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Megatron-Core fuses GatedDeltaNet's [q, k, v, z, beta, alpha] into one in_proj (a TELayerNormColumnParallelLinear) behind one weight and one input quantizer, so Megatron fake quant quantizes the beta / alpha gate rows. Every export keeps them in BF16:
HF PTQ, whose recipes disable *linear_attn.in_proj_a* / *in_proj_b*, since vLLM's in_proj_ba is too narrow for FP4 kernels.
PTQ evaluation and QAD / QARL of Qwen3.5 / Qwen3.6 therefore trained against quantization error on rows that deployment doesn't have. This was raised in NVIDIA-NeMo/Megatron-Bridge#6714 (comment).
_QuantGatedDeltaNet now marks the gate rows of its in_proj, by name from in_proj_split_names / in_proj_split_sections, local to the TP rank. For those rows:
Forward: the quantized TE LayerNormLinear computes the output from the unquantized weight and the unquantized LayerNorm output, i.e. what deployment runs. The quantizers' pre-quant scale and rotation still apply, so an AWQ / SmoothQuant scale the stored weight compensates for stays correct (apply_without_qdq applies a quantizer's transforms without its QDQ).
Backward: the weight gradient of those rows comes from the unquantized input, and the input gradient from their unquantized weight. With no pre-quant scale or rotation on in_proj's quantizers, both match BF16; with them, see the known limitation below.
fold_weight and the vLLM fake-quant exporter (export_mcore_gpt_to_hf_vllm_fq) keep the rows unquantized, so --vllm_fakequant_export and the library export match too.
The qkv / z rows are unchanged, and the fake-quant weight cache still hits, since nothing is modified in place. Other in_proj layer types (non-TE) warn and stay fully quantized.
Not changed: weight calibration (max runs weight_only_quantize on the whole weight) still includes the gate rows in the in_proj weight amax. Both exporters use that same amax for in_proj_qkv / in_proj_z, so fake quant and export stay consistent. Excluding the rows would need a calibration-wide change; GPTQ writes back through iter_weights_for_calibration.
Why mark rows in the fused GEMM rather than the alternatives:
Splitting in_proj into quantized and BF16 linears would change Megatron-Core's GatedDeltaNet module structure, its sharded checkpoint layout and the TP / CP split of in_proj, in Megatron-Core or in a ModelOpt fork of the module.
The quantizer enable / disable mechanisms act on a whole quantizer, i.e. on all of in_proj; there is no per-row switch.
Computing the few gate rows separately inside the existing GEMM patch keeps the module and checkpoints unchanged and costs one small extra GEMM.
Known limitation (pre-existing, every row): the TE LayerNormLinear backward does not apply the input quantizer's pre-quant scale / rotation derivatives in dgrad, nor weight-quantizer transform derivatives in wgrad. The gate rows follow the same convention; a fix for all rows is tracked in OMNIML-6311.
Usage
No change to how models are quantized: it applies whenever a Megatron-Core GatedDeltaNet model is. Adds the helper modelopt.torch.quantization.utils.apply_without_qdq.
Testing
New test_in_proj_keeps_gates_unquantized and test_in_proj_keeps_gates_unquantized_tp2 (TP1 and TP2), with FP8 W8A8 on in_proj:
the gate rows of the output equal the unquantized output, while the other rows don't;
with a loss on the gate rows only, the input and gate-weight gradients match BF16;
VllmFqGPTModelExporter writes the gate weights unquantized;
with a power-of-two pre-quant input scale and the weight divided by it, the gate output is unchanged;
fold_weight keeps the gate weights.
Each check fails without its fix: on main, 21 / 32 gate outputs differ; without the backward fix, the gate-weight gradient; without the exporter change, the exported gate rows; without the input transforms, the scaled-input gate output. The input-gradient check guards the dgrad path, which already used the unquantized gate weight.
New test_mcore_vllm_export_gdn runs export_mcore_gpt_to_hf_vllm_fq on a tiny Qwen3.5-VL. The model is CPU-offloaded, and its in_proj weight quantizer has a weight-only rotate_back, which only runs on CUDA.
The written in_proj_b / in_proj_a weights equal the Megatron rows with only that transform applied.
They have no quantizers in quantizer_state.pth / quant_recipe.yaml, while in_proj_qkv / in_proj_z keep their input quantizers.
It fails without the exporter fix, and with the gate rows outside _fakequant_weight's CUDA staging (Expected x.is_cuda()).
It runs at TP1, like the other Qwen3.5 export tests.
tests/gpu_megatron/torch/quantization/plugins/test_megatron_gated_delta_net.py, test_transformer_engine.py, tests/gpu_megatron/torch/export/test_unified_export_megatron.py, test_quantized_mbridge_export.py on 2× RTX 6000 Ada in nemo:26.10.rc9 (fla-core 0.5.1), with two Megatron-Core versions:
the Megatron-Bridge pin of this repo's CI, 0652ea7: 129 passed, 4 skipped, 1 failed;
Megatron-Bridge#6714 (Megatron-LM a296523510, as in Megatron-Bridge main e42042fa8): the same.
The failure, test_matches_megatron_exporter[llama-nvfp4], fails the same way on main in that container: TE's fused RoPE has no kernel for this GPU.
On the latest commit: test_megatron_gated_delta_net.py and test_vllm_fakequant_megatron_export.py, 11 passed, in the same container with the CI Megatron-Bridge pin.
Cost at Qwen3.6-35B-A3B in_proj size (2048 → 12352, 64 gate rows, 8192 tokens, FP8 W8A8 fwd + bwd): the gate GEMM and weight concat add about 0.07 ms per call, within run-to-run noise of the ~10 ms total. Peak memory is unchanged.
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ (numerics of the gate rows change to match the exported checkpoint)
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
Fixed fake quantization of Gated DeltaNet so beta and alpha gates remain in BF16 during evaluation, quantization-aware distillation, weight folding, and vLLM export. This keeps gate values and outputs consistent with the unquantized reference.
Other input-projection layers continue to be quantized; a warning is shown when their gates are also quantized.
