[RL][Reload] Preserve graph-visible runtime state across weight reloads

[new-TonyWang]

Summary

This PR introduces ReloadArena, layer-owned storage that preserves the identity of graph-visible runtime tensors and Python runtime objects across in-place weight reloads.

During process_weights_after_loading (PWAL), quantization methods may rebuild kernel wrappers, expert objects, derived tensors, or workspaces. If a CUDA graph or runtime object retains an old tensor address, replacing that tensor can leave execution pointing to stale or freed storage. ReloadArena makes the persistent layer own that state so rebuilt paths reacquire the same logical slot.

This PR:

• adds tensor and object slots with model-wide and per-layer identity verification;
• integrates arena snapshot and commit-time verification into layerwise reload;
• updates MLA, Machete/Marlin, CUTLASS MoE, compressed-tensor MoE, RDNA3, FlashInfer B12x, and related paths to use stable arena-owned state;
• supports storage allocated eagerly during PWAL and lazily on first forward;
• preserves the identity of B12x wrappers through object slots following the lifecycle used by [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538;
• validates arena/global-manifest state even when reload itself raises, while preserving the original load exception and marking the worker unsafe to continue serving;
• adds capture-time/dataflow discovery and registry-wide tests for graph-visible storage;
• documents the lifecycle, API, guarantees, and boundaries in docs/design/reload_arena.md.

Why

Layerwise reload already preserves parameter and registered-buffer storage, but PWAL can create runtime tensors and objects that are not checkpoint state. Those values may still be retained by kernels or captured accelerator graphs.

graph capture                       reload
-------------                       ------
graph records address A             PWAL creates tensor at address B
graph continues using address A     Python/kernel object now uses B

An identity-reload output comparison is insufficient to detect this: address A may temporarily contain the same value while still being stale or unsafe for a later update.

Update mechanisms

Checkpoint state and arena-managed runtime state have different publication requirements.

Parameters and registered buffers

Layerwise reload stages new checkpoint state and copies processed parameters and buffers back into the original graph-visible storage at the layer commit boundary.

Arena tensor slots

Arena slots distinguish two semantics:

1. Value-bearing derived tensors (put): PWAL computes derived values which can be staged and published into the existing live slot at the layer commit boundary. The design and caller constraints for this policy are documented in the design document.
2. Runtime workspaces/scratch buffers (get_or_alloc): callers acquire the stable destination directly. These buffers are initialized or populated by the runtime and do not need value copy-back.

This distinction matters because MLA-like tensors carry persistent derived values, while MoE workspaces may only reserve storage during PWAL and receive meaningful contents during apply or first forward.

Arena object slots

Some graph-visible state is a Python object whose identity must remain stable, not just one tensor allocation. Object slots retain the canonical object and let reload-time construction adopt/reuse it. They intentionally have no device argument: the object itself owns its backend/device-specific state, and an incompatible backend, dtype, or device transition is outside legal in-place reload scope.

Object slots are included in snapshot and verification. Replacing, removing, or changing their declared type is rejected.

Lifecycle and guarantees

• Initial PWAL publishes graph-visible runtime state through the owning layer's arena.
• Lazy users retain the selected arena so first-forward allocation remains layer-owned.
• Reload snapshots tensor and object slots before mutation.
• PWAL rebuilds transient state and reacquires the same logical slots.
• Value-bearing arena updates can be published at the layer commit boundary; workspaces remain direct stable allocations.
• Per-layer and model-wide verification compare normalized finding sets and reject moved, missing, replaced, or respecified slots.
• If reload raises, post-failure validation still runs diagnostically without masking the original exception. Because values may already be partially mutated even when identities remain clean, the worker must be restarted rather than returned to service.
• Slots first created after a snapshot are allowed for legitimate lazy initialization; later snapshots require them to remain stable.

The arena does not make arbitrary tensors or objects safe. Consumers must use arena-returned state, and every rebuild path must use the same logical slot. Shape, dtype, device, layout, backend, and object-type changes remain incompatible with in-place reload.

Discussion points

• Is ReloadArena the clearest name for stable graph-visible runtime state ownership?
• Is the value-bearing versus workspace slot distinction sufficiently explicit at call sites?
• Should staged value publication become mandatory for all value-bearing slots, considering its memory/lifecycle cost and the fact that full rollback still requires worker recovery or a shadow model?
• Should object slots remain narrowly scoped to runtime objects whose identity is externally retained?

Scope

This PR is specifically about reload-time identity and publication safety for graph-visible runtime state. It does not provide:

• a shadow model or full reload rollback;
• arbitrary shape/dtype/device/backend changes;
• checkpoint loading or completion receipts;
• safety for consumers that bypass arena-returned state.

Validation

Coverage includes:

• arena allocation, slot specification, snapshot, and verification;
• tensor slots and Python object slots;
• per-layer and model-wide verification in layerwise reload;
• successful reload and reload-body failure paths;
• eager PWAL-derived tensors and lazy first-forward storage;
• Machete/Marlin, MLA, CUTLASS MoE, compressed-tensor MoE, RDNA3, and FlashInfer B12x paths;
• registry-wide and dataflow-based discovery of post-load graph-visible storage;
• expert-storage stability and reload dispatch auditing.

