V-JEPA 2: Convert PyTorch Model To Hugging Face

Hey guys! So, you've trained your V-JEPA 2 model and now you're looking to convert it into a Hugging Face model so you can share it with the world? Awesome! It's a pretty common goal, but you've probably noticed that the keys in your PyTorch checkpoint and the Hugging Face model don't exactly line up. Don't worry, it's a hurdle many people face. Let's break down how you can tackle this and get your model ready for the Hugging Face Hub.

Understanding the Key Differences

Before we dive into the conversion process, it's really important to understand why the keys are different in the first place. Typically, Hugging Face models have a specific architecture and naming convention that's different from the raw PyTorch model you trained. This is often because Hugging Face's transformers library is designed to be modular and flexible, fitting various pre-trained models into a unified API. This means the way layers are named, and parameters are structured can vary quite a bit.

Your trained V-JEPA 2 model probably uses a naming scheme that's specific to its original implementation. When you're converting to a Hugging Face model, you need to map the layers and parameters from your model to the corresponding layers in the Hugging Face model. This often involves inspecting both models, understanding their architecture, and then writing code to correctly transfer the weights. It can be a bit tedious, but trust me, it's doable!

To start, let's explore the architecture of both models: your trained PyTorch model and the expected Hugging Face model. You'll need to load both models and print their state_dict to inspect the keys. Look for patterns and try to identify which layers in your model correspond to the layers in the Hugging Face model. For example, a linear layer named fc1 in your model might be named classifier or dense in the Hugging Face model. Once you understand the mapping, you can start writing the conversion code.

Step-by-Step Conversion Process

Okay, let's get into the nitty-gritty. Here's a step-by-step guide to converting your trained V-JEPA 2 model to a Hugging Face model:

1. Load Your Trained Model

First, load your trained PyTorch checkpoint. Make sure you have the necessary code to instantiate your model architecture. This is the model you trained with your data. You'll load the state dictionary from your checkpoint into this model.

import torch

# Assuming you have your model class defined as VJEPA2Model
model = VJEPA2Model()
checkpoint = torch.load('path/to/your/checkpoint.pth')
model.load_state_dict(checkpoint['state_dict'])
model.eval()

2. Load the Hugging Face Model

Next, load the Hugging Face model you want to convert to. This will be the target model. Ensure you're using the correct configuration for your V-JEPA 2 model. If there isn't an existing V-JEPA 2 model in the transformers library, you might need to create a custom configuration and model class.

from transformers import AutoModel

# If there's a pre-existing V-JEPA 2 model in Hugging Face
hf_model = AutoModel.from_pretrained('some/huggingface/vjepa2_model')

# If you need to define a custom model
class CustomVJEPA2Model(torch.nn.Module):
    def __init__(self, config):
        super().__init__()
        # Define your layers here based on the config
        self.embedding = torch.nn.Embedding(config.vocab_size, config.hidden_size)
        self.transformer = TransformerBlock(config.hidden_size, config.num_attention_heads)
        self.classifier = torch.nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, input_ids):
        embedded = self.embedding(input_ids)
        transformed = self.transformer(embedded)
        logits = self.classifier(transformed)
        return logits

# Create a configuration
class VJEPA2Config:
    def __init__(self, vocab_size, hidden_size, num_attention_heads, num_labels):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_labels = num_labels

config = VJEPA2Config(vocab_size=10000, hidden_size=768, num_attention_heads=12, num_labels=2)
hf_model = CustomVJEPA2Model(config)

3. Inspect the Model Keys

Print the state_dict of both models to understand the key differences. This is crucial for mapping the weights correctly.

print("Trained Model Keys:", model.state_dict().keys())
print("Hugging Face Model Keys:", hf_model.state_dict().keys())

4. Map and Transfer the Weights

Create a mapping between the keys of your trained model and the Hugging Face model. Then, transfer the weights accordingly. This is where the magic happens. You'll iterate through the state dictionary of your trained model and copy the weights to the corresponding layers in the Hugging Face model.

