Implement and explain positional encoding
Implement Positional Encodings for a Transformer Language Model
You are tasked with building a Transformer-based language model. A fundamental characteristic of Transformers is their permutation-equivariance, meaning they inherently lack information about the order of tokens in a sequence. To address this, you must inject positional information into the model.
Your task is to implement positional encodings and integrate them into a minimal PyTorch Transformer-based language model.
Requirements
- Positional Encoding Choice: Select either sinusoidal or learned positional encodings. You may optionally demonstrate both approaches.
- PyTorch Implementation: Provide PyTorch code that accomplishes the following:
- Computes the chosen positional encodings.
- Adds these encodings to token embeddings, ensuring correct tensor shapes and broadcasting.
- Integrates the positional encoding mechanism into a simple, functional Transformer-based language model.
- Explanation: Clearly explain the underlying equations and the tensor shapes involved in your implementation.
- Symptom Analysis: Discuss the expected training and inference symptoms or performance degradation if positional information were entirely omitted from the Transformer model.
- Empirical Verification: Describe how you would empirically verify the effectiveness of your positional encoding implementation. This should include details on ablation studies, relevant metrics, and sanity checks.
AI interview prep
Core dilemma
Injecting sequential order and relative position information into a permutation-equivariant neural network architecture (Transformer) without losing the benefits of parallel computation and global context provided by self-attention.
2 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Implement and explain positional encoding
Implement Positional Encodings for a Transformer Language Model
You are tasked with building a Transformer-based language model. A fundamental characteristic of Transformers is their permutation-equivariance, meaning they inherently lack information about the order of tokens in a sequence. To address this, you must inject positional information into the model.
Your task is to implement positional encodings and integrate them into a minimal PyTorch Transformer-based language model.
Requirements
- Positional Encoding Choice: Select either sinusoidal or learned positional encodings. You may optionally demonstrate both approaches.
- PyTorch Implementation: Provide PyTorch code that accomplishes the following:
- Computes the chosen positional encodings.
- Adds these encodings to token embeddings, ensuring correct tensor shapes and broadcasting.
- Integrates the positional encoding mechanism into a simple, functional Transformer-based language model.
- Explanation: Clearly explain the underlying equations and the tensor shapes involved in your implementation.
- Symptom Analysis: Discuss the expected training and inference symptoms or performance degradation if positional information were entirely omitted from the Transformer model.
- Empirical Verification: Describe how you would empirically verify the effectiveness of your positional encoding implementation. This should include details on ablation studies, relevant metrics, and sanity checks.