Unblock Qwen3.5/3.6 QAD: Megatron export, calibration, and distillation fixes

[kevalmorabia97]

What does this PR do?

Type of change: Bug fix

Everything that blocked running QAD on a quantized Qwen3.5 / Qwen3.6 MoE checkpoint: two
Megatron-Core → HuggingFace export bugs that make it unservable (§1–2), the dead code the first
leaves behind (§3), a no-op flag (§4), a multi-GPU calibration deadlock (§5), and four
distillation / data-prep bugs that stopped QAD itself from running (§6).

1. Routed experts were exported packed, and vLLM cannot load that

AttributeError: Layer language_model.model.layers.23.mlp.experts has no parameter
  'w2_weight_weight_scale_2' for checkpoint weight ...experts.down_proj_weight_scale_2

mcore_qwen35vl.py used GroupedMLPPacking, mirroring the BF16 upstream checkpoint, which
really is packed. But that mapping is only used for quantized export, and vLLM's quantized MoE
loader needs per-expert scales — both released NVFP4 checkpoints (Qwen3.6-35B-A3B-NVFP4 via
hf_ptq, Nemotron-3.5-Lightning-30B-A3B-NVFP4 via Megatron-LM) are per-expert.

_grouped_mlp_slicing gains gate_proj_name / up_proj_name to split each expert's fused gate+up
and slice its per-block weight_scale; GroupedGatedMLPSlicing wires it up. The Megatron
checkpoint layout is unchanged, so affected checkpoints need only a re-export.

_verify_exported_keys is relaxed to match: exported modules now contribute their ancestor
prefixes, so expanding one source module into many is not reported as ~82 dropped tensors. A module
genuinely absent still has nothing beneath its prefix and is still caught.

2. A quantized output_layer (lm_head) could not be checkpointed

GPTModel.sharded_state_dict drops output_layer._extra_state and asserts it is empty. ModelOpt
keeps quantizer state there, so saving raised and — since that method also backs the load plan —
loading silently restored the layer unquantized.

keep_gpt_output_layer_extra_state() retains it, applied from
megatron_replace_quant_module_hook so every Megatron model gets it (Megatron-LM and NeMo
users included, neither of whom can import mbridge, which needs megatron.bridge). It matches
the upstream body by AST before replacing it and self-disables otherwise.
NVIDIA/Megatron-LM#7086 is closed, not
merged: nemo:26.10 migrates GPTModel to HybridModel, whose sharded_state_dict has no
pop-and-assert, so this side keeps the workaround.

Not cosmetic: lm_head is 248320×2048 = 509M params, 34.6% of per-token weight traffic on a
model with ~2.9B active params.

3. Cleanup

Nothing maps GroupedMLPPacking once qwen3_5 is switched over; it is removed with
_grouped_mlp_packing and the quantize= / record_quant_config= parameters that existed only to
serve it. Llama-4's PackNameRemapping is unaffected.

Two smaller review-driven fixes: the gated-split shape checks raise ValueError rather than
assert (stripped under -O), and per-expert quant metadata is recorded for
local_expert_indices rather than every global id, fixing non-contiguous EP.

4. Remove the no-op --moe_calib_experts_ratio from the Megatron quantize example

examples/megatron_bridge/quantize.py accepted the flag and threaded it into the mtq config, but
_moe_calib_experts_ratio exists only in plugins/huggingface.py (9 refs) and never in
plugins/megatron.py (0); mode.py:247 only assigns it to modules already exposing the attribute.
On a Megatron MoE model it was accepted and silently ignored — a trap, since on a 256-expert model
it reads like a major quality lever. hf_ptq.py keeps it, where it works.

5. Fix multi-GPU image-text (VLM) calibration deadlocking

VLM calibration hung for 30 minutes and died on a gloo timeout whenever world_size > 1, with no
error until the timeout fired.

NemotronTarPlusJsonlIterable split its budget with truncating division, so the stream supplied
fewer samples than requested (1024 over 3 subsets → 341×3 = 1023). _ShardedIterable gives
rank r items r, r+W, r+2W…, so a stream that is not a multiple of world_size leaves the
trailing rank one short — it exits the forward loop early and the others block on the next
collective. The arithmetic predicts both observed hangs exactly: 1024 → stall at 255/256,
512 (yielding 510) → 127/128.

Fixed both ends: subset budgets are distributed with divmod so they sum exactly, and
_ShardedIterable truncates every rank to floor(len / world) — which also covers num_samples
not being divisible by world_size, as the first fix alone does not.

Verified on Qwen3.6-35B-A3B (EP=4, nemotron_vlm_dataset_v2, 1024 samples): the configuration that
hung twice now completes 256/256 and exports. Unit tests cover both fixes and fail without them.

6. Fix the distillation path so QAD can actually run

Four independent bugs, all hit while running QAD end to end on Qwen3.6-35B-A3B. Each blocks a
different configuration, and together they made every sequence length OOM or abort.

• Context parallel aborts. The DDP config derived average_in_collective from --sft alone,
  but context parallel also needs per-token loss reduction, so any --cp_size > 1 run died on
  Cannot average in collective when calculating per-token loss.
• TopKLogitsKLLoss was not memory-efficient. Despite documenting "without gathering full
  logits", it cast the whole vocabulary to FP32 before selecting the top-k, allocating two
  [seq, vocab] tensors — 30.3 GiB each at seq 32768 on this model's 248k vocab. Reducing before
  the cast is equivalent: widening is exact and temperature scaling is monotonic, so the selected
  entries and the loss are unchanged.
• MTP cross-entropy ran when it had nothing to recover. skip_lm_loss exempts the MTP heads
  unconditionally, so their CE materialised another FP32 [seq, vocab] tensor even when the MTP
  head is excluded from quantization — as it is in every recipe here (775 of 906
  exclude_modules, zero MTP weight_scale tensors exported). It is now skipped only when the
  model is quantized and MTP is left out of it; plain distillation such as pruning recovery still
  trains the MTP head. test_mtp_excluded_from_quantization pins all four cases.
• One bad record deadlocked data prep. megatron_preprocess_data re-raised chat-template
  failures out of a pool worker, stalling the whole job until it timed out — three malformed
  records cost a multi-hour tokenization run. They are now skipped with a warning, matching the
  existing handling of malformed JSONL a few lines above.

Also exposes --logit_kl_topk, which DistillationConfig has supported for a while but the
example never passed through; test_qad now exercises that path.

§4, §5 and §6 are independent of §1–3; happy to split them out if reviewers prefer.

Usage

No API change. Exported names now match the released checkpoints:

model.language_model.layers.0.mlp.experts.<E>.{gate,up,down}_proj.{weight,weight_scale,weight_scale_2}
lm_head.{weight,weight_scale,weight_scale_2}

Testing

• test_mcore_export_mappings.py — qwen3_5 mappings emit per-expert rules. Verified these fail
  without the fix (2 failed / 11 passed), with Qwen3MoeForCausalLM / NemotronHForCausalLM as
  controls.
• test_unified_export_megatron.py — the gate/up split, per-block scale slicing, the 0-dim scalar
  fallback, and both directions of the _verify_exported_keys relaxation.
• test_megatron.py::TestKeepGptOutputLayerExtraState — 15 cases: payload detection, no-op second
  call, warn-and-skip on an unrecognised sharded_state_dict, and test_patches_stock_megatron_core
  which installs a replica of the real pre-fix upstream body (verified against be08ce5b1~1) so the
  patched path is exercised whichever megatron-core is installed.
• test_qad.py — CI caught that its reference comparison still assumed packed experts; fixed.

End to end on Qwen/Qwen3.6-35B-A3B (35B MoE, 256 experts), 4×GB200, nemo:26.08:

	before	after
export self-check	Export dropped 82 tensor(s)	passes
expert tensors	mlp.experts.gate_up_proj (packed)	mlp.experts.<E>.{gate,up,down}_proj
vLLM v0.28.0 load	AttributeError, engine never starts	Loading weights took 25.61 s
NEL eval (GPQA-D, MMMU-Pro)	FAILED	SUCCESS

Results these fixes unblocked

The export fix is what made a Megatron-produced NVFP4 MoE checkpoint servable at all, so it enabled
a full PTQ study on Qwen3.6-35B-A3B. Accuracy deltas are against a BF16 baseline measured on the
same harness, from paired per-question tests:

recipe	throughput vs BF16	GPQA-D	SciCode ×8	MMMU-Pro	IFBench
W4A16 (weight-only)	0.64–0.86× — slower	−0.06	−0.15	+0.48	−0.44
W4A4	8/12 shapes faster	−0.60	−0.70	−1.48 (p=0.019)	−0.53
W4A4 + 4-bit lm_head	9/12 shapes, up to 1.30×	+0.03 (p=0.96)	−0.81	−1.16 (p=0.016)	−1.65 (ns)

Repeats: GPQA-D is pass@1[avg-of-16]; SciCode is 8 pooled runs per recipe; MMMU-Pro is 3 runs per
side and IFBench 2–3 for BF16 and the last row, 1 elsewhere. AA-LCR (68.33 → 71.33, p=0.25, 3 runs
per side) and τ²-Telecom (94.25 → 94.25, 3 runs per side) are on par; at 100 questions and 114
tasks they cannot resolve below ~5 pp and ~3 pp, so they carry no claim either way.

QAD status (what §6 unblocked)

With the §6 fixes in place, QAD runs end to end on this model: 32 nodes, TP=1 PP=1 CP=1 EP=8,
seq 32768, gbs 512, ~38 s/iter, 124 GB/GPU peak. First accuracy read, MMMU-Pro at iteration 50
(0.84 B tokens), 3 runs per side, paired per-question:

	MMMU-Pro	vs BF16
BF16	74.55	—
W4A4 + 4-bit lm_head (PTQ)	73.39	−1.16, p=0.016
+ QAD, iteration 50	73.78	−0.77, p=0.089 (ns)

The PTQ deficit that motivated this work is no longer statistically significant after 50 QAD
iterations. The improvement itself (+0.39 vs PTQ) is not significant at p=0.41, and 50
iterations is 10% of the planned budget, so this is a direction rather than a result. A full
six-benchmark sweep at iterations 50 and 300 is running; these numbers will be superseded.

