[avifenesh]

Overview

Load ModelOpt NVFP4 MLA projections into GLM-5.x's BF16 targets. Consume unused input_scale tensors from W4A4 checkpoints instead of failing with a KeyError.

Claims

• Dequantize packed NVFP4 q_a_proj, kv_a_proj_with_mqa, q_b_proj and o_proj weights when their model targets stay BF16. Actual quantized targets keep the normal loader path.
• Ignore checkpoint activation scales only when the target has no matching scale parameter. This covers the W4A4 review feedback for vroomfondel/GLM-5.3-Flash-NVFP4-W4A4.
• Use the same helper in the main model and MTP. Preserve NoPE padding and fused-shard loading.

Validation

Refresh on 2026-10-20:

• Merged upstream a6c898e2a9db6c8ee49a9edc5751f809749f3a5b. The MTP import resolution preserves both _try_load_nvfp4_attn_proj and upstream's rounded sparse-top-k buffer helper.
• The fix is still needed: that upstream commit fails all 48 synthetic NVFP4-to-BF16 loading cases across main/MTP, q_b_proj/o_proj, both activation-scale positions and all six weight/scale/global-scale orders. The refreshed PR passes all 48 with exact independent BF16 weight and projection-output references.
• The complete 72-case CPU matrix also checks both FP8 scale aliases/dtypes in either order and native NVFP4 parameter/scale transfers through the real load_weights methods. All 72 pass. These use tiny parameters, not a full checkpoint or GPU kernel.
• Review found that E4M3 scales could be consumed by the wrong helper. Shared pending metadata now resolves the format from the weight dtype; registered quantization scales stay on their normal loader path, and NVFP4-to-BF16 conversion requires a BF16 target. Five focused regressions failed before these guards were fixed. The suite now passes 13 tests, including the added FP32 inverse-scale controls.
• Current pre-commit hooks, including mypy for source and tests, pass. The old manual mypy-3.12 hook no longer exists upstream; the current hook is mypy.
• Upstream CI did not run the code checks: pre-run-check rejects authors with fewer than four merged PRs (this account has zero) unless a verified, ready, or ready-run-all-tests label is present. Formatting/pre-commit jobs were skipped. This is an eligibility block, not a test failure; no CI bypass or label request was made.
• Full-model GPU serving, TP/PP and cross-call partial buffers were not revalidated. The dequantization and padding arithmetic helpers are unchanged; the original serving measurements below are historical, not new results.

OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 \
CUDA_VISIBLE_DEVICES='' VLLM_TARGET_DEVICE=cpu \
.venv/bin/python -m pytest tests/models/glm5next/test_nvfp4_attn_proj_load.py -q
.venv/bin/pre-commit run --files vllm/models/glm5next/common/model.py vllm/models/glm5next/common/mtp.py tests/models/glm5next/test_nvfp4_attn_proj_load.py
.venv/bin/pre-commit run mypy --files vllm/models/glm5next/common/model.py vllm/models/glm5next/common/mtp.py

Follow-up validation on 2026-10-10:

OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 \
CUDA_VISIBLE_DEVICES='' VLLM_TARGET_DEVICE=cpu \
.venv/bin/python -m pytest tests/models/glm5next/test_nvfp4_attn_proj_load.py -q
pre-commit run --files vllm/models/glm5next/common/model.py tests/models/glm5next/test_nvfp4_attn_proj_load.py
pre-commit run mypy-3.12 --hook-stage manual --files vllm/models/glm5next/common/model.py

• Focused suite: 6 passed. With the added regression coverage and the original loader, 3 failed and 2 passed before the fix. Tests cover activation scales before and after weight loading, BF16 dequantization, quantized-target fallthrough and registered activation-scale preservation.
• Separate synthetic CPU reproducer through the real main-model and MTP load_weights methods: all 8 baseline cases raised the input_scale KeyError. All 8 patched cases loaded and matched independent BF16 weight and projection-output references exactly. Cases cover q_b_proj and o_proj, with activation scales first or last.
• Pre-commit and mypy-3.12 passed. Independent follow-up diff review found no issues.
• Full serving and model evaluation of the reviewer's W4A4 checkpoint have not been rerun. The follow-up changes loading of unused metadata, with no arithmetic change. Upstream pre-commit is blocked by the contributor eligibility gate (0 merged PRs; no eligibility label). Read the Docs stopped at its pre-run check because that same gate failed, before building docs.

