{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP2: Image-to-sequence (Handwritten Text Recognition)\n",
    "\n",
    "## Goals\n",
    "- Implement transformer-based model for an image-to-sequence task\n",
    "- Visualize attention maps\n",
    "\n",
    "\n",
    "We will use a modified version of the MNIST dataset with multiple digits within the same image. The goal is to train a model to recognize all digits in the correct order (from left to right)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File ‘mnist_variable_len_1k.data’ already there; not retrieving.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Config used: torch==2.4.1 \n",
    "\n",
    "# Download the data\n",
    "!wget -nc https://people.irisa.fr/Denis.Coquenet/courses/content/M2-DLV/TP2/mnist_variable_len_1k.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making experiment reproducible\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "def set_deterministic():\n",
    "    torch.manual_seed(0)\n",
    "    torch.cuda.manual_seed(0)\n",
    "    np.random.seed(0)\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "set_deterministic()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset\n",
    "\n",
    "This part focuses on defining the custom dataset with images of variable widths (containing between 1 and 5 concatenated MNIST digits).  \n",
    "\n",
    "You have nothing to code here by yourself.  \n",
    "\n",
    "TODO: read and understand how the dataset is handled.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "\n",
    "class MNISTDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Custom dataset class to manipulate a specific version of the MNIST dataset in which samples consists in the concatenation of single digits from the original MNIST dataset.\n",
    "    \"\"\"\n",
    "\n",
    "    pad_label_value = None\n",
    "\n",
    "    def __init__(self, filepath, set_name):\n",
    "        self.set_name = set_name\n",
    "        data = torch.load(filepath)\n",
    "        self.classes = data[\"classes\"]  # associate each class to an int\n",
    "        self.samples = data[f\"{set_name}_samples\"]  # the input images (containing between 1 and 5 digits)\n",
    "        self.targets = data[f\"{set_name}_targets\"]  # the expected output (numpy array of class indices)\n",
    "        \n",
    "        self.token_set = [c[0] for c in self.classes] + [\"<e>\", \"<b>\", \"<p>\"]  # digits + begin / end / padding special tokens\n",
    "        if MNISTDataset.pad_label_value is None:\n",
    "           MNISTDataset.pad_label_value = self.token_set.index(\"<p>\")\n",
    "\n",
    "        self.samples = [torch.tensor(s, dtype=torch.float).unsqueeze(0) for s in self.samples]  # preprocess the inputs once and for all\n",
    "        self.targets = [self.preformat_label(t) for t in self.targets]  # preprocess the targets once and for all\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        Compute the number of samples in the dataset\n",
    "        \"\"\"\n",
    "        return len(self.samples)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \"\"\"\n",
    "        Return the sample at index idx (input and ground truth) as a dict\n",
    "        \"\"\"\n",
    "        return {\n",
    "            \"input_img\": self.samples[idx], \n",
    "            \"ground_truth\": self.targets[idx]\n",
    "        }\n",
    "    \n",
    "    def preformat_label(self, label):\n",
    "        \"\"\"\n",
    "        Format the labels to process them in an image-to-sequence task.\n",
    "        It casts the label from numpy to torch and adds a <b> token before\n",
    "        the label tokens, and a <e> token after.\n",
    "        \"\"\"\n",
    "        new_label = torch.ones((len(label)+2), dtype=torch.long)\n",
    "        new_label[0] = self.token_set.index(\"<b>\")\n",
    "        new_label[1:-1] = torch.tensor(label)\n",
    "        new_label[-1] = self.token_set.index(\"<e>\")\n",
    "        return new_label\n",
    "    \n",
    "    def decode_tokens(self, tokens):\n",
    "        \"\"\"\n",
    "        tokens: iterable of int\n",
    "        Return the string corresponding to the token sequence\n",
    "        \"\"\"\n",
    "        return [self.token_set[i] for i in tokens if i <= 10]\n",
    "\n",
    "    @staticmethod\n",
    "    def pad_images(img_list, padding_value):\n",
    "        \"\"\"\n",
    "        Function that puts some torch images together to process them as a mini-batch.\n",
    "        Smaller images are padded.\n",
    "        A torch image size is as follows (C, H, W)\n",
    "        H: height, W: width, C: number of channels (1 for grayscale, 3 for RGB)\n",
    "        \"\"\"\n",
    "        num_imgs = len(img_list)\n",
    "        channels = img_list[0].size(0)\n",
    "        height = img_list[0].size(1)\n",
    "        max_width = max([img.size(2) for img in img_list])\n",
    "        batch_imgs = torch.full(size=(num_imgs, channels, height, max_width), \n",
    "                                fill_value=padding_value,\n",
    "                                dtype=img_list[0].dtype)\n",
    "        for i, img in enumerate(img_list):\n",
    "            batch_imgs[i, 0, :, :img.size(2)] = img\n",
    "        return batch_imgs\n",
    "    \n",
    "    @classmethod\n",
    "    def pad_labels(cls, label_list):\n",
    "        \"\"\"\n",
    "        Function that puts some labels together to process them as a mini-batch.\n",
    "        Shorter labels are padded.\n",
    "        batch_data: list of list of int\n",
    "        \"\"\"\n",
    "        num_labels = len(label_list)\n",
    "        max_len = max([label.size(0) for label in label_list])\n",
    "        batch_labels = torch.full(size=(num_labels, max_len),\n",
    "                                  fill_value=cls.pad_label_value,\n",
    "                                  dtype=label_list[0].dtype)\n",
    "        for i, label in enumerate(label_list):\n",
