[Model] Add Granite Speech Support (#16246)

Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com>
Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com>

docs/source/models/supported_models.md

@@ -895,6 +895,13 @@ See [this page](#generative-models) for more information on how to use generativ
   * ✅︎
   * ✅︎
   * ✅︎
+- * `GraniteSpeechForConditionalGeneration`
+  * Granite Speech
+  * T + A
+  * `ibm-granite/granite-speech-3.3-8b`
+  * ✅︎
+  * ✅︎
+  * ✅︎
 - * `H2OVLChatModel`
   * H2OVL
   * T + I<sup>E+</sup>

examples/offline_inference/audio_language.py

@@ -38,6 +38,37 @@ class ModelRequestData(NamedTuple):
 # Unless specified, these settings have been tested to work on a single L4.
 
 
+# Granite Speech
+def run_granite_speech(question: str, audio_count: int) -> ModelRequestData:
+    # NOTE - the setting in this example are somehat different than what is
+    # optimal for granite speech, and it is generally recommended to use beam
+    # search. Check the model README for suggested settings.
+    # https://huggingface.co/ibm-granite/granite-speech-3.3-8b
+    model_name = "ibm-granite/granite-speech-3.3-8b"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=2048,
+        max_num_seqs=2,
+        enable_lora=True,
+        max_lora_rank=64,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
+
+    # The model has an audio-specific lora directly in its model dir;
+    # it should be enabled whenever you pass audio inputs to the model.
+    speech_lora_path = model_name
+    audio_placeholder = "<|audio|>" * audio_count
+    prompts = f"<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>{audio_placeholder}{question}<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"  # noqa: E501
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompts,
+        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
+    )
+
+
 # MiniCPM-O
 def run_minicpmo(question: str, audio_count: int) -> ModelRequestData:
     model_name = "openbmb/MiniCPM-o-2_6"
@@ -209,6 +240,7 @@ def run_whisper(question: str, audio_count: int) -> ModelRequestData:
 
 
 model_example_map = {
+    "granite_speech": run_granite_speech,
     "minicpmo": run_minicpmo,
     "phi4_mm": run_phi4mm,
     "qwen2_audio": run_qwen2_audio,

tests/conftest.py

@@ -21,6 +21,7 @@ from transformers.models.auto.auto_factory import _BaseAutoModelClass
 from tests.models.utils import (TokensTextLogprobs,
                                 TokensTextLogprobsPromptLogprobs)
 from vllm import LLM, SamplingParams
+from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
 from vllm.config import TaskOption, _get_and_verify_dtype
@@ -103,10 +104,25 @@ class _VideoAssets(_VideoAssetsBase):
         return [prompts["sample_demo_1"]]
 
 
+class _AudioAssetsBase(UserList[AudioAsset]):
+    pass
+
+
+class _AudioAssets(_AudioAssetsBase):
+
+    def __init__(self) -> None:
+        super().__init__([
+            AudioAsset("mary_had_lamb"),
+            AudioAsset("winning_call"),
+        ])
+
+
 IMAGE_ASSETS = _ImageAssets()
 """Singleton instance of :class:`_ImageAssets`."""
 VIDEO_ASSETS = _VideoAssets()
 """Singleton instance of :class:`_VideoAssets`."""
+AUDIO_ASSETS = _AudioAssets()
+"""Singleton instance of :class:`_AudioAssets`."""
 
 
 @pytest.fixture(scope="function", autouse=True)
@@ -263,6 +279,11 @@ def video_assets() -> _VideoAssets:
     return VIDEO_ASSETS
 
 
+@pytest.fixture(scope="session")
+def audio_assets() -> _AudioAssets:
+    return AUDIO_ASSETS
+
+
 _T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict)
 _R = TypeVar("_R")
 
@@ -390,10 +411,15 @@ class HfRunner:
                 processor_kwargs["images"] = image
             if videos is not None and (video := videos[i]) is not None:
                 processor_kwargs["videos"] = video
-            if audios is not None and (audio_tuple := audios[i]) is not None:
+            if audios is not None and (audio_inputs := audios[i]) is not None:
-                audio, sr = audio_tuple
+                # HACK - not all processors take sampling_rate; we should
+                # clean this up in the future.
+                if len(audio_inputs) == 2:
+                    audio, sr = audio_inputs
                     processor_kwargs["audio"] = audio
                     processor_kwargs["sampling_rate"] = sr
+                else:
+                    processor_kwargs["audio"] = audio_inputs
 
             inputs = self.processor(**processor_kwargs)
             if isinstance(inputs, BatchFeature):

