Strategies for Parallelizing LLMs Masterclass

Learn how parallelism powers large language model training by splitting tasks across multi-gpu clusters, reducing training time for massive models like GPT-3 with tens of terabytes of data.

Explore the single GPU strategy for LLM training, highlighting memory limits, sequential processing, bottlenecks, and how parallelism boosts efficiency and scalability.

Distribute workloads across GPUs in the multi-GPU parallel strategy, expanding memory and throughput while managing communication overhead via interconnects for scalable distributed training.

Show how single-gpu sequential processing contrasts with multi-gpu parallelism for llms, delivering faster training, better resource utilization, and a GPT-3 scale example with 175 billion parameters.

Explore optional IT fundamentals, including CPUs, GPUs, memory units, and RAM, to understand how computation powers parallelizing LLMs.

Define computers as input processors that convert input into output using hardware and software, with the CPU and RAM driving fast data processing to deliver smooth, usable results.

Explore how computers store data in RAM for fast processing and how file systems organize formats like PNG, JPEG, and PDF across hard drives, SSDs, and cloud storage.

Explore a visual overview of a typical file system, detailing directories and a tree-like hierarchy. See how the system enforces access permissions for files and folders to store data.

Understand how local area networks connect computers and devices via switches, routers, and modems, with wired and wireless links and WANs enabling internet access.

Explore how the internet operates as a global, distributed network of computers and servers that uses standardized protocols, enabling fast, resilient communication across unique IP addresses.

Shows how internet communication happens between a client and a server using IP addresses and the domain name system to translate names into addresses.

Understand how servers provide resources to clients through a request-response model governed by http and ftp protocols, supporting web, file, and database servers across the internet.

Examine the hardware trinity of gpu, cpu, and ram and their symbiotic role in parallel workloads for training large language models, with gpu as the primary engine.

Explain GPU architecture for large language model training, detailing thousands of compute cores, CUDA and tensor cores, memory hierarchy from VRAM to L1 cache, and attention and matrix operations.

Explore why this architecture excels for large language model training, highlighting the hardware trinity, parallelism, tensor cores, and memory bandwidth.

Explore the fundamentals of machine learning and deep learning, and how they underpin AI, as a basis for understanding the strategies for parallelizing LMS.

Explore the fundamentals of deep learning and machine learning, including neural networks, data handling, feature extraction, and the model life cycle from training to deployment.

Understand deep learning's key aspects: automatic feature learning, hierarchical structure, and end-to-end learning from raw input to output. Contrast with traditional stepwise methods through driving analogies.

Explore how deep neural networks process data through input, hidden, and output layers, using activation functions and backpropagation to refine weights for accurate predictions.

Explore how a single neuron computes output by summing inputs multiplied by weights, adding biases, and applying an activation function across input, hidden, and output layers.

Explore how weights function in neural networks through a restaurant analogy, adjusting recipe proportions from base stock to spices based on customer feedback to update the weights.

Explore activation functions as quality checks in neural networks, comparing ReLU, sigmoid, and softmax, and map weights, biases, and activation to a restaurant workflow.

Summarizes deep learning fundamentals: neural networks with input, hidden, and output layers, bias and weights, activation functions, and how learning transforms data into outputs for pattern recognition, classification, and prediction.

Explore how machine learning is a broad framework with deep learning as a subset, contrasting manual feature engineering and data needs with automatic learning and natural language processing applications.

Explore supervised, unsupervised, and reinforcement learning with real-world tasks like fraud detection, recommendations, image recognition, natural language, and classification and predictions, plus DL vs ML contrasts.

Explore the three-layer framework of deep learning, machine learning, and artificial intelligence, comparing neural networks, pattern recognition, and data-based learning, with applications from image and language processing to autonomous systems.

Explore the transformer architecture and why it underpins large language models, offering a quick overview of how it works to equip you for strategies to parallelize large language models.

Explore how the transformer architecture, a deep learning model for text and natural language processing, uses tokenization, embeddings, positional encoding, and self-attention to capture long-range dependencies.

Explore how self-attention in transformer models uses query, key, and value to compute attention scores, apply weighted sums, and produce contextual representations.

