add qwen3

vllm-v0.6.2/examples/offline_inference_vision_language.py

@@ -0,0 +1,537 @@
+"""
+This example shows how to use vLLM for running offline inference with
+the correct prompt format on vision language models for text generation.
+
+For most models, the prompt format should follow corresponding examples
+on HuggingFace model repository.
+"""
+from transformers import AutoTokenizer
+
+from vllm import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
+from vllm.assets.video import VideoAsset
+from vllm.utils import FlexibleArgumentParser
+from vllm_mlu._mlu_utils import USE_PAGED
+
+# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
+# lower-end GPUs.
+# Unless specified, these settings have been tested to work on a single L4.
+
+
+# LLaVA-1.5
+def run_llava(question: str, modality: str):
+    assert modality == "image"
+
+    prompt = f"USER: <image>\n{question}\nASSISTANT:"
+
+    llm = LLM(model="llava-hf/llava-1.5-7b-hf", max_model_len=4096)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# LLaVA-1.6/LLaVA-NeXT
+def run_llava_next(question: str, modality: str):
+    assert modality == "image"
+
+    prompt = f"[INST] <image>\n{question} [/INST]"
+    llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf", max_model_len=8192)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# LlaVA-NeXT-Video
+# Currently only support for video input
+def run_llava_next_video(question: str, modality: str):
+    assert modality == "video"
+
+    prompt = f"USER: <video>\n{question} ASSISTANT:"
+    llm = LLM(model="llava-hf/LLaVA-NeXT-Video-7B-hf", max_model_len=8192)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# LLaVA-OneVision
+def run_llava_onevision(question: str, modality: str):
+
+    if modality == "video":
+        prompt = f"<|im_start|>user <video>\n{question}<|im_end|> \
+        <|im_start|>assistant\n"
+
+    elif modality == "image":
+        prompt = f"<|im_start|>user <image>\n{question}<|im_end|> \
+        <|im_start|>assistant\n"
+
+    llm = LLM(model="llava-hf/llava-onevision-qwen2-7b-ov-hf",
+              max_model_len=16384)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Fuyu
+def run_fuyu(question: str, modality: str):
+    assert modality == "image"
+
+    prompt = f"{question}\n"
+    llm = LLM(model="adept/fuyu-8b", max_model_len=2048, max_num_seqs=2)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Phi-3-Vision
+def run_phi3v(question: str, modality: str):
+    assert modality == "image"
+
+    prompt = f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n"  # noqa: E501
+    # Note: The default setting of max_num_seqs (256) and
+    # max_model_len (128k) for this model may cause OOM.
+    # You may lower either to run this example on lower-end GPUs.
+
+    # In this example, we override max_num_seqs to 5 while
+    # keeping the original context length of 128k.
+
+    # num_crops is an override kwarg to the multimodal image processor;
+    # For some models, e.g., Phi-3.5-vision-instruct, it is recommended
+    # to use 16 for single frame scenarios, and 4 for multi-frame.
+    #
+    # Generally speaking, a larger value for num_crops results in more
+    # tokens per image instance, because it may scale the image more in
+    # the image preprocessing. Some references in the model docs and the
+    # formula for image tokens after the preprocessing
+    # transform can be found below.
