CHANGELOG.md

Changelog

NVIDIA Megatron Core 0.9.0

• Uneven pipeline parallelism
  • Enable pipeline parallelism where first and last ranks have fewer transformer layers than the intermediate ranks
• Per layer CUDAGraph support for GPT training with Transformer Engine modules
• Enable different TP sizes for the vision encoder
• Enable pipeline parallelism for T5 & Llava models
• Support multi-tile multi-image input in Llava models
• MoE
  • FP8 support
  • Runtime upcycling support
  • Dispatcher implementation optimizations
  • Shared expert support with overlapping optimizations
    • Qwen Model support
• Known Issues
  • When using sequence parallel, during the transformer block forward pass, dropout is not using the appropriate rng context.

NVIDIA Megatron Core 0.8.0

• Multimodal
  • Added initial support for training vision language models using the LLaVA architecture
  • Added initial support for inference with multimodal inputs
  • End-to-end multimodal example from data collection to training to evaluation is provided in examples/multimodal
• MoE
  • Context Parallel support.
  • Distributed checkpoint support for grouped GEMM.
• Mamba

NVIDIA Megatron Core 0.7.0

• MoE
  • Token drop support
  • Several efficiency optimizations
  • Improved model parallelism
  • Memory optimizations
• Distributed checkpointing
  • Enabled for Retro
  • Asynchronous checkpoint saving
• Several minor bug fixes, speed improvements, and memory optimizations

NVIDIA Megatron Core 0.6.0

• MoE (Mixture of Experts)
  • Performance optimization
    • Communication optimization for multi GPU and Single GPU
    • 23% improvement (323 TFLOPS/GPU) over MCore 0.5.0 on Mixtral with Hopper BF16
    • GroupedMLP enhancement for Hopper
    • DP Overlapping. Support overlapping computation with gradient reduction and parameter gathering.
  • All-to-All based Token Dispatcher
  • Layer-wise logging for load balancing loss.
  • Improved expert parallel support including distributed optimizer.
• Distributed optimizer
• RETRO
  • Data processing
• BERT
  • Distributed checkpointing
• Dist checkpointing
  • PyTorch native distributed backend
  • Improved saving/loading speed
• TensorRT-LLM Export
  • Integration with TensorRT Model Optimizer Post-training quantization (PTQ)
  • Text generation driver to perform PTQ in Megatron-LM
  • Llama2 and Nemotron3-8b examples to use TensorRT-LLM unified build API to build engine after training.
• Several minor enhancements, bug fixes, and documentation updates

NVIDIA Megatron Core 0.5.0

Key Features and Enhancements

Megatron core documentation is now live!

Model Features

• MoE (Mixture of Experts)
  • Support for Z-loss, Load balancing and Sinkhorn
  • Layer and communications refactor
  • Richer parallelism mappings and EP can be combined with other model parallel techniques for larger MoE variants, e.g. EP + TP + DP + SP + PP
  • Token dropless architecture with Top-K routing
  • Performance optimization with with GroupedGEMM when number of local experts is > 1
  • Distributed checkpointing
• Interleaved rotary embedding

Datasets

• Masked WordPiece datasets for BERT and T5
• Raw and mock datasets

Parallelism

Performance

• Activation offloading to CPU
• Rope and Swiglu fusion
• Sliding window attention (via Transformer Engine)

General Improvements

• Timers

NVIDIA Megatron Core 0.4.0

Key Features and Enhancements

Models

• BERT
• RETRO
• T5

Parallelism

• Mixture of Experts support for GPT
• Model parallel efficient Distributed Data Parallel (DDP)
• Context Parallel (2D Tensor Parallel) support

Datasets

• GPT Dataset
• Blended Dataset