The H200 arena suite previously completed with 62 passed, 21 skipped. The newest failure-path tests could not be rerun because the H200 Jupyter terminal endpoint returned HTTP 404; this is an infrastructure access failure rather than a test failure.

Notes

• The branch is based on releases/v0.25.1 to keep the prototype diff reviewable.
• This work implements the storage-identity portion of RFC #48312 — Weight Reload Correctness for RL.
• This does not duplicate another open PR; [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538 is referenced specifically for the FlashInfer B12x lifecycle pattern, while this PR provides the general reload-owned storage/object identity mechanism and verification gate.
• AI assistance was used; the submitter is responsible for reviewing every changed line and validating the final design and implementation.

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[mergify]

Documentation preview: https://vllm--49789.org.readthedocs.build/en/49789/

[RyanClark2k]

Thanks for writing up the design doc. I read through the reload paths in the branch, and I think you can settle section 8's ownership question from where the caches live. Two things got me there.

First, on section 5. You write that completion and arena findings get reported without a universal gate enforcing them. The code is more lopsided than that. layerwise.py:451, 477, and 481 append to _LAYER_COMPLETION_FINDINGS, and only test_reload_completion_set.py calls get_layer_completion_findings(), so nothing outside tests reads them at all. _DUMMY_BASELINE_FAILURES raises at finalize (layerwise.py:872), and no other completion finding does. Your arena findings already fail closed and default to strict at gpu_model_runner.py:5540-5554, so the doc undersells that half. You already list the wiring as item 1 in section 12, and I'd move it up.

Second, on C4d. You name prefix KV serving generation-N state as a problem to solve, and it's worth knowing how little exists today. Reload ends with reset_encoder_cache() and reset_mm_cache() at gpu_model_runner.py:5590-5591 and never touches prefix KV. The runner has no reset_prefix_cache at all. You have to call EngineCore.reset_prefix_cache (engine/core.py:680) to invalidate prefix blocks, or pass clear_cache=True to pause_generation so _reset_caches at 707 runs. Reload calls neither. The transfer path doesn't either, since finish_weight_update (weight_transfer/base.py:227) only finalizes layerwise reloading and nothing under weight_transfer/ touches the prefix cache. So no weight-update path flushes anything today, unconditionally or otherwise. Flushing on commit looks like a much smaller job than the full generation protocol, and the two might be worth splitting. How much it matters depends on the loop, since plenty of RL setups tolerate KV from an older policy.

The second one points at section 8. The runner owns the encoder and mm caches, so it can reset those itself. The scheduler owns the KV cache manager (sched/scheduler.py:262), so gpu_model_runner.reload_weights cannot reach prefix blocks at all. So whatever owns this has to sit above the worker, with reach into both the scheduler's caches and the rank fan-out. Section 8 asks who owns nesting and reentrancy, where rank-local reports are collected, and which component prevents resume. I think all three land in the same place, and the start_weight_update / update_weights / finish_weight_update sequence at entrypoints/llm.py:876-897 already has the shape of it. You list an extension of the WeightTransferEngine lifecycle as one of the section 8 candidates, and that's the worker half of the same thing. Both the cache flush and the report check have to go on the caller side, since the worker can reach neither.

Section 6 might need less than the doc implies. LoadManifestReport holds tuples and ints in a frozen dataclass, and collective_rpc already returns list[_R] and takes a timeout (executor/abstract.py:153-160). The RL lifecycle already fans out through it, at entrypoints/llm.py:876-897, where start_weight_update, update_weights, and finish_weight_update each go out as a collective_rpc. So the worker side of finish_weight_update (gpu_worker.py:1208) could return its report instead of None, and the caller could require every rank to come back ok before anything resumes. __repr__ at llm.py:903 already collects per-rank results that way. You'd get the timeout from the RPC layer and you wouldn't need a new barrier primitive. You'd only have to stop workers deciding locally.

For section 9, I think you can shrink the problem instead of choosing between a shadow model and an undo log. _copy_and_restore_kernel_tensors at layerwise.py:1004 already copies staged values into the original storage, so you already stage each layer and publish it at the end. The checks run on the wrong side of that though. _verify_layer_arena runs at layerwise.py:974, five lines after the copy-back at 969, and it appends to _LAYER_ARENA_FINDINGS rather than stopping the layer. The comment there describes that placement as symmetric to the copy-back, so it looks deliberate. If you verify after publishing, a violation can't prevent the publish.

You say at line 143 that transaction validation and COMMIT belong after finalize_layerwise_reload, and I agree for the global checks. I'm suggesting a cheap per-layer check inside it rather than moving your transaction validation.

I'd change three things.

