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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Transformer From Scratch\n",
    "\n",
    "Implement the [Attention Is All You Need](https://arxiv.org/abs/1706.03762) paper from scratch using PyTorch, focusing on building a Seq2Seq Transformer architecture for translating text from English to Italian."
   ],
   "metadata": {
    "id": "uNugwGz8D2VT"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -r requirements.txt"
   ],
   "metadata": {
    "id": "3sMY6zG2bHep"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import math\n",
    "from dataclasses import dataclass, asdict\n",
    "from time import time\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam\n",
    "from torch.utils.data import DataLoader\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoTokenizer\n",
    "from sklearn.metrics import f1_score\n",
    "import pandas as pd"
   ],
   "metadata": {
    "id": "ehiYYIMgbNnR"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Embedding\n",
    "Mapping input token indices to vectors, transforming discrete tokens into continuous numerical representations.\n",
    "\n",
    "* **Initialization**\n",
    "  * `vocab_size`: the number of unique tokens in the vocabulary.\n",
    "  * `d_model`: the embedding dimension.\n",
    "\n",
    "* **Forward**:\n",
    "  * `sequence`: input token indices.\n",
    "  * `returns`: embedding vector corresponding to the input tokens.\n"
   ],
   "metadata": {
    "id": "QIuklCDOsKM1"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class Embedding(nn.Module):\n",
    "  def __init__(self, vocab_size, d_model):\n",
    "    super().__init__()\n",
    "\n",
    "    self.embedding = nn.Embedding(vocab_size, d_model)\n",
    "\n",
    "  def forward(self, sequence):\n",
    "    return self.embedding(sequence)"
   ],
   "metadata": {
    "id": "JYx1ALU7sOxC"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Positional Encoding\n",
    "Injects positional information into the input embeddings to capture the order of tokens in the sequence.\n",
    "\n",
    "* **Initialization**:\n",
    "  * `max_sequence_length`: the maximum length of the input sequences.\n",
    "  * `d_model`: the embedding dimension.\n",
    "  \n",
    "* **Forward**:\n",
    "  * `embedding`: the input embedded tokens.\n",
    "  * `returns`: the sum of the input embeddings & the positional encodings."
   ],
   "metadata": {
    "id": "dyrnFeD7sPJ7"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "  def __init__(self,max_sequence_length, d_model):\n",
    "    super().__init__()\n",
    "\n",
    "    pe = torch.zeros(max_sequence_length, d_model)\n",
    "    position = torch.arange(0, max_sequence_length, dtype=torch.float).unsqueeze(1)\n",
    "\n",
    "    _2i = torch.arange(0, d_model, 2, dtype=torch.float)\n",
    "    scaler = torch.exp(-_2i * (math.log(10000) / d_model))\n",
    "\n",
    "    pe[:, 0::2] = torch.sin(position * scaler)\n",
    "    pe[:, 1::2] = torch.cos(position * scaler)\n",
    "\n",
    "    self.register_buffer(\"pe\", pe.unsqueeze(0))\n",
    "\n",
    "\n",
    "  def forward(self, embedding):\n",
    "      return embedding + self.pe[:, :embedding.size(1)]"
   ],
   "metadata": {
    "id": "x1NbzI0EsTL_"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Multi Head Self Attention\n",
    "\n",
    "\n",
    "Implements the multi-head attention mechanism, allowing the model to focus on different parts of the input sequence in parallel.\n",
    "\n",
    "* **Initialization**:\n",
    "  * `d_model`: the input & output vectors dimensionality.\n",
    "  * `h`: the number of attention heads.\n",
    "\n",
    "* **Methods**:\n",
    "  * `split()`: splits the input matrix into multiple heads.\n",
    "  * `attention()`: computes the scaled dot-product attention.\n",
    "  * `concatenate()`: concatenates the attention heads.\n",
    "    \n",
    "* **Forward**:\n",
    "    * `Q`: the queries matrix.\n",
    "    * `K`: the keys matrix.\n",
    "    * `V`: the values matrix.\n",
    "    * `mask`: the optional attention mask.\n",
    "    * `returns`: output matrix after applying multi-head attention & linear projection.\n"
   ],
   "metadata": {
    "id": "GINq_nKgD2h3"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "  def __init__(self, d_model, h) -> None:\n",