    "            batch_labels[i, :label.size(0)] = label \n",
    "        return batch_labels\n",
    "    \n",
    "    @staticmethod\n",
    "    def batch_samples(batch_data):\n",
    "        \"\"\"\n",
    "        Function that puts some samples together to process them as a mini-batch.\n",
    "        batch_data: list of samples\n",
    "        \"\"\"\n",
    "        input_imgs = [data[\"input_img\"] for data in batch_data]\n",
    "        ground_truths = [data[\"ground_truth\"] for data in batch_data]\n",
    "        return {\n",
    "            \"imgs\": MNISTDataset.pad_images(input_imgs, padding_value=0),\n",
    "            \"labels\": MNISTDataset.pad_labels(ground_truths),\n",
    "            \"original_widths\": [data[\"input_img\"].size(2) for data in batch_data],\n",
    "            \"label_lengths\": [data[\"ground_truth\"].size(0) for data in batch_data],\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([512, 1, 32, 160]) torch.Size([512, 7])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21269/2784281161.py:12: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  data = torch.load(filepath)\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "train_dataset = MNISTDataset(set_name=\"train\", filepath=\"./mnist_variable_len_1k.data\")\n",
    "train_dataloader = DataLoader(dataset=train_dataset, \n",
    "                              batch_size=512, \n",
    "                              shuffle=True,\n",
    "                              collate_fn=MNISTDataset.batch_samples)\n",
    "\n",
    "\n",
    "# Let's check the first mini-batch\n",
    "for batch_data in train_dataloader:\n",
    "    x = batch_data[\"imgs\"]\n",
    "    y = batch_data[\"labels\"]\n",
    "    print(x.size(), y.size())\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's display some samples\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(10, 10))\n",
    "for i in range(5):\n",
    "    for j in range(2):\n",
    "        sample_index = 5*j+i\n",
    "        axes[i][j].imshow(x[sample_index, 0], cmap=\"gray\")\n",
    "        axes[i][j].set_title(f\"Target: {y[sample_index].numpy()}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Architecture\n",
    "\n",
    "We will follow the architecture of the Document Attention Network, i.e., we will rely on an FCN encoder and a transformer decoder to handle this image-to-sequence task.  \n",
    "\n",
    "We will follow a step-by-step strategy to design the network, successively focusing on the different components:  \n",
    "- The fully convolutional network encoder  \n",
    "- The transformer decoder  \n",
    "- The positional encoding  \n",
    "- Finally, merging all of these together  \n",
    "\n",
    "<img src=\"https://people.irisa.fr/Denis.Coquenet/courses/content/M2-DLV/TP2/DAN_model.png\" height=\"600\"/>  <br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoder\n",
    "\n",
    "We will rely on a rather simple encoder made up of some convolutional layers. It aims at extracting 2D features from the input image.  \n",
    "\n",
    "**TODO**: based on the convolutional layer configurations, compute the downsampling factor applied by the encoder, i.e., by which factor the height and the width of the input image are divided (replace the ??? in the following cell).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Module, InstanceNorm2d, Conv2d, ReLU\n",
    "\n",
    "class FCN_Encoder(Module):\n",
    "    \"\"\"\n",
    "    This encoder will take as input images of size (32, N*32), N being the number of digits in the image\n",
    "    and output features of size (1, N)\n",
    "    \"\"\"\n",
    "\n",
    "    downsampling_factor = 16\n",
    "\n",
    "    def __init__(self, out_dim):\n",
    "        super().__init__()\n",
    "        start_dim=32\n",
    "        self.conv1 = Conv2d(in_channels=1, out_channels=start_dim, kernel_size=2, stride=2, padding=0)  # dim/2\n",
    "        self.conv2 = Conv2d(in_channels=start_dim, out_channels=2*start_dim, kernel_size=2, stride=2, padding=0)  # dim/2\n",
    "        self.conv3 = Conv2d(in_channels=2*start_dim, out_channels=4*start_dim, kernel_size=2, stride=2, padding=0) # dim/2\n",
    "        self.conv4 = Conv2d(in_channels=4*start_dim, out_channels=out_dim, kernel_size=2, stride=2, padding=0) # dim/2\n",
    "\n",
    "        self.norm1 = InstanceNorm2d(2*start_dim)\n",
    "        self.norm2 = InstanceNorm2d(4*start_dim)\n",
    "        self.activation = ReLU()\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.activation(self.conv1(x))\n",
    "        x = self.activation(self.conv2(x))\n",
    "        x = self.norm1(x)\n",
    "        x = self.activation(self.conv3(x))\n",
    "        x = self.norm2(x)\n",
    "        x = self.activation(self.conv4(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformer Decoder\n",
    "\n",
    "The goal now is to implement a transformer decoder layer (we will only use one layer in the network). Its goal is to iteratively apply the transformer attention mechanisms to focus on a specific part of the input image.  \n",
    "\n",
    "To do so, the transformer decoder relies on two kinds of attention:  \n",
    "- the self-attention, which is applied between a set of tokens, and itself (queries, keys and values are from the same source).  \n",
    "- the cross attention, which is applied between two sets of tokens (queries are from source tokens, and keys and values are from target tokens).  \n",
    "\n",
    "When performing these attention mechanisms, it is important to mask some tokens to avoid accessing padding tokens, whether it is from the source (text tokens), or from the target (image tokens).  \n",
    "\n",
    "Also, when applying teacher forcing, it is required to mask \"the future\", so the mask remains causal in the self-attention.  \n",
    "\n",
    "TODO: implement these mask functions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# L: length of the token sequence, B: mini-batch size, H: height of the image, W: width of the image\n",
    "\n",
    "class MaskHelper:\n",
    "\n",
    "    @staticmethod\n",
    "    def compute_tgt_padding_mask(target_text_sequence, target_lengths):\n",
    "        \"\"\"\n",
    "        Compute the mask of valid position for the target text sequence       \n",
    "        target_text_sequence: tensor of size (L, B, C)\n",
    "        target_lengths: list of int\n",
    "        True = padded location, False = real text token location\n",
    "        Return tensor of size (B, L)\n",
    "        \"\"\"\n",
    "        L, B, C = target_text_sequence.size()\n",
    "        mask = torch.ones((B, L), dtype=torch.bool)\n",
    "        for i, len_ in enumerate(target_lengths):\n",
    "            mask[i, :len_] = False\n",
    "        return mask\n",
    "    \n",
    "    @staticmethod\n",
    "    def compute_image_padding_mask(batch_img_features, original_widths, downsampling_factor):\n",
    "        \"\"\"\n",
    "        Compute the mask of valid position for the image\n",
    "        batch_img_features: tensor of size (B, C, H, W)\n",
    "        original_widths: list of int\n",
    "        downsampling_factor: int\n",
    "        Must return a boolean flattened tensor of shape (B, H, W)\n",
    "        True = padded location, False = real image location\n",
    "        \"\"\"\n",
    "        B, C, H, W = batch_img_features.size()\n",
    "        mask = torch.ones((B, H, W), dtype=torch.bool)\n",
    "        for i, width in enumerate(original_widths):\n",
    "            mask[i, :, :width//downsampling_factor] = False\n",
    "        return mask\n",
    "    \n",
    "    \n",
    "    @staticmethod\n",
    "    def compute_tgt_attn_mask(target_text_sequence):\n",
    "        \"\"\"\n",
    "        target_text_sequence: tensor of size (L, B, C)\n",
    "        Return a mask tensor of size (L, L) indicating which text token can attend to which text tokens in self-attention\n",
    "        mask[i, j] specifies if token i can attend to token j\n",
    "        False = can attend, True = cannot attend\n",
    "        \"\"\"\n",
    "        len_ = target_text_sequence.size(0)\n",
    "        return torch.triu(torch.ones((len_, len_), dtype=torch.bool), diagonal=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the mask functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "# visualizing tgt_attn_mask\n",
    "\n",
    "L = 5\n",
    "B = 4\n",
    "C = 256\n",
    "H = 32\n",
    "W = 160\n",
    "Hf, Wf = H // FCN_Encoder.downsampling_factor, W // FCN_Encoder.downsampling_factor\n",
    "\n",
    "target = torch.zeros((L, B, C))  \n",
    "target_len = torch.tensor([5, 2, 2, 3])  # (B, )\n",
    "images = torch.zeros((B, C, Hf, Wf))\n",
    "original_widths = torch.tensor([160, 64, 64, 96])  # (B, )\n",
    "\n",
    "tgt_attn_mask = MaskHelper.compute_tgt_attn_mask(target)  # (L, L)\n",
    "\n",
    "print(tgt_attn_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True]])\n"
     ]
    }
   ],
   "source": [
    "# visualizing tgt_pad_mask\n",
    "tgt_pad_mask = MaskHelper.compute_tgt_padding_mask(target, target_len)  # (B, L)\n",
    "print(tgt_pad_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[False, False, False, False, False, False, False, False, False, False],\n",
      "         [False, False, False, False, False, False, False, False, False, False]],\n",
      "\n",
      "        [[False, False, False, False,  True,  True,  True,  True,  True,  True],\n",
      "         [False, False, False, False,  True,  True,  True,  True,  True,  True]],\n",
      "\n",
      "        [[False, False, False, False,  True,  True,  True,  True,  True,  True],\n",
      "         [False, False, False, False,  True,  True,  True,  True,  True,  True]],\n",
      "\n",
      "        [[False, False, False, False, False, False,  True,  True,  True,  True],\n",
      "         [False, False, False, False, False, False,  True,  True,  True,  True]]])\n"
     ]
    }
   ],
   "source": [
    "# visualizing memory_pad_mask\n",
    "memory_pad_mask = MaskHelper.compute_image_padding_mask(images, original_widths, FCN_Encoder.downsampling_factor)  # (B, Hf, Wf)\n",
    "torch.set_printoptions(profile=\"full\")\n",
    "print(memory_pad_mask)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformer decoder layer\n",
    "\n",
    "We now focus on the decoder layer itself. We will rely on the multi-head attention implementation of Pytorch.   \n",
    "\n",
    "In the following cell, layers are already initialized with appropriate parameters.  \n",
    "\n",
    "**TODO**: based on the Document Attention Network figure (gray box), implement the forward function. \n",
    "\n",
    "Documentation for the used layers can be found here: [MultiheadAttention](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html), [Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear), [LayerNorm](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html#torch.nn.LayerNorm), [ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import MultiheadAttention, Linear, LayerNorm, ReLU\n",
    "\n",
    "class TransformerDecoder(Module):\n",
    "\n",
    "    def __init__(self, embed_dim, num_heads=2, dim_feedforward=128):\n",
    "        super().__init__()\n",
    "        self.cross_attention = MultiheadAttention(embed_dim, \n",
    "                                                  num_heads=num_heads)\n",
    "        \n",
    "        self.norm1 = LayerNorm(embed_dim)\n",
    "        self.self_attention = MultiheadAttention(embed_dim, \n",
    "                                                  num_heads=num_heads)\n",
    "        self.norm2 = LayerNorm(embed_dim)\n",
    "        \n",
    "        # feedforward is defined as an MLP\n",
    "        self.linear1 = Linear(embed_dim, dim_feedforward)\n",
    "        self.activation = ReLU()\n",
    "        self.linear2 = Linear(dim_feedforward, embed_dim)\n",
    "\n",
