# 2.5D Tensor Parallelism

Author: Zhengda Bian, Yongbin Li

Prerequisite

Example Code

Related Paper

## Introduction​

Compared with 1D tensor parallelism, 2D parallelism reduces the memory cost, but may introduce more communication. Therefore, a 2.5D tensor parallelism algorithm was proposed based on 2.5D SUMMA to reduce communication by using more devices.

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

$\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \\ X_{10} & X_{11} \\ X_{00} & X_{01}\end{matrix} \right],$

which can be reshaped into $d$ layers as

$\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \end{matrix} \right].$

Also, the weight $A$ is split into

$\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right].$

For each layer of $X$, we use the SUMMA algorithm to multiply $X$ and $A$. Then, we have the output

$\left[\begin{matrix} Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \\ Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right] \text{~and~}$
$\left[\begin{matrix} Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \\ Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \end{matrix} \right].$

## Efficiency​

Given $P=q \times q \times d$ 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 2.5D tensor parallelism.

ComputationMemory (parameters)Memory (activations)Communication (bandwidth)Communication (latency)
$O(1/dq^2)$$O(1/q^2)$$O(1/dq^2)$$\small O(3(q-1)(d+1)/dq)$$O(6(q-1))$

## Usage​

To enable 2.5D tensor parallelism for our model, e.g. on 8 GPUs, we need to configure the parallism setting as below.

CONFIG = dict(parallel=dict(    data=1,    pipeline=1,    tensor=dict(size=8, mode='2.5d', depth=2),))

Then Colossal-AI will automatically apply 2.5D parallelism to all the layers from colossalai.nn.

Let's define a model that consists of a two-layer multi-layer perceptron (MLP) as below.

import colossalaiimport colossalai.nn as col_nnimport torchfrom colossalai.utils import print_rank_0class MLP(torch.nn.Module):    def __init__(self, dim: int = 256):        super().__init__()        intermediate_dim = dim * 4        self.dense_1 = col_nn.Linear(dim, intermediate_dim)        print_rank_0(f'Weight of the first linear layer: {self.dense_1.weight.shape}')        self.activation = torch.nn.GELU()        self.dense_2 = col_nn.Linear(intermediate_dim, dim)        print_rank_0(f'Weight of the second linear layer: {self.dense_2.weight.shape}')        self.dropout = col_nn.Dropout(0.1)    def forward(self, x):        x = self.dense_1(x)        print_rank_0(f'Output of the first linear layer: {x.shape}')        x = self.activation(x)        x = self.dense_2(x)        print_rank_0(f'Output of the second linear layer: {x.shape}')        x = self.dropout(x)        return x

Launch Colossal-AI on 8 GPUs and build the model

parser = colossalai.get_default_parser()colossalai.launch(config=CONFIG,                  rank=args.rank,                  world_size=args.world_size,                  local_rank=args.local_rank,                  host=args.host,                  port=args.port)m = MLP()

We will see the shapes of partitioned parameters(e.g. weights) in the MLP model.

Weight of the first linear layer: torch.Size([128, 512])Weight of the second linear layer: torch.Size([512, 128])

The complete weight of the first linear layer is supposed to have the shape [256, 1024]. After the partitioning of 2.5D parallelism, it becomes [128, 512] on each GPU. Similarly, the second layer partitions the weight [1024, 256] into [512, 128].

We can run the model with some random inputs.

from colossalai.context import ParallelModefrom colossalai.core import global_context as gpcfrom colossalai.utils import get_current_devicex = torch.randn((16, 256), device=get_current_device())# partition inputtorch.distributed.broadcast(x, src=0)x = torch.chunk(x, 2, dim=0)[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)]x = torch.chunk(x, 2, dim=0)[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)]x = torch.chunk(x, 2, dim=-1)[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)]print_rank_0(f'Input: {x.shape}')x = m(x)

Then we can see the shapes of activation results.

Input: torch.Size([4, 128])Output of the first linear layer: torch.Size([4, 512])Output of the second linear layer: torch.Size([4, 128])

The activation tensors in 2.5D parallelism are all split by $d \times q$ in the row and $q$ in the column. E.g. the output of the first linear layer has the shape [4, 512]), while the second layer has the output of [4, 128]. Note, 2.5D parallelism use the same partition method as 2D parallelism for weights, where the difference is the partition of input.