• Move _verify_layer_arena above the copy-back, between PWAL at layerwise.py:964 and the copy at 968-969, and skip the copy-back when it produces findings. The move on its own does nothing, since the function only appends to _LAYER_ARENA_FINDINGS and never raises, so the refusal does the actual work. The reorder shouldn't change what it sees, since _copy_and_restore_kernel_tensors only copies into info.kernel_tensors and re-registers them, and never touches arena._slots. I tried the reorder locally and your per-layer arena tests still pass, including test_rebinding_arena_is_caught_per_layer, so the earlier position doesn't lose detection.
• Run the completion and receipt checks per layer as well. _check_set_completion and _record_load_audit_findings run at layerwise.py:864-865 during finalize, though _layerwise_process already holds the same info. A layer missing a fragment could then refuse its own copy-back, instead of showing up only at the end.
• You'd need to handle arena writes separately. Backends put() during PWAL at layerwise.py:964, straight into persistent storage, so the arena gets its new values before the parameters do and skips staging entirely. You could make put() two-phase, landing in a shadow slot and copying into live storage on commit. Most slots hold workspaces and permutations, so a shadow copy is cheap there, but MLA puts W_UV and W_UK_T at mla_attention.py:969-971 and those are derived weight matrices. On a DeepSeek-shaped model that's real memory, so you'd probably want this opt-in per slot rather than across the board.

You'd still need rollback for the genuinely global failures. There would just be far fewer of them.

One more thing, on getting completion to strict. I don't think you can as written, because required_keys comes from observing a full load, so a deliberately partial update produces missing keys that are correct. Your own C4 row points at #45835 for the partial-FP8 preserve-or-reject policy, and I think that's the same question. If the reload entry point took an explicit scope, full or a named subset, you could check completion against that, and that also answers your section 8 question about representing scopes.

Unrelated and much smaller. CUDAGraphWrapper already records entry.input_addresses at capture (vllm/compilation/cuda_graph.py:279-282) and asserts them on replay at 346-355, behind is_debugging_mode. That might help with the piecewise capture window you scoped out in the Tier 3 commit message. It only walks the args tensors, so it misses the weights and derived tensors the region reads out of the module. It does already run at capture though, so you could record a baseline there.

[new-TonyWang]

Thanks for writing up the design doc. I read through the reload paths in the branch, and I think you can settle section 8's ownership question from where the caches live. Two things got me there.

First, on section 5. You write that completion and arena findings get reported without a universal gate enforcing them. The code is more lopsided than that. layerwise.py:451, 477, and 481 append to _LAYER_COMPLETION_FINDINGS, and only test_reload_completion_set.py calls get_layer_completion_findings(), so nothing outside tests reads them at all. _DUMMY_BASELINE_FAILURES raises at finalize (layerwise.py:872), and no other completion finding does. Your arena findings already fail closed and default to strict at gpu_model_runner.py:5540-5554, so the doc undersells that half. You already list the wiring as item 1 in section 12, and I'd move it up.

Second, on C4d. You name prefix KV serving generation-N state as a problem to solve, and it's worth knowing how little exists today. Reload ends with reset_encoder_cache() and reset_mm_cache() at gpu_model_runner.py:5590-5591 and never touches prefix KV. The runner has no reset_prefix_cache at all. You have to call EngineCore.reset_prefix_cache (engine/core.py:680) to invalidate prefix blocks, or pass clear_cache=True to pause_generation so _reset_caches at 707 runs. Reload calls neither. The transfer path doesn't either, since finish_weight_update (weight_transfer/base.py:227) only finalizes layerwise reloading and nothing under weight_transfer/ touches the prefix cache. So no weight-update path flushes anything today, unconditionally or otherwise. Flushing on commit looks like a much smaller job than the full generation protocol, and the two might be worth splitting. How much it matters depends on the loop, since plenty of RL setups tolerate KV from an older policy.

The second one points at section 8. The runner owns the encoder and mm caches, so it can reset those itself. The scheduler owns the KV cache manager (sched/scheduler.py:262), so gpu_model_runner.reload_weights cannot reach prefix blocks at all. So whatever owns this has to sit above the worker, with reach into both the scheduler's caches and the rank fan-out. Section 8 asks who owns nesting and reentrancy, where rank-local reports are collected, and which component prevents resume. I think all three land in the same place, and the start_weight_update / update_weights / finish_weight_update sequence at entrypoints/llm.py:876-897 already has the shape of it. You list an extension of the WeightTransferEngine lifecycle as one of the section 8 candidates, and that's the worker half of the same thing. Both the cache flush and the report check have to go on the caller side, since the worker can reach neither.

Section 6 might need less than the doc implies. LoadManifestReport holds tuples and ints in a frozen dataclass, and collective_rpc already returns list[_R] and takes a timeout (executor/abstract.py:153-160). The RL lifecycle already fans out through it, at entrypoints/llm.py:876-897, where start_weight_update, update_weights, and finish_weight_update each go out as a collective_rpc. So the worker side of finish_weight_update (gpu_worker.py:1208) could return its report instead of None, and the caller could require every rank to come back ok before anything resumes. __repr__ at llm.py:903 already collects per-rank results that way. You'd get the timeout from the RPC layer and you wouldn't need a new barrier primitive. You'd only have to stop workers deciding locally.