Megatron fuses [q, k, v, z, beta, alpha] into one in_proj behind one weight
and input quantizer, so fake quant quantized the gate rows, while every export
(export_mcore_gpt_to_hf, Megatron-Bridge, HF PTQ) keeps in_proj_a / in_proj_b
in BF16. The TE LayerNormLinear quantized forward now computes those output rows
from the unquantized weight and input (straight-through in backward), and
fold_weight keeps them, so PTQ evaluation and QAD match deployment.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Walkthrough
Megatron fake quantization now leaves the fused Gated DeltaNet in_proj beta and alpha rows unquantized. Transformer Engine preserves these rows during forward computation, gradient calculation, and weight folding. The vLLM fake-quant exporter also preserves them in exported weights.
Changes: Gated DeltaNet Fake Quantization
Layer / File(s)
Summary
Select and validate gate rows modelopt/torch/quantization/plugins/megatron.py, tests/gpu_megatron/torch/quantization/plugins/test_megatron_gated_delta_net.py, CHANGELOG.rst
Megatron marks the final beta and alpha rows when in_proj has the expected module type and split names. Distributed tests check outputs, gradients, input scaling, folding, and export. The changelog describes this behavior and the warning for other in_proj types.
Preserve selected rows in Transformer Engine modelopt/torch/quantization/utils/core_utils.py, modelopt/torch/quantization/plugins/transformer_engine.py
apply_without_qdq applies quantizer transforms without QDQ and restores each quantizer’s prior disabled state. Transformer Engine uses it for selected rows during forward computation, backward gradients, and weight folding.
Preserve selected rows in export modelopt/torch/export/plugins/vllm_fakequant_megatron.py, tests/gpu_megatron/torch/export/test_vllm_fakequant_megatron_export.py
The exporter restores selected weight rows using the quantizer without QDQ. Tests check that gate weights remain unchanged while other weights are quantized.
No merge-blocking issue was established in the supported workflows checked.
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The title clearly and concisely describes the primary change: keeping GatedDeltaNet beta and alpha gates unquantized in Megatron fake quantization.
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Inline comments:
Review comments at @modelopt/torch/quantization/plugins/transformer_engine.py:
- Around line 318-351: Update _patched_general_gemm so weight gradients for
unquantized_rows are computed from the original input, matching their forward
computation, rather than from input_quantizer(input). Preserve the existing
quantized-input gradient path for all other weight rows.
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… fq exporter
- Backward: the gate rows' weight gradient comes from the unquantized input.
- Gate rows apply the quantizers' pre-quant scale and rotation without QDQ
(new apply_without_qdq), so AWQ / SmoothQuant scaling stays compensated.
- VllmFqGPTModelExporter keeps the gate rows unquantized, like fold_weight.
- fold_weight clones the rows before folding.
- CHANGELOG entry moved to Bug Fixes and scoped to the TE in_proj.
- Test: gate-only gradients vs BF16, exporter rows, pre-quant input scale.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Changes requested: scaled/rotated gate gradients still disagree with the forward computation, although the earlier unscaled-gradient fix is present.
Needs action:
Fix transform-aware gate gradients in transformer_engine.py; extend the GDN regression with scaled and rotated backward comparisons (inline).
💬 Author added a private snapshot check — still add GDN coverage through export_mcore_gpt_to_hf_vllm_fq, checking written gate weights and sidecars (inline).
Explain in the PR body why the cross-path row marker is preferred over a split-linear implementation and existing quantizer disable/enable mechanisms.
Document the circular-import reason inside apply_without_qdq, or move its import to module scope, as CONTRIBUTING.md requires.
Shorten the CHANGELOG.rst entry to at most two sentences.
No action needed:
✔️ Earlier unscaled weight-gradient, reciprocal forward-scaling, exporter snapshot, and changelog placement/scope concerns are addressed. The existing test-helper edit preserves coverage.
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Bot comment.
[P2] The gate branch now preserves transforms in its forward values, but its backward still needs their derivatives. For example, set the input pre-quant scale to a constant 2 and divide the stored weight by 2, as the regression does: gate output matches BF16, but the unchanged first dgrad GEMM computes dY_gate @ (W_gate / 2) and passes that directly into LayerNorm backward, missing the input transform's factor of 2. With rotation, gate dgrad likewise needs the input rotation's VJP; the replacement wgrad here is in transformed-weight coordinates and also needs the weight transform's VJP. These transforms execute inside the custom autograd forward, so PyTorch will not differentiate them automatically. Apply the gate-specific transform derivatives before LayerNorm backward and before returning gate wgrad, preserving the other rows' existing path. Extend the current numerical backward check to non-unit scaling and paired rotation; the scaled check currently runs only under no_grad().
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This isn't specific to the gate rows. _QuantLayerNormLinearFunc.backward has never applied the input quantizer's transform derivatives for any row: the dgrad GEMM is dY @ Q(W / s), with no factor of s and no rotation VJP, for the quantized rows on main too. The gate rows now follow the same convention.
The gate wgrad is already correct for a pre-quant scale: it is taken with respect to the stored weight W / s, from X * s, the same coordinates as the forward pass.
Making the TE LayerNormLinear backward transform-aware (input scale / rotation VJP in dgrad, weight-transform VJP in wgrad) for all rows is a separate change to AWQ / SmoothQuant / rotation QAT on TE layers. It's out of scope for this fix, so I'll track it as a follow-up rather than special-case the gate rows here.
… changelog
- test_mcore_vllm_export_gdn runs export_mcore_gpt_to_hf_vllm_fq on a tiny
Qwen3.5-VL: in_proj_b / in_proj_a are written unquantized without quantizers,
in_proj_qkv / in_proj_z keep their input quantizers.
- The gate test runs explicitly at TP1 and TP2.
- Explain the local import in apply_without_qdq; two-sentence changelog entry.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at
@tests/gpu_megatron/torch/export/test_vllm_fakequant_megatron_export.py:
- Around line 475-478: Update test_mcore_vllm_export_gdn to skip when the
optional fla dependency is unavailable by checking it with pytest.importorskip
before calling create_tiny_qwen3_5_vl_dir; briefly document that the check is
for the optional dependency.
