Fix mxfp8 param gather numerical issue when DP overlap is off - #4800
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Signed-off-by: qiyuw <qiyuw@nvidia.com>
Signed-off-by: qiyuw <qiyuw@nvidia.com>
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svcnvidia-nemo-ci
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This was referenced May 15, 2026
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/ok to test fc3471a |
Signed-off-by: qiyuw <qiyuw@nvidia.com>
deepakn94
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May 22, 2026
| deferred_bucket_groups = [] | ||
| deferred_bucket_group_ids = set() | ||
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| if defer_param_sync: |
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Can we re-factor this into a separate method? This is making the optimizer.step_with_ready_grads method extremely complicated...
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/ok to test 0479d84 |
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/ok to test c2b4f6d |
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/ok to test 752d246 |
deepakn94
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ericharper
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🔄 Merge queue validation started! You can track the progress here: https://github.com/NVIDIA/Megatron-LM/actions/runs/26309015669 |
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com>
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* origin/main: (50 commits) Drain predecessor reduce-scatter at dispatch time (NVIDIA#4940) ci: Add allow_failure flag to gpt and moe recipes that are failing in nightlies (NVIDIA#4905) fix(tests): initialize num_microbatches calculator in vision cudagraph tests (NVIDIA#4986) test: re-enable test_pp2_create_cudagraphs_first_stage on TE 2.15+ (NVIDIA#4985) ci: Add support for MBridge job gating based on PR labels (NVIDIA#4926) test(ci): re-enable 8experts2parallel_multi_dist_optimizer_instances_1node (NVIDIA#4984) test: re-enable paged stashing MoE tests (NVIDIA#4978) Fix elastification unwrap_model import (NVIDIA#4972) Avoid offsetting functional test master port (NVIDIA#4973) test: enable NVTE_CUTEDSL_FUSED_GROUPED_MLP via pytest fixture (NVIDIA#4931) chore(beep boop 🤖): Bump (main) (2026-05-25) test(release): add release goldens for deepseekv3/nemotron3 and set tp2pp2 exit-interval (NVIDIA#4932) Fix `get_batch` return order to ignore BlendedDataset provenance fields (NVIDIA#4952) ci: restore perf test torchrun logs (NVIDIA#4951) Various training utils (NVIDIA#4872) ci: Update training script paths in BERT and T5 (NVIDIA#4939) [MXFP8/FP4-param-gather] Post processing after forced param AG in eval (NVIDIA#4562) Fix mxfp8 param gather numerical issue when DP overlap is off (NVIDIA#4800) Add TEFusedDenseMLP for Dense+Grouped GEMM fusion on SM100+ (NVIDIA#4318) (NVIDIA#4786) Fix paged stashing test submodules lookup (NVIDIA#4925) ... # Conflicts: # megatron/training/training.py
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com>
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com>
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com> Signed-off-by: yhgalaxy <yhgalaxy@outlook.com>
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com> Signed-off-by: Jon Barker <jbarker@aws-cmh-slurm-1-vscode-02.cm.cluster>
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com>
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…#4800) Signed-off-by: qiyuw <qiyuw@nvidia.com> Co-authored-by: Philip Petrakian <ppetrakian@nvidia.com> Signed-off-by: Dmytro Pykhtar <dpykhtar@nvidia.com>
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What does this PR do ?
dev: #4769
With MXFP8 grad-buffer reuse and non-overlap param gather, each DistOpt stages its own updated main-param shards into its param buffers during step. However, param sync is a DDP model-chunk operation: model_chunk.start_param_sync() gathers both dense and expert bucket groups, copies gathered values into model weights, and zeros the shared MXFP8 param/grad buffers. For MoE, dense and expert DistOpts may share the same model chunk, so defer param sync until all chained optimizers have staged their params, then sync each model chunk once. Otherwise the expert DistOpt may copy the zeroes into expert model weights.
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