Merge branch 'v0.6.2-audio' into v0.6.2-dev

docs/source/models/supported_models.rst

@@ -284,6 +284,11 @@ Multimodal Language Models
     - Image\ :sup:`E+`
     - :code:`Qwen/Qwen-VL`, :code:`Qwen/Qwen-VL-Chat`, etc.
     -
+  * - :code:`Qwen2AudioForConditionalGeneration`
+    - Qwen2-Audio
+    - T + A\ :sup:`+`
+    - :code:`Qwen/Qwen2-Audio-7B-Instruct`
+    -
   * - :code:`Qwen2VLForConditionalGeneration`
     - Qwen2-VL 
     - Image\ :sup:`+` / Video\ :sup:`+`

examples/offline_inference_audio_language.py

@@ -12,14 +12,15 @@ from vllm.assets.audio import AudioAsset
 from vllm.utils import FlexibleArgumentParser
 
 audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
-question_per_audio_count = [
-    "What is recited in the audio?",
-    "What sport and what nursery rhyme are referenced?"
-]
+question_per_audio_count = {
+    0: "What is 1+1?",
+    1: "What is recited in the audio?",
+    2: "What sport and what nursery rhyme are referenced?"
+}
 
 
 # Ultravox 0.3
-def run_ultravox(question, audio_count):
+def run_ultravox(question: str, audio_count: int):
     model_name = "fixie-ai/ultravox-v0_3"
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -42,9 +43,29 @@ def run_ultravox(question, audio_count):
     return llm, prompt, stop_token_ids
 
 
-model_example_map = {
-    "ultravox": run_ultravox,
-}
+# Qwen2-Audio
+def run_qwen2_audio(question: str, audio_count: int):
+    model_name = "Qwen/Qwen2-Audio-7B-Instruct"
+
+    llm = LLM(model=model_name,
+              max_model_len=4096,
+              max_num_seqs=5,
+              limit_mm_per_prompt={"audio": audio_count})
+
+    audio_in_prompt = "".join([
+        f"Audio {idx+1}: "
+        f"<|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(audio_count)
+    ])
+
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              "<|im_start|>user\n"
+              f"{audio_in_prompt}{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+model_example_map = {"ultravox": run_ultravox, "qwen2_audio": run_qwen2_audio}
 
 
 def main(args):
@@ -54,7 +75,7 @@ def main(args):
 
     audio_count = args.num_audios
     llm, prompt, stop_token_ids = model_example_map[model](
-        question_per_audio_count[audio_count - 1], audio_count)
+        question_per_audio_count[audio_count], audio_count)
 
     # We set temperature to 0.2 so that outputs can be different
     # even when all prompts are identical when running batch inference.
@@ -62,16 +83,18 @@ def main(args):
                                      max_tokens=64,
                                      stop_token_ids=stop_token_ids)
 
-    assert args.num_prompts > 0
-    inputs = {
-        "prompt": prompt,
-        "multi_modal_data": {
+    mm_data = {}
+    if audio_count > 0:
+        mm_data = {
             "audio": [
                 asset.audio_and_sample_rate
                 for asset in audio_assets[:audio_count]
             ]
-        },
-    }
+        }
+        
+    assert args.num_prompts > 0
+    inputs = {"prompt": prompt, "multi_modal_data": mm_data}
+    
     if args.num_prompts > 1:
         # Batch inference
         inputs = [inputs] * args.num_prompts
@@ -100,7 +123,7 @@ if __name__ == "__main__":
     parser.add_argument("--num-audios",
                         type=int,
                         default=1,
-                        choices=[1, 2],
+                        choices=[0, 1, 2],
                         help="Number of audio items per prompt.")
 
     args = parser.parse_args()

vllm/entrypoints/chat_utils.py

@@ -168,6 +168,9 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         elif modality == "audio":
             if model_type == "ultravox":
                 return "<|reserved_special_token_0|>"
+            if model_type == "qwen2_audio":
+                return (f"Audio {current_count}: "
+                        f"<|audio_bos|><|AUDIO|><|audio_eos|>")
             raise TypeError(f"Unknown model type: {model_type}")
         elif modality == "video":
             if model_type == "qwen2_vl":

vllm/inputs/__init__.py

-from .data import (EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
+from .data import (DecoderOnlyInputs, EncoderDecoderLLMInputs, ExplicitEncoderDecoderPrompt,
                    LLMInputs, PromptInputs, SingletonPromptInputs, TextPrompt,
-                   TokensPrompt, build_explicit_enc_dec_prompt,
-                   to_enc_dec_tuple_list, zip_enc_dec_prompts)
+                   TokensPrompt, 
+                   build_explicit_enc_dec_prompt, to_enc_dec_tuple_list, 
+                   token_inputs, zip_enc_dec_prompts)
 from .registry import InputContext, InputRegistry
 
