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[optim] bucket checkpoint save to avoid CPU memory spike during ckpt saving - #61

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yueming-yuan wants to merge 26 commits into
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yueming/ckpt-save-staging
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[optim] bucket checkpoint save to avoid CPU memory spike during ckpt saving#61
yueming-yuan wants to merge 26 commits into
miles-mainfrom
yueming/ckpt-save-staging

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Problem

Synchronous torch_dist save stages all write buckets to host memory before any file write starts (FileSystemWriterAsync.preload_tensors runs as one shot in AsyncRequest.execute_sync). The host transient equals the rank's full checkpoint shard.

On GLM-5.2 744B RL training (16x GB300, TP8/PP4/DP2, distributed optimizer), that is ~650 GB/node (fp32 mains + Adam moments + bf16 params) against ~780 GB available — the first save deterministically OOM-killed the job across all nodes within a minute.

Change

Two opt-in args, defaults preserve existing behavior exactly:

  • --ckpt-save-staging-buckets N (default 0): sync save stages and writes at most N buckets at a time. The wave loop lives inside write_preloaded_data_multiproc: preload one wave D2H → fork writers → join → free → next. Results are merged across waves and put to the results queue once, so the retrieve_write_results protocol is unchanged. Incompatible with --async-save (asserted): waved D2H must run in the training process, and async staging must complete before training resumes anyway.
  • --ckpt-save-thread-count N (default 2): exposes the existing thread_count (buckets/files per rank) so waves have useful granularity.

Host peak becomes waves × bucket_size instead of the full shard: with thread-count 8, staging-buckets 2 on the workload above, ~160 GB/node instead of ~650 GB.

Exactness

Bucket split, file names, and file contents are fixed at planning time (prepare_write_data); this change only reorders when D2H and writes happen. With the same thread_count, checkpoints with staging on/off are byte-identical. The async path is untouched, and staging_max_buckets=0 degenerates to a single wave with no internal preload (current behavior).

Overhead

~1-3% of save wall time (per-wave join stragglers + D2H gaps between waves; writes remain the bottleneck and write concurrency per rank is unchanged). Total D2H bytes and per-bucket fsync behavior are identical to the current path.

Validation

  • GLM-5.2 744B, 16x GB300, --save-interval 1 --ckpt-save-thread-count 8 --ckpt-save-staging-buckets 2: 10.4 TB checkpoint written successfully; MemAvailable stable at 585-632 GB/node throughout the save, Dirty ~0 (previous run with unbounded staging was OOM-killed at the same point).
  • Wave slicing/result-merging logic unit-checked (global index mapping, single queue put, exception short-circuit, staging=0 equivalence).

yueming-yuan and others added 26 commits February 25, 2026 19:22
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

Co-authored-by: Yueming Yuan <yym022502@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
- Detach output layer params to prevent MTP gradient flowing to output layer
- Add mtp_kwargs interface for flexible MTP label/loss_mask passing
- Roll mtp_labels and loss_mask for RL training compatibility

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Yueming Yuan <yym022502@gmail.com>
…yers (#10)

- Add is_mtp flag to MoE layers and multi_token_prediction module
- Bypass routing replay for MTP layers (MTP uses fresh routing)
- Replace rdxa/dev's built-in RouterReplay with miles.utils.routing_replay:
  - moe_utils.py: use get_routing_replay_compute_topk() wrapper
  - router.py: use register_routing_replay() for initialization

Co-authored-by: Yueming Yuan <yym022502@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Yueming Yuan <yym022502@gmail.com>
After bumping Megatron (rdxa/dev), colocated IPC weight update fails with
torch.AcceleratorError: CUDA error: invalid argument during
torch.multiprocessing serialization of CUDA tensors.

Root cause: Megatron's new TMS hook (PR NVIDIA#3048) alters allocator behavior
in training flow, causing allocations via cuMemCreate/cuMemMap which are
incompatible with CUDA IPC (_share_cuda_() fails).

Fix: resolve mapping.py and dynamic_context.py conflicts to isolate
hook side effects so TMS/allocator state remains IPC-compatible during
the weight update phase.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- merge(): truncate dp_reshardable padding on optimizer/param_state path
- load_parameter_state_from_dp_reshardable: tolerate missing 'padding' key
- ShardedTensor: relax flattened_range to deprecation warning

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This PR rebases from radixark Megatron fork [miles-20260218](https://github.com/radixark/Megatron-LM/tree/miles-20260218) and resolve conflicts.

Upgrade Megatron from Dec 17 (3714d81) to Feb 13 (1dcf0da)

PR link: #13

Co-authored-by: Yueming Yuan <yym022502@gmail.com>
Made-with: Cursor
…se `--disable-weight-backuper` in miles (#18)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com>
Co-authored-by: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com>
Squash merge of the dense true-on-policy Megatron branch.

Co-authored-by: zju-stu-lizheng <lizheng.cs@zju.edu.cn>
Co-authored-by: zyxiyy02 <282300612+zyxiyy02@users.noreply.github.com>
Co-authored-by: Yi Zhang <1109276519@qq.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com>
Co-authored-by: Zhiyao Jiang <jessicajiang324@gmail.com>
Co-authored-by: zyzshishui <82826991+zyzshishui@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
@yueming-yuan yueming-yuan changed the title Bound host memory in sync torch_dist checkpoint save via waved staging [optim] bucket checkpoint save to avoid CPU memory spike during ckpt saving Jul 6, 2026
@yueming-yuan
yueming-yuan marked this pull request as ready for review July 6, 2026 22:01
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6 participants