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Skip MTP forward and recomputation when freeze_mtp is enabled - #2891
--freeze_mtp previously froze the student's MTP-specific parameters and skipped its cross-entropy loss, but both teacher and student still executed the MTP transformer. The student's MTP activations could also be recomputed during backward through shared backbone tensors.
Explicitly freezing MTP now bypasses its forward and loss paths for both models. The GPT wrapper temporarily disables both MTP gates using a shallow config copy, leaving the decoder's shared config intact for MoE auxiliary losses and activation recomputation. The original config and MTP flags are restored even if forward raises, and MTP parameters remain available in checkpoints and exports. The default behavior and the automatic freeze rule are unchanged.
Usage
Add --freeze_mtp to an existing examples/megatron_bridge/distill.py command.
Testing
Validated the exact signed commit d14cfcab33d5b6bc212c0da049ac91641f1e5076 on Volc A800-SXM4-80GB GPUs, using the existing NeMo 26.08 workspace image (PyTorch 2.13.0a0+8145d630e8.nv26.06, CUDA 13.3). CUDA extensions were compiled for sm_80.
Run
Result
Full Megatron distillation module, TP2
26 passed; no failures, errors or skips
MTP regressions, one GPU
11 passed; no failures, errors or skips
Same new freeze checks with the pre-fix plugin, TP2
2 expected failures; dense and MoE both report Frozen MTP must not execute forward or recomputation
For the negative control, only modelopt/torch/distill/plugins/megatron.py was restored from parent commit d8abfb447d; the new tests and test helper remained unchanged. The selected cases were freeze_mtp_with_quantized_mtp and True-1.0.
The extended existing training-step test covers dense/MoE models with full activation recomputation, teacher/student MTP execution guards, backbone gradients, unchanged frozen MTP weights after AdamW, MTP checkpoint keys, and config restoration after normal and exceptional forward. The quantizers in this test mark quantization state; it does not perform numerical PTQ or a full-model QAD benchmark.
JUnit reports confirm the counts above. Task t-20261020201232-kq9xt completed with exit code 0, and source hashes matched the signed commit before and after execution. The two positive runs took 273.60 s and 33.65 s, and the negative run took 258.16 s; these include fixture setup and CUDA compilation and are not performance benchmarks.
Pre-commit on all five changed files (including Ruff, mypy, Bandit and Markdown checks) and git diff --check also passed.
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ The API, checkpoint layout, and behavior when the flag is omitted are unchanged.
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?: ✅ Extended two existing GPU tests; no new test functions or unit-test overhead.
Did you update Changelog?: N/A. Follow-up fix for freeze_mtp in the same unreleased cycle.
Did you get Claude approval on this PR?: ❌ Not requested.
Additional Information
Related: #2784 changes the freeze override API; this PR retains the current API and addresses execution when freeze_mtp=True.
Summary by CodeRabbit
New Features
With --freeze_mtp, MTP forward passes and loss computation are bypassed for both the teacher and student. Frozen MTP weights remain available in checkpoints and exports, and the modules are not executed or activation-recomputed during training.
Documentation
Updated command-line help and tutorial guidance to clarify the behavior of --freeze_mtp and distinguish it from the default quantization-based MTP loss skip.
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When freeze_mtp is true, Megatron distillation temporarily bypasses MTP processing during teacher and student forwards. The changes update option descriptions and add GPU test coverage for loss calls, recomputation, retained MTP state, and optional MoE configurations.
Changes: Frozen MTP execution
Layer / File(s)
Summary
Configure and apply the MTP bypass modelopt/torch/distill/plugins/megatron.py, examples/megatron_bridge/distill.py, examples/megatron_bridge/tutorials/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/README.md
The freeze_mtp documentation, warning, CLI help, and tutorial describe bypassing teacher and student MTP forward and loss paths. A context manager temporarily disables MTP processing and sets the MTP layer count to zero during both forwards, then restores the original state.
With --freeze_mtp, pipeline-parallel models that tie embeddings may compute logits from a different output weight than the one checkpointed. Configurations that name an MTP module in intermediate distillation losses may also fail during training. Both should be addressed or explicitly accepted before merge. Default behavior without the flag is unchanged.
Pre-merge checks: 5 · 1
Failed checks: 1 warning
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Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 5 passed
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Title check
The title clearly and concisely describes the main change: skipping MTP forward processing and recomputation when freeze_mtp is enabled.
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Security Anti-Patterns
No listed security anti-pattern was introduced. The production change only adds a context manager and shallow config copy; it adds no unsafe torch.load, numpy.load(..., allow_pickle=True), hardcod…
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No listed security anti-pattern was introduced. The production change only adds a context manager and shallow config copy; it adds no unsafe torch.load, numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, string eval/exec, or # nosec. The example's existing trust_remote_code value remains caller-configurable. No dependency manifest changed.
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Write docstrings for the functions missing them to satisfy the coverage threshold.
