[optim] bucket checkpoint save to avoid CPU memory spike during ckpt saving#61
Open
yueming-yuan wants to merge 1 commit into
Open
[optim] bucket checkpoint save to avoid CPU memory spike during ckpt saving#61yueming-yuan wants to merge 1 commit into
yueming-yuan wants to merge 1 commit into
Conversation
yueming-yuan
marked this pull request as ready for review
July 6, 2026 22:01
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Problem
Synchronous
torch_distsave stages all write buckets to host memory before any file write starts (FileSystemWriterAsync.preload_tensorsruns as one shot inAsyncRequest.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 insidewrite_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 theretrieve_write_resultsprotocol 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 existingthread_count(buckets/files per rank) so waves have useful granularity.Host peak becomes
waves × bucket_sizeinstead of the full shard: withthread-count 8, staging-buckets 2on 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 samethread_count, checkpoints with staging on/off are byte-identical. The async path is untouched, andstaging_max_buckets=0degenerates 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
--save-interval 1 --ckpt-save-thread-count 8 --ckpt-save-staging-buckets 2: 10.4 TB checkpoint written successfully;MemAvailablestable at 585-632 GB/node throughout the save,Dirty~0 (previous run with unbounded staging was OOM-killed at the same point).