[OMNIML-3232] Support full TE spec for NemotronH HF-to-Megatron import by yueshen2016 · Pull Request #884 · NVIDIA/Model-Optimizer

yueshen2016 · 2026-02-12T22:52:20Z

What does this PR do?

Type of change: new feature

Overview: Enable full TE spec support for NemotronH (Mamba hybrid) models during HF-to-Megatron weight import via import_mcore_gpt_from_hf.

Previously, importing HF weights into a Megatron model built with the full TE spec (TELayerNormColumnParallelLinear, TEGroupedMLP, etc.) failed for NemotronH models due to two issues:

Grouped expert prefix bug: The experts.linear_fc1/fc2 import rules had a hard-coded mtp.layers.{} prefix, which was only correct for MTP layers. When regular decoder MoE layers use TEGroupedMLP (via the full TE spec), the importer generated incorrect HF keys (e.g., mtp.layers.27.mixer.experts.0.up_proj.weight instead of backbone.layers.27.mixer.experts.0.up_proj.weight).
Fused layer norm loading: In the full TE spec, layer norms are fused into TELayerNormColumnParallelLinear modules as layer_norm_weight. The importer's _name_remapping would crash trying to load layer_norm_weight from a non-existent HF path (e.g., backbone.layers.X.mixer.in_proj.layer_norm_weight), when the actual HF norm weight lives at backbone.layers.X.norm.weight.

Changes

mcore_nemotron.py:

Fixed grouped expert prefix from mtp.layers.{} to backbone.layers.{}. The _grouped_mlp_merging function already handles backbone → mtp replacement when is_mtp=True, so both decoder and MTP layers work correctly.
Added mapping={"layer_norm_weight": None} to in_proj and linear_fc1 rules to skip layer_norm_weight during _name_remapping (loaded separately via fused_norm).
Added fused_norm rule (NameRemapping("backbone.layers.{}.norm.weight")) to load HF norm weights into fused TE modules.

megatron_importer.py:

Added source_key is None check in _name_remapping to skip keys mapped to None in the mapping dict (keeps existing value instead of crashing on missing HF key).
Added fused norm loading in _import_mamba_layer: after loading in_proj, loads layer_norm_weight from HF via fused_norm rule when layer.norm is IdentityOp.
Added fused norm loading in _import_transformer_layer: loads layer_norm_weight into linear_qkv (when input_layernorm is IdentityOp) and into linear_fc1 (when pre_mlp_layernorm is IdentityOp).

Usage

The full TE spec is enabled via the --full-te-spec flag on the Megatron-LM side (separate PR). On the ModelOpt side, no user-facing changes are needed -- the import rules automatically handle both local spec and full TE spec models.

# Convert HF checkpoint to Megatron with full TE spec (megatron-lm side)
unset MLM_MODEL_CKPT && export MLM_MODEL_SAVE=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16_mlm && export HF_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
export PP=2
export MLM_EXTRA_ARGS="--full-te-spec"
bash convert.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

# Quantize the converted checkpoint (megatron-lm side)
export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16_mlm
export MLM_MODEL_SAVE=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm
export HF_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
export PP=2 && export TP=4 && export EP=4 && export ETP=1
export MLM_EXTRA_ARGS="--full-te-spec"
bash quantize.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 FP8_DEFAULT_CFG

# Generate
export PP=2 && export TP=4 && export EP=4 && export ETP=1
export MLM_EXTRA_ARGS="--full-te-spec"
export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm && ./generate.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

# MMLU
export PP=2 && export TP=4 && export EP=4 && export ETP=1
export MLM_EXTRA_ARGS="--full-te-spec"
export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm && export MLM_EXTRA_ARGS="--fraction 0.05 --disable-tqdm" && ./mmlu.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

Testing

Tested end-to-end: HF → Megatron conversion → FP8 quantization → inference (generate) → MMLU evaluation with Nemotron-3-Nano-30B-A3B-BF16.
Verified the resulting model structure matches Megatron-Bridge's TE spec output (TELayerNormColumnParallelLinear, TEGroupedMLP, IdentityOp norms, etc.).
Verified quantized model produces coherent text generation outputs.
Verified backward compatibility: all changes are no-ops for existing local-spec pipelines (guarded by IdentityOp checks, hasattr checks, and "fused_norm" in self.rules checks).

