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README
MIT

简介

MindSpeed 是针对华为昇腾设备的大模型加速库。

大模型训练是一种非常复杂的过程,涉及到许多技术和挑战,其中大模型训练需要大量的显存资源是一个难题,对计算卡提出了不小的挑战。 为了在单个计算卡显存资源不足时,可以通过多张计算卡进行计算,业界出现了类似 Megatron、DeepSpeed 等第三方大模型加速库,对模型、输入数据等进行切分并分配到不同的计算卡上,最后再通过集合通信对结果进行汇总。

昇腾提供 MindSpeed 加速库,使能客户大模型业务快速迁移至昇腾设备,并且支持昇腾专有算法,确保开箱可用。

安装

1. 安装依赖

在安装MindSpeed之前,请参考版本配套表,安装最新昇腾软件栈:https://www.hiascend.com/zh/

依赖软件
Driver
Firmware
CANN
Kernel
PyTorch
torch_npu
apex

2. 安装 MindSpeed

下载源码安装:

git clone https://gitee.com/ascend/MindSpeed.git
pip install -e MindSpeed

如需使用ATB算子,请在安装前添加环境变量ENABLE_ATB=1,例如:

git clone https://gitee.com/ascend/MindSpeed.git
ENABLE_ATB=1 pip install -e MindSpeed

3. 获取 Megatron-LM 并指定 commit id

# 目前版本基于core_r0.6.0的release版本
git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
git checkout core_r0.6.0

快速上手

以 GPT 模型为例:

  1. 在 Megatron-LM 目录下修改pretrain_gpt.py文件,在import torch下新增一行import mindspeed.megatron_adaptor

     import os
     import torch
    +import mindspeed.megatron_adaptor
     from torch import Tensor
     from functools import partial
     from typing import Union
  2. 在 Megatron-LM 目录下修改pretrain_gpt.py文件,在model_provider函数中删除assert(args.context_parallel_size == 1), "Context parallelism is only supported with Megatron Core!"

    else:
    -   assert(args.context_parallel_size == 1), "Context parallelism is only supported with Megatron Core!"
    
        model = megatron.model.GPTModel(
            config,
            num_tokentypes=0,
            parallel_output=True,
            pre_process=pre_process,
            post_process=post_process
        )
  3. 在 Megatron-LM 目录下,准备好训练数据,并在示例脚本中填写对应路径,然后执行。

    bash examples/pretrain_gpt_distributed.sh

特性介绍

特性 介绍
Megatron 数据并行 link
Megatron 张量并行 link
Megatron 流水并行 link
Megatron 虚拟流水并行 link
Megatron 序列并行 link
Megatron 重计算 link
Megatron 分布式优化器 link
Megatron 异步DDP link
Megatron 权重更新通信隐藏 link
Ascend TP 重计算通信优化 link
Ascend 内存碎片优化 link
Ascend 自适应选择重计算 link
Ascend 激活函数重计算 link
Ascend 计算通信并行优化 link
Ascend BF16 参数副本复用 link
Ascend rms_norm 融合算子 link
Ascend swiglu 融合算子 link
Ascend rotary_embedding 融合算子 link
Ascend MoE token重排性能优化 link
Ascend flash attention 适配 link
【Prototype】Ulysses 长序列并行 link
【Prototype】Ring Attention 长序列并行 link
【Prototype】混合长序列并行 link
【Prototype】Ascend MC2 link
【Prototype】alibi link
【Prototype】PP自动并行 link
【Prototype】其他昇腾亲和优化 暂无

自定义算子

算子 介绍
npu_dropout_add_layer_norm link
【Prototype】ffn link
【Prototype】fusion_attention link
【Prototype】rms_norm link
【Prototype】swiglu link
【Prototype】lcal_coc link
【Prototype】npu_mm_all_reduce_add_rms_norm link
【Prototype】npu_mm_all_reduce_add_rms_norm_ link
【Prototype】npu_grouped_mat_mul_all_reduce link
【Prototype】npu_rotary_position_embedding link

版本配套表

PyTorch Extension版本号采用{PyTorch版本}-{昇腾版本}命名规则,前者为PyTorch Extension匹配的PyTorch版本,后者用于匹配CANN版本,详细匹配如下:

MindSpeed版本 PyTorch版本 Extension版本 Python版本
1.0 2.1.0 2.1.0.post3 Python3.8.x, Python3.9.x, Python3.10.x

昇腾辅助软件中有更多关于PyTorch和CANN的版本信息。

安全声明

MindSpeed 安全声明

The following applies to all files unless otherwise noted; Copyright (c) 2023, Huawei Technologies Co., Ltd Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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Python 等 4 种语言
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