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