# 3D Tensor Parallelism

Author: Zhengda Bian, Yongbin Li

**Prerequisite**
- [1D Tensor Parallelism](./1D_tensor_parallel.md)
- [2D Tensor Parallelism](./2D_tensor_parallel.md)

**Example Code**
- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md)

**Related Paper**
- [Maximizing Parallelism in Distributed Training for Huge Neural Networks](https://arxiv.org/pdf/2105.14450.pdf)

## Introduction

The [3D tensor parallelism](https://arxiv.org/pdf/2105.14450.pdf) is an approach to parallelize the computation of neural models, hoping to obtain the optimal communication cost.

Let's still take a linear layer $Y = XA$ as an example.
Given $P=q \times q \times q$ processors (necessary condition), e.g. $q=2$, we split the input $X$ and weight $A$ into

$$
\left[\begin{matrix}
            X_{000} & X_{001} \\
            X_{010} & X_{011} \\
            X_{100} & X_{101} \\
            X_{110} & X_{111} \end{matrix}
\right]
\text{~and~}
\left[\begin{matrix}
            A_{000} & A_{001} & A_{010} & A_{011} \\
            A_{100} & A_{101} & A_{110} & A_{111} \end{matrix}
\right]
\text{~respectively,}$$
where each $X_{ijl}$ and $A_{lji}$ are stored at processor $(i,j,l)$, as shown in the figure below.

<center>
<img src="https://s2.loli.net/2022/02/17/JevO6SED5z4PFdp.png" width = "200" height = "250" />
<img src="https://s2.loli.net/2022/02/17/qvtwjdfNXMAb4nF.png" width = "200" height = "250" />
<img src="https://s2.loli.net/2022/02/17/WFzm2N4IwKf1jXZ.png" width = "200" height = "250" />
<img src="https://s2.loli.net/2022/02/17/r2dZQ4hKxwTuIv6.png" width = "200" height = "250" />
</center>

Then we all-gather $X_{ijl}$ across $(i, 0...q,l)$, as well as $A_{lji}$ across $(0...q, j, l)$.
So, we have $X_{il}$ and $A_{lj}$ on each processor $(i,j,l)$ to get $X_{il}A_{lj}$.
Finally, we reduce-scatter the results across $(i, j, 0...q)$ to get $Y_{ijl}$, which forms
$$
Y=
\left[\begin{matrix}
            Y_{000} & Y_{001} \\
            Y_{010} & Y_{011} \\
            Y_{100} & Y_{101} \\
            Y_{110} & Y_{111} \end{matrix}
\right].
$$

We also need to note that in the backward pass, we need to all-gather the gradient $\dot{Y_{ijl}}$, and then reduce-scatter the gradient $\dot{X_{il}}=\dot{Y_{ij}}A_{lj}^T$ and $\dot{A_{lj}}=X_{il}^T\dot{Y_{ij}}$.

## Efficiency
Given $P=q \times q \times q$ processors, we present the theoretical computation and memory cost, as well as the communication cost based on the ring algorithm in both the forward and backward pass of 3D tensor parallelism.

| Computation | Memory (parameters) | Memory (activations) | Communication (bandwidth) | Communication (latency) |
| :-:         | :-:              | :-:                  | :-:                       | :-:                     |
| $O(1/q^3)$  | $O(1/q^3)$       | $O(1/q^3)$           | $O(6(q-1)/q^3)$           | $O(6(q-1))$             |

## Usage

Currently the newest version of ColossalAI doesn't support 3D tensor parallelism, but this feature will be integrated into `Shardformer` in future releases.
For more details about ideas and usages of `Shardformer`, please refer to [Shardformer Doc](./shardformer.md).

For users of older version of ColossalAI, please refer to [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md).

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