2021-12-lecture_AttentionV_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "601f30a4-5182-4e10-aada-a2350cb49a84",
   "metadata": {},
   "source": [
    "# Attention and Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c35ae45-1350-4f50-a991-ea34369fc9d7",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to use self-attention to process a set of objects. This is the basic building block of a transformer (note: the original transformer paper introduces two parts, encoder and decoder, and uses both self-attention and attention; but other transformer implementations may use only the self-attention=encoding part as we are doing it here; BERT is an example).\n",
    "\n",
    "2022 F.Marquardt and the MPL theory division machine learning discussion group;\n",
    "MIT License"
   ]
  },
  {
   "attachments": {
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WkYPV+IYOpuF7BBBAAAEEEEAAAQQQQAABBBA4DARIyB8Gg0SICCCAAAIIIIBAdwRi1dtl931fk+ZtS7tzekbOyTlipoz49K0SGTQibXtvv/eu/Pqh+6W6rjbteZk6eOvjj8ilp50uX738ilQy3K7dNpXc/vvzz8iDc15WibSk3WkZrS/MzZObrrhKzplxUtp2D9TXyc/uu1veWb0q7XmZPDh+xEj50Wc/L2OHDU/b7HubNspP771L9lRXpT0vUwdve/JROfvEmfLtf/tUl5MZHntzi9z1zw3SohLKbpTsaEiuP3eSfGL22LTd6WTtb59YKa8t35P2vEweHFaSKz+4eppMGTMobbNb9tbL/z6wXLbsbUh7XiYPnqSSy9+7aloq2Zyu3VeW7JLbn1mTSsSnOy9Tx257eo1cdcYR8tmzJ6r5Rx/MdDA0rid53PHsGnl2wXY1schwggNVpYVZ8q1PHCOzjip3oHWaRAABBBBAAAEEEEAAAQQQQAABpwS6t1zCqd5pFwEEEEAAAQQQQCAjAvG6fbLt1stdTcbrwJu3LJStt14miXr7xKxOxv/gL390LRmv49KryJ948zX5yT/+rj/alt898qDc+/KLriXjdSB1TY3y03vulBffmW8bV1NLi9z0h9+4mozXwWzatVNu/N3NqZ0C7IJbvXWLfOO237mWjO+I45XFC+V7d9yWdqzu/ddGueO5ta4l43VsLWqV+x9VYvb+OZs6QrV81aumv//3Ra4m43UQetX2t/+8UNbuqLHE1FGxo7JR/v2PC1xNxuu+31lbKd/80zvS3JroCMXy9cWFO+QXD69wLRmvA4irsbpPjeVtT6+2xNNRoXcT+On9y+Xp+e4l43Xf1XWt8p93L5F31G4BFAQQQAABBBBAAAEEEEAAAQQQOHwESMgfPmNFpAgggAACCCCAgK1AxbP/KwmVlPeiJGr2SMULvzB2rbep1yvjvSpvLF8qc99dbux+hVrl/fTbbxqPuVH5+0cflHqVnDeVe/75gmzbt9d0yPG6huYm+f2jDxn70YnIXz5wj9oC3j6JarwwQ5Xvbtogz74919jaTpVYvueVDcZjblTerfreXd1k7OoZlbhdtc0+KW68KEOVeiX3bx5b+cFrK6yN3vrUKmlUq/e9KHr7/gdeM09k0K8duE2tjPeq6GT7qm0HjN3PXblP3lrlze9bvVP/bx9fqV4R4c4OEEYAKhFAAAEEEEAAAQQQQAABBBBAoEcCJOR7xMXJCCCAAAIIIICA/wSSLQ1St+wZTwOrW/yEtMXU++o7lXnqnfFubVPfqesPPz47z5zAtav/8EKHv2lubZU5SxZbetHb6D8//21LvZsV+t3w+w7st3S5cstm2bJnt6XezYpnbMbzxUU7Xds63HS/bWrf8pcW7zQdkuff2WGsd6tSv3t97Q7r6yL2qhX0SzZUuxWGsZ8X3tlpnCzwqtraX+8+4GWxG7fnFng7nlVqpbzeYYCCAAIIIIAAAggggAACCCCAAAKHhwAJ+cNjnIgSAQQQQAABBBCwFWjdu06kzZsVrh1BtSdj0lqxsePjh1837PQ2caUDsYvBrv7D4F34xhRDZU2N1Da69y5vu9vU29d3Lht9MJ5bdu9KvZKgc2ybdtd1rnL9sykGvV39tgrvx3OjwWfj7nrXjTp3WKNWwuut2DsXk2Xnc5z+vMnGZ/MePzxr3o+d0/60jwACCCCAAAIIIIAAAggggEB/ESAh319GkvtAAAEEEEAAgQErkGw0b6vsNkiy0bqiurbB+0SkXXLbr7HV+CAZr5+dGsPY2Vm6+ay1qT2765usW8PXNsXcDMPYV21j3FJf1xRXK8At1a5X6C3gOxdTXedz3PhsGjs/xGYXg55E4HWpafA+Bq8N6B8BBBBAAAEEEEAAAQQQQACBw0WAhPzhMlLEiQACCCCAAAII2Ai0J1psjrhb3R63rnKNJawJSnejEkkkk8Ytsf0QWyxu9THVuW2m+zP5+CY2g5t+V7rXJRa3brHuh7jeH0+rT1yt3vdDicWtccT8MJ6GGJLKzA8TLPwydn54fogBAQQQQOBQgcChH/mEAAIIIIAAAggg4AMBEvI+GARCQAABBBBAAAEE+iTgh+xQ6gasy4DbfRKbXlXdufghtnaxxuWLbJ/CalfvRO9cTI6dz3Hjs2nsDOG6EcohfRgeM/Vee6vjIRe59MFkZqpzKZxDujHFYao75CIXPphi8MNzpm/d9PPpAkm/7CIg1tRVIGCt65c334ebMhGZ6vrQRa8u9UMMPQncL/H6JY6e2Hl9rsnMz787/BKbyc3rsaR/BBBAAAEEEEDADQES8m4o0wcCCCCAAAIIIOCkgE8SfqZEsj9SkSp5ZTAyVDk5Ssa2TTH4xsw0WcB4Fz6p9AGccYKFT3hMYZieP9N5Ttf5YOi6fYt+GePDySwtbsAHfyVhiCHok4yRKY5A0B+TBQIGNz8k/II+jUv/HJhjS/sT4tpB0yNvqnMtoIM6Mj3ypp+Ngy5x5VvT8+4Xs8MtNlcGjE4QQAABBBBAAAGPBXzwX78eC9A9AggggAACCCCAAAIIIIAAAgi4LhCIZLneZ+cOTTFEI5HOp3ny2RRHNOyP2LLCYYuJH2KLRkxxWesswbtQETGahVzouesuomFrHNGIta7rljJ7Rjik9rAwZLmjEe//OjMatsYQCVnrMivSvdZMPqZ4u9daZs/ySxyZvStaQwABBBBAAAEEuhbwx78pdh0nZyCAAAIIIIAAAggggAACCCCAQD8SCOeVen434XxrDEV5+Z7HpQMozi+wxFGc7+fYrPFabsDhCpOZTuj6YUxNsRXnRx0W6V7zpjiK87yPzS6GIj/EZhg7k2P3RiCzZ5nc/BBbUG13kJ/jj0lFmRWnNQQQQAABBBBAoGsBEvJdG3EGAggggAACCCCAAAIIIIAAAghkWCB75NQMt9jz5rJHWGPQK5mPGDa8541l+IpJo0ZbWjTVWU5yuGJwcbEUF1iT736IbdJIq5nmmGywdJjJ0vzk0dbYxg7JF70K3MuiVyyPHpxnCWHiyEJLndsVE0cWGbucOMIHsY2wxja4KFtMyXDjTThYafKZaIjXwRCMTY8fViAh0zsIjGdTiQACCCCAAAII9C8BEvL9azy5GwQQQAABBBBAAAEEEEAAAQQOC4HiWVerF2t7ty12IBSVoplXGq0uPe0jxnq3Ko8ee4RMHDnK0t2w0jKZedQUS72bFZeedrqxu7NPnCk5Wd6+huASm3Gzi9l4Iw5UDistlRlHHm1pOScrLGedMMJS72bFuSeOFNP29LOOLJcylWD2slw8yzqJQcdjV+9WrPk5YTnj2KGW7vRuDBfNsv7cWk50sOKUo8ultNA6bkeNLpYJw72dyOD1uDnITtMIIIAAAggggECXAiTkuyTiBAQQQAABBBBAAAEEEEAAAQQQyLRAtGyslF/wvUw32+32yi/5oUSKhxnPv+TUj8jxEycbjzldmRPNkv+4+lrbbr515dVSmGtd0Wx7QQYPHDl6jFz1sbONLRbm5cm3r7zGeMyNyk+ffZ5xEoPue/axx8nHTjjRjTAsfYTUpJMffOo6tRLePPnkSxdMliGDcizXuVExrCRXPn/eJGNXEbVy/rufnCpeLWg+Z/oIOenIwcbYdHL5itljjcfcqPzmx4+RvGzz1utXf3S86JXgXhS9Ov9rl1onfnTE8p1PHiNZEW/+KvjESWVy3oyRHaHwFQEEEEAAAQQQGHAC3vxb2IBj5oYRQAABBBBAAAEEEEAAAQQQQKCzQOnHbpShV/xcgjnW7Z87n5upz6HcYhn2b7+Vktmfs21SJ09/+ZWvyYUnn6oSku5tKT5+xEi5/ZvflXHD7VdNDy8bLHd8+3ty1JgjbON34sCZ02fI7772TZXQMycidZ/nzJwlP7n+S1JS4N5KXL0q/8bLrpAvXXJ52tv+z89cL1efeY5tYjztxb08OEKN1e++/g05bqI56a2b1e9Dv+XGWTJ9Ymkve+ndZTMnl8ktX50lBbn246mTqL/8wgwZ6uKEgUgoKNd8bJx8R00GSFe+ctGR8oXzJ0l2xDzRId21vT1WWpglP/nsCWp1vHkij243OxqS337lJDl9mnUFfW/77c51R48pllu/Nivt5A69bf3vb5gl+lUJbhU9oeNitWvA/yg3tqt3S51+EEAAAQQQQMCPAmE/BkVMCCCAAAIIIIAAAggggAACCCAwMAQGnXKtFJ5wuTSufV1a922QtuY6aU/GpL29LSMAgUBQAuFoKumfNWSi5B/5UQlm5XbZdnY0Kt+75jPymXMvkEVrV8ve/fulpbVVEm1JFVuXl3d5gs7z62R/ViSaeif7UWPGyrHjJ4re9rqrMqp8iPz5O9+XVVs2y8otm2R/XZ20xuPSpswyFZte2Z2jDMpLSmT6pCNF99mdcsbx0+XkY6bJwjWrZMue3VLX2CjxRELa9f8y4KbNwuGQ5GfnpGI66egpKqnc9Y4BepLFDZd9Qj750bNSse2prpKmlhaJJ1VsGYirYzwj4YgU5+er1fqjZfrkI1USsuu1MHpr+F99caZs2l0n727eL9V1rdISS6rxzIxZR2w6WVxWlCXHjiuVcd1cxX3CxDK55z8+Iks2Vqfiq22MSSyunjP1MGTKTSfhc7JCMrw0V2ZMHqwmdHT96gP9c6JXo1940ihZuLZSdlU1SWNLQo1n5n4G9E+i3imgMDcqRwzLFz1BIaqeva5Kfk5E/uvTx8uOigZZqtwqa1tS45lsy+x46tXu2mrK2EGidw3oTpk0skj+9q3TUs/Zuh21cqAhpn4+9bOWufEMhwLqd0c4NTlAm3m1A0R3PDgHAQQQQAABBBBwS4CEvFvS9IMAAggggAACCCCAAAIIIIAAAkaBUHa+FB53kfGY15X6ve16C3s/lilHjBP9x29Fr6KfPe241B+/xVZWVCQXzDrFb2Gl4hmv3vGt//ithFTCfKZKlOs/fis6WX7WCfY7SngZ76jyfDV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"
    }
   },
   "cell_type": "markdown",
