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[EXPERIMENT][WIP][OpenVINO] Add support for NemotronH#1724

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[EXPERIMENT][WIP][OpenVINO] Add support for NemotronH#1724
mlukasze wants to merge 9 commits into
huggingface:mainfrom
mlukasze:feat/add-nemotron-h-support

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@mlukasze

@mlukasze mlukasze commented May 6, 2026 •
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⚠️ AUTOMATICALLY GENERATED BY MEAT AGENT — REQUIRES HUMAN REVIEW ⚠️
This PR was created by an AI agent as part of automated model enablement.
A human maintainer must review and approve it before it can be considered for merge.
Do NOT merge without human review and sign-off.

What does this PR do?

Adds OpenVINO export support for NemotronH (model_type = "nemotron_h") — NVIDIA's hybrid Mamba-2 + MoE architecture used in nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.

Architecture:

  • 52 hybrid layers defined by hybrid_override_pattern (e.g. "MEMEM*EMEMEM*...")
    • M = Mamba-2 SSM layer (23 layers)
    • E = standard attention layer (6 layers)
    • * = Mixture-of-Experts layer (23 layers, 128 routed + 1 shared expert)
  • 30B total / ~3B active parameters
  • 262K token context window

Changes:

  1. model_configs.py: NemotronHNormalizedTextConfig — maps NemotronH's layers_block_type attribute to the layer_types interface expected by GraniteMoeHybridOpenVINOConfig
  2. model_configs.py: NemotronHOpenVINOConfig — extends GraniteMoeHybridOpenVINOConfig to reuse hybrid cache handling (Mamba-2 SSM states + KV cache)
  3. model_patcher.py: NemotronHModelPatcher — parses the hybrid_override_pattern string; fixes TorchScript tracing closure scoping; robust MoE forward with API fallbacks

Installation instructions

pip install optimum-intel[openvino] \
    --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly \
    openvino openvino-tokenizers

Exporting the model

optimum-cli export openvino \
    --model nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
    --task text-generation-with-past \
    --weight-format int4 \
    --trust-remote-code \
    ./nemotron-h-int4/

Inference script

from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer

model_dir = "./nemotron-h-int4/"
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = OVModelForCausalLM.from_pretrained(model_dir, device="CPU", trust_remote_code=True)

inputs = tokenizer("The capital of France is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Before submitting

  • This PR fixes a typo or improves the docs
  • Did you make sure to update the documentation with your changes?
  • Did you write any new necessary tests?

mlukasze and others added 2 commits May 6, 2026 09:46
@mlukasze
Add nemotron_h support to OpenVINO export
916ee10 
- Add NemotronHOpenVINOConfig class extending TextDecoderOnnxConfig
- Register nemotron_h model type for text-generation and text-generation-with-past tasks
- Configure DUMMY_INPUT_GENERATOR_CLASSES and NORMALIZED_CONFIG_CLASS for proper export support

This enables exporting NVIDIA Nemotron H models to OpenVINO IR format.
@mlukasze
[OpenVINO] Add support for NemotronH (nemotron_h) hybrid Mamba-2/MoE
deae127 
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@mlukasze mlukasze marked this pull request as draft May 6, 2026 08:57
@mlukasze mlukasze changed the title Add nemotron_h support to OpenVINO export [EXPERIMENT][WIP][OpenVINO] Add support for NemotronH May 6, 2026
mlukasze and others added 7 commits May 6, 2026 11:07
@mlukasze
[OpenVINO] Point NemotronH test registry at target model
4cfb1ea 
Use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 for the nemotron_h test entry on the feature branch.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@mlukasze
[EXPERIMENT][WIP] Add NemotronH (nemotron_h) OpenVINO export support
3295597 
NemotronH is NVIDIA's hybrid Mamba-2 + MoE architecture used in
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16. Architecture pattern:
- 23 Mamba-2 SSM layers (M)
- 6 standard attention layers (E)
- 23 MoE layers (*)
- hybrid_override_pattern: 'MEMEM*EMEMEM*...'