    "    super().__init__()\n",
    "\n",
    "    assert d_model % h == 0, \"d_model must be divisible by h\"\n",
    "\n",
    "    self.d_model = d_model\n",
    "    self.h = h\n",
    "    self.d_k = d_model // h\n",
    "\n",
    "    self.W_q = nn.Linear(d_model, d_model)\n",
    "    self.W_k = nn.Linear(d_model, d_model)\n",
    "    self.W_v = nn.Linear(d_model, d_model)\n",
    "    self.W_o = nn.Linear(d_model, d_model)\n",
    "\n",
    "\n",
    "  def split(self, M):\n",
    "    batch_size, sequence_length, d_model = M.size()\n",
    "\n",
    "    splitted_heads = M.view(batch_size, sequence_length, self.h, self.d_k).transpose(1, 2)\n",
    "\n",
    "    return splitted_heads\n",
    "\n",
    "\n",
    "  def attention(self, Q, K, V, mask=None):\n",
    "    scaled_attention = torch.matmul(input=Q, other=K.transpose(-1, -2))\n",
    "\n",
    "    scaled_attention_score = scaled_attention / math.sqrt(self.d_k)\n",
    "\n",
    "    if mask is not None:\n",
    "      scaled_attention_score.masked_fill(mask == 0, float(\"-inf\"))\n",
    "\n",
    "    attention_weights = torch.softmax(input=scaled_attention_score, dim=-1)\n",
    "\n",
    "    weighted_sum = torch.matmul(input=attention_weights, other=V)\n",
    "\n",
    "    return weighted_sum\n",
    "\n",
    "\n",
    "  @staticmethod\n",
    "  def concatenate(M):\n",
    "    batch_size, h, sequence_length, d_k = M.size()\n",
    "\n",
    "    concatenated_heads = M.transpose(1, 2).contiguous().view(batch_size, sequence_length, h * d_k)\n",
    "\n",
    "    return concatenated_heads\n",
    "\n",
    "\n",
    "  def forward(self, Q, K, V, mask=None):\n",
    "    Q = self.W_q(Q)\n",
    "    K = self.W_k(K)\n",
    "    V = self.W_v(V)\n",
    "\n",
    "    Q = self.split(Q)\n",
    "    K = self.split(K)\n",
    "    V = self.split(V)\n",
    "\n",
    "    weighted_sum = self.attention(Q, K, V, mask)\n",
    "\n",
    "    concatenated_heads = self.concatenate(weighted_sum)\n",
    "\n",
    "    output = self.W_o(concatenated_heads)\n",
    "\n",
    "    return output"
   ],
   "metadata": {
    "id": "SDrvXX_OdMDz"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Position Wise Feed Forward Network\n",
    "\n",
    "Implements a feed-forward network applied to each position separately.\n",
    "\n",
    "* **Initialization**:\n",
    "  * `d_model`: the input & output vectors dimensions.\n",
    "  * `d_ff`: the feed-forward layer's dimension.\n",
    "\n",
    "* **Forward**:\n",
    "  * `x`: the input matrix.\n",
    "  * `returns`: output matrix with the same shape as input after applying the feed-forward network.\n"
   ],
   "metadata": {
    "id": "N5AuUv89D2kS"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class PositionWiseFeedForward(nn.Module):\n",
    "  def __init__(self, d_model, d_ff):\n",
    "    super().__init__()\n",
    "\n",
    "    self.layer1 = nn.Linear(d_model, d_ff)\n",
    "    self.layer2 = nn.Linear(d_ff, d_model)\n",
    "\n",
    "    self.activation = nn.ReLU()\n",
    "\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.layer1(x)\n",
    "    x = self.activation(x)\n",
    "    x = self.layer2(x)\n",
    "\n",
    "    return x"
   ],
   "metadata": {
    "id": "6A_RRlaadMkO"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Encoder\n",
    "\n",
    "Gathering the encoder component, which processes input sequences through multi-head self-attention & position-wise feed-forward networks.\n",
    "\n",
    "* **Initialization**:\n",
    "  * `d_model`: the input & output vectors dimensions.\n",
    "  * `d_ff`:the feed-forward layer's dimension.\n",
    "  * `h`: the number of attention heads.\n",
    "  * `dropout`: the dropout rate for regularization.\n",
    "\n",
    "* **Components**:\n",
    "  * `self_attention`: the multi-head self-attention sub-layer.\n",
    "  * `feed_forward`: the position-wise feed forward sub-layer.\n",
    "  * `norm1`: a normalization layer applied after the self-attention sub-layer.\n",
    "  * `norm2`: a normalization layer applied after the feed-forward sub-layer.\n",
    "  * `dropout`: the dropout layer applied to the outputs of each sub-layer.\n",
    "\n",
    "* **Forward**:\n",
    "  * `x`: the input matrix.\n",
    "  * `mask`: the optional attention mask.\n",