    "        self.norm3 = LayerNorm(embed_dim)\n",
    "\n",
    "    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,\n",
    "                tgt_key_padding_mask=None, memory_key_padding_mask=None):\n",
    "        \"\"\"\n",
    "        tgt: target text sequence (Lt, B, C)\n",
    "        memory: flattened image features (Li, B, C)\n",
    "        tgt_mask: mask for the target (which item can attend to which one, useful for teacher forcing) - bool tensor (Lt, Lt)\n",
    "        memory_mask: mask for the image features (Li, Li) - not useful here as no self attention over the image\n",
    "        tgt_key_padding_mask: mask to discard padded position in target - bool tensor (B, Lt)\n",
    "        memory_key_padding_mask: mask to discard padded position in features - bool tensor (B, Li)\n",
    "        \n",
    "        Output: tuple including\n",
    "        - the output sequence (Lt, B, C)\n",
    "        - the attention weights of the cross-attention layer (B, Lt, Li)\n",
    "        \"\"\"\n",
    "        \n",
    "        new_tgt, _ = self.self_attention(query=tgt,\n",
    "                                      key=tgt,\n",
    "                                      value=tgt,\n",
    "                                      attn_mask=tgt_mask,\n",
    "                                      key_padding_mask=tgt_key_padding_mask,\n",
    "                                      need_weights=False,\n",
    "        )\n",
    "        tgt = self.norm1(new_tgt + tgt)\n",
    "\n",
    "        new_tgt, attn_weights = self.cross_attention(query=tgt,\n",
    "                                    key=memory,\n",
    "                                    value=memory,\n",
    "                                    attn_mask=memory_mask,\n",
    "                                    key_padding_mask=memory_key_padding_mask,\n",
    "                                    need_weights=True,\n",
    "        )\n",
    "\n",
    "        tgt = self.norm2(new_tgt + tgt)\n",
    "        new_tgt = self.linear2(self.activation(self.linear1(tgt)))\n",
    "        tgt = self.norm3(new_tgt + tgt)\n",
    "        return tgt, attn_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Positional encoding\n",
    "\n",
    "The multi-head attention is permutation-equivariant, so we need to inject positional information to the input sets.  \n",
    "Each position is encoded as a positional embedding vector such that $\\text{PE}(p, k)$ corresponds to the $k^\\text{th}$ value of the positional embedding vector for position $p$.\n",
    "\n",
    "The original positional encoding (from transformer paper) is defined as follows:\n",
    "\\begin{equation*}\n",
    "\\begin{split}\n",
    "  & \\mathrm{PE}(p, 2k) = \\sin(w_k \\cdot p)\\\\\n",
    "  & \\mathrm{PE}(p, 2k+1) = \\cos(w_k \\cdot p)\\\\\n",
    "\\end{split}\n",
    "\\end{equation*}  \n",
    "\n",
    "$\\forall k \\in [0, d/2]$, with $w_k = 1/10000^{2k/d}$  \n",
    "\n",
    "where $d$ is the dimension of the token embedding.\n",
    "\n",
    "**TODO**: complete the forward function of the following Module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Parameter\n",
    "\n",
    "class PositionalEncoding(Module):\n",
    "\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "\n",
    "        # We use exponential properties for numerical stability\n",
    "        # w_k = e^(-alog(b)) = e^(log(1/b^a)) = 1/(b^a)\n",
    "        # with b = 10,000 and a = 2k/d\n",
    "        a = torch.arange(0., dim, 2) / dim\n",
    "        b = torch.tensor(10000.0)\n",
    "        self.wk = Parameter(torch.exp(-a * torch.log(b)).unsqueeze(0), requires_grad=False)  # (1, dim/2)\n",
    "\n",
    "    def forward(self, indices):\n",
    "        \"\"\"\n",
    "        Input: indices (B, L) - e.g.: [0, 1, 2, 3, ..., L-1]\n",
    "        Output: positional embedding (B, L, C)\n",
    "        \"\"\"\n",
    "        emb_indices = torch.zeros((indices.size(0), indices.size(1), self.dim), device=indices.device)  # (B, L, C)\n",
    "        emb_indices[:, :, ::2] = torch.sin(indices.unsqueeze(2) * self.wk)\n",
    "        emb_indices[:, :, 1::2] = torch.cos(indices.unsqueeze(2) * self.wk)\n",
    "        return emb_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "index_sequence = torch.arange(0,500).unsqueeze(0)  # (1, 500)\n",
    "C = 1000\n",
    "\n",
    "positional_encoding = PositionalEncoding(C)  \n",
    "positional_emb = positional_encoding(index_sequence)[0] # (500, 1000)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(positional_emb)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's put all of these components together!\n",
    "\n",
    "The following cell implements the end-to-end image-to-sequence model.  It takes as input the image, and outputs the predicted text sequence.  \n",
    "\n",
    "TODO: implement the *decode* function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Embedding\n",
    "\n",
    "class I2SModel(Module):\n",
    "\n",
    "    \"\"\"\n",
    "    The whole image-to-sequence model.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, token_set, embed_dim=128, max_preds=6):\n",
    "        super().__init__()\n",
    "        self.max_preds = max_preds  # set a maximum number of predictions to avoid infinite loops\n",
    "        self.token_set = token_set  # list of all tokens (digits + special tokens <b>, <e> and <p>)\n",
    "\n",
    "        self.encoder = FCN_Encoder(embed_dim)  # to extract image features\n",
    "        self.decoder = TransformerDecoder(embed_dim=embed_dim)  # to iteratively focus on a subpart of the features\n",
    "        self.positional_encoding = PositionalEncoding(embed_dim)  # to inject positional information in the attention process\n",
    "        self.emb_layer = Embedding(num_embeddings=len(token_set), embedding_dim=embed_dim)  # to associate a vector embedding to each text token\n",
    "        self.decision_layer = Linear(in_features=embed_dim, out_features=len(token_set)-2)  # to make token predictions (-2: no need to predict <b> and <p>)\n",