tests/models/decoder_only/audio_language/test_granite_speech.py

@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from collections.abc import Sequence
+from typing import Optional
+
+import pytest
+from transformers import AutoModelForSpeechSeq2Seq
+
+from vllm.lora.request import LoRARequest
+from vllm.sequence import SampleLogprobs
+
+from ....conftest import HfRunner, PromptAudioInput, VllmRunner, _AudioAssets
+from ...registry import HF_EXAMPLE_MODELS
+from ...utils import check_logprobs_close
+
+HF_AUDIO_PROMPT = "<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|><|audio|>can you transcribe the speech into a written format?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"  # noqa: E501
+
+
+def vllm_to_hf_output(
+    vllm_output: tuple[list[int], str, Optional[SampleLogprobs]],
+) -> tuple[list[int], str, Optional[SampleLogprobs]]:
+    """Sanitize hf output to be comparable with vllm output."""
+    output_ids, output_str, out_logprobs = vllm_output
+
+    hf_output_str = output_str + "<|end_of_text|>"
+
+    return output_ids, hf_output_str, out_logprobs
+
+
+MODEL_NAME = "ibm-granite/granite-speech-3.3-8b"
+# Audio lora co-exists directly in the model directory, but
+# currently still needs to be passed directly to vLLM.
+audio_lora_path = MODEL_NAME
+models = [MODEL_NAME]
+
+
+def run_test(
+    hf_runner: type[HfRunner],
+    vllm_runner: type[VllmRunner],
+    inputs: Sequence[tuple[list[str], PromptAudioInput]],
+    model: str,
+    *,
+    max_model_len: int,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
+    """Inference result should be the same between hf and vllm.
+
+    All the audio fixtures for the test are from AUDIO_ASSETS.
+    For huggingface runner, we provide the audio as input.
+    For vllm runner, we provide MultiModalDataDict objects
+    and corresponding MultiModalConfig as input.
+    Note, the text input is also adjusted to abide by vllm contract.
+    The text output is sanitized to be able to compare with hf.
+    """
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+    # max_model_len should be greater than image_feature_size
+    with vllm_runner(
+            model,
+            task="generate",
+            max_model_len=max_model_len,
+            max_num_seqs=1,
+            dtype=dtype,
+            limit_mm_per_prompt={"audio": 1},
+            tensor_parallel_size=tensor_parallel_size,
+            distributed_executor_backend=distributed_executor_backend,
+            enable_lora=True,
+            max_lora_rank=64,
+            enforce_eager=True,
+    ) as vllm_model:
+        lora_request = LoRARequest("audio", 1, audio_lora_path)
+        vllm_outputs_per_case = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                audios=audios,
+                                                lora_request=lora_request)
+            for prompts, audios in inputs
+        ]
+
+    with hf_runner(model, dtype=dtype,
+                   auto_cls=AutoModelForSpeechSeq2Seq) as hf_model:
+
+        hf_processor = hf_model.processor
+        eos_token_id = hf_processor.tokenizer.eos_token_id
+
+        hf_outputs_per_case = [
+            hf_model.generate_greedy_logprobs_limit(prompts,
+                                                    max_tokens,
+                                                    num_logprobs=num_logprobs,
+                                                    audios=[audios],
+                                                    eos_token_id=eos_token_id)
+            for prompts, audios in inputs
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
+                                        vllm_outputs_per_case):
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=[
+                vllm_to_hf_output(output) for output in vllm_outputs
+            ],
+            name_0="hf",
+            name_1="vllm",
+        )
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_model_len", [2048])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [10])
+def test_models(hf_runner, vllm_runner, model: str, audio_assets: _AudioAssets,
+                dtype: str, max_model_len: int, max_tokens: int,
+                num_logprobs: int) -> None:
+    model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
+    model_info.check_available_online(on_fail="skip")
+    model_info.check_transformers_version(on_fail="skip")
+
+    audio, sr = audio_assets[0].audio_and_sample_rate
+    # This model expects 16k sample rate, which our test audio
+    # already is; if this changes, it may break this test,
+    # so we check it directly
+    assert sr == 16000
+    run_test(
+        hf_runner,
+        vllm_runner,
+        [
+            ([HF_AUDIO_PROMPT], [audio]),
+        ],
+        model,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -11,7 +11,7 @@ from transformers import AutoModel, AutoTokenizer
 from vllm.multimodal.audio import resample_audio_librosa
 from vllm.sequence import SampleLogprobs
 
-from ....conftest import HfRunner, VllmRunner
+from ....conftest import HfRunner, VllmRunner, _AudioAssets
 from ....utils import RemoteOpenAIServer
 from ...registry import HF_EXAMPLE_MODELS
 from ...utils import check_logprobs_close
@@ -31,12 +31,6 @@ CHUNKED_PREFILL_KWARGS = {
 }
 
 
-@pytest.fixture(scope="session")
-def audio_assets():
-    from vllm.assets.audio import AudioAsset
-    return [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
-
-
 @pytest.fixture(scope="module", params=("mary_had_lamb", "winning_call"))
 def audio(request):
     from vllm.assets.audio import AudioAsset
@@ -59,7 +53,7 @@ def params_kwargs_to_cli_args(params_kwargs: dict[str, Any]) -> list[str]:
     pytest.param({}, marks=pytest.mark.cpu_model),
     pytest.param(CHUNKED_PREFILL_KWARGS),
 ])
-def server(request, audio_assets):
+def server(request, audio_assets: _AudioAssets):
     args = [
         "--dtype", "bfloat16", "--max-model-len", "4096", "--enforce-eager",
         "--limit-mm-per-prompt",
@@ -230,8 +224,9 @@ def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int,
     pytest.param({}, marks=pytest.mark.cpu_model),
     pytest.param(CHUNKED_PREFILL_KWARGS),
 ])
-def test_models_with_multiple_audios(vllm_runner, audio_assets, dtype: str,
+def test_models_with_multiple_audios(vllm_runner, audio_assets: _AudioAssets,
-                                     max_tokens: int, num_logprobs: int,
+                                     dtype: str, max_tokens: int,
+                                     num_logprobs: int,
                                      vllm_kwargs: dict) -> None:
 
