[trainer] feat: Add Nemo-Automodel as alternative training engine

[HuiyingLi]

What does this PR do?

Add NeMo-Automodel as a training engine. The SFT trainer is tested with Qwen2.5-0.5B.

• automodel engine matches exactly with FSDP engine for SFT trainer (TP1/TP2/rmpad=True/False)
• use_remove_padding=True matches use_remove_padding=False
• EP support tested with kimi moonlight 16B

Relevant PRs:

• RFC [RFC] support Nemo-AutoModel as an alternative training backend #5245
• Add VeOmni [trainer] feat: Implemented VeomniEngine as a alternative training backend #4072
• Add TorchTitan [trainer] feat: Add Torchtitan as alternative training engine #5051

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Test

automodel backend 1gpu & 4gpu tp1/tp2 against FSDP backend 1gpu, rmpad true/false.

automodel backend finetuning moonlight 16B with ep8 8H100

automodel backend finetuning Qwen3 30B with ep8 8H100

automodel backend finetuning qwen2.5-7b with 4H100 fsdp2

API and Usage Example

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# Add code snippet or script demonstrating how to use this

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[CLAassistant]

All committers have signed the CLA.

[gemini-code-assist]

Code Review

This pull request introduces a new automodel SFT backend, which leverages nemo_automodel for distributed training. The changes include adding the engine implementation, configuration files, and test scripts. I've identified a configuration issue in the test script and a maintainability concern in the engine implementation. Overall, this is a significant feature addition.

[gemini-code-assist]

The model's output is conditionally wrapped in a SimpleNamespace if it's a raw tensor. This suggests an inconsistent return type from self.module, which can make the code harder to maintain and reason about. It would be more robust to enforce a consistent, structured return type (like CausalLMOutput) from the model to avoid such conditional handling.

[ISEEKYAN]

hi @HuiyingLi , thanks to your great contribution.
I found the MFU of automodel is lower than FSDP on 0.5B model and the MFU is less than 1% on 16B MoE model, is this supposed to be right? Could you provide some fair comparison on popular models such as 7B dense or 30B MoE?

[HuiyingLi]

hi @HuiyingLi , thanks to your great contribution. I found the MFU of automodel is lower than FSDP on 0.5B model and the MFU is less than 1% on 16B MoE model, is this supposed to be right? Could you provide some fair comparison on popular models such as 7B dense or 30B MoE?

Hi @ISEEKYAN ,
Thank you!

• for 0.5B model, FSDP engine is single GPU while automodel is 4 GPU. I've updated the chart with automodel single GPU for comparison.
• for the low mfu on 16B model: due to very small seqlen and batch size. I've updated the chart with larger seqlen and gbs, and added charts for Qwen 30B MoE and Qwen 7B dense.

[ISEEKYAN]

great, is your exp on H100? If true, the MFU looks good but it will be better to have a fair comparison with FSDP or Megatron. But this is not a blocker of merging this PR. It is just a good reference for the users to adopt AutoModel, and it will be better to add a doc to show the comparison and an example for the user to easily hand on

[ETOgaosion]

@HuiyingLi Thanks for your great contribution! Could you please sign the CLA~

[ETOgaosion]

Maybe in another PR, we should refactor docs/start/install.rst to support all model engines and rollout engine install methods and use some displays for better choices.

[ETOgaosion]

We should also prepare some CI tests for Nemo-Automodel