    "  * `returns`: output matrix with the same shape as the input, after applying self-attention, feed-forward, dropout, and normalization.\n"
   ],
   "metadata": {
    "id": "0jTLE3xGs27-"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class Encoder(nn.Module):\n",
    "  def __init__(self, d_model, d_ff, h, dropout):\n",
    "    super().__init__()\n",
    "\n",
    "    self.self_attention = MultiHeadAttention(d_model, h)\n",
    "    self.feed_forward = PositionWiseFeedForward(d_model, d_ff)\n",
    "\n",
    "    self.norm1 = nn.LayerNorm(d_model)\n",
    "    self.norm2 = nn.LayerNorm(d_model)\n",
    "    self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "\n",
    "  def forward(self, x, mask):\n",
    "    self_attention_output = self.dropout(self.self_attention(x, x, x, mask))\n",
    "    x = self.norm1(x + self_attention_output)\n",
    "\n",
    "    feed_forward_output = self.dropout(self.feed_forward(x))\n",
    "    x = self.norm2(x + feed_forward_output)\n",
    "\n",
    "    return x"
   ],
   "metadata": {
    "id": "ZtTlXqkos8GC"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Decoder\n",
    "\n",
    "Gathering the decoder component, which processes the target sequences through multi-head self-attention & cross-attention with the encoder output, & position-wise feed-forward networks.\n",
    "\n",
    "* **Initialization**:\n",
    "  * `d_model`: the input & output vectors dimensions.\n",
    "  * `d_ff`: the feed-forward layer's dimension.\n",
    "  * `h`: the number of attention heads.\n",
    "  * `dropout`: the dropout rate for regularization.\n",
    "\n",
    "* **Components**:\n",
    "  * `self_attention`: the multi-head self-attention sub-layer.\n",
    "  * `cross_attention`: the multi-head attention sub-layer that attends to the encoder's output.\n",
    "  * `feed_forward`: the position-wise feed forward sub-layer.\n",
    "  * `norm1`: a normalization layer applied after the self-attention sub-layer.\n",
    "  * `norm2`: a normalization layer applied after the cross-attention sub-layer.\n",
    "  * `norm3`: a normalization layer applied after the feed-forward sub-layer.\n",
    "  * `dropout`: the dropout layer applied to the outputs of each sub-layer.\n",
    "\n",
    "* **Forward**:\n",
    "  * `x`: the input matrix (decoder input).\n",
    "  * `encoder_output`: the output matrix from the encoder.\n",
    "  * `encoder_mask`: the attention mask for the encoder's output.\n",
    "  * `decoder_mask`: the attention mask for the decoder's self-attention.\n",
    "  * `returns`: output matrix with the same shape as the input, after applying self-attention, cross-attention, feed-forward, dropout, and normalization.\n"
   ],
   "metadata": {
    "id": "nUQBmvFgs3Eu"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class Decoder(nn.Module):\n",
    "  def __init__(self, d_model, d_ff, h, dropout):\n",
    "    super().__init__()\n",
    "\n",
    "    self.self_attention = MultiHeadAttention(d_model, h)\n",
    "    self.cross_attention = MultiHeadAttention(d_model, h)\n",
    "    self.feed_forward = PositionWiseFeedForward(d_model, d_ff)\n",
    "\n",
    "    self.norm1 = nn.LayerNorm(d_model)\n",
    "    self.norm2 = nn.LayerNorm(d_model)\n",
    "    self.norm3 = nn.LayerNorm(d_model)\n",
    "    self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "\n",
    "  def forward(self, x, encoder_output, encoder_mask, decoder_mask):\n",
    "    self_attention_output = self.dropout(self.self_attention(x, x, x, decoder_mask))\n",
    "    x = self.norm1(x + self_attention_output)\n",
    "\n",
    "    cross_attention_output = self.dropout(self.self_attention(x, encoder_output, encoder_output, encoder_mask))\n",
    "    x = self.norm2(x + cross_attention_output)\n",
    "\n",
    "    feed_forward_output = self.dropout(self.feed_forward(x))\n",
    "    x = self.norm3(x + feed_forward_output)\n",
    "\n",
    "    return x"
   ],
   "metadata": {
    "id": "rfzItaTOs8t_"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Transformer\n",
    "\n",
    "Implements the transformer model, integrating encoder and decoder components with embeddings, positional encodings, and masking.\n",
    "\n",
    "* **Initialization**:\n",
    "  * `source_pad_idx`: padding token index for source sequences.\n",
    "  * `target_pad_idx`: padding token index for target sequences.\n",
