Transformer Architecture

Tokenizers

What it does: encode string(like utf-8 encoded text) into a list of integers(token ids).

Why we need it: if we use utf-8 as the vocabulary directly, it would be too large and sparse.

  1. Too BIG: 154,998 characters.
  2. Too SPARSE: some characters don’t appear so often.

Purpose: create a way of encoding to make the vocabulary efficient.

Different types of tokenizers:

  1. Word-level tokenizer: map each word to token ids.
    • Too much words.
    • out-of-vocabulary issue: there are always new words.
  2. Byte-level tokenizer: directly use raw bytes as token.
    • Solves out-of-vocabulary issue.
    • Scale Issues: Results in extremely LONG input sequence, which: slows down model training, creates long-term dependencies in data.
  3. Subword-level tokenizer: a trade-off between the above.
    • larger vocabulary size(the number of possible tokens) <==> better compression of the input byte sequence.

Key Concepts:

Byte-Pair Encoding(GPT-2)

  1. Pre-tokenizer: divide a sentence into parts.
    • 中文如何pre-tokenize?
  2. Special tokens: like <|endoftext|> should always be treated as a whole. Determines when to stop generating, etc.

Parameter Initialization

For a linear model like \(y = Wx\), we need to keep y from blow up(overflow) when input dimension grows.

Simply, we initialize parameters like \(\frac{W}{\sqrt{input\_dims}}\), use a gaussian distribution to initialize, and truncate for extra safety.

All(1/3) you need is attention.

Post-Norm vs Pre-Norm

Activation functions

Positional Embedding

Hyper-Parameters

Feedforward layer, two dimensions: the feedforward dim(\(d_{ff}\)) and model dim(\(d_{model}\)), their relationship:

\[d_{ff} = 4 \ d_{model}\]

Almost always true.

Exception #1 - GLU variant: scale down by 2/3. So most GLU variants have \(d_{ff} = 8/3 \ d_{model}\). Exception #2 - T5 - 64 multipliers.

Takeaway: for \(d_{ff} / d_{model}\) ratio, there are conventions, but the choice isn’t written in stone.

Multi-head vs. Single-head self-attention.

If the ratio is 1, it’s computationally efficient compared to single-head.

\[\frac{d_{head} \times N_{heads}}{d_{model}} \approx 1\]

Aspect ratio, how deep should my model be?

\[\frac{d_{model}}{n_{layer}}\]

GPT2 is 33, GPT3/QWen is 128, LLaMA/LLaMA2 is 102.

Extremely deep models are harder to parallelize and have higher latency.

Distribute computation for each layer to different GPUs -> wait for previous layer. This unlike width, which can be easily parallelized over thousands of devices.

Sweet spot of aspect ratio is around 100 [Kaplan et el 2020].

Vocabulary size?

Monolingual models ~ 30-50k. Original transformer - 37,000, GPT2/3 - 50257, LLaMA - 32,000.

Multilingual / Production systems ~ 100 - 250k. GPT4 - 255,000. Deepseek - 100,000. Qwen 15B - 152,064.

Regularization

Do we need regularization during pre-training like dropout and weigh decay(e.g. AdamW)?

Argument against - not likely to overfit:

  1. Too much data » parameters.
  2. SGD only does a single pass on a corpus(hard to memorize).

In practice, many older models used dropout during pre-training. Newer models (except Qwen) rely only on weight decay.

Why weight decay LLMs [Andriushchenko et al 2023]?

  1. It’s not to control overfitting(has no effect), because no improvement over validation loss.
  2. But there are some improvement over training loss. Weight decay interacts with learning rates(cosine schedule). There are some complex interactions between optimizer and weight decay to cause this effect.

Takeaways: we still “regularize” LMs but its effects are primarily on optimization dynamics.

Stability

Problem child - softmax not very numerically stable.

  1. exponential.
  2. divide by zero.

Solution 1: Z-Loss(for output layer). Solution 2: QK-Norm. Solution 3: Logit soft-capping -> might have performance issues.

Attention heads

1:15:12

· LLM, Paper