Before your PR is "Ready for review"

Make sure you read and follow Contributor guidelines and your commits are signed.
Is this change backward compatible?: Yes -- all changes are guarded by conditions that only activate for full TE spec models. Local spec models follow the exact same code paths as before.
Did you write any new necessary tests?: No
Did you add or update any necessary documentation?: No
Did you update Changelog?: No

Additional Information

Companion megatron-lm changes (separate PR):

megatron/core/post_training/modelopt/mamba/model_specs.py: Added use_full_te_spec parameter to return canonical mamba_stack_spec from mamba_layer_specs.py.
megatron/post_training/model_builder.py: Passes use_full_te_spec=args.full_te_spec to get_mamba_stack_modelopt_spec.
megatron/post_training/arguments.py: Added --full-te-spec CLI flag.
examples/post_training/modelopt/convert_model.py: Skip moe_grouped_gemm=False override when --full-te-spec is set.

Summary by CodeRabbit

New Features
- Added support for loading fused normalization weights during model import.
Bug Fixes
- Improved weight mapping logic to correctly skip redundant layer norm weights in specialized model architectures.
Refactor
- Reorganized expert model parallel configuration paths for better compatibility with mixed parallel processing settings.

coderabbitai · 2026-02-12T22:52:36Z

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📝 Walkthrough

Walkthrough

The changes update model import/export handling to support fused layer normalization in transformer-based models. Mapping configurations skip direct layer_norm_weight loading, while new logic routes these weights through dedicated fused_norm rules. Expert layer paths are reworked from MTP to backbone-based paths.

Changes

Cohort / File(s)	Summary
Nemotron Plugin Mappings `modelopt/torch/export/plugins/mcore_nemotron.py`	Updated in_proj and linear_fc1 mappings to skip layer_norm_weight via mapping hints. Introduced new fused_norm rule to load norm weights via dedicated path. Reworked expert layer mappings to use backbone paths with COL_ETP/ROW_ETP, removing is_mtp annotations.
Megatron Importer Fusion Logic `modelopt/torch/export/plugins/megatron_importer.py`	Added support for None mapping values to preserve keys. Implemented TE-specific fused norm handling blocks in _import_mamba_layer and _import_transformer_layer that detect IdentityOp norms and route layer_norm_weight through fused_norm rule when available.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

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Merge Conflict Detection	⚠️ Warning	❌ Merge conflicts detected (2 files): ⚔️ `modelopt/torch/export/plugins/mcore_nemotron.py` (content) ⚔️ `modelopt/torch/export/plugins/megatron_importer.py` (content) These conflicts must be resolved before merging into `main`.	Resolve conflicts locally and push changes to this branch.

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Title check	✅ Passed	The title clearly and concisely summarizes the main objective: adding full Tensor-Expression spec support for NemotronH model imports, which is the primary change in this pull request.

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Commit unit tests in branch yueshen/TE-spec-MLM

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codecov · 2026-02-12T23:04:47Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 73.74%. Comparing base (ae69d5d) to head (5ddcbe7).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #884   +/-   ##
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  Coverage   73.74%   73.74%           
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kevalmorabia97 · 2026-02-13T02:43:39Z

Can we make sure previous modelopt spec continues to work? Pruning doesnt support full TE spec yet

kevalmorabia97 · 2026-02-13T08:46:18Z

No concerns from pruning-support POV where we still need to use older non-full-TE modelopt spec

yueshen2016 · 2026-02-13T23:02:32Z

Can we make sure previous modelopt spec continues to work? Pruning doesnt support full TE spec yet

Yes, previous one is still working.

