karpathy/llm.c

Features

• Large Language Model Training Frameworks - A high-performance framework for training large language models using native C and CUDA kernels.
• Bare-Metal Deep Learning Engines - Executes neural network training directly on hardware accelerators using low-level code to eliminate high-level software overhead.
• Distributed Training Frameworks - A system for scaling neural network training across multiple graphics processors and compute nodes using collective communication primitives.
• Low-Level Neural Network Trainers - Provides a low-dependency toolkit for executing tensor operations and gradient backpropagation directly on hardware accelerators.
• Language Model Pretraining - Trains foundational language models from scratch using high-performance hardware kernels.
• Backpropagation - Implements manual gradient backpropagation to facilitate iterative parameter adjustment during model training.
• Large-Scale Training Frameworks - Scales neural network training across multiple graphics processors and compute nodes to reduce total training time.
• Tensor Computation Primitives - Implements custom neural network layers and operations directly in C and CUDA for maximum efficiency.
• Hardware Acceleration Kernels - Runs optimized computational routines tailored to processor architectures to accelerate complex neural network operations.
• Backpropagation Implementations - Calculates weight adjustments through explicit mathematical implementation rather than relying on automated differentiation engines.
• Attention Backends - Implements optimized attention backends using hardware-specific kernels to accelerate transformer model training.
• Distributed Training - Parallelizes model training workloads across multiple hardware accelerators and compute nodes to maximize processing efficiency.
• Training Memory Management - Manages training memory by adjusting batch sizes and sequence lengths to fit within hardware constraints.
• Large Language Models - LLM training implemented in raw C/CUDA.
• Systems Programming - High-performance implementation of large language models in C.
• Collective Communication Operations - Synchronizes gradient updates across multiple graphics processors and compute nodes using collective communication operations.
• Gradient Checkpointing - Utilizes gradient checkpointing to reduce memory consumption by recomputing intermediate activations during the backward pass.
• Normalization Layers - Includes layer normalization components to stabilize activation scales and improve training convergence.
• Hyperparameter Sweep Orchestrators - Automates the execution of multiple training iterations with varying configuration parameters to identify optimal model settings.
• Model Checkpoints - Initializes model structures by reading pre-trained weights and configuration data from binary files directly into hardware memory.
• Tensor Mappings - Converts multi-dimensional tensor structures into linear memory addresses to maximize hardware cache efficiency.
• Data Ingestion and Preparation - Processes large-scale datasets into binary streams formatted for direct consumption by low-level training routines.
• Binary Stream Loaders - Reads pre-processed tokenized data directly from disk into memory to bypass input-output bottlenecks during training.
• Numerical Accuracy Validators - Provides diagnostic tools to verify numerical consistency of tensor outputs and gradients during the training lifecycle.

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