Original change validation, before the input_scale follow-up

• New tests: 3 passed. tests/models/glm5next/: 28 passed, 8 skipped. Pre-commit and mypy-3.12 were clean.
• CPU and RTX 5090 CUDA dequantization with dequantize_to_dtype(..., swizzle=False): bit-exact against an independent e2m1 reference at 1536x6144 (q_a) and 576x6144 (kv_a).
• End to end on 2x DGX Spark (GB10, TP2), tiyuvta/GLM-5.3-Flash-NVFP4 (ModelOpt 0.46.0 W4A16), using an sm121 image built from vLLM 0.1.dev20051+g487ecf187 with the helper backported into that version's glm5next/nvidia/model.py. Without the change, loading failed with a shape assertion. With it, both ranks loaded and served. Single-stream decode was 47 to 51 tok/s; four-stream aggregate was 60 to 64 tok/s. These are the original reported measurements, not new follow-up measurements.

Details

fused_qkv_a_proj is always BF16. The existing loader only dequantizes FP8 checkpoints, so ModelOpt NVFP4 MLA weights fail with assert param_data.shape == loaded_weight.shape. The helper buffers packed uint8 weight, e4m3 per-16 weight_scale and fp32 weight_scale_2, then loads their BF16 dequantization. It runs before the FP8 helper because both accept .weight_scale.

W4A4 checkpoints also store activation input_scale. BF16 projections have no destination parameter for it, so it must be consumed without buffering or marking a weight as loaded. A registered activation-scale parameter still uses normal loading.

This extends the existing PR in response to review. The original duplicate search found no other PR or issue covering NVFP4 MLA projections in the GLM-5.x loader (fused_qkv_a_proj nvfp4, glm5next nvfp4 q_a_proj, kv_a_proj_with_mqa nvfp4, and the assertion text). The NVFP4 MoE scale mismatch in #54974 and #55073 is separate. A follow-up open-PR search for glm5next input_scale found no overlap.

AI assistance: Claude for the original change; Codex for the input-scale follow-up and independent review; Hermes for this upstream refresh, routing guards and CPU relevance verification.

---

Pull Request Checklist

• I used vLLM's /pr-checklist skill.
• AI assistance was used during the creation of this PR.
• Design Fit: Reuses the shared loader helper without changing core interfaces.
• Testing and Validation: Regression failure confirmed before the fix; CPU checks passed. Full W4A4 checkpoint serving remains unverified.
• Code Quality and Style: Narrow change; pre-commit and type checks passed.
• Pull Request Contents: Claims, root cause, validation and limitations are described above.

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[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.

[lesj0610]

W4A4 NVFP4 checkpoints with quantized MLA projections still fail to load because their input_scale tensors are not handled. Details inline.

[lesj0610]

Could we also ignore input_scale when the target projection is kept in BF16? vroomfondel/GLM-5.3-Flash-NVFP4-W4A4 stores it for q_b_proj and o_proj. Both are built with quant_config=None, so their weights are dequantized here, but input_scale falls through to params_dict[name] and raises a KeyError.

[avifenesh]

Fixed in be8e956. The shared main-model/MTP helper now consumes input_scale when the target has no activation-scale parameter. Quantized targets and registered scales keep the normal loader path.

The regression suite covers q_b_proj, o_proj and the fused projections, with activation scales before and after weight loading: 6 passed. The added BF16 cases failed before the fix. A separate CPU reproducer through both public loaders raised the reported KeyError in all 8 baseline cases; all 8 patched cases loaded and matched the BF16 weight and projection-output references exactly. Pre-commit and mypy-3.12 passed.

I have not rerun full serving of vroomfondel/GLM-5.3-Flash-NVFP4-W4A4.

[lesj0610]

Confirmed in be8e956a69; the input_scale case is covered now.

[lesj0610]

My comment is addressed. This looks ready for a maintainer review.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @avifenesh.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork