Current Implementation of TP in MambaMixer2

Oct 13, 2025 by sorusgentrification.com 71 views

Bug: Quantization In MambaMixer2 Not Supported when Tensor Parallel is enabled

Tensor parallelism (TP) is a technique used in deep learning to improve the performance of large models by splitting the model into smaller parts and processing them in parallel. However, when it comes to quantization, a process that reduces the precision of model weights and activations, the current implementation of TP in MambaMixer2 has some limitations. In this article, we will discuss the issue of quantization not being supported when TP is enabled in MambaMixer2.

Current Implementation of TP in MambaMixer2

The current implementation of TP in MambaMixer2 is complicated due to the fusion of multiple components into a single layer. The gate, projection, state space, and heads are all combined into a single layer, making it difficult to implement TP. Additionally, the implementation of TP is simplified to only support the case where num_groups == 1 if num_groups does not divide num_heads. This limitation is due to the complexity of the implementation and the need to consider different possibilities when the number of groups divides the number of heads.

Limitations of Current Implementation

The current implementation of TP in MambaMixer2 has several limitations. Firstly, it does not support TP > 1 when the Mamba2 mixer is quantized. This means that even if the model is large and could benefit from TP > 1, it is not possible to use this technique when the model is quantized. Secondly, the implementation is limited to the case where num_groups == 1 if num_groups does not divide num_heads. This limitation is due to the complexity of the implementation and the need to consider different possibilities when the number of groups divides the number of heads.

Need for Support of TP > 1 with Quant Layers

For large models, it may be useful to support TP > 1 with quant layers, even in some special cases of num_heads and num_groups. This would allow for the use of TP > 1 even when the model is quantized, which could improve the performance of the model. However, this would require a more complex implementation of TP, which would need to consider different possibilities when the number of groups divides the number of heads.

In conclusion, the current implementation of TP in MambaMixer2 has several limitations, including the lack of support for TP > 1 when the model is quantized. To improve the performance of large models, it may be useful to support TP > 1 with quant layers, even in some special cases of num_heads and num_groups. However, this would require a more complex implementation of TP, which would need to consider different possibilities when the number of groups divides the number of heads.

Based on the analysis of the current implementation of TP in MambaMixer2, the following recommendations are made:

Improve the implementation of TP to support TP > 1 when the model is quantized.
Consider different possibilities when the number of groups divides the number of heads.
Develop a more complex implementation of TP that can handle different cases of num_heads and num_groups.

Future work on this issue could include:

Developing a more complex implementation of TP that can handle different cases of num_heads and num_groups.
Testing the performance of the model with TP > 1 when the model is quantized.
Evaluating the effectiveness of TP > 1 with quant layers for large models.

The following is a summary of the key points discussed in this article:

The current implementation of TP in MambaMixer2 has several limitations, including the lack of support for TP > 1 when the model is quantized.
To improve the performance of large models, it may be useful to support TP > 1 with quant layers, even in some special cases of num_heads and num_groups.
A more complex implementation of TP is needed to handle different cases of num_heads and num_groups.
Future work could include developing a more complex implementation of TP, testing the performance of the model with TP > 1 when the model is quantized, and evaluating the effectiveness of TP > 1 with quant layers for large models.
Q&A: Bug: Quantization In MambaMixer2 Not Supported when Tensor Parallel is enabled

Q: What is the current implementation of Tensor Parallelism (TP) in MambaMixer2?

A: The current implementation of TP in MambaMixer2 is complicated due to the fusion of multiple components into a single layer. The gate, projection, state space, and heads are all combined into a single layer, making it difficult to implement TP.

Q: Why is the current implementation of TP limited to the case where `num_groups == 1` if `num_groups` does not divide `num_heads`?

A: The current implementation of TP is limited to the case where num_groups == 1 if num_groups does not divide num_heads due to the complexity of the implementation and the need to consider different possibilities when the number of groups divides the number of heads.

Q: Why is TP > 1 not supported when the Mamba2 mixer is quantized?

A: TP > 1 is not supported when the Mamba2 mixer is quantized because the current implementation of TP is simplified and does not consider the case where the model is quantized.

Q: Why is it useful to support TP > 1 with quant layers?

A: Supporting TP > 1 with quant layers is useful for large models because it can improve the performance of the model. Even in some special cases of num_heads and num_groups, TP > 1 with quant layers can be beneficial.

Q: What are the limitations of the current implementation of TP in MambaMixer2?

A: The current implementation of TP in MambaMixer2 has several limitations, including the lack of support for TP > 1 when the model is quantized and the limitation to the case where num_groups == 1 if num_groups does not divide num_heads.

Q: What is the recommended solution to improve the performance of large models?

A: The recommended solution to improve the performance of large models is to support TP > 1 with quant layers, even in some special cases of num_heads and num_groups. This would require a more complex implementation of TP that can handle different cases of num_heads and num_groups.

Q: What are the future work recommendations for this issue?

A: The future work recommendations for this issue include developing a more complex implementation of TP that can handle different cases of num_heads and num_groups, testing the performance of the model with TP > 1 when the model is quantized, and evaluating the effectiveness of TP > 1 with quant layers for large models.

Q: What are the references for this issue?

A: The references for this issue include [1] https://github.com/vllm-project/vllm/pull/13660 and [2] https://github.com/vllm-project/vllm/pull/14617.

Q: What is the appendix for this issue?

A: The appendix for this issue is a summary of the key points discussed in this article, including the limitations of the current implementation of TP in MambaMixer2, the need to support TP > 1 with quant layers, and the future work recommendations for this issue.