   "id": "93ae2676-a330-42a8-9884-8d6114f5dffc",
   "metadata": {},
   "source": [
    "![image.png](attachment:46564db9-6681-4933-879a-8e0b509f8106.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c387a301-95f4-4e3c-8a15-ab7da5e9572b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T10:25:13.826589Z",
     "iopub.status.busy": "2022-01-15T10:25:13.826242Z",
     "iopub.status.idle": "2022-01-15T10:25:13.835338Z",
     "shell.execute_reply": "2022-01-15T10:25:13.834518Z",
     "shell.execute_reply.started": "2022-01-15T10:25:13.826562Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as tfk\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.facecolor'] = 'beige'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c31427-e200-42f1-b09f-001a014b11d0",
   "metadata": {},
   "source": [
    "### All components of the Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "a9d85850-60fc-4e71-a097-e020e8c9de67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T18:08:28.794641Z",
     "iopub.status.busy": "2022-01-15T18:08:28.794324Z",
     "iopub.status.idle": "2022-01-15T18:08:28.819918Z",
     "shell.execute_reply": "2022-01-15T18:08:28.819644Z",
     "shell.execute_reply.started": "2022-01-15T18:08:28.794612Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TransformerBlock:\n",
    "    def __init__(self,dim_data,dim_key,hidden_neurons_NN):\n",
    "        \"\"\"\n",
    "        Produce a transformer block.\n",
    "        \n",
    "        dim_data:\n",
    "            dimensionality of data vector (one such vector per object)\n",
    "        dim_key:\n",
    "            dim. of key vectors (extracted from data)\n",
    "        hidden_neurons_NN:\n",
    "            number of hidden neurons in the fully connected network\n",
    "            \n",
    "        Apply the resulting transformer block instance like you would apply\n",
    "        a function, to a tensor that represents data (set of objects).\n",
    "        \"\"\"\n",
    "        self.WQ=tf.Variable(np.random.randn(dim_data,dim_key),dtype='float32')\n",
    "        self.WK=tf.Variable(np.random.randn(dim_data,dim_key),dtype='float32')\n",
    "        self.WV=tf.Variable(np.random.randn(dim_data,dim_data),dtype='float32')\n",
    "        self.NN=tfk.Sequential([tfk.layers.Input(shape=(dim_data,)),tfk.layers.Dense(hidden_neurons_NN,activation='relu'),\n",
    "                        tfk.layers.Dense(dim_data)])\n",
    "        self.layer_normalization=[]\n",
    "        for k in range(2):\n",
    "            self.layer_normalization.append(tfk.layers.LayerNormalization())\n",
    "        self.trainable_variables=([self.WQ,self.WK,self.WV]+self.NN.trainable_variables+\n",
    "                                  self.layer_normalization[0].trainable_variables+\n",
    "                                  self.layer_normalization[1].trainable_variables)\n",
    "\n",
    "    def attention(self,queries,keys,values):\n",
    "        \"\"\"\n",
    "        General (multi-head) attention operation.\n",
    "\n",
    "        Take dot products between queries and keys to produce\n",
    "        scores, and then return the values weighted according to the\n",
    "        exponentials of those scores (i.e. using softmax).\n",
    "\n",
    "        This is written such that it can be used both with and without\n",
    "        multiple 'attention heads'.\n",
    "\n",
    "        An arbitrary number num_queries of queries can be made to\n",
    "        an arbitrary number num_objects of objects. In many applications,\n",
    "        and especially for self-attention, these numbers are the\n",
    "        same and represent the 'sequence length' (e.g. max. number of words\n",
    "        in a sentence, possibly padded), where each object in the\n",
    "        sequence generates a query, which is then compared against the\n",
    "        keys of all objects.\n",
    "\n",
    "        dim_key must be the same for the key and query vectors.\n",
    "\n",
    "        queries.shape = [batchsize,(num_heads),num_queries,dim_key]\n",
    "        keys.shape = [batchsize,(num_heads),num_objects,dim_key]\n",
    "        values.shape = [batchsize,(num_heads),num_objects, dim_value]\n",
    "\n",
    "        Returns weights (of shape [batchsize,(num_heads),num_queries,num_objects])\n",
    "        and return_values (of shape [batchsize,(num_heads),num_queries, dim_value])\n",
    "        \"\"\"\n",
    "        sqrt_dk=tf.sqrt( tf.cast( tf.shape(keys)[-1], dtype='float32' ) )\n",
    "        # take dot product between queries and keys along dim_key dimension,\n",
    "        # leaving behind shape [batchsize,(num_heads),num_queries,num_objects]\n",
    "        dot_product = tf.reduce_sum( queries[...,None,:] * keys[...,None,:,:] , axis=-1 )\n",
    "        # get weights via softmax applied to the scaled dot product\n",
    "        weights = tf.nn.softmax(  dot_product / sqrt_dk ) # [batchsize,num_queries,num_objects]\n",
    "        # return weights and weighted values\n",
    "        return weights, tf.reduce_sum( weights[...,None] * values[...,None,:,:], axis=-2 )\n",
    "\n",
    "\n",
    "    def __call__(self,data,store_attention_weights=None):\n",
    "        \"\"\"\n",
    "        Apply the 'transformer block' to data. \n",
    "        (This works by just calling the TransformerBlock instance on data like a function)\n",
    "        \n",
    "        data.shape=[batchsize,num_objects,dim_data]\n",
    "        \n",
    "        This first applies attention,\n",
    "        after extracting queries, keys, and values, using the matrices WQ,WK,WV,\n",
    "        and afterwards it also applies the neural network NN. \n",
    "        Note that both the attention part and\n",
    "        the NN part are applied with the \"Add+Norm\" operation, i.e. with a\n",
    "        residual-type connection and a Layer Normalization. As explained\n",
