OpenAI-Clip: Optimized for Mobile Deployment

Multi-modal foundational model for vision and language tasks like image/text similarity and for zero-shot image classification

Contrastive Language-Image Pre-Training (CLIP) uses a ViT like transformer to get visual features and a causal language model to get the text features. Both the text and visual features can then be used for a variety of zero-shot learning tasks.

This model is an implementation of OpenAI-Clip found here. This repository provides scripts to run OpenAI-Clip on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

• Model Type: Image classification
• Model Stats:
  • Model checkpoint: ViT-B/16
  • Image input resolution: 224x224
  • Text context length: 77
  • Number of parameters (CLIPTextEncoder): 76.0M
  • Model size (CLIPTextEncoder): 290 MB
  • Number of parameters (CLIPImageEncoder): 115M
  • Model size (CLIPImageEncoder): 437 MB

Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	TFLite	5.809 ms	0 - 2 MB	FP16	NPU	CLIPTextEncoder.tflite
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	TFLite	39.036 ms	0 - 3 MB	FP16	NPU	CLIPImageEncoder.tflite
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	QNN Model Library	6.029 ms	0 - 22 MB	FP16	NPU	CLIPTextEncoder.so
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	QNN Model Library	39.047 ms	0 - 54 MB	FP16	NPU	CLIPImageEncoder.so

Installation

This model can be installed as a Python package via pip.

pip install "qai-hub-models[openai_clip]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.openai_clip.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.openai_clip.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

• Performance check on-device on a cloud-hosted device
• Downloads compiled assets that can be deployed on-device for Android.
• Accuracy check between PyTorch and on-device outputs.

python -m qai_hub_models.models.openai_clip.export

Profile Job summary of CLIPTextEncoder
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 6.16 ms
Estimated Peak Memory Range: 0.15-0.15 MB
Compute Units: NPU (377) | Total (377)

Profile Job summary of CLIPImageEncoder
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 31.39 ms
Estimated Peak Memory Range: 0.57-0.57 MB
Compute Units: NPU (369) | Total (369)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.openai_clip import CLIPTextEncoder,CLIPImageEncoder

# Load the model
text_encoder_model = CLIPTextEncoder.from_pretrained()
image_encoder_model = CLIPImageEncoder.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
text_encoder_input_shape = text_encoder_model.get_input_spec()
text_encoder_sample_inputs = text_encoder_model.sample_inputs()

traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()])

# Compile model on a specific device
text_encoder_compile_job = hub.submit_compile_job(
    model=traced_text_encoder_model ,
    device=device,
    input_specs=text_encoder_model.get_input_spec(),
)

# Get target model to run on-device
text_encoder_target_model = text_encoder_compile_job.get_target_model()
# Trace model
image_encoder_input_shape = image_encoder_model.get_input_spec()
image_encoder_sample_inputs = image_encoder_model.sample_inputs()

traced_image_encoder_model = torch.jit.trace(image_encoder_model, [torch.tensor(data[0]) for _, data in image_encoder_sample_inputs.items()])

# Compile model on a specific device
image_encoder_compile_job = hub.submit_compile_job(
    model=traced_image_encoder_model ,
    device=device,
    input_specs=image_encoder_model.get_input_spec(),
)

# Get target model to run on-device
image_encoder_target_model = image_encoder_compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

text_encoder_profile_job = hub.submit_profile_job(
    model=text_encoder_target_model,
    device=device,
)
image_encoder_profile_job = hub.submit_profile_job(
    model=image_encoder_target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

text_encoder_input_data = text_encoder_model.sample_inputs()
text_encoder_inference_job = hub.submit_inference_job(
    model=text_encoder_target_model,
    device=device,
    inputs=text_encoder_input_data,
)
text_encoder_inference_job.download_output_data()
image_encoder_input_data = image_encoder_model.sample_inputs()
image_encoder_inference_job = hub.submit_inference_job(
    model=image_encoder_target_model,
    device=device,
    inputs=image_encoder_input_data,
)
image_encoder_inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

• TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

• QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on OpenAI-Clip's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

• The license for the original implementation of OpenAI-Clip can be found here.
• The license for the compiled assets for on-device deployment can be found here

References

• Learning Transferable Visual Models From Natural Language Supervision
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.