 INPUT_REGISTRY = InputRegistry()
@@ -20,6 +21,8 @@ __all__ = [
     "SingletonPromptInputs",
     "ExplicitEncoderDecoderPrompt",
     "LLMInputs",
+    "token_inputs",
+    "DecoderOnlyInputs",
     "EncoderDecoderLLMInputs",
     "build_explicit_enc_dec_prompt",
     "to_enc_dec_tuple_list",

vllm/inputs/data.py

-from typing import (TYPE_CHECKING, Generic, Iterable, List, Optional, Tuple,
-                    Union)
+from typing import (TYPE_CHECKING, Any, Dict, Generic, Iterable, List, Literal,
+                    Optional, Tuple, Union)
 
 from typing_extensions import NotRequired, TypedDict, TypeVar
 
 if TYPE_CHECKING:
-    from vllm.multimodal import MultiModalDataDict
+    from vllm.multimodal import MultiModalDataDict, MultiModalPlaceholderDict
+    from vllm.multimodal.inputs import MultiModalInputsV2
 
 
 class TextPrompt(TypedDict):
@@ -18,6 +19,14 @@ class TextPrompt(TypedDict):
     Optional multi-modal data to pass to the model,
     if the model supports it.
     """
+    
+    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    """
+    Optional multi-modal processor kwargs to be forwarded to the
+    multimodal input mapper & processor. Note that if multiple modalities
+    have registered mappers etc for the model being considered, we attempt
+    to pass the mm_processor_kwargs to each of them.
+    """
 
 
 class TokensPrompt(TypedDict):
@@ -28,10 +37,18 @@ class TokensPrompt(TypedDict):
 
     multi_modal_data: NotRequired["MultiModalDataDict"]
     """
-    Optional multi-modal data to pass to the model,
+    DEPRECATED: Optional multi-modal data to pass to the model,
     if the model supports it.
     """
 
+    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    """
+    DEPRECATED: Optional multi-modal processor kwargs to be forwarded to the
+    multimodal input mapper & processor. Note that if multiple modalities
+    have registered mappers etc for the model being considered, we attempt
+    to pass the mm_processor_kwargs to each of them.
+    """
+
 
 SingletonPromptInputs = Union[str, TextPrompt, TokensPrompt]
 """
@@ -87,6 +104,8 @@ class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
     encoder_prompt: _T1_co
 
     decoder_prompt: Optional[_T2_co]
+    
+    mm_processor_kwargs: NotRequired[Dict[str, Any]]
 
 
 PromptInputs = Union[SingletonPromptInputs, ExplicitEncoderDecoderPrompt]
@@ -101,6 +120,71 @@ both decoder-only and encoder/decoder input types:
 """
 
 
+
+class TokenInputs(TypedDict):
+    """Represents token-based inputs."""
+
+    type: Literal["token"]
+    """The type of inputs."""
+
+    prompt_token_ids: List[int]
+    """The token IDs of the prompt."""
+
+    prompt: NotRequired[str]
+    """
+    The original prompt text corresponding to the token IDs, if available.
+    """
+
+    multi_modal_data: NotRequired["MultiModalDataDict"]
+    """
+    Optional multi-modal data to pass to the model,
+    if the model supports it.
+    """
+
+    multi_modal_placeholders: NotRequired["MultiModalPlaceholderDict"]
+    """
+    Placeholder ranges for the multi-modal data.
+    """
+
+    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    """
+    Optional multi-modal processor kwargs to be forwarded to the
+    multimodal input mapper & processor. Note that if multiple modalities
+    have registered mappers etc for the model being considered, we attempt
+    to pass the mm_processor_kwargs to each of them.
+    """
+    
+    
+def token_inputs(
+    prompt_token_ids: List[int],
+    prompt: Optional[str] = None,
+    multi_modal_data: Optional["MultiModalDataDict"] = None,
+    multi_modal_placeholders: Optional["MultiModalPlaceholderDict"] = None,
+    mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+) -> TokenInputs:
+    """Construct :class:`TokenInputs` from optional values."""
+    inputs = TokenInputs(type="token", prompt_token_ids=prompt_token_ids)
+
+    if prompt is not None:
+        inputs["prompt"] = prompt
+    if multi_modal_data is not None:
+        inputs["multi_modal_data"] = multi_modal_data
+    if multi_modal_placeholders is not None:
+        inputs["multi_modal_placeholders"] = multi_modal_placeholders
+    if mm_processor_kwargs is not None:
+        inputs["mm_processor_kwargs"] = mm_processor_kwargs
+
+    return inputs
+
+
+DecoderOnlyInputs = Union[TokenInputs, "MultiModalInputsV2"]
+"""
+The inputs in :class:`~vllm.LLMEngine` before they are
+passed to the model executor.
+This specifies the data required for decoder-only models.
+"""
+
+
 class LLMInputs(TypedDict):
     """
     The inputs in :class:`~vllm.LLMEngine` before they are
@@ -121,8 +205,8 @@ class LLMInputs(TypedDict):
     Optional multi-modal data to pass to the model,
     if the model supports it.
     """
-
-
+    
+    
 class EncoderDecoderLLMInputs(LLMInputs):
     """
     The inputs in :class:`~vllm.LLMEngine` before they are
@@ -146,6 +230,32 @@ class EncoderDecoderLLMInputs(LLMInputs):
     """
 