- Line 477: Update the source setup using create_tiny_qwen3_5_vl_dir so it uses
the offline tiny-model builder and saves the resulting model as the local export
source. Avoid enabling processor creation or loading reference model/tokenizer
files; the test only needs the model for export and weight assertions.
Review comments at
@tests/gpu_megatron/torch/quantization/plugins/test_megatron_gated_delta_net.py:
- Line 246: Update test_in_proj_keeps_gates_unquantized to declare its
worker-pool fixture statically in the test signature, so pool startup occurs
during setup and pytest can collect the fixture name for rerun logic. Since the
fixture name depends on tp_size, split the parametrized test into separate TP1
and TP2 tests using dist_workers_size_1 and dist_workers_size_2, each running
_test_in_proj_gates_unquantized_helper.
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modelopt/torch/quantization/utils/core_utils.py
CHANGELOG.rst
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Could we use the same CUDA staging as _fakequant_weight() here? With CPU-offloaded weights and weight-only rotate_back, the full-weight export is staged onto CUDA, but this call applies the CUDA-only Hadamard transform to the original CPU slice and fails. A CPU-offloaded export test with weight rotation would cover this.
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Good catch. Fixed in 0338cf9: the gate rows are now handled inside _fakequant_weight, so they are moved to CUDA and cast to the export dtype together with the rest of the weight. test_mcore_vllm_export_gdn now exports a CPU-offloaded model whose in_proj weight quantizer has a weight-only rotate_back. It fails on the previous head with Expected x.is_cuda() and passes now.
- The vLLM fake-quant exporter now keeps the gate rows inside _fakequant_weight,
so they get its CUDA staging and dtype; CPU-offloaded weights with a CUDA-only
transform (Hadamard rotate_back) failed before.
- test_mcore_vllm_export_gdn exports a CPU-offloaded model with a weight-only
rotate_back, builds its source offline and skips without fla.
- Split the TP1 / TP2 gate tests so their worker fixtures are static.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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LGTM: prior correctness concerns are resolved; human sign-off remains for the justified existing test-helper edit.
Needs action:
Confirm the _make_model edit in test_megatron_gated_delta_net.py: it preserves the existing one-head default while enabling explicit TP2 regression coverage.
No action needed:
✔️ 14 distinct prior concerns addressed or settled, including gate-weight gradients and public fake-quant export with CPU staging. The shared transform-gradient limitation is now scoped and tracked in the PR body.
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Looks good. One follow-up: HF allows alpha/beta quantization to be configured, while the Megatron fake-quant/export path always keeps them in BF16. We can address that configurability gap in a separate PR.
…2a instead of forcing eager (#3074)
> [!NOTE]
> **[2/2]**: #3122 ([1/2]) is this PR's expert-parallel correctness fix,
split out on 2026-10-29 at a reviewer's request so that it lands on its
own, with a Bug Fixes changelog entry. This PR keeps the two
backward-breaking changes, calibration on `grouped_mm` by default and
the new `calibrate()` error, and sat on #3122 until #3122 merged on
2026-10-30. Before that, it was [3/3] of the OMNIML-6073 split, after
#3088 (FSDP2's weight-access window) and #2745 (`grouped_mm`
quantization), both merged. #3067 (Nemotron-H) sits on this PR.
>
> **Base:** `main`. #3122 merged on 2026-10-30, so this PR sits on main,
its own diff unchanged.
>
> **In the tree:** #2741's helper base merges #3067, which carries this
PR and #2745.
<details><summary>Series layout</summary>
- `main`
- #3088 FSDP2 weight-access window (merged)
- #2745 fused experts under grouped_mm (merged)
- #3122 expert-parallel experts calibrated and folded on each rank's own
experts (merged)
- **#3074 calibration on grouped_mm (this PR)**
- #3067 Nemotron-H's experts under grouped_mm
- #2739 meta-DCP checkpoint load (merged)
- #2736 distribution plan, run group, collectives (merged)
- #2740 calibration on per-rank data
- #2750 keep-fused fold
- #2751 distributed-writer pieces
- #2752 no-gather export (on #2751, #2750, #2779 and #2740)
- #2753 expert-parallel export
- #2737 plan decision
- #2779 load routing
- #2741 expert-parallel loader (on #2779, #3067 and #2740)
- #2742 dense sharding
- #2743 block-FP8 dequantization
- #2780 expert-parallel load routing
- #2746 expert parallelism in the model specs
- #2747 on-the-fly decompression
- #2748 Kimi-K2.5 and Kimi-VL adapters
- #2749 Kimi-K3 adapter
- #2754 expert-parallel PTQ from hf_ptq.py (on #2746 and #2753)
</details>
### What does this PR do?
Type of change: New feature, with a backward-incompatible change to MoE
calibration (see the changelog entry)
#2745 quantizes the `grouped_mm` experts forward, transformers' default,
while calibration still runs every model on eager. This PR moves
calibration onto `grouped_mm` and the `torch_a2a` expert-parallel
forward, and makes everything that reads the experts' inputs or weights
during calibration work there. Each gap below otherwise failed silently,
by collecting no amax or the wrong one rather than by raising.
- **Backend normalization.** `normalize_experts_impl_on_the_fly`
replaces `force_eager_experts_impl_on_the_fly`. `eager`, `grouped_mm`
and `torch_a2a` are kept; a backend nothing intercepts (`batched_mm`,
the opaque `sonicmoe` kernel, hub kernels) is moved to `grouped_mm`
before calibration, or to `eager` on a transformers without
`_grouped_linear`. A model whose experts quantize only on the eager
forward keeps main's forced eager: native FP8 experts, a class with its
own wrapper (GPT-OSS's quantizes `down_proj`'s input by intercepting the
eager forward's matmuls), and Nemotron-H's non-gated experts until
#3067. Such a model is refused on `torch_a2a`, before any config
changes, instead of calibrating on raw FP8 bytes or not at all.
