
+        data = {"audio": [(audio, sampling_rate)] * audio_count}
+
+        return ProcessorInputs(
+            prompt_text="<|audio|>" * audio_count,
+            mm_data=data,
+            mm_processor_kwargs={},
+        )
 
 
 class StackAudioFrames(nn.Module):
@@ -332,11 +305,9 @@ def forward(
         return hidden_states
 
 
-@MULTIMODAL_REGISTRY.register_input_mapper("audio", input_mapper_for_ultravox)
 @MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
     "audio", get_ultravox_max_audio_tokens)
-@INPUT_REGISTRY.register_dummy_data(dummy_data_for_ultravox)
-@INPUT_REGISTRY.register_input_processor(input_processor_for_ultravox)
+@MULTIMODAL_REGISTRY.register_processor(UltravoxProcessor)
 class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/multimodal/processing.py

@@ -594,14 +594,10 @@ def _find_placeholders(
         return list(
             iter_placeholders(all_prompt_repls, new_token_ids, mm_item_counts))
 
-    def _apply_hf_processor(
+    def _get_processor_data(
         self,
-        prompt: str,
         mm_data: MultiModalDataDict,
-        mm_processor_kwargs: Mapping[str, object],
     ) -> BatchFeature:
-        hf_processor = self._get_hf_processor(**mm_processor_kwargs)
-
         processor_data = dict[str, Any]()
         passthrough_data = dict[str, Any]()
         for k, v in mm_data.items():
@@ -619,6 +615,19 @@ def _apply_hf_processor(
                     processor_data[f"{k}s"] = v
             else:
                 processor_data[k] = v
+        return processor_data, passthrough_data
+
+    def _apply_hf_processor(
+        self,
+        prompt: str,
+        mm_data: MultiModalDataDict,
+        mm_processor_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        # some mm_processor_kwargs may be used in processor initialization
+        # instead of processor call
+        hf_processor = self._get_hf_processor(**mm_processor_kwargs)
+
+        processor_data, passthrough_data = self._get_processor_data(mm_data)
 
         assert callable(hf_processor)
         mm_processor_kwargs = self.ctx.resolve_hf_processor_call_kwargs(