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The mechanism looks right: in the Megatron-LM I checked, GPTModel and the hybrid model gate MTP only through mtp_process and config.mtp_num_layers, both of which this bypasses, and the dense and MoE tests with the forward pre-hooks and negative control cover it well.
Needs action:
Add a short real-model run with --freeze_mtp on an MTP model, ideally hybrid and with PP>1 (the README you edit targets Nemotron-3.5-Super-VL). The tests only use GPT with PP=1, and the hybrid _forward takes a different path. Report that it trains, the MTP weights are identical in the saved checkpoint, export keeps the MTP keys, and step time and memory against a run without the flag.
No action needed:
The shallow config copy per forward is cheap, and the original config is restored in finally.
#2784 changes the same flag's override API, so whichever merges second needs a rebase.
The mechanism looks right: in the Megatron-LM I checked, GPTModel and the hybrid model gate MTP only through mtp_process and config.mtp_num_layers, both of which this bypasses, and the dense and MoE tests with the forward pre-hooks and negative control cover it well.
Needs action:
Add a short real-model run with --freeze_mtp on an MTP model, ideally hybrid and with PP>1 (the README you edit targets Nemotron-3.5-Super-VL). The tests only use GPT with PP=1, and the hybrid _forward takes a different path. Report that it trains, the MTP weights are identical in the saved checkpoint, export keeps the MTP keys, and step time and memory against a run without the flag.在 MTP 模型上使用 --freeze_mtp 添加一次简短的真实模型运行，最好采用混合模式且 PP>1（你编辑的 README 面向 Nemotron-3.5-Super-VL）。测试仅使用 PP=1 的 GPT，而混合 _forward 走的是不同路径。报告其能够训练、保存的检查点中 MTP 权重完全一致、导出保留 MTP 键，以及与不带该标志的运行相比的步时和内存。
No action needed:
The shallow config copy per forward is cheap, and the original config is restored in finally.
The non-last-PP-stage comment is in commit 8f166bd.
I additionally validated the real NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 checkpoint using MCore HybridModel (actual runtime class: modelopt.torch.opt.dynamic.DistillHybridModel): 52 body layers (23 Mamba, 23 MoE, 6 Attention), with its native Attention+MoE MTP block. Both conditions used TP2/PP2/EP2/ETP1 on the same four A80080GB GPUs, BF16 weights/gradients, precision-aware optimizer with full CPU optimizer offload, seq 256/MBS 1/GBS 2, and the same seed and initial weights.
Both freeze and control completed 8 steps with finite losses and no skipped iterations. All non-MTP body parameters remained trainable and body samples changed on every rank. Freeze had zero student/teacher MTP module hooks and unchanged MTP hashes. Without the flag, MTP hooks were active and 133/139 and 135/139 MTP parameter tensors changed on the two last-stage ranks.
The saved step 8 checkpoint's 14 MTP tensor entries reconstructed all 278 rank-local parameter hashes exactly. A fresh-process 1-step model-only reload matched both saved body samples and MTP hashes. The native standalone HF export retained all 270 MTP keys with identical dtype, shape and SHA256; 16 exported body samples matched the trained checkpoint and differed from the original weights.
Measured median step time (max rank per step, steps 3–8) was 13.786s with freeze versus 12.948s without. Step peak allocated memory was 51.315 versus 52.512GiB; reserved was 68.680 versus 71.498GiB. This short CPU-offloaded pair did not demonstrate a speedup: freeze was 6.48% slower, although allocator memory was lower. Initialization/checkpoint/export are excluded from these step metrics.
Scope: synthetic tokens, one ordered short pair, model-only recovery (optimizer state was not restored), and the recorded locally patched Bridge/MCore snapshots (base revisions 2a8fcdd05181ffcef261192f71ff5910d0005bf2 / 4fe0cc3fdfb5ca0e5d0c7f25897c661632469672). This validates the training/checkpoint/export mechanics.
Sources consulted:
- https://raw.githubusercontent.com/NVIDIA/Megatron-LM/core_v0.16.0/megatron/core/models/gpt/gpt_model.py
- Megatron-LM/megatron/core/models/gpt/gpt_model.py at core_v0.16.0 · NVIDIA/Megatron-LM · GitHub — https://github.com/NVIDIA/Megatron-LM/blob/core_v0.16.0/megatron/core/models/gpt/gpt_model.py
- Megatron-LM/megatron/core/models/gpt/gpt_model.py at main · NVIDIA/Megatron-LM · GitHub — https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/models/gpt/gpt_model.py
- core.models.gpt.gpt_model — Megatron Core — https://docs.nvidia.com/megatron-core/developer-guide/0.16.0/apidocs/core/core.models.gpt.gpt_model.html
- …and 18 more
Script executed:
python3 - <<'PY'from urllib.request import urlopenurl = "https://raw.githubusercontent.com/NVIDIA/Megatron-LM/core_v0.16.0/megatron/core/models/gpt/gpt_model.py"text = urlopen(url, timeout=20).read().decode()lines = text.splitlines()for start, end in ((210, 245), (470, 585), (660, 688)): print(f"--- upstream gpt_model.py:{start}-{end} ---") for number in range(start, min(end, len(lines)) + 1): print(f"{number:4} {lines[number-1]}")PY
Repository: NVIDIA/Model-Optimizer
Length of output: 8735
Preserve tied output-weight selection during the MTP bypass.