    "        in the original Transformer paper.\n",
    "\n",
    "        store_attention_weights: if not None, points to a list, to which the\n",
    "            current attention weights will be appended (used for visualization)\n",
    "            \n",
    "        Returns the processed set of objects, i.e. a new 'data' Tensor.\n",
    "        \"\"\"\n",
    "        Q=data @ self.WQ # @ == matrix multiplication (acting on last index of left operand)\n",
    "        K=data @ self.WK\n",
    "        V=data @ self.WV\n",
    "        intermediate = self.layer_normalization[0](data+self.attention(Q,K,V)[1])\n",
    "        if store_attention_weights is not None:\n",
    "            store_attention_weights.append(attention(Q,K,V)[0]) # call again (bit inefficient)\n",
    "        return self.layer_normalization[1](intermediate+self.NN(intermediate))\n",
    "\n",
    "# intermediate.shape = [batchsize,num_objects,dim_data]\n",
    "\n",
    "class Transformer:\n",
    "    def __init__(self,num_layers,dim_data,dim_key,hidden_neurons_NN):\n",
    "        \"\"\"\n",
    "        Initialize a Transformer, consisting of num_layers TransformerBlock instances,\n",
    "        each of them initialized according to dim_data,dim_key, and hidden_neurons_NN.\n",
    "        \"\"\"\n",
    "        self.layers=[TransformerBlock(dim_data,dim_key,hidden_neurons_NN) for _ in range(num_layers)]\n",
    "        self.W_final=tf.Variable(np.eye(dim_data)+0.01*np.random.randn(dim_data,dim_data),dtype='float32')\n",
    "        self.b_final=tf.Variable(0.01*np.random.randn(dim_data),dtype='float32')\n",
    "        self.trainable_variables=([self.W_final,self.b_final]+\n",
    "                                  sum([ly.trainable_variables for ly in self.layers],[]))\n",
    "    \n",
    "    def __call__(self,data,store_attention_weights=None,\n",
    "                     store_data_layers=None):\n",
    "        \"\"\"\n",
    "        Apply several transformer layers to data and return new data.\n",
    "\n",
    "        data.shape=[batchsize,num_objects,dim_data]\n",
    "\n",
    "        WQ,WK,WV,NN are lists containing the weight matrices and networks for\n",
    "        all the layers.\n",
    "\n",
    "        layer_norms is a list of layer normalization layers (of length len(NN)*2)\n",
    "\n",
    "        W_final and b_final are a matrix and vector for the last, linear layer,\n",
    "        applied to the final output.\n",
    "\n",
    "        store_attention_weights: if not None, points to a list, to which the attention\n",
    "            weights are appended, for each layer\n",
    "        store_data_layers: if not None, points to a list, to which data (including input)\n",
    "            will be appended for each layer\n",
    "        \"\"\"\n",
    "        if store_data_layers is not None:\n",
    "            store_data_layers.append(data)\n",
    "        for block in self.layers:\n",
    "            data=block(data,store_attention_weights=store_attention_weights) # apply transformer block\n",
    "            if store_data_layers is not None:\n",
    "                store_data_layers.append(data)\n",
    "        data = data @ self.W_final + self.b_final # final linear operation\n",
    "        if store_data_layers is not None:\n",
    "            store_data_layers.append(data)\n",
    "        return data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85832fd2-bc9f-45d5-83a9-692e95457339",
   "metadata": {},
   "source": [
    "### Toy example for attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "35103d4d-e58f-492f-8ae6-de1cf154e870",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T18:08:30.253568Z",
     "iopub.status.busy": "2022-01-15T18:08:30.253276Z",
     "iopub.status.idle": "2022-01-15T18:08:30.291579Z",
     "shell.execute_reply": "2022-01-15T18:08:30.290898Z",
     "shell.execute_reply.started": "2022-01-15T18:08:30.253544Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query #0: [0. 1.]\n",
      "Key [1.  0.5] -> Weight: 34.0%  / Value [1. 0. 0. 0.]\n",
      "Key [0.  0.8] -> Weight: 42.1%  / Value [0. 2. 0. 0.]\n",
      "Key [-1.  0.] -> Weight: 23.9%  / Value [0. 0. 3. 0.]\n",
      "Return Value [0.34 0.84 0.72 0.  ]\n",
      "Query #1: [1. 0.]\n",
      "Key [1.  0.5] -> Weight: 57.6%  / Value [1. 0. 0. 0.]\n",
      "Key [0.  0.8] -> Weight: 28.4%  / Value [0. 2. 0. 0.]\n",
      "Key [-1.  0.] -> Weight: 14.0%  / Value [0. 0. 3. 0.]\n",
      "Return Value [0.58 0.57 0.42 0.  ]\n"
     ]
    }
   ],
   "source": [
    "# very first simple toy test: (only attention)\n",
    "\n",
    "# batchsize = 1, two queries with dim_key = 2\n",
    "queries=tf.constant( [ [ [0.0, 1.0] , [1.0,0.0] ] ] )\n",
    "# batchsize = 1, three keys with dim_key = 2\n",
    "keys   =tf.constant( [ [ [1.0,0.5], [0.0, 0.8] , [-1.0,0.0]  ] ] )\n",
    "# batchsize = 1, three values with dim_value = 4\n",
    "values =tf.constant( [ [ [1.0,0,0,0], [0,2,0,0], [0,0.0,3,0] ]])\n",
    "\n",
    "# initialize a TransformerBlock (but we will only use its attention method)\n",
    "test_block=TransformerBlock(dim_data=4,dim_key=2,hidden_neurons_NN=10)\n",
    "\n",
    "weights, return_values = test_block.attention(queries,keys,values)\n",
    "\n",
    "for query_idx in range(2):\n",
    "    print(f\"Query #{query_idx}: {queries[0,query_idx,:]}\")\n",
    "    for object_idx in range(3):\n",
    "        print(f\"Key {keys[0,object_idx,:]} \\\n",
    "-> Weight: {100*weights[0,query_idx,object_idx]:.1f}%  / Value {values[0,object_idx,:]}\")\n",
    "    print(f\"Return Value {np.round(return_values[0,query_idx,:],2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79b53d7-5746-43fa-9caf-b5bb42ecfc4d",
   "metadata": {},
   "source": [
    "## Particular Example: Database lookup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927f32cb-a313-4c4d-b35e-4ec7bc93839b",
   "metadata": {},
   "source": [
