[Model] Refactoring of MiniCPM-V and add MiniCPM-o-2.6 support for vLLM

docs/source/models/supported_models.md

@@ -690,6 +690,13 @@ See [this page](#generative-models) for more information on how to use generativ
   -
   - ✅︎
   - ✅︎
+* - `MiniCPMO`
+  - MiniCPM-o
+  - T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup>
+  - `openbmb/MiniCPM-o-2_6`, etc.
+  - ✅︎
+  - ✅︎
+  -
 * - `MiniCPMV`
   - MiniCPM-V
   - T + I<sup>E+</sup>

examples/offline_inference/audio_language.py

@@ -67,7 +67,37 @@ def run_qwen2_audio(question: str, audio_count: int):
     return llm, prompt, stop_token_ids
 
 
-model_example_map = {"ultravox": run_ultravox, "qwen2_audio": run_qwen2_audio}
+def run_minicpmo(question: str, audio_count: int):
+    model_name = "openbmb/MiniCPM-o-2_6"
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    llm = LLM(model=model_name,
+              trust_remote_code=True,
+              max_model_len=4096,
+              max_num_seqs=5,
+              limit_mm_per_prompt={"audio": audio_count})
+
+    stop_tokens = ['<|im_end|>', '<|endoftext|>']
+    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+
+    audio_placeholder = "(<audio>./</audio>)" * audio_count
+    audio_chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n<|spk_bos|><|spk|><|spk_eos|><|tts_bos|>' }}{% endif %}"  # noqa: E501
+    messages = [{
+        'role': 'user',
+        'content': f'{audio_placeholder}\n{question}'
+    }]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True,
+                                           chat_template=audio_chat_template)
+    return llm, prompt, stop_token_ids
+
+
+model_example_map = {
+    "ultravox": run_ultravox,
+    "qwen2_audio": run_qwen2_audio,
+    "minicpmo": run_minicpmo
+}
 
 
 def main(args):

examples/offline_inference/vision_language.py

@@ -265,8 +265,9 @@ def run_mantis(question: str, modality: str):
 
 
 # MiniCPM-V
-def run_minicpmv(question: str, modality: str):
-    assert modality == "image"
+def run_minicpmv_base(question: str, modality: str, model_name):
+    assert modality in ["image", "video"]
+    # If you want to use `MiniCPM-o-2_6` with audio inputs, check `audio_language.py` # noqa
 
     # 2.0
     # The official repo doesn't work yet, so we need to use a fork for now
@@ -277,7 +278,15 @@ def run_minicpmv(question: str, modality: str):
     # model_name = "openbmb/MiniCPM-Llama3-V-2_5"
 
     # 2.6
-    model_name = "openbmb/MiniCPM-V-2_6"
+    # model_name = "openbmb/MiniCPM-V-2_6"
+    # o2.6
+
+    # modality supports
+    # 2.0: image
+    # 2.5: image
+    # 2.6: image, video
+    # o2.6: image, video, audio
+    # model_name = "openbmb/MiniCPM-o-2_6"
     tokenizer = AutoTokenizer.from_pretrained(model_name,
                                               trust_remote_code=True)
     llm = LLM(
@@ -294,20 +303,33 @@ def run_minicpmv(question: str, modality: str):
     # 2.5
     # stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]
 
-    # 2.6
+    # 2.6 / o2.6
     stop_tokens = ['<|im_end|>', '<|endoftext|>']
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
 
+    modality_placeholder = {
+        "image": "(<image>./</image>)",
+        "video": "(<video>./</video>)",
+    }
+
     messages = [{
         'role': 'user',
-        'content': f'(<image>./</image>)\n{question}'
+        'content': f'{modality_placeholder[modality]}\n{question}'
     }]
     prompt = tokenizer.apply_chat_template(messages,
                                            tokenize=False,
                                            add_generation_prompt=True)
     return llm, prompt, stop_token_ids
 
 
+def run_minicpmo(question: str, modality: str):
+    return run_minicpmv_base(question, modality, "openbmb/MiniCPM-o-2_6")
+
+
+def run_minicpmv(question: str, modality: str):
+    return run_minicpmv_base(question, modality, "openbmb/MiniCPM-V-2_6")
+
+
 # LLama 3.2
 def run_mllama(question: str, modality: str):
     assert modality == "image"
@@ -523,6 +545,7 @@ def run_qwen2_vl(question: str, modality: str):
     "llava-next-video": run_llava_next_video,
     "llava-onevision": run_llava_onevision,
     "mantis": run_mantis,
+    "minicpmo": run_minicpmo,
     "minicpmv": run_minicpmv,
     "mllama": run_mllama,
     "molmo": run_molmo,