For section 9, I think you can shrink the problem instead of choosing between a shadow model and an undo log. _copy_and_restore_kernel_tensors at layerwise.py:1004 already copies staged values into the original storage, so you already stage each layer and publish it at the end. The checks run on the wrong side of that though. _verify_layer_arena runs at layerwise.py:974, five lines after the copy-back at 969, and it appends to _LAYER_ARENA_FINDINGS rather than stopping the layer. The comment there describes that placement as symmetric to the copy-back, so it looks deliberate. If you verify after publishing, a violation can't prevent the publish.

You say at line 143 that transaction validation and COMMIT belong after finalize_layerwise_reload, and I agree for the global checks. I'm suggesting a cheap per-layer check inside it rather than moving your transaction validation.

I'd change three things.

• Move _verify_layer_arena above the copy-back, between PWAL at layerwise.py:964 and the copy at 968-969, and skip the copy-back when it produces findings. The move on its own does nothing, since the function only appends to _LAYER_ARENA_FINDINGS and never raises, so the refusal does the actual work. The reorder shouldn't change what it sees, since _copy_and_restore_kernel_tensors only copies into info.kernel_tensors and re-registers them, and never touches arena._slots. I tried the reorder locally and your per-layer arena tests still pass, including test_rebinding_arena_is_caught_per_layer, so the earlier position doesn't lose detection.
• Run the completion and receipt checks per layer as well. _check_set_completion and _record_load_audit_findings run at layerwise.py:864-865 during finalize, though _layerwise_process already holds the same info. A layer missing a fragment could then refuse its own copy-back, instead of showing up only at the end.
• You'd need to handle arena writes separately. Backends put() during PWAL at layerwise.py:964, straight into persistent storage, so the arena gets its new values before the parameters do and skips staging entirely. You could make put() two-phase, landing in a shadow slot and copying into live storage on commit. Most slots hold workspaces and permutations, so a shadow copy is cheap there, but MLA puts W_UV and W_UK_T at mla_attention.py:969-971 and those are derived weight matrices. On a DeepSeek-shaped model that's real memory, so you'd probably want this opt-in per slot rather than across the board.

You'd still need rollback for the genuinely global failures. There would just be far fewer of them.

One more thing, on getting completion to strict. I don't think you can as written, because required_keys comes from observing a full load, so a deliberately partial update produces missing keys that are correct. Your own C4 row points at #45835 for the partial-FP8 preserve-or-reject policy, and I think that's the same question. If the reload entry point took an explicit scope, full or a named subset, you could check completion against that, and that also answers your section 8 question about representing scopes.

Unrelated and much smaller. CUDAGraphWrapper already records entry.input_addresses at capture (vllm/compilation/cuda_graph.py:279-282) and asserts them on replay at 346-355, behind is_debugging_mode. That might help with the piecewise capture window you scoped out in the Tier 3 commit message. It only walks the args tensors, so it misses the weights and derived tensors the region reads out of the module. It does already run at capture though, so you could record a baseline there.

[new-TonyWang]

Thanks for writing up the design doc. I read through the reload paths in the branch, and I think you can settle section 8's ownership question from where the caches live. Two things got me there.

First, on section 5. You write that completion and arena findings get reported without a universal gate enforcing them. The code is more lopsided than that. layerwise.py:451, 477, and 481 append to _LAYER_COMPLETION_FINDINGS, and only test_reload_completion_set.py calls get_layer_completion_findings(), so nothing outside tests reads them at all. _DUMMY_BASELINE_FAILURES raises at finalize (layerwise.py:872), and no other completion finding does. Your arena findings already fail closed and default to strict at gpu_model_runner.py:5540-5554, so the doc undersells that half. You already list the wiring as item 1 in section 12, and I'd move it up.

Second, on C4d. You name prefix KV serving generation-N state as a problem to solve, and it's worth knowing how little exists today. Reload ends with reset_encoder_cache() and reset_mm_cache() at gpu_model_runner.py:5590-5591 and never touches prefix KV. The runner has no reset_prefix_cache at all. You have to call EngineCore.reset_prefix_cache (engine/core.py:680) to invalidate prefix blocks, or pass clear_cache=True to pause_generation so _reset_caches at 707 runs. Reload calls neither. The transfer path doesn't either, since finish_weight_update (weight_transfer/base.py:227) only finalizes layerwise reloading and nothing under weight_transfer/ touches the prefix cache. So no weight-update path flushes anything today, unconditionally or otherwise. Flushing on commit looks like a much smaller job than the full generation protocol, and the two might be worth splitting. How much it matters depends on the loop, since plenty of RL setups tolerate KV from an older policy.

