Stories with Tag Self-Attention

• Writing an LLM from scratch, part 8 – trainable self-attention

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  Posted: 2025-03-05 01:41:14

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  This blog post details the implementation of trainable self-attention, a crucial component of transformer-based language models, within the author's ongoing project to build an LLM from scratch. It focuses on replacing the previously hardcoded attention mechanism with a learned version, enabling the model to dynamically weigh the importance of different parts of the input sequence. The post covers the mathematical underpinnings of self-attention, including queries, keys, and values, and explains how these are represented and calculated within the code. It also discusses the practical implementation details, like matrix multiplication and softmax calculations, necessary for efficient computation. Finally, it showcases the performance improvements gained by using trainable self-attention, demonstrating its effectiveness in capturing contextual relationships within the text.

  This blog post, the eighth in a series on building a Large Language Model (LLM) from scratch, delves into the crucial concept of trainable self-attention, a mechanism that allows the model to weigh different parts of the input sequence differently when generating output. The author begins by recapping the previous implementation of self-attention, which relied on fixed, pre-computed attention weights based on the relative positions of tokens in the input sequence. This approach, while functional, lacked the flexibility and adaptability of a truly learned attention mechanism. He emphasizes that the core objective of this post is to enable the model to learn these attention weights during the training process, allowing the model to discover contextually relevant relationships between tokens that go beyond simple positional proximity.

  The transition to trainable self-attention involves introducing learnable parameters, specifically weight matrices, into the attention calculation. The author meticulously outlines the mathematical operations involved, starting with projecting the input embeddings into three distinct vector spaces: Query (Q), Key (K), and Value (V). These projections are accomplished through matrix multiplications with the corresponding weight matrices (W_Q, W_K, and W_V). The attention weights are then calculated by performing a dot product between the Query vector of each token and the Key vectors of all other tokens in the sequence. This dot product operation captures the affinity or relevance between different token pairs. These raw attention scores are then scaled down by the square root of the embedding dimension to prevent them from becoming too large and to stabilize training. A softmax function is then applied to these scaled scores, converting them into probabilities that sum to one for each token. Finally, these attention probabilities are used to compute a weighted average of the Value vectors, effectively allowing the model to attend to different parts of the input with varying degrees of focus.

  The author highlights the importance of backpropagation for training these newly introduced weight matrices. During backpropagation, the error signal from the output is propagated back through the network, and the gradients with respect to the attention weights are calculated. These gradients are then used to update the weight matrices via an optimization algorithm, typically stochastic gradient descent, thereby refining the attention mechanism over successive iterations of training.

  The post then provides a detailed walkthrough of the Python code implementation of this trainable self-attention mechanism, using the Jax framework for automatic differentiation and efficient computation. The code includes the necessary steps for initializing the weight matrices, performing the forward pass to calculate the attention-weighted output, and implementing the backward pass for gradient calculation and weight updates. The author stresses the clarity and conciseness of the Jax implementation, emphasizing its advantages for building and training complex models like LLMs. He concludes by reiterating the significance of this step in the development of a full-fledged LLM, paving the way for more sophisticated language understanding and generation capabilities.

  • Large Language Models
  • LLMs
  • natural language processing
  • NLP
  • artificial intelligence
  • AI
  • Self-Attention
  • attention mechanism
  • deep learning
  • machine learning
  • neural networks
  • training
  • from scratch
  • Implementation
  • Tutorial
  • coding
  • Software Development
  • Transformer Model
  • Language Model Training

  Summary of Comments ( 24 )
  https://news.ycombinator.com/item?id=43261650

  Verbose tl;dr

  Hacker News users discuss the blog post's approach to implementing self-attention, with several praising its clarity and educational value, particularly in explaining the complexities of matrix multiplication and optimization for performance. Some commenters delve into specific implementation details, like the use of torch.einsum and the choice of FlashAttention, offering alternative approaches and highlighting potential trade-offs. Others express interest in seeing the project evolve to handle longer sequences and more complex tasks. A few users also share related resources and discuss the broader landscape of LLM development. The overall sentiment is positive, appreciating the author's effort to demystify a core component of LLMs.