 
+class EncoderDecoderInputs(TypedDict):
+    """
+    The inputs in :class:`~vllm.LLMEngine` before they are
+    passed to the model executor.
+
+    This specifies the required data for encoder-decoder models.
+    """
+    encoder: Union[TokenInputs, "MultiModalInputsV2"]
+    """The inputs for the encoder portion."""
+
+    decoder: Union[TokenInputs, "MultiModalInputsV2"]
+    """The inputs for the decoder portion."""
+
+
+SingletonInputs = Union[TokenInputs, "MultiModalInputsV2"]
+"""
+A processed :class:`SingletonPrompt` which can be passed to
+:class:`vllm.sequence.Sequence`.
+"""
+
+
+ProcessorInputs = Union[DecoderOnlyInputs, EncoderDecoderInputs]
+"""
+The inputs to :data:`vllm.inputs.InputProcessor`.
+"""
+
 _T1 = TypeVar("_T1",
               bound=SingletonPromptInputs,
               default=SingletonPromptInputs)

vllm/model_executor/models/__init__.py

@@ -103,6 +103,7 @@ _MULTIMODAL_MODELS = {
     "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
     "Qwen2VLForConditionalGeneration": ("qwen2_vl",
                                         "Qwen2VLForConditionalGeneration"),
+    "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),  # noqa: E501
     "UltravoxModel": ("ultravox", "UltravoxModel"),
     "MllamaForConditionalGeneration": ("mllama",
                                        "MllamaForConditionalGeneration"),

vllm/model_executor/models/interfaces.py

-from typing import (ClassVar, Dict, List, Literal, Optional, Protocol, Type,
+from typing import (TYPE_CHECKING, ClassVar, Dict, List, Literal, Optional, Protocol, Type,
                     Union, overload, runtime_checkable)
 
+import torch
 from typing_extensions import TypeIs
 
 from vllm.config import LoRAConfig, MultiModalConfig, SchedulerConfig
 from vllm.logger import init_logger
 
+if TYPE_CHECKING:
+    from vllm.config import LoRAConfig, MultiModalConfig, SchedulerConfig
+    from vllm.sequence import IntermediateTensors
+
 logger = init_logger(__name__)
 
 
@@ -145,6 +150,41 @@ def _supports_lora(
     return isinstance(model, SupportsLoRA)
 
 
+@runtime_checkable
+class SupportsPP(Protocol):
+    """The interface required for all models that support pipeline parallel."""
+
+    supports_pp: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports pipeline parallel.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def make_empty_intermediate_tensors(
+        self,
+        batch_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> "IntermediateTensors":
+        """Called when PP rank > 0 for profiling purposes."""
+        ...
+
+    def forward(
+        self,
+        *,
+        intermediate_tensors: Optional["IntermediateTensors"],
+    ) -> Union[torch.Tensor, "IntermediateTensors"]:
+        """
+        Accept :class:`IntermediateTensors` when PP rank > 0.
+
+        Return :class:`IntermediateTensors` only for the last PP rank.
+        """
+        ...
+        
+        
 @runtime_checkable
 class HasInnerState(Protocol):
     """The interface required for all models that has inner state."""

vllm/model_executor/models/qwen2.py

@@ -51,7 +51,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA
-from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers, make_empty_intermediate_tensors_factory
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.utils import pad_weight, gemm_bank_conf
@@ -265,6 +265,10 @@ class Qwen2Model(nn.Module):
             prefix=f"{prefix}.layers",
         )
 