    "  * `source_vocab_size`: vocabulary size for source language.\n",
    "  * `target_vocab_size`: vocabulary size for target language.\n",
    "  * `h`: the number of attention heads.\n",
    "  * `n`: the number of encoder and decoder layers.\n",
    "  * `d_model`: the input & output vectors dimensions.\n",
    "  * `d_ff`: the feed-forward layer's dimension.\n",
    "  * `max_sequence_length`: the maximum sequence length of the input sequences for positional encoding.\n",
    "  * `dropout`: the dropout rate for regularization.\n",
    "\n",
    "* **Components**:\n",
    "  * `encoder_embedding`: embedding layer for source sequences.\n",
    "  * `decoder_embedding`: embedding layer for target sequences.\n",
    "  * `positional_encoding`: adds positional information to embeddings.\n",
    "  * `encoders`: stack of encoder layers.\n",
    "  * `decoders`: stack of decoder layers.\n",
    "  * `pre_softmax`: linear projection layer that produces logits.\n",
    "  * `dropout`: the dropout layer applied to embeddings.\n",
    "\n",
    "* **Methods**:\n",
    "  * `masking()`: generates source and target masks for attention.\n",
    "    \n",
    "* **Forward**:\n",
    "  * `source_sequence`: the source input sequence.\n",
    "  * `target_sequence`: the target input sequence.\n",
    "  * `returns`: logits.\n"
   ],
   "metadata": {
    "id": "gCpQyeBLs3L4"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class Transformer(nn.Module):\n",
    "  def __init__(self, source_pad_idx, target_pad_idx, source_vocab_size, target_vocab_size, h, n, d_model, d_ff, max_sequence_length, dropout):\n",
    "    super().__init__()\n",
    "\n",
    "    self.source_pad_idx = source_pad_idx\n",
    "    self.target_pad_idx = target_pad_idx\n",
    "\n",
    "    self.encoder_embedding = Embedding(source_vocab_size, d_model)\n",
    "    self.decoder_embedding = Embedding(target_vocab_size, d_model)\n",
    "\n",
    "    self.positional_encoding = PositionalEncoding(max_sequence_length, d_model)\n",
    "\n",
    "    self.encoders = nn.ModuleList([Encoder(d_model, d_ff, h, dropout) for _ in range(n)])\n",
    "    self.decoders = nn.ModuleList([Decoder(d_model, d_ff, h, dropout) for _ in range(n)])\n",
    "\n",
    "    self.pre_softmax = nn.Linear(d_model, target_vocab_size)\n",
    "    self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "\n",
    "  def masking(self, source_sequence, target_sequence):\n",
    "    device = source_sequence.device\n",
    "\n",
    "    source_mask = (source_sequence != self.source_pad_idx).unsqueeze(1).unsqueeze(2).to(device)\n",
    "    target_mask = (target_sequence != self.target_pad_idx).unsqueeze(1).unsqueeze(3).to(device)\n",
    "\n",
    "    sequence_length = target_sequence.size(1)\n",
    "    ones = torch.ones(sequence_length, sequence_length, device=device)\n",
    "\n",
    "    future_mask = torch.tril(ones, diagonal=0).bool()\n",
    "    target_mask = target_mask & future_mask\n",
    "\n",
    "    return source_mask, target_mask\n",
    "\n",
    "\n",
    "  def forward(self, source_sequence, target_sequence):\n",
    "    encoder_embedded = self.dropout(self.positional_encoding(self.encoder_embedding(source_sequence)))\n",
    "    decoder_embedded = self.dropout(self.positional_encoding(self.decoder_embedding(target_sequence)))\n",
    "\n",
    "    encoder_mask, decoder_mask = self.masking(source_sequence, target_sequence)\n",
    "\n",
    "    encoder_output, decoder_output = encoder_embedded, decoder_embedded\n",
    "\n",
    "    for encoder in self.encoders:\n",
    "       encoder_output = encoder(encoder_output, encoder_mask)\n",
    "\n",
    "    for decoder in self.decoders:\n",
    "       decoder_output = decoder(decoder_output, encoder_output, encoder_mask, decoder_mask)\n",
    "\n",
    "    logits = self.pre_softmax(decoder_output)\n",
    "\n",
    "    return logits"
   ],
   "metadata": {
    "id": "kTrmiJMFtIjG"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training\n"
   ],
   "metadata": {
    "id": "nSegNixWvLnV"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Loading the dataset\n",
    "\n",
    "Loads the [europarl_en-it](https://huggingface.co/datasets/g8a9/europarl_en-it) dataset from Hugging Face, specifying that it should be divided into training and validation sets.\n",
    "\n",
    "\n",
    "* training set: 100,000 sentences.\n",
    "* validation set: 20,000 sentences."
   ],
   "metadata": {
    "id": "JUca5MOWkfli"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "dataset = load_dataset(\"g8a9/europarl_en-it\", split=[\"train\", \"validation\"])\n",
    "\n",
    "train = dataset[0].take(100000)\n",
    "validation = dataset[1].take(20000)"
   ],
   "metadata": {
    "id": "iC9Trf3FvK9s",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345,
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    "outputId": "c3c364bb-ac11-4652-d1af-dd12dcad1e2c"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "display_data",
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       "version_major": 2,
       "version_minor": 0,
       "model_id": "529a9384109d4f2ea23e5b5f241bd380"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Load the tokenizers for both English & Italian\n",
    "\n",
    "Load and initialize tokenizers for `English-to-Italian` and `Italian-to-English` translation.\n",
    "\n",
    "* **English-to-Italian Tokenizer**: used to tokenize english sentences.\n",
    "  * Checkpoint: `\"Helsinki-NLP/opus-mt-en-it\"`\n",
    "\n",
    "\n",
    "* **Italian-to-English Tokenizer**: used to tokenize italian sentences.\n",
    "  * Checkpoint: `\"Helsinki-NLP/opus-mt-it-en\"`\n",
    "  \n"
   ],
   "metadata": {
    "id": "rxiyDgMVmlbC"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "english_checkpoint = \"Helsinki-NLP/opus-mt-en-it\"\n",
    "english_tokenizer = AutoTokenizer.from_pretrained(english_checkpoint)\n",
    "\n",
    "italian_checkpoint = \"Helsinki-NLP/opus-mt-it-en\"\n",
    "italian_tokenizer = AutoTokenizer.from_pretrained(italian_checkpoint)"
   ],
   "metadata": {
    "id": "0jZRTuugl9oX",
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    {
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     "output_type": "stream",
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     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
      "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
     ]
    },
    {
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  },
  {
   "cell_type": "markdown",
   "source": [
    "### Prepare the data loaders\n",
    "\n",
    "* `collate_fn`: prepares batches of tokenized english and italian sentences for training by applying tokenization, truncation, and padding.\n",
    "\n",
    "* `train_loader`: provides batches of tokenized sentences for training the model, with shuffling enabled to ensure varied mini-batches.\n",
    "\n",
    "* `validation_loader`: provides batches of tokenized sentences for evaluating the model’s performance during validation.\n"
   ],
   "metadata": {
    "id": "edfwXnltndhC"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def collate_fn(batch):\n",
    "    english_sentences = [sample[\"sent_en\"] for sample in batch]\n",
    "    italian_sentences = [sample[\"sent_it\"] for sample in batch]\n",
    "\n",
    "    english_encoding = english_tokenizer.batch_encode_plus(english_sentences, truncation=True, padding=\"max_length\", max_length=100, return_tensors=\"pt\")\n",
    "    italian_encoding = italian_tokenizer.batch_encode_plus(italian_sentences, truncation=True, padding=\"max_length\", max_length=100, return_tensors=\"pt\")\n",
    "\n",
    "    english_batch = english_encoding[\"input_ids\"]\n",
    "    italian_batch = italian_encoding[\"input_ids\"]\n",
    "\n",
    "    return english_batch, italian_batch\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train, batch_size=64, collate_fn=collate_fn, shuffle=True)\n",
    "validation_loader = DataLoader(dataset=validation, batch_size=64, collate_fn=collate_fn)"
   ],
   "metadata": {
    "id": "64HhPqmznW4C"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Model configures and training hyperparameters\n",
    "\n",
    "* `Config`: defines model hyperparameters and configuration details:\n",
    "  * `source_pad_idx`: padding token index for source sequences.\n",
    "  * `target_pad_idx`: padding token index for target sequences.\n",
    "  * `source_vocab_size`: vocabulary size for source language.\n",
    "  * `target_vocab_size`: vocabulary size for target language.\n",
    "  * `h`: the number of attention heads.\n",
    "  * `n`: the number of encoder and decoder layers.\n",
