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[Draft][Reload] Add manifest-driven load receipts and update scopes - #50251

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[Draft][Reload] Add manifest-driven load receipts and update scopes#50251
new-TonyWang wants to merge 19 commits into
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Summary

This draft splits the load-completion and receipt work out of #49789 into a focused branch on top of releases/v0.25.1:

  • observe the exact rank-local source-to-target fragment manifest during the first real load;
  • use that manifest, rather than copied element counts, as the reload completion contract;
  • add structured LoadReceipt support and collision/schema audits for packed, merged, and MoE loaders;
  • validate checkpoint and IPC/NCCL source manifests;
  • support explicit full/partial update scopes, with exact scope enforcement for legal partial updates;
  • probe dummy-load target manifests and expose the resulting baseline so training can construct valid update scopes.

Why this is not a duplicate

This is the receipt/completion half split from draft #49789. After this split, feat/reload-arena contains only arena/storage-stability work, while this PR contains only reload receipt, completion, source-manifest, update-scope, dummy-probe, and baseline API work. It does not include ReloadArena or CUDAGraph storage changes.

PR #50075 also introduces manifest-driven completion, but this draft additionally provides structured receipts, fragment/collision auditing, source-manifest validation, explicit partial-update scopes, dummy-load probing, and a baseline API. The overlapping completion mechanism should be reconciled during review rather than merged independently without coordination.

Completion semantics

  • Full reload treats the initial rank-local manifest as a lower bound: every expected consumed application must be observed; additional accepted applications are allowed.
  • Loader calls declined with consumed=False are not part of the baseline or observed completion set, including non-local EP experts offered by a broadcasting sender.
  • Explicit partial updates enforce both bounds: all selected events must be observed and no consumed event may escape the declared scope.

Validation

Tests run on NVIDIA H200:

  • update-scope, completion-set, and dummy-probe tests: 54 passed;
  • partial-update baseline API test: 1 passed;
  • worker weight-transfer tests: 6 passed.

AI assistance

AI assistance was used to analyze the existing reload paths, implement portions of the change, split the original branch, and prepare this draft. The human submitter is responsible for reviewing every changed line, understanding the design end-to-end, and validating the final change before it is marked ready for review.

new-TonyWang and others added 16 commits July 29, 2026 17:13
The reload completion criterion (load_numel >= load_numel_total) is the root
of vllm-project#44814 / vllm-project#37334 / vllm-project#38746, and those three split into two kinds. vllm-project#44814 is
a wrong UNIT: counting elements made a composed loader's two internal copies
look like two parameters, so the layer finalized before mixer.D arrived.
tensors no loader ever loads (EP bookkeeping, a shared bias copy), so the
count could never be reached.

Switching the unit from elements to keys fixes the first kind. It does not
fix the second, because "the layer's parameters minus SKIP_TENSORS" is the
same prediction that was wrong, just re-expressed -- and SKIP_TENSORS is a
hand-maintained, case-by-case allowlist that a new model with a new
never-loaded buffer would have to extend again.

So do not predict the expected side at all: observe it. The first load is
correct by construction (the model serves correctly afterward, and it does
not use the reload accounting -- it just runs the loaders), so the set of
keys it consumes is a ground-truth "required" with no prediction in it.
record_load_consumption wraps every effective loader before the first
load_weights and records the names that actually fire; finalize_load_recording
removes the wrappers immediately after, leaving the loaders as they were.
A tensor the checkpoint never drives simply never fires, so it stays out of
required_keys without any exclusion list. This is the "capturing how many
weights are loaded on first pass" that make_online_process_loader's own
comment anticipated.

LayerReloadingInfo gains required_keys (the observed baseline, preserved
across reset() like restore_metadata) and received_keys (per-reload,
cleared). The online loader records received_keys; _check_set_completion runs
inside _layerwise_process, where received_keys is still alive -- it cannot be
deferred to the finalize reset loop, because required_keys survives reset and
received_keys does not, which would report every key as missing.

Dual-run: numel stays authoritative and the set criterion only records
findings (get_layer_completion_findings), so a disagreement is visible before
either becomes load-bearing alone.

Known scope: this is the completion criterion only, and it validates that a
reload consumes what the known-good first load consumed -- not absolute
correctness, since a first load that itself dropped a key would bake that
into the baseline. Parameters registered during the load itself are missed,
the same limitation the reload path's late-registration handling has. Set
membership does not yet capture how MUCH or how MANY TIMES a key loads; a
per-tensor profile is the next step.

Tested on H200: 5 CPU tests (observation excludes a never-loaded buffer with
no SKIP_TENSORS consulted, the baseline survives reset, a dropped key is
caught via the delayed-processing path, a clean reload is silent, no baseline
means no dual-run), full reload suite 83 passed / 21 skipped, and end-to-end
on Qwen3-30B-A3B-W4A8: the model loads and generates correctly through the
recording wrappers, 19 layers have an observed baseline, and a real reload
produces zero findings.

via [HAPI](https://hapi.run)

Co-Authored-By: HAPI <noreply@hapi.run>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: OpenAI Codex <codex@openai.com>
Co-authored-by: OpenAI Codex <codex@openai.com>
Co-authored-by: OpenAI Codex <codex@openai.com>
Signed-off-by: tony <864832769@qq.com>
Signed-off-by: tony <864832769@qq.com>
Signed-off-by: tony <864832769@qq.com>
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mergify Bot commented Jul 29, 2026

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Documentation preview: https://vllm--50251.org.readthedocs.build/en/50251/

@mergify mergify Bot added documentation Improvements or additions to documentation deepseek Related to DeepSeek models frontend gpt-oss Related to GPT-OSS models v1 labels Jul 29, 2026
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Signed-off-by: tony <864832769@qq.com>
Signed-off-by: tony <864832769@qq.com>
Signed-off-by: tony <864832769@qq.com>
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