[Model] Expose Phi3v num_crops as a mm_processor_kwarg

[alex-jw-brooks]

FIX #7861. This PR should be merged after #8657; it exposes num_crops as a processor_kwarg for phi3v models (see last commit) and adds a bunch of tests to ensure it's properly handled everywhere in:

• The max token count
• The dummy data
• The input processor
• The default input mapper (which wraps the default HF image processor)

Examples

1. Offline batch inference

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset

question = "What is the content of this image?"
prompt = f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n"
image = ImageAsset("cherry_blossom").pil_image.convert("RGB")

llm = LLM(
    model="microsoft/Phi-3-vision-128k-instruct",
    trust_remote_code=True,
    max_num_seqs=5,
    mm_processor_kwargs={"num_crops": 4}
)

sampling_params = SamplingParams(temperature=0.2, max_tokens=64)

outputs = llm.generate(
    {
        "prompt": prompt,
        "multi_modal_data": {"image": image}
    }, 
    sampling_params=sampling_params
)

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)

Sample response: The image captures a serene scene of a tall, white tower with a golden dome, standing majestically against a clear blue sky. The tower is partially obscured by a tree adorned with pink cherry blossoms, adding a touch of nature's beauty to the urban landscape.
The example for offline inference for phi3v has also been updated to pass it in case users end up looking at it.

2. Through the server

python vllm/entrypoints/openai/api_server.py \
    --device cuda \
    --model microsoft/Phi-3-vision-128k-instruct \
    --tokenizer microsoft/Phi-3-vision-128k-instruct \
    --trust-remote-code \
    --api-key token-abc123 \
    --max_model_len 32000 \
    --disable-frontend-multiprocessing \
    --mm_processor_kwargs '{"num_crops": 4}' &

Client:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123")

completion = client.chat.completions.create(
  model="microsoft/Phi-3-vision-128k-instruct",
  messages=[
    {
        "role": "user", "content": [
          {"type": "image_url", "image_url": {"url": "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"}},
          {"type": "text", "text": "Describe this image. "},
        ]
    }
  ]
)

print(completion.choices[0].message)

ChatCompletionMessage(content=" The radar chart displays performance metrics for four different models across various evaluation datasets. Each axis represents a dataset, with metrics such as 'BLEU-4', 'MME', 'PPE', 'SMOG', 'TED-LIUM', 'VLEN', 'Vocabulary', and 'Word Error Rate' ranging from 0 to 100. The chart includes the following models: 'BLIP-2', 'InstructBLIP', 'Qwen-VL-Chat', and 'LLaVA-1.5'. Each model has a distinct line and color on the chart, with their performance in each dataset marked along the corresponding axis. Data points are annotated with their values. The chart is titled 'Performance Metrics of Translation Models on NLP Datasets' and includes a legend for model identification.", refusal=None, role='assistant', function_call=None, tool_calls=[])

---

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[DarkLight1337]

Marking this as draft to make it clear that #8657 should be merged first.

[DarkLight1337]

Can you link to the HF repo explaining how to use num_crops?

Please update the multi-image input example as well.

[DarkLight1337]

Otherwise the PR looks good to me (the tests pass locally). You can mark the PR as ready once you have addressed the above comment.

[alex-jw-brooks]

Awesome, sounds good, thanks @DarkLight1337! Updated both examples with a short description about num_crops.

The values are based on the docs for Phi-3.5-vision-instruct - for Phi-3-vision-128k-instruct(what we use in the single image example) it doesn't explicitly say in the README, but uses the value recommended by Phi-3.5-vision-instruct for single frame in its config, so it seems reasonable to use the same recommended values to me