- **Per-expert captures.** `grouped_mm` and `torch_a2a` feed a
projection's input quantizer one expert-sorted batch. The weight-input
captures (local Hessian, imatrix, GPTQ) split it back per expert by the
cumulative token offsets the grouped interception records, following
whichever path ran. The input quantizers calibrate on the rows the
grouped GEMM computes only: under transformers' own expert parallelism,
the rows routed to other ranks' experts are sorted past the offsets, and
before transformers 5.17 the down projection's input holds them
uninitialized.
- **Expert parallelism.** #3122 makes everything that reads the expert
weights outside the forward index each rank's own experts: max
calibration's weight pass, `fold_weight`, the weight-input captures and
GPTQ. Here the grouped captures split the expert-sorted batch per local
expert. The grouped weight quantization needs no indexing: transformers
swaps expert-parallel DTensors for their local shards around the experts
forward.
- **Fail loudly.** `calibrate()` raises, naming the modules, when a
fused-experts forward that had to reach a quantizer ran but neither hook
fired: a hand-written `bmm` forward on a class without
`@use_experts_implementation`, which no config change redirects. Each
`calibrate()` judges its own forwards and raises with the model back in
its training mode. Under the expert parallelism of transformers 5.16 and
later a module's verdict is reduced over its experts' expert-parallel
group, so every rank of the group raises, not just the ranks whose
experts saw rows. Not reported: experts no calibration token reached
(Ernie-4.5-VL's vision experts under text-only calibration, which fall
back to weight-derived scales as on main), an eager forward whose rows
were all routed to other ranks' experts, and weight-only experts under
max or MSE, whose weight quantizers calibrate on the weights alone.
**Behavior change.** main forced MoE calibration onto eager;
transformers resolves an unrequested backend to `grouped_mm`, so
fused-experts models now calibrate there. A single-GPU Qwen3-30B-A3B
export, NVFP4 or FP8, matches main in every weight and weight scale, but
4,647 activation scales move, by up to 10.2%. main's own repeat is
byte-identical, and loading with `experts_implementation="eager"`
reproduces main's checkpoint byte for byte (all 74,835 tensors for NVFP4
and 56,211 for FP8), so the backend alone moves them. The changelog says
so.
| Moved activation scales, Qwen3-30B-A3B NVFP4, 1 GPU | Tensors |
|---|---|
| Experts' `down_proj` input (33 layers, 128 experts each) | 4,224 |
| Experts' shared `gate_proj` and `up_proj` input (1 layer) | 256 |
| Attention input and KV-cache | 167 |
| Total, in 45 of 48 layers (none in layers 0, 1 and 5) | 4,647 |
The largest moves are 10.2% on the experts (layer 25's `down_proj`
input) and 7.8% on attention.
*Why they move.* Both forwards feed an input quantizer the same rows: a
single GPU has no padding rows (transformers' sentinel rows exist only
under its own expert parallelism, where calibration skips them;
transformers zeroes them only from 5.17), the routing weights apply
after `down_proj` on both, and neither casts. They round the block's
output differently. Eager adds a token's 8 expert outputs into a bf16
buffer with `index_add_`, rounding after each add, while
`grouped_mm_experts_forward` sums them in fp32 and rounds once, and its
GEMMs run other kernels. From the first MoE block's output on, the
residual stream differs by rounding, and where a router's 8th and 9th
scores nearly tie, the routing flips. A flipped token feeds `down_proj`
another expert's activations, and `down_proj`'s input scale is a max
over routed (token, expert) rows, so it moves by more than rounding
would. The moved scales fit that: the experts' shared
`gate_proj`/`up_proj` input, whose rows are every token however it is
routed, moved in one layer only, against 33 for `down_proj`.
*Why `grouped_mm` off expert parallelism too,* rather than eager there
and `grouped_mm` on `torch_a2a` only: it is the forward transformers
runs the model on when nothing else is asked for, its fp32 sum is the
more precise of the two combines, and expert-parallel calibration runs
the grouped path anyway (`torch_a2a` runs the local experts through
`_grouped_linear`), so a model calibrates on one forward whether or not
it is sharded. `experts_implementation="eager"` keeps earlier releases'
checkpoints.
### Usage
```python
# To calibrate exactly as earlier releases did:
model = AutoModelForCausalLM.from_pretrained(path, experts_implementation="eager")
```
```bash
# The same from hf_ptq.py:
python hf_ptq.py --pyt_ckpt_path <model> --qformat nvfp4 --experts_implementation eager # or grouped_mm
```
### Testing
- `tests/unit/torch/quantization/plugins/test_fused_experts.py`, against
synthetic and real transformers experts modules:
- backend normalization, including the fallback to eager on a
transformers without `_grouped_linear`;
- GPTQ's Hessians and local Hessian's captures under `grouped_mm` and
`torch_a2a` match eager's per expert, and a capture follows whichever
path ran;
- the interception check raises when the forward ran past both hooks,
also after an earlier calibration reached them, and not for unreached
experts, an eager forward routed only to other ranks' experts, or
weight-only experts beside a calibrated dense input quantizer under max
(each expert's weight amax equals its weight's per-channel abs max).
Under local Hessian and GPTQ, which read the experts' inputs, those
weight-only experts still raise;
- GPT-OSS, a real tiny model, calibrating `down_proj`'s input on the
eager forward.
- Expert parallelism, on CPU under gloo:
- the tests that experts sharded the way transformers shards them
calibrate, fold, capture and build GPTQ helpers on each rank's own
experts moved to #3122 with that fix;
- every rank of the experts' expert-parallel group raises when only one
rank's experts ran past both hooks;
- with fake quantizers inserted, experts split over two ranks train
through the `torch_a2a` forward like the whole quantized module: the
fake-quantized case of `test_moe_a2a.py`'s whole-module test;
- the transport's hop slicing under a graph: `test_moe_a2a.py`'s
chunked-hops test also runs forward and backward through hops sliced
below the limit, and checks the output and input gradients bit for bit
against the unsliced run.
- The transport's (#2738, merged) docs say again that ModelOpt quantizes
its local experts, now that the `_grouped_linear` override does. Its
hop-slicing comments say what the slicing guarantees, every hop within
`all_to_all_single`'s int32 element offsets, rather than that it
prevents a hang: with the slicing disabled, hops past 2**31 elements
completed exactly on NCCL 2.29, 2.30 and 2.31, including at 32 ranks
over the network on 2.29 and 2.30.
- Against main, on tiny real models: GPT-OSS calibrates on eager with
`down_proj`'s input amax equal to main's; Ernie-4.5-VL-MoE quantizes in
FP8 and NVFP4 under text-only calibration, as on main.
- Byte-for-byte checkpoint comparison against main on one GPU, measured
on earlier revisions of #2745: Qwen3-30B-A3B, eager and `grouped_mm`,
NVFP4 and FP8; Qwen2.5-0.5B (dense) is byte-identical. On GPU,
Nemotron-3-Nano-30B-A3B's NVFP4 calibration with its experts pinned to
eager and #3067's with `experts_implementation="eager"` give
byte-identical exports, all 18,012 tensors.
- The GPU tests of QAT under expert parallelism are in #2742, the first
PR with an FSDP2-sharded dense part they also run.
- Review fixes, 2026-10-27, carried from #2745: the FSDP2 window's
ownership test (unnamed meshes, every shard state); an empty experts
forward is no work the interception check expects; the non-gated experts
are pinned to eager whichever plugin runs first.
- Review fixes, 2026-10-27, last round, each with a test that fails
without it:
- `grouped_mm` is kept, and redirected to, only where `_grouped_linear`
can be intercepted; one capability constant gates the interception, the
supported set and the redirect target;
- an eager experts forward counts as work for the interception check
only when it routes a row to this rank's experts, so a rank whose
experts calibration routed nothing to no longer raises alone;
- an enabled expert weight quantizer needs the interception only when
the calibration captures the experts' inputs to calibrate their weights
(GPTQ, local Hessian, imatrix), so weight-only experts under max or MSE
no longer raise; the message names what falls back.
- Split from #2745 on 2026-10-27, night: #2745's and this PR's commits
together give #2745's previous tree exactly, plus this round's fixes.
Pre-commit, mypy included, and this PR's unit tests pass at this head on
transformers 5.18 and 5.5.4 and on torch 2.8, and the series' PRs merged
together add no unit-test failure over main f03651e
(tests/unit/torch utils, export, quantization and models). This
revision's GPU tests run in CI only.
- Review suggestions, 2026-10-28: the weight-input captures (local
Hessian, imatrix, GPTQ) read the path that ran, not the one the config
named when they were registered, so they follow whichever forward runs
and the refusal is gone, pinned by a test that fails on the code before;
the interception check's forward count, and the host sync its routing
test costs, stop after the first hook call of each calibration; the
check's docstring says it is rank-local. Rebased over #2745's fixes,
taking back the `torch_a2a` test fixtures #2745 no longer carries.
- Rebased over #2745's second-round fixes: the grouped interceptor
records the offsets and clears the eager expert index, and #2745's test
that captures skip the grouped batch gives way to this PR's per-expert
capture tests.
- Review follow-up, 2026-10-28, third round: drops the note #2745 now
adds, that the grouped captures skip their batch, since here they split
it per expert. A new test pins that a calibration forward stops
counting, and skips its host-syncing routing test, once a hook has
fired; it fails with that gate removed.
- Review suggestions, 2026-10-28, fourth round: the experts forward
clears `_grouped_offs` when it returns, so a capture reached outside a
forward skips instead of splitting its input by a finished forward's
offsets; the interception check's docstring says that a rank whose
experts saw no rows waits in its next collective until the launcher
stops it or the collective times out, so the error is in another rank's
log. Rebased over #2745's cleanup.
- Rebased on 2026-10-28, evening, onto #2745's head `e7c449fcf87`, after
#2745 moved onto main `7092db6ac5b` and took its last review rounds.
Conflicts only in the changelog (both sides' entries kept),
`_QuantFusedExperts`'s docstring (it keeps #2745's note that the
`grouped_mm` fallback does not call `F.linear`) and the fused-experts
tests (#2745's transposed-container tests kept). The FP8 experts
wrapper's comment from #2745 now names
`normalize_experts_impl_on_the_fly`, this PR's name for the function
that keeps it on eager. pre-commit, mypy included, passes at this head
against main.
- Review fix, 2026-10-28, evening: `calibrate()`'s interception check
reads the HF plugin from `sys.modules` instead of importing it, so a
plugin that failed to load (which `import_plugin` only warns about) no
longer fails every calibration with its error.
`test_a_plugin_that_failed_to_load_leaves_calibration_alone` breaks the
plugin's import and fails without the change. pre-commit, mypy included,
passes at this head.
- Split, 2026-10-28, evening, at the bot review's request: the FSDP2
window's ownership filter and its ownership test moved to #3088, with a
test that the window leaves an ignored expert-parallel DTensor sharded.
This PR sits on a helper base merging #2745 and #3088, and its source
diff is 334 lines. At this head `test_fused_experts.py`,
`test_moe_a2a.py` and `test_utils.py` pass on CPU (200 passed, 6
skipped), and pre-commit, mypy included, passes against main.
- Review suggestions, 2026-10-28, evening: `_local_experts` refuses a
DTensor whose local shard does not hold the module's experts (one
replicated over the ranks, say) instead of indexing the global experts,
and the native FP8 wrapper's `unpack_weight` refuses expert weights
sharded over the experts instead of filling a buffer for every expert.
One two-rank test covers both and fails without either. The suggested
all-reduce in the interception check is declined (a collective in every
distributed `calibrate()`). Focused tests pass (201 passed, 6 skipped),
and pre-commit passes against main.
