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

Model	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
CLIPTextEncoder	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	5.704 ms	0 - 2 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	4.73 ms	0 - 23 MB	FP16	NPU	OpenAI-Clip.so
CLIPTextEncoder	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	35.21 ms	0 - 131 MB	FP16	NPU	OpenAI-Clip.onnx
CLIPTextEncoder	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	4.08 ms	0 - 195 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	3.379 ms	0 - 72 MB	FP16	NPU	OpenAI-Clip.so
CLIPTextEncoder	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	25.241 ms	0 - 536 MB	FP16	NPU	OpenAI-Clip.onnx
CLIPTextEncoder	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	3.991 ms	0 - 109 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	2.754 ms	0 - 65 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	23.811 ms	0 - 315 MB	FP16	NPU	OpenAI-Clip.onnx
CLIPTextEncoder	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	5.664 ms	0 - 1004 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	QCS8550 (Proxy)	QCS8550 Proxy	QNN	4.838 ms	0 - 1 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	SA8255 (Proxy)	SA8255P Proxy	TFLITE	5.761 ms	0 - 2 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	SA8255 (Proxy)	SA8255P Proxy	QNN	4.832 ms	0 - 1 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	SA8775 (Proxy)	SA8775P Proxy	TFLITE	5.675 ms	0 - 2 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	SA8775 (Proxy)	SA8775P Proxy	QNN	4.864 ms	0 - 1 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	SA8650 (Proxy)	SA8650P Proxy	TFLITE	5.702 ms	0 - 2 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	SA8650 (Proxy)	SA8650P Proxy	QNN	4.893 ms	0 - 2 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	SA8295P ADP	SA8295P	TFLITE	7.762 ms	0 - 87 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	SA8295P ADP	SA8295P	QNN	6.779 ms	0 - 6 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	6.582 ms	0 - 167 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPTextEncoder	QCS8450 (Proxy)	QCS8450 Proxy	QNN	5.291 ms	0 - 68 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	5.229 ms	0 - 0 MB	FP16	NPU	Use Export Script
CLIPTextEncoder	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	38.327 ms	127 - 127 MB	FP16	NPU	OpenAI-Clip.onnx
CLIPImageEncoder	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	36.525 ms	0 - 3 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	27.189 ms	0 - 51 MB	FP16	NPU	OpenAI-Clip.so
CLIPImageEncoder	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	29.37 ms	0 - 666 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	20.849 ms	0 - 170 MB	FP16	NPU	OpenAI-Clip.so
CLIPImageEncoder	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	116.721 ms	1 - 3571 MB	FP16	NPU	OpenAI-Clip.onnx
CLIPImageEncoder	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	25.793 ms	0 - 461 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	19.399 ms	0 - 172 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	109.572 ms	1 - 2716 MB	FP16	NPU	OpenAI-Clip.onnx
CLIPImageEncoder	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	36.934 ms	0 - 2 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	QCS8550 (Proxy)	QCS8550 Proxy	QNN	21.964 ms	1 - 2 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	SA8255 (Proxy)	SA8255P Proxy	TFLITE	36.858 ms	0 - 2 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	SA8255 (Proxy)	SA8255P Proxy	QNN	22.523 ms	1 - 2 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	SA8775 (Proxy)	SA8775P Proxy	TFLITE	37.002 ms	0 - 3 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	SA8775 (Proxy)	SA8775P Proxy	QNN	22.519 ms	1 - 2 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	SA8650 (Proxy)	SA8650P Proxy	TFLITE	37.107 ms	1 - 3 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	SA8650 (Proxy)	SA8650P Proxy	QNN	22.501 ms	1 - 2 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	SA8295P ADP	SA8295P	TFLITE	42.408 ms	0 - 359 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	SA8295P ADP	SA8295P	QNN	26.489 ms	1 - 6 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	36.678 ms	0 - 547 MB	FP16	NPU	OpenAI-Clip.tflite
CLIPImageEncoder	QCS8450 (Proxy)	QCS8450 Proxy	QNN	31.003 ms	0 - 165 MB	FP16	NPU	Use Export Script
CLIPImageEncoder	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	161.714 ms	188 - 188 MB	FP16	NPU	OpenAI-Clip.onnx

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

Profiling Results
------------------------------------------------------------
CLIPTextEncoder
Device                          : Samsung Galaxy S23 (13)  
Runtime                         : TFLITE                   
Estimated inference time (ms)   : 5.7                      
Estimated peak memory usage (MB): [0, 2]                   
Total # Ops                     : 660                      
Compute Unit(s)                 : NPU (658 ops) CPU (2 ops)

------------------------------------------------------------
CLIPImageEncoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 36.5                   
Estimated peak memory usage (MB): [0, 3]                 
Total # Ops                     : 659                    
Compute Unit(s)                 : NPU (659 ops)

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

Community

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