[Reload] Probe dummy load manifests - #50418
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Layerwise reload decided a layer was complete by summing `numel()` over its tensors and counting `aten.copy_` elements against that total. Alignment padding and loader-derived buffers are in that sum but no checkpoint tensor ever writes them, so such layers never reach the threshold: their incoming tensors stay buffered on device until finalization, which is the peak-memory window layerwise processing exists to bound. Record what the initial checkpoint load actually applies instead. Each `weight_loader` application is keyed by (canonical source tensor, destination tensor, non-tensor arguments) and frozen as that layer's contract; later reloads tick arrivals off the list. Padding and derived buffers are never on the list, so no per-model annotation is needed. Completion is a lower bound -- every expected application must arrive, extras are absorbed -- because an expert-parallel rank loads a filtered set from disk yet is offered every expert by a broadcasting sender. A loader that declines via `return_success` writes nothing and is neither buffered nor counted. An update that fails to deliver the contract now raises at finalization, naming the missing keys, instead of logging a warning and continuing to serve a half-updated model. Signed-off-by: aoshen02 <aoshen@inferact.ai> Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
An expert parallel rank is offered every expert during the initial load and its MoE loader declines the ones it does not own by returning False. The recorder wrote the key before invoking that loader, so declined experts entered the contract and every later update waited for an application that would never arrive, failing `finish_weight_update` with `expected=1, observed=0` on a routed expert. Record after the call and skip anything that returns False, matching what `deepseek_v2.py` already does with `return_success`. The streaming path already had this ordering; only the recorder disagreed. Also drop `_freeze_selector`, whose only effect was to reword the `TypeError` a Counter insert raises anyway, and rename `_layerwise_process`'s `keep_transaction` to `mid_update`: there is no rollback here, only the question of whether the update has ended. Its `info.reset()` moves to the one caller that relied on it, leaving finalize the sole owner of lifecycle reset. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Ao Shen <aoshen@inferact.ai>
Once a layer publishes, its parameters are the live kernel tensors again, so probing a loader to see whether it declines could write checkpoint-format data into them and only then raise. Decide from the loader signature and the contract instead: a loader that implements `return_success` may be offered an application absent from the contract, which holds only applications that wrote, and one that cannot decline has no business writing at all. Also warn when an application outside the contract is absorbed before publish. Absorbing is deliberate, since an update may offer a target the startup checkpoint never did, but a misrouted tensor name arrives the same way and was silent. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Ao Shen <aoshen@inferact.ai>
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Port metadata-only dummy load probing onto the manifest completion work in PR vllm-project#50075 while retaining its signature-based selector discovery. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: tony <864832769@qq.com>
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Summary
Stacked on #50075. This ports the dummy-load metadata probing flow from #50251 onto #50075's manifest-driven completion implementation.
model.load_weightsrouting with meta safetensors while suppressing tensor writes;DummyModelLoaderopt into probing withmodel_loader_extra_config.enable_load_probe;LoadPlankey;loaded_shard_id,expert_id, and other non-tensor selectors;This intentionally excludes
LoadReceipt, LoRA manifests, partial-update scopes, weight-transfer/API/protocol changes, and serving entrypoints from #50251.Dependency and duplicate check
This is not an independent duplicate of #50075 or #50251. It is a selective stacked port: #50075 remains the completion foundation, while #50251 contains a broader receipt/update-scope design based on
releases/v0.25.1. GitHub cannot use #50075's cross-fork head as this PR's base, so the PR temporarily targetskimi-k3; after #50075 lands, its three commits will drop from this diff. Searches for open dummy-load-probe and weight-loader-receipt PRs found only #50251 and its parent #49789.Validation
Run on NVIDIA H200 using the shared vLLM environment:
The single failure is
test_padded_derived_layer_completes_online_without_annotation: the untouched padding row contains uninitialized data after meta materialization. The same failure was reproduced on a clean #50075 worktree on the same H200, so it is not introduced by this port and is intentionally left for separate follow-up.python -m compileall -q tests/model_executor/model_loader/test_load_probe.py vllm/model_executor/model_loader/dummy_loader.py vllm/model_executor/model_loader/reload vllm/model_executor/models/gpt_oss.py # passedAI assistance
AI assistance was used to analyze #50075/#50251, adapt the probe mechanism, write tests, and prepare this PR. The human submitter is responsible for reviewing every changed line and validating the design end-to-end.