    "\n",
    "\n",
    "    def forward(self, x, original_widths, tgt=None, tgt_lengths=None):\n",
    "        \"\"\"\n",
    "        x: a mini-batch input tensor of images of variable widths (B, C, H, W)\n",
    "        original_widths: a list of int (useful for masking)\n",
    "        tgt: a target mini-batch output tensor of token indices (for training only = teacher forcing) (B, L)\n",
    "        tgt_lengths: a list of int (target sequences can be of variable lengths, for training only)\n",
    "        \"\"\"\n",
    "\n",
    "        # Encoder part (performed only once)\n",
    "        features = self.encoder(x)  # extracting features from image (B, dim, H/16, W/16)\n",
    "        flattened_features = torch.flatten(features, start_dim=2, end_dim=3).permute(2, 0, 1)  # flatten for transformer requirements (Li, B, dim) with Li=(H/16)*(W/16)\n",
    "        Li, B, C = flattened_features.size()\n",
    "        img_position = self.generate_positional_embedding(B, Li)  # generate positional embedding\n",
    "        flattened_features = flattened_features + img_position  # adding positional embedding\n",
    "        img_pad_mask = MaskHelper.compute_image_padding_mask(features, \n",
    "                                                       original_widths, \n",
    "                                                       downsampling_factor=self.encoder.downsampling_factor)  # generate mask for features\n",
    "        img_pad_mask = torch.flatten(img_pad_mask, start_dim=1, end_dim=2)  # flatten mask for transformer requirements (B, Li)\n",
    "        \n",
    "        # Decoder part (iterative)\n",
    "        # The iterative prediction process can be parallelized at training time through teacher forcing, but not at evaluation time\n",
    "        if tgt is None:\n",
    "            # eval mode  \n",
    "            B = x.size(0)\n",
    "            tgt = torch.full((B, 1), dtype=torch.long, fill_value=self.token_set.index(\"<b>\"))  # start with the <b> token to initiate the decoding stage\n",
    "            tgt_lengths = [1 for _ in range(B)]  # keep track for prediction lengths\n",
    "            for i in range(1, self.max_preds+1):  # repeat the decoding step x times\n",
    "                output, attn_weights = self.decode(tgt, tgt_lengths, flattened_features, img_pad_mask)  # perform one decoding step\n",
    "                new_pred = torch.argmax(output[:, -1], dim=-1, keepdim=True)  # get the most probable token\n",
    "                tgt = torch.cat([tgt, new_pred], dim=-1)  # add this most probable token to the target/query sequence\n",
    "                for j in range(B):  # check for each sample in the mini-batch\n",
    "                    if tgt_lengths[j] == i and new_pred[j].item() != self.token_set.index(\"<e>\"):  # increase prediction length only if sequence is not already ended (a <e> token has been predicted)\n",
    "                        tgt_lengths[j] += 1\n",
    "            \n",
    "            # Reshape attention weights to get back in 2 dimensions\n",
    "            B, _, H, W = x.size()\n",
    "            _, Lt, _ = attn_weights.size() # (B, Lt, Li)\n",
    "            attn_weights = attn_weights.reshape(B, Lt, H//self.encoder.downsampling_factor, W//self.encoder.downsampling_factor) # (B, Lt, H/16, W/16)\n",
    "\n",
    "            return tgt, tgt_lengths, attn_weights\n",
    "        else:  \n",
    "            # Training mode\n",
    "            # All predictions are performed at once based on the ground truth, i.e., we use the ground truth as if it was what we predicted so far\n",
    "            return self.decode(tgt, tgt_lengths, flattened_features, img_pad_mask)\n",
    "        \n",
    "    def generate_positional_embedding(self, batch_size, length):\n",
    "        \"\"\"\n",
    "        Compute a positional embedding tensor for position starting from 0 to length-1\n",
    "        The same positions are used for each sample in the mini-batch.\n",
    "        \"\"\"\n",
    "        positions = torch.arange(0, length)  # [0,1,2,..., length-1]\n",
    "        batch_positions = torch.repeat_interleave(positions.unsqueeze(1), repeats=batch_size, dim=1)  # (length, batch_size)\n",
    "        batched_position_emb = self.positional_encoding(batch_positions)  # (length, batch_size, dim)\n",
    "        return batched_position_emb\n",
    "        \n",
    "\n",
    "    def decode(self, tgt, tgt_lengths, features, img_pad_mask):\n",
    "        \"\"\"\n",
    "        tgt: tensor of token indices (B, Lt)\n",
    "        tgt_lengths: list of int\n",
    "        features: flattened image features extracted from the encoder (Li, B, C)\n",
    "        img_pad_mask: bool mask tensor for the flattened features (B, Li)\n",
    "\n",
    "        Returns a tuple:\n",
    "        - the predictions of size (B, Lt, N)\n",
    "        - the attention weights of size (B, Lt, Li)\n",
    "        \"\"\"\n",
    "\n",
    "        # Token indices to token embeddings\n",
    "        tgt = self.emb_layer(tgt) #  (B, Lt, C)\n",
    "        tgt = tgt.permute(1, 0, 2) # (Lt, B, C)\n",
    "\n",
    "        # Adding positional encoding to token embedding\n",
    "        Lt, B, _ = tgt.size()\n",
    "        tgt_positional_embedding = self.generate_positional_embedding(B, Lt)\n",
    "        tgt = tgt + tgt_positional_embedding\n",
    "\n",
    "        # Compute tgt masks\n",
    "        tgt_pad_mask = MaskHelper.compute_tgt_padding_mask(tgt, tgt_lengths)  # (B, Lt)\n",
    "        tgt_attn_mask = MaskHelper.compute_tgt_attn_mask(tgt)  # (Lt, Lt)\n",
    "\n",
    "        # Apply transformer decoder layer\n",
    "        output, attn_weights = self.decoder(tgt=tgt,\n",
    "                                memory=features,\n",
    "                                tgt_mask=tgt_attn_mask,\n",
    "                                tgt_key_padding_mask=tgt_pad_mask,\n",
