machine learning

Breaking Down a Rust Machine Learning Model with Candle

Breaking Down a Rust Machine Learning Model with Candle
Photo by Kevin Ku / Unsplash

In this article, we'll break down a Rust code snippet that defines a deep learning model for Language Modeling (LLM) using the Candle library. The architecture seems to resemble a GPT-like transformer model. We'll go through the code step by step, explaining the functions, structures, and underlying concepts, even for those new to Rust and deep learning.

Rust Sample (we'll be breaking down the model.rs file):

candle/candle-examples/examples/bigcode at main · huggingface/candle
Minimalist ML framework for Rust. Contribute to huggingface/candle development by creating an account on GitHub.
GitHubhuggingface

Modules and Imports

use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{Embedding, LayerNorm, Linear, VarBuilder};

These lines import the necessary components from the candle and candle_nn libraries:

  • candle: A general-purpose tensor computation library for GPU computing.
  • candle_nn: A library that provides neural network layers and functions.

Functions

1. linear()

fn linear(size1: usize, size2: usize, bias: bool, vb: VarBuilder) -> Result<Linear> {
    let weight = vb.get((size2, size1), "weight")?;
    let bias = if bias { Some(vb.get(size2, "bias")?) } else { None };
    Ok(Linear::new(weight, bias))
}

This function helps to create a linear layer, often used in neural networks to apply a linear transformation to the data. It's sometimes called a fully connected layer.

  • size1 and size2: These define the input and output sizes, respectively, of the layer.
  • bias: A boolean flag to determine whether to include a bias term.
  • vb: A variable builder used to get the weight and bias tensors.
  • Result<Linear>: The function returns a Linear layer object that can be part of a neural network.

2. embedding()

fn embedding(vocab_size: usize, hidden_size: usize, vb: VarBuilder) -> Result<Embedding> {
    let embeddings = vb.get((vocab_size, hidden_size), "weight")?;
    Ok(Embedding::new(embeddings, hidden_size))
}

This function helps create an embedding layer, commonly used in natural language processing to represent words or tokens in continuous vector space.

  • vocab_size: The number of unique words or tokens in the vocabulary.
  • hidden_size: The size of the embedding vector for each token.

3. layer_norm()

fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
    let weight = vb.get(size, "weight")?;
    let bias = vb.get(size, "bias")?;
    Ok(LayerNorm::new(weight, bias, eps))
}

Layer normalization is a method used to standardize the inputs to a layer, which often helps in training deep networks.

  • size: Specifies the size of the layer.
  • eps: A small value to ensure numerical stability.

4. make_causal_mask()

fn make_causal_mask(t: usize, device: &Device) -> Result<Tensor> {
    let mask: Vec<_> = (0..t).flat_map(|i| (0..t).map(move |j| u8::from(j <= i))).collect();
    let mask = Tensor::from_slice(&mask, (t, t), device)?;
    Ok(mask)
}

This function creates a causal mask to ensure that each position in a sequence only attends to previous positions. This is essential for models that predict the next word in a sequence, such as the GPT model.

  • t: Specifies the size of the mask, which corresponds to the length of the sequence.

Configuration, Attention, MLP, Transformer Block, GPTBigCode Structures

These structures are the building blocks of the model, each playing a unique role:

  • Config: Blueprint for hyperparameters like vocabulary size, the number of hidden layers, attention heads, etc.
  • Attention: Defines the attention mechanism, including query, key, value transformations, and more.
  • MLP (Multi-Layer Perceptron): Creates two linear layers with GELU activation between, a common structure in neural networks.
  • Transformer Block: Combines layer normalization, attention, and MLP to form a block.
  • GPTBigCode: Represents the whole model, comprising embedding, transformer blocks, normalization, and prediction layer.

Conclusion

The provided code represents a sophisticated transformer model, implemented in Rust using the Candle library. For someone new to Rust, this code offers an example of strong typing and memory safety. For those new to deep learning, it's a practical introduction to key concepts like linear layers, embedding, normalization, attention mechanisms, and more. Together, these elements form a powerful and efficient model for language processing tasks.

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