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  {
   "cell_type": "markdown",
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    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Multi-GPU Computation Implementation from Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:12.539924Z",
     "start_time": "2019-07-05T23:40:07.289355Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Jul  5 23:40:11 2019       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 418.67       Driver Version: 418.67       CUDA Version: 10.1     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla V100-SXM2...  Off  | 00000000:00:1B.0 Off |                    0 |\n",
      "| N/A   47C    P0    54W / 300W |      0MiB / 16130MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  Tesla V100-SXM2...  Off  | 00000000:00:1C.0 Off |                    0 |\n",
      "| N/A   45C    P0    55W / 300W |   2005MiB / 16130MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  Tesla V100-SXM2...  Off  | 00000000:00:1D.0 Off |                    0 |\n",
      "| N/A   46C    P0    58W / 300W |      0MiB / 16130MiB |      4%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  Tesla V100-SXM2...  Off  | 00000000:00:1E.0 Off |                    0 |\n",
      "| N/A   43C    P0    40W / 300W |     11MiB / 16130MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|    1     49887      C   /home/ubuntu/miniconda3/bin/python          1001MiB |\n",
      "|    1     59096      C   /home/ubuntu/miniconda3/bin/python           993MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import d2l\n",
    "from mxnet import autograd, np, npx, gluon\n",
    "npx.set_np()\n",
    "\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Initializing LeNet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:12.554122Z",
     "start_time": "2019-07-05T23:40:12.542050Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "10"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize model parameters\n",
    "scale = 0.01\n",
    "W1 = np.random.normal(scale=scale, size=(20, 1, 3, 3))\n",
    "b1 = np.zeros(20)\n",
    "W2 = np.random.normal(scale=scale, size=(50, 20, 5, 5))\n",
    "b2 = np.zeros(50)\n",
    "W3 = np.random.normal(scale=scale, size=(800, 128))\n",
    "b3 = np.zeros(128)\n",
    "W4 = np.random.normal(scale=scale, size=(128, 10))\n",
    "b4 = np.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 = npx.convolution(data=X, weight=params[0], bias=params[1],\n",
    "                              kernel=(3, 3), num_filter=20)\n",
    "    h1_activation = npx.relu(h1_conv)\n",
    "    h1 = npx.pooling(data=h1_activation, pool_type='avg', kernel=(2, 2),\n",
    "                    stride=(2, 2))\n",
    "    h2_conv = npx.convolution(data=h1, weight=params[2], bias=params[3],\n",
    "                              kernel=(5, 5), num_filter=50)\n",
    "    h2_activation = npx.relu(h2_conv)\n",
    "    h2 = npx.pooling(data=h2_activation, pool_type='avg', kernel=(2, 2),\n",
    "                    stride=(2, 2))\n",
    "    h2 = h2.reshape((h2.shape[0], -1))\n",
    "    h3_linear = np.dot(h2, params[4]) + params[5]\n",
    "    h3 = npx.relu(h3_linear)\n",
    "    y_hat = np.dot(h3, params[6]) + params[7]\n",
    "    return y_hat\n",
    "\n",
    "# Cross-entropy loss function\n",
    "loss = gluon.loss.SoftmaxCrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Copy model parameters to a specific GPU and initializes gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:12.559083Z",
     "start_time": "2019-07-05T23:40:12.555984Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "12"
    }
   },
   "outputs": [],
   "source": [
    "def get_params(params, ctx):\n",
    "    new_params = [p.copyto(ctx) for p in params]\n",
    "    for p in new_params:\n",
    "        p.attach_grad()\n",
    "    return new_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:15.975756Z",
     "start_time": "2019-07-05T23:40:12.561391Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b1 weight: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] @gpu(0)\n",
      "b1 grad: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] @gpu(0)\n"
     ]
    }
   ],
   "source": [
    "new_params = get_params(params, d2l.try_gpu(0))\n",
    "print('b1 weight:', new_params[1])\n",
    "print('b1 grad:', new_params[1].grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Sum the data on all GPUs and then broadcast it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:15.980945Z",
     "start_time": "2019-07-05T23:40:15.977226Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "14"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def allreduce(data):\n",
    "    for i in range(1, len(data)):\n",
    "        data[0][:] += data[i].copyto(data[0].context)\n",
    "    for i in range(1, len(data)):\n",
    "        data[0].copyto(data[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:19.358921Z",
     "start_time": "2019-07-05T23:40:15.982270Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before allreduce:\n",
      " [[1. 1.]] @gpu(0) \n",
      " [[2. 2.]] @gpu(1)\n",
      "after allreduce:\n",
      " [[3. 3.]] @gpu(0) \n",
      " [[3. 3.]] @gpu(1)\n"
     ]
    }
   ],
   "source": [
    "data = [np.ones((1, 2), ctx=d2l.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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Split a data batch into multiple GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:19.367325Z",
     "start_time": "2019-07-05T23:40:19.360370Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input =\n",
      " [[ 0.  1.  2.  3.  4.  5.]\n",
      " [ 6.  7.  8.  9. 10. 11.]\n",
      " [12. 13. 14. 15. 16. 17.]\n",
      " [18. 19. 20. 21. 22. 23.]]\n",
      "output =\n",
      " [[ 0.  1.  2.  3.  4.  5.]\n",
      " [ 6.  7.  8.  9. 10. 11.]] @gpu(0) \n",
      " [[12. 13. 14. 15. 16. 17.]\n",
      " [18. 19. 20. 21. 22. 23.]] @gpu(1)\n"
     ]
    }
   ],
   "source": [
    "data = np.arange(24).reshape((4, 6))\n",
    "ctx = d2l.try_all_gpus()\n",
    "splitted = gluon.utils.split_and_load(data, ctx)\n",
    "print('input =\\n', data)\n",
    "print('output =\\n', splitted[0], '\\n', splitted[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We need to split both the features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:19.372751Z",
     "start_time": "2019-07-05T23:40:19.369328Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    }
   },
   "outputs": [],
   "source": [
    "def split_batch(X, y, ctx_list):\n",
    "    \"\"\"Split X and y into multiple devices specified by ctx\"\"\"\n",
    "    assert X.shape[0] == y.shape[0]\n",
    "    return (gluon.utils.split_and_load(X, ctx_list),\n",
    "            gluon.utils.split_and_load(y, ctx_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Multi-GPU training on a single mini-batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:19.379649Z",
     "start_time": "2019-07-05T23:40:19.374330Z"
    },
    "attributes": {
     "classes": [],
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     "n": "10"
    }
   },
   "outputs": [],
   "source": [
    "def train_batch(X, y, gpu_params, ctx_list, lr):\n",
    "    gpu_Xs, gpu_ys = split_batch(X, y, ctx_list)\n",
    "    with autograd.record():  # Loss is calculated separately on each GPU\n",
    "        ls = [loss(lenet(gpu_X, gpu_W), gpu_y)\n",
    "              for gpu_X, gpu_y, gpu_W in zip(gpu_Xs, gpu_ys, gpu_params)]\n",
    "    for l in ls:  # Back Propagation is performed separately on each GPU\n",
    "        l.backward()\n",
    "    # Sum all the gradients from each GPU and then broadcast them \n",
    "    for i in range(len(gpu_params[0])):\n",
    "        allreduce([gpu_params[c][i].grad for c in range(len(ctx_list))])\n",
    "    # The model parameters are updated separately on each GPU\n",
    "    for param in gpu_params:\n",
    "        d2l.sgd(param, lr, X.shape[0])  # Here, we use a full-size batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:19.387640Z",
     "start_time": "2019-07-05T23:40:19.380970Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "61"
    }
   },
   "outputs": [],
   "source": [
    "def train(num_gpus, batch_size, lr):\n",
    "    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
    "    ctx_list = [d2l.try_gpu(i) for i in range(num_gpus)]\n",
    "    # Copy model parameters to num_gpus GPUs\n",
    "    gpu_params = [get_params(params, c) for c in ctx_list]\n",
    "    num_epochs, times, acces = 10, [], []\n",
    "    animator = d2l.Animator('epoch', 'test acc', xlim=[1, num_epochs])\n",
    "    timer = d2l.Timer()\n",
    "    for epoch in range(num_epochs):\n",
    "        timer.start()\n",
    "        for X, y in train_iter:\n",
    "            train_batch(X, y, gpu_params, ctx_list, lr)\n",
    "            npx.waitall()\n",
    "        timer.stop()\n",
    "        animator.add(epoch+1, (d2l.evaluate_accuracy_gpu(\n",
    "            lambda x: lenet(x, gpu_params[0]), test_iter, ctx[0]),))\n",
    "    print('test acc: %.2f, %.1f sec/epoch on %s' % (\n",
    "            animator.Y[0][-1], timer.avg(), ctx_list))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training with a singe GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-05T23:40:43.841379Z",
     "start_time": "2019-07-05T23:40:19.388943Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "62"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acc: 0.80, 1.8 sec/epoch on [gpu(0)]\n"
     ]
    },
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