     vllm_prompt = _get_prompt(len(audio_assets),
@@ -250,7 +245,7 @@ def test_models_with_multiple_audios(vllm_runner, audio_assets, dtype: str,
 
 
 @pytest.mark.asyncio
-async def test_online_serving(client, audio_assets):
+async def test_online_serving(client, audio_assets: _AudioAssets):
     """Exercises online serving with/without chunked prefill enabled."""
 
     messages = [{

tests/models/multimodal/processing/test_common.py

@@ -254,6 +254,7 @@ def _test_processing_correctness_mistral(
     "adept/fuyu-8b",
     "google/gemma-3-4b-it",
     "THUDM/glm-4v-9b",
+    "ibm-granite/granite-speech-3.3-8b",
     "h2oai/h2ovl-mississippi-800m",
     "OpenGVLab/InternVL2-1B",
     "HuggingFaceM4/Idefics3-8B-Llama3",

tests/models/registry.py

@@ -301,6 +301,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                 hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]}),  # noqa: E501
     "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
     "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it"),
+    "GraniteSpeechForConditionalGeneration": _HfExamplesInfo("ibm-granite/granite-speech-3.3-8b",  # noqa: E501
+                                                             min_transformers_version="4.52.0"),  # noqa: E501
     "GLM4VForCausalLM": _HfExamplesInfo("THUDM/glm-4v-9b",
                                         trust_remote_code=True,
                                         hf_overrides={"architectures": ["GLM4VForCausalLM"]}),  # noqa: E501

vllm/entrypoints/chat_utils.py

@@ -517,7 +517,7 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
 
             raise TypeError(f"Unknown {modality} model type: {model_type}")
         elif modality == "audio":
-            if model_type == "ultravox":
+            if model_type in ("ultravox", "granite_speech"):
                 return "<|audio|>"
             if model_type == "phi4mm":
                 return f"<|audio_{current_count}|>"

vllm/model_executor/models/blip2.py

@@ -60,6 +60,7 @@ class Blip2QFormerMultiHeadAttention(nn.Module):
         quant_config: Optional[QuantizationConfig],
         cache_config: Optional[CacheConfig],
         is_cross_attention: bool = False,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -139,7 +140,7 @@ class Blip2QFormerMultiHeadAttention(nn.Module):
 
 class Blip2QFormerSelfOutput(nn.Module):
 
-    def __init__(self, config: Blip2QFormerConfig) -> None:
+    def __init__(self, config: Blip2QFormerConfig, prefix: str = "") -> None:
         super().__init__()
 
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
@@ -167,6 +168,7 @@ class Blip2QFormerAttention(nn.Module):
         quant_config: Optional[QuantizationConfig],
         cache_config: Optional[CacheConfig],
         is_cross_attention: bool = False,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -175,9 +177,10 @@ class Blip2QFormerAttention(nn.Module):
             quant_config=quant_config,
             cache_config=cache_config,
             is_cross_attention=is_cross_attention,
+            prefix=f"{prefix}.attention",
         )
 
-        self.output = Blip2QFormerSelfOutput(config)
+        self.output = Blip2QFormerSelfOutput(config, prefix=f"{prefix}.output")
 
     def forward(
         self,
@@ -195,7 +198,7 @@ class Blip2QFormerAttention(nn.Module):
 
 class Blip2QFormerIntermediate(nn.Module):
 
-    def __init__(self, config: Blip2QFormerConfig) -> None:
+    def __init__(self, config: Blip2QFormerConfig, prefix: str = "") -> None:
         super().__init__()
 
         self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
@@ -209,7 +212,7 @@ class Blip2QFormerIntermediate(nn.Module):
 
 class Blip2QFormerOutput(nn.Module):
 
-    def __init__(self, config: Blip2QFormerConfig) -> None:
+    def __init__(self, config: Blip2QFormerConfig, prefix: str = "") -> None:
         super().__init__()
 
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
@@ -237,6 +240,7 @@ class Blip2QFormerLayer(nn.Module):
         quant_config: Optional[QuantizationConfig],
         cache_config: Optional[CacheConfig],
         layer_idx: int,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -244,7 +248,8 @@ class Blip2QFormerLayer(nn.Module):
         self.seq_len_dim = 1
         self.attention = Blip2QFormerAttention(config,
                                                quant_config=quant_config,
-                                               cache_config=cache_config)
+                                               cache_config=cache_config,
+                                               prefix=f"{prefix}.attention")
 
         self.layer_idx = layer_idx
 
@@ -253,13 +258,16 @@ class Blip2QFormerLayer(nn.Module):
                 config,
                 quant_config=quant_config,
                 cache_config=cache_config,
-                is_cross_attention=True)
+                is_cross_attention=True,
+                prefix=f"{prefix}.crossattention")
             self.has_cross_attention = True
         else:
             self.has_cross_attention = False
 
