ColossalAI/colossalai/tensor/d_tensor/README.md

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# đŸ”ĸ Distributed Tensor
## 📚 Table of Contents
- [đŸ”ĸ Distributed Tensor](#-distributed-tensor)
- [📚 Table of Contents](#-table-of-contents)
- [🔗 Introduction](#-introduction)
- [📝 Design](#-design)
- [🔨 Usage](#-usage)
- [🎈 Progress Log](#-progress-log)
## 🔗 Introduction
Distributed tensor is a type of tensor that is distributed across multiple devices. It is a wrapper of PyTorch tensor, and it is used to support distributed training.
It can represent the device topology and tensor placement over the devices in the topology. It also provides a set of APIs to manipulate the distributed tensor.
## 📝 Design
Our implementation is inspired by the work [Alpa](https://arxiv.org/abs/2201.12023), which unifies data parallelism and tensor parallelism as intra-op parallelism. It uses notations `S` to represent the sharded dimension and `R` to represent the replicated dimension. For example, given a 2D matrix, `[S, R]` represents the tensor is sharded over the first dimension.
Each sharded dimension will have a subscript to represent its placement over the devices. Assuming we have 4 GPUs and the GPUs are arranged in a 2 x 2 manner. Let's say we have a 2D matrix like below:
```text
[1, 2, 3, 4 ]
A = [4, 5, 6, 7 ]
[8, 9, 10, 11]
[12, 13, 14, 15]
```
`[S0, R]` would mean that the first dimension is sharded over the rows in the device topology.
```text
| --------------------—————————————————————-|
| | |
| [1, 2, 3, 4 ] | [1, 2, 3, 4 ] |
| [4, 5, 6, 7 ] | [4, 5, 6, 7 ] |
| | |
| --------------------——————————————————-----
| | |
| [8, 9, 10, 11] | [8, 9, 10, 11] |
| [12, 13, 14, 15] | [12, 13, 14, 15] |
| | |
| --------------------——————————————————-----
```
`[S01, R]` would mean that the first dimension is sharded over both the row and column in the device topology.
```text
| --------------------—————————————————————-|
| | |
| [1, 2, 3, 4 ] | [4, 5, 6, 7 ] |
| | |
| --------------------——————————————————-----
| | |
| [8, 9, 10, 11] | [12, 13, 14, 15] |
| | |
| --------------------——————————————————-----
```
## 🔨 Usage
A sample API usage is given below.
```python
import torch
import colossalai
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.d_tensor import DTensor, ShardingSpec
colossalai.launch_from_torch()
# define your device mesh
# assume you have 4 GPUs
physical_mesh_id = torch.arange(0, 4)
mesh_shape = (2, 2)
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
# define a tensor
a = torch.rand(16, 32).cuda()
# create sharding spec for the tensor
# assume the sharding spec is [S0, R]
dim_partition_dict = {0: [0]}
sharding_spec = ShardingSpec(a.dim(), dim_partition_dict)
# create a distributed tensor
d_tensor = DTensor(a, device_mesh, sharding_spec)
print(d_tensor)
global_tensor = d_tensor.to_global()
print(global_tensor)
```
## 🎈 Progress Log
- [x] Support layout conversion
- [x] Support sharding on 2D device mesh
- [ ] Support sharding on 3D device mesh
- [ ] Support sharding 4D device mesh
- [ ] Support sharding info saving and offline tensor merge (we can save tensor as dtensor and gather the tensors back to the global tensor based on the sharding info in a single process in CPU, useful for distributed training checkpoint load and save.)