[Bugfix] Fail loudly on unregistered checkpoint tensors in fused linear load_weights

[afierka-intel]

Summary

MergedColumnParallelLinear.load_weights() and QKVParallelLinear.load_weights() used the layer module itself as a "not found" sentinel:

param = getattr(self.get_submodule(submodule), attr, self)   # <- self as sentinel
...
if param is None and name == "bias":
    continue
param.weight_loader(param, loaded_weight, shard_id)

A checkpoint tensor with no registered param therefore reached param.weight_loader() with param bound to the layer, and crashed inside the loader with an opaque AttributeError: 'QKVParallelLinear' object has no attribute 'data'.

Both implementations were byte-identical, so name resolution and the skip/raise decision now live in one helper, _resolve_loadable_param(). A bias the layer registered as None is skipped, exactly as before; every other unregistered tensor raises a ValueError naming the layer and the tensor.

Three things change for a reader of the code, so this is not a rename: an unloadable checkpoint tensor now reports which layer and which tensor instead of has no attribute 'data'; a dotted sub.bias that main crashes on is handled; and the duplicated resolution block is gone from both classes.

What the skip set actually is (measured, not assumed)

An earlier revision of this PR skipped three names — bias, g_idx, g_idx_sort_indices — and justified the last two with "GPTQ exporters emit them even when desc_act=False". That justification was wrong and the two entries were dead code. I instrumented _resolve_loadable_param and loaded a real desc_act=False GPTQ checkpoint (Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4, bits=4, group_size=128) with the engine in-process so the spy observes the loading code:

SPY create_weights (layer, g_idx registered) -> {('QKVParallelLinear', True): 24,
                                                 ('MergedColumnParallelLinear', True): 24,
                                                 ('RowParallelLinear', True): 48}
SPY resolve: param found -> {'g_idx': 120, 'qweight': 120, 'qzeros': 120, 'scales': 120, 'bias': 72}
SPY resolve: skipped (None) -> {'bias': 48}
SPY resolve total calls: 600

AutoGPTQLinearMethod.create_weights registers g_idx unconditionally (auto_gptq.py — only the kernel config gets has_g_idx=desc_act), so g_idx resolves to a real RowvLLMParameter 120/120 times and can never reach the skip branch. g_idx_sort_indices never appears at all: for a linear layer its only producer is marlin_sort_g_idx in process_weights_after_loading, so it is not a checkpoint key. bias is the one name that actually fires (48 skips).

The allowlist is therefore frozenset(("bias",)) — exactly the reachable set — and it is the same set upstream already skipped. I verified the g_idx registration on each platform separately, because kernel selection is platform-dependent:

platform	QKVParallelLinear params with desc_act=False
H200 NVL (sm90)	['g_idx', 'qweight', 'qzeros', 'scales']
B200 (sm100)	['g_idx', 'qweight', 'qzeros', 'scales']
Intel Arc Pro B-series (XPU)	['g_idx', 'qweight', 'qzeros', 'scales']

Behavior change, stated

For a checkpoint carrying an unregistered tensor other than bias, this raises ValueError where main raises AttributeError from inside weight_loader. Both are hard failures — such a load never succeeded — so this changes the diagnostic, not whether any model loads.

One case flips from crash to skip: a dotted name such as sub.bias, resolving to a submodule param registered as None. main compared the full name against the literal "bias", so it fell through and died on AttributeError: 'NoneType' object has no attribute 'weight_loader'; the helper compares the resolved attribute name and skips it. There is a test for it. I have not seen a checkpoint that reaches this, so I am not claiming it as the motivating bug.

Why it raises instead of skipping more names

Names that genuinely can reach the raise are ones where skipping would be wrong, not harmless:

• compressed-tensors W4A16 weight_g_idx (registered only when actorder == GROUP) — dropping it runs the layer on weights whose row permutation was never applied.
• fp8 input_scale (registered only when activation_scheme == "static") — dropping it silently discards the activation scale.

Both currently fail on main with the opaque AttributeError and still fail here, now with a message naming the layer, the tensor and the fact that config and checkpoint disagree. A blanket skip is not hypothetical harm: an earlier revision of this change did skip broadly and hid a packed_modules_mapping propagation bug (now afierka-intel#12) by quietly discarding weights instead of reporting them.

Test plan

New file tests/model_executor/layers/test_linear_load_weights.py (7 cases). Each test asserts its own premise, so a test whose premise decays fails instead of passing for the wrong reason.

python3 -m pytest tests/model_executor/layers/test_linear_load_weights.py -q

Run against pristine main and against this branch. Baseline files were restored from the CI image rather than by reverse-applying a patch, and every file swap was followed by a sha256sum plus an in-process assertion of the loaded allowlist, so no result is attributed to a tree that was not actually in place:

platform	pristine main	this PR
H200 NVL (sm90)	5 failed, 2 passed	7 passed
B200 (sm100)	5 failed, 2 passed	7 passed
Intel Arc Pro B-series (XPU)	5 failed, 2 passed	7 passed

The 5 failures on main are the four "unregistered tensor must be diagnosable" cases (g_idx on both layer classes, qweight, and the message-content check) plus the sub.bias case; on main they die with 'MergedColumnParallelLinear' object has no attribute 'data', 'QKVParallelLinear' object has no attribute 'data' and 'NoneType' object has no attribute 'weight_loader'. The 2 that pass on both sides pin pre-existing behaviour (bare bias skip, matched-weight load) and are labelled as such in the file — they are not evidence for this change.

linear.py contains no platform checks, but it sits on every model's weight-loading path, so all three platforms were measured rather than argued from one.