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+        
         if get_pp_group().is_last_rank:
             self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         else:
@@ -370,6 +374,9 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA):
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
         
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+        
         self.quant_method = None
         if quant_config is not None:
             self.quant_method=quant_config.get_name()

vllm/model_executor/models/qwen2_audio.py

+# coding=utf-8
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2-Audio model compatible with HuggingFace weights."""
+from functools import lru_cache
+from typing import Iterable, List, Mapping, Optional, Tuple, TypedDict, Union
+
+import librosa
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import Qwen2AudioConfig, Qwen2AudioEncoder
+
+from vllm.attention import AttentionMetadata
+from vllm.config import CacheConfig, MultiModalConfig
+from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
+                         token_inputs)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.models.qwen2 import Qwen2Model
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs
+from vllm.sequence import IntermediateTensors, SequenceData
+
+from .interfaces import SupportsMultiModal, SupportsPP
+
+logger = init_logger(__name__)
+
+_KEYS_TO_MODIFY_MAPPING = {
+    "language_model.lm_head": "lm_head",
+    "language_model.model": "language_model",
+}
+
+
+# # === Audio Inputs === #
+class Qwen2AudioInputs(TypedDict):
+    input_features: torch.Tensor
+    """Shape: 
+    `(num_audios, num_mel_bins, 3000)`
+    """
+
+    feature_attention_mask: torch.Tensor
+    """Shape: `(num_audios, 3000)`
+    """
+
+
+# === Audio Encoder === #
+
+
+class Qwen2AudioMultiModalProjector(nn.Module):
+
+    def __init__(self, audio_hidden_size: int, text_hidden_size: int):
+        super().__init__()
+        self.linear = nn.Linear(audio_hidden_size, text_hidden_size, bias=True)
+
+    def forward(self, audio_features):
+        hidden_states = self.linear(audio_features)
+        return hidden_states
+
+
+def dummy_data_for_qwen2_audio(ctx: InputContext, seq_len: int,
+                               mm_counts: Mapping[str, int]):
+    num_audios = mm_counts["audio"]
+    max_llm_audio_tokens = get_max_qwen2_audio_audio_tokens(ctx) * num_audios
+    if seq_len - max_llm_audio_tokens - 2 < 0:
+        raise RuntimeError(
+            f"Qwen2-Audio cannot process {num_audios} audios in a prompt, "
+            "please increase max_model_len or reduce audio limit by "
+            "--limit-mm-per-prompt.")
+
+    audio_token_index = ctx.model_config.hf_config.audio_token_index
+
+    dummy_seqdata = SequenceData.from_prompt_token_counts(
+        (audio_token_index, max_llm_audio_tokens),
+        (0, seq_len - max_llm_audio_tokens),
+    )
+    dummy_audio = np.full((max_llm_audio_tokens * 2 * 2 * 160, ), 0.)
+    return dummy_seqdata, {"audio": [(dummy_audio, 16000)] * num_audios}
+
+
+def get_processor(
+    processor_name: str,
+    *args,
+    trust_remote_code: bool = False,
+    **kwargs,
+):
+    """Gets a processor for the given model name via HuggingFace.
+    Derived from `vllm.transformers_utils.image_processor.get_image_processor`.
+    """
+    # don't put this import at the top level
+    # it will call torch.cuda.device_count()
+    from transformers import AutoProcessor
+
+    try:
+        processor = AutoProcessor.from_pretrained(
+            processor_name,
+            *args,
+            trust_remote_code=trust_remote_code,
+            **kwargs)
+    except ValueError as e:
+        # If the error pertains to the processor class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        # Unlike AutoTokenizer, AutoProcessor does not separate such errors
+        if not trust_remote_code:
+            err_msg = (
+                "Failed to load the processor. If the processor is "
+                "a custom processor not yet available in the HuggingFace "
+                "transformers library, consider setting "
+                "`trust_remote_code=True` in LLM or using the "
+                "`--trust-remote-code` flag in the CLI.")
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+
+    return processor
+
+
+cached_get_processor = lru_cache(get_processor)
+
+
+def _get_feat_extract_output_lengths(input_lengths: torch.LongTensor):
+    """
+    Computes the output length of the convolutional layers
+    and the output length of the audio encoder
+    """
+    input_lengths = (input_lengths - 1) // 2 + 1
+    output_lengths = (input_lengths - 2) // 2 + 1
+    return input_lengths, output_lengths
+
+
+def get_max_qwen2_audio_audio_tokens(ctx: InputContext) -> int:
+    max_source_position = (