    "We have a set of objects that carry both a label and an attribute. In some objects the attribute is missing, which is indicated by some flag being 0 (otherwise, it is 1). The goal of our transformer will be, for each 'placeholder' object (where the attribute information is missing) to look up an object with the same label among all objects, and copy the attribute from there.\n",
    "\n",
    "This demonstrates how transformers can operate on some unordered set of objects and exploit the attention mechanism to look up relevant information.\n",
    "\n",
    "Labels and attributes will be provided as one-hot vectors, composing the full data vector for any object like shown in the example below. In a placeholder object, the attribute is just a zero vector."
   ]
  },
  {
   "attachments": {
    "1c596ffd-57c9-4f00-a705-863f2713d49d.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "bb712662-f6cd-46ab-a8d4-d17c8f96a42a",
   "metadata": {},
   "source": [
    "![image.png](attachment:1c596ffd-57c9-4f00-a705-863f2713d49d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "28558d22-6880-4c67-bb1a-9cb86ffef4b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T18:08:32.412935Z",
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   "source": [
    "# Generate training sample for a database lookup scenario\n",
    "\n",
    "# Example: The following set of objects...\n",
    "# (0,  0,0,1,0,0,     0,0,0,0,0,0   )\n",
    "# (1,  0,0,0,1,0,     0,0,1,0,0,0   )\n",
    "# (1,  0,0,1,0,0,     0,0,0,0,1,0   )\n",
    "# (0,  0,0,0,1,0,     0,0,0,0,0,0   )\n",
    "# will be turned into: (* marks the changes)\n",
    "# (0,  0,0,1,0,0,     0,0,0,0,*1*,0   )\n",
    "# (1,  0,0,0,1,0,     0,0,1,0,0,0   )\n",
    "# (1,  0,0,1,0,0,     0,0,0,0,1,0   )\n",
    "# (0,  0,0,0,1,0,     0,0,*1*,0,0,0   )\n",
    "\n",
    "\n",
    "def generate_batch_lookup_example(batchsize,num_objects, vector_length,\n",
    "                                 provide_permutation=None):\n",
    "    \"\"\"\n",
    "    Produce batch of sets of objects for the lookup\n",
    "    training example: randomly sorted collections of objects of\n",
    "    type (0,j,0) or (1,j,v_j) where v_j is a random one-hot vector, and\n",
    "    there is always exactly one of each, for every j.\n",
    "    The 'label' j is also treated as a one-hot vector in this version.\n",
    "    \n",
    "    So:\n",
    "    \n",
    "    (1,   0,1,0,   0,0,1,0,0)\n",
    "    (0,   0,1,0,   0,0,0,0,0)\n",
    "    \n",
    "    should be turned into:\n",
    "    \n",
    "    (1,   0,1,0,   0,0,1,0,0)\n",
    "    (0,   0,1,0,   0,0,**1**,0,0)\n",
    "    \n",
    "    where we marked the change with **\n",
    "    \n",
    "    Returns: input_batch,target_batch\n",
    "    \"\"\"\n",
    "    assert num_objects%2==0, \"need even number of objects.\"\n",
    "    \n",
    "    def single_sample():\n",
    "        sample=np.zeros([num_objects,(1+num_objects//2+vector_length)])\n",
    "        sample[:num_objects//2,0]=1\n",
    "        for i in range(num_objects//2):\n",
    "            sample[i,1+i] = 1\n",
    "            sample[i+num_objects//2, 1+i] = 1\n",
    "            random_int = np.random.randint(low=0, high=vector_length)\n",
    "            sample[i, 1+num_objects//2+random_int] = 1\n",
    "        \n",
    "        target_sample=np.copy(sample)\n",
    "        target_sample[num_objects//2:,1:]=sample[:num_objects//2,1:]\n",
    "        return sample,target_sample\n",
    "    \n",
    "    \n",
    "    input_batch=[]\n",
    "    output_batch=[]\n",
    "    \n",
    "    # for each sample in the batchsize:\n",
    "    # generate sample (a set of objects), and inside that sample randomly shuffle\n",
    "    # the objects (in principle this is not needed, since the\n",
    "    # transformer by construction is insensitive to the order\n",
    "    # of objects, unless we add position encoding; but it is\n",
    "    # nice to demonstrate that).\n",
    "    for _ in range(batchsize):\n",
    "        sample,target_sample=single_sample()\n",
    "        permutation=np.random.permutation(num_objects)\n",
    "        if provide_permutation is not None:\n",
    "            provide_permutation.append(permutation)\n",
    "        input_batch.append(sample[permutation,:])\n",
    "        output_batch.append(target_sample[permutation,:])\n",
    "        \n",
    "    return np.array(input_batch),np.array(output_batch)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "9cbdc154-253d-4add-b950-15c9a54bd9c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T18:08:33.076400Z",
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    },
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   "outputs": [],
   "source": [
    "def train_step(input_batch, output_batch):\n",
    "    \"\"\"\n",
    "    Apply the transformer to the input_batch, calculate mean-square-error\n",
    "    against desired output_batch, and apply gradient descent on all trainable\n",
    "    parameters (i.e. the W matrices in all transformer layers and the\n",
    "    NN weights in all layers).\n",
    "    \"\"\"\n",
    "    with tf.GradientTape() as tape:\n",
    "        tape.watch(transformer.trainable_variables)\n",
    "        data=input_batch\n",
    "        data=transformer(data)\n",
    "        loss=tf.reduce_sum((data-output_batch)**2)\n",
    "    grads=tape.gradient(loss,transformer.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(grads,transformer.trainable_variables))\n",
    "    return loss\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93997f4-fb37-41ee-a0fd-d99fb547c0d1",
   "metadata": {},
   "source": [
    "### Prepare Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "a3a25d32-86be-4756-9acf-8fbbf73afa47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T20:12:01.849417Z",
     "iopub.status.busy": "2022-01-15T20:12:01.848692Z",
     "iopub.status.idle": "2022-01-15T20:12:01.914107Z",
