[Bugfix][Reload] Preserve unmanaged tensor addresses across weight reload

[new-TonyWang]

Summary

• Recursive tensor collector for unmanaged CUDA tensor address preservation across reload
• MoE kernel rebuild guard for all 8 compressed-tensors MoE methods (Layerwise reload crashes with CUDA illegal memory access on compressed-tensors channel-wise FP8 MoE #41670)
• W4A8 MoE reload fix: Cache QuantFP8 in convert_to_w4a8_moe_kernel_format to prevent CustomOp crash during reload
• RotaryEmbedding false warning fix
• 38 tests across 3 suites + W4A8 MoE e2e + FlashInfer CUTLASS validation, all PASS

Why the MoE Kernel Guard is Needed

process_weights_after_loading calls make_*_moe_kernel() which allocates internal workspace tensors (_permute_scratch, ab_strides, etc.). These tensors get new GPU addresses each time the kernel is recreated. CUDA graphs hard-code tensor addresses at capture time.

Experimental verification on H200 (DeepSeek-V3 FP8_DYNAMIC 2-layer, 3 reloads each):

Mode	Guard Removed	Result
enforce_eager=True (no CUDA graph)	3 reloads	ALL PASS — outputs identical
enforce_eager=False (CUDA graph enabled)	1st reload → generation	CRASH — CUDA error: illegal memory access
Normal (guard present + CUDA graph)	3 reloads	ALL PASS

Root cause: when the kernel is recreated, _permute_scratch's 9 internal sub-tensors (token_expert_indices, expert_first_token_offset, sorted_row_idx, etc.) get new addresses. tensor_collector records these paths but cannot resolve them after kernel recreation (logged as extra-tensor path unresolvable after reload). The old tensors lose all Python references → GC → CUDA allocator reclaims the memory → CUDA graph replay writes to freed addresses → illegal memory access.

The guard (if self.moe_kernel is None:) prevents kernel recreation entirely. Since workspace tensors don't carry model weights (they're scratch buffers), skipping recreation is safe — tensor_collector handles weight tensor address preservation via copy_back.

Additional guard experiment (NVFP4 / OAI Triton path):

Mode	Guard	CUDA Graph	Result
gpt-oss-20b-2layer, OAI Triton	present	off	PASS (12 extras, 0 drifted)
gpt-oss-20b-2layer, OAI Triton	removed	off	PASS (12 extras, 0 drifted)
gpt-oss-20b-2layer, OAI Triton	removed	on	PASS (38 extras, 0 drifted)

The NVFP4/OAI Triton path passes without the guard because tensor_collector's copy_back successfully resolves all extra tensor paths. The FP8_DYNAMIC path fails because _permute_scratch sub-tensors become unresolvable after kernel recreation. The guard is a defense-in-depth measure that prevents both failure modes.

FlashInfer CUTLASS MoE Coverage

Per #48312 comment, FlashInferExperts allocates SwiGLU constants (gemm1_alpha, gemm1_beta, gemm1_clamp_limit) in __init__ that drift on kernel rebuild.

Confirmed on H200 (gpt-oss-20b-2layer, moe_backend=flashinfer_cutlass, FlashInfer 0.6.13 JIT-compiled for SM90):

Expert type: FlashInferExperts (2 layers × 32 experts)
tensor_collector found per layer:
  quant_method.moe_kernel.impl.fused_experts.gemm1_alpha      shape=[32] dtype=float32
  quant_method.moe_kernel.impl.fused_experts.gemm1_beta       shape=[32] dtype=float32
  quant_method.moe_kernel.impl.fused_experts.gemm1_clamp_limit shape=[32] dtype=float32
Total extras: 10 (3 gemm1 constants + 2 attn per layer × 2 layers)

_walk discovers these at depth=5 via the path quant_method.moe_kernel.impl.fused_experts.gemm1_*. The guard prevents kernel rebuild (which would reallocate these tensors), and tensor_collector preserves their addresses via copy_back.