Two findings worth flagging beyond this PR:

• Weight-only NVFP4 is slower than BF16 on Blackwell. W4A16 leaves activations in BF16, so vLLM
  cannot use the FP4 tensor cores and falls back to MarlinNvFp4LinearKernel / 'MARLIN' MoE.
  W4A4 selects FLASHINFER_TRTLLM + FlashInferCuteDslNvFp4LinearKernel and beats W4A16 in
  12/12 shapes. The Marlin line count tracks the recipe exactly (one W4A16 layer ⇒ one Marlin
  line ⇒ zero once lm_head is W4A4).
• The only accuracy cost is multimodal: −1.2 pp on MMMU-Pro for the fastest recipe,
  confirmed over 3 runs per side (p=0.016). GPQA-D, SciCode, IFBench, AA-LCR and τ²-Telecom show no
  significant regression.

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• Is this change backward compatible?: ✅
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Additional Information

Both export bugs were found while reproducing nvidia/Qwen3.6-35B-A3B-NVFP4 through
examples/megatron_bridge/. Follow-up to #2332. Upstream counterpart
NVIDIA/Megatron-LM#7086 is closed — see §2.
Labeled cherry-pick-0.47.0.

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📝 Walkthrough

Walkthrough

The change adds per-expert Qwen MoE export with split projection tensors and quantization metadata. It also preserves quantized GPT output-layer state during Megatron-Bridge checkpoint workflows and documents the fixes.

Changes

Per-expert MoE export

Layer / File(s)	Summary
Grouped expert slicing and metadata modelopt/torch/export/unified_export_megatron.py, tests/gpu_megatron/torch/export/test_unified_export_megatron.py	Grouped slicing validates paired projections, splits fused weights and scales, and records per-projection quantization metadata. Tests cover scale handling, metadata, exclusions, and key validation.
Qwen mapping and checkpoint validation modelopt/torch/export/plugins/mcore_custom.py, modelopt/torch/export/plugins/mcore_qwen35vl.py, tests/gpu_megatron/torch/export/plugins/test_mcore_export_mappings.py, tests/_test_utils/torch/transformers_models.py, CHANGELOG.rst	Qwen routed experts use per-expert projection mappings. Tests verify mapping functions and released checkpoint paths. Documentation distinguishes packed BF16 fixtures from per-expert quantized exports.

Megatron-Bridge output-layer state

Layer / File(s)	Summary
Guarded GPT state preservation modelopt/torch/utils/plugins/mbridge.py	A guarded compatibility patch preserves populated output_layer._extra_state and removes empty placeholders.
Checkpoint workflow integration examples/megatron_bridge/distill.py, examples/megatron_bridge/export_quantized_megatron_to_hf.py, examples/megatron_bridge/quantize.py	The workflows enable output-layer state preservation before model construction or checkpoint loading.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: 🟡 Moderate · up to 0f46e

The export now emits per-expert Qwen MoE projections and preserves quantized GPT output-layer state, but sparse expert placement can produce mismatched quantization metadata and optimized Python can bypass shape checks needed for valid exported projections. These export-correctness issues should be fixed before merge.

Sequence Diagram(s)

sequenceDiagram
  participant QwenMapping
  participant UnifiedExporter
  participant Checkpoint
  QwenMapping->>UnifiedExporter: configure per-expert gate_proj and up_proj slicing
  UnifiedExporter->>UnifiedExporter: split weights, scales, and quantization metadata
  UnifiedExporter->>Checkpoint: emit gate_proj, up_proj, and down_proj tensors

Loading

Possibly related PRs

• NVIDIA/Model-Optimizer#2276: Both changes modify Qwen3.5-VL and MoE export mappings, with this change refining packed-expert handling into per-expert projection exports.

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Bot review (claude-opus-5) — DM the bot to share feedback.

The fix itself looks right: Qwen3_5MoeForConditionalGeneration has no import mapping, so switching the routed-expert rules affects export only, use_moe_grouped_gemm still finds experts.linear_fc1 (existing test_moe_layout_choice stays green), and the gate/up split mirrors what _gated_mlp_slicing already does for the SequentialMLP path. Two things block a clean approval:

1. An existing GPU test is very likely broken and was not updated. tests/gpu_megatron/torch/export/test_unified_export_megatron.py runs qwen3_5_moe_vl_grouped (NVFP4 + FP8) and qwen3_5_moe_vl_sequential (NVFP4) against create_tiny_qwen3_5_moe_vl_dir, whose fixture is deliberately repacked to mlp.experts.gate_up_proj / mlp.experts.down_proj by _pack_qwen3_5_moe_experts. Those cases then call assert_exported_checkpoint_matches(..., allow_missing=()), which asserts every reference tensor is present in the export. With per-expert names the two packed reference tensors per layer are now absent → "N reference tensor(s) absent from the export". The fixture comment ("every real Qwen3.5 checkpoint stores them packed") and the in-test comment ("Both layouts must reach the same packed HF tensors, via GroupedMLPPacking … and PackNameRemapping") also become wrong. Please update that test (and the helper's allowances, e.g. an expert-layout-aware comparison) in this PR — otherwise CI regresses even though the fix is correct.