Changes:
- model_configs.py: NemotronHNormalizedTextConfig mapping layers_block_type->layer_types
  NemotronHOpenVINOConfig extending GraniteMoeHybridOpenVINOConfig
- model_patcher.py: NemotronHModelPatcher with:
  * _parse_nemotron_h_pattern() for hybrid_override_pattern parsing
  * Robust MoE forward with fallbacks
  * Fixed closure scoping for TorchScript tracing
  * backbone.layers access pattern

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@mlukasze
Add comprehensive NemotronH documentation and test suite
ec38bae 
Features added:
- Comprehensive documentation for NemotronH (NVIDIA Nemotron Hybrid Mamba-2 + MoE)
  * Architecture overview and characteristics
  * Configuration parameters (Mamba SSM, MoE, hybrid layers)
  * Export and inference examples with code samples
  * Performance characteristics and optimization tips
  * Troubleshooting guide for common issues
  * Implementation details and internal architecture

- Complete test suite for NemotronH support (13 tests):
  * Model type registration in TasksManager
  * Config inheritance validation (GraniteMoeHybridOpenVINOConfig, OnnxConfig)
  * NemotronHOpenVINOConfig initialization
  * NemotronHNormalizedTextConfig attribute mapping
  * Layer type mapping for hybrid architecture (Mamba, Attention, MoE)
  * MoE and SSM parameter handling
  * Hybrid architecture validation (alternating layers, override patterns)
  * Task support (text-generation, text-generation-with-past)

- Integration with existing test suites:
  * Added nemotron_h to test_exporters_cli.py SUPPORTED_ARCHITECTURES (TF >= 5.0)
  * Added nemotron_h to _NUM_EXPECTED_TOKENIZERS with value 2
  * Added nemotron_h to test_decoder.py with decoder count 1

All tests pass (13/13 in test_nemotron_h.py):
- Registration tests: 3/3 passed
- Config tests: 1/1 passed
- Normalized config tests: 4/4 passed
- Hybrid architecture tests: 2/2 passed
- Task support tests: 3/3 passed

Changes are ready for model export, inference optimization, and production deployment.

Co-authored-by: Copilot <copilot@users.noreply.github.com>
@mlukasze
Fix NemotronH CPU export: make torch.cuda.stream conditional
e14b97d 
NemotronHBlock.forward() unconditionally calls torch.cuda.stream()
which fails on systems without NVIDIA GPU driver. Added
_patch_cuda_stream_for_cpu() in NemotronHModelPatcher to make
the CUDA stream call a no-op when running on CPU.

This allows exporting the model to OpenVINO IR on CPU-only Intel
systems (no NVIDIA driver required).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@mlukasze
Fix NemotronH config attribute mappings for Mamba-2 dummy inputs
f4f4617 
NemotronH uses different attribute names than standard Mamba config:
- mamba_expand -> expand
- mamba_d_conv -> conv_kernel
- mamba_d_state -> ssm_state_size
- mamba_ngroups -> n_groups
- mamba_headdim -> head_dim
- mamba_nheads -> mamba_num_heads
- mamba_d_ssm -> (computed from expand*hidden_size/nheads)
- mamba_dt_rank -> (computed from hidden_size/16)

Added comprehensive property-based mapping in NemotronHNormalizedTextConfig
to support GraniteMoeHybrid dummy input generator for Mamba-2 blocks.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@mlukasze
fix: eliminate NemotronH tensor slice tracerwarning
0e96626 
Use torch.narrow for the padded prefill crop so the export path no longer
creates a tensor-valued Python slice at the NemotronH mamba output trim.
This removes the model_patcher.py:8186 tracerwarning seen during export.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@mlukasze
fix: finalize NemotronH OpenVINO support
b172b33 
- declare NemotronH normalized config supports past
- add temporary .model alias for NemotronH patching
- point NemotronH test registry to tiny random model

Validated with: PYTHONPATH=$PWD pytest -q tests/openvino/test_nemotron_h.py -q

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@rkazants

rkazants commented May 7, 2026

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We need other representation for mamba block. The current one is not correct. This arch review matters.

@rkazants rkazants closed this May 7, 2026
@mlukasze

mlukasze commented Jun 30, 2026

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current PR: #1789

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2 participants

@mlukasze @rkazants