The second one points at section 8. The runner owns the encoder and mm caches, so it can reset those itself. The scheduler owns the KV cache manager (sched/scheduler.py:262), so gpu_model_runner.reload_weights cannot reach prefix blocks at all. So whatever owns this has to sit above the worker, with reach into both the scheduler's caches and the rank fan-out. Section 8 asks who owns nesting and reentrancy, where rank-local reports are collected, and which component prevents resume. I think all three land in the same place, and the start_weight_update / update_weights / finish_weight_update sequence at entrypoints/llm.py:876-897 already has the shape of it. You list an extension of the WeightTransferEngine lifecycle as one of the section 8 candidates, and that's the worker half of the same thing. Both the cache flush and the report check have to go on the caller side, since the worker can reach neither.

Section 6 might need less than the doc implies. LoadManifestReport holds tuples and ints in a frozen dataclass, and collective_rpc already returns list[_R] and takes a timeout (executor/abstract.py:153-160). The RL lifecycle already fans out through it, at entrypoints/llm.py:876-897, where start_weight_update, update_weights, and finish_weight_update each go out as a collective_rpc. So the worker side of finish_weight_update (gpu_worker.py:1208) could return its report instead of None, and the caller could require every rank to come back ok before anything resumes. __repr__ at llm.py:903 already collects per-rank results that way. You'd get the timeout from the RPC layer and you wouldn't need a new barrier primitive. You'd only have to stop workers deciding locally.

For section 9, I think you can shrink the problem instead of choosing between a shadow model and an undo log. _copy_and_restore_kernel_tensors at layerwise.py:1004 already copies staged values into the original storage, so you already stage each layer and publish it at the end. The checks run on the wrong side of that though. _verify_layer_arena runs at layerwise.py:974, five lines after the copy-back at 969, and it appends to _LAYER_ARENA_FINDINGS rather than stopping the layer. The comment there describes that placement as symmetric to the copy-back, so it looks deliberate. If you verify after publishing, a violation can't prevent the publish.

You say at line 143 that transaction validation and COMMIT belong after finalize_layerwise_reload, and I agree for the global checks. I'm suggesting a cheap per-layer check inside it rather than moving your transaction validation.

I'd change three things.

• Move _verify_layer_arena above the copy-back, between PWAL at layerwise.py:964 and the copy at 968-969, and skip the copy-back when it produces findings. The move on its own does nothing, since the function only appends to _LAYER_ARENA_FINDINGS and never raises, so the refusal does the actual work. The reorder shouldn't change what it sees, since _copy_and_restore_kernel_tensors only copies into info.kernel_tensors and re-registers them, and never touches arena._slots. I tried the reorder locally and your per-layer arena tests still pass, including test_rebinding_arena_is_caught_per_layer, so the earlier position doesn't lose detection.
• Run the completion and receipt checks per layer as well. _check_set_completion and _record_load_audit_findings run at layerwise.py:864-865 during finalize, though _layerwise_process already holds the same info. A layer missing a fragment could then refuse its own copy-back, instead of showing up only at the end.
• You'd need to handle arena writes separately. Backends put() during PWAL at layerwise.py:964, straight into persistent storage, so the arena gets its new values before the parameters do and skips staging entirely. You could make put() two-phase, landing in a shadow slot and copying into live storage on commit. Most slots hold workspaces and permutations, so a shadow copy is cheap there, but MLA puts W_UV and W_UK_T at mla_attention.py:969-971 and those are derived weight matrices. On a DeepSeek-shaped model that's real memory, so you'd probably want this opt-in per slot rather than across the board.

You'd still need rollback for the genuinely global failures. There would just be far fewer of them.

One more thing, on getting completion to strict. I don't think you can as written, because required_keys comes from observing a full load, so a deliberately partial update produces missing keys that are correct. Your own C4 row points at #45835 for the partial-FP8 preserve-or-reject policy, and I think that's the same question. If the reload entry point took an explicit scope, full or a named subset, you could check completion against that, and that also answers your section 8 question about representing scopes.

Unrelated and much smaller. CUDAGraphWrapper already records entry.input_addresses at capture (vllm/compilation/cuda_graph.py:279-282) and asserts them on replay at 346-355, behind is_debugging_mode. That might help with the piecewise capture window you scoped out in the Tier 3 commit message. It only walks the args tensors, so it misses the weights and derived tensors the region reads out of the module. It does already run at capture though, so you could record a baseline there.

Thanks for writing up the design doc. I read through the reload paths in the branch, and I think you can settle section 8's ownership question from where the caches live. Two things got me there.

First, on section 5. You write that completion and arena findings get reported without a universal gate enforcing them. The code is more lopsided than that. layerwise.py:451, 477, and 481 append to _LAYER_COMPLETION_FINDINGS, and only test_reload_completion_set.py calls get_layer_completion_findings(), so nothing outside tests reads them at all. _DUMMY_BASELINE_FAILURES raises at finalize (layerwise.py:872), and no other completion finding does. Your arena findings already fail closed and default to strict at gpu_model_runner.py:5540-5554, so the doc undersells that half. You already list the wiring as item 1 in section 12, and I'd move it up.

