feat(flux): FSDP2 fp32/bf16 optimizers + fp8 all-gather#808
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…DP2 refactor (#808) PR #808 (FSDP2 fp32/bf16 optimizers + fp8 all-gather) replaced Megatron's FSDP2 wrapper with PrimusTorchFullyShardedDataParallel (installed as megatron.training.training.torch_FSDP). ODC still hooked the stock megatron.core...TorchFullyShardedDataParallel.__init__, which is no longer instantiated, so odc_init/_ensure_odc_ready never ran, reduction_service stayed None, and odc_nopad crashed at iter2 with NoneType.clear_accumulations. Hook the class the trainer actually uses, with an ImportError fallback to the stock class. Verified: single-node 1.5B odc_nopad runs 100/100 (lm loss 9.970).
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Part of an 18-PR series splitting the Flux diffusion-training feature (training Flux, a DiT text-to-image diffusion model, on Primus/Megatron) out of one large branch for reviewability. Targets
feat/flux/core— review after it. The diff here is only this layer.What this changes
The FSDP2 optimization layer used by Flux training: fp32 and bf16-master-weight optimizer variants, incremental grad-norm, the FSDP2 fp8 all-gather path, and the related torch-FSDP2 / fp8-cache / optimizer-registration patches.
Dependencies
Sequenced after the CI-pins PR (
feat/flux/ci-env); builds onfeat/flux/core. It is the parent of the turbo layer, whose float8 extension lazily imports this layer's fp8 all-gather.Test plan
pytest tests/unit_tests/optimizer tests/unit_tests/backends/megatron/diffusion/distributed. Validated locally on an AMD GPU container: 87 passed.Files
14 (FSDP2 optimizers, fp8 all-gather, optimizer/FSDP2 patches + tests).