Posts

MoE Parallelism Principles: GShard, Expert Parallel, and All-to-All Mixture-of-Experts is sparse compute wrapped in a communication problem. The model idea is simple: replace a dense FFN with many expert FFNs and route each token to a few of them. The distributed-training problem starts when those experts live on different GPUs. Shazeer et al. introduced the sparsely-gated MoE layer in Outrageously Large Neural Networks. GShard made the pattern practical at scale with top-2 routing, capacity, load-balancing loss, and all-to-all dispatch in GShard. Switch simplified the router to top-1 in Switch Transformers. This post explains the shared systems model underneath them. ...

ZeRO: Partitioning Optimizer State, Gradients, and Parameters Plain DDP is clean but wasteful. Every data-parallel rank stores the same parameters, gradients, fp32 master weights, and optimizer moments. At small scale that redundancy is convenient. At LLM scale it is the memory wall. ZeRO, the Zero Redundancy Optimizer, keeps data-parallel semantics and removes the redundant storage one category at a time. ZeRO-1 shards optimizer state. ZeRO-2 shards optimizer state and gradients. ZeRO-3 shards optimizer state, gradients, and parameters (arXiv:1910.02054). ...

Megatron Internals III: Mixed Precision, Loss Scaling, and Grad Clipping Mixed precision training is not just “turn on fp16.” It is a state machine: fast low-precision tensors do forward and backward, stable fp32 tensors receive optimizer updates, and every rank agrees whether the step is valid before any shard changes. The classic recipe comes from Micikevicius et al., Mixed Precision Training: use lower precision where hardware is fast, keep fp32 master weights for updates, and use loss scaling when fp16 gradients underflow. Megatron wraps that recipe around tensor, pipeline, and data parallelism. ...

Megatron Internals II: Column/Row Parallel Linear and Vocab Parallel Embedding Tensor parallelism is not “split every tensor somehow.” In Megatron, it is a small set of layer contracts: which dimension is local, which collective completes the dense math, and which gradient path communicates. The original Megatron-LM paper is still the cleanest starting point: split transformer matrix multiplies so each GPU does useful dense GEMM, then communicate only where the algebra requires it. This post walks the implementation-level contracts behind ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding, and parallel cross entropy. ...

Megatron Internals I: Building the DP / TP / PP Process Groups Megatron-LM’s first trick is not tensor-parallel matmul. It is rank bookkeeping. Before the model runs, every process must know the small set of peers it will communicate with for tensor parallelism, pipeline parallelism, data parallelism, embeddings, and optimizer state. The original Megatron-LM paper introduced intra-layer tensor parallelism for transformer training. The later Megatron-LM scaling paper put tensor parallelism, pipeline parallelism, and data parallelism into one training system. This post is the control plane underneath that system: how a flat list of ranks becomes a DP / PP / TP mesh. ...