Unpack Qwen3.5 MoE routed experts and keep quantized lm_head on Megatron export

[kevalmorabia97]

What does this PR do?

Type of change: Bug fix

Two Megatron-Core → HuggingFace export bugs that together make a quantized Qwen3.5 / Qwen3.6 MoE
checkpoint unservable, plus the dead code the first one leaves behind.

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
  'language_model.model.layers.23.mlp.experts.down_proj_weight_scale_2'

Quantization itself is fine — vLLM auto-detects quantization=modelopt_mixed,
kv_cache_dtype=fp8_e4m3 and selects Marlin. It fails at weight load, on layout.

mcore_qwen35vl.py used GroupedMLPPacking / PackNameRemapping, faithfully mirroring the
BF16 upstream checkpoint, which really is packed (mlp.experts.gate_up_proj, 40 tensors).
That mapping is only used for quantized export, and vLLM's quantized MoE loader needs
per-expert scales. Both released NVFP4 checkpoints are per-expert:

checkpoint	produced by	routed-expert layout
Qwen/Qwen3.6-35B-A3B (BF16 source)	Qwen	packed, 40 tensors
nvidia/Qwen3.6-35B-A3B-NVFP4	hf_ptq	per-expert, 10240
nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4	Megatron-LM	per-expert, 2944

The Nemotron row is the important one: Megatron export already does this correctly via
GroupedMLPSlicing / NameRemapping in mcore_nemotron.py. Only the qwen3_5 mapping packed.

_grouped_mlp_slicing gains gate_proj_name / up_proj_name, splitting each expert's fused
gate+up and slicing a per-block weight_scale with it; GroupedGatedMLPSlicing wires it up and
mcore_qwen35vl.py switches the TEGroupedMLP path over. The SequentialMLP path needed no qwen3_5
rule at all — qwen3_causal_lm_export already splits per expert — so the two rules that duplicated
it were dropped and both layouts now agree by inheriting one definition. use_moe_grouped_gemm still returns True, so the Megatron checkpoint layout is
unchanged — affected checkpoints need only a re-export, not re-quantization.

_verify_exported_keys had to be relaxed to match. It compares module prefixes against the BF16
source, so expanding one source module (...mlp.experts) into many (...mlp.experts.<E>.gate_proj)
looked like 82 dropped tensors. Exported modules now contribute their ancestor prefixes; a module
that is genuinely absent still has nothing beneath its prefix and is still reported. This never
fired for Qwen3-MoE or NemotronH because their BF16 sources are already per-expert.

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, for
compatibility with GPT checkpoints that only stored the output-layer weight. ModelOpt keeps
quantizer state there, so quantizing output_layer raised
RuntimeError: Boolean value of Tensor with more than one value is ambiguous on save — and since
that method also backs the load plan, it silently restored the layer unquantized on load.
MambaModel has no such rule, which is why nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4 ships a
quantized lm_head today and Qwen cannot.

keep_gpt_output_layer_extra_state() retains the entry. It runs from
megatron_replace_quant_module_hook, which is registered in CUSTOM_MODEL_PLUGINS and therefore
fires for every Megatron model, on both paths — the hook runs during modelopt-state restore,
before the sharded load plan is built. It deliberately does not live in
modelopt/torch/utils/plugins/mbridge.py: that module imports megatron.bridge, so Megatron-LM
and NeMo users could never reach it there. It matches the upstream body by AST before replacing
it, self-disables against its own work, and warns once if it meets a sharded_state_dict it does
not recognise.

The upstream counterpart NVIDIA/Megatron-LM#7086
is closed rather than merged: nemo:26.10 migrates GPTModel to HybridModel, whose
sharded_state_dict carries no pop-and-assert, so the bug retires with the deprecated class
instead of needing a change to it. This side therefore keeps the workaround for the interim
containers, and the TODO points at that migration.

This is not cosmetic: on Qwen3.6-35B-A3B, lm_head is 248320x2048 = 509M params,
34.6% of per-token weight traffic in BF16 on a model with only ~2.9B active params. Leaving it
unquantized costs about a quarter of the decode-side win.

Two smaller correctness fixes came out of review and are folded in: the gated-split shape checks
raise ValueError rather than assert (stripped under -O, where the failure mode is silently
unequal gate/up halves), and per-expert quant metadata is recorded for local_expert_indices
instead of every global id — _gather_layer_config_dict and _gather_exclude_modules already
all-gather across ranks, and the global loop misnamed experts under non-contiguous EP assignment.

3. Cleanup

With qwen3_5 switched over, nothing maps GroupedMLPPacking. It is removed along with
_grouped_mlp_packing and the quantize= / record_quant_config= parameters of
_grouped_mlp_slicing that existed only to serve it, so one grouped-expert export path remains
instead of two where only one is reachable. Llama-4's separate PackNameRemapping path is
unaffected.