Not yet tested (hardware/software constraints):

• TRT-LLM gemm1_alpha/beta/clamp_limit + g1_scale_c: SM100+ required
• fake_input_scale on mxfp8 path: requires mxfp8 activation quantization model
• Machete g_idx_sort_indices: covered by separate PR [Bugfix] Preserve Machete act-order permutation storage across weight reload #48539

H200 Validation (rl_learning, 2026-07-17/18)

Category 1: Storage Identity

Test	Type	Result
W4A8 MoE b_strides (Qwen3-30B-A3B-2layer-W4A8)	H200 model reload	PASS (46 extras, 8 b_strides, 0 drifted)
FlashInfer CUTLASS gemm1 (gpt-oss-20b-2layer)	H200 model load	PASS (10 extras incl. gemm1_alpha/beta/clamp)
FP8 MoE strides (DeepSeek-V3 FP8_DYNAMIC)	H200 model reload	PASS (19 extras, 0 drifted)
MLA W_UV/W_UK_T (DeepSeek-V3 BF16)	H200 model reload	PASS (6 extras, 0 drifted)
Machete W4A16 (Qwen3-0.6B-W4A16-G128)	H200 model reload	PASS (114 extras, 0 drifted)
BF16 baseline (Qwen3-0.6B)	H200 model reload	PASS (30 extras, 0 drifted)
FP8 online (Qwen3-0.6B)	H200 model reload	PASS (114 extras, 0 drifted)
W4A8 b_strides collection	Unit test (real tensor_collector)	PASS
W4A8 copy-back preserves addr	Unit test (real copy_back)	PASS
W4A8 stride drift detection	Unit test (replacement)	PASS
Mismatched quant config	Unit test (FP8 vs W4A8 schemes)	PASS

Category 2: WNA8O8 / Value Refresh

Test	Type	Result
WNA8O8 scale discovery	Unit test (real tensor_collector)	PASS
WNA8O8 scale copy-back	Unit test (real copy_back)	PASS
WNA8O8 no-copy-back drift	Unit test	PASS
WNA8O8 wrong-dtype detection	Unit test	PASS
WNA8O8 model reload	NOT_AVAILABLE	0 HF results for wna8o8
Qwen3 BF16 perplexity	H200 model reload	PASS
Qwen3 W4A16 perplexity	H200 model reload	PASS
DeepSeek-V3 FP8 perplexity	H200 model reload	PASS
DeepSeek-V3 BF16 perplexity	H200 model reload	PASS

Category 3: Loader Lifecycle

Test	Type	Result
FP8 MoE 2-reload + generation	H200 model reload	PASS
BF16 MoE 2-reload + generation	H200 model reload	PASS
Guard preserves kernel id	Unit test	PASS
Guard bypass rebuilds kernel	Unit test	PASS
WITH vs WITHOUT guard monkeypatch	Unit test	PASS

Category 4: State Preservation

Test	Type	Result
9 upstream test_reload.py CPU tests	H200 pytest	9/9 PASS
Reload-cycle buffer corruption	Unit test (real LayerReloadingInfo)	PASS
Unloaded buffer metadata preservation	Unit test (real capture/restore)	PASS

Totals: 14 unit + 16 H200 comprehensive + 9 upstream = 39/39 PASS

W4A8 MoE Bug Found & Fixed

convert_to_w4a8_moe_kernel_format creates QuantFP8(static=False, group_shape=PER_TOKEN) on every call. During reload, QuantFP8.__init__ → CustomOp.dispatch_forward → get_current_vllm_config() crashes because vLLM config context is not set. Fix: cache the QuantFP8 instance as a function attribute.

NOT_AVAILABLE (with evidence)

Item	Investigated	Reason
WNA8O8 model reload	HF API: 0 results for wna8o8/w8a8o8	No public model exists
TRT-LLM NVFP4 g1_scale_c	SM100+ only (TrtLlmNvfp4ExpertsModular)	No B200/B100 hardware
MXFP8 fake_input_scale	Requires mxfp8 activation quant model	No model available
AITER MLA	AMD ROCm only	No ROCm hardware

Fixes #48312 (Category 1), Fixes #41670
Related: #48251, #48438, #48539

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[mergify]

Documentation preview: https://vllm--48902.org.readthedocs.build/en/48902/

[RyanClark2k]

@new-TonyWang the snapshot/copy-back approach makes sense to me, and keeping the old tensors alive through the reload window is a nice property since it rules out the reclaim failure mode for anything the collector can see. Some thoughts on the approach:

1. I might be misreading this, but it looks like copy-back can only restore plain attribute paths. _walk records paths like foo['scale'], fn.args[0], fn.__closure__[2], while resolve_path and set_by_path split on . and getattr each segment, so anything with brackets fails to resolve and gets skipped at debug level. A tensor that's only reachable through a dict/list/partial/closure ends up recorded but never repaired, and you'd never see it in normal logs. Maybe collect only attr paths so the slot list matches what copy-back can restore, or at least log the skips at warning.