2. The behavior that actually changed has no test. The two added cases only assert on entries in the mapping table; they never execute _grouped_mlp_slicing or _verify_exported_keys. The new gate/up shard loop (per-block scale slicing, scalar-scale fallback, weight_scale_2 duplication, per-shard quant-config recording) and the ancestor-prefix relaxation in the self-check are both testable without a GPU — test_unified_export_megatron.py already has _FakeTEGroupedMLP + _make_exporter_for_grouped_mlp helpers that would cover the first, and the self-check "expansion accepted / genuine drop still raised" pair the PR body says was verified manually should be a unit test.

Minor: GroupedMLPPacking / _grouped_mlp_packing now have no production caller (only the mapping this PR removes used them), and _grouped_mlp_slicing's quantize= / record_quant_config= parameters exist only to serve it — worth removing or noting why they stay. The transpose=False comment in _pack_name_remapping ("Qwen3.5 keeps Megatron's orientation") is now stale too.

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Inline comments:
In `@modelopt/torch/export/plugins/mcore_custom.py`:
- Line 131: Add GroupedGatedMLPSlicing to the module’s __all__ and re-export it
through the package public API using the existing from .module import * pattern.

In `@tests/gpu_megatron/torch/export/plugins/test_mcore_export_mappings.py`:
- Around line 60-69: Add a focused GPU regression test that exercises the real
quantized Qwen export path through GroupedGatedMLPSlicing and
GPTModelExporter._grouped_mlp_slicing, rather than only inspecting mapping
configuration. Verify every expert emits gate_proj, up_proj, and down_proj
tensors together with their corresponding quantization scales, using the
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Codecov Report

❌ Patch coverage is 91.37931% with 10 lines in your changes missing coverage. Please review.
✅ Project coverage is 78.00%. Comparing base (f13a796) to head (bfa7757).
⚠️ Report is 15 commits behind head on main.

Files with missing lines	Patch %	Lines
modelopt/torch/utils/nemotron_vlm_dataset_utils.py	57.14%	3 Missing ⚠️
modelopt/torch/quantization/plugins/megatron.py	93.93%	2 Missing ⚠️
...pt/torch/utils/plugins/megatron_preprocess_data.py	0.00%	2 Missing ⚠️
modelopt/torch/utils/vlm_dataset_utils.py	88.88%	2 Missing ⚠️
modelopt/torch/export/unified_export_megatron.py	97.43%	1 Missing ⚠️

Additional details and impacted files

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##             main    #2334      +/-   ##
==========================================
- Coverage   79.31%   78.00%   -1.32%     
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🧹 Nitpick comments (1)

modelopt/torch/utils/plugins/mbridge.py (1)

297-297: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Add keep_gpt_output_layer_extra_state to mbridge.py::__all__.

The helper is imported by multiple entry points but is absent from the module’s public export list. Keep the package-level mbridge import disabled because __init__.py documents a circular-dependency constraint.

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In `@modelopt/torch/utils/plugins/mbridge.py` at line 297, Add
keep_gpt_output_layer_extra_state to mbridge.py’s __all__ export list so
existing entry points can import it publicly, while leaving the package-level
mbridge import in __init__.py unchanged due to the circular-dependency
constraint.

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Nitpick comments:
In `@modelopt/torch/utils/plugins/mbridge.py`:
- Line 297: Add keep_gpt_output_layer_extra_state to mbridge.py’s __all__ export
list so existing entry points can import it publicly, while leaving the
package-level mbridge import in __init__.py unchanged due to the
circular-dependency constraint.

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Inline comments:
In `@modelopt/torch/export/unified_export_megatron.py`:
- Around line 1491-1493: Replace the assertion guarding the gated expert weight
row count with an explicit ValueError when weight.shape[0] is odd, preserving
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In `@modelopt/torch/export/unified_export_megatron.py`:
- Line 1456: Update the metadata-recording logic guarded by seen_qformat to
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[kevalmorabia97]

/claude review

[cjluo-nv]

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Re-review of #2334 (11 files, +292/-130).