+        ctx.model_config.hf_config.audio_config.max_source_positions)
+    output_lengths = (max_source_position - 2) // 2 + 1
+    return output_lengths
+
+
+def input_processor_for_qwen2_audio(
+        ctx: InputContext, inputs: DecoderOnlyInputs) -> DecoderOnlyInputs:
+    multi_modal_data = inputs.get("multi_modal_data")
+    if multi_modal_data is None or "audio" not in multi_modal_data:
+        return inputs
+
+    audios = multi_modal_data["audio"]
+    if not isinstance(audios, list):
+        audios = [audios]
+
+    if len(audios) == 0:
+        return inputs
+
+    processor = cached_get_processor(ctx.model_config.model)
+    resampled_audios = [
+        librosa.resample(audio,
+                         orig_sr=sampling_rate,
+                         target_sr=processor.feature_extractor.sampling_rate)
+        for audio, sampling_rate in audios
+    ]
+    audio_input_lengths = np.array(
+        [min(3000, _.shape[0] // 160 + 1) for _ in resampled_audios])
+
+    audio_feat_lengths, audio_output_lengths = _get_feat_extract_output_lengths(
+        audio_input_lengths)
+
+    audio_token_index = ctx.model_config.hf_config.audio_token_index
+
+    input_ids = inputs['prompt_token_ids']
+
+    new_input_ids = []
+    audio_num = input_ids.count(audio_token_index)
+    assert len(audio_input_lengths) == audio_num, \
+        (f'The text input contains {audio_num} audio tokens, '
+         f'but {len(audio_input_lengths)} audios provided')
+    start = 0
+    for audio_idx in range(audio_num):
+        end = input_ids.index(audio_token_index, start)
+        new_input_ids.extend(input_ids[start:end])  # text part
+
+        new_input_ids.extend([audio_token_index] *
+                             audio_output_lengths[audio_idx])
+        start = end + 1
+    new_input_ids.extend(input_ids[start:])
+
+    return token_inputs(
+        prompt_token_ids=new_input_ids,
+        prompt=inputs['prompt'],
+        multi_modal_data=multi_modal_data,
+    )
+
+
+def input_mapper_for_qwen2_audio(
+    ctx: InputContext,
+    multi_modal_data: Union[np.ndarray, List[np.ndarray]],
+) -> MultiModalInputs:
+    """Input mapper for Qwen2-Audio."""
+    if not isinstance(multi_modal_data, list):
+        multi_modal_data = [multi_modal_data]
+
+    if len(multi_modal_data) == 0:
+        return MultiModalInputs()
+
+    processor = cached_get_processor(ctx.model_config.model)
+    audio_feature_extractor = processor.feature_extractor
+    if audio_feature_extractor is None:
+        raise RuntimeError(
+            "No HuggingFace audio_feature_extractor is available "
+            "to process the audio object")
+
+    try:
+        resampled_audios = [
+            librosa.resample(
+                audio,
+                orig_sr=sampling_rate,
+                target_sr=processor.feature_extractor.sampling_rate)
+            for audio, sampling_rate in multi_modal_data
+        ]
+        batch_data = audio_feature_extractor(resampled_audios,
+                                             sampling_rate=16000,
+                                             return_attention_mask=True,
+                                             padding="max_length",
+                                             return_tensors="pt").data
+        batch_data["feature_attention_mask"] = batch_data.pop("attention_mask")
+    except Exception:
+        logger.error("Failed to process audio (%s)", multi_modal_data)
+        raise
+
+    return MultiModalInputs(batch_data)
+
+
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen2_audio)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen2_audio)
+@MULTIMODAL_REGISTRY.register_input_mapper("audio",
+                                           input_mapper_for_qwen2_audio)
+@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
+    "audio", get_max_qwen2_audio_audio_tokens)
+class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                         SupportsPP):
+
+    def __init__(self,
+                 config: Qwen2AudioConfig,
+                 multimodal_config: MultiModalConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
+        super().__init__()
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.audio_tower = Qwen2AudioEncoder(config.audio_config)
+        self.multi_modal_projector = Qwen2AudioMultiModalProjector(
+            config.audio_config.d_model, config.text_config.hidden_size)
+
+        self.quant_config = quant_config
+
+        self.language_model = Qwen2Model(config.text_config, cache_config,
+                                         quant_config)
+        self.unpadded_vocab_size = config.text_config.vocab_size
+        if config.text_config.tie_word_embeddings:
+            self.lm_head = self.language_model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(config.text_config.vocab_size,