2. Question about the moe_kernel is None guard: in compressed_tensors_moe_w4a4_nvfp4.py it also skips moe_kernel.fused_experts.process_weights_after_loading(layer) on reload, and the WNA16 kernel keeps the marlin_args computed at first load. If any of that derives from weight values (rather than holding references to layer params, which copy-back refreshes in place), it stays frozen after a weight update, which is category 2 in the RFC's taxonomy. Did you check that for the guarded classes? Another option is to rebuild the kernel but reuse the storage, like [Bugfix] Preserve Marlin runtime tensor storage across weight reload #48438 does with existing= for the marlin workspace. Then derived state gets recomputed on every reload but lands at the same addresses.

3. You should be able to test the FlashInfer CUTLASS MoE row on the H200 you used for validation. It's in the "not tested (Blackwell SM100+)" list, but the (kMxfp4Static, None) scheme is gated on is_device_capability(90) exactly, and H200 is SM90. gpt-oss-20b with kernel_config={"moe_backend": "flashinfer_cutlass"} reaches FlashInferExperts with all three gemm1 tensors allocated. That's the config I used to confirm the bug on an H100, numbers are in the RFC thread. Heads up that if the machine's CUDA toolkit is older than 12.8, engine start fails for an unrelated flashinfer build reason; installing flashinfer-jit-cache fixes it ([Bug]: FlashInfer CUTLASS MoE selected on fp4-less builds (CUDA toolkit < 12.8); gpt-oss dies at engine start #48541).

4. Since the doc frames this as a safety net under [RFC] Fail-Closed Graph Storage Contract for Weight Reload #48478: if the collector only fixes things silently, an unregistered tensor never shows up anywhere, which works against the fail-closed contract it's meant to back up. A warn-once per repaired path (or a strict flag that raises) would also give you a free detection tool for the registry migration.

[RyanClark2k]

The bracket-path handling and the migration warnings look good. Two things on the newest commits.

I don't think the mxfp4/nvfp4 full-PWAL guard is safe in the upstream layerwise reload path, and I suspect the failure it fixes came from a different flow. Reload metadata is recorded at model construction (model_loader/utils.py:64), so initialize_layerwise_reload restores the checkpoint-format names before any loading: w13_weight_packed comes back, the checkpoint streams into it, and PWAL re-runs the rename from a clean slate.

I checked this on CPU with the real reload machinery and a mock method that reproduces the rename pattern from compressed_tensors_moe_w4a4_mxfp4.py:149-158. Without the guard, reload works end to end: new values land in the original w13_weight parameter object at the original address. With the guard, reload raises AttributeError in _copy_and_restore_kernel_tensors, because the pre-reload snapshot holds w13_weight while the layer only has w13_weight_packed (the rename never ran). There's also live evidence for the marlin branch of this exact class: the #48438 validation ran a W4A4 MXFP4 MoE checkpoint through capture/reload/replay on a 4090 with PWAL re-running, and post-reload generation was normal.

So "w13_weight_packed already deleted" shouldn't be reachable in the layerwise path. Was the crash you saw from a flow that calls process_weights_after_loading without the meta-restore step (direct load_weights-style updates)? If so, the guard fixes that flow but breaks this one.

CPU repro for both directions

import torch

from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
from vllm.model_executor.model_loader.reload.layerwise import (
    get_layerwise_info,
    initialize_layerwise_reload,
    record_metadata_for_reloading,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader


class RenameQuantMethod(QuantizeMethodBase):
    """Reproduces compressed_tensors_moe_w4a4_mxfp4.py:149-158."""

    def __init__(self, guard: bool):
        self.moe_kernel = None
        self.guard = guard

    def create_weights(self, layer):
        p = torch.nn.Parameter(torch.zeros(4, 8), requires_grad=False)
        p.weight_loader = default_weight_loader
        layer.register_parameter("w13_weight_packed", p)

    def apply(self, layer, *a, **k):
        raise NotImplementedError

    def process_weights_after_loading(self, layer):
        if self.guard and self.moe_kernel is not None:
            print("    [guard] PWAL skipped (moe_kernel exists)")
            return
        layer.w13_weight = torch.nn.Parameter(
            layer.w13_weight_packed.data, requires_grad=False
        )
        delattr(layer, "w13_weight_packed")
        self.moe_kernel = object()


def run(guard: bool) -> None:
    label = "B (guard: skip PWAL on reload)" if guard else "A (upstream: rerun PWAL)"
    print(f"=== Scenario {label}")

    method = RenameQuantMethod(guard)
    layer = torch.nn.Module()
    layer.quant_method = method
    method.create_weights(layer)

    record_metadata_for_reloading(layer)

    layer.w13_weight_packed.data.copy_(torch.full((4, 8), 1.0))
    method.process_weights_after_loading(layer)