  The Hacker News post titled "Writing an LLM from scratch, part 8 – trainable self-attention" has generated several comments discussing various aspects of the linked blog post.

  Several commenters praise the author's clear and accessible explanation of complex concepts related to LLMs and self-attention. One commenter specifically appreciates the author's approach of starting with a simple, foundational model and gradually adding complexity, making it easier for readers to follow along. Another echoes this sentiment, highlighting the benefit of the step-by-step approach for understanding the underlying mechanics.

  There's a discussion around the practical implications of implementing such a model from scratch. A commenter questions the real-world usefulness of building an LLM from the ground up, given the availability of sophisticated pre-trained models and libraries. This sparks a counter-argument that emphasizes the educational value of such an endeavor, allowing for a deeper understanding of the inner workings of these models, even if it's not practically efficient for production use. The idea of building from scratch being a valuable learning experience, even if not practical for deployment, is a recurring theme.

  One commenter dives into a more technical discussion about the author's choice of softmax for the attention mechanism, suggesting alternative approaches like sparsemax. This leads to further conversation exploring the tradeoffs between different attention mechanisms in terms of performance and computational cost.

  Another thread focuses on the challenges of scaling these models. A commenter points out the computational demands of training large language models and how this limits accessibility for individuals or smaller organizations. This comment prompts a discussion on various optimization techniques and hardware considerations for efficient LLM training.

  Finally, some commenters express excitement about the ongoing series and look forward to future installments where the author will cover more advanced topics. The overall sentiment towards the blog post is positive, with many praising its educational value and clarity.

• The FFT Strikes Back: An Efficient Alternative to Self-Attention

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  Posted: 2025-02-26 09:57:23

  Verbose tl;dr

  The paper "The FFT Strikes Back: An Efficient Alternative to Self-Attention" proposes using Fast Fourier Transforms (FFTs) as a more efficient alternative to self-attention mechanisms in Transformer models. It introduces a novel architecture called the Fast Fourier Transformer (FFT), which leverages the inherent ability of FFTs to capture global dependencies within sequences, similar to self-attention, but with significantly reduced computational complexity. Specifically, the FFT Transformer achieves linear complexity (O(n log n)) compared to the quadratic complexity (O(n^2)) of standard self-attention. The paper demonstrates that the FFT Transformer achieves comparable or even superior performance to traditional Transformers on various tasks including language modeling and machine translation, while offering substantial improvements in training speed and memory efficiency.

  The arXiv preprint "The FFT Strikes Back: An Efficient Alternative to Self-Attention" proposes a novel approach to sequence modeling that leverages the Fast Fourier Transform (FFT) as a compelling alternative to the computationally demanding self-attention mechanism prevalent in Transformer models. The authors argue that the core strength of self-attention, its ability to capture long-range dependencies within a sequence, can be effectively replicated and even surpassed by exploiting the inherent properties of the FFT.

  The paper introduces a new model architecture termed "SFFT," which stands for "Sparse Fast Fourier Transform." This architecture centers around a sparse variant of the FFT algorithm, carefully designed to selectively attend to relevant frequency components within the input sequence. This sparsity is crucial for managing computational complexity and preventing the model from being overwhelmed by irrelevant information. The authors meticulously construct this sparsity pattern by learning a binary mask that determines which frequency components are considered important for each input. This learned mask allows the SFFT mechanism to dynamically adapt its focus to different input sequences, effectively mimicking the adaptive attention mechanism of Transformers.

  A key advantage of the SFFT approach lies in its computational efficiency. Unlike self-attention, which scales quadratically with the sequence length, the FFT and its variants, including the proposed SFFT, scale quasi-linearly (N log N). This represents a significant improvement, particularly for long sequences, making the SFFT architecture more suitable for processing extensive data like lengthy text passages or high-resolution images.

