Enable phi-4 with vision and audio

docker/llm/serving/xpu/docker/audio_language.py

@@ -0,0 +1,131 @@
+import os
+from dataclasses import asdict
+from typing import NamedTuple, Optional
+
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer
+
+from vllm import LLM, EngineArgs, SamplingParams
+from ipex_llm.vllm.xpu.engine import IPEXLLMClass as LLM
+from vllm.assets.audio import AudioAsset
+from vllm.utils import FlexibleArgumentParser
+
+audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
+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?"
+}
+
+model_path = "/llm/models/whisper-large-v3-turbo"
+#model_path = "/llm/models/whisper-medium"
+#model_path = "/llm/models/Phi-4-multimodal-instruct"
+
+# Phi-4-multimodal-instruct
+def run_phi4mm(question: str, audio_count: int):
+    placeholders = "".join([f"<|audio_{i+1}|>" for i in range(audio_count)])
+
+    prompt = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
+
+    return prompt
+
+
+# Whisper
+def run_whisper(question: str, audio_count: int):
+    assert audio_count == 1, (
+        "Whisper only support single audio input per prompt")
+
+    prompt = "<|startoftranscript|>"
+
+    return prompt
+
+
+model_example_map = {
+    "phi4mm": run_phi4mm,
+    "whisper": run_whisper,
+}
+
+
+if "whisper" in model_path:
+    model_len=448
+    low_bit="fp16"
+else:
+    model_len = 5500
+    low_bit="sym_int4"
+
+def main(args):
+    audio_count = args.num_audios
+
+    llm = LLM(
+            model=model_path,
+            device="xpu",
+            dtype="float16",
+            limit_mm_per_prompt={"audio": audio_count},
+            enforce_eager=True,
+            mm_processor_kwargs=None,
+            load_in_low_bit=low_bit,
+            tensor_parallel_size=1,
+            max_num_seqs=8,
+            gpu_memory_utilization=0.95,
+            disable_async_output_proc=True,
+            distributed_executor_backend="ray",
+            max_model_len=model_len,
+            trust_remote_code=True,
+            block_size=8,
+            max_num_batched_tokens=model_len)
+
+    model = llm.llm_engine.model_config.hf_config.model_type
+    if model not in model_example_map:
+        raise ValueError(f"Model type {model} is not supported.")
+
+    prompt = model_example_map[model](question_per_audio_count[audio_count], audio_count)
+
+    sampling_params = SamplingParams(temperature=0.1,
+                                     top_p=0.001,
+                                     repetition_penalty=1.05,
+                                     max_tokens=128,
+                                     skip_special_tokens=False
+                                     )
+
+    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
+
+    outputs = llm.generate(inputs, sampling_params=sampling_params)
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description='Demo on using vLLM for offline inference with '
+        'audio language models')
+    parser.add_argument('--num-prompts',
+                        type=int,
+                        default=1,
+                        help='Number of prompts to run.')
+    parser.add_argument("--num-audios",
+                        type=int,
+                        default=1,
+                        choices=[0, 1, 2],
+                        help="Number of audio items per prompt.")
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Set the seed when initializing `vllm.LLM`.")
+
+    args = parser.parse_args()
+    main(args)

docker/llm/serving/xpu/docker/vllm_offline_inference_vision_language.py

@@ -10,6 +10,7 @@
 model_path = "/llm/models/InternVL2-8B"
 model_path = "/llm/models/gemma-3-12b-it"
 model_path = "/llm/models/Qwen2.5-VL-7B-Instruct"
+model_path = "/llm/models/Phi-4-multimodal-instruct"
 
 prompt = "What is in the image?"
 
@@ -77,6 +78,18 @@ def run_qwen2_vl(question, modality):
     stop_token_ids = None
     return prompt, stop_token_ids
 
+# Phi-4-multimodal-instruct
+def run_phi4mm(question, modality):
+    """
+    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
+    show how to process image inputs.
+    """
+    assert modality == "image"
+
+    prompt = f"<|user|><|image_1|>{question}<|end|><|assistant|>"
+    stop_token_ids = None   
+    return prompt, stop_token_ids
+
 model_example_map = {
     "minicpmv": run_minicpmv,
     "qwen2_vl": run_qwen2_vl,
@@ -85,6 +98,7 @@ def run_qwen2_vl(question, modality):
     "chatglm": run_glm4v,
     "internvl_chat": run_internvl,
     "gemma3": run_gemma3,
+    "phi4mm": run_phi4mm,
 }
 
 if "glm-4v" in model_path:

docs/mddocs/DockerGuides/vllm_docker_quickstart.md

@@ -438,6 +438,8 @@ docker logs CONTAINER_NAME
 ## 8. Advanced Features
 
 #### Multi-modal Model
+
+##### Vision model
 <details>
 vLLM serving with IPEX-LLM supports multi-modal models, such as [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6), which can accept image and text input at the same time and respond.
 
@@ -478,6 +480,40 @@ curl http://localhost:8000/v1/chat/completions \
 ```
 </details>
 
+##### Audio model
+<details>
+vLLM serving with IPEX-LLM supports multi-modal models, such as [Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)(only offine now) and whisper series model([whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) and [whisper-medium](https://huggingface.co/openai/whisper-medium)), which can accept audio input and respond text output.
+
+Offline test:
+```bash
+export VLLM_USE_V1=0
+python3 audio_language.py
+```
+
+Online test:
+1. Start vLLM service: change the `model` and `served_model_name` value in `/llm/start-vllm-service.sh`
+
+2. Download or get a audio file first.
+```python
+# python3 load.py
+from vllm.assets.audio import AudioAsset
+import soundfile as sf
+
+audio, sr = AudioAsset("winning_call").audio_and_sample_rate
+
+sf.write("output.wav", audio, sr)
+```
+
+3. Send request with audio file and prompt text(optional). 
+
+```bash
+curl http://localhost:8000/v1/audio/transcriptions \
+-H "Content-Type: multipart/form-data" \
+-F file="@/llm/models/test/output.wav" \
+-F model="whisper-large-v3-turbo"
+```
+</details>
+
 #### Preifx Caching
 <details>
 Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache if it shares the same prefix with one of the existing queries, allowing the new query to skip the computation of the shared part.

python/llm/src/ipex_llm/vllm/xpu/model_convert.py

@@ -129,6 +129,8 @@ def _ipex_llm_load_model(self) -> None:
                 if "glm-4v" in self.vllm_config.model_config.model.lower() and \
                         low_bit in ("sym_int4", "woq_int4"):
                     modules = ["dense_4h_to_h"]
+                if "phi4mm" in self.vllm_config.model_config.hf_config.model_type:
+                    modules = ["vision_encoder", "embed_tokens_extend"]
                 if low_bit == "fp16":
                     # to fix qwen2.5-vl and glm-4v
                     modules = ["vision", "visual"]