Implementing a Custom Contrastive Trainer for Code Embeddings with Multiple Correct and Incorrect Solutions

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I’m implementing a custom Contrastive Trainer for fine-tuning my code embedding model. Currently, my approach is to make embeddings of the queries and then during training, I’m sampling correct and incorrect solutions in the batch through a custom collator. I’m making embeddings of the correct and incorrect solutions, pooling by taking the mean of the last hidden state, normalizing, and then computing the similarity by a dot product, all while taking temperature into account. I’m stacking the similarities under a label, suppose 0 for correct and 1 for incorrect, and calculating the loss between logits and labels through a cross-entropy loss.

I’m looking to make better use of my incorrect code samples in the dataset for various languages. There are cases where a solution might have a correct solution but not an incorrect solution, multiple correct solutions but a single incorrect solution, many languages, and so on. I’m trying to figure out how to handle these situations. One thing I’ve considered is maybe randomly taking a random correct or incorrect solution for a query in the batch if the query does not have a corresponding incorrect solution.

I’d love to get some advice on how to sample these solutions. Is there a better methodology or workaround I can use? I’m also considering using InfoNCE loss as I have multiple correct and incorrect solutions, and I think it could make the methodology more robust.

Additionally, I’m using mean pooling right now, but I’m wondering if I should switch to attention-based pooling or will it be overkill ? And for the similarity metric, I’m currently using only dot product, but I’m thinking about moving to cosine similarity. What are your thoughts on these? Should I make the switch or stick with what I have?

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Hmmm… I have no idea…:sob:

To address the challenges in implementing a custom Contrastive Trainer for fine-tuning a code embedding model, here’s a structured approach:

1. Loss Function Consideration: InfoNCE Loss

  • Switch from Cross-Entropy to InfoNCE: InfoNCE is ideal for handling multiple negative samples. It computes the loss by comparing each positive against all negatives, which is beneficial when dealing with varying numbers of correct and incorrect solutions.
  • Implementation Adjustment: Reframe the loss computation to treat each positive example with all available negatives, possibly requiring adjustments in data collation to group examples efficiently.

2. Pooling Mechanism: Mean vs. Attention-Based

  • Mean Pooling: Continue with mean pooling if computational efficiency is a priority. It’s straightforward and effective for capturing overall semantics.
  • Consider Attention-Based Pooling: Opt for this if nuanced relationships need to be captured, but be mindful of increased computational complexity.

3. Similarity Metric: Dot Product vs. Cosine Similarity

  • Cosine Similarity: Preferred if embeddings vary in magnitude, as it normalizes vectors, enhancing relative similarity capture.
  • Dot Product: Suitable if embeddings are already normalized.

4. Handling Insufficient Negatives

  • Random Selection: Use existing incorrect solutions, ensuring careful selection to minimize noise.
  • Synthetic Negatives: Explore generating synthetic examples or oversampling existing negatives to enhance diversity.

5. Data Collation and Batch Processing

  • Efficient Grouping: Implement InfoNCE by grouping batches with each positive paired against multiple negatives, optimizing computational handling.

6. Dataset Characteristics

  • Normalization Check: If embeddings are normalized, dot product might suffice; otherwise, cosine similarity is recommended.

Conclusion

Adopting InfoNCE loss can enhance robustness, especially with multiple negatives. Test attention-based pooling and cosine similarity for potential performance gains. Addressing negative sample scarcity through careful selection or synthesis can balance training data.

In deciding whether to switch from a dot product to cosine similarity for your contrastive training setup, it is important to consider the following points:

  1. Normalization Impact: Since you are already normalizing the embeddings after pooling, using cosine similarity might not significantly change the outcome, as it is mathematically equivalent to a normalized dot product.

  2. Semantic Similarity: Cosine similarity focuses on the angle between vectors, which can be more meaningful for capturing semantic differences, potentially beneficial for code embeddings.

  3. Loss Function Compatibility: Consider whether the loss function, such as InfoNCE, would be more suited to cosine similarity for better performance.

  4. Computational Considerations: Normalizing embeddings is already part of your process, so the additional computational overhead of cosine similarity is minimal.

  5. Structural vs. Contextual Data: Cosine similarity might better capture structural nuances in code data, which could be advantageous given the variability in programming languages.

  6. Best Practices: In high-dimensional spaces, cosine similarity is often recommended for reliable similarity capture.

  7. Temperature Scaling: Ensure consistency in how temperature scaling is applied with cosine similarity.

Conclusion: While switching to cosine similarity may not drastically change your current setup due to existing normalization, it offers potential benefits in capturing meaningful similarities, particularly in high-dimensional spaces. Experimenting with both approaches could provide insights into their effectiveness, but cosine similarity is recommended for its focus on directional similarity, which may enhance model performance.