Second, on C4d. You name prefix KV serving generation-N state as a problem to solve, and it's worth knowing how little exists today. Reload ends with reset_encoder_cache() and reset_mm_cache() at gpu_model_runner.py:5590-5591 and never touches prefix KV. The runner has no reset_prefix_cache at all. You have to call EngineCore.reset_prefix_cache (engine/core.py:680) to invalidate prefix blocks, or pass clear_cache=True to pause_generation so _reset_caches at 707 runs. Reload calls neither. The transfer path doesn't either, since finish_weight_update (weight_transfer/base.py:227) only finalizes layerwise reloading and nothing under weight_transfer/ touches the prefix cache. So no weight-update path flushes anything today, unconditionally or otherwise. Flushing on commit looks like a much smaller job than the full generation protocol, and the two might be worth splitting. How much it matters depends on the loop, since plenty of RL setups tolerate KV from an older policy.

The second one points at section 8. The runner owns the encoder and mm caches, so it can reset those itself. The scheduler owns the KV cache manager (sched/scheduler.py:262), so gpu_model_runner.reload_weights cannot reach prefix blocks at all. So whatever owns this has to sit above the worker, with reach into both the scheduler's caches and the rank fan-out. Section 8 asks who owns nesting and reentrancy, where rank-local reports are collected, and which component prevents resume. I think all three land in the same place, and the start_weight_update / update_weights / finish_weight_update sequence at entrypoints/llm.py:876-897 already has the shape of it. You list an extension of the WeightTransferEngine lifecycle as one of the section 8 candidates, and that's the worker half of the same thing. Both the cache flush and the report check have to go on the caller side, since the worker can reach neither.

Section 6 might need less than the doc implies. LoadManifestReport holds tuples and ints in a frozen dataclass, and collective_rpc already returns list[_R] and takes a timeout (executor/abstract.py:153-160). The RL lifecycle already fans out through it, at entrypoints/llm.py:876-897, where start_weight_update, update_weights, and finish_weight_update each go out as a collective_rpc. So the worker side of finish_weight_update (gpu_worker.py:1208) could return its report instead of None, and the caller could require every rank to come back ok before anything resumes. __repr__ at llm.py:903 already collects per-rank results that way. You'd get the timeout from the RPC layer and you wouldn't need a new barrier primitive. You'd only have to stop workers deciding locally.

For section 9, I think you can shrink the problem instead of choosing between a shadow model and an undo log. _copy_and_restore_kernel_tensors at layerwise.py:1004 already copies staged values into the original storage, so you already stage each layer and publish it at the end. The checks run on the wrong side of that though. _verify_layer_arena runs at layerwise.py:974, five lines after the copy-back at 969, and it appends to _LAYER_ARENA_FINDINGS rather than stopping the layer. The comment there describes that placement as symmetric to the copy-back, so it looks deliberate. If you verify after publishing, a violation can't prevent the publish.

You say at line 143 that transaction validation and COMMIT belong after finalize_layerwise_reload, and I agree for the global checks. I'm suggesting a cheap per-layer check inside it rather than moving your transaction validation.

I'd change three things.

• Move _verify_layer_arena above the copy-back, between PWAL at layerwise.py:964 and the copy at 968-969, and skip the copy-back when it produces findings. The move on its own does nothing, since the function only appends to _LAYER_ARENA_FINDINGS and never raises, so the refusal does the actual work. The reorder shouldn't change what it sees, since _copy_and_restore_kernel_tensors only copies into info.kernel_tensors and re-registers them, and never touches arena._slots. I tried the reorder locally and your per-layer arena tests still pass, including test_rebinding_arena_is_caught_per_layer, so the earlier position doesn't lose detection.
• Run the completion and receipt checks per layer as well. _check_set_completion and _record_load_audit_findings run at layerwise.py:864-865 during finalize, though _layerwise_process already holds the same info. A layer missing a fragment could then refuse its own copy-back, instead of showing up only at the end.
• You'd need to handle arena writes separately. Backends put() during PWAL at layerwise.py:964, straight into persistent storage, so the arena gets its new values before the parameters do and skips staging entirely. You could make put() two-phase, landing in a shadow slot and copying into live storage on commit. Most slots hold workspaces and permutations, so a shadow copy is cheap there, but MLA puts W_UV and W_UK_T at mla_attention.py:969-971 and those are derived weight matrices. On a DeepSeek-shaped model that's real memory, so you'd probably want this opt-in per slot rather than across the board.

You'd still need rollback for the genuinely global failures. There would just be far fewer of them.

One more thing, on getting completion to strict. I don't think you can as written, because required_keys comes from observing a full load, so a deliberately partial update produces missing keys that are correct. Your own C4 row points at #45835 for the partial-FP8 preserve-or-reject policy, and I think that's the same question. If the reload entry point took an explicit scope, full or a named subset, you could check completion against that, and that also answers your section 8 question about representing scopes.

Unrelated and much smaller. CUDAGraphWrapper already records entry.input_addresses at capture (vllm/compilation/cuda_graph.py:279-282) and asserts them on replay at 346-355, behind is_debugging_mode. That might help with the piecewise capture window you scoped out in the Tier 3 commit message. It only walks the args tensors, so it misses the weights and derived tensors the region reads out of the module. It does already run at capture though, so you could record a baseline there.