     "shell.execute_reply": "2022-01-15T20:12:01.913697Z",
     "shell.execute_reply.started": "2022-01-15T20:12:01.849284Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Now prepare Transformer:\n",
    "\n",
    "vector_length = 5 # length of the random one-hot vectors that represent the data stored\n",
    "num_objects=8 # here: will keep it simple and act on a fixed number of objects\n",
    "\n",
    "dim_data=1+num_objects//2 + vector_length # length of data vectors\n",
    "\n",
    "dim_key=num_objects//2 # length of key vectors\n",
    "\n",
    "num_layers=2 # how many Transformer layers (blocks) to apply\n",
    "\n",
    "transformer=Transformer(num_layers=num_layers,dim_data=dim_data,dim_key=dim_key,hidden_neurons_NN=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ba9fc9-0244-48ff-b105-123632bc99c7",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "3d377341-b376-48a5-850e-e226915b62ea",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2022-01-15T20:12:02.803554Z",
     "iopub.status.busy": "2022-01-15T20:12:02.803260Z",
     "iopub.status.idle": "2022-01-15T20:12:59.490529Z",
     "shell.execute_reply": "2022-01-15T20:12:59.490145Z",
     "shell.execute_reply.started": "2022-01-15T20:12:02.803525Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 968 Loss 12.188364028930664     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-15 21:12:15.296920: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 960).\n",
      "2022-01-15 21:12:15.297290: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 960).\n",
      "2022-01-15 21:12:15.297679: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 960).\n",
      "2022-01-15 21:12:15.297954: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 960).\n",
      "2022-01-15 21:12:15.341127: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 964).\n",
      "2022-01-15 21:12:15.341357: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 964).\n",
      "2022-01-15 21:12:15.341740: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 964).\n",
      "2022-01-15 21:12:15.341939: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 964).\n",
      "2022-01-15 21:12:15.363073: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 966).\n",
      "2022-01-15 21:12:15.363335: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 966).\n",
      "2022-01-15 21:12:15.363709: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 966).\n",
      "2022-01-15 21:12:15.363981: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 966).\n",
      "2022-01-15 21:12:15.384671: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 968).\n",
      "2022-01-15 21:12:15.384884: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 968).\n",
      "2022-01-15 21:12:15.385282: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 968).\n",
      "2022-01-15 21:12:15.385473: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 968).\n",
      "2022-01-15 21:12:15.395892: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 969).\n",
      "2022-01-15 21:12:15.396102: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 969).\n",
      "2022-01-15 21:12:15.396488: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 969).\n",
      "2022-01-15 21:12:15.396752: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 969).\n",
      "2022-01-15 21:12:15.418737: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 971).\n",
      "2022-01-15 21:12:15.418969: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 971).\n",
      "2022-01-15 21:12:15.419405: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 971).\n",
      "2022-01-15 21:12:15.419706: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 971).\n",
      "2022-01-15 21:12:15.430344: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 972).\n",
      "2022-01-15 21:12:15.430558: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 972).\n",
      "2022-01-15 21:12:15.430934: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 972).\n",
      "2022-01-15 21:12:15.431201: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 972).\n",
      "2022-01-15 21:12:15.441809: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 973).\n",
      "2022-01-15 21:12:15.442082: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 973).\n",
      "2022-01-15 21:12:15.442469: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 973).\n",
      "2022-01-15 21:12:15.442662: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 973).\n",
      "2022-01-15 21:12:15.463804: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 975).\n",
      "2022-01-15 21:12:15.464034: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 975).\n",
      "2022-01-15 21:12:15.464492: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 975).\n",
      "2022-01-15 21:12:15.464770: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 975).\n",
      "2022-01-15 21:12:15.475664: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 976).\n",
      "2022-01-15 21:12:15.476001: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 976).\n",
      "2022-01-15 21:12:15.476390: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 976).\n",
      "2022-01-15 21:12:15.476653: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 976).\n",
      "2022-01-15 21:12:15.487193: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 977).\n",
      "2022-01-15 21:12:15.487406: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 977).\n",
      "2022-01-15 21:12:15.487786: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 977).\n",
      "2022-01-15 21:12:15.488021: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 977).\n",
      "2022-01-15 21:12:15.498158: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 978).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 985 Loss 9.140467643737793      \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-15 21:12:15.498372: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 978).\n",