+    #
+    # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally
+    # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194
+    llm = LLM(
+        model="microsoft/Phi-3-vision-128k-instruct",
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=2,
+        # Note - mm_processor_kwargs can also be passed to generate/chat calls
+        mm_processor_kwargs={"num_crops": 16},
+    )
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# PaliGemma
+def run_paligemma(question: str, modality: str):
+    assert modality == "image"
+
+    # PaliGemma has special prompt format for VQA
+    prompt = "caption en"
+    llm = LLM(model="google/paligemma-3b-mix-224")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Chameleon
+def run_chameleon(question: str, modality: str):
+    assert modality == "image"
+
+    prompt = f"{question}<image>"
+    llm = LLM(model="facebook/chameleon-7b", max_model_len=4096)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# MiniCPM-V
+def run_minicpmv(question: str, modality: str):
+    assert modality == "image"
+
+    # 2.0
+    # The official repo doesn't work yet, so we need to use a fork for now
+    # For more details, please see: See: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630 # noqa
+    # model_name = "HwwwH/MiniCPM-V-2"
+
+    # 2.5
+    # model_name = "openbmb/MiniCPM-Llama3-V-2_5"
+
+    #2.6
+    model_name = "openbmb/MiniCPM-V-2_6"
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    llm = LLM(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=2,
+        trust_remote_code=True,
+    )
+    # NOTE The stop_token_ids are different for various versions of MiniCPM-V
+    # 2.0
+    # stop_token_ids = [tokenizer.eos_id]
+
+    # 2.5
+    # stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]
+
+    # 2.6
+    stop_tokens = ['<|im_end|>', '<|endoftext|>']
+    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+
+    messages = [{
+        'role': 'user',
+        'content': f'(<image>./</image>)\n{question}'
+    }]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+    return llm, prompt, stop_token_ids
+
+
+# H2OVL-Mississippi
+def run_h2ovl(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "h2oai/h2ovl-mississippi-2b"
+
+    llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=8192,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    messages = [{'role': 'user', 'content': f"<image>\n{question}"}]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+
+    # Stop tokens for H2OVL-Mississippi
+    # https://huggingface.co/h2oai/h2ovl-mississippi-2b
+    stop_token_ids = [tokenizer.eos_token_id]
+    return llm, prompt, stop_token_ids
+
+
+# InternVL
+def run_internvl(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "OpenGVLab/InternVL2-2B"
+
+    llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=4096,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    messages = [{'role': 'user', 'content': f"<image>\n{question}"}]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+
+    # Stop tokens for InternVL
+    # models variants may have different stop tokens
+    # please refer to the model card for the correct "stop words":
+    # https://huggingface.co/OpenGVLab/InternVL2-2B#service
+    stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"]
+    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+    return llm, prompt, stop_token_ids
+
+
+# NVLM-D
+def run_nvlm_d(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "nvidia/NVLM-D-72B"
+
+    # Adjust this as necessary to fit in GPU
+    llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=4096,
+        tensor_parallel_size=4,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    messages = [{'role': 'user', 'content': f"<image>\n{question}"}]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# BLIP-2
+def run_blip2(question: str, modality: str):
+    assert modality == "image"
+
+    # BLIP-2 prompt format is inaccurate on HuggingFace model repository.
+    # See https://huggingface.co/Salesforce/blip2-opt-2.7b/discussions/15#64ff02f3f8cf9e4f5b038262 #noqa
+    prompt = f"Question: {question} Answer:"
+    llm = LLM(model="Salesforce/blip2-opt-2.7b")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Qwen
+def run_qwen_vl(question: str, modality: str):
+    assert modality == "image"
+
+    llm = LLM(
+        model="Qwen/Qwen-VL",
+        trust_remote_code=True,
+        max_model_len=1024,
+        max_num_seqs=2,
+    )
+
+    prompt = f"{question}Picture 1: <img></img>\n"
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Qwen2-VL
+def run_qwen2_vl(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "Qwen/Qwen2-VL-7B-Instruct"
+
+    llm = LLM(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        # Note - mm_processor_kwargs can also be passed to generate/chat calls
+        mm_processor_kwargs={
+            "min_pixels": 28 * 28,
+            "max_pixels": 1280 * 28 * 28,
+        },
+    )
+
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
+              f"{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Pixtral HF-format
+def run_pixtral_hf(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "mistral-community/pixtral-12b"
+
+    llm = LLM(
+        model=model_name,
+        max_model_len=8192,
+    )
+
+    prompt = f"<s>[INST]{question}\n[IMG][/INST]"
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# LLama 3.2
+def run_mllama(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "/data/vllm/models/Llama-3.2-11B-Vision-Instruct"
+
+    # Note: The default setting of max_num_seqs (256) and
+    # max_model_len (131072) for this model may cause OOM.
+    # You may lower either to run this example on lower-end GPUs.
+
+    # The configuration below has been confirmed to launch on a single L40 GPU.
+    llm_args = {
+        'model' : model_name,
+        'max_model_len' : 4096,
+        'max_num_seqs' : 16,
+        'enforce_eager' : True,
+     }
+    if not USE_PAGED:
+        # Batch size should be no smaller than input_len(6404) + output_len(64).