- Review suggestions, 2026-10-28, late evening: the per-expert split of
a grouped batch requires exactly one exclusive end per expert, within
the rows, as transformers' `grouped_mm` and `a2a_moe` build the offsets,
and raises on any other form (a leading zero would credit each expert
with the previous one's rows). A test feeds three such forms and fails
without the check. The `_grouped_offs` comment names all three captures
and the attribute's role as the which-forward-ran signal. Rebased onto
the helper base re-merged with #3088's update; focused tests pass (204
passed, 6 skipped), and pre-commit passes against main.
- Review suggestion, 2026-10-28, late evening: `_local_experts` checks
for a DTensor through the plugin's guarded `torch.distributed.tensor`
import, so a torch without distributed does not fail every fused-MoE
call. Rebased onto the helper base re-merged with #3088's update;
focused tests pass (204 passed, 6 skipped), and pre-commit passes
against main.
- 2026-10-28, night: `hf_ptq.py` gains `--experts_implementation`,
passed on every load path: `get_model` (the compressed-tensors branch
included), the low-memory load, and the FSDP2 load, through the config
its meta model is built from. The changelog's way back to earlier
releases' scales is now reachable from the script. A test loads a tiny
Qwen3-MoE with `eager` and fails without the plumbing. The
`tests/examples/hf_ptq` unit tests pass (110), and pre-commit passes
against main.
- Review fixes, 2026-10-28, night: on the FSDP2 path the flag is set on
the pre-fetched config's `_experts_implementation`, as
`build_meta_causal_lm` sets `attn_implementation`, and a test captures
the config the FSDP2 loader receives (it fails without the assignment);
`get_model` passes the flag only when given, on every branch.
`calibrate()`'s new error for a fused-experts forward that reaches
neither hook has its own changelog entry. Rebased onto #2745, itself
rebased onto main `bc0b6e03237` after #3088 merged; focused and
`tests/examples/hf_ptq` tests pass (316 passed, 6 skipped), and
pre-commit passes against main.
- Review suggestions and a rebase, 2026-10-28, late night: the backend
normalization warns once for each change it makes to a caller's choice,
but not for a `None` backend pinned to eager, which already dispatches
to the eager forward. Rebased onto #2745 on main `e9f432affbb` (#3091):
`DTensor` now comes from the plugin's plain `torch.distributed.tensor`
import, since the guarded import is gone with the torch 2.10 floor.
Focused and `tests/examples/hf_ptq` tests pass (325 passed, 6 skipped),
and pre-commit passes against main.
- Review suggestion and a rebase, 2026-10-28, late night: the
backend-redirect notice goes through `warn_rank_0`, so an
expert-parallel run prints one copy. Rebased onto #2745's comment and
warning-text update; `_is_fused_experts_module`'s new docstring names
`normalize_experts_impl_on_the_fly`, which keeps GPT-OSS on eager here.
`test_fused_experts.py` and `test_utils.py` pass (197), and pre-commit
passes against main.
- Bot review of `2e253a509d0`, 2026-10-28, late night: the FSDP2 test
runs a Qwen3.5-MoE config too and checks the text config its experts
dispatch on, which the one assignment reaches through transformers'
recursive `_experts_implementation` setter (5.5.4 and 5.18; a tiny
Qwen3.5-MoE built on meta dispatches its experts on `eager` with the
flag and on `grouped_mm` without it). The test fails when the value
reaches the root config only, and the comment at the assignment says the
setter carries it. Declined: resolving the routing indices' parameter
name from the forward's signature, since every transformers MoE block
passes them positionally. The `tests/examples/hf_ptq` unit tests pass on
5.18 and 5.5.4 (120), and pre-commit passes against main.
- Bot review of `042e4bf1e55` (approved), 2026-10-28, late night: the
interception error names the ways forward: a class no backend change
redirects needs its own quantized wrapper, and meanwhile the rest of the
model quantizes with these modules' quantizers disabled, which the check
honours. Declined: caching the grouped offsets' host copy across the two
projections' captures (one small sync per experts module per batch under
GPTQ, local Hessian and imatrix, whose per-expert matmuls dominate).
pre-commit passes against main.
- Rebased on 2026-10-28, late night, onto main `1ca8bd1fc4d` after #2745
merged (squashed with the tree of its last head, so this PR's tree is
unchanged); the base is now `main`, so the GPU tests run. pre-commit
passes against main.
- CI fix, 2026-10-28, late night: the first run on main failed two cases
of `tests/examples/hf_ptq/test_hf_ptq_vision_quantization.py` in the
trtllm `hf_ptq` lane (2 failed, 267 passed): its stand-in for hf_ptq's
parsed arguments lacked the new `experts_implementation`, which
`load_model` now reads. The stand-in gains it; the file passes on
transformers 5.18 and 5.5.4.
- Rebased on 2026-10-28, late night, onto main `bdc45368d10` (#2736
merged), with its own diff unchanged. Its tests with #3067's, #2736's
`test_distributed.py` and the `hf_ptq` unit tests pass (376), and
pre-commit passes against main.
- CI, 2026-10-29: the first full run on main at `df2e3fe4a76`, GPU lanes
included, passes.
- Split, 2026-10-29, at a reviewer's request: the expert-parallel
indexing fix, its tests and a Bug Fixes changelog entry moved to #3122
([1/2], on main), and this PR now sits on it, its own diff otherwise
unchanged. The review's three smaller items are taken: the interception
check reduces its verdict over the experts' expert-parallel group (a
two-rank test fails with the rank-local check); a test checks that every
experts class transformers decorates with `use_experts_implementation`
takes the routing indices second (transformers 5.5.4 and 5.18); and
`--experts_implementation` offers `eager` and `grouped_mm` as argparse
choices. pre-commit, mypy included, passes against main; this PR's
tests, `test_moe_a2a.py` and the `hf_ptq` unit tests pass (315).
- Review suggestion (Claude), 2026-10-29: the interception check reads
the expert-parallel group from the experts' weight, a DTensor sharded
over the experts from transformers 5.16 on; from 5.6 to 5.15 the shards
are plain tensors that name no group, so the check stays rank-local
there, as before. The docstrings say so. Restacked onto #3122's two
follow-ups; pre-commit passes against main.