Signed-off-by: James Shen <yueshen@nvidia.com>

NVIDIA#884) ## What does this PR do? **Type of change:** new feature **Overview:** Enable full TE spec support for NemotronH (Mamba hybrid) models during HF-to-Megatron weight import via `import_mcore_gpt_from_hf`. Previously, importing HF weights into a Megatron model built with the full TE spec (`TELayerNormColumnParallelLinear`, `TEGroupedMLP`, etc.) failed for NemotronH models due to two issues: 1. **Grouped expert prefix bug**: The `experts.linear_fc1/fc2` import rules had a hard-coded `mtp.layers.{}` prefix, which was only correct for MTP layers. When regular decoder MoE layers use `TEGroupedMLP` (via the full TE spec), the importer generated incorrect HF keys (e.g., `mtp.layers.27.mixer.experts.0.up_proj.weight` instead of `backbone.layers.27.mixer.experts.0.up_proj.weight`). 2. **Fused layer norm loading**: In the full TE spec, layer norms are fused into `TELayerNormColumnParallelLinear` modules as `layer_norm_weight`. The importer's `_name_remapping` would crash trying to load `layer_norm_weight` from a non-existent HF path (e.g., `backbone.layers.X.mixer.in_proj.layer_norm_weight`), when the actual HF norm weight lives at `backbone.layers.X.norm.weight`. ### Changes **`mcore_nemotron.py`**: - Fixed grouped expert prefix from `mtp.layers.{}` to `backbone.layers.{}`. The `_grouped_mlp_merging` function already handles `backbone` → `mtp` replacement when `is_mtp=True`, so both decoder and MTP layers work correctly. - Added `mapping={"layer_norm_weight": None}` to `in_proj` and `linear_fc1` rules to skip `layer_norm_weight` during `_name_remapping` (loaded separately via `fused_norm`). - Added `fused_norm` rule (`NameRemapping("backbone.layers.{}.norm.weight")`) to load HF norm weights into fused TE modules. **`megatron_importer.py`**: - Added `source_key is None` check in `_name_remapping` to skip keys mapped to `None` in the mapping dict (keeps existing value instead of crashing on missing HF key). - Added fused norm loading in `_import_mamba_layer`: after loading `in_proj`, loads `layer_norm_weight` from HF via `fused_norm` rule when `layer.norm` is `IdentityOp`. - Added fused norm loading in `_import_transformer_layer`: loads `layer_norm_weight` into `linear_qkv` (when `input_layernorm` is `IdentityOp`) and into `linear_fc1` (when `pre_mlp_layernorm` is `IdentityOp`). ## Usage The full TE spec is enabled via the `--full-te-spec` flag on the Megatron-LM side (separate PR). On the ModelOpt side, no user-facing changes are needed -- the import rules automatically handle both local spec and full TE spec models. ```bash # Convert HF checkpoint to Megatron with full TE spec (megatron-lm side) unset MLM_MODEL_CKPT && export MLM_MODEL_SAVE=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16_mlm && export HF_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 export PP=2 export MLM_EXTRA_ARGS="--full-te-spec" bash convert.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 # Quantize the converted checkpoint (megatron-lm side) export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16_mlm export MLM_MODEL_SAVE=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm export HF_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 export PP=2 && export TP=4 && export EP=4 && export ETP=1 export MLM_EXTRA_ARGS="--full-te-spec" bash quantize.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 FP8_DEFAULT_CFG # Generate export PP=2 && export TP=4 && export EP=4 && export ETP=1 export MLM_EXTRA_ARGS="--full-te-spec" export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm && ./generate.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 # MMLU export PP=2 && export TP=4 && export EP=4 && export ETP=1 export MLM_EXTRA_ARGS="--full-te-spec" export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm && export MLM_EXTRA_ARGS="--fraction 0.05 --disable-tqdm" && ./mmlu.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 ``` ## Testing - Tested end-to-end: HF → Megatron conversion → FP8 quantization → inference (generate) → MMLU evaluation with Nemotron-3-Nano-30B-A3B-BF16. - Verified the resulting model structure matches Megatron-Bridge's TE spec output (TELayerNormColumnParallelLinear, TEGroupedMLP, IdentityOp norms, etc.). - Verified quantized model produces coherent text generation outputs. - Verified backward compatibility: all changes are no-ops for existing local-spec pipelines (guarded by `IdentityOp` checks, `hasattr` checks, and `"fused_norm" in self.rules` checks). ## Before your PR is "*Ready for review*" - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes -- all changes are guarded by conditions that only activate for full TE spec models. Local spec models follow the exact same code paths as before. - **Did you write any new necessary tests?**: No - **Did you add or update any necessary documentation?**: No - **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**: No ## Additional Information Companion megatron-lm changes (separate PR): - `megatron/core/post_training/modelopt/mamba/model_specs.py`: Added `use_full_te_spec` parameter to return canonical `mamba_stack_spec` from `mamba_layer_specs.py`. - `megatron/post_training/model_builder.py`: Passes `use_full_te_spec=args.full_te_spec` to `get_mamba_stack_modelopt_spec`. - `megatron/post_training/arguments.py`: Added `--full-te-spec` CLI flag. - `examples/post_training/modelopt/convert_model.py`: Skip `moe_grouped_gemm=False` override when `--full-te-spec` is set.  ## Summary by CodeRabbit * **New Features** * Added support for loading fused normalization weights during model import. * **Bug Fixes** * Improved weight mapping logic to correctly skip redundant layer norm weights in specialized model architectures. * **Refactor** * Reorganized expert model parallel configuration paths for better compatibility with mixed parallel processing settings.  Signed-off-by: James Shen <yueshen@nvidia.com> Signed-off-by: Hung-Yueh <hungyueh.chiang@gmail.com>

