examples/train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install layer-to-layer-pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class M(nn.Module):\n",
    "    def __init__(self, depth: int, dim: int, hidden_dim: Optional[int] = None):\n",
    "        super().__init__()\n",
    "        hidden_dim = hidden_dim or dim\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                nn.Sequential(\n",
    "                    nn.Linear(dim, hidden_dim),\n",
    "                    nn.BatchNorm1d(hidden_dim),\n",
    "                    nn.LeakyReLU(),\n",
    "                )\n",
    "            ]\n",
    "            + [\n",
    "                nn.Sequential(\n",
    "                    nn.Linear(hidden_dim, hidden_dim),\n",
    "                    nn.BatchNorm1d(hidden_dim),\n",
    "                    nn.LeakyReLU(),\n",
    "                )\n",
    "                for i in range(depth)\n",
    "            ]\n",
    "            + [nn.Linear(hidden_dim, dim), nn.Sigmoid()]\n",
    "        )\n",
    "        \n",
    "    def __len__(self) -> int:\n",
    "        return len(self.layers)\n",
    "\n",
    "    def forward(self, batch: torch.Tensor) -> torch.Tensor:\n",
    "        x = batch\n",
    "        for l in self.layers:\n",
    "            x = l(x)\n",
    "\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = M(depth=5, dim=40, hidden_dim=100)\n",
    "\n",
    "l2l_model = Layer2Layer(model, layers_attr=\"layers\", microbatch_size=100, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand(1_000, 40)\n",
    "y = torch.rand(1_000, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[layers.0.0.weight]: True\n",
      "[layers.0.0.bias]: True\n",
      "[layers.0.1.weight]: True\n",
      "[layers.0.1.bias]: True\n",
      "[layers.1.0.weight]: True\n",
      "[layers.1.0.bias]: True\n",
      "[layers.1.1.weight]: True\n",
      "[layers.1.1.bias]: True\n",
      "[layers.2.0.weight]: True\n",
      "[layers.2.0.bias]: True\n",
      "[layers.2.1.weight]: True\n",
      "[layers.2.1.bias]: True\n",
      "[layers.3.0.weight]: True\n",
      "[layers.3.0.bias]: True\n",
      "[layers.3.1.weight]: True\n",
      "[layers.3.1.bias]: True\n",
      "[layers.4.0.weight]: True\n",
      "[layers.4.0.bias]: True\n",
      "[layers.4.1.weight]: True\n",
      "[layers.4.1.bias]: True\n",
      "[layers.5.0.weight]: True\n",
      "[layers.5.0.bias]: True\n",
      "[layers.5.1.weight]: True\n",
      "[layers.5.1.bias]: True\n",
      "[layers.6.weight]: True\n",
      "[layers.6.bias]: True\n"
     ]
    }
   ],
   "source": [
    "for name, param in l2l_model.model.named_parameters():\n",
    "    print(f\"[{name}]: {param.requires_grad}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8427a73755ee488a9eabea522dd89327",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0] loss = 0.09251299779862165\n",
      "[50] loss = 0.04679687600582838\n",
      "[100] loss = 0.024244420928880572\n",
      "[150] loss = 0.012858417234383523\n",
      "[200] loss = 0.007469074393156916\n",
      "[250] loss = 0.004840212088311091\n",
      "[300] loss = 0.0034365870524197817\n",
      "[350] loss = 0.0025724523584358394\n",
      "[400] loss = 0.0020228293433319777\n",
      "[450] loss = 0.001649867874220945\n",
      "[500] loss = 0.001370955680613406\n",
      "[550] loss = 0.001156660437118262\n",
      "[600] loss = 0.0010158663790207356\n",
      "[650] loss = 0.0008867938231560402\n",
      "[700] loss = 0.0008164640748873353\n",
      "[750] loss = 0.0007635831498191692\n",
      "[800] loss = 0.0006904192341607995\n",
      "[850] loss = 0.0006020103282935452\n",
      "[900] loss = 0.000585917350690579\n",
      "[950] loss = 0.0005711435842385981\n",
      "[1000] loss = 0.0005517072750080843\n",
      "[1050] loss = 0.00042251174090779386\n",
      "[1100] loss = 0.00043898035073652864\n",
      "[1150] loss = 0.000410193380957935\n",
      "[1200] loss = 0.00040243057810585015\n",
      "[1250] loss = 0.00041606493323342875\n",
      "[1300] loss = 0.0003635622069850797\n",
      "[1350] loss = 0.0003410119952604873\n",
      "[1400] loss = 0.000347757559211459\n",
      "[1450] loss = 0.00033284839992120396\n",
      "[1500] loss = 0.00028941587333974894\n",
      "[1550] loss = 0.00031473712442675605\n",
      "[1600] loss = 0.00026775159130920656\n",
      "[1650] loss = 0.0002649552479851991\n",
      "[1700] loss = 0.00023840018729970325\n",
      "[1750] loss = 0.000247815372858895\n",
      "[1800] loss = 0.0002410621236776933\n",
      "[1850] loss = 0.0002461158837832045\n",
      "[1900] loss = 0.00026135327789234\n",
      "[1950] loss = 0.00024941059928096365\n",
      "\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.AdamW(l2l_model.main_params)\n",
    "\n",
    "for i in trange(2000):\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",
    "    losses.append(loss_value)\n",
    "\n",
    "    l2l_model.backward()\n",
    "    optimizer.step()\n",
    "    l2l_model.update_main_model_params() # Sync params with CPU\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"102aad6b-17ee-40dd-8d64-cbcc267c96fd\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        '102aad6b-17ee-40dd-8d64-cbcc267c96fd',\n",
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('102aad6b-17ee-40dd-8d64-cbcc267c96fd');\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",
       "}}\n",
       "\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",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=np.arange(5000), y=losses))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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