<div align="center"> <h1><img src="docs/logo.png" height="28px" /> BMTrain</h1>

Efficient Training for Big Models

<p align="center"> <a href="#overview">Overview</a> • <a href="#documentation">Documentation</a> • <a href="#install">Installation</a> • <a href="#usage">Usage</a> • <a href="#performance">Performance</a> • <a href="./README-ZH.md" target="_blank">简体中文</a> <br> </p> <p align="center"> <a href='https://bmtrain.readthedocs.io/en/latest/?badge=latest'> <img src='https://readthedocs.org/projects/bmtrain/badge/?version=latest' alt='Documentation Status' /> </a> <a href="https://github.com/OpenBMB/BMTrain/releases"> <img alt="GitHub release (latest by date including pre-releases)" src="https://img.shields.io/github/v/release/OpenBMB/BMTrain?include_prereleases"> </a> <a href="https://github.com/OpenBMB/BMTrain/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/OpenBMB/BMTrain"> </a> </p> </div>

What's New

2024/02/26 BMTrain 1.0.0 released. Code refactoring and Tensor parallel support. See the detail in update log
2023/08/17 BMTrain 0.2.3 released. See the update log.
2022/12/15 BMTrain 0.2.0 released. See the update log.
2022/06/14 BMTrain 0.1.7 released. ZeRO-2 optimization is supported!
2022/03/30 BMTrain 0.1.2 released. Adapted to OpenPromptand OpenDelta.
2022/03/16 BMTrain 0.1.1 has publicly released the first stable version, which fixes many bugs that were in the beta version.
2022/02/11 BMTrain 0.0.15 has publicly released the first beta version.

Overview

BMTrain is an efficient large model training toolkit that can be used to train large models with tens of billions of parameters. It can train models in a distributed manner while keeping the code as simple as stand-alone training.

Documentation

Our documentation provides more information about the package.

Installation

From pip （recommend） : pip install bmtrain
From source code: download the package and run pip install .

Installing BMTrain may take a few to ten minutes, as it requires compiling the c/cuda source code at the time of installation. We recommend compiling BMTrain directly in the training environment to avoid potential problems caused by the different environments.

Usage

Step 1: Initialize BMTrain

Before you can use BMTrain, you need to initialize it at the beginning of your code. Just like using the distributed module of PyTorch requires the use of init_process_group at the beginning of the code, using BMTrain requires the use of init_distributed at the beginning of the code.

import bmtrain as bmt
bmt.init_distributed(
    seed=0,
    # ...
)

NOTE: Do not use PyTorch's distributed module and its associated communication functions when using BMTrain.

Step 2: Enable ZeRO Optimization

To enable ZeRO optimization, you need to make some simple replacements to the original model's code.

torch.nn.Module -> bmtrain.DistributedModule
torch.nn.Parameter -> bmtrain.DistributedParameter

And wrap the transformer blocks with bmtrain.Block.

Here is an example.

Original

import torch
class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.param = torch.nn.Parameter(torch.empty(1024))
        self.module_list = torch.nn.ModuleList([
            SomeTransformerBlock(),
            SomeTransformerBlock(),
            SomeTransformerBlock()
        ])
    
    def forward(self):
        x = self.param
        for module in self.module_list:
            x = module(x, 1, 2, 3)
        return x

Replaced

import torch
import bmtrain as bmt
class MyModule(bmt.DistributedModule): # changed here
    def __init__(self):
        super().__init__()
        self.param = bmt.DistributedParameter(torch.empty(1024)) # changed here
        self.module_list = torch.nn.ModuleList([
            bmt.Block(SomeTransformerBlock(), zero_level=3), # changed here, support 2 and 3 now
            bmt.Block(SomeTransformerBlock(), zero_level=3), # changed here, support 2 and 3 now
            bmt.Block(SomeTransformerBlock(), zero_level=3)  # changed here, support 2 and 3 now
        ])
    
    def forward(self):
        x = self.param
        for module in self.module_list:
            x = module(x, 1, 2, 3)
        return x

Step 3: Enable Communication Optimization

To further reduce the extra overhead of communication and overlap communication with computing time, TransformerBlockList can be used for optimization.

You can enable them by making the following substitutions to the code:

torch.nn.ModuleList -> bmtrain.TransformerBlockList
for module in self.module_list: x = module(x, ...) -> x = self.module_list(x, ...)

Original

import torch
import bmtrain as bmt
class MyModule(bmt.DistributedModule):
    def __init__(self):
        super().__init__()
        self.param = bmt.DistributedParameter(torch.empty(1024))
        self.module_list = torch.nn.ModuleList([
            bmt.Block(SomeTransformerBlock()),
            bmt.Block(SomeTransformerBlock()),
            bmt.Block(SomeTransformerBlock())
        ])
    
    def forward(self):
        x = self.param
        for module in self.module_list:
            x = module(x, 1, 2, 3)
        return x

Replaced

import torch
import bmtrain as bmt
class MyModule(bmt.DistributedModule):
    def __init__(self):
        super().__init__()
        self.param = bmt.DistributedParameter(torch.empty(1024))
        self.module_list = bmt.TransformerBlockList([ # changed here
            bmt.Block(SomeTransformerBlock()),
            bmt.Block(SomeTransformerBlock()),
            bmt.Block(SomeTransformerBlock())
        ])
    
    def forward(self):
        x = self.param
        for module in self.module_list:
            x = module(x, 1, 2, 3)
        return x

Step 4: Launch Distributed Training

BMTrain uses the same launch command as the distributed module of PyTorch.

You can choose one of them depending on your version of PyTorch.

${MASTER_ADDR} means the IP address of the master node.
${MASTER_PORT} means the port of the master node.
${NNODES} means the total number of nodes.
${GPU_PER_NODE} means the number of GPUs per node.
${NODE_RANK} means the rank of this node.

torch.distributed.launch

$ python3 -m torch.distributed.launch --master_addr ${MASTER_ADDR} --master_port ${MASTER_PORT} --nproc_per_node ${GPU_PER_NODE} --nnodes ${NNODES} --node_rank ${NODE_RANK} train.py

torchrun

$ torchrun --nnodes=${NNODES} --nproc_per_node=${GPU_PER_NODE} --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} train.py

For more information, please refer to the documentation.

Example

We provide an example of training GPT-2 based on BMTrain. The code mainly consists of the following parts.

Part 1: Model Definition

├── layers
│   ├── attention.py
│   ├── embedding.py
│   ├── feedforward.py
│   ├── __init__.py
│   ├── layernorm.py
│   └── linear.py
└── models
    ├── gpt.py
    └── __init__.py

Above is the directory structure of the code in the part of Model Definition.

We defined all the layers needed in GPT-2 and used BMTrain's DistributedModule and DistributedParameter to enable ZeRO optimization.

Part 2: BMTrain Initialization

bmtrain.init_distributed(seed=0)

model = GPT(
    num_layers=8,
    vocab_size=10240, 
    dim_model=2560,
    dim_head=80,
    num_heads=32,
    dim_ff=8192,
    max_distance=1024,
    bias=True,
    dtype=torch.half
)

bmtrain.init_parameters(model) # or loading checkpoint use `bmtrain.load`

# ... other initialization (dataset) ...

bmtrain.init_distributed(seed=0) is used to initialize the distributed training environment and set the random seed for reproducibility.

bmtrain.init_parameters(model) is used to initialize the distributed parameters of the model.

Part 3: Intialization of the Optimizer and LR Scheduler

loss_func = torch.nn.CrossEntropyLoss(ignore_index=-100)
optimizer = bmtrain.optim.AdamOffloadOptimizer(model.parameters(), weight_decay=1e-2)
lr_scheduler = bmtrain.lr_scheduler.Noam(optimizer, start_lr=1e-3, warmup_iter=40, end_iter=1000, num_iter=0)

BMTrain supports all the PyTorch native optimizers and loss functions, and you can also use the fused optimizer provided by BMTrain for mixed-precision training.

In addition, BMTrain also provides the common LRScheduler in the bmtrain.lr_scheduler module.

Part 4: Training Loop

# create a new instance of optimizer manager
optim_manager = bmtrain.optim.OptimManager(loss_scale=1024)
# let optim_manager handle all the optimizer and (optional) their corresponding lr_scheduler
optim_manager.add_optimizer(optimizer, lr_scheduler)
# add_optimizer can be called multiple times to add other optimizers.

for iteration in range(1000):
    # ... load data for each rank ...

    # forward pass and calculate loss
    pos = torch.arang

BMTrain

Install / Use

README