[QUESTION]how to incorporate MOE into hybrid mamba efficiently

[sunying2018]

how to incorporate MOE into hybrid mamba efficiently
Hi,

You provided code pretrain_mamba.py contains the mamba spec to contain transformer, mamba and mlp layers. I'm wondering if there is an easier way to incorporate MOE to replace some of MLP layers? Since I found args.spec conflicts with simply specified moe args setting. What considerations require these two to be set as mutually exclusive? Thank you so much!

[Advait251206]

The short answer is that MoE is not currently designed to be "plug-and-play" with the hybrid Mamba model specification, which is why --spec and the MoE-related arguments are treated as mutually exclusive.

The pretrain_mamba.py example uses a custom layer specification (spec) that explicitly defines the sequence of blocks (e.g., Transformer, Mamba, MLP). In contrast, Megatron's MoE implementation expects to build transformer blocks through its own layer specifications, where the feed-forward network (FFN) can be replaced by an MoE layer based on the configuration.

As a result, both mechanisms are trying to control the model architecture, leading to the conflict you observed.

---

Why are --spec and MoE mutually exclusive?

The spec mechanism gives complete control over the model topology.

For example, a hybrid Mamba model might look conceptually like

Transformer
↓
Mamba
↓
Transformer
↓
Mamba
↓
MLP

where every block is explicitly instantiated according to the provided specification.

On the other hand, enabling MoE through arguments such as

--num-experts
--expert-model-parallel-size
--moe-router-topk

expects the model builder to replace the standard FFN inside transformer layers with an MoE implementation.

If both are enabled simultaneously, Megatron has two different sources of truth for constructing the network:

• the custom spec
• the MoE-aware transformer specification

Rather than attempting to merge these automatically (which could lead to ambiguous or inconsistent architectures), Megatron currently treats them as mutually exclusive.

---

Is there an easy way to replace only some MLP layers with MoE?

At the moment, not automatically.

If your goal is something like

Transformer
↓
Mamba
↓
Transformer
↓
MoE
↓
Mamba
↓
Transformer
↓
MLP

you'll likely need to define a custom hybrid specification where the desired FFN blocks are explicitly replaced with MoE layers.

In other words, the layer specification itself would need to know which positions should instantiate:

• Standard MLP
• MoE FFN
• Mamba block
• Transformer block

instead of relying solely on command-line flags.

---

What would be required?

A clean solution would be to extend the hybrid model specification so that each block can independently specify its feed-forward implementation.

Conceptually, something like

Layer 0 : Transformer + Dense MLP
Layer 1 : Mamba
Layer 2 : Transformer + MoE
Layer 3 : Mamba
Layer 4 : Transformer + Dense MLP

would provide much greater flexibility than a global "enable MoE" switch.

This would also allow different routing strategies or expert configurations to be applied only where beneficial, without affecting the rest of the architecture.

---

Is this a reasonable feature request?

I think so.

Hybrid architectures are becoming increasingly common, and combining Mamba with sparse MoE layers is an interesting direction for improving parameter efficiency and scaling.

Supporting MoE as another configurable building block within the spec system—rather than making it mutually exclusive—would make the architecture significantly more flexible while preserving the explicit control that the specification mechanism already provides.

---

In summary: the conflict exists because both --spec and the MoE arguments are responsible for defining the model architecture. The current implementation doesn't automatically merge these two mechanisms, so selectively replacing MLP layers with MoE in a hybrid Mamba model requires extending the custom layer specification rather than simply enabling the existing MoE flags.

---

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GitHub: https://github.com/Advait251206