-        self.intermediate_query = Blip2QFormerIntermediate(config)
+        self.intermediate_query = Blip2QFormerIntermediate(
-        self.output_query = Blip2QFormerOutput(config)
+            config, prefix=f"{prefix}.intermediate_query")
+        self.output_query = Blip2QFormerOutput(config,
+                                               prefix=f"{prefix}.output_query")
 
     def forward(
         self,
@@ -325,6 +333,7 @@ class Blip2QFormerEncoder(nn.Module):
         *,
         quant_config: Optional[QuantizationConfig],
         cache_config: Optional[CacheConfig],
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -334,7 +343,8 @@ class Blip2QFormerEncoder(nn.Module):
             Blip2QFormerLayer(config,
                               quant_config=quant_config,
                               cache_config=cache_config,
-                              layer_idx=layer_idx)
+                              layer_idx=layer_idx,
+                              prefix=f"{prefix}.layer.{layer_idx}")
             for layer_idx in range(config.num_hidden_layers)
         ])
 
@@ -365,6 +375,7 @@ class Blip2QFormerModel(nn.Module):
         *,
         quant_config: Optional[QuantizationConfig],
         cache_config: Optional[CacheConfig],
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -376,7 +387,8 @@ class Blip2QFormerModel(nn.Module):
 
         self.encoder = Blip2QFormerEncoder(config,
                                            quant_config=quant_config,
-                                           cache_config=cache_config)
+                                           cache_config=cache_config,
+                                           prefix=f"{prefix}.encoder")
 
     def forward(
         self,
@@ -511,7 +523,8 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
 
         self.qformer = Blip2QFormerModel(config.qformer_config,
                                          cache_config=cache_config,
-                                         quant_config=quant_config)
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.qformer")
 
         self.language_projection = nn.Linear(
             config.qformer_config.hidden_size,