    "  * `d_model`: the input & output vectors dimensions.\n",
    "  * `d_ff`: the feed-forward layer's dimension.\n",
    "  * `max_sequence_length`: the maximum sequence length of the input sequences for positional encoding.\n",
    "  * `dropout`: the dropout rate for regularization.\n",
    "\n",
    "* `device`: sets up the computation device, selecting GPU if available or falling back to CPU.\n",
    "\n",
    "* `transformer`: initializes the Transformer model.\n",
    "\n",
    "* `loss_function`: cross-entropy loss function.\n",
    "* `optimizer`: Adam optimizer.\n",
    "\n"
   ],
   "metadata": {
    "id": "ICLI8-4oo9Yc"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "@dataclass(frozen=True)\n",
    "class Config:\n",
    "    source_pad_idx: int\n",
    "    target_pad_idx: int\n",
    "    source_vocab_size: int\n",
    "    target_vocab_size: int\n",
    "    h: int\n",
    "    n: int\n",
    "    d_model: int\n",
    "    d_ff: int\n",
    "    max_sequence_length: int\n",
    "    dropout: float\n",
    "\n",
    "\n",
    "source_pad_idx = english_tokenizer.pad_token_id\n",
    "target_pad_idx = italian_tokenizer.pad_token_id\n",
    "\n",
    "source_vocab_size = english_tokenizer.vocab_size\n",
    "target_vocab_size = italian_tokenizer.vocab_size\n",
    "\n",
    "cfg = Config(source_pad_idx, target_pad_idx, source_vocab_size, target_vocab_size, 8, 6, 512, 2048, 100, 0.1)\n",
    "cfg = asdict(cfg)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "transformer = Transformer(**cfg).to(device)\n",
    "\n",
    "loss_function = nn.CrossEntropyLoss(ignore_index=italian_tokenizer.pad_token_id)\n",
    "optimizer = Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)\n",
    "\n",
    "transformer"
   ],
   "metadata": {
    "id": "btHd4QSOXs_v",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "cada01b3-bcfc-4f66-83e1-e91409fe10fa"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Transformer(\n",
       "  (encoder_embedding): Embedding(\n",
       "    (embedding): Embedding(80035, 512)\n",
       "  )\n",
       "  (decoder_embedding): Embedding(\n",
       "    (embedding): Embedding(80379, 512)\n",
       "  )\n",
       "  (positional_encoding): PositionalEncoding()\n",
       "  (encoders): ModuleList(\n",
       "    (0-5): 6 x Encoder(\n",
       "      (self_attention): MultiHeadAttention(\n",
       "        (W_q): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_k): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_v): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_o): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (feed_forward): PositionWiseFeedForward(\n",
       "        (layer1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "        (layer2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "        (activation): ReLU()\n",
       "      )\n",
       "      (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "  )\n",
       "  (decoders): ModuleList(\n",
       "    (0-5): 6 x Decoder(\n",
       "      (self_attention): MultiHeadAttention(\n",
       "        (W_q): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_k): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_v): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_o): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (cross_attention): MultiHeadAttention(\n",
       "        (W_q): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_k): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_v): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (W_o): Linear(in_features=512, out_features=512, bias=True)\n",
       "      )\n",
       "      (feed_forward): PositionWiseFeedForward(\n",
       "        (layer1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "        (layer2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "        (activation): ReLU()\n",
       "      )\n",
       "      (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "  )\n",
       "  (pre_softmax): Linear(in_features=512, out_features=80379, bias=True)\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Trainer\n",
    "\n",
    "Trains the Transformer model on the provided data loader (train loader) by computing predictions, calculating the loss, and updating model parameters. It aggregates the loss and performance metrics (F1 score) over all batches and returns the average loss and F1 score."
