This "mistake" is not really a mistake: start with four classic papers to understand what is "wrong" with the Transformer architecture diagram

Some time ago, a tweet pointing out the inconsistency between the Transformer architecture diagram and the code in the Google Brain team's paper "Attention Is All You Need" triggered a lot of discussion.

Some people think that Sebastian’s discovery was an unintentional mistake, but at the same time it is also strange. After all, given the popularity of the Transformer paper, this inconsistency should have been mentioned a thousand times over.

Sebastian Raschka said in response to netizen comments that the "most original" code is indeed consistent with the architecture diagram, but the code version submitted in 2017 was modified, but the architecture was not updated at the same time. picture. This is also the root cause of "inconsistent" discussions.

Subsequently, Sebastian published an article on Ahead of AI specifically describing why the original Transformer architecture diagram was inconsistent with the code, and cited multiple papers to briefly explain the development and changes of Transformer.

##The following is the original text of the article, let us take a look at what the article is about:

A few months ago I shared Understanding Large Language Models: A Cross-Section of the Most Relevant Literature To Get Up to Speed ​​and the positive feedback was very encouraging! Therefore, I've added a few papers to keep the list fresh and relevant.

At the same time, it is crucial to keep the list concise and concise so that everyone can get up to speed in a reasonable amount of time. There are also some papers that contain a lot of information and should probably be included.

I would like to share four useful papers to understand Transformer from a historical perspective. While I'm just adding them directly to the Understanding Large Language Models article, I'm also sharing them separately in this article so that they can be more easily found by those who have read Understanding Large Language Models before.

On Layer Normalization in the Transformer Architecture (2020)

Although the original image of Transformer in the picture below (left) (https://arxiv.org/abs/1706.03762) is a useful summary of the original encoder-decoder architecture, but there is a small difference in the diagram. For example, it does layer normalization between residual blocks, which does not match the official (updated) code implementation included with the original Transformer paper. The variant shown below (middle) is called the Post-LN Transformer.

The layer normalization in the Transformer architecture paper shows that Pre-LN works better and can solve the gradient problem as shown below. Many architectures adopt this approach in practice, but it can lead to a breakdown in representation.

So, while there is still discussion about using Post-LN or Pre-LN, there is also a new paper that proposes applying both together: "ResiDual: Transformer with Dual Residual" Connections" (https://arxiv.org/abs/2304.14802), but whether it will be useful in practice remains to be seen.

Illustration: Source https://arxiv.org/abs/1706.03762 ( Left & Center) and https://arxiv.org/abs/2002.04745 (Right)

##Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Neural Networks (1991)

This article is recommended for those interested in historical tidbits and early methods that are basically similar to the modern Transformer.

For example, in 1991, 25 years before the Transformer paper, Juergen Schmidhuber proposed an alternative to recurrent neural networks (https://www.semanticscholar.org/paper/Learning-to-Control- Fast-Weight-Memories:-An-to-Schmidhuber/bc22e87a26d020215afe91c751e5bdaddd8e4922), called Fast Weight Programmers (FWP). Another neural network that achieves fast weight changes is the feedforward neural network involved in the FWP method that learns slowly using the gradient descent algorithm.

This blog (https://people.idsia.ch//~juergen/fast-weight-programmer-1991-transformer.html#sec2) compares it with a modern Transformer The analogy is as follows:

In today's Transformer terminology, FROM and TO are called key and value respectively. The input to which the fast network is applied is called a query. Essentially, queries are handled by a fast weight matrix, which is the sum of the outer products of keys and values ​​(ignoring normalization and projection). We can use additive outer products or second-order tensor products to achieve end-to-end differentiable active control of rapid changes in weights because all operations of both networks support differentiation. During sequence processing, gradient descent can be used to quickly adapt fast networks to the problems of slow networks. This is mathematically equivalent to (except for the normalization) what has come to be known as a Transformer with linearized self-attention (or linear Transformer).

As mentioned in the excerpt above, this approach is now known as linear Transformer or Transformer with linearized self-attention. They come from the papers "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" (https://arxiv.org/abs/2006.16236) and "Rethinking Attention with Performers" (https://arxiv. org/abs/2009.14794).

In 2021, the paper "Linear Transformers Are Secretly Fast Weight Programmers" (https://arxiv.org/abs/2102.11174) clearly shows that linearized self-attention and the 1990s Equivalence between fast weight programmers.

##Photo source: https://people.idsia.ch// ~juergen/fast-weight-programmer-1991-transformer.html#sec2

##Universal Language Model Fine-tuning for Text Classification (2018)

This is another very interesting paper from a historical perspective. It was written a year after the release of the original Attention Is All You Need, and doesn't involve transformers, focusing instead on recurrent neural networks, but it's still worth watching. Because it effectively proposes pre-trained language models and downstream tasks of transfer learning. Although transfer learning is well established in computer vision, it has not yet become popular in the field of natural language processing (NLP). ULMFit (https://arxiv.org/abs/1801.06146) was one of the first papers to show that pre-trained language models can produce SOTA results on many NLP tasks when fine-tuned on a specific task.

ULMFit’s proposed language model fine-tuning process is divided into three stages:

1. Training the language on a large text corpus Model;
2. Fine-tune the pre-trained language model based on task-specific data so that it can adapt to the specific style and vocabulary of the text;
3. Fine-tune classifiers on task-specific data to avoid catastrophic forgetting by gradually unfreezing layers.

This method of training a language model on a large corpus and then fine-tuning it on downstream tasks is based on Transformer models and basic models (such as BERT, GPT -2/3/4, RoBERTa, etc.).

However, as a key part of ULMFiT, progressive unfreezing is usually not performed in practice because Transformer architecture usually fine-tunes all layers at once.

Gopher is a particularly good paper (https://arxiv.org/abs/2112.11446) that includes extensive analysis to understand LLM training. The researchers trained an 80-layer, 280 billion parameter model on 300 billion tokens. This includes some interesting architectural modifications, such as using RMSNorm (root mean square normalization) instead of LayerNorm (layer normalization). Both LayerNorm and RMSNorm are better than BatchNorm because they are not limited to batch size and do not require synchronization, which is an advantage in distributed settings with smaller batch sizes. RMSNorm is generally considered to stabilize training in deeper architectures.

Besides the interesting tidbits above, the main focus of this article is to analyze task performance analysis at different scales. An evaluation on 152 different tasks shows that increasing model size is most beneficial for tasks such as comprehension, fact-checking, and identifying toxic language, while architecture expansion is less beneficial for tasks related to logical and mathematical reasoning.

##Illustration: Source https://arxiv.org/abs/2112.11446

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