Real checkpoint

The GPTQ load above is also the proof that the changed line is actually executed on a real load (600 resolutions) rather than only in unit tests. Same script without the spy, greedy decoding, 3 prompts, seed=42 — output is byte-identical between pristine main and this PR on both H200 and B200:

OUT: '______.\nA. London\nB. Berlin\nC. Madrid\nD'
OUT: ' 4\n2 * 2 = 4\n2 / 2 ='
OUT: ' there was a little girl who loved to read. She read every day, and'

Accuracy

lm_eval --model vllm --tasks gsm8k --limit 200 --batch_size 16 --seed 42 (5-shot), same checkpoint, H200:

build	flexible-extract	strict-match
pristine main	0.450 ± 0.0353	0.365 ± 0.0341
this PR, run 1	0.445 ± 0.0352	0.355 ± 0.0339
this PR, run 2	0.440 ± 0.0352	0.365 ± 0.0341

Two runs of the same build differ by 0.005 / 0.010, i.e. the run-to-run noise floor is as large as the main-to-PR delta, and every value sits well inside one stderr. That is the honest reading: no measurable accuracy change. Mechanically none is possible either — on a successful load the helper takes the same decisions as main (param found → same weight_loader call; bias-as-None → skip), which the spy counters confirm.

Performance

Not benchmarked, and I claim no perf effect: the change is one getattr plus a set membership test per checkpoint tensor, executed during weight loading only, never in the forward path.

Known evidence gap

The real-checkpoint spy and the accuracy run were done on CUDA only. On the XPU box the container has no HF cache mounted and the card had ~6 GB free, and the GPTQ-MoE load path there additionally depends on afierka-intel#12, which would confound the result. To close it: an XPU container from vllm-release-repo:<commit>-xpu with -v ~/.cache/huggingface:/root/.cache/huggingface, #12 applied, then the same in-process spy and lm_eval invocations on Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4. The XPU-side facts that are measured: the 7 unit tests (5→7 delta) and the per-platform g_idx registration probe above.

ruff check + ruff format --check clean (ruff 0.14.0, as pinned in .pre-commit-config.yaml); full pre-commit run --files on both changed files green, including mypy.

Why this is not duplicating an existing PR

gh pr list --repo vllm-project/vllm --state open --search ... for MergedColumnParallelLinear load_weights, _resolve_loadable_param, getattr(self, name, self), optional checkpoint tensors, skip unregistered checkpoint tensor. No open PR touches MergedColumnParallelLinear.load_weights / QKVParallelLinear.load_weights.

The nearest neighbour is #43892 ([Bugfix][DeepSeekV4] Handle optional scale tensors for W4A16-FP8 artifacts), which fixes the same class of failure — a checkpoint tensor with no materialized param, including the identical AttributeError: 'ColumnParallelLinear' object has no attribute ... signature — but does so inside vllm/models/deepseek_v4/, with no file overlap with this PR. It skips a model-specific set of optional scale names; this PR deliberately does not generalize that skip into shared code, for the reason given above. #41170 touches WEIGHT_LOADER_V2_SUPPORTED in the same file but not this code path.

Relationship to other PRs

Split out of afierka-intel#12 on review feedback: that PR is the minimal GPTQ-MoE loading fix (XPU allowlist + packed_modules_mapping propagation), while this one touches every model's load path and deserves its own review. This PR is not required for that model to load — with #12 applied the checkpoint loads and generates correctly without this change. Supersedes #9, a self-review staging PR for the same change.

Base is pinned to the commit the CI images above were built from, so every number here corresponds to exactly one tree.

---

AI assistance was used (Claude Code); every changed line was reviewed and all commands above were run personally on real hardware.

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[afierka-intel]

This description is too detailed and too long. Keep it short, but informative.

[afierka-intel]

Is the test file complete? Does it cover all real-life scenarios/cases? Maybe it is too long and we test some synthetic cases?

[afierka-intel]

Closing — superseded upstream before publication.

vllm-project/vllm#53118 (Tejas-Raj01, opened 2026-08-20T14:11Z, "Resolves vllm-project#53107") fixes the same two load_weights call sites in linear.py with the same outcome: an unresolved checkpoint tensor raises a ValueError naming it instead of crashing later on param.data. AGENTS.md §1 is explicit — if an open PR already addresses the same fix, do not open another.

The third-party issue that justified this branch, #53107 (jschmied, 2026-08-20T13:00Z), is claimed by that PR.

What was handed over to vllm-project#53118 instead of duplicated: the real reproducing checkpoint (shanjiaz/gemma4-dflash-speculator-fp8-block), the root cause behind it (speculators/base.py:61 builds the draft config from transformer_layer_config only, and no updater in speculators/algos.py copies quantization_config, so the draft linears are built unquantized while 36 weight_scale tensors exist in the index), the allowlist-versus-type-check difference, and an offer of the 129 lines of B70/H200-validated tests.

Branch afierka/linear-load-weights-optional-tensors kept for those tests.

Residual, unclaimed and worth more than this PR was: make quantized DFlash drafts keep their quantization_config.