Thanks for the detailed review. I know how much time it takes to go through something like this line by line, cross-referencing against the actual code at that level of detail, and I really appreciate that you did.

1. Completion findings are currently observational rather than a production commit gate
   You are right. _check_set_completion() and _record_load_audit_findings() append to _LAYER_COMPLETION_FINDINGS, but the production reload/update paths do not currently consume those findings to reject the update. _DUMMY_BASELINE_FAILURES is the exception because it raises during finalization.
2. For Section 6, I will try "finish_weight_update() -> LoadManifestReport" rather than return a "None".
3. I will try to implement those “Three things need to change”
4. Agree with “deliberately partial update produces missing keys that are correct”. The current "required_keys" represents the full-load baseline, so comparing every update against it incorrectly rejects intentional partial updates.
   I think the weight reload entry point should require an explicit immutable scope such as "a layer" ," one MoE expert shard group", "a quant weight + scale group" or someting else.

full
sources={...}
layers={...}
model_role=target/draft/MTP

The transaction then projects the full manifest onto that scope.

expected_events = full_manifest.project(scope)

and compares the received events only against that projected manifest. The scope must be declared before transfer; it cannot be inferred from the weights that happened to arrive, otherwise accidental omissions would look like valid partial updates.

5. "CUDAGraphWrapper" is a useful tool for capturing tensor addresses. I will take a closer look at it and test whether it can detect tensor address drift.

Thanks for looking over my plan. I really appreciate it.

[mergify]

Documentation preview: https://vllm--49789.org.readthedocs.build/en/49789/

[mergify]

Documentation preview: https://vllm--49789.org.readthedocs.build/en/49789/

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[new-TonyWang]

@claude review

[RyanClark2k]

I went through the current branch end to end. Dropping the moe_kernel guards and rebuilding the kernels under arena_scope resolves the layerwise-path concern from the #48902 thread, and routing the lazily allocated permute scratch through the arena covers a bug that none of the open point fixes handle. The set_current_vllm_config boundary in reload_weights should also help with the config problems from #44613/#45989. Findings below, plus an answer to the open question in the description.

1. FlashInferB12xExperts still has a graph-visible storage bug, and the sweep now reports the class as migrated. The class builds its B12xMoEWrapper lazily in _ensure_wrapper with use_cuda_graph=True, so captured graphs hold the addresses of the wrapper's pre-allocated workspaces. A rebuilt experts object starts with _wrapper = None and builds a fresh wrapper at new addresses on the first post-reload forward. I hit this live on SM120 and the first post-reload generation hangs the engine. [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538 has the details and a fix that caches the wrapper on the layer. With the scale migration in this PR, classify() puts the class in the arena bucket, because the only raw-allocation marker left in the file sits inside your arena.put call, so test_no_enumerated_moe_backend_has_raw_runtime_allocations stays green. Widening _sources would not help, since the allocation happens inside flashinfer's constructor and no torch.* marker appears in any method of the class. I would record FlashInferB12xExperts as an explicit expected-unsafe worklist entry pointing at [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538. Your docstring calls the static layer advisory and your Boundaries section says the arena cannot own storage a third-party extension allocates internally, so the worklist entry would make the test agree with both.

2. The commit gate cannot see the sinks fix. FlashInferImpl creates a private ReloadArena on the impl object, and snapshot_model_arenas walks named_modules, so the sinks_f32 slot never reaches the gate. _finalize_attention_layer already opens arena_scope(get_reload_arena(layer)) around the impl's PWAL, and your model_loader/utils.py change opens the same scope on the initial-load path. If the impl resolves current_arena() instead of owning a private arena, the slot lands in the layer's arena on both the first PWAL and every reload, and the per-layer verification in _finalize_attention_layer covers it for free.

3. The compressed-tensors NVFP4 CUTLASS scale chain from the RFC's high-risk table still allocates raw. The nvfp4 oracle builds 1 / a13_scale and 1 / a2_scale for the graph-visible a1/a2_gscale, the quant config retains the fused g1/g2_alphas storage, and the kernel rebuild replaces all of it. That construction runs during PWAL where your ambient scope is already open, so the current_arena() pattern you used for trtllm_fp8.g1_alphas should drop in.

4. On the update-policy question in the description, I would split the two categories and put the commit point at the layer's copy-back rather than at one model-wide boundary. Scratch and workspaces have no value worth staging, so I would keep those direct. For value-bearing slots, a staged put can land in a per-layer shadow, and _copy_and_restore_kernel_tensors can publish the shadow into the live slot together with that layer's parameters. That gives arena values the same publication boundary parameters already have, and the shadow stays transient, so peak overhead is one layer's value-bearing slots at a time. Even MLA W_UV/W_UK_T on a DeepSeek-shaped model stay in the tens of megabytes when the shadow lives for one layer's window, while holding every layer's shadow until a global commit would cost gigabytes. Your API already answers the declaration sub-question, since put marks a slot value-bearing and get_or_alloc marks it scratch. A staged put still has to return the live slot so rebuilt consumers bind the captured address, which means the returned tensor holds stale values until copy-back, so PWAL code has to derive follow-on values from the source tensors rather than from what put returned. The TRT-LLM classes derive g1_scale_c from g1_alphas in the same pass today, so that pattern already exists in the tree. I would not gate this PR on any of it. Direct update plus the strict gate seems like a reasonable first state as long as the doc keeps saying that a strict failure means the worker needs recovery.