Previous comments — status

• Critical: qwen3_5_moe_vl_{grouped,sequential} GPU test would regress — only half fixed. allow_missing=("mlp.experts.gate_up_proj", "mlp.experts.down_proj") silences the "reference tensor(s) absent" assert, but assert_exported_checkpoint_matches also asserts on the other direction: every exported key not present in the reference and not ending in a quant suffix is "unexpected". The export now writes ...mlp.experts.<E>.{gate,up,down}_proj.weight, none of which exist in the packed fixture, and allow_unexpected is still only ("mlp.gate.expert_bias",). Both parametrizations should fail with Export produced unexpected tensors. See inline.
• Critical: gate/up split and _verify_exported_keys relaxation untested — addressed (three CPU tests over _FakeTEGroupedMLP covering the split, per-block scale slicing, weight_scale_2 replication, both _record_layer_quant_config prefixes and the 0-dim fallback, plus the parametrized expansion/drop test for the self-check).
• Minor: dead GroupedMLPPacking / _grouped_mlp_packing / quantize= / record_quant_config= / stale transpose=False comment — addressed (removed in 8874b85).
• Minor (CodeRabbit): __all__ for GroupedGatedMLPSlicing — declined with a reasonable rationale (no sibling mcore_*.py defines __all__, and mcore_custom is not star-imported by plugins/__init__.py).

New scope in this revision (design gate). The MoE half introduces no new abstraction — it reuses GroupedMLPSlicing/GatedMLPSlicing and deletes a competing path, which is the right direction. The lm_head half does introduce a new mechanism: an AST-fingerprinted monkeypatch of megatron.core GPTModel.sharded_state_dict, invoked from three example main()s. The repo already has an in-tree owner for exactly this upstream assert — modelopt/torch/quantization/plugins/megatron.py (quant_module_get_extra_state returns {} for an unquantized output_layer precisely because "GPTModel.sharded_state_dict pops output_layer._extra_state and asserts it carries no data", and megatron_replace_quant_module_hook already walks the model at quantize time). The PR body doesn't say why the workaround lives in utils/plugins/mbridge.py behind three explicit call sites instead of there; as written, any other entry point (Megatron-LM examples, NeMo, direct library users) still hits the save-time RuntimeError / silent unquantized restore. It also has no test, unlike the MoE half. Please justify the placement in the PR body or move it, and add coverage.

Otherwise the export logic reads correctly: Qwen3_5MoeForConditionalGeneration has no import mapping so this is export-only; the grouped split at shape[0] // 2 with per-block scale slicing mirrors _gated_mlp_slicing; the ancestor-prefix loop in _verify_exported_keys is idempotent and correctly short-circuits.

[claude]

Claude review — 3 IMPORTANT, 3 SUGGESTION, 0 CRITICAL

Reviewed all 11 changed files (small PR, no coverage cap applied): modelopt/torch/export/unified_export_megatron.py, plugins/mcore_custom.py, plugins/mcore_qwen35vl.py, modelopt/torch/utils/plugins/mbridge.py, the three examples/megatron_bridge/ call sites, both test files, the shared tests/_test_utils/ fixture, and CHANGELOG.rst. Also read tests/_test_utils/torch/export/unified_checkpoint.py and plugins/megatron_importer.py for context the diff lacked.

The two root causes are correctly diagnosed and the per-expert gate/up split itself is right. The findings are about blast radius, not the core fix.

Most impactful

1. The GPU tests for the new layout should fail as written (comment). assert_exported_checkpoint_matches checks reference→export and export→reference. Only allow_missing was widened; the newly emitted ...mlp.experts.<E>.gate_proj/up_proj/down_proj.weight keys are absent from the packed fixture, do not end in a QUANT_SUFFIXES entry, and are not in allow_unexpected — so assert not unexpected should trip for all three qwen3_5_moe_vl_* parametrizations. These are the only end-to-end coverage of the change, so worth running on GPU before merge.

2. export_distilled_megatron_to_hf.py is missing keep_gpt_output_layer_extra_state() (comment). It loads a ModelOpt Megatron checkpoint but never applies the patch, so the QAD student whose quantized lm_head distill.py now saves hits the original failure on the distill→export path. Suggest calling it from load_modelopt_megatron_checkpoint rather than from each main(), so a fourth entry point cannot forget.

3. exclude_modules bookkeeping for unquantized grouped experts is lost (comment). The deleted _grouped_mlp_packing had an if qformat in (None, QUANTIZATION_NONE): _record_excluded_module(prefix) branch; _grouped_mlp_slicing has no equivalent, and exclude_modules is an explicit list rather than a complement. On a mixed-precision export leaving routed experts in BF16, hf_quant_config.json lists them neither as quantized nor as excluded. Pre-existing for Nemotron's GroupedMLPSlicing, but a regression for Qwen3.5, which previously used the packing path.

Plus suggestions on a missing scale-shape assertion before the gate/up split, the width of the _verify_exported_keys ancestor relaxation (a partial expansion that drops down_proj would now pass), and keep_gpt_output_layer_extra_state not being added to mbridge.__all__.