vllm/model_executor/models/granite_speech.py

@@ -0,0 +1,777 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2025 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 IBM Granite speeech model."""
+import math
+from typing import Iterable, Mapping, Optional, Set, Tuple, TypedDict, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import BatchFeature, PretrainedConfig
+
+from vllm.config import CacheConfig, VllmConfig
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.sampler import get_sampler
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (AudioProcessorItems, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .blip2 import Blip2QFormerModel
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .utils import (AutoWeightsLoader, embed_multimodal,
+                    init_vllm_registered_model, maybe_prefix)
+
+
+### Audio Input
+class GraniteSpeechAudioInputs(TypedDict):
+
+    input_features: torch.Tensor
+    """Shape: `(bsz, num_features, 160)`"""
+
+    input_features_mask: torch.Tensor
+    """Shape: `(bsz, num_features)`"""
+
+    audio_embed_sizes: list[int]
+    """List of length `bsz`"""
+
+
+class GraniteSpeechMultiModalProcessingInfo(BaseProcessingInfo):
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"audio": 1}
+
+    # There is no limit to the maximum number of audio tokens that can be
+    # encoded as features; we pick ~5000 as a number that is probably higher
+    # than we would expect to encounter. The sequence of length
+    # get_max_audio_len() produces get_max_audio_tokens().
+    def get_max_audio_tokens(self):
+        return 5001
+
+    def get_max_audio_len(self):
+        return 8000000
+
+
+### Input Processing  & Multimodal utils
+class GraniteSpeechMultiModalProcessor(
+        BaseMultiModalProcessor[GraniteSpeechMultiModalProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_hf_processor().audio_processor
+        sampling_rate = feature_extractor.melspec_kwargs["sample_rate"]
+        return MultiModalDataParser(target_sr=sampling_rate)
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            input_features=MultiModalFieldConfig.batched("audio"),
+            audio_embed_sizes=MultiModalFieldConfig.batched("audio"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        feature_extractor = processor.audio_processor
+        vocab = tokenizer.get_vocab()
+
+        # Use getattr with default to be compatible with transformers<4.48
+        audio_token = getattr(processor, "audio_token", "<|audio|>")
+        audio_token_id = vocab[audio_token]
+
+        def get_replacement(item_idx: int):
+            audios = mm_items.get_items("audio", AudioProcessorItems)
+            audio = audios.get(item_idx)
+            audio_length = audio.shape[-1]
+            num_projector_features = feature_extractor._get_num_audio_features(
+                [audio_length])[0]
+            return [audio_token_id] * num_projector_features
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=[audio_token_id],
+                replacement=get_replacement,
+            )
+        ]
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        mm_data = dict(mm_data)
+        audios = mm_data.pop("audios", [])
+
+        if audios:
+            # GraniteSpeechFeatureExtractor accepts "audio"
+            mm_data["audio"] = audios
+
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+        )
+
+        if "audio" in mm_data:
+            # Calculate the number of audio tokens per entry in the batch;
+            # This is used to split the batch back out after padding.
+            audio_token_index = self.info.get_hf_config().audio_token_index
+            processed_outputs["audio_embed_sizes"] = [
+                torch.sum(indices == audio_token_index).item()
+                for indices in processed_outputs["input_ids"]
+            ]
+
+        return processed_outputs
+
+
+class GraniteSpeechDummyInputsBuilder(
+        BaseDummyInputsBuilder[GraniteSpeechMultiModalProcessingInfo]):
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_audios = mm_counts.get("audio", 0)
+        return {
+            "audio":
+            self._get_dummy_audios(
+                length=self.info.get_max_audio_len(),
+                num_audios=num_audios,
+            )
+        }
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_audios = mm_counts.get("audio", 0)
+        hf_processor = self.info.get_hf_processor()
+        audio_token = getattr(hf_processor, "audio_token", "<|audio|>")
+        return audio_token * num_audios
+
+
+### QFormer Projector
+class GraniteSpeechEncoderProjector(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: CacheConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.projector_config.hidden_size
+        self.downsample_rate = config.downsample_rate
+        self.window_size = config.window_size
+        self.num_queries = config.window_size // config.downsample_rate
+
+        self.query = nn.Parameter(
+            torch.zeros(1, self.num_queries,
+                        config.projector_config.hidden_size))
+
+        # NOTE - this is implemented generically in transformers,
+        # but for now we create the QFormer model directly since
+        # all existing models use this for the projector.
+        self.qformer = Blip2QFormerModel(
+            config.projector_config,
+            quant_config=quant_config,
+            cache_config=cache_config,
+            prefix=f"{prefix}.qformer",
+        )
+        self.linear = nn.Linear(config.projector_config.hidden_size,
+                                config.text_config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, seq_len, dim = hidden_states.size()
+        nblocks = math.ceil(seq_len / self.window_size)
+        pad = nblocks * self.window_size - seq_len
+        hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, pad),
+                                          "constant", 0)
+        hidden_states = hidden_states.view(batch_size * nblocks,
+                                           self.window_size, dim)
+
+        last_hidden_state = self.qformer(
+            query_embeds=self.query.data,
+            encoder_hidden_states=hidden_states,
+        )
+
+        query_proj = self.linear(
+            last_hidden_state.view(
+                batch_size,
+                nblocks * self.window_size // self.downsample_rate,
+                -1,
+            ))
+        return query_proj
+
+
+# Encoder - conformer is adapted from: https://github.com/lucidrains/conformer.git
+# NOTE - it would be nice to see if we can align this with other models using
+# conformer in vLLM, e.g., phi4mm audio.
+class GraniteSpeechConformerFeedForward(nn.Module):
