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Eli Bendersky: Notes on implementing Attention
- The article provides insights on implementing attention blocks in pure Python + Numpy with a focus on the exact code implementation and explanation of shapes throughout the process.
- It starts with a basic scaled dot product self-attention for a single sequence of tokens without masking, utilizing weight matrices Wk, Wq, and Wv.
- The article introduces a Numpy implementation of self-attention and explains the 'scaled' part by dividing by the square root of the head size to manage dot product values.
- Batched self-attention is discussed for processing batches of input sequences efficiently, leveraging parallelism and Numpy matrix operations.
- Multi-head attention, a common technique in modern models, is covered, where multiple heads handle attention separately and results are concatenated and linearly projected.
- The article delves into masked (causal) self-attention, essential in decoder blocks to prevent tokens from attending to future tokens, ensuring proper generative model training.
- Cross-attention is explained as a variant where elements of one sequence attend to elements in another sequence, commonly seen in decoder blocks of models like the AIAYN paper.
- A vectorized implementation of multi-head attention is provided to optimize the code for accelerators like GPUs and TPUs, concatenating weight matrices for efficiency.
- The article concludes by offering detailed code samples, including tests, available in a repository for further exploration and understanding of attention mechanism implementations.
- The article emphasizes the importance of attention in neural network architectures and presents clear examples for better comprehension and implementation.
- Attention mechanisms like self-attention, multi-head attention, and masked attention play a crucial role in enhancing model performance and training for various NLP tasks.
- Different variants of attention blocks, including cross-attention, cater to diverse requirements in neural network design and enable advanced functionalities in model learning and inference.
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