Verified as correct (no action)

• Name templating end to end: GroupedGatedMLPSlicing("model.layers.{}.mlp.experts.{{}}") — _custom_mapping_to_lambda's prefix.format(layer_id) leaves {} for _grouped_mlp_slicing to fill, resolving to model.language_model.layers.L.mlp.experts.E.gate_proj.. with_language_model_prefix's type(m)(...) re-instantiation preserves gate_proj_name/up_proj_name through the **func_kwargs default merge.
• Dropping use_packed_local_experts correctly routes SequentialMLP to per-expert iteration (unified_export_megatron.py:707); the flag is still honored for Llama-4 / GPT-OSS and by megatron_importer.py:701.
• Replicating weight_scale_2 to both shards is safe — the exporter already enforces a scalar weight_scale_2, and both projections sharing the fused global scale is numerically valid (coarser than hf_ptq's per-projection amax, not wrong).
• _get_weight_scales pops weight_scale/weight_scale_2 out of name_to_value, so the trailing replicate loop cannot clobber the sliced per-shard scales; input_scale / pre_quant_scale replicate to both shards as HF expects.
• Splitting on weight.shape[0] // 2 rather than config.ffn_hidden_size is the right call for grouped experts, whose width is moe_ffn_hidden_size.
• Cleanup leaves nothing dangling: no remaining GroupedMLPPacking references anywhere, and _merge_nvfp4_expert_scales is still reachable from _pack_name_remapping (line 1830).
• The AST guard is genuinely idempotent — a second call parses the replacement's [Assign, Assign, Assign, If, Return] against the expected [..., Assert, Return] and no-ops — and super(GPTModel, self) keeps the MRO correct for GPTModel subclasses and VLM nesting.
• CHANGELOG entries are user-facing, one to two sentences, filed under the existing 0.47.0 Bug Fixes section.

Risk: moderate. The library changes are well-scoped and the export-side reasoning holds up. Risk is concentrated in (a) CI — the modified GPU tests look like they will fail, leaving the layout change unverified in automation until finding 1 is resolved — and (b) coverage of the _extra_state workaround, applied per-script and already with one gap. Both are mechanical to fix.

[kevalmorabia97]

/claude review

[cjluo-nv]

Bot review (claude-opus-5) — DM the bot to share feedback.

Re-review of #2334 (9 files, +478/-140). All previously flagged blockers now look resolved; what remains is owner sign-off rather than new defects.

Previous comments — status

• Critical: qwen3_5_moe_vl_{grouped,sequential} GPU tests would fail on the unexpected side — addressed. allow_unexpected now gains "mlp.experts." alongside the widened allow_missing, and _assert_per_expert_experts_complete re-adds a positive check that every (layer, expert) emits all three projections.
• Critical: gate/up split + _verify_exported_keys relaxation untested — addressed (CPU tests over _FakeTEGroupedMLP cover the split, per-block scale slicing at half, weight_scale_2 replication, both _record_layer_quant_config prefixes, the 0-dim fallback, and the parametrized expansion/drop pair for the self-check).
• Critical: exclude_modules bookkeeping lost when _grouped_mlp_packing was deleted — addressed; the per-expert loop now records exclusions for qformat in (None, QUANTIZATION_NONE) with a dedicated test, and this also fixes the pre-existing Nemotron gap.
• Critical (design gate): placement of keep_gpt_output_layer_extra_state — addressed; moved out of utils/plugins/mbridge.py into modelopt/torch/quantization/plugins/megatron.py next to quant_module_get_extra_state, invoked from megatron_replace_quant_module_hook (CUSTOM_MODEL_PLUGINS), the three example main() call sites reverted, and 15 tests added. The PR body now explains why mbridge.py was the wrong home (it imports megatron.bridge, unreachable for Megatron-LM/NeMo users).
• Minor: dead GroupedMLPPacking / quantize= / record_quant_config= / stale transpose=False comment — removed. Minor: __all__ for GroupedGatedMLPSlicing — declined with a reasonable rationale. Minor: asserts → ValueError, local_expert_indices instead of a global range, duplicated SequentialMLP rules — all applied.

I re-verified the export path end to end: GroupedGatedMLPSlicing("...experts.{{}}") → prefix.format(layer).format(expert) resolves to model.language_model.layers.L.mlp.experts.E.gate_proj.; with_language_model_prefix's type(m)(...) preserves the gate_proj_name/up_proj_name defaults; _get_weight_scales pops the scales so the trailing replicate loop cannot clobber the sliced ones; the ancestor loop in _verify_exported_keys is idempotent and short-circuits correctly.

Why nudge rather than approve

• 💬 Author replied on the GPU-test fix: "These are GPU tests so I cannot run them here; the completeness logic is unit-verified." — still worth a human eye because allow_unexpected=("mlp.experts.",) waives the routed experts from both key checks, so the per-expert tensors never enter shared and the helper's shape cross-check no longer sees them. Coverage for the new layout in the only end-to-end test is now presence/completeness only, and CI green on qwen3_5_moe_vl_grouped (NVFP4 + FP8) and _sequential hasn't been observed yet.
• 💬 Author replied on the lm_head placement: "moved to megatron_replace_quant_module_hook so no caller can forget it; mbridge.py imports megatron.bridge and could never reach Megatron-LM/NeMo." — reasonable, and the tests are good. Flagging anyway because the mechanism is a permanent, process-global monkeypatch of megatron.core GPTModel.sharded_state_dict, gated on an AST statement-kind fingerprint, installed as a side effect of every mtq.quantize on any Megatron model (including HybridModel/teacher models). The upstream PR is closed, so this is the permanent home, not a stopgap — that's a maintenance commitment a human owner should sign off on.
• Licensing signal: tests/gpu_megatron/torch/quantization/plugins/test_megatron.py adds _stock_gpt_sharded_state_dict, described in the PR body as "a replica of the real pre-fix upstream body (verified against be08ce5b1~1 in Megatron-LM)". It's a handful of lines from a sibling Apache-2.0 NVIDIA project in test code, so likely fine, but per policy a copied-from-external-repo block shouldn't be auto-approved — a short attribution comment naming the source commit would settle it.