    "                                memory_mask=None,\n",
    "                                memory_key_padding_mask=img_pad_mask)\n",
    "        # output (Lt, B, C)\n",
    "        # attn_weights (B, Lt, Li)\n",
    "\n",
    "        # Prediction\n",
    "        output = output.permute(1, 0, 2) # (B, Lt, C)\n",
    "        output = self.decision_layer(output) # (B, Lt, N)\n",
    "\n",
    "        assert not torch.any(torch.isnan(output))  # check that everything worked fine\n",
    "        return output, attn_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "\n",
    "We now have a dataset and a network. Last stage is to train the network on that dataset!  \n",
    "\n",
    "TODO: implement the *accuracy* function to compute the top-1 accuracy metric, carefully ignoring padding tokens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EPOCH 0 - loss: 2.4034 ; acc : 15.82: 100%|██████████| 2/2 [00:00<00:00,  2.40it/s]\n",
      "EPOCH 1 - loss: 2.2566 ; acc : 25.44: 100%|██████████| 2/2 [00:00<00:00,  4.27it/s]\n",
      "EPOCH 2 - loss: 2.1683 ; acc : 30.43: 100%|██████████| 2/2 [00:00<00:00,  4.15it/s]\n",
      "EPOCH 3 - loss: 2.0881 ; acc : 31.65: 100%|██████████| 2/2 [00:00<00:00,  3.87it/s]\n",
      "EPOCH 4 - loss: 2.0186 ; acc : 34.51: 100%|██████████| 2/2 [00:00<00:00,  4.19it/s]\n",
      "EPOCH 5 - loss: 1.9337 ; acc : 37.24: 100%|██████████| 2/2 [00:00<00:00,  4.39it/s]\n",
      "EPOCH 6 - loss: 1.8168 ; acc : 39.33: 100%|██████████| 2/2 [00:00<00:00,  4.17it/s]\n",
      "EPOCH 7 - loss: 1.7098 ; acc : 43.57: 100%|██████████| 2/2 [00:00<00:00,  3.81it/s]\n",
      "EPOCH 8 - loss: 1.6058 ; acc : 47.46: 100%|██████████| 2/2 [00:00<00:00,  3.33it/s]\n",
      "EPOCH 9 - loss: 1.5080 ; acc : 51.07: 100%|██████████| 2/2 [00:00<00:00,  3.27it/s]\n",
      "EPOCH 10 - loss: 1.4136 ; acc : 55.12: 100%|██████████| 2/2 [00:00<00:00,  3.13it/s]\n",
      "EPOCH 11 - loss: 1.3333 ; acc : 58.09: 100%|██████████| 2/2 [00:00<00:00,  3.02it/s]\n",
      "EPOCH 12 - loss: 1.2373 ; acc : 60.90: 100%|██████████| 2/2 [00:00<00:00,  3.30it/s]\n",
      "EPOCH 13 - loss: 1.1644 ; acc : 62.75: 100%|██████████| 2/2 [00:00<00:00,  4.10it/s]\n",
      "EPOCH 14 - loss: 1.0800 ; acc : 65.90: 100%|██████████| 2/2 [00:00<00:00,  4.24it/s]\n",
      "EPOCH 15 - loss: 1.0019 ; acc : 68.90: 100%|██████████| 2/2 [00:00<00:00,  4.01it/s]\n",
      "EPOCH 16 - loss: 0.9238 ; acc : 70.78: 100%|██████████| 2/2 [00:00<00:00,  4.02it/s]\n",
      "EPOCH 17 - loss: 0.8475 ; acc : 74.45: 100%|██████████| 2/2 [00:00<00:00,  3.87it/s]\n",
      "EPOCH 18 - loss: 0.7769 ; acc : 76.25: 100%|██████████| 2/2 [00:00<00:00,  4.31it/s]\n",
      "EPOCH 19 - loss: 0.6981 ; acc : 78.88: 100%|██████████| 2/2 [00:00<00:00,  3.35it/s]\n",
      "EPOCH 20 - loss: 0.6247 ; acc : 82.05: 100%|██████████| 2/2 [00:00<00:00,  2.94it/s]\n",
      "EPOCH 21 - loss: 0.5620 ; acc : 83.62: 100%|██████████| 2/2 [00:00<00:00,  3.56it/s]\n",
      "EPOCH 22 - loss: 0.5027 ; acc : 85.67: 100%|██████████| 2/2 [00:00<00:00,  3.77it/s]\n",
      "EPOCH 23 - loss: 0.4442 ; acc : 87.79: 100%|██████████| 2/2 [00:00<00:00,  3.41it/s]\n",
      "EPOCH 24 - loss: 0.4000 ; acc : 89.30: 100%|██████████| 2/2 [00:00<00:00,  3.05it/s]\n",
      "EPOCH 25 - loss: 0.3458 ; acc : 91.20: 100%|██████████| 2/2 [00:00<00:00,  4.10it/s]\n",
      "EPOCH 26 - loss: 0.3061 ; acc : 92.54: 100%|██████████| 2/2 [00:00<00:00,  3.72it/s]\n",
      "EPOCH 27 - loss: 0.2655 ; acc : 93.73: 100%|██████████| 2/2 [00:00<00:00,  2.88it/s]\n",
      "EPOCH 28 - loss: 0.2297 ; acc : 94.68: 100%|██████████| 2/2 [00:00<00:00,  3.01it/s]\n",
      "EPOCH 29 - loss: 0.1994 ; acc : 95.77: 100%|██████████| 2/2 [00:00<00:00,  3.72it/s]\n",
      "EPOCH 30 - loss: 0.1688 ; acc : 96.62: 100%|██████████| 2/2 [00:00<00:00,  3.16it/s]\n",
      "EPOCH 31 - loss: 0.1478 ; acc : 97.47: 100%|██████████| 2/2 [00:00<00:00,  3.21it/s]\n",
      "EPOCH 32 - loss: 0.1254 ; acc : 98.19: 100%|██████████| 2/2 [00:00<00:00,  3.34it/s]\n",
      "EPOCH 33 - loss: 0.1084 ; acc : 98.67: 100%|██████████| 2/2 [00:00<00:00,  3.14it/s]\n",
      "EPOCH 34 - loss: 0.0918 ; acc : 99.13: 100%|██████████| 2/2 [00:00<00:00,  3.78it/s]\n",
      "EPOCH 35 - loss: 0.0807 ; acc : 99.21: 100%|██████████| 2/2 [00:00<00:00,  4.20it/s]\n",
      "EPOCH 36 - loss: 0.0669 ; acc : 99.60: 100%|██████████| 2/2 [00:00<00:00,  4.32it/s]\n",
      "EPOCH 37 - loss: 0.0591 ; acc : 99.63: 100%|██████████| 2/2 [00:00<00:00,  3.97it/s]\n",
      "EPOCH 38 - loss: 0.0500 ; acc : 99.75: 100%|██████████| 2/2 [00:00<00:00,  4.14it/s]\n",
      "EPOCH 39 - loss: 0.0442 ; acc : 99.78: 100%|██████████| 2/2 [00:00<00:00,  4.42it/s]\n"
     ]
    }
   ],
   "source": [
    "from torch.optim import Adam\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "def accuracy(x, y, padding_value):\n",
    "    \"\"\"\n",
    "    Compute the top-1 accuracy between predictions x (B, L, N) and ground truth y (L, N) \n",
    "    B: mini-batch size; L: target sequence length; N: token set size\n",
    "    \"\"\"\n",
    "    mask = y != padding_value  # ignoring padding tokens\n",
    "    top1_preds = torch.argmax(x, dim=-1)\n",
    "    return torch.mean(top1_preds[mask] == y[mask], dtype=torch.float).item()\n",
    "\n",
    "def train(model, dataloader, num_epochs=40):\n",
    "    \"\"\"\n",
    "    Training loop\n",
    "    \"\"\"\n",
    "    optimizer=Adam(model.parameters(), lr=0.001)\n",
    "    loss_fn = CrossEntropyLoss(ignore_index=MNISTDataset.pad_label_value)\n",
    "    model.train()\n",
    "    for i in range(num_epochs):\n",
    "        losses = []\n",
    "        accuracies = []\n",
    "        progress_bar = tqdm(dataloader)\n",
    "        for batch_data in progress_bar:\n",
    "            # remove previous gradients (from previous mini-batch)\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # get data of current mini-batch\n",
    "            x = batch_data[\"imgs\"]\n",
    "            y = batch_data[\"labels\"]\n",