requirements-cpu.txt

@@ -4,5 +4,6 @@
 # Dependencies for CPUs
 torch==2.5.1+cpu; platform_machine != "ppc64le" and platform_machine != "aarch64" and platform_system != "Darwin"
 torch==2.5.1; platform_machine == "aarch64" or platform_system == "Darwin" 
+torchaudio; platform_machine != "ppc64le"  # required for the image processor of minicpm-o-2_6, this must be updated alongside torch
 torchvision; platform_machine != "ppc64le"  # required for the image processor of phi3v, this must be updated alongside torch
 datasets # for benchmark scripts

requirements-cuda.txt

@@ -5,6 +5,7 @@
 ray[default] >= 2.9
 nvidia-ml-py >= 12.560.30 # for pynvml package
 torch == 2.5.1
+torchaudio==2.5.1
 # These must be updated alongside torch
 torchvision == 0.20.1 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 xformers == 0.0.28.post3; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch 2.5.1

requirements-test.in

@@ -12,13 +12,16 @@ decord # required for video tests
 einops # required for MPT, qwen-vl and Mamba
 httpx
 librosa # required for audio tests
+vector_quantize_pytorch # required for minicpmo_26 test
+vocos # required for minicpmo_26 test
 peft
 pqdm
 ray[adag]==2.40.0
 sentence-transformers # required for embedding tests
 soundfile # required for audio tests
 timm # required for internvl test
 torch==2.5.1
+torchaudio==2.5.1
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[opencv] >= 1.5.0 # required for pixtral test

requirements-test.txt

@@ -106,9 +106,17 @@ dnspython==2.7.0
 docutils==0.16
     # via awscli
 einops==0.8.0
-    # via -r requirements-test.in
+    # via
+    #   -r requirements-test.in
+    #   encodec
+    #   vector-quantize-pytorch
+    #   vocos
+einx==0.3.0
+    # via vector-quantize-pytorch
 email-validator==2.2.0
     # via pydantic
+encodec==0.1.1
+    # via vocos
 evaluate==0.4.3
     # via lm-eval
 fastparquet==2024.11.0
@@ -125,6 +133,8 @@ filelock==3.16.1
     #   triton
 fonttools==4.54.1
     # via matplotlib
+frozendict==2.4.6
+    # via einx
 frozenlist==1.5.0
     # via
     #   aiohttp
@@ -159,6 +169,7 @@ huggingface-hub==0.26.2
     #   timm
     #   tokenizers
     #   transformers
+    #   vocos
 idna==3.10
     # via
     #   anyio
@@ -261,6 +272,8 @@ numpy==1.26.4
     #   cupy-cuda12x
     #   datasets
     #   decord
+    #   einx
+    #   encodec
     #   evaluate
     #   fastparquet
     #   genai-perf
@@ -283,6 +296,7 @@ numpy==1.26.4
     #   torchvision
     #   transformers
     #   tritonclient
+    #   vocos
 nvidia-cublas-cu12==12.4.5.8
     # via
     #   nvidia-cudnn-cu12
@@ -455,6 +469,7 @@ pyyaml==6.0.2
     #   responses
     #   timm
     #   transformers
+    #   vocos
 ray[adag]==2.40.0
     # via -r requirements-test.in
 redis==5.2.0
@@ -517,6 +532,7 @@ scipy==1.13.1
     #   scikit-learn
     #   sentence-transformers
     #   statsmodels
+    #   vocos
 sentence-transformers==3.2.1
     # via -r requirements-test.in
 sentencepiece==0.2.0
@@ -540,7 +556,9 @@ sqlitedict==2.1.0
 statsmodels==0.14.4
     # via genai-perf
 sympy==1.13.1
-    # via torch
+    # via
+    #   einx
+    #   torch
 tabledata==1.3.3
     # via pytablewriter
 tabulate==0.9.0
@@ -568,12 +586,21 @@ torch==2.5.1
     #   -r requirements-test.in
     #   accelerate
     #   bitsandbytes
+    #   encodec
     #   lm-eval
     #   peft
     #   sentence-transformers
     #   tensorizer
     #   timm
+    #   torchaudio
     #   torchvision
+    #   vector-quantize-pytorch
+    #   vocos
+torchaudio==2.5.1
+    # via
+    #   -r requirements-test.in
+    #   encodec
+    #   vocos
 torchvision==0.20.1
     # via timm
 tqdm==4.66.6
@@ -584,6 +611,7 @@ tqdm==4.66.6
     #   lm-eval
     #   nltk
     #   peft
+    #   pqdm
     #   sentence-transformers
     #   tqdm-multiprocess
     #   transformers
@@ -615,6 +643,7 @@ typing-extensions==4.12.2
     #   huggingface-hub
     #   librosa
     #   mistral-common
+    #   pqdm
     #   pydantic
     #   pydantic-core
     #   torch
@@ -626,6 +655,10 @@ urllib3==2.2.3
     #   requests
     #   responses
     #   tritonclient
+vector-quantize-pytorch==1.21.2
+    # via -r requirements-test.in
+vocos==0.1.0
+    # via -r requirements-test.in
 word2number==1.1
     # via lm-eval
 xxhash==3.5.0

