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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nirzu97/pyprobml/blob/multi-gpu-training-torch/notebooks/multi_gpu_training_torch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UeG2yP57tJBX"
      },
      "source": [
        "#Train a CNN on multiple GPUs using data parallelism.\n",
        "\n",
        "Based on sec 12.5 of http://d2l.ai/chapter_computational-performance/multiple-gpus.html.\n",
        "\n",
        "Note: in colab, we only have access to 1 GPU, so the code below just simulates the effects of multiple GPUs, so it will not run faster. You may not see a speedup eveen on a machine which really does have multiple GPUs, because the model and data are too small. But the example should still illustrate the key ideas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_2hHiscs-cH"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "from IPython import display \n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils import data\n",
        "from torchvision import transforms\n",
        "\n",
        "import random\n",
        "import os\n",
        "import time\n",
        "\n",
        "np.random.seed(seed=1)\n",
        "torch.manual_seed(1)\n",
        "!mkdir figures # for saving plots"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngd9TmIVuAN6"
      },
      "source": [
        "# Model\n",
        "\n",
        "We use a slightly modified version of the LeNet CNN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgrwcqORtJdL"
      },
      "source": [
        "# Initialize model parameters\n",
        "scale = 0.01\n",
        "torch.random.manual_seed(0)\n",
        "W1 = torch.randn(size=(20, 1, 3, 3)) * scale\n",
        "b1 = torch.zeros(20)\n",
        "W2 = torch.randn(size=(50, 20, 5, 5)) * scale\n",
        "b2 = torch.zeros(50)\n",
        "W3 = torch.randn(size=(800, 128)) * scale\n",
        "b3 = torch.zeros(128)\n",
        "W4 = torch.randn(size=(128, 10)) * scale\n",
        "b4 = torch.zeros(10)\n",
        "params = [W1, b1, W2, b2, W3, b3, W4, b4]\n",
        "\n",
        "# Define the model\n",
        "def lenet(X, params):\n",
        "    h1_conv = F.conv2d(input=X, weight=params[0], bias=params[1])\n",
        "    h1_activation = F.relu(h1_conv)\n",
        "    h1 = F.avg_pool2d(input=h1_activation, kernel_size=(2, 2), stride=(2, 2))\n",
        "    h2_conv = F.conv2d(input=h1, weight=params[2], bias=params[3])\n",
        "    h2_activation = F.relu(h2_conv)\n",
        "    h2 = F.avg_pool2d(input=h2_activation, kernel_size=(2, 2), stride=(2, 2))\n",
        "    h2 = h2.reshape(h2.shape[0], -1)\n",
        "    h3_linear = torch.mm(h2, params[4]) + params[5]\n",
        "    h3 = F.relu(h3_linear)\n",
        "    y_hat = torch.mm(h3, params[6]) + params[7]\n",
        "    return y_hat\n",
        "\n",
        "# Cross-entropy loss function\n",
        "loss = nn.CrossEntropyLoss(reduction='none')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkQPS6zAuP6t"
      },
      "source": [
        "# Copying parameters across devices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8U6mWcduCuy"
      },
      "source": [
        "def get_params(params, device):\n",
        "    new_params = [p.clone().to(device) for p in params]\n",
        "    for p in new_params:\n",
        "        p.requires_grad_()\n",
        "    return new_params"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIYjX1lRuc2K",
        "outputId": "f712afcb-67d0-4c4b-8a5d-26055e6bacf6"
      },
      "source": [
        "# Copy the params to GPU0\n",
        "gpu0 = torch.device('cuda:0')\n",
        "new_params = get_params(params, gpu0)\n",
        "print('b1 weight:', new_params[1])\n",
        "print('b1 grad:', new_params[1].grad)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b1 weight: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
            "       device='cuda:0', requires_grad=True)\n",
            "b1 grad: None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82NV4DDtvCxa",
        "outputId": "36f3197c-d967-4b5a-e475-0dc10d129069"
      },
      "source": [
        "# Copy the params to GPU1\n",
        "gpu1 = torch.device('cuda:0') # torch.device('cuda:1')\n",
        "new_params = get_params(params, gpu1)\n",
        "print('b1 weight:', new_params[1])\n",
        "print('b1 grad:', new_params[1].grad)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b1 weight: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
            "       device='cuda:0', requires_grad=True)\n",
            "b1 grad: None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMvps-jrvQmD"
      },
      "source": [
        "All-reduce will copy data (eg gradients) from all devices to device 0, add them, and then broadcast the result back to each device."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kkd7DqszuuCY"
      },
      "source": [
        "def allreduce(data):\n",
        "    for i in range(1, len(data)):\n",
        "        data[0][:] += data[i].to(data[0].device)\n",
        "    for i in range(1, len(data)):\n",
        "        data[i] = data[0].to(data[i].device)\n",
        "\n",
        "def try_gpu(i=0):\n",
        "    \"\"\"Return gpu(i) if exists, otherwise return cpu().\"\"\"\n",
        "    if torch.cuda.device_count() >= i + 1:\n",
        "      return torch.device(f'cuda:{i}')\n",
        "    return torch.device('cpu')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYkATwb0vhbP",
        "outputId": "b32101ce-7e1b-491e-bcbd-2eb10a14cfcc"
      },
      "source": [
        "data_ = [torch.ones((1, 2), device=try_gpu(i)) * (i + 1) for i in range(2)]\n",
        "print('before allreduce:\\n', data_[0], '\\n', data_[1])\n",
        "allreduce(data_)\n",
        "print('after allreduce:\\n', data_[0], '\\n', data_[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "before allreduce:\n",
            " tensor([[1., 1.]], device='cuda:0') \n",
            " tensor([[2., 2.]])\n",
            "after allreduce:\n",
            " tensor([[3., 3.]], device='cuda:0') \n",
            " tensor([[3., 3.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPF2qz7Xvn0p"
      },
      "source": [
        "# Distribute data across GPUs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0St4qm0RvhmV",
        "outputId": "a706001b-e363-4b22-ebb0-eec50a41342a"
      },
      "source": [
        "data_ = torch.arange(20).reshape(4, 5)\n",
        "#devices = [torch.device('cuda:0'), torch.device('cuda:1')]\n",
        "devices = [torch.device('cuda:0'), torch.device('cuda:0')]\n",
        "split = nn.parallel.scatter(data_, devices)\n",
        "print('input :', data_)\n",
        "print('load into', devices)\n",
        "print('output:', split)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input : tensor([[ 0,  1,  2,  3,  4],\n",
            "        [ 5,  6,  7,  8,  9],\n",
            "        [10, 11, 12, 13, 14],\n",
            "        [15, 16, 17, 18, 19]])\n",
            "load into [device(type='cuda', index=0), device(type='cuda', index=0)]\n",
            "output: (tensor([[0, 1, 2, 3, 4],\n",
            "        [5, 6, 7, 8, 9]], device='cuda:0'), tensor([[10, 11, 12, 13, 14],\n",
            "        [15, 16, 17, 18, 19]], device='cuda:0'))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9Oo0Htvv4vK"
      },
      "source": [
        "Split data and labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrQymW40vqQQ"
      },
      "source": [
        "\n",
        "def split_batch(X, y, devices):\n",
        "    \"\"\"Split `X` and `y` into multiple devices.\"\"\"\n",
        "    assert X.shape[0] == y.shape[0]\n",
        "    return (nn.parallel.scatter(X, devices), nn.parallel.scatter(y, devices))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gp-2SIaUv_Hh"
      },
      "source": [
        "# Training on Fashion MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilYJZjkq6C9D"
      },
      "source": [
        "def load_data_fashion_mnist(batch_size, resize=None):\n",
        "    \"\"\"Download the Fashion-MNIST dataset and then load it into memory.\"\"\"\n",
        "    trans = [transforms.ToTensor()]\n",
        "    if resize:\n",
        "        trans.insert(0, transforms.Resize(resize))\n",
        "    trans = transforms.Compose(trans)\n",
        "    mnist_train = torchvision.datasets.FashionMNIST(root=\"../data\",\n",
        "                                                    train=True,\n",
        "                                                    transform=trans,\n",
        "                                                    download=True)\n",
        "    mnist_test = torchvision.datasets.FashionMNIST(root=\"../data\",\n",
        "                                                   train=False,\n",
        "                                                   transform=trans,\n",
        "                                                   download=True)\n",
        "    return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
        "                            num_workers=4),\n",
        "            data.DataLoader(mnist_test, batch_size, shuffle=False,\n",
        "                            num_workers=4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tA_QeZH5rp1o"
      },
      "source": [
        "class Animator:\n",
        "    \"\"\"For plotting data in animation.\"\"\"\n",
        "    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n",
        "                 ylim=None, xscale='linear', yscale='linear',\n",
        "                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n",
        "                 figsize=(3.5, 2.5)):\n",
        "        # Incrementally plot multiple lines\n",
        "        if legend is None:\n",
        "            legend = []\n",
        "        display.set_matplotlib_formats('svg')\n",
        "        self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)\n",
        "        if nrows * ncols == 1:\n",
        "            self.axes = [self.axes,]\n",
        "        # Use a lambda function to capture arguments\n",
        "        self.config_axes = lambda: set_axes(self.axes[\n",
        "            0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n",
        "        self.X, self.Y, self.fmts = None, None, fmts\n",
        "\n",
        "    def add(self, x, y):\n",
        "        # Add multiple data points into the figure\n",
        "        if not hasattr(y, \"__len__\"):\n",
        "            y = [y]\n",
        "        n = len(y)\n",
        "        if not hasattr(x, \"__len__\"):\n",
        "            x = [x] * n\n",
        "        if not self.X:\n",
        "            self.X = [[] for _ in range(n)]\n",
        "        if not self.Y:\n",
        "            self.Y = [[] for _ in range(n)]\n",
        "        for i, (a, b) in enumerate(zip(x, y)):\n",
        "            if a is not None and b is not None:\n",
        "                self.X[i].append(a)\n",
        "                self.Y[i].append(b)\n",
        "        self.axes[0].cla()\n",
        "        for x, y, fmt in zip(self.X, self.Y, self.fmts):\n",
        "            self.axes[0].plot(x, y, fmt)\n",
        "        self.config_axes()\n",
        "        display.display(self.fig)\n",
        "        display.clear_output(wait=True)\n",
        "\n",
        "class Timer:\n",
        "    \"\"\"Record multiple running times.\"\"\"\n",
        "    def __init__(self):\n",
        "        self.times = []\n",
        "        self.start()\n",
        "\n",
        "    def start(self):\n",
        "        \"\"\"Start the timer.\"\"\"\n",
        "        self.tik = time.time()\n",
        "\n",
        "    def stop(self):\n",
        "        \"\"\"Stop the timer and record the time in a list.\"\"\"\n",
        "        self.times.append(time.time() - self.tik)\n",
        "        return self.times[-1]\n",
        "\n",
        "    def avg(self):\n",
        "        \"\"\"Return the average time.\"\"\"\n",
        "        return sum(self.times) / len(self.times)\n",
        "\n",
        "    def sum(self):\n",
        "        \"\"\"Return the sum of time.\"\"\"\n",
        "        return sum(self.times)\n",
        "\n",
        "    def cumsum(self):\n",
        "        \"\"\"Return the accumulated time.\"\"\"\n",
        "        return np.array(self.times).cumsum().tolist()\n",
        "\n",
        "class Accumulator:\n",
        "    \"\"\"For accumulating sums over `n` variables.\"\"\"\n",
        "    def __init__(self, n):\n",
        "        self.data = [0.0] * n\n",
        "\n",
        "    def add(self, *args):\n",
        "        self.data = [a + float(b) for a, b in zip(self.data, args)]\n",
        "\n",
        "    def reset(self):\n",
        "        self.data = [0.0] * len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.data[idx]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqnIuSGwxG0L"
      },
      "source": [
        "def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):\n",
        "    \"\"\"Set the axes for matplotlib.\"\"\"\n",
        "    axes.set_xlabel(xlabel)\n",
        "    axes.set_ylabel(ylabel)\n",
        "    axes.set_xscale(xscale)\n",
        "    axes.set_yscale(yscale)\n",
        "    axes.set_xlim(xlim)\n",
        "    axes.set_ylim(ylim)\n",
        "    if legend:\n",
        "        axes.legend(legend)\n",
        "    axes.grid()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBqC89zOr_Si"
      },
      "source": [
        "def accuracy(y_hat, y):\n",
        "    \"\"\"Compute the number of correct predictions.\"\"\"\n",
        "    if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n",
        "        y_hat = torch.argmax(y_hat, axis=1)\n",
        "    cmp_ = y_hat.type(y.dtype) == y\n",
        "    return float(cmp_.type(y.dtype).sum())\n",
        "\n",
        "def evaluate_accuracy_gpu(net, data_iter, device=None):\n",
        "    \"\"\"Compute the accuracy for a model on a dataset using a GPU.\"\"\"\n",
        "    if isinstance(net, torch.nn.Module):\n",
        "        net.eval()  # Set the model to evaluation mode\n",
        "        if not device:\n",
        "            device = next(iter(net.parameters())).device\n",
        "    # No. of correct predictions, no. of predictions\n",
        "    metric = Accumulator(2)\n",
        "    for X, y in data_iter:\n",
        "        X = X.to(device)\n",
        "        y = y.to(device)\n",
        "        metric.add(accuracy(net(X), y), y.numel())\n",
        "    return metric[0] / metric[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prMQdt8MO8nf"
      },
      "source": [
        "## Train function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21qD8zr0wYCk"
      },
      "source": [
        "def sgd(params, lr, batch_size):\n",
        "    \"\"\"Minibatch stochastic gradient descent.\"\"\"\n",
        "    with torch.no_grad():\n",
        "        for param in params:\n",
        "            param -= lr * param.grad / batch_size\n",
        "            param.grad.zero_()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCAo71EDv6FL"
      },
      "source": [
        "def train_batch(X, y, device_params, devices, lr):\n",
        "    X_shards, y_shards = split_batch(X, y, devices)\n",
        "    # Loss is calculated separately on each GPU\n",
        "    losses = [\n",
        "        loss(lenet(X_shard, device_W),\n",
        "             y_shard).sum() for X_shard, y_shard, device_W in zip(\n",
        "                 X_shards, y_shards, device_params)]\n",
        "    for l in losses:  # Back Propagation is performed separately on each GPU\n",
        "        l.backward()\n",
        "    # Sum all gradients from each GPU and broadcast them to all GPUs\n",
        "    with torch.no_grad():\n",
        "        for i in range(len(device_params[0])):\n",
        "            allreduce([device_params[c][i].grad for c in range(len(devices))])\n",
        "    # The model parameters are updated separately on each GPU\n",
        "    ndata = X.shape[0] # gradient is summed over the full minibatch\n",
        "    for param in device_params:\n",
        "        sgd(param, lr, ndata)   "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APWm5BzdwQHi"
      },
      "source": [
        "def train(num_gpus, batch_size, lr):\n",
        "    train_iter, test_iter = load_data_fashion_mnist(batch_size)\n",
        "    devices = [try_gpu(i) for i in range(num_gpus)]\n",
        "    # Copy model parameters to num_gpus GPUs\n",
        "    device_params = [get_params(params, d) for d in devices]\n",
        "    # num_epochs, times, acces = 10, [], []\n",
        "    num_epochs = 5\n",
        "    animator = Animator('epoch', 'test acc', xlim=[1, num_epochs])\n",
        "    timer = Timer()\n",
        "    for epoch in range(num_epochs):\n",
        "        timer.start()\n",
        "        for X, y in train_iter:\n",
        "            # Perform multi-GPU training for a single minibatch\n",
        "            train_batch(X, y, device_params, devices, lr)\n",
        "            torch.cuda.synchronize()\n",
        "        timer.stop()\n",
        "        # Verify the model on GPU 0\n",
        "        animator.add(epoch + 1, (evaluate_accuracy_gpu(\n",
        "            lambda x: lenet(x, device_params[0]), test_iter, devices[0]),))\n",
        "    print(f'test acc: {animator.Y[0][-1]:.2f}, {timer.avg():.1f} sec/epoch '\n",
        "          f'on {str(devices)}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jG99sNIE9gAd"
      },
      "source": [
        "# Learning curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278,
          "referenced_widgets": [
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        },
        "id": "ysVlYgDQwhQ7",
        "outputId": "71a0fe79-5454-4244-8b4f-fabb468d53e5"
      },
      "source": [
        "train(num_gpus=1, batch_size=256, lr=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test acc: 0.74, 4.4 sec/epoch on [device(type='cuda', index=0)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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