   ],
   "metadata": {
    "id": "T5ukd84gnv1C"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def train(transformer, data_loader, loss_function, optimizer, device):\n",
    "    transformer.train()\n",
    "\n",
    "    total_loss = 0\n",
    "    num_batches = len(data_loader)\n",
    "\n",
    "    all_predictions = []\n",
    "    all_targets = []\n",
    "\n",
    "    for source_batch, target_batch in data_loader:\n",
    "        source_batch, target_batch = source_batch.to(device), target_batch.to(device)\n",
    "\n",
    "        target_input = target_batch[:, :-1]\n",
    "        target_output = target_batch[:, 1:]\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = transformer(source_batch, target_input)\n",
    "        loss = loss_function(output.view(-1, output.size(-1)), target_output.contiguous().view(-1))\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "        predictions = output.argmax(dim=-1).cpu().numpy()\n",
    "        targets = target_output.cpu().numpy()\n",
    "\n",
    "        all_predictions.extend(predictions.flatten())\n",
    "        all_targets.extend(targets.flatten())\n",
    "\n",
    "    f1 = f1_score(all_targets, all_predictions, average=\"weighted\")\n",
    "    average_loss = total_loss / num_batches\n",
    "\n",
    "    return average_loss, f1"
   ],
   "metadata": {
    "id": "Y1T7J8fhSyJv"
   },
   "execution_count": 27,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evaluator\n",
    "Evaluates the Transformer's performanceon a the validation dataset without updating weights. It calculates the average loss and F1 score based on the model’s predictions compared to the true values."
   ],
   "metadata": {
    "id": "tj58ba75nyeZ"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def evaluate(transformer, data_loader, loss_function, device):\n",
    "    transformer.eval()\n",
    "\n",
    "    total_loss = 0\n",
    "    num_batches = len(data_loader)\n",
    "\n",
    "    all_predictions = []\n",
    "    all_targets = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for source_batch, target_batch in data_loader:\n",
    "            source_batch, target_batch = source_batch.to(device), target_batch.to(device)\n",
    "\n",
    "            target_input = target_batch[:, :-1]\n",
    "            target_output = target_batch[:, 1:]\n",
    "\n",
    "            output = transformer(source_batch, target_input)\n",
    "            loss = loss_function(output.view(-1, output.size(-1)), target_output.contiguous().view(-1))\n",
    "\n",
    "            total_loss += loss.item()\n",
    "\n",
    "            predictions = output.argmax(dim=-1).cpu().numpy()\n",
    "            targets = target_output.cpu().numpy()\n",
    "\n",
    "            all_predictions.extend(predictions.flatten())\n",
    "            all_targets.extend(targets.flatten())\n",
    "\n",
    "    f1 = f1_score(all_targets, all_predictions, average=\"weighted\")\n",
    "    average_loss = total_loss / num_batches\n",
    "\n",
    "    return average_loss, f1"
   ],
   "metadata": {
    "id": "CeelVVQTTzqJ"
   },
   "execution_count": 28,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Training and evaluation loops\n",
    "\n",
    "iterates over a `5` epochs to train and evaluate the model\n",
    "\n",
    "- **Training**: train the model on the training set using the `train` function.\n",
    "- **Evaluation**: evaluate the model on the validation set using the `evaluate` function.\n"
   ],
   "metadata": {
    "id": "pMPIek93uyep"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "epochs = 5\n",
