twotower-marginrankingloss.ipynb

twotower-marginrankingloss.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/abpai/b49ac40c91309ebbb4767ca9e8aea77e/twotower-marginrankingloss.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install structlog"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "id": "MaD8dU2VVod7",
        "outputId": "fe8538bf-e488-4b70-d484-56864c060f29"
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      "execution_count": 1,
      "outputs": [
        {
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          "text": [
            "Collecting structlog\n",
            "  Downloading structlog-25.3.0-py3-none-any.whl.metadata (8.0 kB)\n",
            "Downloading structlog-25.3.0-py3-none-any.whl (68 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m68.2/68.2 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: structlog\n",
            "Successfully installed structlog-25.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Minimal ranking demo\n",
        "--------------------\n",
        "\n",
        "• 500 toy queries and 500 toy docs\n",
        "• Negative = next doc (cyclic roll)\n",
        "• Two-tower encoder: Embedding → mean-pool → Linear → L2-norm\n",
        "• Trains with MarginRankingLoss\n",
        "• Evaluates Recall@5 over the full corpus (1 doc / query)\n",
        "\"\"\"\n",
        "\n",
        "import time\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import structlog\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn.functional import normalize\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "logger = structlog.get_logger(__name__)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "logger.info('Using device', device=device)\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 1.  Synthetic data  (N queries  ==  N docs  ==  corpus size)\n",
        "# ---------------------------------------------------------------------\n",
        "def build_synthetic(\n",
        "  n_queries=500, vocab=100, q_len=4, doc_len=16, overlap=0.6, seed=42\n",
        "):\n",
        "  rng = torch.Generator().manual_seed(seed)\n",
        "  queries = torch.randint(0, vocab, (n_queries, q_len), generator=rng)\n",
        "  docs_pos = torch.randint(0, vocab, (n_queries, doc_len), generator=rng)\n",
        "\n",
        "  for i in range(n_queries):\n",
        "    num_copy = int(overlap * q_len)\n",
        "    copy_idx = torch.randperm(q_len, generator=rng)[:num_copy]\n",
        "    docs_pos[i, :num_copy] = queries[i, copy_idx]\n",
        "\n",
        "  docs_neg = docs_pos.roll(shifts=1, dims=0)\n",
        "  return queries, docs_pos, docs_neg\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 2.  Dataset  (triples: q  |  pos  |  neg)\n",
        "# ---------------------------------------------------------------------\n",
        "class TripleDS(Dataset):\n",
        "  def __init__(self, q, p, n):\n",
        "    self.q, self.p, self.n = q, p, n\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.q)\n",
        "\n",
        "  def __getitem__(self, i):\n",
        "    return {'q': self.q[i], 'd_pos': self.p[i], 'd_neg': self.n[i]}\n",
        "\n",
        "\n",
        "def collate(batch):\n",
        "  result = {}\n",
        "  for k in batch[0]:\n",
        "    result[k] = torch.stack([item[k] for item in batch]).to(device)\n",
        "  return result\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 3.  Two-tower model\n",
        "# ---------------------------------------------------------------------\n",
        "class TwoTower(nn.Module):\n",
        "  def __init__(self, vocab, emb_dim=36, proj_dim=18):\n",
        "    super().__init__()\n",
        "    self.embedding = nn.Embedding(vocab, emb_dim)\n",
        "    self.proj = nn.Linear(emb_dim, proj_dim, bias=False)\n",
        "\n",
        "  def encode(self, toks):\n",
        "    # Ensure input tensors are on the correct device within the model\n",
        "    toks = toks.to(next(self.parameters()).device)\n",
        "    x = self.embedding(toks).mean(dim=1)  # (B, emb_dim), mean-pooling\n",
        "    return normalize(self.proj(x), dim=-1)  # (B, proj_dim)  ‖·‖₂ = 1\n",
        "\n",
        "  def forward(self, q, d):\n",
        "    qv, dv = self.encode(q), self.encode(d)\n",
        "    return (qv * dv).sum(dim=-1)  # cosine similarity\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 4.  Train\n",
        "# ---------------------------------------------------------------------\n",
        "\n",
        "# --- Hyperparameters ---\n",
        "n_queries = 500\n",
        "vocab = 50\n",
        "q_len = 16\n",
        "doc_len = 48\n",
        "overlap = 0.8\n",
        "seed = 1337\n",
        "batch_size = 16\n",
        "margin = 0.25\n",
        "lr = 3e-4\n",
        "epochs = 10\n",
        "emb_dim = 48\n",
        "proj_dim = 72\n",
        "\n",
        "logger.info('Building Synthetic Data')\n",
        "queries, docs_pos, docs_neg = build_synthetic(\n",
        "  n_queries, vocab, q_len, doc_len, overlap, seed\n",
        ")\n",
        "\n",
        "logger.info('Creating DataLoader')\n",
        "train_dataset = TripleDS(queries, docs_pos, docs_neg)\n",
        "loader = DataLoader(\n",
        "  train_dataset,\n",
        "  batch_size=batch_size,\n",
        "  shuffle=True,\n",
        "  collate_fn=collate,  # to handle device placement\n",
        ")\n",
        "\n",
        "logger.info('Initializing Model and Optimizer')\n",
        "model = TwoTower(vocab=vocab, emb_dim=emb_dim, proj_dim=proj_dim).to(device)\n",
        "opt = torch.optim.AdamW(model.parameters(), lr=lr)\n",