# Create a dictionary to map the keys
key_mapping = {
    'embedding.weight': 'embedding.weight',
    'transformer.0.self_attn.q_proj.weight': 'transformer.encoder.layer.0.attention.self.query.weight',
    # Add more mappings here
}

# Create a new state dictionary for the Hugging Face model
hf_state_dict = {}
for k, v in model.state_dict().items():
    if k in key_mapping:
        hf_state_dict[key_mapping[k]] = v
    else:
        print(f"Key {k} not found in mapping")

# Load the new state dictionary into the Hugging Face model
hf_model.load_state_dict(hf_state_dict, strict=False)

Important: The strict=False argument in load_state_dict is important because it allows you to load only the weights that have been mapped. Any layers in the Hugging Face model that don't have corresponding weights in your mapping will retain their initial values. After loading, double-check that the weights have been correctly transferred by comparing the outputs of both models for the same input.

5. Save the Hugging Face Model

Save the converted Hugging Face model. You can save both the model and the configuration.

# Save the model
hf_model.save_pretrained('path/to/save/huggingface_model')

# If you have a custom configuration, save it too
config.save_pretrained('path/to/save/huggingface_model')

6. Test the Converted Model

Before uploading, make sure your converted model works as expected. Load the model and run some inference to verify the outputs.

from transformers import AutoModel

loaded_model = AutoModel.from_pretrained('path/to/save/huggingface_model')
loaded_model.eval()

# Example input
input_ids = torch.tensor([[1, 2, 3, 4, 5]])

# Get the output
with torch.no_grad():
    output = loaded_model(input_ids)

print(output)

Uploading to Hugging Face Hub

Alright, you've converted your model and tested it locally. Now it's time to share it with the world! Here’s how you can upload your model to the Hugging Face Hub:

1. Install huggingface_hub

Make sure you have the huggingface_hub library installed. If not, you can install it using pip:

pip install huggingface_hub

2. Login to Your Hugging Face Account

Login to your Hugging Face account using the huggingface-cli tool. If you don't have an account, create one on the Hugging Face website. It's free!

huggingface-cli login

You'll be prompted to enter your username and API token. Once you're logged in, you can proceed to upload your model.

3. Upload Your Model

Use the push_to_hub method to upload your model to the Hugging Face Hub. Make sure to specify the repository name.

from transformers import AutoModel

# Load your model
model = AutoModel.from_pretrained('path/to/save/huggingface_model')

# Push to the hub
model.push_to_hub('your_username/your_model_name', commit_message="Add V-JEPA 2 model")

If you have a custom configuration, you should upload it separately:

config.push_to_hub('your_username/your_model_name', commit_message="Add V-JEPA 2 configuration")

4. Add a Model Card

A model card is a README file that describes your model, its intended use, limitations, and other relevant information. It’s a crucial part of sharing your model responsibly. Create a README.md file in your model repository and add the necessary details.

Here’s an example of what a model card might include:

# V-JEPA 2 Model

This model is a V-JEPA 2 model trained on [your dataset]. It can be used for [specific tasks, e.g., image recognition, object detection].

## Usage

To use this model, you can load it using the `transformers` library:

```python
from transformers import AutoModel

model = AutoModel.from_pretrained('your_username/your_model_name')

Limitations

This model may not perform well on data that is significantly different from the training data.

License

[Specify the license, e.g., Apache 2.0]

Commit and push the `README.md` file to your repository.

## Troubleshooting Common Issues

### Key Mapping Errors

If you encounter errors during the key mapping process, double-check your mapping dictionary. Make sure the keys in your trained model match the keys in the Hugging Face model. Use the `state_dict().keys()` method to inspect the keys and ensure they are correct.

### Model Performance Issues

If your converted model doesn’t perform as well as your original model, it could be due to incorrect weight transfer. Verify that the weights have been correctly mapped and transferred. You can also try fine-tuning the converted model on a small dataset to improve its performance.

### Dependency Issues

Make sure you have all the necessary dependencies installed. Check the requirements of both your original model and the Hugging Face model. Install any missing dependencies using pip.

## Conclusion

Converting a trained V-JEPA 2 model to a Hugging Face model and uploading it can seem daunting at first, but with a systematic approach, it’s definitely achievable. By understanding the key differences, mapping the weights correctly, and testing the converted model, you can successfully share your model with the community. So, go ahead, convert your model, and contribute to the world of open-source AI! Happy converting!

For additional information on Hugging Face models and how to use them, check out the **[Hugging Face documentation](https://huggingface.co/docs)**. It's a great resource for all things related to transformers and model sharing.