Integrating TorchTPU into TorchTitan and Optimizing Models for TPU

TorchTitan TPU is a Google-maintained adaptation of Meta’s PyTorch-based TorchTitan framework that enables large-scale dense, mixture-of-experts, and diffusion-model training on Google Cloud TPUs through the native TorchTPU backend, avoiding major model-code rewrites or reliance on legacy torch_xla. It combines plain PyTorch model definitions with configurable TPU-specific execution features, including SimpleFSDP for compiler-visible parameter sharding, Graph Trainer for capturing full training steps as XLA programs, activation offloading, scan and graph-splitting techniques for very large models, and Pallas-based kernels for attention, fused cross-entropy, and MoE operations. The accompanying optimization playbook emphasizes progressing from eager execution to compiled or full-graph training, tuning sharding, precision, memory usage, batch size, kernels, compiler settings, and communication based on profiling. Reported TPU v7x results include roughly 57% model FLOPs utilization for Llama 3 405B on up to 1,024 chips, about 32% for Qwen3 235B MoE at scale, 43.4% for Muse Glimmer 30B, and near-parity with JAX MaxDiffusion for FLUX.1. The project also offers a Hugging Face Transformers backend for broadly onboarding supported models, agent-assisted porting guidance, and plans for improved production tooling, float8 support, future TPU hardware, expert parallelism, long-context training, and larger MoE architectures.

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