[Misc] Add Phi4-MM example

examples/offline_inference/audio_language.py

@@ -6,10 +6,14 @@
 For most models, the prompt format should follow corresponding examples
 on HuggingFace model repository.
 """
+import os
+
+from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer
 
 from vllm import LLM, SamplingParams
 from vllm.assets.audio import AudioAsset
+from vllm.lora.request import LoRARequest
 from vllm.utils import FlexibleArgumentParser
 
 audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
@@ -51,6 +55,39 @@ def run_minicpmo(question: str, audio_count: int):
     return llm, prompt, stop_token_ids
 
 
+# Phi-4-multimodal-instruct
+def run_phi4mm(questions: str, audio_count: int):
+    """
+    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
+    show how to process audio inputs.
+    """
+    model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
+    # Since the vision-lora and speech-lora co-exist with the base model,
+    # we have to manually specify the path of the lora weights.
+    speech_lora_path = os.path.join(model_path, "speech-lora")
+    placeholders = "".join([f"<|audio_{i+1}|>" for i in range(audio_count)])
+
+    prompts = f"<|user|>{placeholders}{questions}<|end|><|assistant|>"
+
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=2,
+        enable_lora=True,
+        max_lora_rank=320,
+        lora_extra_vocab_size=0,
+    )
+    lora_request = LoRARequest("speech", 1, speech_lora_path)
+    # To maintain code compatibility in this script, we add LoRA here.
+    llm.llm_engine.add_lora(lora_request=lora_request)
+    # You can also add LoRA using:
+    # llm.generate(prompts, lora_request=lora_request,...)
+
+    stop_token_ids = None
+    return llm, prompts, stop_token_ids
+
+
 # Qwen2-Audio
 def run_qwen2_audio(question: str, audio_count: int):
     model_name = "Qwen/Qwen2-Audio-7B-Instruct"
@@ -113,6 +150,7 @@ def run_whisper(question: str, audio_count: int):
 
 model_example_map = {
     "minicpmo": run_minicpmo,
+    "phi4_mm": run_phi4mm,
     "qwen2_audio": run_qwen2_audio,
     "ultravox": run_ultravox,
     "whisper": run_whisper,

examples/offline_inference/vision_language.py

@@ -6,13 +6,16 @@
 For most models, the prompt format should follow corresponding examples
 on HuggingFace model repository.
 """
+import os
 import random
 
+from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer
 
 from vllm import LLM, SamplingParams
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
+from vllm.lora.request import LoRARequest
 from vllm.utils import FlexibleArgumentParser
 
 # NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
@@ -519,6 +522,40 @@ def run_phi3v(questions: list[str], modality: str):
     return llm, prompts, stop_token_ids
 
 
+# Phi-4-multimodal-instruct
+def run_phi4mm(questions: list[str], modality: str):
+    """
+    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
+    show how to process image inputs.
+    """
+    assert modality == "image"
+    model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
+    # Since the vision-lora and speech-lora co-exist with the base model,
+    # we have to manually specify the path of the lora weights.
+    vision_lora_path = os.path.join(model_path, "vision-lora")
+    prompts = [
+        f"<|user|><|image_1|>{question}<|end|><|assistant|>"
+        for question in questions
+    ]
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=2,
+        enable_lora=True,
+        max_lora_rank=320,
+        lora_extra_vocab_size=0,
+    )
+    lora_request = LoRARequest("vision", 1, vision_lora_path)
+    # To maintain code compatibility in this script, we add LoRA here.
+    llm.llm_engine.add_lora(lora_request=lora_request)
+    # You can also add LoRA using:
+    # llm.generate(prompts, lora_request=lora_request,...)
+
+    stop_token_ids = None
+    return llm, prompts, stop_token_ids
+
+
 # Pixtral HF-format
 def run_pixtral_hf(questions: list[str], modality: str):
     assert modality == "image"
@@ -644,6 +681,7 @@ def run_qwen2_5_vl(questions: list[str], modality: str):
     "paligemma": run_paligemma,
     "paligemma2": run_paligemma2,
     "phi3_v": run_phi3v,
+    "phi4_mm": run_phi4mm,
     "pixtral_hf": run_pixtral_hf,
     "qwen_vl": run_qwen_vl,
     "qwen2_vl": run_qwen2_vl,