Explore how the transformer architecture uses embeddings, self-attention, information exchange, and processing with context to generate output and translate English to Spanish, illustrated by an animation.

Explore the Hugging Face transformers library as a modular toolkit for accessing state-of-the-art transformer models, tokenizers, and pipelines for tasks like classification, question answering, and generation.

Master the fundamentals of parallel computing, including nodes, CPU and GPU memory, and how multiple nodes with GPUs train large language models, guided by Amdahl's and Gustafson's laws.

Learn parallel computing fundamentals with CPU, memory, and GPU architectures, shared memory, and data transfer, and apply Amdahl's law and Gustafson's law to scale training of large language models.

Explore the types of parallelism in large language model training, including data, model, pipeline, and tensor parallelism, along with bottlenecks, communication models, and memory considerations for scalable training.

explore data parallelism, distributing training data across multiple GPUs with identical model copies, processing different batches in parallel, and gradient synchronization via allreduce to update parameters.

Data parallelism offers near-linear throughput scaling with more GPUs, enables processing trillions of tokens, provides memory efficiency by storing one model copy per GPU, and remains straightforward to implement.

Leverage data parallelism to train GPT-3 across thousands of GPU clusters processing trillions of tokens from millions of documents, with high-speed interconnects and all-reduce gradients for months instead of years.

Explore model parallelism for training large language models by distributing the model across multiple gpus, using tensor parallelism with horizontal splitting and layer parallelism with vertical splitting.

Master model parallelism for scaling language models with billions to trillions of parameters, addressing memory needs for parameters, optimizer states, and activations while highlighting graph partitioning and load balancing.

Compare data and model parallelism to reveal their tradeoffs. Data parallelism boosts throughput with full model copies and infrequent gradient synchronization, while model parallelism enables large models with frequent communication.

compare data parallelism and model parallelism using a chef and recipe analogy, highlighting independent parallel work versus sequential handoffs, specialist roles, and memory and scaling limits.

Demonstrates data and model parallelism by visualizing how identical model copies on multiple GPUs process different data batches, and how a single model splits across GPUs for sequential layers.

Hybrid parallelism combines model and data parallelism to overcome size limits and enable multidimensional scaling, improving hardware utilization and memory efficiency via pipeline parallelism and micro-batching.

Combine model and data parallelism to form hybrid parallelism, using a restaurant franchise analogy to show specialized stations processing batches of orders to maximize throughput.

Learn pipeline parallelism, a distributed training technique that divides the model's layers across multiple GPUs or TPUs, enabling overlapped computation across mini batches and reducing device idle time.

Understand pipeline parallelism by partitioning the model into sequential stages across GPUs and using mini-batch processing with micro-batching to reduce bubbles and idle time.

Explore pipeline parallelism concepts, highlighting micro-batching and pipeline bubbles, including warm up and cool down phases that create idle times and impact efficiency across stages.

Explore pipeline schedules for parallelizing llms, comparing Gpipe and 1F1B strategies. Learn how forward and backward passes, memory use, and micro-batch flow affect efficiency, especially for deep models.

Explore activation recomputation, or gradient checkpointing, which trades computation for memory by discarding forward activations and recomputing them in the backward pass to train large language models.

Understand neural networks with neurons and trainable weights, where forward passes produce activations that feed layers and backward passes propagate gradients through backpropagation with memory of activations, including activation recomputation.

Explore activation recomputation to reduce memory by storing checkpoints and recomputing activations during backward pass, and compare standard training with gradient checkpointing for larger language models.

This visualization compares standard training and activation recomputation, showing how recomputation lowers memory usage by storing checkpoints and recomputing activations during backprop, at the cost of extra computation.

Compare activation recomputation with the standard approach using a road trip analogy. Learn how checkpoints and stored activations reduce memory use at the cost of extra recomputation.

Explore how activation recomputation dramatically reduces memory during llm training, enabling GPT-4-scale models and larger batch sizes, using optimal checkpointing, hierarchical checkpointing, and activation compression.

Explore pipeline parallelism frameworks like Gpipe, Pipedream, Megatron-LM, and the zero redundancy optimizer (zero for short) to understand combining data, tensor, and pipeline parallelism for scalable large-model training.