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Python vlm_chat_sample that supports VLM models

This example showcases inference of text-generation Vision Language Models (VLMs): miniCPM-V-2_6 and other models with the same signature. The application doesn't have many configuration options to encourage the reader to explore and modify the source code. For example, change the device for inference to GPU. The sample features openvino_genai.VLMPipeline and configures it for the chat scenario. There is also a Jupyter notebook which provides an example of Visual-language assistant.

There are three sample files:

  • visual_language_chat.py demonstrates basic usage of the VLM pipeline.
  • video_to_text_chat.py demonstrates video to text usage of the VLM pipeline.
  • benchmark_vlm.py shows how to benchmark a VLM in OpenVINO GenAI. The script includes functionality for warm-up iterations, generating text and calculating various performance metrics.
  • milebench_eval_vlm.py provides MileBench validation for VLMs, enabling evaluation of image–text reasoning and visual QA tasks across multiple subsets designed to assess the MultImodal Long-contExt capabilities of MLLMs.

Download and convert the model and tokenizers​

The --upgrade-strategy eager option is needed to ensure optimum-intel is upgraded to the latest version.

Install ../../export-requirements.txt to convert a model.

pip install --upgrade-strategy eager -r ../../export-requirements.txt

Then, run the export with Optimum CLI:

optimum-cli export openvino --model openbmb/MiniCPM-V-2_6 --trust-remote-code MiniCPM-V-2_6

Alternatively, you can do it in Python code:

from optimum.exporters.openvino.convert import export_tokenizer
from optimum.intel import OVModelForVisualCausalLM
from transformers import AutoTokenizer

output_dir = "MiniCPM-V-2_6"

model = OVModelForVisualCausalLM.from_pretrained("openbmb/MiniCPM-V-2_6", export=True, trust_remote_code=True)
model.save_pretrained(output_dir)

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-V-2_6")
export_tokenizer(tokenizer, output_dir)

Install deployment-requirements.txt via pip install -r ../../deployment-requirements.txt to run VLM samples.

Run image-to-text chat sample:​

This image can be used as a sample image.

python visual_language_chat.py ./miniCPM-V-2_6/ 319483352-d5fbbd1a-d484-415c-88cb-9986625b7b11.jpg

See https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md#supported-models for the list of supported models.

Run video-to-text chat sample:​

A model that supports video input is required to run this sample, for example llava-hf/LLaVA-NeXT-Video-7B-hf.

This video can be used as a sample video.

python video_to_text_chat.py ./LLaVA-NeXT-Video-7B-hf/ sample_demo_1.mp4

Supported models with video input are listed in this section.

Discrete GPUs (dGPUs) usually provide better performance compared to CPUs. It is recommended to run larger models on a dGPU with 32GB+ RAM. # TODO: examples of larger models Modify the source code to change the device for inference to the GPU.

Run benchmark:​

python benchmark_vlm.py [OPTIONS]

Options​

  • -m, --model(default: .): Path to the model and tokenizers base directory.
  • -p, --prompt (default: None): The prompt to generate text. If without -p and -pf, the default prompt is "What is on the image?"
  • -pf, --prompt_file Read prompt from file.
  • -i, --image (default: image.jpg): Path to the image.
  • -nw, --num_warmup (default: 1): Number of warmup iterations.
  • -mt, --max_new_tokens (default: 20): Maximal number of new tokens.
  • -n, --num_iter (default: 3): Number of iterations.
  • -d, --device (default: "CPU"): Device to run the model on.

Output:​

python benchmark_vlm.py -m miniCPM-V-2_6 -i 319483352-d5fbbd1a-d484-415c-88cb-9986625b7b11.jpg -n 3
Load time: 1982.00 ms
Generate time: 13820.99 ± 64.62 ms
Tokenization time: 1.26 ± 0.09 ms
Detokenization time: 0.33 ± 0.05 ms
Embeddings preparation time: 5733.85 ± 26.34 ms
TTFT: 11246.98 ± 80.55 ms
TPOT: 135.45 ± 4.73 ms/token
Throughput: 7.38 ± 0.26 tokens/s

For more information how performance metrics are calculated please follow performance-metrics tutorial.

Troubleshooting​

Unicode characters encoding error on Windows​

Example error:

UnicodeEncodeError: 'charmap' codec can't encode character '\u25aa' in position 0: character maps to <undefined>

If you encounter the error described in the example when sample is printing output to the Windows console, it is likely due to the default Windows encoding not supporting certain Unicode characters. To resolve this:

  1. Enable Unicode characters for Windows cmd - open Region settings from Control panel. Administrative->Change system locale->Beta: Use Unicode UTF-8 for worldwide language support->OK. Reboot.
  2. Enable UTF-8 mode by setting environment variable PYTHONIOENCODING="utf8".