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370 lines
17 KiB
370 lines
17 KiB
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "colossal_cifar_demo.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "uhrbvVEh2iJd"
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},
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"source": [
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"# Train an image classifier\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "vP7LvCpG23a2",
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"outputId": "b37f7203-8a02-4736-c527-603f2bb34d7d"
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},
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"source": [
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"!pip install ColossalAI deepspeed"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: ColossalAI in /usr/local/lib/python3.7/dist-packages (0.1)\n",
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"Requirement already satisfied: deepspeed in /usr/local/lib/python3.7/dist-packages (0.5.4)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from deepspeed) (21.0)\n",
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"Requirement already satisfied: triton in /usr/local/lib/python3.7/dist-packages (from deepspeed) (1.1.1)\n",
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"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from deepspeed) (4.62.3)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from deepspeed) (1.19.5)\n",
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"Requirement already satisfied: tensorboardX==1.8 in /usr/local/lib/python3.7/dist-packages (from deepspeed) (1.8)\n",
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"Requirement already satisfied: ninja in /usr/local/lib/python3.7/dist-packages (from deepspeed) (1.10.2.2)\n",
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"Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from deepspeed) (1.9.0+cu111)\n",
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"Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from deepspeed) (5.4.8)\n",
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"Requirement already satisfied: protobuf>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX==1.8->deepspeed) (3.17.3)\n",
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"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tensorboardX==1.8->deepspeed) (1.15.0)\n",
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"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->deepspeed) (2.4.7)\n",
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"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->deepspeed) (3.7.4.3)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from triton->deepspeed) (3.3.0)\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "UVKEurtS4SFS",
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"outputId": "99fb6050-5da7-4f27-b4eb-9b3ccf830efb"
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},
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"source": [
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"import colossalai\n",
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"from colossalai.engine import Engine, NoPipelineSchedule\n",
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"from colossalai.trainer import Trainer\n",
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"from colossalai.context import Config\n",
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"import torch"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Please install apex to use FP16 Optimizer\n",
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"Apex should be installed to use the FP16 optimizer\n",
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"apex is required for mixed precision training\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "PpFfhNBD7NSn"
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},
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"source": [
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"First, we should initialize distributed environment. Though we just use single GPU in this example, we still need initialize distributed environment for compatibility. We just consider the simplest case here, so we just set the number of parallel processes to 1."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "8yF7Lc-K7NAS",
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"outputId": "01312349-a8b0-4de4-9103-7d1b48e6cc36"
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},
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"source": [
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"parallel_cfg = Config(dict(parallel=dict(\n",
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" data=dict(size=1),\n",
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" pipeline=dict(size=1),\n",
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" tensor=dict(size=1, mode=None),\n",
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")))\n",
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"colossalai.init_dist(config=parallel_cfg,\n",
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" local_rank=0,\n",
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" world_size=1,\n",
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" host='127.0.0.1',\n",
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" port=8888,\n",
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" backend='nccl')"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"colossalai - torch.distributed.distributed_c10d - 2021-10-15 03:27:51,596 INFO: Added key: store_based_barrier_key:1 to store for rank: 0\n",
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"colossalai - torch.distributed.distributed_c10d - 2021-10-15 03:27:51,598 INFO: Rank 0: Completed store-based barrier for 1 nodes.\n",
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"colossalai - torch.distributed.distributed_c10d - 2021-10-15 03:27:51,602 INFO: Added key: store_based_barrier_key:2 to store for rank: 0\n",
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"colossalai - torch.distributed.distributed_c10d - 2021-10-15 03:27:51,605 INFO: Rank 0: Completed store-based barrier for 1 nodes.\n",
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"colossalai - torch.distributed.distributed_c10d - 2021-10-15 03:27:51,608 INFO: Added key: store_based_barrier_key:3 to store for rank: 0\n",
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"colossalai - torch.distributed.distributed_c10d - 2021-10-15 03:27:51,610 INFO: Rank 0: Completed store-based barrier for 1 nodes.\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"process rank 0 is bound to device 0\n",
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"initialized seed on rank 0, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.TENSOR: 1124,the default parallel seed is ParallelMode.DATA.\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ppjmMxc_81TK"
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},
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"source": [
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"Load and normalize the CIFAR10 training and test datasets using `colossalai.nn.data`. Note that we have wrapped `torchvision.transforms`, so that we can simply use the config dict to use them."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ZyGhyD47-dUY",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "98bbf2d1-a1c4-4bb4-b6df-600777b1e8f5"
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},
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"source": [
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"transform_cfg = [\n",
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" dict(type='ToTensor'),\n",
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" dict(type='Normalize',\n",
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" mean=[0.4914, 0.4822, 0.4465],\n",
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" std=[0.2023, 0.1994, 0.2010]),\n",
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"]\n",
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"\n",
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"batch_size = 128\n",
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"\n",
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"trainset = colossalai.nn.data.CIFAR10Dataset(transform_cfg, root='./data', train=True)\n",
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"trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
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"\n",
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"testset = colossalai.nn.data.CIFAR10Dataset(transform_cfg, root='./data', train=False)\n",
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"testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Files already downloaded and verified\n",
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"Files already downloaded and verified\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NvPbfLLR9NzC"
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},
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"source": [
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"We just define a simple Convolutional Neural Network here."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "cQ_y7lBG09LS"
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},
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"source": [
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"\n",
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"\n",
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"class Net(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.conv1 = nn.Conv2d(3, 6, 5)\n",
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" self.pool = nn.MaxPool2d(2, 2)\n",
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" self.conv2 = nn.Conv2d(6, 16, 5)\n",
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" self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
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" self.fc2 = nn.Linear(120, 84)\n",
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" self.fc3 = nn.Linear(84, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.pool(F.relu(self.conv1(x)))\n",
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" x = self.pool(F.relu(self.conv2(x)))\n",
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" x = torch.flatten(x, 1) # flatten all dimensions except batch\n",
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" x = F.relu(self.fc1(x))\n",
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" x = F.relu(self.fc2(x))\n",
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" x = self.fc3(x)\n",
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" return x\n",
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"\n",
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"\n",
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"model = Net().cuda()"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "tgsszAmM9dYZ"
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},
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"source": [
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"Define a Loss function and optimizer. And then we use them to initialize `Engine` and `Trainer`. We provide various training / evaluating hooks. In this case, we just use the simplest hooks which can compute and print loss and accuracy."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "YtaDoCax1BCf",
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"outputId": "b33b1641-03d8-4597-c8c2-1a4c1d61e9b0"
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},
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"source": [
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"import torch.optim as optim\n",
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"\n",
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"criterion = nn.CrossEntropyLoss()\n",
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"optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
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"schedule = NoPipelineSchedule()\n",
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"engine = Engine(\n",
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" model=model,\n",
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" criterion=criterion,\n",
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" optimizer=optimizer,\n",
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" lr_scheduler=None,\n",
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" schedule=schedule\n",
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" )\n",
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"trainer = Trainer(engine=engine,\n",
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" hooks_cfg=[dict(type='LossHook'), dict(type='LogMetricByEpochHook'), dict(type='AccuracyHook')],\n",
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" verbose=True)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"colossalai - rank_0 - 2021-10-15 03:27:56,018 WARNING: No gradient handler is set up, please make sure you do not need to all-reduce the gradients after a training step.\n",
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"colossalai - rank_0 - 2021-10-15 03:27:56,024 INFO: build LogMetricByEpochHook for train, priority = 1\n",
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"colossalai - rank_0 - 2021-10-15 03:27:56,026 INFO: build LossHook for train, priority = 10\n",
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"colossalai - rank_0 - 2021-10-15 03:27:56,029 INFO: build AccuracyHook for train, priority = 10\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "_JR2TuvH99Ik"
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},
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"source": [
|
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"Then we set training configs. We train our model for 10 epochs and it will be evaluated every 1 epoch. Set `display_progress` to `True` to display the training / evaluating progress bar."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "w-J3IP-J1sfx",
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"outputId": "bdb76939-04f1-4124-ce5e-3af44c0d902c"
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},
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"source": [
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"num_epochs = 10\n",
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"test_interval = 1\n",
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"trainer.fit(\n",
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" train_dataloader=trainloader,\n",
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" test_dataloader=testloader,\n",
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" max_epochs=num_epochs,\n",
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" display_progress=True,\n",
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" test_interval=test_interval\n",
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" )"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"[Epoch 0 train]: 0%| | 0/391 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n",
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" return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
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"[Epoch 0 train]: 100%|██████████| 391/391 [00:14<00:00, 26.82it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:28:11,088 INFO: Training - Epoch 1 - LogMetricByEpochHook: Loss = 2.29158\n",
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"[Epoch 0 val]: 100%|██████████| 79/79 [00:02<00:00, 28.66it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:28:14,040 INFO: Testing - Epoch 1 - LogMetricByEpochHook: Loss = 2.26517, Accuracy = 0.14820\n",
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"[Epoch 1 train]: 100%|██████████| 391/391 [00:14<00:00, 26.31it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:28:29,059 INFO: Training - Epoch 2 - LogMetricByEpochHook: Loss = 2.15763\n",
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"[Epoch 1 val]: 100%|██████████| 79/79 [00:02<00:00, 28.50it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:28:32,007 INFO: Testing - Epoch 2 - LogMetricByEpochHook: Loss = 2.00450, Accuracy = 0.27850\n",
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"[Epoch 2 train]: 100%|██████████| 391/391 [00:14<00:00, 26.08it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:28:47,167 INFO: Training - Epoch 3 - LogMetricByEpochHook: Loss = 1.85409\n",
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"[Epoch 2 val]: 100%|██████████| 79/79 [00:02<00:00, 27.89it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:28:50,168 INFO: Testing - Epoch 3 - LogMetricByEpochHook: Loss = 1.73788, Accuracy = 0.35990\n",
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"[Epoch 3 train]: 100%|██████████| 391/391 [00:14<00:00, 26.09it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:05,330 INFO: Training - Epoch 4 - LogMetricByEpochHook: Loss = 1.69363\n",
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"[Epoch 3 val]: 100%|██████████| 79/79 [00:02<00:00, 28.43it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:08,290 INFO: Testing - Epoch 4 - LogMetricByEpochHook: Loss = 1.65005, Accuracy = 0.39350\n",
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"[Epoch 4 train]: 100%|██████████| 391/391 [00:15<00:00, 25.97it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:23,530 INFO: Training - Epoch 5 - LogMetricByEpochHook: Loss = 1.61387\n",
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"[Epoch 4 val]: 100%|██████████| 79/79 [00:02<00:00, 27.75it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:26,515 INFO: Testing - Epoch 5 - LogMetricByEpochHook: Loss = 1.57507, Accuracy = 0.42430\n",
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"[Epoch 5 train]: 100%|██████████| 391/391 [00:15<00:00, 25.92it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:41,764 INFO: Training - Epoch 6 - LogMetricByEpochHook: Loss = 1.55712\n",
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"[Epoch 5 val]: 100%|██████████| 79/79 [00:02<00:00, 27.51it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:44,778 INFO: Testing - Epoch 6 - LogMetricByEpochHook: Loss = 1.53242, Accuracy = 0.43700\n",
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"[Epoch 6 train]: 100%|██████████| 391/391 [00:14<00:00, 26.13it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:29:59,927 INFO: Training - Epoch 7 - LogMetricByEpochHook: Loss = 1.51618\n",
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"[Epoch 6 val]: 100%|██████████| 79/79 [00:02<00:00, 28.31it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:02,884 INFO: Testing - Epoch 7 - LogMetricByEpochHook: Loss = 1.49720, Accuracy = 0.45430\n",
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"[Epoch 7 train]: 100%|██████████| 391/391 [00:14<00:00, 26.23it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:17,968 INFO: Training - Epoch 8 - LogMetricByEpochHook: Loss = 1.47857\n",
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"[Epoch 7 val]: 100%|██████████| 79/79 [00:02<00:00, 27.97it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:20,967 INFO: Testing - Epoch 8 - LogMetricByEpochHook: Loss = 1.45808, Accuracy = 0.46320\n",
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"[Epoch 8 train]: 100%|██████████| 391/391 [00:14<00:00, 26.11it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:36,129 INFO: Training - Epoch 9 - LogMetricByEpochHook: Loss = 1.44656\n",
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"[Epoch 8 val]: 100%|██████████| 79/79 [00:02<00:00, 28.18it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:39,096 INFO: Testing - Epoch 9 - LogMetricByEpochHook: Loss = 1.44903, Accuracy = 0.46580\n",
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"[Epoch 9 train]: 100%|██████████| 391/391 [00:15<00:00, 25.97it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:54,342 INFO: Training - Epoch 10 - LogMetricByEpochHook: Loss = 1.41120\n",
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"[Epoch 9 val]: 100%|██████████| 79/79 [00:02<00:00, 28.05it/s]\n",
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"colossalai - rank_0 - 2021-10-15 03:30:57,332 INFO: Testing - Epoch 10 - LogMetricByEpochHook: Loss = 1.41242, Accuracy = 0.48500\n"
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]
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}
|
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]
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}
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]
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} |