+                                          config.text_config.hidden_size,
+                                          quant_config=quant_config)
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.text_config.vocab_size,
+                                                logit_scale)
+        self.sampler = Sampler()
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _validate_and_reshape_mm_tensor(self,
+                                        mm_input: Union[torch.Tensor,
+                                                        List[torch.Tensor]],
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_audio_input(
+            self, **kwargs: object) -> Optional[Qwen2AudioInputs]:
+        input_features = kwargs.pop('input_features', None)
+        feature_attention_mask = kwargs.pop('feature_attention_mask', None)
+        if input_features is None:
+            return None
+        input_features = self._validate_and_reshape_mm_tensor(
+            input_features, 'input_features')
+        feature_attention_mask = self._validate_and_reshape_mm_tensor(
+            feature_attention_mask, 'feature_attention_mask')
+        if not isinstance(input_features, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of audio input features. "
+                             f"Got type: {type(input_features)}")
+        return Qwen2AudioInputs(input_features=input_features,
+                                feature_attention_mask=feature_attention_mask)
+
+    def _process_audio_input(self,
+                             audio_input: Qwen2AudioInputs) -> torch.Tensor:
+
+        input_features = audio_input["input_features"]
+        feature_attention_mask = audio_input["feature_attention_mask"]
+
+        audio_feat_lengths, audio_output_lengths = (
+            self.audio_tower._get_feat_extract_output_lengths(
+                feature_attention_mask.sum(-1)))
+
+        batch_size, _, max_mel_seq_len = input_features.shape
+        max_seq_len = (max_mel_seq_len - 2) // 2 + 1
+        # Create a sequence tensor of shape (batch_size, max_seq_len)
+        seq_range = (torch.arange(
+            0,
+            max_seq_len,
+            dtype=audio_feat_lengths.dtype,
+            device=audio_feat_lengths.device).unsqueeze(0).expand(
+                batch_size, max_seq_len))
+        lengths_expand = audio_feat_lengths.unsqueeze(-1).expand(
+            batch_size, max_seq_len)
+        # Create mask
+        padding_mask = seq_range >= lengths_expand
+
+        audio_attention_mask_ = padding_mask.view(
+            batch_size, 1, 1, max_seq_len).expand(batch_size, 1, max_seq_len,
+                                                  max_seq_len)
+        audio_attention_mask = audio_attention_mask_.to(
+            dtype=self.audio_tower.conv1.weight.dtype,
+            device=self.audio_tower.conv1.weight.device)
+        audio_attention_mask[audio_attention_mask_] = float("-inf")
+
+        audio_outputs = self.audio_tower(input_features,
+                                         attention_mask=audio_attention_mask)
+        selected_audio_feature = audio_outputs.last_hidden_state
+        audio_features = self.multi_modal_projector(selected_audio_feature)
+        num_audios, max_audio_tokens, embed_dim = audio_features.shape
+        audio_features_mask = torch.arange(max_audio_tokens).expand(
+            num_audios, max_audio_tokens
+        ).to(audio_output_lengths.device) < audio_output_lengths.unsqueeze(1)
+        masked_audio_features = audio_features[audio_features_mask].view(
+            -1, embed_dim)
+
+        return masked_audio_features
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+        else:
+            audio_input = self._parse_and_validate_audio_input(**kwargs)
+
+            if audio_input is None:
+                inputs_embeds = None
+            else:
+                inputs_embeds = self.language_model.embed_tokens(input_ids)
+                masked_audio_features = self._process_audio_input(audio_input)
+                # merge llm embeddings and audio features
+                mask = (input_ids == self.config.audio_token_index)
+                inputs_embeds[mask, :] = masked_audio_features
+
+                input_ids = None
+
+        hidden_states = self.language_model(
+            input_ids=input_ids,
+            positions=positions,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if (self.config.text_config.tie_word_embeddings
+                    and "lm_head.weight" in name):
+                continue
+            for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
+                if key_to_modify in name:
+                    name = name.replace(key_to_modify, new_key)
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name or 'audio' in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
\ No newline at end of file

vllm/model_executor/models/ultravox.py

@@ -117,6 +117,9 @@ def dummy_data_for_ultravox(
 def input_mapper_for_ultravox(ctx: InputContext, data: object):
     if not isinstance(data, list):
         data = [data]
+        
+    if len(data) == 0:
+        return MultiModalInputs()
 
     audio_features = []
     for audio_input in data:

vllm/multimodal/__init__.py

 from .base import (BatchedTensorInputs, MultiModalDataBuiltins,
                    MultiModalDataDict, MultiModalInputs, MultiModalPlugin,
                    NestedTensors)
+from .inputs import MultiModalPlaceholderDict
 from .registry import MultiModalRegistry
 
 MULTIMODAL_REGISTRY = MultiModalRegistry()
@@ -17,6 +18,7 @@ __all__ = [
     "MultiModalDataBuiltins",
     "MultiModalDataDict",
     "MultiModalInputs",
+    "MultiModalPlaceholderDict",
     "MultiModalPlugin",
     "NestedTensors",
     "MULTIMODAL_REGISTRY",

vllm/multimodal/inputs.py

+from collections import UserDict, defaultdict
+from typing import (Any, Dict, List, Literal, Mapping, Sequence, Tuple,
+                    TypedDict, TypeVar, Union, cast, final)
+
+import numpy as np
+import torch
+import torch.types
+from PIL.Image import Image
+from typing_extensions import TypeAlias
+
+from vllm.utils import JSONTree, is_list_of, json_map_leaves
+
+_T = TypeVar("_T")
+
+# yapf: disable
+ImageItem: TypeAlias = Union[Image, np.ndarray, torch.Tensor]
+"""
+A :class:`transformers.image_utils.ImageInput` representing a single image,
+which can be passed to a HuggingFace :code:`ImageProcessor`.
+"""
+
+VideoItem: TypeAlias = Union[
+    List[Image],
+    np.ndarray,
+    torch.Tensor,
+    List[np.ndarray],
+    List[torch.Tensor],
+]
+"""
+
+A :class:`transformers.image_utils.VideoInput` representing a single video,
+which can be passed to a HuggingFace :code:`VideoProcessor`.
+"""
+
+AudioItem: TypeAlias = Union[
+    np.ndarray,
+    List[float],
+    Tuple[np.ndarray, float],  # DEPRECATED: Use mm_processor_kwargs instead
+]
+"""
+Represents a single audio that can be inputted to a HuggingFace
+:code:`AudioProcessor`.
+"""
+# yapf: enable
+
+MultiModalData: TypeAlias = Union[_T, List[_T]]
+"""
+Either a single data item, or a list of data items.
+
+The number of data items allowed per modality is restricted by
+:code:`--limit-mm-per-prompt`.
+"""
+
+
+@final
+class MultiModalDataBuiltins(TypedDict, total=False):
+    """Type annotations for modality types predefined by vLLM."""
+
+    image: MultiModalData[ImageItem]
+    """The input image(s)."""
+
+    video: MultiModalData[VideoItem]
+    """The input video(s)."""
+
+    audio: MultiModalData[AudioItem]
+    """The input audio(s)."""
+
+
+MultiModalDataDict: TypeAlias = Mapping[str, MultiModalData[Any]]
+"""
+A dictionary containing an entry for each modality type to input.
+
+Note:
+    This dictionary also accepts modality keys defined outside
+    :class:`MultiModalDataBuiltins` as long as a customized plugin
+    is registered through the :class:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
+    Read more on that :ref:`here <adding_multimodal_plugin>`.
+"""
+
+
+class PlaceholderRange(TypedDict):
+    """
+    Placeholder location information for multi-modal data.
+
+    For example:
+        Prompt: AAAA BBBB What is in these images?
+        Images A and B will have:
+            A: { "offset": 0, "length": 4 }
+            B: { "offset": 5, "length": 4 }
+    """
+
+    offset: int
+    """The start index of the placeholder in the prompt."""
+
+    length: int
+    """The length of the placeholder."""
+
+
+NestedTensors = Union[List["NestedTensors"], List[torch.Tensor], torch.Tensor]
+"""
+Uses a list instead of a tensor if the dimensions of each element do not match.
+"""
+
+BatchedTensorInputs: TypeAlias = Dict[str, NestedTensors]
+"""
+A dictionary containing nested tensors which have been batched via
+:meth:`MultiModalKwargs.batch`.
+"""
+
+
+class MultiModalKwargs(UserDict[str, NestedTensors]):
+    """
+    A dictionary that represents the keyword arguments to
+    :meth:`~torch.nn.Module.forward`.
+    """
+
+    @staticmethod
+    def _try_stack(nested_tensors: NestedTensors) -> NestedTensors:
+        """
+        Stack the inner dimensions that have the same shape in
+        a nested list of tensors.
+
+        Thus, a dimension represented by a list means that the inner
+        dimensions are different for each element along that dimension.
+        """
+        if isinstance(nested_tensors, torch.Tensor):
+            return nested_tensors
+
+        # TODO: Remove these once all models have been migrated
+        if isinstance(nested_tensors, np.ndarray):
+            return torch.from_numpy(nested_tensors)
+        if isinstance(nested_tensors, (int, float)):
+            return torch.tensor(nested_tensors)
+
+        stacked = [MultiModalKwargs._try_stack(t) for t in nested_tensors]
+        if not is_list_of(stacked, torch.Tensor, check="all"):
+            # Only tensors (not lists) can be stacked.
+            return stacked
+
+        tensors_ = cast(List[torch.Tensor], stacked)
+        if any(t.shape != tensors_[0].shape for t in tensors_):
+            # The tensors have incompatible shapes and can't be stacked.
+            return tensors_
+
+        return torch.stack(tensors_)
+
+    @staticmethod
+    def batch(inputs_list: List["MultiModalKwargs"]) -> BatchedTensorInputs:
+        """
+        Batch multiple inputs together into a dictionary.
+
+        The resulting dictionary has the same keys as the inputs.
+        If the corresponding value from each input is a tensor and they all
+        share the same shape, the output value is a single batched tensor;
+        otherwise, the output value is a list containing the original value
+        from each input.
+        """
+        if len(inputs_list) == 0:
+            return {}
+
+        # We need to consider the case where each item in the batch
+        # contains different modalities (i.e. different keys).
+        item_lists: Dict[str, List[NestedTensors]] = defaultdict(list)
+
+        for inputs in inputs_list:
+            for k, v in inputs.items():
+                item_lists[k].append(v)
+
+        return {
+            k: MultiModalKwargs._try_stack(item_list)
+            for k, item_list in item_lists.items()
+        }
+
+    @staticmethod
+    def as_kwargs(
+        batched_inputs: BatchedTensorInputs,
+        *,
+        device: torch.types.Device,
+    ) -> BatchedTensorInputs:
+        json_inputs = cast(JSONTree[torch.Tensor], batched_inputs)
+
+        json_mapped = json_map_leaves(
+            lambda x: x.to(device, non_blocking=True),
+            json_inputs,
+        )
+
+        return cast(BatchedTensorInputs, json_mapped)
+
+
+MultiModalPlaceholderDict = Mapping[str, Sequence[PlaceholderRange]]
+"""
+A dictionary containing placeholder ranges.
+"""
+
+
+class MultiModalInputsV2(TypedDict):
+    """
+    Represents the outputs of :class:`vllm.multimodal.MultiModalProcessor`,
+    ready to be passed to vLLM internals.
+    """
+
+    type: Literal["multimodal"]
+    """The type of inputs."""
+
+    prompt: str
+    """
+    The original, unprocessed prompt text.
+
+    Note:
+        Since prompt text is not required by vLLM internals, we leave this
+        unprocessed to save CPU computation. You can still call
+        :code:`tokenizer.decode(prompt_token_ids)` to get the processed text.
+    """
+
+    prompt_token_ids: List[int]
+    """The processed token IDs which includes placeholder tokens."""
+
+    mm_kwargs: MultiModalKwargs
+    """Keyword arguments to be directly passed to the model after batching."""
+
+    mm_placeholders: MultiModalPlaceholderDict
+    """
+    For each modality, information about the placeholder tokens in
+    :code:`prompt_token_ids`.
+    """
\ No newline at end of file