    "results = []\n",
    "\n",
    "start = time()\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    train_loss, f1_train = train(transformer, train_loader, loss_function, optimizer, device)\n",
    "\n",
    "    validation_loss, f1_validation = evaluate(transformer, validation_loader, loss_function, device)\n",
    "\n",
    "    results.append({\"Epochs\": epoch + 1, \"Loss(train)\": train_loss, \"Loss(validation)\": validation_loss, \"F1(train)\": f1_train, \"F1(validation)\": f1_validation})\n",
    "\n",
    "    print(f\"Completed: {(((epoch + 1) / epochs) * 100):.2f}%\")\n",
    "\n",
    "end = time()"
   ],
   "metadata": {
    "id": "7_REB_PsajNt",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "6e5bdca9-5f00-4ea7-90d6-d591c4437edf"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Completed: 20.00%\n",
      "Completed: 40.00%\n",
      "Completed: 60.00%\n",
      "Completed: 80.00%\n",
      "Completed: 100.00%\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Results\n",
    "\n",
    "After completing the training and evaluation process, the results are summarized in a dataFrame and include the following:\n",
    "\n",
    "- Total epochs number.\n",
    "- Train loss.\n",
    "- Validation loss.\n",
    "- Train F1 score.\n",
    "- Validation F1 score.\n",
    "- Total training time taken in minutes.\n",
    "- The device used for training."
   ],
   "metadata": {
    "id": "Is08wAqiwsFe"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "results_df = pd.DataFrame(results).iloc[-1:]\n",
    "\n",
    "training_time = round((end - start) / 60)\n",
    "device_used = \"1 x T4 GPU\"\n",
    "\n",
    "results_df.insert(5, \"Time(minutes)\", training_time)\n",
    "results_df.insert(6, \"Device\", device_used)\n",
    "\n",
    "results_df.style.hide(axis=\"index\")"
   ],
   "metadata": {
    "id": "VpdZG-wruU0G",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "outputId": "b6a1dd99-c4f3-4e98-bf99-ca5d469d407e"
   },
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fa122ff8b80>"
      ],
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c52a0\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_c52a0_level0_col0\" class=\"col_heading level0 col0\" >Epochs</th>\n",
       "      <th id=\"T_c52a0_level0_col1\" class=\"col_heading level0 col1\" >Loss(train)</th>\n",
       "      <th id=\"T_c52a0_level0_col2\" class=\"col_heading level0 col2\" >Loss(validation)</th>\n",
       "      <th id=\"T_c52a0_level0_col3\" class=\"col_heading level0 col3\" >F1(train)</th>\n",
       "      <th id=\"T_c52a0_level0_col4\" class=\"col_heading level0 col4\" >F1(validation)</th>\n",
       "      <th id=\"T_c52a0_level0_col5\" class=\"col_heading level0 col5\" >Time(minutes)</th>\n",
       "      <th id=\"T_c52a0_level0_col6\" class=\"col_heading level0 col6\" >Device</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_c52a0_row0_col0\" class=\"data row0 col0\" >5</td>\n",
       "      <td id=\"T_c52a0_row0_col1\" class=\"data row0 col1\" >0.116378</td>\n",
       "      <td id=\"T_c52a0_row0_col2\" class=\"data row0 col2\" >0.091345</td>\n",
       "      <td id=\"T_c52a0_row0_col3\" class=\"data row0 col3\" >0.262342</td>\n",
       "      <td id=\"T_c52a0_row0_col4\" class=\"data row0 col4\" >0.273207</td>\n",
       "      <td id=\"T_c52a0_row0_col5\" class=\"data row0 col5\" >170</td>\n",
       "      <td id=\"T_c52a0_row0_col6\" class=\"data row0 col6\" >1 x T4 GPU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ]
  }
 ]
}