With PP>1 and tied embeddings under Megatron-Core v0.16.0, setting mtp_process to False changes the main-logit selector from embedding.word_embeddings.weight to output_layer.weight. The last pipeline stage can therefore compute logits from a separate output-layer weight while checkpoint handling still preserves the embedding weight as the shared copy.
Keep the shared-weight selection while skipping MTP. Add a PP2 tied-embedding regression test that checks the main logits and checkpoint weights.
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Review comment at @modelopt/torch/distill/plugins/megatron.py at line 837:
Update the MTP bypass around `lm.mtp_process` so it skips MTP processing without
switching tied-embedding main-logit selection to `output_layer.weight`; preserve
selection of the shared embedding weight. Add a PP2 tied-embedding regression
test that verifies both main logits and checkpoint weights use the shared
weight.
After applying the fix, consider running `coderabbit review --agent` for local
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@wiederse The supplied code confirms the new DistillationConfig.__post_init__ validation. It rejects MTP references on either side of intermediate_layer_pairs when freeze_mtp=True.
However, this thread concerns tied output-weight selection during the MTP bypass. The configuration validation does not establish that this separate concern is fixed.
Did you intend this update for another thread? For this thread, please provide the corresponding fix or PP2 tied-embedding regression results that check main logits and checkpoint weights.
Learnings used
Learnt from: CR
Repo: NVIDIA/Model-Optimizer PR: 0
File: AGENTS.md:0-0
Timestamp: 2026-10-20T14:44:36.749Z
Learning: Source excerpt:
# Agent Instructions for ModelOpt
## Pre-push review pass
- **Correctness.** Validate before mutating, so a refusal leaves state
unchanged, and fail loudly rather than silently ignore an option or route.
Cover the layouts the change can meet: dtypes, tied or shared weights,
offloaded, FSDP2 and meta tensors, fused or grouped modules.
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What does this PR do?
Type of change: Bug fix.
--freeze_mtppreviously froze the student's MTP-specific parameters and skipped its cross-entropy loss, but both teacher and student still executed the MTP transformer. The student's MTP activations could also be recomputed during backward through shared backbone tensors.Explicitly freezing MTP now bypasses its forward and loss paths for both models. The GPT wrapper temporarily disables both MTP gates using a shallow config copy, leaving the decoder's shared config intact for MoE auxiliary losses and activation recomputation. The original config and MTP flags are restored even if forward raises, and MTP parameters remain available in checkpoints and exports. The default behavior and the automatic freeze rule are unchanged.
Usage
Add
--freeze_mtpto an existingexamples/megatron_bridge/distill.pycommand.Testing
Validated the exact signed commit
d14cfcab33d5b6bc212c0da049ac91641f1e5076on Volc A800-SXM4-80GB GPUs, using the existing NeMo 26.08 workspace image (PyTorch2.13.0a0+8145d630e8.nv26.06, CUDA 13.3). CUDA extensions were compiled forsm_80.Frozen MTP must not execute forward or recomputationFor the negative control, only
modelopt/torch/distill/plugins/megatron.pywas restored from parent commitd8abfb447d; the new tests and test helper remained unchanged. The selected cases werefreeze_mtp_with_quantized_mtp and True-1.0.The extended existing training-step test covers dense/MoE models with full activation recomputation, teacher/student MTP execution guards, backbone gradients, unchanged frozen MTP weights after AdamW, MTP checkpoint keys, and config restoration after normal and exceptional forward. The quantizers in this test mark quantization state; it does not perform numerical PTQ or a full-model QAD benchmark.
JUnit reports confirm the counts above. Task
t-20261020201232-kq9xtcompleted with exit code 0, and source hashes matched the signed commit before and after execution. The two positive runs took 273.60 s and 33.65 s, and the negative run took 258.16 s; these include fixture setup and CUDA compilation and are not performance benchmarks.Pre-commit on all five changed files (including Ruff, mypy, Bandit and Markdown checks) and
git diff --checkalso passed.Before your PR is "Ready for review"
CONTRIBUTING.md: N/A.freeze_mtpin the same unreleased cycle.Additional Information
Related: #2784 changes the freeze override API; this PR retains the current API and addresses execution when
freeze_mtp=True.Summary by CodeRabbit
--freeze_mtp, MTP forward passes and loss computation are bypassed for both the teacher and student. Frozen MTP weights remain available in checkpoints and exports, and the modules are not executed or activation-recomputed during training.--freeze_mtpand distinguish it from the default quantization-based MTP loss skip.