Non-blocking nit: keep_gpt_output_layer_extra_state recognises its own work only by identity against the module-global _patched_gpt_sharded_state_dict. In the new test fixture (which restores GPTModel.sharded_state_dict to a previously installed replacement while the global points at a newer closure), a later cache_clear() + call will fingerprint our own replacement ([Assign, Assign, If, Return]), miss, and emit the misleading "not the version ModelOpt patches" warning. Unreachable in production because of @cache, but it makes the warning less trustworthy in test logs.

[claude]

Claude review passed — no blocking issues found. LGTM

Findings: CRITICAL 0 / IMPORTANT 0 / SUGGESTION 3 (all posted inline; none block).

Scope: full review, modelopt/ first then tests/. 9 changed files; reviewed all 5 non-generated source/test files plus CHANGELOG.rst. The workflow/skills/uv.lock churn present in the working diff is unrelated to this PR and was not opened.

What I verified — per-expert Qwen3.5 MoE export

• Gate/up split offset is weight.shape[0] // 2 on the local weight{i}, which is the right choice: Megatron s gated linear_fc1 local shard is [gate_local; up_local] (mirroring TEGroupedMLP s torch.chunk(x, 2, dim=-1) GLU), so the split holds under ETP and does not depend on the global config.ffn_hidden_size the way _gated_mlp_slicing does.
• Scale handling is right across formats: NVFP4 per-block [out, in/16] and FP8/INT4 per-channel [out(,1)] slice along dim 0; the FP8 per-tensor 0-dim case is shared; a scale whose dim 0 is not the output dim now raises instead of silently mis-slicing. weight_scale_2 is the fused tensor s global scale, so replicating it to both halves keeps dequantized values bit-identical to the packed path — and vLLM s fused MoE loader requires the w13 gate/up scale_2 to be equal, which replication guarantees.
• Moving per-expert quant-config recording from "all global ids on every rank" to local_expert_indices is sound: _gather_layer_config_dict / _gather_exclude_modules both all_gather_object over the full world group and are called on every rank before the is_writer_rank branch, so EP ranks jointly cover all ids. It also fixes the misnaming under non-contiguous EP assignment and shrinks the per-rank gather payload.
• Dropping use_packed_local_experts and the local_experts.* rules is safe: Qwen3_5MoeForConditionalGeneration appears only in all_mcore_hf_export_mapping and all_mcore_hf_vision_passthrough_mapping, not in all_mcore_hf_import_mapping, so megatron_importer.py s use of that flag is untouched; the SequentialMLP path now inherits qwen3_causal_lm_export s per-expert GatedMLPSlicing rules. Llama-4 / GPT-OSS keep their own use_packed_local_experts + PackNameRemapping path, and _merge_nvfp4_expert_scales still has a live caller in _pack_name_remapping.
• The doubled-brace template survives with_language_model_prefix, and test_qwen3_5_moe_expert_names_match_released_checkpoint pins that.
• Unquantized gated shards are written as non-overlapping views of one storage; safetensors _filter_shared_not_shared splits non-overlapping regions into singletons, so this does not trip the shared-memory check, and torch.save / all_gather_object for EP>1 dedups the parent storage.
• _verify_exported_keys ancestor-prefix relaxation: the walk-up-with-early-break preserves the "ancestors are always present" invariant, and a genuinely absent module still has nothing under its prefix. It does weaken the check for a sibling dropped under a container that was already expanded — the PR is upfront about that, pins it with test_verify_exported_keys_cannot_see_a_dropped_sibling_under_an_expanded_container, and compensates with _assert_per_expert_experts_complete.

What I verified — quantized output_layer / lm_head

• _output_layer_extra_state_has_data lines up with the actual payloads: quant_module_get_extra_state already returns {} for an output_layer with nothing quantized (so the empty placeholder is still popped, preserving upstream behaviour), and a populated entry s .data is the multi-element tensor that produced the reported Boolean value of Tensor with more than one value is ambiguous.
• super(GPTModel, self) in the replacement resolves the same MRO slot as the original in-class super(), including for GPTModel subclasses.
• Calling it from megatron_replace_quant_module_hook (registered in CUSTOM_MODEL_PLUGINS) does get it in before the sharded load plan is built, which is what retires the silent-unquantized-restore half of the bug rather than just the save-side raise.