#884) ## What does this PR do? **Type of change:** new feature **Overview:** Enable full TE spec support for NemotronH (Mamba hybrid) models during HF-to-Megatron weight import via `import_mcore_gpt_from_hf`. Previously, importing HF weights into a Megatron model built with the full TE spec (`TELayerNormColumnParallelLinear`, `TEGroupedMLP`, etc.) failed for NemotronH models due to two issues: 1. **Grouped expert prefix bug**: The `experts.linear_fc1/fc2` import rules had a hard-coded `mtp.layers.{}` prefix, which was only correct for MTP layers. When regular decoder MoE layers use `TEGroupedMLP` (via the full TE spec), the importer generated incorrect HF keys (e.g., `mtp.layers.27.mixer.experts.0.up_proj.weight` instead of `backbone.layers.27.mixer.experts.0.up_proj.weight`). 2. **Fused layer norm loading**: In the full TE spec, layer norms are fused into `TELayerNormColumnParallelLinear` modules as `layer_norm_weight`. The importer's `_name_remapping` would crash trying to load `layer_norm_weight` from a non-existent HF path (e.g., `backbone.layers.X.mixer.in_proj.layer_norm_weight`), when the actual HF norm weight lives at `backbone.layers.X.norm.weight`. ### Changes **`mcore_nemotron.py`**: - Fixed grouped expert prefix from `mtp.layers.{}` to `backbone.layers.{}`. The `_grouped_mlp_merging` function already handles `backbone` → `mtp` replacement when `is_mtp=True`, so both decoder and MTP layers work correctly. - Added `mapping={"layer_norm_weight": None}` to `in_proj` and `linear_fc1` rules to skip `layer_norm_weight` during `_name_remapping` (loaded separately via `fused_norm`). - Added `fused_norm` rule (`NameRemapping("backbone.layers.{}.norm.weight")`) to load HF norm weights into fused TE modules. **`megatron_importer.py`**: - Added `source_key is None` check in `_name_remapping` to skip keys mapped to `None` in the mapping dict (keeps existing value instead of crashing on missing HF key). - Added fused norm loading in `_import_mamba_layer`: after loading `in_proj`, loads `layer_norm_weight` from HF via `fused_norm` rule when `layer.norm` is `IdentityOp`. - Added fused norm loading in `_import_transformer_layer`: loads `layer_norm_weight` into `linear_qkv` (when `input_layernorm` is `IdentityOp`) and into `linear_fc1` (when `pre_mlp_layernorm` is `IdentityOp`). ## Usage The full TE spec is enabled via the `--full-te-spec` flag on the Megatron-LM side (separate PR). On the ModelOpt side, no user-facing changes are needed -- the import rules automatically handle both local spec and full TE spec models. ```bash # Convert HF checkpoint to Megatron with full TE spec (megatron-lm side) unset MLM_MODEL_CKPT && export MLM_MODEL_SAVE=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16_mlm && export HF_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 export PP=2 export MLM_EXTRA_ARGS="--full-te-spec" bash convert.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 # Quantize the converted checkpoint (megatron-lm side) export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16_mlm export MLM_MODEL_SAVE=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm export HF_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 export PP=2 && export TP=4 && export EP=4 && export ETP=1 export MLM_EXTRA_ARGS="--full-te-spec" bash quantize.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 FP8_DEFAULT_CFG # Generate export PP=2 && export TP=4 && export EP=4 && export ETP=1 export MLM_EXTRA_ARGS="--full-te-spec" export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm && ./generate.