    kernel_obj = layer.w13_weight
    kernel_ptr = kernel_obj.data_ptr()

    initialize_layerwise_reload(layer)
    print(f"  restore brings packed name back: "
          f"{hasattr(layer, 'w13_weight_packed')}")

    try:
        param = layer.w13_weight_packed
        param.weight_loader(param, torch.full((4, 8), 2.0))
    except Exception as e:
        print(f"  RELOAD FAILED: {type(e).__name__}: {e}")
        return

    print(f"  original object restored: {layer.w13_weight is kernel_obj}; "
          f"original address: {layer.w13_weight.data_ptr() == kernel_ptr}; "
          f"value now: {layer.w13_weight.flatten()[0].item()} "
          f"(2.0 = new weights arrived)")


run(guard=False)
print()
run(guard=True)

Output:

=== Scenario A (upstream: rerun PWAL)
  restore brings packed name back: True
  original object restored: True; original address: True; value now: 2.0 (2.0 = new weights arrived)

=== Scenario B (guard: skip PWAL on reload)
  restore brings packed name back: True
    [guard] PWAL skipped (moe_kernel exists)
  RELOAD FAILED: AttributeError: 'Module' object has no attribute 'w13_weight'

Separately, 4c5c7db drops the persistent= handling in restore_layer_on_meta along with the __dict__ cleanup (the cleanup itself looks right to me). With that change every restored buffer comes back persistent, so things like cos_sin_cache end up in the state dict after the first reload. The early snapshot of _non_persistent_buffers_set exists upstream specifically because restore runs after the live set gets mutated. Was removing it intentional?

[new-TonyWang]

@new-TonyWang the snapshot/copy-back approach makes sense to me, and keeping the old tensors alive through the reload window is a nice property since it rules out the reclaim failure mode for anything the collector can see. Some thoughts on the approach:

1. I might be misreading this, but it looks like copy-back can only restore plain attribute paths. _walk records paths like foo['scale'], fn.args[0], fn.__closure__[2], while resolve_path and set_by_path split on . and getattr each segment, so anything with brackets fails to resolve and gets skipped at debug level. A tensor that's only reachable through a dict/list/partial/closure ends up recorded but never repaired, and you'd never see it in normal logs. Maybe collect only attr paths so the slot list matches what copy-back can restore, or at least log the skips at warning.
2. Question about the moe_kernel is None guard: in compressed_tensors_moe_w4a4_nvfp4.py it also skips moe_kernel.fused_experts.process_weights_after_loading(layer) on reload, and the WNA16 kernel keeps the marlin_args computed at first load. If any of that derives from weight values (rather than holding references to layer params, which copy-back refreshes in place), it stays frozen after a weight update, which is category 2 in the RFC's taxonomy. Did you check that for the guarded classes? Another option is to rebuild the kernel but reuse the storage, like [Bugfix] Preserve Marlin runtime tensor storage across weight reload #48438 does with existing= for the marlin workspace. Then derived state gets recomputed on every reload but lands at the same addresses.
3. You should be able to test the FlashInfer CUTLASS MoE row on the H200 you used for validation. It's in the "not tested (Blackwell SM100+)" list, but the (kMxfp4Static, None) scheme is gated on is_device_capability(90) exactly, and H200 is SM90. gpt-oss-20b with kernel_config={"moe_backend": "flashinfer_cutlass"} reaches FlashInferExperts with all three gemm1 tensors allocated. That's the config I used to confirm the bug on an H100, numbers are in the RFC thread. Heads up that if the machine's CUDA toolkit is older than 12.8, engine start fails for an unrelated flashinfer build reason; installing flashinfer-jit-cache fixes it ([Bug]: FlashInfer CUTLASS MoE selected on fp4-less builds (CUDA toolkit < 12.8); gpt-oss dies at engine start #48541).
4. Since the doc frames this as a safety net under [RFC] Fail-Closed Graph Storage Contract for Weight Reload #48478: if the collector only fixes things silently, an unregistered tensor never shows up anywhere, which works against the fail-closed contract it's meant to back up. A warn-once per repaired path (or a strict flag that raises) would also give you a free detection tool for the registry migration.

Replying to this comment

Thanks for the thorough probe work on Machete, the gemm1 constants, and the FlashInfer CUTLASS confirmation. Here's where our PR stands against each finding.

