LLama-from-scratch

An LLM from Scratch in Pure C++/CUDA

Note: This project is currently a work in progress.

Welcome to the LLama-from-scratch project! Our goal is to build a large language model (LLM) entirely from scratch using C++ and CUDA, leveraging the power of parallel computing for efficient training and inference.

Project Overview

This project aims to implement a full-fledged LLM by following these key steps:

1. Tensor Operations
2. CUDA Parallelization
3. Backpropagation for Tensor Class
4. Enhanced Parallelization
5. SentencePiece Tokenizer
6. Implementing Embeddings
7. Feed-Forward Networks (FFNs)
8. Flash Attention Mechanism
9. Rope Scaling and Other Peripheral Functions
10. Building Encoders
11. Integration and Cohesion
12. Training and Inference
13. Instruction Fine-Tuning

1. Tensor Operations

• Objective: Develop a robust Tensor class to handle multidimensional arrays and basic tensor operations such as addition, subtraction, and multiplication.
• Implementation:
  • Define tensor data structures and initialize tensors with various data types.
  • Implement tensor operations with type safety and memory management.

2. CUDA Parallelization

• Objective: Leverage CUDA to parallelize tensor operations for performance improvements.
• Implementation:
  • Identify computationally intensive operations within the Tensor class.
  • Offload these operations to the GPU using CUDA kernels.

3. Backpropagation for Tensor Class

• Objective: Implement backpropagation to support training of neural networks.
• Implementation:
  • Extend the Tensor class to store gradients and support gradient computation.
  • Implement backward operations for each tensor operation.

5. SentencePiece Tokenizer

• Objective: Implement the SentencePiece tokenizer for efficient text processing.
• Implementation:
  • Integrate the SentencePiece library to tokenize and detokenize input text.
  • Ensure compatibility with the Tensor class for processing tokenized data.

6. Implementing Embeddings

• Objective: Develop embedding layers to convert tokens into dense vectors.
• Implementation:
  • Implement word, positional, and segment embeddings.
  • Optimize embedding lookup operations using CUDA.

7. Feed-Forward Networks (FFNs)

• Objective: Build FFNs as core components of the neural network.
• Implementation:
  • Develop fully connected layers with activation functions.
  • Optimize forward and backward passes using parallelization.

8. Flash Attention Mechanism

• Objective: Implement an efficient attention mechanism using Flash Attention.
• Implementation:
  • Design attention layers with scaled dot-product attention.
  • Optimize memory access patterns and computation using CUDA.

9. Rope Scaling and Other Peripheral Functions

• Objective: Implement additional features and scaling techniques for model robustness.
• Implementation:
  • Incorporate rotary position encodings (RoPE) for better sequence modeling.
  • Develop auxiliary functions and utilities to support training and inference.

11. Integration and Cohesion

• Objective: Integrate all components to form a cohesive LLM framework.
• Implementation:
  • Ensure seamless data flow between components.
  • Validate the integrated model through rigorous testing.

12. Training and Inference

• Objective: Train the LLM and perform efficient inference.
• Implementation:
  • Develop training loops with backpropagation and optimization algorithms.
  • Implement inference mechanisms for real-time text generation.

13. Instruction Fine-Tuning

• Objective: Fine-tune the trained LLM for specific instructions and tasks.
• Implementation:
  • Use supervised fine-tuning techniques with task-specific datasets.
  • Optimize the model for low-latency inference and high accuracy.

Contributions

Contributions are welcome, as I prolly don't even know what I am doing lmao. Even this readme was generated by chatgpt to give the readers a crude idea of what I am trying to accomplish. So, if you've got anything, just open a PR and I'll most prolly merge it.

License

This project is licensed under the MIT License.