[megatron] feat: fused kernel suppport for new model engine

[HollowMan6]

What does this PR do?

Add fused_forward_no_padding method so that model engine can also support fused kernels.

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Test

Tested on 8*A800 GPUs with triton version 3.5.1 using DAPO-Math-17k dataset with DAPO and using Moonlight-16B-A3B, the training curves are closely matched by either disabling or enabling fused kernels, and even enabling fused kernels has better rollout training mismatch level.

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[Copilot]

Pull request overview

This PR wires up fused-kernel support for the Megatron-based training engine when using the “no padding” (nested tensor) data path, aligning the training engine with the model’s fused-kernel capabilities.

Changes:

• Propagates use_fused_kernels from model config into engine config and worker defaults so fused-kernel usage is consistently configured across components.
• Adds a Megatron engine hook (_maybe_enable_fused_kernels) that conditionally patches Megatron GPT models with fused forward kernels (disabled for value models and when MTP is enabled).
• Introduces a fused no-padding forward path in verl.models.mcore (fused_forward_no_padding_gen + get_mcore_forward_fused_no_padding_fn) and routes MegatronEngine’s forward_step through it when use_fused_kernels is enabled under DatasetPadMode.NO_PADDING.

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
verl/workers/engine_workers.py	Syncs engine_config.use_fused_kernels with model_config.use_fused_kernels and uses it when injecting default non-tensor flags into training/inference batches.
verl/workers/engine/megatron/transformer_impl.py	Adds _maybe_enable_fused_kernels to patch Megatron GPT forward with fused kernels, and extends forward_step to call a new fused no-padding forward function when use_fused_kernels is requested.
verl/models/mcore/registry.py	Registers get_mcore_forward_fused_no_padding_fn to return the appropriate fused no-padding forward generator based on the HF architecture (VLM vs. standard LM).
verl/models/mcore/model_forward_fused.py	Implements fused_forward_no_padding_gen, which runs a fused, no-padding (nested tensor) forward pass with optional VLM handling, and reuses existing THD preprocess/postprocess utilities.
verl/models/mcore/__init__.py	Re-exports get_mcore_forward_fused_no_padding_fn as part of the public mcore API so engines can obtain the fused no-padding forward function.

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[Copilot]

Pull request overview

Copilot reviewed 6 out of 6 changed files in this pull request and generated 1 comment.

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[ISEEKYAN]

hi @HollowMan6, we found the fused kernel is broken with the speed after triton3.3 about 3 months ago, since then we don't maintain recommend the fused kernel. Do you observe a speed down after enabling this fused kernel.

As a option, my colleagues have rewrite the fused kernel and integrated it into the MCore, see NVIDIA/Megatron-LM#3076 and NVIDIA/Megatron-LM#2739. But unfortunately now this kernel only supports Blackwell, not even for Hopper. And we have another PR to adopt the mcore version of fused_kernel at #5130.

I think the best way to use this kernel is to enable it through transformer_config, but this can't help Hopper and Ampere users.
The existing way is OK but I want you to check if the speed is slow down significantly, thanks!

[ISEEKYAN]

The code changes are OK, it only depends on the fused kernel perf

[HollowMan6]

Hi @ISEEKYAN ! Thanks for your reviewing and that's interesting to know! Actually, I didn't find a speed degradation on A800 when training Moonlight-16B-A3B with triton 3.5.1, please check the last figure in the PR description, in contrast, it improves the speed a lot (step time around 340 -> around 270)!

If you were benchmarking this on Hopper GPUs 3 months ago and found the speed degradation, actually there's a PR to improve it about 1 and a half months ago and the speed up was also very obvious: #4576 I've also tested it with triton 3.5.0 together with this support fix #4676 on L20x and also didn't notice any performance degradation at that time as well.

Unfortunately I don't have blackwell GPUs at hand and can't really help try out/test the new kernel, but since I still observe performance improvement when enabling fused kernel on verl side for now and #4576 also does some improvement on Hopper side, maybe we can still go back and recommend the fused kernel (though for now I no longer have Hopper GPUs on my side and can't really tell how good it is there).

Since I didn't make fused kernel the default option for model engine in this PR, I think it's safe and also good to ask other people to try this out and check the performance benefit, so I'll go ahead and merge this one.

[ISEEKYAN]

thanks @HollowMan6, good to know that the great works in #4576