+    """Feedforward module for conformer encoder blocks."""
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(config.hidden_dim)
+
+        self.up_proj = ColumnParallelLinear(
+            input_size=config.hidden_dim,
+            output_size=config.hidden_dim * config.feedforward_mult,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        self.silu = nn.SiLU()
+
+        self.down_proj = RowParallelLinear(
+            input_size=config.hidden_dim * config.feedforward_mult,
+            output_size=config.hidden_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(hidden_states)
+        hidden_states, _ = self.up_proj(hidden_states)
+        hidden_states = self.silu(hidden_states)
+        hidden_states, _ = self.down_proj(hidden_states)
+        return hidden_states
+
+
+class GraniteSpeechConformerAttention(nn.Module):
+    """Attention for conformer blocks using Shaw's relative positional
+    embeddings. See the following [paper](https://arxiv.org/pdf/1803.02155)
+    for more details.
+    """
+
+    def __init__(self, config: PretrainedConfig, prefix: str = ""):
+        super().__init__()
+
+        inner_dim = config.dim_head * config.num_heads
+        self.max_pos_emb = config.max_pos_emb
+        self.context_size = config.context_size
+        self.num_heads = config.num_heads
+        self.dim_head = config.dim_head
+        self.scale = self.dim_head**-0.5
+        self.pre_norm = nn.LayerNorm(config.hidden_dim)
+        self.to_q = nn.Linear(config.hidden_dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(config.hidden_dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, config.hidden_dim)
+        self.rel_pos_emb = nn.Embedding(2 * self.max_pos_emb + 1,
+                                        self.dim_head)
+
+        if self.context_size <= 0 or self.context_size > self.max_pos_emb:
+            raise ValueError(
+                "Context size is either less than 0 or exceeds the max_pos_emb"
+            )
+
+    def forward(self, hidden_states: torch.Tensor,
+                attention_dists: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(hidden_states)
+        bsz, num_features, _ = hidden_states.shape
+
+        num_blocks = math.ceil(num_features / self.context_size)
+        remainder = num_features % self.context_size
+        if remainder > 0:
+            # right padding to reach block size
+            hidden_states = torch.nn.functional.pad(
+                hidden_states, (0, 0, 0, self.context_size - remainder))
+
+        # NOTE: would be nice to try to use qkvparallellinear
+        # here for this block attention implementation if possible
+        query_states = self.to_q(hidden_states)
+        key_states, value_states = self.to_kv(hidden_states).chunk(2, dim=-1)
+
+        query_states = query_states.reshape(bsz, num_blocks, self.context_size,
+                                            self.num_heads,
+                                            -1).transpose(2, 3)
+        key_states = key_states.reshape(bsz, num_blocks, self.context_size,
+                                        self.num_heads, -1).transpose(2, 3)
+        value_states = value_states.reshape(bsz, num_blocks, self.context_size,
+                                            self.num_heads,
+                                            -1).transpose(2, 3)
+
+        # shaw's relative positional embedding
+        dist = attention_dists.to(hidden_states.device)
+        rel_pos_emb = self.rel_pos_emb(dist)
+        rel_pos_emb_expanded = rel_pos_emb.view([1, 1, 1] +
+                                                list(rel_pos_emb.shape))
+        pos_attn = torch.sum(query_states.unsqueeze(-2) * rel_pos_emb_expanded,
+                             dim=-1) * self.scale
+
+        if remainder > 0:
+            # masked attention in the extended block
+            mask = torch.ones(self.context_size,
+                              self.context_size,
+                              dtype=bool,
+                              device=hidden_states.device)
+            mask[:remainder, :remainder] = 0
+            mask_value = -torch.finfo(pos_attn.dtype).max
+            pos_attn[:, -1, :].masked_fill_(mask, mask_value)
+
+        with torch.nn.attention.sdpa_kernel(
+                torch.nn.attention.SDPBackend.MATH):
+            out = F.scaled_dot_product_attention(query_states,
+                                                 key_states,
+                                                 value_states,
+                                                 attn_mask=pos_attn,
+                                                 scale=self.scale)
+        out = out.transpose(2, 3).reshape(bsz, hidden_states.shape[1], -1)
+        return self.to_out(out[:, :num_features, :])
+
+
+class GraniteSpeechConformerDepthWiseConv1d(nn.Module):
+    """Wrapper for padded 1D pointwise convolution."""
+
+    def __init__(self,
+                 chan_in: int,
+                 chan_out: int,
+                 kernel_size: int,
+                 prefix: str = ""):
+        super().__init__()
+        # Padding for the 1D conv is symmetric or close (i.e., offset by one).
+        pad = kernel_size // 2
+        pad_offset = (kernel_size + 1) % 2
+        self.padding = (pad, pad - pad_offset)
+
+        self.conv = nn.Conv1d(chan_in,
+                              chan_out,
+                              kernel_size,
+                              groups=chan_in,
+                              bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = F.pad(hidden_states, self.padding)
+        return self.conv(hidden_states)
+
+
+class GraniteSpeechConformerConvModule(nn.Module):
+    """Conformer conv module consisting of several 1D/depthwise 1D
+    convolutional layers.
+    """
+
+    def __init__(self, config: PretrainedConfig, prefix: str = ""):
+        super().__init__()
+        inner_dim = config.hidden_dim * config.conv_expansion_factor
+
+        self.norm = nn.LayerNorm(config.hidden_dim)
+        self.up_conv = nn.Conv1d(config.hidden_dim, inner_dim * 2, 1)
+        self.glu = nn.GLU(dim=1)
+        self.depth_conv = GraniteSpeechConformerDepthWiseConv1d(
+            inner_dim,
+            inner_dim,
+            kernel_size=config.conv_kernel_size,
+            prefix=f"{prefix}.depth_conv",
+        )
+        self.silu = nn.SiLU()
+        self.batch_norm = nn.BatchNorm1d(inner_dim)
+        self.down_conv = nn.Conv1d(inner_dim, config.hidden_dim, 1)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.up_conv(hidden_states.permute(0, 2, 1))
+        hidden_states = self.glu(hidden_states)
+        hidden_states = self.depth_conv(hidden_states)