examples/offline_inference/vision_language_multi_image.py

@@ -4,13 +4,16 @@
 multi-image input on vision language models for text generation,
 using the chat template defined by the model.
 """
+import os
 from argparse import Namespace
 from typing import NamedTuple, Optional
 
+from huggingface_hub import snapshot_download
 from PIL.Image import Image
 from transformers import AutoProcessor, AutoTokenizer
 
 from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
 from vllm.multimodal.utils import fetch_image
 from vllm.utils import FlexibleArgumentParser
 
@@ -294,6 +297,46 @@ def load_phi3v(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_phi4mm(question: str, image_urls: list[str]) -> ModelRequestData:
+    """
+    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
+    show how to process multi images inputs.
+    """
+
+    model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
+    # Since the vision-lora and speech-lora co-exist with the base model,
+    # we have to manually specify the path of the lora weights.
+    vision_lora_path = os.path.join(model_path, "vision-lora")
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        max_model_len=10000,
+        max_num_seqs=2,
+        limit_mm_per_prompt={"image": len(image_urls)},
+        enable_lora=True,
+        max_lora_rank=320,
+        lora_extra_vocab_size=0,
+    )
+    lora_request = LoRARequest("vision", 1, vision_lora_path)
+    # To maintain code compatibility in this script, we add LoRA here.
+    llm.llm_engine.add_lora(lora_request=lora_request)
+    # You can also add LoRA using:
+    # llm.generate(prompts, lora_request=lora_request,...)
+
+    placeholders = "".join(f"<|image_{i}|>"
+                           for i, _ in enumerate(image_urls, start=1))
+    prompt = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
+    stop_token_ids = None
+
+    return ModelRequestData(
+        llm=llm,
+        prompt=prompt,
+        stop_token_ids=stop_token_ids,
+        image_data=[fetch_image(url) for url in image_urls],
+        chat_template=None,
+    )
+
+
 def load_qwen_vl_chat(question: str,
                       image_urls: list[str]) -> ModelRequestData:
     model_name = "Qwen/Qwen-VL-Chat"
@@ -459,6 +502,7 @@ def load_qwen2_5_vl(question, image_urls: list[str]) -> ModelRequestData:
     "mllama": load_mllama,
     "NVLM_D": load_nvlm_d,
     "phi3_v": load_phi3v,
+    "phi4_mm": load_phi4mm,
     "pixtral_hf": load_pixtral_hf,
     "qwen_vl_chat": load_qwen_vl_chat,
     "qwen2_vl": load_qwen2_vl,

vllm/model_executor/models/phi4mm.py

@@ -25,6 +25,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead)
 from vllm.model_executor.models.llama import LlamaModel
+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 MultiModalInputs, NestedTensors
@@ -1421,7 +1422,6 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
     """
     Implements the Phi-4-multimodal-instruct model in VLLM.
     """
-    # LoRA specific attributes
     packed_modules_mapping = {
         "qkv_proj": [
             "qkv_proj",
@@ -1430,12 +1430,6 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
             "gate_up_proj",
         ],
     }
-    supported_lora_modules = [
-        "qkv_proj", "o_proj", "gate_up_proj", "down_proj"
-    ]
-    # Phi4MMForCausalLM does not apply LoRA to the embedding layer.
-    embedding_modules = {}
-    embedding_padding_modules = []
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -1801,3 +1795,13 @@ def sample(
     ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="model.",
+            connector=["audio_projection_for_vision", "audio_projection"],
+            tower_model=["vision_encoder", "embed_tokens_extend"],
+        )