      "2022-01-15 21:12:15.498961: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 978).\n",
      "2022-01-15 21:12:15.499160: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 978).\n",
      "2022-01-15 21:12:15.510996: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 979).\n",
      "2022-01-15 21:12:15.511257: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 979).\n",
      "2022-01-15 21:12:15.511819: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 979).\n",
      "2022-01-15 21:12:15.512101: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 979).\n",
      "2022-01-15 21:12:15.537813: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 981).\n",
      "2022-01-15 21:12:15.538094: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 981).\n",
      "2022-01-15 21:12:15.538525: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 981).\n",
      "2022-01-15 21:12:15.538791: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 981).\n",
      "2022-01-15 21:12:15.551672: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 982).\n",
      "2022-01-15 21:12:15.552642: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 982).\n",
      "2022-01-15 21:12:15.553353: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 982).\n",
      "2022-01-15 21:12:15.553844: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 982).\n",
      "2022-01-15 21:12:15.565729: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 983).\n",
      "2022-01-15 21:12:15.565932: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 983).\n",
      "2022-01-15 21:12:15.566477: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 983).\n",
      "2022-01-15 21:12:15.566734: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 983).\n",
      "2022-01-15 21:12:15.577387: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 984).\n",
      "2022-01-15 21:12:15.577628: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 984).\n",
      "2022-01-15 21:12:15.578024: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 984).\n",
      "2022-01-15 21:12:15.578230: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 984).\n",
      "2022-01-15 21:12:15.588556: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 985).\n",
      "2022-01-15 21:12:15.588758: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 985).\n",
      "2022-01-15 21:12:15.589125: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 985).\n",
      "2022-01-15 21:12:15.589314: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 985).\n",
      "2022-01-15 21:12:15.599353: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 986).\n",
      "2022-01-15 21:12:15.599573: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 986).\n",
      "2022-01-15 21:12:15.599960: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 986).\n",
      "2022-01-15 21:12:15.600218: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 986).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 4999 Loss 0.05711992084980011      \r"
     ]
    }
   ],
   "source": [
    "# need to train for at least around 5000 steps for good performance in this example.\n",
    "num_training_steps=5000\n",
    "batchsize=10\n",
    "\n",
    "# a learning rate schedule (ramping up and then down again, like in original Transformer paper)\n",
    "# note: learning also works without this, but if this is chosen carefully, it speeds up\n",
    "# convergence\n",
    "class WarmUpSchedule(tfk.optimizers.schedules.LearningRateSchedule):\n",
    "    def __init__(self, max_learning_rate, num_steps_until_maximum):\n",
    "        self.max_learning_rate=max_learning_rate\n",
    "        self.num_steps_until_maximum=num_steps_until_maximum\n",
    "\n",
    "    def __call__(self, step):\n",
    "        s=step/self.num_steps_until_maximum\n",
    "        if s<1:\n",
    "            return self.max_learning_rate*s\n",
    "        else:\n",
    "            return self.max_learning_rate*(s**(-0.5))\n",
    "\n",
    "optimizer = tfk.optimizers.Adam(learning_rate=WarmUpSchedule(max_learning_rate=1e-2,\n",
    "                                                             num_steps_until_maximum=1000))\n",
    "\n",
    "losses=[] # comment out this line if continuing training!\n",
    "\n",
    "for n_step in range(num_training_steps):\n",
    "    input_batch,output_batch=generate_batch_lookup_example(batchsize,num_objects, vector_length)\n",
    "    tf_input_batch = tf.convert_to_tensor(input_batch, tfk.backend.floatx())\n",
    "    tf_output_batch = tf.convert_to_tensor(output_batch, tfk.backend.floatx())\n",
    "    loss = train_step(tf_input_batch, tf_output_batch)\n",
    "    print(f\"Step {n_step} Loss {loss.numpy()}\",end=\"     \\r\")\n",
    "    losses.append(loss.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "480e409f-3bb9-4f07-9fba-6b2dd4823e20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T20:13:17.682590Z",
     "iopub.status.busy": "2022-01-15T20:13:17.681128Z",
     "iopub.status.idle": "2022-01-15T20:13:18.056321Z",
     "shell.execute_reply": "2022-01-15T20:13:18.055942Z",