+        llm_args['block_size'] = 8192
+        llm_args['tensor_parallel_size'] = 4
+        llm_args['dtype'] = 'float16'
+    llm = LLM(**llm_args)
+
+    prompt = f"<|image|><|begin_of_text|>{question}"
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# Molmo
+def run_molmo(question, modality):
+    assert modality == "image"
+
+    model_name = "allenai/Molmo-7B-D-0924"
+
+    llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        dtype="bfloat16",
+    )
+
+    prompt = question
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+# GLM-4v
+def run_glm4v(question: str, modality: str):
+    assert modality == "image"
+    model_name = "THUDM/glm-4v-9b"
+
+    llm = LLM(model=model_name,
+              max_model_len=2048,
+              max_num_seqs=2,
+              trust_remote_code=True,
+              enforce_eager=True)
+    prompt = question
+    stop_token_ids = [151329, 151336, 151338]
+    return llm, prompt, stop_token_ids
+
+
+# Idefics3-8B-Llama3
+def run_idefics3(question: str, modality: str):
+    assert modality == "image"
+    model_name = "HuggingFaceM4/Idefics3-8B-Llama3"
+
+    llm = LLM(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=2,
+        enforce_eager=True,
+        # if you are running out of memory, you can reduce the "longest_edge".
+        # see: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3#model-optimizations
+        mm_processor_kwargs={
+            "size": {
+                "longest_edge": 3 * 364
+            },
+        },
+    )
+    prompt = (
+        f"<|begin_of_text|>User:<image>{question}<end_of_utterance>\nAssistant:"
+    )
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
+model_example_map = {
+    "llava": run_llava,
+    "llava-next": run_llava_next,
+    "llava-next-video": run_llava_next_video,
+    "llava-onevision": run_llava_onevision,
+    "fuyu": run_fuyu,
+    "phi3_v": run_phi3v,
+    "paligemma": run_paligemma,
+    "chameleon": run_chameleon,
+    "minicpmv": run_minicpmv,
+    "blip-2": run_blip2,
+    "h2ovl_chat": run_h2ovl,
+    "internvl_chat": run_internvl,
+    "NVLM_D": run_nvlm_d,
+    "qwen_vl": run_qwen_vl,
+    "qwen2_vl": run_qwen2_vl,
+    "pixtral_hf": run_pixtral_hf,
+    "mllama": run_mllama,
+    "molmo": run_molmo,
+    "glm4v": run_glm4v,
+    "idefics3": run_idefics3,
+}
+
+
+def get_multi_modal_input(args):
+    """
+    return {
+        "data": image or video,
+        "question": question,
+    }
+    """
+    if args.modality == "image":
+        # Input image and question
+        image = ImageAsset("cherry_blossom") \
+            .pil_image.convert("RGB")
+        img_question = "What is the content of this image?"
+
+        return {
+            "data": image,
+            "question": img_question,
+        }
+
+    if args.modality == "video":
+        # Input video and question
+        video = VideoAsset(name="sample_demo_1.mp4",
+                           num_frames=args.num_frames).np_ndarrays
+        vid_question = "Why is this video funny?"
+
+        return {
+            "data": video,
+            "question": vid_question,
+        }
+
+    msg = f"Modality {args.modality} is not supported."
+    raise ValueError(msg)
+
+
+def main(args):
+    model = args.model_type
+    if model not in model_example_map:
+        raise ValueError(f"Model type {model} is not supported.")
+
+    modality = args.modality
+    mm_input = get_multi_modal_input(args)
+    data = mm_input["data"]
+    question = mm_input["question"]
+
+    llm, prompt, stop_token_ids = model_example_map[model](question, modality)
+
+    # We set temperature to 0.2 so that outputs can be different
+    # even when all prompts are identical when running batch inference.
+    sampling_params = SamplingParams(temperature=0.2,
+                                     max_tokens=64,
+                                     stop_token_ids=stop_token_ids)
+
+    assert args.num_prompts > 0
+    if args.num_prompts == 1:
+        # Single inference
+        inputs = {
+            "prompt": prompt,
+            "multi_modal_data": {
+                modality: data
+            },
+        }
+
+    else:
+        # Batch inference
+        inputs = [{
+            "prompt": prompt,
+            "multi_modal_data": {
+                modality: data
+            },
+        } for _ in range(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 '
+        'vision language models for text generation')
+    parser.add_argument('--model-type',
+                        '-m',
+                        type=str,
+                        default="llava",
+                        choices=model_example_map.keys(),
+                        help='Huggingface "model_type".')
+    parser.add_argument('--num-prompts',
+                        type=int,
+                        default=4,
+                        help='Number of prompts to run.')
+    parser.add_argument('--modality',
+                        type=str,
+                        default="image",
+                        choices=['image', 'video'],
+                        help='Modality of the input.')
+    parser.add_argument('--num-frames',
+                        type=int,
+                        default=16,
+                        help='Number of frames to extract from the video.')
+    args = parser.parse_args()
+    main(args)