  The paper provides a detailed mathematical analysis of the SFFT mechanism, demonstrating its ability to approximate the functionality of self-attention while maintaining a lower computational footprint. Furthermore, the authors conduct extensive experiments across various benchmark datasets, including Long Range Arena and image classification tasks. These empirical results demonstrate that the SFFT model achieves competitive performance compared to state-of-the-art Transformer models, while exhibiting significantly improved computational efficiency, especially for long sequences. This superior efficiency translates into faster training and inference times, making the SFFT architecture a promising candidate for resource-constrained environments and applications demanding real-time performance.

  The authors conclude that the SFFT mechanism offers a viable and efficient alternative to self-attention, opening up new avenues for research in sequence modeling. They suggest that the proposed architecture could be particularly beneficial in scenarios involving extremely long sequences where the quadratic complexity of self-attention becomes prohibitive. The paper further encourages exploration of different sparsity patterns and learning strategies for the binary mask to potentially further enhance the performance and efficiency of the SFFT approach.

  • Fast Fourier Transform
  • FFT
  • Self-Attention
  • transformers
  • deep learning
  • machine learning
  • AI
  • artificial intelligence
  • Algorithm
  • Efficiency
  • Computational Complexity
  • sequence modeling
  • natural language processing
  • NLP
  • Computer Science
  • Signal Processing

  Summary of Comments ( 62 )
  https://news.ycombinator.com/item?id=43182325

  Verbose tl;dr

  Hacker News users discussed the potential of the Fast Fourier Transform (FFT) as a more efficient alternative to self-attention mechanisms. Some expressed excitement about the approach, highlighting its lower computational complexity and potential to scale to longer sequences. Skepticism was also present, with commenters questioning the practical applicability given the constraints imposed by the theoretical framework and the need for further empirical validation on real-world datasets. Several users pointed out that the reliance on circular convolution inherent in FFTs might limit its ability to capture long-range dependencies as effectively as attention. Others questioned whether the performance gains would hold up on complex tasks and datasets, particularly in domains like natural language processing where self-attention has proven successful. There was also discussion around the specific architectural choices and hyperparameters, with some users suggesting modifications and further avenues for exploration.

  The Hacker News post "The FFT Strikes Back: An Efficient Alternative to Self-Attention" (https://news.ycombinator.com/item?id=43182325) discussing the arXiv paper (https://arxiv.org/abs/2502.18394) has a modest number of comments, focusing primarily on the technical aspects and potential implications of the proposed method.

  Several commenters discuss the core idea of the paper, which uses Fast Fourier Transforms (FFTs) as a more efficient alternative to self-attention mechanisms. One commenter highlights the intriguing aspect of revisiting FFTs in this context, especially given their historical precedence over attention mechanisms. They emphasize the cyclical nature of advancements in machine learning, where older techniques are sometimes rediscovered and refined. Another commenter points out the computational advantages of FFTs, particularly their lower complexity compared to the quadratic complexity often associated with self-attention. This difference in scaling is mentioned as a potential game-changer for larger models and datasets.

  The discussion also delves into the specific techniques used in the paper. One commenter asks for clarification on the "low-rank" property mentioned, and how it relates to the efficiency gains. Another comment thread explores the connection between FFTs and convolution operations, with one user suggesting that the proposed method could be interpreted as a form of global convolution. This sparked further discussion about the implications for receptive fields and the ability to capture long-range dependencies within data.

  Some commenters express cautious optimism about the proposed method. While acknowledging the potential of FFTs for improved efficiency, they also raise questions about the potential trade-offs in terms of performance and expressiveness compared to self-attention. One commenter specifically wonders about the ability of FFT-based methods to capture the nuanced relationships often modeled by attention mechanisms. Another comment emphasizes the need for further empirical evaluation to determine the practical benefits of the proposed approach across various tasks and datasets.

  Finally, a few comments touch upon the broader context of the research. One user mentions the ongoing search for efficient alternatives to self-attention, driven by the computational demands of large language models. They suggest that this work represents a valuable contribution to this effort. Another comment points out the cyclical nature of research in machine learning, where older techniques often find new relevance and application in light of new advancements.