+        hidden_states = self.silu(self.batch_norm(hidden_states))
+        hidden_states = self.down_conv(hidden_states).permute(0, 2, 1)
+        return hidden_states
+
+
+class GraniteSpeechConformerBlock(nn.Module):
+    """Conformer block, consisting largely of linear layers,
+    attention, and convolutional layers."""
+
+    def __init__(self, config: PretrainedConfig, prefix: str = ""):
+        super().__init__()
+        self.ff1 = GraniteSpeechConformerFeedForward(config,
+                                                     prefix=f"{prefix}.ff1")
+        self.attn = GraniteSpeechConformerAttention(config,
+                                                    prefix=f"{prefix}.attn")
+        self.conv = GraniteSpeechConformerConvModule(config,
+                                                     prefix=f"{prefix}.conv")
+        self.ff2 = GraniteSpeechConformerFeedForward(config,
+                                                     prefix=f"{prefix}.ff2")
+        self.post_norm = nn.LayerNorm(config.hidden_dim)
+
+    def forward(self, hidden_states: torch.Tensor,
+                attention_dists: torch.Tensor) -> torch.Tensor:
+        hidden_states = 0.5 * self.ff1(hidden_states) + hidden_states
+        hidden_states = self.attn(
+            hidden_states, attention_dists=attention_dists) + hidden_states
+        hidden_states = self.conv(hidden_states) + hidden_states
+        hidden_states = 0.5 * self.ff2(hidden_states) + hidden_states
+        hidden_states = self.post_norm(hidden_states)
+        return hidden_states
+
+
+class GraniteSpeechCTCEncoder(nn.Module):
+    """CTC Encoder comprising conformer blocks and additional linear layers."""
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 prefix: str,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+
+        # Precompute clamped relative positional encoding distances
+        seq = torch.arange(config.context_size)
+        relpos_dist = seq.view(-1, 1) - seq.view(1, -1)
+        self.attention_dists = torch.clamp(
+            relpos_dist, -config.context_size,
+            config.context_size) + config.max_pos_emb
+
+        self.input_linear = nn.Linear(config.input_dim,
+                                      config.hidden_dim,
+                                      bias=True)
+        self.layers = nn.ModuleList([
+            GraniteSpeechConformerBlock(
+                config,
+                prefix=f"{prefix}.layers.{idx}",
+            ) for idx in range(config.num_layers)
+        ])
+
+        self.out = ColumnParallelLinear(
+            input_size=config.hidden_dim,
+            output_size=config.output_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out",
+        )
+
+        self.out_mid = RowParallelLinear(
+            input_size=config.output_dim,
+            output_size=config.hidden_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_mid",
+        )
+        self.softmax = nn.Softmax(dim=-1)
+        self.num_layers = config.num_layers
+
+    def forward(self, hidden_states: torch.Tensor):
+        hidden_states = self.input_linear(hidden_states)
+        for idx, layer in enumerate(self.layers, start=1):
+            hidden_states = layer(hidden_states,
+                                  attention_dists=self.attention_dists)
+
+            if idx == self.num_layers // 2:
+                hidden_states_mid = hidden_states.clone()
+                hidden_states_mid, _ = self.out(hidden_states_mid)
+                hidden_states_mid = self.softmax(hidden_states_mid)
+                hidden_states_mid, _ = self.out_mid(hidden_states_mid)
+                hidden_states += hidden_states_mid
+        return hidden_states
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    GraniteSpeechMultiModalProcessor,
+    info=GraniteSpeechMultiModalProcessingInfo,
+    dummy_inputs=GraniteSpeechDummyInputsBuilder)
+class GraniteSpeechForConditionalGeneration(
+        nn.Module,
+        SupportsMultiModal,
+        SupportsPP,
+        SupportsLoRA,
+):
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        cache_config = vllm_config.cache_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.cache_config = cache_config
+        self.sampler = get_sampler()
+
+        # The language model is typically a Granite LLM
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        # Conformer encoder
+        self.encoder = GraniteSpeechCTCEncoder(
+            config=config.encoder_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder",
+        )
+
+        # Blip2 QFormer
+        self.projector = GraniteSpeechEncoderProjector(
+            config=config,
+            quant_config=quant_config,
+            cache_config=cache_config,
+            prefix=f"{prefix}.projector",
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_audio_input(
+        self,
+        **kwargs: object,
+    ) -> Optional[GraniteSpeechAudioInputs]:
+        input_features = kwargs.pop("input_features", None)
+        input_features_mask = kwargs.pop("input_features_mask", None)
+        audio_embed_sizes = kwargs.pop("audio_embed_sizes", None)
+        if input_features is None:
+            return None
+
+        # If we have a batch of variable feature length audio clips, we need
+        # to mask the features; usually we would get an input_features_mask
+        # from the processor, but we handle rebuilding it here since
+        # vLLM generally processes everything independently + batches.
+        if input_features_mask is None:
+            input_features_mask = self._build_input_features_mask(
+                audio_embed_sizes)
+
+        if not isinstance(input_features, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of audio input features. "
+                             f"Got type: {type(input_features)}")
+
+        if input_features_mask is not None and not isinstance(
+                input_features_mask, torch.Tensor):
+            raise ValueError("Incorrect type of audio input features mask. "
+                             f"Got type: {type(input_features_mask)}")
+
+        if isinstance(input_features, torch.Tensor):
+            # Granite speech currently only allows one audio token per instance
+            # and features are already unsqueezed in the processor, so one
+            # instance will have shape [1, {num_features}, 160]. As such,
+            # input features will usually be of shape
+            # [bsz, 1, num_features, 160], which we squeeze to be 3D here.
+            if len(input_features.shape) == 4:
+                input_features = input_features.squeeze(1)
+            if len(input_features.shape) != 3:
+                raise ValueError(
+                    "Squeezed input features should be 3D but are of shape "
+                    f"{input_features.shape}")
+            input_features = input_features.to(
+                self.encoder.input_linear.weight.dtype)
+
+        else:
+            # Otherwise we have a list of tensors, which are almost certainly
+            # differing in their respective numbers of audio features;
+            # stack them into a 3D tensor of size [bsz, most_num_features, 160].
+            input_features = self._pad_and_stack_input_features(
+                input_features, ).to(self.encoder.input_linear.weight.dtype)
+
+        return GraniteSpeechAudioInputs(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            audio_embed_sizes=audio_embed_sizes.flatten().tolist(),
+        )
+
+    def _build_input_features_mask(
+        self,
+        audio_embed_sizes: torch.Tensor,
+    ) -> torch.Tensor:
+        """Calculate the input features mask, which will generally be used
+        to mask the the padded features for all entries in the batch except
+        for those with the most audio features.
+
+        Args:
+            audio_embed_sizes: torch.Tensor
+                Tensor of num features in each seq in the batch.
+        Returns:
+            torch.Tensor: Mask of shape (bsz, num_features) to be applied to
+            the audio features prior to splitting the audio embeddings.
+        """
+        most_audio_features = torch.max(audio_embed_sizes).item()
+        mask_indices = torch.arange(
+            most_audio_features,
+            device=audio_embed_sizes.device,
+        ).view(1, -1)
+        input_features_mask = mask_indices < audio_embed_sizes.view(-1, 1)
+        return input_features_mask
+
+    def _pad_and_stack_input_features(
+        self,
+        input_features: list[torch.Tensor],
+    ) -> torch.Tensor:
+        """Given a list of input features of varying length, pad them to the
+        same length and stack them into a torch.Tensor.
+
+        NOTE: Usually, padding is done in the input processor/feature extractor
+        and zero padded prior to the computation of the Mel features; the
+        resulting values are only constant within a batch and generally nonzero
+        (i.e., slightly negative nums); we should validate that this is okay
+        since we don't use a feature attention mask, but the more important
+        thing is that we apply the input_features_mask with variable len
+        batches.
+
+        Args:
+            input_features: list[torch.Tensor]
+                Input features to be coerced into a tensor.
+        Returns:
+            torch.Tensor: Tensor of shape [bsz, num_features, 160], where
+            num_features is the max number of features of any entry in the
+            batch.
+        """
+        # Input features are of shape [bsz, num_features, 160]
+        feat_lens = [feats.shape[1] for feats in input_features]
+        padding = [max(feat_lens) - length for length in feat_lens]
+        # TODO (Alex) - Validate that it's okay to zero pad like this;
+        # in transformers we zero pad prior to calculating the speech features,
+        # so the value is not zero and is dependent on the batched features.
+        padded = [
+            torch.nn.functional.pad(feats, (0, 0, 0, pad, 0, 0))
+            for feats, pad in zip(input_features, padding)
+        ]
+        stacked_features = torch.cat(padded, dim=0).to(input_features[0])
+        return stacked_features
+
+    def _process_audio_input(
+        self,
+        audio_input: GraniteSpeechAudioInputs,
+    ) -> tuple[torch.Tensor]:
+        """Compute the audio features to be merged into the LLM embeddings.
+        
+        Args:
+            audio_input: GraniteSpeechAudioInputs
+                Audio inputs object containing Mel features, an input features
+                mask, and the (flattened) number of audio tokens per instance.
+        Returns:
+            tuple[torch.Tensor]: List of length bsz.
+        """
+        # TODO (Alex) - support embedding inputs
+        encoder_embeds = self.encoder(audio_input["input_features"])
+        # [bsz, <max feature size>, 4096]
+        projected_embeds = self.projector(encoder_embeds)
+        # Apply mask on variable length audio features
+        masked_embeds = projected_embeds[audio_input["input_features_mask"]]
+        # Split variable length features into a tuple
+        return torch.split(masked_embeds, audio_input["audio_embed_sizes"])
+
+    def get_multimodal_embeddings(
+        self,
+        **kwargs: object,
+    ) -> Optional[MultiModalEmbeddings]:
+        """Compute the audio embeddings if audio inputs are present."""
+        audio_input = self._parse_and_validate_audio_input(**kwargs)
+        if audio_input is None:
+            return None
+        audio_features = self._process_audio_input(audio_input)
+        return audio_features
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        """Compute the merged LLM / audio embeddings."""
+        if multimodal_embeddings is None:
+            return self.language_model.get_input_embeddings(input_ids)
+
+        inputs_embeds = embed_multimodal(
+            input_ids,
+            self.config.audio_token_index,
+            self.language_model.model.get_input_embeddings,
+            multimodal_embeddings,
+        )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            audio_embeds = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids, audio_embeds)
+            input_ids = None
+
+        model_output = self.language_model(input_ids, positions,
+                                           intermediate_tensors, inputs_embeds)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(
+            hidden_states,
+            sampling_metadata,
+        )
+
+    def load_weights(
+        self,
+        weights: Iterable[Tuple[str, torch.Tensor]],
+    ) -> Set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """Get the module prefix in multimodal models."""
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="projector",
+            tower_model="encoder",
+        )

vllm/model_executor/models/registry.py

@@ -178,6 +178,7 @@ _MULTIMODAL_MODELS = {
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
     "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
     "GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
+    "GraniteSpeechForConditionalGeneration": ("granite_speech", "GraniteSpeechForConditionalGeneration"),  # noqa: E501
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),
     "Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),