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 # MMLU export PP=2 && export TP=4 && export EP=4 && export ETP=1 export MLM_EXTRA_ARGS="--full-te-spec" export MLM_MODEL_CKPT=/models/NVIDIA-Nemotron-3-Nano-30B-A3B-fp8_mlm && export MLM_EXTRA_ARGS="--fraction 0.05 --disable-tqdm" && ./mmlu.sh nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 ``` ## Testing - Tested end-to-end: HF → Megatron conversion → FP8 quantization → inference (generate) → MMLU evaluation with Nemotron-3-Nano-30B-A3B-BF16. - Verified the resulting model structure matches Megatron-Bridge's TE spec output (TELayerNormColumnParallelLinear, TEGroupedMLP, IdentityOp norms, etc.). - Verified quantized model produces coherent text generation outputs. - Verified backward compatibility: all changes are no-ops for existing local-spec pipelines (guarded by `IdentityOp` checks, `hasattr` checks, and `"fused_norm" in self.rules` checks). ## Before your PR is "*Ready for review*" - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes -- all changes are guarded by conditions that only activate for full TE spec models. Local spec models follow the exact same code paths as before. - **Did you write any new necessary tests?**: No - **Did you add or update any necessary documentation?**: No - **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**: No ## Additional Information Companion megatron-lm changes (separate PR): - `megatron/core/post_training/modelopt/mamba/model_specs.py`: Added `use_full_te_spec` parameter to return canonical `mamba_stack_spec` from `mamba_layer_specs.py`. - `megatron/post_training/model_builder.py`: Passes `use_full_te_spec=args.full_te_spec` to `get_mamba_stack_modelopt_spec`. - `megatron/post_training/arguments.py`: Added `--full-te-spec` CLI flag. - `examples/post_training/modelopt/convert_model.py`: Skip `moe_grouped_gemm=False` override when `--full-te-spec` is set.  ## Summary by CodeRabbit * **New Features** * Added support for loading fused normalization weights during model import. * **Bug Fixes** * Improved weight mapping logic to correctly skip redundant layer norm weights in specialized model architectures. * **Refactor** * Reorganized expert model parallel configuration paths for better compatibility with mixed parallel processing settings.  Signed-off-by: James Shen <yueshen@nvidia.com>

yueshen2016 requested a review from a team as a code owner February 12, 2026 22:52

yueshen2016 requested a review from sugunav14 February 12, 2026 22:52

yueshen2016 requested review from ChenhanYu, jenchen13 and kevalmorabia97 February 12, 2026 22:52

jenchen13 approved these changes Feb 13, 2026

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ChenhanYu approved these changes Feb 13, 2026

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Support full TE spec for NemotronH HF-to-Megatron import

5ddcbe7

Signed-off-by: James Shen <yueshen@nvidia.com>

yueshen2016 force-pushed the yueshen/TE-spec-MLM branch from 45399c9 to 5ddcbe7 Compare February 13, 2026 23:07

yueshen2016 enabled auto-merge (squash) February 13, 2026 23:08

yueshen2016 merged commit 5c4ef8e into main Feb 14, 2026
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yueshen2016 deleted the yueshen/TE-spec-MLM branch February 14, 2026 00:34

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[OMNIML-3232] Support full TE spec for NemotronH HF-to-Megatron import#884

[OMNIML-3232] Support full TE spec for NemotronH HF-to-Megatron import#884
yueshen2016 merged 1 commit intomainfrom
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