     "shell.execute_reply.started": "2022-01-15T20:13:17.682543Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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8fUmm9enTK3796+81Cls/99yLcffdDzS7Xm82T28CAAAAAADtIXgOKejevfEPEdvyg72dr9n5B5PQle3cDzv/0D0VO/fYzn3b3LmO7OVsPSdoztq16+K4486KF15Y3HCuX7/ecfPNV8a4caNS3kdvpnYvvUl77Lvv3nHrrddE3769G85dddX1cfPNdzS7Xl+mdi99SUREfX19XHzxpbF585aIiBg9enhceumHMn4ffZnavfQlmTB9+pQ49ND9G5274457ml2rN1O7l94EAAAAAADSIXgOKejZs0ej482bt0RV1ea09li9+vVGx5WVPdtdF+wqKisb99iaNWvTun7TpqomPzxvrsey2cvZek6ws/XrN8YJJ5wTTz/9XMO53r0r46abroxJk8antZfebEpv0hFGjRoen/nMBxud+973ft7sWn3ZlL6kJb/+9e/j7rvvbzj+wQ8ui27dyjJ+H33ZlL6kIx1++AGNjp966tlm1+nNpvQmAAAAAADQXoLnkIK+fXtH796Vjc4tXboirT2WLl3e6HjcuJHtrgt2FWPHjmp0vHO/tGbJksbr+/Tp1aRnI7Lby9l6TrCjjRs3xUknvTcee+yphnM9e3aPv/zlVzF16qS099ObTelNOso73nFco+OHHloQ69ZtaLJOXzalL2nJZZd9v+HXRx55UIwdOzJeeWVZ0v9WrVrTaI+ampoma7Zv395ojb5sSl/SkUaOHNbouKXwtd5sSm8CAAAAAADtJXgOKZowYWyj45deeiWt6xcvXrrTfuPaXRPsKnburxdfTK+/Xn65cX9NnNhyf2Wrl7P5nCAioqpqc5xyyvvjoYcWNJzr3r0i/vznX8Xee09t0556sym9SUcZMKBvo+BXXV1dk6+3CH3ZHH1JS7ZufXuq79//fldMmnRQq/+dc85HGu2xYsWqJmsWLlzUaI2+bEpf0pHKykobHW/duq3ZdXqzKb0JAAAAAAC0l+A5pGjSpPGNjh988LGUr62q2hxPPfVc0v2gK9u5H5566rnYvHlLytfff/+jSfdL9lhH9XI2nxNs2bI13vGO8+K++x5pOFde3i1uuOHnMWfOXm3eV282pTfpSEVFRY2Od56qHKEvm6MvyTV92ZS+pCO9/vobjY779u3d7Dq92ZTeBAAAAAAA2kvwHFJ0+OEHNDq+994HU772P/95OGpqahqOp06dFAMH9stYbdDZDR48IKZMmdhwXFNT0yhA25qd+/GIIw5oYWX2ejmbz4mubevWbfHOd14Q99zz9tdMWVlp/OEPl8f++89q1956sym9SUfZunVbkyDdgAFNv471ZVP6klzTl03pSzrSI4883uh48OABza7Tm03pTQAAAAAAoL0EzyFFhx02N7p1K2s4fvDBx+K5515M6dprr/1To+MTTjgio7XBruCEEw5vdHz11TekdN1zz70YDz/8dvCgoqI8Dj10bovrs9nL2XpOdF3bt2+P97znovjXv/7TcK60tCR+97ufxsEH75uRe+jNt+lNOtJdd90XdXV1Dcfl5d1iyJCBza7Vl2/TlySzYsVjUVW1KK3/br31mkZ7jBgxtMmaqVMnNbmXvnybvqQjbd26LW688e+Nzs2dO7vF9XrzbXoTAAAAAADIBMFzSFF5ebc46aSjGp377nevaPW6F15YHDfddHvDcVFRUbzrXcdnvD7o7N797hOjsLCw4fimm/4eixa93Op1O/fhKaccE2VlpS2uz2YvZ+s50TXV1NTEvHkfittvv7vhXHFxcVx77Y+aTFxsD73Zcj16k0ypq6uLb37zR43OHX74AVFSUtLsen3Zcj36klzRly3Xoy/JpO9+9/JYsWJVw3FhYWEcddTBLa7Xmy3XozcBAAAAAIC2EDyHNFx66YeiuLi44fiaa26IW265s8X1W7duiwsv/HRs317dcO7ss98ZY8aM7NA6oTMaN25UnHHGyQ3H27dXxwUXfCq2bt3W4jU333xHXHPN29PdSkqK43Ofu6TVe2Wrl7P5nOhaamtr49xzPxY33/z2121RUVFcddX34+ijD8novfTmm/QmqfjpT6+KlStfS+ua6urq+MAHPttoAmlExPnnn9niNfryTfqSfKIv36QvSdVvf/vnWLVqTVrX/PrXv4uvf/2Hjc6deeYpMWLE0Bav0Ztv0psAAAAAAECmCJ5DGkaPHhEXXXR2o3NnnHFJ/OxnV8X27dsbnX/22UVx7LHz4oEH5jec69u3d3z2s36wR+e0fPnKeOWVZU3+2zksUFNT0+y6V15ZFmvWrE16j89//sPRu3dlw/EDD8yP4447q8lblG/bti1++tOr4swzP9To/Ic+9L6koYO3ZLOXs/Wc6FouvPAzccMNf2t07ktf+nhMnTqpxf5r6b9kAZW36E29SWquvPKPscceh8S5534s/va3f8TGjZtaXLtly9b4wx/+Gvvsc0KjIFhExHvec1IcdNA+Se+lL/Ul+Udf6ktSd9VVf4zJkw+K8877ZNx227+iqmpzi2vnz38y3vOei+KDH/x81NfXN5wfMmRgfOELH2v1XnpTbwIAAAAAAJmTqKpaVN/6MuAttbW18Y53nB+33353o/P9+/eNadMmR48eFbF48dJYsODpRj8QLSkpjptvvir2229mtkuGjNh99wNjyZLl7drjjDNOiSuu+FbSNffc80CceOJ7G019SyQSMX36lBg9enisX78xFix4ukmI/eijD47f//5njd5yPJls9nK2nhNdR0XFuIztdeut18QBB8xpdZ3ebPtzouuYM+f4ePLJhQ3HiUQixo4dGSNGDI1evXpGcXFxbNpUFUuWrIhnn10U1dXVTfY4+uiD49prfxSlpaWt3k9ftv05QUvuueeBOProt99xYMSIobFw4d1Jrmh6vb5s23OiaznqqNPj3nsfajguKCiIceNGxYgRQ6OyskcUFBTG2rVvxJNPPhuvvdZ0MnqfPr3ittt+G5Mnj0/pfnqz7c8JAAAAAABgR4Ln0AabNlXFxRd/Lq6//paU1vfv3zeuuOJbccQRB3ZwZdBxshU8j4i47ba74oILPtXqhPS3vPOdx8ePf/y1qKgoT6uebPZytp4TXUMugucRejNCb5LczsHzdHTrVhaf/vRF8ZGPnBfFxcUpX6cv9SWZ1d7geYS+jNCXtG7n4Hk6Djpo37jiiv8XQ4cOTus6vak3AQAAAACA9ivIdQHQGXXvXhFXXvn9uOaaH8asWdNaXNenT68477zT4+GH/yZ0Dmk46qiD4uGHb433v//0Rm8fvrNZs6bFtdf+KH7zm++16Qfn2ezlbD0n6Eh6U2+S3I9//LX49Kcvjtmzp0dpaUlK10yYMDa+8IWPxuOP3xGf/ORFaYXOI/SlviQf6Ut9SesuuuiceNe7TogRI4amtL6iojxOOOGIuPnmq+KWW65KO3QeoTf1JgAAAAAAkAkmnkMGvPzym2+TvHLla1FVtTkGDuwfI0YMiX32mRElJamFjoDmbd++Pe6//9FYsmRFrFq1OioqymPIkIExdeqkGDVqeEbvla1ezuZzgo6iNyG56urqePbZF+Pll5fGihWvxqZNm6OmpiYqKsqjR4/uMXLksJg6dVLSkFi69CXkH30JrVu3bkMsXPhCLFu2Ml57bU1s3rwl6uvro7KyR/TqVRkTJoyNPfaYGIWFhRm7p94EAAAAAABoG8FzAAAAAAAAAAAAAACSKsh1AQAAAAAAAAAAAAAA5DfBcwAAAAAAAAAAAAAAkhI8BwAAAAAAAAAAAAAgKcFzAAAAAAAAAAAAAACSEjwHAAAAAAAAAAAAACApwXMAAAAAAAAAAAAAAJISPAcAAAAAAAAAAAAAICnBcwAAAAAAAAAAAAAAkhI8BwAAAAAAAAAAAAAgKcFzAAAAAAAAAAAAAACSEjwHAAAAAAAAAAAAACApwXMAAAAAAAAAAAAAAJISPAcAAAAAAAAAAAAAICnBcwAAAAAAAAAAAAAAkhI8BwAAAAAAAAAAAAAgKcFzAAAAAAAAAAAAAACSEjwHAAAAAAAAAAAAACApwXMAAAAAAAAAAAAAAJISPAcAAAAAAAAAAAAAICnBcwAAAAAAAAAAAAAAkhI8BwAAAAAAAAAAAAAgKcFzAAAAAAAAAAAAAACSEjwHAAAAAAAAAAAAACApwXMAAAAAAAAAAAAAAJISPAcAAAAAAAAAAAAAICnBcwAAAAAAAAAAAAAAkhI8BwAAAAAAAAAAAAAgKcFzAAAAAAAAAAAAAACSEjwHAAAAAAAAAAAAACApwXMAAAAAAAAAAAAAAJISPAcAAAAAAAAAAAAAICnBcwAAAAAAAAAAAAAAkhI8BwAAAAAAAAAAAAAgKcFzAAAAAAAAAAAAAACSEjwHAAAAAAAAAAAAACApwXMAAAAAAAAAAAAAAJISPAcAAAAAAAAAAAAAIKn/Dztm1+moKw9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 3600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(ncols=2,dpi=300,figsize=(12,4))\n",
    "ax[0].semilogy(losses)\n",
    "ax[0].set_title(\"Loss\")\n",
    "ax[1].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1632fa5f-14b7-45ac-a074-07bfe1c3ff0c",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "2f2489c5-d943-4db8-a074-f3471364131e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T20:15:02.069469Z",
     "iopub.status.busy": "2022-01-15T20:15:02.068233Z",
     "iopub.status.idle": "2022-01-15T20:15:02.333260Z",
     "shell.execute_reply": "2022-01-15T20:15:02.332958Z",
     "shell.execute_reply.started": "2022-01-15T20:15:02.069356Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain a test batch, but also store the random\n",
    "# permutation of the objects (useful for obtaining the\n",
    "# expected attention matrix later)\n",
    "batch_permutations=[]\n",
    "test_batch_in,test_batch_out=generate_batch_lookup_example(1,num_objects,vector_length,\n",
    "                                                          provide_permutation=batch_permutations)\n",
    "batch_permutations=batch_permutations[0] # was a batch of size 1, drop batch index\n",
    "\n",
    "# apply transformer, but also receive weights for each layer,\n",
    "# and also store the data vectors for all objects, for each layer\n",
    "test_weights=[]\n",
    "test_data_layers=[]\n",
    "result=transformer(test_batch_in,                        \n",
    "                         store_attention_weights=test_weights,\n",
    "                        store_data_layers=test_data_layers)\n",
    "\n",
    "# plot results (input/output/prediction)\n",
    "fig, ax = plt.subplots(1,3, figsize=(10,3),dpi=300)\n",
    "for idx,values in enumerate([test_batch_in[0], test_batch_out[0], result[0].numpy()]):\n",
    "    ax[idx].imshow(values, vmin = -1., vmax = 1.,origin='lower')\n",
    "ax[0].set_title(\"Input\")\n",
    "ax[1].set_title(\"Desired Output\")\n",
    "ax[2].set_title(\"Transformer Prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "f501da05-59cd-401a-8735-b14befdeabc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T20:15:02.537786Z",