vllm/sequence.py

@@ -29,6 +29,11 @@ VLLM_TOKEN_ID_ARRAY_TYPE = "l"
 VLLM_INVALID_TOKEN_ID = -1
 
 
+def array_full(token_id: int, count: int):
+    """:class:`array` equivalent of :func:`numpy.full`."""
+    return array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
+
+
 # We use dataclass for now because it is used for
 # openai server output, and msgspec is not serializable.
 # TODO(sang): Fix it.
@@ -174,6 +179,26 @@ class SequenceData(msgspec.Struct,
 
     _first_step_flag: bool = True
 
+    @staticmethod
+    def from_prompt_token_counts(
+            *token_counts: Tuple[int, int]) -> "SequenceData":
+        """
+        Construct a :class:`SequenceData` instance by concatenating
+        prompt token sequences.
+
+        Each tuple represents one token sequence, expressed in the form
+        :code:`(token_id, count)`.
+        """
+        if len(token_counts) == 0:
+            return SequenceData.from_seqs([])
+
+        prompt_token_ids_arr = reduce(
+            array.__iadd__,
+            (array_full(token_id, count) for token_id, count in token_counts),
+        )
+
+        return SequenceData(prompt_token_ids_arr)
+    
     @staticmethod
     def from_token_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
         if len(token_counts) == 0: