examples/train_fp16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Optional\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from tqdm.auto import tqdm, trange\n",
    "from tqdm.contrib import tenumerate, tzip\n",
    "\n",
    "from layer_to_layer_pytorch import Layer2Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layers = nn.ModuleList([\n",
    "            nn.Linear(5, 5),\n",
    "            nn.Linear(5, 5),\n",
    "        ])\n",
    "        \n",
    "    def forward(self, x):\n",
    "        for l in self.layers:\n",
    "            x = l(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Model()\n",
    "nnet = Model()\n",
    "for s, t in zip(net.parameters(), nnet.parameters()):\n",
    "    t.data.copy_(s.data)\n",
    "l2l_model = Layer2Layer(net, microbatch_size=20, mixed_precision=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand((80, 5))\n",
    "y = torch.rand((80, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b8ded8f432f4f64b222bd8564d27a63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=600.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "criterion = nn.MSELoss(reduction=\"mean\")\n",
    "optimizer = optim.Adam(nnet.parameters())\n",
    "r_loss = []\n",
    "for i in trange(600):\n",
    "    optimizer.zero_grad()\n",
    "    out = nnet(x)\n",
    "    loss = criterion(out, y)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    r_loss.append(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f7aca4385554856a905261c8e93dda0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=600.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0] loss = 0.40304672718048096\n",
      "[50] loss = 0.29087238758802414\n",
      "[100] loss = 0.21799978241324425\n",
      "[150] loss = 0.15478910133242607\n",
      "[200] loss = 0.1116107702255249\n",
      "[250] loss = 0.09347709640860558\n",
      "[300] loss = 0.08926261961460114\n",
      "[350] loss = 0.08778456225991249\n",
      "[400] loss = 0.08662739209830761\n",
      "[450] loss = 0.08561820536851883\n",
      "[500] loss = 0.08475672826170921\n",
      "[550] loss = 0.08399175852537155\n",
      "\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.Adam(l2l_model.main_params)\n",
    "w_loss = []\n",
    "for i in trange(600):\n",
    "    l2l_model.zero_grad()\n",
    "    _ = l2l_model.forward(x)\n",
    "    loss_value = l2l_model.compute_loss(y, criterion)\n",
    "    \n",
    "    if i % 50 == 0:\n",
    "        tqdm.write(f\"[{i}] loss = {loss_value}\")\n",
    "    w_loss.append(loss_value)\n",
    "\n",
    "    l2l_model.backward()\n",
    "    optimizer.step()\n",
    "    l2l_model.update_main_model_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ffdd5337-2f54-4b64-b8e4-a0389620f12d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Scatter(x=np.arange(600), y=w_loss, name=\"L2L wrapper\"),\n",
    "        go.Scatter(x=np.arange(600), y=r_loss, name=\"Usual model\"),\n",
    "    ]\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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