    "            widths = batch_data[\"original_widths\"]\n",
    "            lens = batch_data[\"label_lengths\"]\n",
    "\n",
    "            # forward pass\n",
    "            output, _ = model(x, original_widths=widths, tgt=y[:, :-1], tgt_lengths=lens)\n",
    "\n",
    "            # loss computation\n",
    "            loss = loss_fn(output.permute(0, 2, 1), y[:, 1:])\n",
    "\n",
    "            # backward pass\n",
    "            loss.backward()\n",
    "\n",
    "            # weight update\n",
    "            optimizer.step()\n",
    "            \n",
    "            # display loss and metric\n",
    "            losses.append(loss.item())\n",
    "            accuracies.append(accuracy(output, y[:, 1:], MNISTDataset.pad_label_value))\n",
    "            progress_bar.set_description(f\"EPOCH {i} - loss: {np.mean(losses):.4f} ; acc : {np.mean(accuracies)*100:.2f}\")\n",
    "\n",
    "model = I2SModel(train_dataset.token_set)\n",
    "train(model, train_dataloader)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction\n",
    "\n",
    "The network should reach nearly perfect results on the training set. Let's make a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21269/2784281161.py:12: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  data = torch.load(filepath)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 37427\n"
     ]
    }
   ],
   "source": [
    "def display_image(x):\n",
    "    plt.subplots()\n",
    "    plt.imshow(x[0], cmap=\"gray\")\n",
    "    plt.show()\n",
    "\n",
    "def predict(model, x):\n",
    "    model.eval()\n",
    "    x = x.unsqueeze(0)\n",
    "    widths = [x.size(3)]\n",
    "    output, tgt_lengths, attn_weights = model(x, widths)\n",
    "    output = output[0][:tgt_lengths[0]+1]\n",
    "    token_pred = train_dataset.decode_tokens(output)\n",
    "    str_pred = \"\".join([p for p in token_pred if p != \"<e>\"])\n",
    "    return token_pred, str_pred, attn_weights[0]\n",
    "\n",
    "test_dataset = MNISTDataset(set_name=\"test\", filepath=\"./mnist_variable_len_1k.data\")\n",
    "data = test_dataset[3]\n",
    "x = data[\"input_img\"]\n",
    "display_image(x)\n",
    "token_pred, str_pred, attn_weights = predict(model, x)\n",
    "print(f\"Prediction: {str_pred}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "\n",
    "Now, let's analyze the attention weights, iteration per iteration.  \n",
    "\n",
    "Try with some examples. What kinds of error does the model make?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3000x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "def show_iterations(img, weights, preds):\n",
    "    \"\"\"\n",
    "    img: Tensor of size (1, H, W)\n",
    "    weights: Tensor of size (L, H, W)\n",
    "    preds: String of length L\n",
    "    \"\"\"   \n",
    "    img = img.detach().numpy()[0]\n",
    "    weights = [cv2.resize(w.detach().numpy(), (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC) for w in weights]\n",
    "    preds = list(preds)\n",
    "    num_preds = len(preds)\n",
    "    fig, axes = plt.subplots(nrows=num_preds, figsize=(5*num_preds,10))\n",
    "\n",
    "    if not isinstance(axes, np.ndarray):\n",
    "        axes = [axes]\n",
    "\n",
    "    for i in range(num_preds):\n",
    "        axes[i].imshow(img, alpha=0.5)\n",
    "        attn_map = axes[i].imshow(weights[i], cmap='viridis', alpha=0.6)\n",
    "        axes[i].set_title(f\"Prediction #{i+1}: '{preds[i]}'\")\n",
    "        plt.colorbar(attn_map)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "show_iterations(x, attn_weights, token_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 00285\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3000x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prediction #2, #3 an #5: confusing digit with another\n",
    "# Prediction #4: attention is split on two different \"8\" digits (good prediction, wrong method)\n",
    "data = test_dataset[0]\n",
    "x = data[\"input_img\"]\n",
    "token_pred, str_pred, attn_weights = predict(model, x)\n",
    "print(f\"Prediction: {str_pred}\")\n",
    "show_iterations(x, attn_weights, token_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 3333\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looping several times on the same digit\n",
    "data = test_dataset[1]\n",
    "x = data[\"input_img\"]\n",
    "token_pred, str_pred, attn_weights = predict(model, x)\n",
    "print(f\"Prediction: {str_pred}\")\n",
    "show_iterations(x, attn_weights, token_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To go further: attention implementation\n",
    "\n",
    "Reminder: the multi-head attention is defined as follows:\n",
    "\n",
    "$$Q_1 = XW^Q_1 \\quad \\ldots \\quad Q_h = XW^Q_h$$\n",
    "$$K_1 = YW^K_1 \\quad \\ldots \\quad K_h = YW^K_h$$\n",
    "$$V_1 = YW^V_1 \\quad \\ldots \\quad V_h = YW^V_h$$\n",
    "\n",
    "$$O_1 = \\text{softmax} \\left(\\frac{Q_1K_1^T}{\\sqrt{d}}\\right) V_1 \\quad \\ldots \\quad O_h = \\text{softmax}\\left(\\frac{Q_hK_h^T}{\\sqrt{d}}\\right) V_h$$\n",
    "\n",
    "$$Y = \\text{concat}(O_1, \\ldots, O_h)W^O$$\n",
    "\n",
    "$d$ refers to the number of dimensions associated to one head.\n",
    "\n",
    "where $X=Y$ for self-attention.  \n",
    "\n",
    "It has to be noted that the query/key/value projections of all heads can be computed in parallel among the same densely-connected layer for computation efficiency.\n",
    "\n",
    "TODO: complete the following class which implements this attention mechanism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Linear, Module\n",
    "\n",
    "\n",
    "class TransformerAttention(Module):\n",
    "\n",
    "    def __init__(self, dim, num_heads):\n",
    "        \"\"\"\n",
    "        dim: total number of dimensions C\n",
    "        num_heads: number of attention heads h\n",
    "        Each head must have the same number of dimensions d\n",
