TorchDistPackage

TorchDistPackage provides some easy-to-use modules and tools for Distributed Training in PyTorch.

It is under construction. Welcome to use and contribute.

主要特性介绍-中文

安装使用

• install

git clone https://github.com/KimmiShi/TorchDistPackage.git
cd TorchDistPackage
pip install -e . # or pip install . --user

• simple example

import torch
from torchdistpackage import setup_distributed,test_comm,tpc

# init torch disttributed
setup_distributed()

# init process groups
pp_size=2
tp_size=2
dist_config = [('data',world_size/(2*pp_size)), ('pipe',pp_size), ('tensor',tp_size)]
tpc.setup_process_groups(dist_config)

# test communication in groups
tmp = torch.rand([100,1024]).cuda()

# collective
dist.broadcast(tmp, tpc.get_ranks_in_group('model')[0], tpc.get_group('model'))

# p2p
if tpc.is_first_in_pipeline_group():
    dist.send(tmp, tpc.get_next_global_rank('pipe'))
if tpc.is_last_in_pipeline_group():
    dist.recv(tmp, tpc.get_prev_global_rank('pipe'))

特性介绍

0. 简单的纯Python实现DDP - Simple DDP Module in PyTorch

example: TestNaiveDdp

Highlights:

• Python only implementation. Easy to understand and debug.
• overlaps grad reduce with compute like TorchDDP
• For Pipeline Parallelism, only reduce grad at the last micro-batch; and could still overlap comm, which is better than ColossalAI impl.

Drawbacks/TODO:

• the all-reduce launch seems to take more time than TorchDDP in some model

1. 从slurm初始化torch distributed - torch_launch_from_slurm

torch dist init from slurm

example

2. 灵活的通信组划分 - Flexible process group initialization for Mixed Parallelism

详见主要特性介绍

3. 流水并行相关 - For Pipeline Parallelism

• 自定义fwd_fn,bwd_fn的1F1B调度器 pipeline scheduler
• pipeline model partition 流水并行模型切分

使用示例 测例参考

4. MoE-数据并行

在专家并行(Expert Parallel)的基础上，支持 MoE 数据并行：即复制一些expert，相同的expert之间做数据并行（初始参数广播，梯度平均），不同的expert之间做专家并行。

使用示例

5. Tensor Parallel & Sequence Parallel

简单的TP实现。

测例参考

6. Hybrid ZeRO / 节点内ZeRO - 加速ZeRO多卡训练速度

详见主要特性介绍

7. 分片EMA - sharded EMA

节省EMA的显存消耗，见sharded ema example

TOOLS 工具类

1. model profiler

分级时间和显存消耗 参考