"MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods"

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LLMs like Mamba face challenges in deployment due to their large size. MambaQuant addresses this by enabling effective model compression through quantization.

MambaQuant uses a combination of techniques, including Karhunen-Loève Transformation and Hadamard rotation, along with pre-quantization smoothing, using a special Scaled SiLU activation. This approach effectively handles the challenges presented by Mamba's architecture.

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https://arxiv.org/abs/2501.13484

📌 MambaQuant's Karhunen-Loève Transformation enhanced rotation is critical. It acts like a dynamic pre-conditioner for quantization. This directly tackles the core issue of non-uniform data, specific to Mamba's selective state space models.

📌 The Scaled Sigmoid Linear Unit fusion is brilliant. It is not just about smoothing. It strategically shifts scaling into the activation. This prepares weights for optimal quantization, reducing information loss.

📌 MambaQuant provides a comprehensive quantization solution. It uses both pre-processing, with Scaled Sigmoid Linear Unit, and post-processing, with Karhunen-Loève Transformation enhanced rotation. It controls quantization errors at multiple levels of the architecture.

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Methods Explored in this Paper 🔧:

→ "MambaQuant" is introduced. It is a post-training quantization scheme.

→ It employs "Karhunen-Loève Transformation enhanced rotation". This rotation adjusts the rotation matrix. It adapts to various channel data patterns.

→ "Smooth-Fused rotation" is applied. This balances channel variances. It incorporates added parameters into model weights directly.

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Key Insights 💡:

→ Standard quantization does not perform well on Mamba models. The data distribution is highly irregular.

→ MambaQuant's novel rotation technique addresses the uneven distribution. It equalizes or normalizes channel variances.

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Results 📊:

→ Accuracy loss remains under 1% with 8-bit quantization of both weights and activations. This is for both vision and language tasks.

→ Better performance compared to baseline methods is observed in the more demanding W4A8 configuration.

→ MambaQuant achieves, on average, over 12% greater accuracy than previous approaches. This is observed with 8-bit quantization of vision models.