async_processing_2gpus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = 3\n",
    "bs = 3000\n",
    "dim = 200\n",
    "X = torch.randn(bs*nb, dim).to(\"cuda:0\")\n",
    "target = (torch.randn(bs*nb) > 0).float().to(\"cuda:1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 50000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = torch.randn(dim, n_components) / (n_components**0.5)\n",
    "R = R.to(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# artificially amount of operations performed for more evident timing differences\n",
    "\n",
    "class DumbModule(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "    def forward(self, input):\n",
    "        for i in range(3):\n",
    "            torch.mm(input, input.t())\n",
    "        return input\n",
    "    \n",
    "model = nn.Sequential(DumbModule(), nn.Linear(n_components, 1)).to(\"cuda:1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating external accelerator with device 0 and moving to device 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following an external accelerator is simulated with device 0 and device 1 is the GPU supposed to run asynchronously. The accelerator returns output is a synchronization point, so I call synchronize before moving the tensor to the other device. What happens after moving the tensor `y` to device 1 should happen asynchronously with the first line of the next loop, if I'm not mistaken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0008285045623779297\n"
     ]
    }
   ],
   "source": [
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    y = y.to(\"cuda:1\")\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03906726837158203\n"
     ]
    }
   ],
   "source": [
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    torch.cuda.synchronize()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding processing through model on device 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26718616485595703\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9821622371673584\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    torch.cuda.synchronize()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding explicit loss computation on device 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2649667263031006\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    loss = ((y_hat - target[i*bs:(i+1)*bs].view_as(y_hat)) ** 2).mean()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9794397354125977\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    loss = ((y_hat - target[i*bs:(i+1)*bs].view_as(y_hat)) ** 2).mean()\n",
    "    torch.cuda.synchronize()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing with MSELoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7607848644256592\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    loss = F.mse_loss(y_hat, target[i*bs:(i+1)*bs].view_as(y_hat))\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.760429859161377\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    loss = F.mse_loss(y_hat, target[i*bs:(i+1)*bs].view_as(y_hat))\n",
    "    torch.cuda.synchronize()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss().to(\"cuda:1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7595822811126709\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    loss = criterion(y_hat, target[i*bs:(i+1)*bs].view_as(y_hat))\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7597024440765381\n"
     ]
    }
   ],
   "source": [
    "correct = 0.\n",
    "total = 0.\n",
    "t = time()\n",
    "for i in range(nb):\n",
    "    y = torch.mm(X[i*bs:(i+1)*bs], R)\n",
    "    torch.cuda.synchronize()\n",
    "    y = y.to(\"cuda:1\")\n",
    "    y_hat = model(y)\n",
    "    correct += (torch.sign(y_hat)).eq(target[i*bs:(i+1)*bs].view_as(y_hat)).sum()\n",
    "    total += target.numel()\n",
    "    loss = criterion(y_hat, target[i*bs:(i+1)*bs].view_as(y_hat))\n",
    "    torch.cuda.synchronize()\n",
    "print(time()-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of the explicit loss computation there is a timing difference, while using the `MSELoss` from `torch.nn` or `torch.nn.functional` calling a synchronization at the end of each iteration or not does not make any difference."
   ]
  }
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