- Comment, 2026-10-29: at `calibrate()`'s gate for the interception
check, a sentence says that both conditions, the forward loop having run
and the HF plugin being loaded, must come out the same on every rank,
since under expert parallelism the check all-reduces over the experts'
group. pre-commit passes against main.
- Test fix, 2026-10-29:
`tests/gpu/torch/quantization/test_fsdp2_autoquant.py`'s 2-GPU KV
AutoQuantize test builds hf_ptq's arguments by hand and lacked the new
`experts_implementation`, which `load_model`'s FSDP2 branch reads. It
now passes `None`. The 1-GPU PR runners skip that test, and main's
nightly runs it. No other test or example builds hf_ptq's arguments by
hand on a path that reads the flag. pre-commit passes against main.
- Rebased on 2026-10-30 onto #3122 on main `99c3834f62c`, with its own
diff unchanged and no conflicts. pre-commit passes against main.
- Review (sychen52), 2026-10-30: with every weight quantizer collecting
statistics without quantizing (calibration), the grouped forward returns
the stored weight itself instead of building a weight-sized copy slice
by slice on each forward. A CPU test checks the identity and the
collected per-expert amax, and fails on the previous code. On CPU, one
call on a Qwen3-30B-A3B-sized `gate_up_proj` (128 × 1536 × 2048, bf16,
768 MiB) raised peak RSS by 755 MiB before and 0 MiB after; a GPU
`max_memory_allocated` comparison is queued. The changelog entry now
says the experts stay on `grouped_mm` for every forward after
`mtq.quantize` or `mto.restore` (QAT/QAD, AutoQuantize scoring,
fake-quant evaluation), with `experts_implementation="eager"` as the way
back. `test_fused_experts.py` and `test_moe_a2a.py` pass on transformers
5.18 (220) and 5.5.4 (214, 6 skipped); pre-commit passes against main.
- Review (kevalmorabia97), 2026-10-30: the three changes no test covered
now have one each, or are gone. The `_local_experts` call in
`_quantize_grouped_weight` is dropped as unreachable: the weight arrives
as this rank's local shard inside the experts forward. New tests pin
that a grouped forward reaching neither hook is counted whatever its
routing indices hold, and that a capture after a grouped forward does
not split by its offsets. The calibration pass-through calls only
quantizers that calibrate (Claude's suggestion), with a constant-amax
test case. Each test fails with its change reverted.
`test_fused_experts.py`, `test_moe_a2a.py` and `test_hf_ptq_args.py`
pass on transformers 5.18 (283) and 5.5.4 (277, 6 skipped); pre-commit
passes against main.
- GPU calibration peak memory, 2026-10-30 (sychen52's request):
`torch.cuda.max_memory_allocated` around the `mtq.quantize(...)` call
only, after `reset_peak_memory_stats`. Qwen3-30B-A3B on the default
`grouped_mm` backend, recipe `general/ptq/fp8_default-kv_fp8`,
`calib_size` 32, `batch_size` 1, one NVIDIA GB200 for both revisions
(torch 2.11, transformers 5.13.0, CUDA 13.1).
| Revision | Calibration peak (bytes) | MiB |
|---|---|---|
| Before the pass-through change (`3f3544435d8`) | 864,901,632 | 824.8 |
| After (`27c5f77feac`) | 174,759,936 | 166.7 |
| Drop | 690,141,696 | 658.2 |
- Rebased on 2026-10-30 onto main `55898ee4e3b` after #3122 merged,
dropping #3122's pre-squash commits; its own diff is unchanged, with no
conflicts. pre-commit passes against main.
- CI fix, 2026-10-30: the routing-order test read transformers' modeling
sources with the platform's default encoding, cp1252 on Windows, which
16 of them (`modeling_altclip.py`, say) cannot be decoded with; it now
reads them as UTF-8, and the windows unit job's only failure goes with
it. The `linux` job's one failure on the previous head, a NAS sorting
test this PR does not touch, passed on rerun at the same commit.
pre-commit passes against main.
- `eager` against main, byte for byte, 2026-10-30 (re-verifying at this
PR's head the claim that `experts_implementation="eager"` reproduces
main): PTQ of Qwen/Qwen3-30B-A3B with recipe
`general/ptq/nvfp4_default-kv_fp8`, `calib_size` 512, calibration
sequence length 2048, `batch_size` 1, cnn_dailymail, without expert
parallelism (one process), on one NVIDIA GB200 (torch 2.11, transformers
5.13.0, CUDA 13.1). This PR at `27c5f77feac` with
`--experts_implementation eager`, against main at `99c3834f62c`, both
compared at zero tolerance. Later revisions of this PR leave its own
diff unchanged (a rebase) or change tests only.
| Scale kind | Tensors | Differing |
|---|---|---|
| `input_scale` | 18,624 | 0 |
| `weight_scale` | 18,624 | 0 |
| `weight_scale_2` | 18,624 | 0 |
| `k_scale` | 48 | 0 |
| `v_scale` | 48 | 0 |
- Review (juhi10071998), 2026-10-30: under transformers' own expert
parallelism the grouped forward sorts the rows routed to other ranks'
experts past `offs[-1]`, which the grouped GEMM neither reads nor
writes; before transformers 5.17 nothing zeroes them in the down
projection's input, so its input quantizer calibrated on uninitialized
rows. While an input quantizer collects statistics it now sees only the
rows before `offs[-1]` (a host sync, during calibration only). A test
hands the grouped projections a NaN or a large finite tail, as pre-5.17
expert parallelism leaves it, and checks the input amaxes match a run
without one; both cases fail without the change.
`test_fused_experts.py`, `test_moe_a2a.py`, `test_calib.py` and
`test_hf_ptq_args.py` pass on transformers 5.18 (307) and 5.5.4 (301, 6
skipped); pre-commit passes against main.
- Rebased on 2026-10-30 onto main `059588b89a5` (after #3131, #3124,
#2751, #3104, #3128, #3134 and #3149 merged), with its own diff
unchanged and no conflicts. pre-commit passes against main.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ❌ Fused-experts MoE calibration
now runs on `grouped_mm`, so some activation scales differ from earlier
releases; `experts_implementation="eager"` restores the earlier
checkpoint exactly.