Usage

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

model.language_model.layers.0.mlp.experts.0.gate_proj.{weight,weight_scale,weight_scale_2}
model.language_model.layers.0.mlp.experts.0.up_proj.{...}
model.language_model.layers.0.mlp.experts.0.down_proj.{...}
lm_head.{weight,weight_scale,weight_scale_2}

Testing

CPU tests, no GPU required:

• test_mcore_export_mappings.py — the qwen3_5 mappings emit per-expert rules and resolve to the
  released checkpoint's names. Verified these fail without the fix: 2 failed / 11 passed, with
  Qwen3MoeForCausalLM and NemotronHForCausalLM passing either way as controls.
• test_unified_export_megatron.py — the gate/up split itself: per-expert
  gate_proj / up_proj shards, a per-block 2-D weight_scale sliced at half with
  weight_scale_2 replicated and both _record_layer_quant_config prefixes recorded, and the
  0-dim scalar-scale fallback. Plus both directions of the _verify_exported_keys relaxation:
  expansion accepted, genuine drop still raised.
• Fixed the existing qwen3_5_moe_vl_grouped / _sequential cases, which compare the export
  against a fixture that packs experts and would otherwise regress on the per-expert layout.
• test_megatron.py::TestKeepGptOutputLayerExtraState — 15 cases over the output_layer patch:
  the payload check across tensor / bytes / ShardedObject-shaped entries, the no-op second call,
  and warn-and-skip against an unrecognised sharded_state_dict. test_patches_stock_megatron_core
  installs a replica of the real pre-fix upstream body (verified against be08ce5b1~1 in
  Megatron-LM) so the patched path is exercised whichever megatron-core is installed, and
  test_keeps_populated_extra_state fails if neither the patch nor upstream keeps the entry.

End to end on Qwen/Qwen3.6-35B-A3B (35B MoE, 256 experts) quantized W4A16-NVFP4 / FP8-attn /
kv_fp8_cast through examples/megatron_bridge/, on 4x GB200 with 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
checkpoint size	23 GB	23 GB
quant map vs released ckpt	identical apart from expert granularity	same
vLLM v0.28.0 load	AttributeError, engine never starts	Loading weights took 25.61 s, Application startup complete
NEL eval (GPQA-D, MMMU-Pro)	FAILED	SUCCESS

MMMU-Pro matters as a canary because it exercises the quantized MoE and the vision tower together.

For the lm_head half, on Qwen3.5-9B (untied embeddings): PTQ -> save -> export yields
lm_head.{weight,weight_scale,weight_scale_2}, and a QAD save -> resume -> export round trip
preserves them. Qwen3.5-0.8B and 4B have tied embeddings and cannot exercise that path at all,
which is worth knowing before picking a smoke model.

One deliberate deviation: hf_quant_config.json is 5.1 MB vs the released checkpoint's 35 KB,
because per-expert export records one quant-config entry per expert where hf_ptq collapses them to
one mlp.experts entry per layer. Semantically identical, and the released
nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4 enumerates per-expert too (2944 entries) and serves
fine. Collapsing would mean changing recording granularity in a function Nemotron also uses.

Before your PR is "Ready for review"

• Is this change backward compatible?: ✅
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
• Did you write any new necessary tests?: ✅
• Did you update Changelog?: ✅
• Did you get Claude approval on this PR?: ✅

Additional Information

Both bugs were found while reproducing nvidia/Qwen3.6-35B-A3B-NVFP4 through
examples/megatron_bridge/. Follow-up to #2332. The upstream counterpart
NVIDIA/Megatron-LM#7086 is closed — see §2 —
which makes the workaround here the permanent home rather than a stopgap. Labeled
cherry-pick-0.47.0.

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Summary by CodeRabbit

• Bug Fixes

  • Preserved GPT output-layer quantizer state during quantization, checkpoint saving, loading, and resume workflows.
  • Improved Qwen3.5/Qwen3.6 MoE exports for vLLM compatibility with per-expert tensors and split gate/up projections.
  • Added quantization metadata and scale handling for grouped expert exports.
  • Improved export validation for routed MoE architectures and ancestor modules.

• Tests

  • Expanded coverage for per-expert MoE mappings, checkpoint key formats, projection splitting, scale handling, quantization metadata, and exported tensor validation.

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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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Title check	✅ Passed	The title clearly summarizes both primary changes: per-expert Qwen3.5 MoE export and preservation of quantized lm_head state during Megatron export.

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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 95.89041% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 78.68%. Comparing base (f13a796) to head (b2afe06).
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Files with missing lines	Patch %	Lines
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modelopt/torch/export/unified_export_megatron.py	97.43%	1 Missing ⚠️

Additional details and impacted files

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##             main    #2334      +/-   ##
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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
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In `@modelopt/torch/utils/plugins/mbridge.py`:
- Line 297: Add keep_gpt_output_layer_extra_state to mbridge.py’s __all__ export
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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.

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Inline comments:
In `@modelopt/torch/export/unified_export_megatron.py`:
- Around line 1460-1461: Update the expert metadata loop in the export flow to
iterate over the rank-local IDs from local_expert_indices rather than a
contiguous range derived from num_total_experts, while preserving the existing
metadata recording and relying on _gather_layer_config_dict() for cross-rank
merging.
- Around line 1421-1424: Replace the assert-based scale-shape validation in the
gated expert weight handling with an explicit ValueError when _gated_subnames is
set and non-scalar weight_scale_cpu has a first dimension different from
weight.shape[0]. Preserve the existing validation message and allow valid shapes
to proceed to to_quantized_weight.

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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).