setup.py

@@ -656,7 +656,8 @@ def _read_requirements(filename: str) -> List[str]:
     extras_require={
         "tensorizer": ["tensorizer>=2.9.0"],
         "runai": ["runai-model-streamer", "runai-model-streamer-s3", "boto3"],
-        "audio": ["librosa", "soundfile"],  # Required for audio processing
+        "audio": ["librosa", "soundfile",
+                  "vocos"],  # Required for audio processing
         "video": ["decord"]  # Required for video processing
     },
     cmdclass=cmdclass,

tests/models/decoder_only/vision_language/test_models.py

@@ -350,6 +350,20 @@
         postprocess_inputs=model_utils.wrap_inputs_post_processor,
         hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
     ),
+    "minicpmo_26": VLMTestInfo(
+        models=["openbmb/MiniCPM-o-2_6"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
+        max_model_len=4096,
+        max_num_seqs=2,
+        get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
+        postprocess_inputs=model_utils.ignore_inputs_post_processor(
+            "image_sizes"
+        ),
+        hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
+        patch_hf_runner=model_utils.minicpmo_patch_hf_runner
+    ),
     "minicpmv_26": VLMTestInfo(
         models=["openbmb/MiniCPM-V-2_6"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),

tests/models/decoder_only/vision_language/vlm_utils/model_utils.py

@@ -497,6 +497,17 @@ def _generate(self, *args, **kwargs):
     return hf_model
 
 
+def minicpmo_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    orig_generate = hf_model.model.generate
+
+    def _generate(self, *args, **kwargs):
+        return orig_generate(*args, decode_text=False, **kwargs)
+
+    hf_model.model.generate = types.MethodType(_generate, hf_model.model)
+
+    return hf_model
+
+
 def _generate_greedy_logprobs_limit(
     self,
     prompts: List[str],

tests/models/multimodal/processing/test_common.py

@@ -152,6 +152,8 @@ def _test_processing_correctness(
     "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
     "TIGER-Lab/Mantis-8B-siglip-llama3",
     "mistral-community/pixtral-12b",
+    "openbmb/MiniCPM-o-2_6",
+    "openbmb/MiniCPM-V-2_6",
     "Qwen/Qwen-VL-Chat",
     "Qwen/Qwen2-VL-2B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",

tests/models/registry.py

@@ -245,7 +245,9 @@ def check_available_online(
     "LlavaOnevisionForConditionalGeneration": _HfExamplesInfo("llava-hf/llava-onevision-qwen2-0.5b-ov-hf"),  # noqa: E501
     "MantisForConditionalGeneration": _HfExamplesInfo("TIGER-Lab/Mantis-8B-siglip-llama3",  # noqa: E501
                                                       hf_overrides={"architectures": ["MantisForConditionalGeneration"]}),  # noqa: E501
-    "MiniCPMV": _HfExamplesInfo("openbmb/MiniCPM-Llama3-V-2_5",
+    "MiniCPMO": _HfExamplesInfo("openbmb/MiniCPM-o-2_6",
+                                trust_remote_code=True),
+    "MiniCPMV": _HfExamplesInfo("openbmb/MiniCPM-V-2_6",
                                 trust_remote_code=True),
     "MolmoForCausalLM": _HfExamplesInfo("allenai/Molmo-7B-D-0924",
                                         trust_remote_code=True),

vllm/entrypoints/chat_utils.py

@@ -392,7 +392,7 @@ def _placeholder_str(self, modality: ModalityStr,
             if model_type == "phi3_v":
                 # Workaround since this token is not defined in the tokenizer
                 return f"<|image_{current_count}|>"
-            if model_type == "minicpmv":
+            if model_type in ("minicpmo", "minicpmv"):
                 return "(<image>./</image>)"
             if model_type in ("blip-2", "chatglm", "fuyu", "paligemma",
                               "pixtral"):
@@ -424,10 +424,14 @@ def _placeholder_str(self, modality: ModalityStr,
             if model_type == "qwen2_audio":
                 return (f"Audio {current_count}: "
                         f"<|audio_bos|><|AUDIO|><|audio_eos|>")
+            if model_type == "minicpmo":
+                return "(<audio>./</audio>)"
             raise TypeError(f"Unknown model type: {model_type}")
         elif modality == "video":
             if model_type == "qwen2_vl":
                 return "<|vision_start|><|video_pad|><|vision_end|>"
+            if model_type in ("minicpmo", "minicpmv"):
+                return "(<video>./</video>)"
             if model_type.startswith("llava"):
                 return self._cached_token_str(self._tokenizer,
                                               hf_config.video_token_index)