     "iopub.status.busy": "2022-01-15T20:15:02.537396Z",
     "iopub.status.idle": "2022-01-15T20:15:03.152746Z",
     "shell.execute_reply": "2022-01-15T20:15:03.152435Z",
     "shell.execute_reply.started": "2022-01-15T20:15:02.537750Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize data processing in all the layers\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explanation: Neuron activations during transformer processing pipeline, for all layers. Vertical axis: object index. Horizontal: vector component.\n",
      "\n",
      "Visualize queries, keys, and values for all layers\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explanation: For each layer, we show for each object (object index is vertical) the query, key and value vectors, from left to right in reach row.\n",
      "\n",
      "Visualizing attentions\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explanation: Attention weights from object index (vertical axis) to some object index (horizontal axis). Sum of weights along each row is one. First panel shows naive expectation, from construction and random shuffling.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# obtain expected attention matrix\n",
    "# objects with placeholders should attend to corresponding\n",
    "# objects with the actual data; we need the permutation (that was imposed\n",
    "# during the batch generation) to do this properly\n",
    "half_num=num_objects//2\n",
    "expected_attention=((batch_permutations[None,:]%half_num)==\n",
    "                    (batch_permutations[:,None]%half_num))\n",
    "\n",
    "# visualize data processing in the transformer\n",
    "\n",
    "def all_ticks(ax,x_range=num_objects):\n",
    "    ax.set_xticks(np.arange(x_range))\n",
    "    ax.set_yticks(np.arange(num_objects))\n",
    "\n",
    "print(\"Visualize data processing in all the layers\")\n",
    "\n",
    "fig,ax=plt.subplots(ncols=num_layers+2,dpi=300,figsize=(12,2))\n",
    "for layer_idx in range(num_layers+2):\n",
    "    ax[layer_idx].imshow(test_data_layers[layer_idx][0],origin='lower',vmin=-1,vmax=1)\n",
    "    all_ticks(ax[layer_idx],x_range=dim_data)\n",
    "    ax[layer_idx].set_title(str(layer_idx))\n",
    "plt.show()\n",
    "print(\"Explanation: Neuron activations during transformer processing pipeline, for all layers. Vertical axis: object index. Horizontal: vector component.\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Visualize queries, keys, and values for all layers\")\n",
    "\n",
    "fig,ax=plt.subplots(ncols=num_layers,dpi=300,figsize=(12,2))\n",
    "if num_layers==1:\n",
    "    ax=[ax] # so we can access it as list...\n",
    "for layer_idx in range(num_layers):\n",
    "    data = test_data_layers[layer_idx][0]\n",
    "    block = transformer.layers[layer_idx]\n",
    "    Q = data @ block.WQ\n",
    "    K = data @ block.WK\n",
    "    V = data @ block.WV\n",
    "    ax[layer_idx].imshow(Q,origin='lower',vmin=-4,vmax=4,\n",
    "                         extent=(0,dim_key,0,num_objects))\n",
    "    ax[layer_idx].imshow(K,origin='lower',vmin=-4,vmax=4,\n",
    "                         extent=(dim_key,2*dim_key,0,num_objects))\n",
    "    ax[layer_idx].imshow(V,origin='lower',vmin=-4,vmax=4,\n",
    "                         extent=(2*dim_key,2*dim_key+dim_data,0,num_objects))\n",
    "    ax[layer_idx].vlines([dim_key,2*dim_key],0,num_objects,color='white')\n",
    "\n",
    "    all_ticks(ax[layer_idx],x_range=2*dim_key+dim_data)\n",
    "\n",
    "    ax[layer_idx].set_title(str(layer_idx))\n",
    "plt.show()\n",
    "print(\"Explanation: For each layer, we show for each object (object index is vertical) the query, key and value vectors, from left to right in reach row.\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Visualizing attentions\")\n",
    "fig,ax=plt.subplots(ncols=num_layers+1,dpi=300,figsize=(12,2))\n",
    "ax[0].imshow(expected_attention,origin='lower')\n",
    "all_ticks(ax[0])\n",
    "ax[0].set_title(\"expected?\")\n",
    "for layer_idx in range(num_layers):\n",
    "    ax[layer_idx+1].imshow(test_weights[layer_idx][0],origin='lower',vmin=0,vmax=1)\n",
    "    all_ticks(ax[layer_idx+1])\n",
    "    ax[layer_idx+1].set_title(str(layer_idx))\n",
    "plt.show()\n",
    "print(\"Explanation: Attention weights from object index (vertical axis) to some object index (horizontal axis). Sum of weights along each row is one. First panel shows naive expectation, from construction and random shuffling.\")\n",
    "print()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "50e38b04-0918-4d7e-b827-c8f7eecabd47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-15T20:15:03.153582Z",
     "iopub.status.busy": "2022-01-15T20:15:03.153492Z",
     "iopub.status.idle": "2022-01-15T20:15:03.156736Z",
     "shell.execute_reply": "2022-01-15T20:15:03.156454Z",
     "shell.execute_reply.started": "2022-01-15T20:15:03.153572Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Desired Output:\n",
      "[[1. 0. 0. 0. 1. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [1. 0. 0. 1. 0. 0. 0. 0. 1. 0.]\n",
      " [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]]\n",
      "Transformer Prediction:\n",
      "[[ 1. -0.  0. -0.  1.  0.  0. -0.  1. -0.]\n",
      " [-0. -0.  0.  0.  1.  0.  0.  0.  1. -0.]\n",
      " [ 0.  1. -0.  0.  0. -0.  0.  1. -0.  0.]\n",
      " [ 1. -0.  1. -0.  0. -0. -0.  1.  0.  0.]\n",
      " [ 0. -0.  1.  0.  0. -0. -0.  1.  0. -0.]\n",
      " [ 1. -0.  0.  1.  0. -0.  0.  0.  1.  0.]\n",
      " [ 1.  1.  0.  0.  0. -0.  0.  1.  0.  0.]\n",
      " [ 0. -0. -0.  1.  0.  0.  0.  0.  1. -0.]]\n"
     ]
    }
   ],
   "source": [
    "# text output of test:\n",
    "\n",
    "print(\"Desired Output:\")\n",
    "print(np.round(test_batch_out[0],1))\n",
    "print(\"Transformer Prediction:\")\n",
    "print(np.round(result[0].numpy(),1))\n"
   ]
  }
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