Two small things. The dual-run comparison in reload_weights compares len(per_layer) against len(arena_problems), so equal counts of different findings pass silently. Comparing normalized sets costs about the same number of lines. And the marlin sites call arena.put("marlin.workspace", marlin_make_workspace_new(...)), which allocates a throwaway workspace on every pass just to copy zeros into the slot. get_or_alloc with InitPolicy.ZERO matches what your API doc recommends for workspaces, and one site spells the slot "marlin_workspace" while the rest use "marlin.workspace".

[new-TonyWang]

Thank you for taking the time to review this PR so carefully end to end, and especially for identifying the multiple B12x issues that our original sweep did not cover. The SM120 evidence and the analysis in #50538 were very helpful.

I have addressed the findings in follow-up commits:

1. FlashInfer B12x wrapper lifetime

   I followed the ownership flow demonstrated in [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538: the wrapper is now created during the first PWAL rather than lazily on the first post-reload forward, and every rebuilt FlashInferB12xExperts instance binds the same persistent wrapper.

   Since this PR already has a layer-owned arena abstraction, I added a minimal object-slot API:

   arena.get_or_create_object("flashinfer_b12x.wrapper", self._build_wrapper)

   The wrapper is owned by the routed-experts layer arena, while rebuilt experts only borrow it. Its private routing workspaces and output buffer therefore survive PWAL rebuilds. Object slots participate in both per-layer and model-wide verification: snapshots retain the original Python object, and verification reports moved or gone if it is replaced or removed.

   The three exposed B12x runtime tensors remain arena-managed separately. A hardware-independent regression test rebuilds the experts, verifies that the wrapper factory runs once, and checks wrapper identity through the arena gate.

   I also referenced [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538 explicitly in the design document. I could not execute the real B12x kernel on H200, so [Bugfix] Preserve FlashInfer B12x MoE runtime tensors across reload #50538 remains the SM120 red/green evidence for this wrapper-lifetime mechanism. The material difference here is integrating the wrapper into the arena ownership and verification model rather than caching it only as a layer attribute. If maintainers prefer to avoid overlap, I am happy to coordinate how the B12x-specific integration should be split.

2. FlashInferImpl.sinks_f32 ownership

   The private ReloadArena("FlashInferImpl") has been removed. PWAL now resolves current_arena() and publishes flashinfer.sinks_f32 into the owning attention layer arena, so both per-layer verification and the model-wide commit gate can see it.

3. Compressed-tensors NVFP4 CUTLASS scale chain

   For FLASHINFER_CUTLASS and VLLM_CUTLASS, the following graph-visible values now use the ambient layer arena:

   • nvfp4.g1_alphas
   • nvfp4.g2_alphas
   • nvfp4.a1_gscale
   • nvfp4.a2_gscale

   The layer aliases for w13_weight_scale_2 and w2_weight_scale_2 are rebound to the returned stable tensors, so the in-place activation-scale fusion in CutlassExpertsFp4 still updates the same storage retained by the quant config. The regression test covers two PWAL-like passes, including the in-place fusion, and verifies stable pointers and refreshed rather than cumulatively multiplied values.

4. Value-bearing staged publication

   I documented the proposed split without implementing it in this PR:

   • put() identifies value-bearing derived state that could stage into a per-layer shadow;
   • get_or_alloc() remains direct for scratch/workspace;
   • publication would happen at the existing layer copy-back boundary;
   • staged put() must return the live tensor, so same-pass derivations must use the fresh local source rather than the returned stale live value.

   The document also covers publish/discard semantics, per-layer memory bounds, and why this still does not provide model-wide rollback. The current implementation remains direct-update, with strict failure requiring worker recovery.

5. Dual-run comparison

   The model-level and per-layer checks now emit the same normalized module-path finding format and compare frozensets rather than lengths. The warning reports findings missed by the per-layer path as well as findings seen only by it.

6. Marlin workspaces

   All arena-managed Marlin workspace sites now use a shared helper backed by:

   get_or_alloc("marlin.workspace", ..., init=InitPolicy.ZERO)

   This removes the throwaway workspace allocation and copy on every PWAL. The old marlin_workspace spelling is unified to marlin.workspace. Tests dirty the workspace between PWAL passes and verify both pointer stability and zeroing on reacquisition.

The updated H200 arena suite reports:

62 passed, 21 skipped

The skipped paths are unavailable hardware/backend combinations. B12x object lifetime is covered without SM120, while #50538 provides the real SM120 validation.