    "        The number of dimensions is preserved from input to output\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        assert dim % num_heads == 0, 'dim should be divisible by num_heads'\n",
    "        self.num_heads = num_heads\n",
    "        self.head_dim = dim // num_heads\n",
    "        self.scale_factor = self.head_dim ** -0.5\n",
    "\n",
    "        # head projections can be merged in a single Linear to parallelize computations\n",
    "        self.q_proj = Linear(dim, dim, bias=False)\n",
    "        self.k_proj = Linear(dim, dim, bias=False)\n",
    "        self.v_proj = Linear(dim, dim, bias=False)\n",
    "\n",
    "        self.out_proj = Linear(dim, dim, bias=False)  # W^o\n",
    "\n",
    "    def forward(self, query_seq, key_seq, value_seq, key_padding_mask=None, attn_mask=None):\n",
    "        \"\"\"\n",
    "        Input:  query_seq (B, L_t, C)\n",
    "                key_seq (B, L_s, C)\n",
    "                value_seq (B, L_s, C)\n",
    "                key_padding_mask (B, L_s) = which token to consider in source, to ignore padding\n",
    "                attn_mask (L_t, L_s) = which source token to consider for each target token, to ignore future prediction with teacher forcing\n",
    "        Output: (B, L_t, C)\n",
    "        \"\"\"\n",
    "        B, target_len, C = query_seq.size()\n",
    "        source_len = key_seq.size(1)\n",
    "\n",
    "        #1 Projection must lead to Q (B, L_t, C), K (B, L_s, C), V (B, L_s, C)\n",
    "        q = self.q_proj(query_seq)\n",
    "        k = self.k_proj(key_seq)\n",
    "        v = self.v_proj(value_seq)\n",
    "\n",
    "        #2 We will treat all heads in parallel through batch: it must lead to Q (B*h, L_t, d), K (B*h, L_s, d), V (B*h, L_s, d)\n",
    "        #Be careful with data order, do not mix representations of different tokens together\n",
    "        # Hint: you will need permute and reshape functions\n",
    "        q = q.permute(0, 2, 1) # Q (B, C, L_t)\n",
    "        q = q.reshape(B*self.num_heads, self.head_dim, target_len) # Q (B*h, d, L_t)\n",
    "        q = q.permute(0, 2, 1) # Q (B*h, L_t, d)\n",
    "\n",
    "        k = k.permute(0, 2, 1).reshape(B*self.num_heads, self.head_dim, source_len).permute(0, 2, 1) # K (B*h, L_s, d)\n",
    "        v = v.permute(0, 2, 1).reshape(B*self.num_heads, self.head_dim, source_len).permute(0, 2, 1) # V (B*h, L_s, d)\n",
    "\n",
    "        #3 Compute scores S=QK/srqt(d), leading to S (B*h, L_t, L_s)\n",
    "        # S[i, j, k] = how well token j matches with token k with respect to sample/head i\n",
    "        q = q * self.scale_factor\n",
    "        attn_weights = torch.bmm(q, k.transpose(-2, -1))\n",
    "\n",
    "        #4 We need to take into account padded sequences\n",
    "        # Score for padding token must be set to -Inf to be ignored through the softmax operation\n",
    "        # Hint: use masked_fill function\n",
    "        if key_padding_mask is not None:\n",
    "            attn_weights = attn_weights.view(B, self.num_heads, target_len, source_len)\n",
    "            attn_weights = attn_weights.masked_fill(\n",
    "                key_padding_mask.unsqueeze(1).unsqueeze(2),\n",
    "                float(\"-inf\"),\n",
    "            )\n",
    "            attn_weights = attn_weights.view(B * self.num_heads, target_len, source_len)\n",
    "\n",
    "        #5 We need to take into account the causality of the decoder part\n",
    "        #The model should not use tokens that have not yet been processed\n",
    "        if attn_mask is not None:\n",
    "            attn_mask = attn_mask.unsqueeze(0)\n",
    "            attn_weights.masked_fill_(attn_mask, float(\"-inf\"))\n",
    "\n",
    "        #6 Apply softmax to get attention weights alpha (B*h, L_t, L_s)\n",
    "        attn_weights = attn_weights.softmax(dim=-1)\n",
    "\n",
    "        #7 Computation of alpha·V (B*h, L_t, d)\n",
    "        x = torch.bmm(attn_weights, v)\n",
    "\n",
    "        #8 Dissociate heads for projection\n",
    "        x = x.permute(0, 2, 1).reshape(B, C, target_len).permute(0, 2, 1) # (B, L_t, C=d*h)\n",
    "        x = self.out_proj(x)\n",
    "        return attn_weights, x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that it provides the same behavior that the pytorch implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import MultiheadAttention\n",
    "\n",
    "B = 2\n",
    "L = 10\n",
    "C = 64\n",
    "H = 2\n",
    "\n",
    "# Initialize modules\n",
    "custom_attn = TransformerAttention(dim=C, num_heads=H)\n",
    "torch_attn = MultiheadAttention(embed_dim=C, num_heads=H, bias=False, add_bias_kv=False, batch_first=True)\n",
    "\n",
    "# Ensure they have same weights\n",
    "torch_state_dict = torch_attn.state_dict()\n",
    "custom_state_dict = {\n",
    "    \"q_proj.weight\": torch_state_dict[\"in_proj_weight\"][:C],\n",
    "    \"k_proj.weight\": torch_state_dict[\"in_proj_weight\"][C:2*C],\n",
    "    \"v_proj.weight\": torch_state_dict[\"in_proj_weight\"][2*C:],\n",
    "    \"out_proj.weight\": torch_state_dict[\"out_proj.weight\"]\n",
    "}\n",
    "custom_attn.load_state_dict(custom_state_dict)\n",
    "\n",
    "# Generate fake example\n",
    "fake_sample = torch.randn((B, L, C), dtype=torch.float)\n",
    "padding_mask = torch.zeros((B, L), dtype=torch.bool)\n",
    "padding_mask[0, -2:] = True\n",
    "mask = torch.zeros((L, L), dtype=torch.bool)\n",
    "mask[0, 7] = mask[1, 1] = True\n",
    "\n",
    "# forward pass\n",
    "out_torch, weights_torch = torch_attn(fake_sample, fake_sample, fake_sample, key_padding_mask=padding_mask, attn_mask=mask)\n",
    "weights_custom, out_custom = custom_attn(fake_sample, fake_sample, fake_sample, key_padding_mask=padding_mask, attn_mask=mask)\n",
    "\n",
    "# check equal\n",
    "assert torch.allclose(out_torch, out_custom, atol=1e-6)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "course_DLV",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}