Prior-round items

Both blockers from the earlier bot review look resolved: the qwen3_5_moe_vl_grouped / _sequential GPU cases were updated (fixture docstring corrected, _assert_per_expert_experts_complete added so the waiver cannot hide a partial rule), the gate/up split and both directions of the _verify_exported_keys relaxation now have real unit tests, GroupedMLPPacking / _grouped_mlp_packing / the quantize= and record_quant_config= parameters are gone, and the stale transpose=False comment is fixed. No repo-wide references to the removed names remain, and mcore_custom.py has no __all__, so the rename is not a star-import break.

Risk

Low-to-moderate and well contained. The export-layout change is scoped to the two Qwen3.5 experts.* rules; the one cross-architecture effect is that unquantized grouped experts now appear in exclude_modules (NemotronH), which is more correct but changes hf_quant_config.json content — see the inline note suggesting the changelog mention it. The GPTModel.sharded_state_dict monkeypatch is the highest-blast-radius piece, but it is fingerprint-gated, warns and no-ops on an unrecognised body, and preserves upstream semantics for the empty placeholder. My only robustness nit there is the unguarded ast.parse (inline).

[cjluo-nv]

Bot review (claude-opus-5) — DM the bot to share feedback.

Commenting: the export/lm_head half looks settled, but the newly added VLM-calibration fix (§5) does not cover the dataset that produced the reported hang, and its test asserts nothing about production code.

Needs action:

• Fix the _ShardedIterable guard in modelopt/torch/utils/vlm_dataset_utils.py: NemotronTarPlusJsonlIterable has no __len__, so the truncation is skipped for exactly the dataset in §5; and for the wrapper it uses the declared num_samples, not the actual yield count. See inline.
• Rewrite test_nemotron_subset_budget_sums_to_num_samples to drive NemotronTarPlusJsonlIterable; today it re-implements the divmod expression inline and passes even if the fix is reverted.
• Add an attribution comment naming the Megatron-LM source commit above _stock_gpt_sharded_state_dict in tests/gpu_megatron/torch/quantization/plugins/test_megatron.py (raised last round, still open; licensing needs a human call).
• Split §4–§6 into a separate PR as you offered — they touch unrelated subsystems and arrived after §1–3 had already been reviewed four times.

No action needed:

• ✔️ Resolved since the last review: the GPU unexpected-key regression, the exclude_modules gap, the keep_gpt_output_layer_extra_state placement, the ast.parse guard and the dead _grouped_mlp_slicing return.

[cjluo-nv]

Bot review (claude-opus-5) — DM the bot to share feedback.

Nudge: the three code concerns from the last round are fixed, but one new correctness question and the licensing/scope calls still need a human.

Needs action:

• Confirm convert_to_distillation_provider propagates calculate_per_token_loss onto distill_provider in examples/megatron_bridge/distill.py; if it doesn't, average_in_collective silently regresses --sft. Deriving it from args.sft or args.cp_size > 1 avoids the dependency.
• Sign off on the verbatim Apache-2.0 Megatron-LM copy (_stock_gpt_sharded_state_dict in tests/gpu_megatron/torch/quantization/plugins/test_megatron.py) — attribution is now present, but the legal call is a human's.
• Split §4–§6 out as you offered, or say why they ship together — they touch unrelated subsystems and landed after §1–3 had been reviewed several rounds.
• Use count == 0 rather than last is None for the empty-stream check in _ShardedIterable (modelopt/torch/utils/vlm_dataset_utils.py), so a sample that is legitimately None isn't read as an empty stream.

No action needed:

• ✔️ Resolved since the last review: the _ShardedIterable __len__/num_samples guard, the budget test now calling subset_sample_targets, and the Megatron-LM source attribution.

[cjluo-nv]

Bot review (claude-opus-5) — DM the bot to share feedback.

Nudge: both code concerns from the last round are fixed, but the licensing sign-off and the scope call still need a human.

Needs action:

• Sign off on the verbatim Apache-2.0 Megatron-LM copy (_stock_gpt_sharded_state_dict in tests/gpu_megatron/torch/quantization/plugins/test_megatron.py) — attribution is now present, but the legal call is a human's.
• Split §4–§6 out as you offered, or state in the PR body why they ship together — they touch unrelated subsystems and landed after §1–3 had been reviewed several rounds.
• Confirm the qwen3_5_moe_vl_grouped / _sequential GPU cases pass in CI: allow_unexpected=("mlp.experts.",) waives the routed experts from both key checks, so assert_per_expert_experts_complete is now the only coverage of the new layout and it has not been seen green.
• Consider a rank-local warning (not warn_rank_0) for the pad-repeat path in _ShardedIterable (modelopt/torch/utils/vlm_dataset_utils.py) — a shortfall on a non-zero rank is currently silent.

No action needed:

• ✔️ Resolved since the last review: calculate_per_token_loss is now derived once into per_token_loss in distill.py and reused for the DDP config, and the empty-stream guard uses count == 0.