[xuwchen]

• I, the PR author, have personally reviewed every line of this PR.

What does this PR do?

Generalized Tensor Parallelism (GTP) is a light-weight, high-performance and memory-efficient distributed training strategy implemented in Megatron-LM and TransformerEngine. It shards weight tensors across an GTP process group and reconstructs them on-demand via async all-gather, enabling training of larger models without sacrificing throughput by overlapping communication with computation.

• GTP_README.md

  Outline:

  Features
  1.1 Fine-grained, per-weight materialization & gradient reduction
  1.2 CUDA graph compatibility
  1.3 Low-precision gather (native FP8 / NVFP4 param)
  1.4 Composability with TP / SP / EP / DDP
  1.5 Opt-in, minimally invasive integration
  1.6 Optimizer-agnostic (Adam + Muon)
  1.7 Scaling
  1.8 Native distributed checkpointing (DCP)
  Usage
  2.1 Required flags
  2.2 High-priority streams (Blackwell and later)
  2.3 Minimal end-to-end example
  2.4 Tuning knobs
  Implementation details
  3.1 GTP_remat architecture (Mcore ↔ TE integration)
  Class hierarchy: which linears shard
  3.2 DDP buckets with (E)GTP_remat
  3.3 Distributed checkpointing (DCP)
  3.4 Prefetch-chain construction and its design assumptions
  Testing

• TE related MR: TE-3005 (Merged)

GTP's Architecture (Mcore + TE)

• TE ships self-contained: Protocol + dispatcher no-op.
• GTP lives 100% in Mcore as one protocol implementer.

Changes Summary

Convergence Test (07/25)

Config: 1/4 of NT3 Ultra,27Layers512Experts, 32xGB200, GTP16_EP8EGTP2_m1g32_mxfp8_CG, Adam Optimizer, forceBalance, vs. 3D parallel baseline with parallelism=TP8_EP16DP2_m1g32, ~10k steps, dataset=blend_files/1T-phase1var-moresft.json

GTP matches well with the 3d baseline in lm loss curve:

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