- 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](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ Backward Breaking Changes.
- Did you get Claude approval on this PR?: ✅ At `042e4bf1e55`; the
error-message change after it is from that review.
### Additional Information
Split out of #2745 (part 3 of 21 of expert-parallel PTQ for Hugging Face
MoE models), which merged on 2026-10-28.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Behavior Changes**
* Hugging Face MoE calibration now uses Transformers’ default
`grouped_mm` backend instead of forcing eager execution. Calibrated
activation scales may differ; set `experts_implementation="eager"` to
use the earlier calibration behavior.
* Eager-only expert wrappers continue to use eager execution. Using them
with `torch_a2a` raises an error.
* **Bug Fixes**
* Improved calibration support for grouped and expert-parallel
execution, including local expert weights and experts receiving no
routed inputs.
* Calibration now reports when fused experts run without interception or
required inputs are not captured.
* Improved FSDP2 parameter handling during weight access and writeback.
* **New Features**
* Added calibration support for newer Transformers fused-expert
implementations, including Qwen3-MoE and native FP8 experts.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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What does this PR do?
Type of change: Bug fix
Megatron-Core fuses GatedDeltaNet's
[q, k, v, z, beta, alpha]into onein_proj(aTELayerNormColumnParallelLinear) behind one weight and one input quantizer, so Megatron fake quant quantizes the beta / alpha gate rows. Every export keeps them in BF16:export_mcore_gpt_to_hf;save_hf_pretrained_modelopt(feat(quant): export quantized GatedDeltaNet input projections to HF NVIDIA-NeMo/Megatron-Bridge#6714);*linear_attn.in_proj_a*/*in_proj_b*, since vLLM'sin_proj_bais too narrow for FP4 kernels.PTQ evaluation and QAD / QARL of Qwen3.5 / Qwen3.6 therefore trained against quantization error on rows that deployment doesn't have. This was raised in NVIDIA-NeMo/Megatron-Bridge#6714 (comment).
_QuantGatedDeltaNetnow marks the gate rows of itsin_proj, by name fromin_proj_split_names/in_proj_split_sections, local to the TP rank. For those rows:apply_without_qdqapplies a quantizer's transforms without its QDQ).in_proj's quantizers, both match BF16; with them, see the known limitation below.fold_weightand the vLLM fake-quant exporter (export_mcore_gpt_to_hf_vllm_fq) keep the rows unquantized, so--vllm_fakequant_exportand the library export match too.The
qkv/zrows are unchanged, and the fake-quant weight cache still hits, since nothing is modified in place. Otherin_projlayer types (non-TE) warn and stay fully quantized.Not changed: weight calibration (
maxrunsweight_only_quantizeon the whole weight) still includes the gate rows in thein_projweight amax. Both exporters use that same amax forin_proj_qkv/in_proj_z, so fake quant and export stay consistent. Excluding the rows would need a calibration-wide change; GPTQ writes back throughiter_weights_for_calibration.Why mark rows in the fused GEMM rather than the alternatives:
in_projinto quantized and BF16 linears would change Megatron-Core's GatedDeltaNet module structure, its sharded checkpoint layout and the TP / CP split ofin_proj, in Megatron-Core or in a ModelOpt fork of the module.in_proj; there is no per-row switch.Computing the few gate rows separately inside the existing GEMM patch keeps the module and checkpoints unchanged and costs one small extra GEMM.
Known limitation (pre-existing, every row): the TE LayerNormLinear backward does not apply the input quantizer's pre-quant scale / rotation derivatives in dgrad, nor weight-quantizer transform derivatives in wgrad. The gate rows follow the same convention; a fix for all rows is tracked in OMNIML-6311.
Usage
No change to how models are quantized: it applies whenever a Megatron-Core GatedDeltaNet model is. Adds the helper
modelopt.torch.quantization.utils.apply_without_qdq.Testing
New
test_in_proj_keeps_gates_unquantizedandtest_in_proj_keeps_gates_unquantized_tp2(TP1 and TP2), with FP8 W8A8 onin_proj:VllmFqGPTModelExporterwrites the gate weights unquantized;fold_weightkeeps the gate weights.Each check fails without its fix: on
main, 21 / 32 gate outputs differ; without the backward fix, the gate-weight gradient; without the exporter change, the exported gate rows; without the input transforms, the scaled-input gate output. The input-gradient check guards the dgrad path, which already used the unquantized gate weight.New
test_mcore_vllm_export_gdnrunsexport_mcore_gpt_to_hf_vllm_fqon a tiny Qwen3.5-VL. The model is CPU-offloaded, and itsin_projweight quantizer has a weight-onlyrotate_back, which only runs on CUDA.in_proj_b/in_proj_aweights equal the Megatron rows with only that transform applied.quantizer_state.pth/quant_recipe.yaml, whilein_proj_qkv/in_proj_zkeep their input quantizers._fakequant_weight's CUDA staging (Expected x.is_cuda()).tests/gpu_megatron/torch/quantization/plugins/test_megatron_gated_delta_net.py,test_transformer_engine.py,tests/gpu_megatron/torch/export/test_unified_export_megatron.py,test_quantized_mbridge_export.pyon 2× RTX 6000 Ada innemo:26.10.rc9(fla-core 0.5.1), with two Megatron-Core versions:The failure,
test_matches_megatron_exporter[llama-nvfp4], fails the same way onmainin that container: TE's fused RoPE has no kernel for this GPU.On the latest commit:
test_megatron_gated_delta_net.pyandtest_vllm_fakequant_megatron_export.py, 11 passed, in the same container with the CI Megatron-Bridge pin.Cost at Qwen3.6-35B-A3B
in_projsize (2048 → 12352, 64 gate rows, 8192 tokens, FP8 W8A8 fwd + bwd): the gate GEMM and weight concat add about 0.07 ms per call, within run-to-run noise of the ~10 ms total. Peak memory is unchanged.Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
Requested for the Qwen3.6 QAD / QARL studies.
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