Re: Point 1 — Machete act_perm / g_idx_sort_indices

Our tensor_collector._walk discovers closure-captured tensors (including act_perm via __closure__[i]), but cannot fix them via copy_back. We verified this experimentally on H200:

collect_extra_tensors → found: quant_method._apply_fn.__closure__[0] ✓
After PWAL re-execution → path: UNRESOLVABLE (new function object replaces old)
copy_back → SKIPPED (path broken)
closure still uses new address: matches_old=False

Root cause: PWAL creates a new function object (self._apply_fn = apply_fn), so the old closure path breaks — the issue is not cell_contents mutability, but that the entire function is replaced. The correct fix is #48539's approach: register as layer.g_idx_sort_indices and read from the layer at apply time, eliminating the closure capture entirely.

Re: Point 2 — gemm1_alpha/gemm1_beta/gemm1_clamp_limit

Confirmed on H200 (SM90) with moe_backend=flashinfer_cutlass forced on gpt-oss-20b-2layer (FlashInfer 0.6.13 JIT-compiled for SM90, FlashInferExperts selected):

Per layer: 3 SwiGLU constants discovered at depth=5:
  quant_method.moe_kernel.impl.fused_experts.gemm1_alpha      [32] float32
  quant_method.moe_kernel.impl.fused_experts.gemm1_beta       [32] float32
  quant_method.moe_kernel.impl.fused_experts.gemm1_clamp_limit [32] float32
Total extras: 10 (3 gemm1 + 2 attn × 2 layers)

These are plain object attributes (not closures), so copy_back works normally. The MoE kernel guard prevents rebuild, keeping captured addresses stable.

g1_scale_c (TRT-LLM NVFP4, SM100+) and fake_input_scale (mxfp8) are not reachable on SM90 — confirmed absent as expected.

Guard necessity experiments:

Model / Backend	Guard	CUDA Graph	Reload
DeepSeek-V3 FP8_DYNAMIC	✗	eager	PASS × 3
DeepSeek-V3 FP8_DYNAMIC	✗	on	CRASH illegal memory access
gpt-oss-20b NVFP4 (OAI Triton)	✗	eager	PASS
gpt-oss-20b NVFP4 (OAI Triton)	✗	on	PASS

The FP8_DYNAMIC crash is caused by _permute_scratch's 9 sub-tensors becoming path-unresolvable after kernel recreation.

Re: Point 3 — FlashInfer CUTLASS confirmed on Hopper

Your H100 finding (72/72 gemm1 tensors moved, 72/72 storages freed) matches our mechanism. Our guard prevents rebuild, keeping all 72 tensors at captured addresses. Updated the PR description accordingly.

Re: Point 4 — #48478 (GraphStorageRegistry RFC)

Fully agree with the direction. The key advantage of our current tensor_collector approach is that it requires zero changes to existing backend code — no backend needs to add register_graph_storage() calls to get basic protection. This makes it a practical transitional solution while #48478's registry is adopted.

That said, as the Machete closure case demonstrates, recursive _walk has inherent limitations — it can discover but not always fix. Would you be open to the following division of responsibilities going forward?

• tensor_collector._walk → transitions to a static audit tool (DEBUG-level logging), responsible for detecting which tensors are not captured by CUDA graphs / not registered via GraphStorageRegistry. Think of it as a safety net that flags "hey, this tensor at path X is unmanaged and could drift on reload."
• [RFC] Fail-Closed Graph Storage Contract for Weight Reload #48478 GraphStorageRegistry → the production execution path, responsible for register_graph_storage() and enforcing the fail-closed contract at runtime.

This way _walk serves as a completeness checker for the registry — if a backend forgets to register a tensor, the audit log catches it, rather than silently drifting.

[RyanClark2k]

@new-TonyWang yes, that split makes sense to me. The registry should be the thing that actually enforces the contract in production, and the walker fits the audit role well since it doesn't need any backend changes.

One thing about the audit setup though. The walker starts from the layer's instance attributes, so it can only see tensors that some attribute path reaches. A tensor held in module-level state still gets its address baked into the graph, but no walk of the model will ever reach it. The manifest prototype in the RFC thread handles that case with a second recording mode, a TorchDispatchMode that records every tensor flowing through an op during the capture forward, then reports any recorded storage that got freed or rebound after reload. I have a CPU test where a tensor referenced only from a module-level dict is invisible to an attribute walk and the dispatch recorder catches it (test_dispatch_recorder_sees_tensors_no_walk_can_find). So I think the two combine well. The manifest can tell you a tensor got missed, and for anything that does have an attribute path, the walker output shows where the registration needs to go. Happy to help wire that up if you want to try it for the audit tool.

Thanks for running the closure probe on H200 btw, matches what I saw. #48539 has the layer-registration fix if that ends up being the direction.