        "loss_fn = nn.MarginRankingLoss(margin=margin)\n",
        "\n",
        "# ---> Lists to store metrics for plotting <---\n",
        "loss_history = []\n",
        "lr_history = []\n",
        "epoch_times = []\n",
        "\n",
        "logger.info('Starting Training')\n",
        "training_start_time = time.time()\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  epoch_start_time = time.time()\n",
        "  model.train()\n",
        "  total_loss = 0.0\n",
        "\n",
        "  current_lr = opt.param_groups[0]['lr']\n",
        "  lr_history.append(current_lr)\n",
        "\n",
        "  for batch_idx, batch in enumerate(loader):\n",
        "    pos_scores = model(batch['q'], batch['d_pos'])\n",
        "    neg_scores = model(batch['q'], batch['d_neg'])\n",
        "\n",
        "    # Target tensor for MarginRankingLoss: 1 means pos should be > neg\n",
        "    target = torch.ones_like(pos_scores).to(device)\n",
        "\n",
        "    loss = loss_fn(pos_scores, neg_scores, target)\n",
        "\n",
        "    opt.zero_grad()\n",
        "    loss.backward()\n",
        "    opt.step()\n",
        "\n",
        "    total_loss += loss.item()\n",
        "\n",
        "  avg_loss = total_loss / len(loader)\n",
        "  loss_history.append(avg_loss)\n",
        "\n",
        "  epoch_end_time = time.time()\n",
        "  epoch_duration = epoch_end_time - epoch_start_time\n",
        "  epoch_times.append(epoch_duration)\n",
        "\n",
        "  logger.info(\n",
        "    'epoch_summary',\n",
        "    epoch=epoch + 1,\n",
        "    loss=avg_loss,\n",
        "    lr=current_lr,\n",
        "    time=epoch_duration,\n",
        "  )\n",
        "\n",
        "training_end_time = time.time()\n",
        "total_training_time = training_end_time - training_start_time\n",
        "logger.info('Training Finished', total_training_time=total_training_time)\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 5.  Quick diagnostics: accuracy + histograms + LR Plot\n",
        "# ---------------------------------------------------------------------\n",
        "logger.info('Running Diagnostics')\n",
        "model.eval()\n",
        "queries_dev = queries.to(device)\n",
        "docs_pos_dev = docs_pos.to(device)\n",
        "docs_neg_dev = docs_neg.to(device)\n",
        "\n",
        "with torch.no_grad():\n",
        "  # Use the device-specific tensors for evaluation\n",
        "  pos_scores_eval = model(queries_dev, docs_pos_dev)\n",
        "  neg_scores_eval = model(queries_dev, docs_neg_dev)\n",
        "\n",
        "accuracy = (pos_scores_eval > neg_scores_eval).float().mean().item()\n",
        "logger.info('pos>neg accuracy', accuracy=accuracy)\n",
        "\n",
        "# --- Plotting Section ---\n",
        "plt.style.use('seaborn-v0_8-whitegrid')\n",
        "\n",
        "# Figure 1: Loss and Learning Rate\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Subplot 1: Loss Curve\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(range(1, epochs + 1), loss_history, marker='o', linestyle='-', color='b')\n",
        "plt.title('Training Loss per Epoch')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Average Margin Ranking Loss')\n",
        "plt.xticks(range(1, epochs + 1, max(1, epochs // 10)))  # Adjust x-ticks for readability\n",
        "plt.grid(True)\n",
        "\n",
        "# Subplot 2: Learning Rate Schedule\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(range(1, epochs + 1), lr_history, marker='.', linestyle='-', color='r')\n",
        "plt.title('Learning Rate Schedule')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Learning Rate')\n",
        "plt.xticks(range(1, epochs + 1, max(1, epochs // 10)))\n",
        "plt.ylim(bottom=0)\n",
        "plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))\n",
        "plt.grid(True)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# Figure 2: Score Distributions\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Subplot 1: Histograms of Positive vs Negative Scores\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.hist(\n",
        "  pos_scores_eval.cpu().numpy(), bins=50, alpha=0.7, label='pos scores', color='green'\n",
        ")\n",
        "plt.hist(\n",
        "  neg_scores_eval.cpu().numpy(),\n",
        "  bins=50,\n",
        "  alpha=0.7,\n",
        "  label='neg scores (cyclic)',\n",
        "  color='red',\n",
        ")\n",
        "plt.title('Score Distribution (Positive vs. Cyclic Negative)')\n",
        "plt.xlabel('Cosine Similarity Score')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "plt.grid(True, axis='y', alpha=0.5)\n",
        "\n",
        "# Subplot 2: Histogram of Margin (pos - neg)\n",
        "plt.subplot(1, 2, 2)\n",
        "margin_diff = (pos_scores_eval - neg_scores_eval).cpu().numpy()\n",
        "plt.hist(margin_diff, bins=50, color='purple', alpha=0.8)\n",
        "plt.axvline(\n",
        "  x=margin, color='k', linestyle='--', label=f'Target Margin ({margin})'\n",
        ")  # Show target margin\n",
        "plt.axvline(x=0, color='grey', linestyle=':', label='Zero Margin')  # Show zero margin\n",
        "plt.title('Margin Distribution (pos_score - neg_score)')\n",
        "plt.xlabel('Score Difference')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "plt.grid(True, axis='y', alpha=0.5)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 6.  Recall@k *against the entire corpus*\n",
        "# ---------------------------------------------------------------------\n",
        "@torch.no_grad()\n",
        "def recall_at_k_corpus(model, q, docs, k=10, batch_size_eval=256):\n",
        "  \"\"\"\n",
        "  Each query has exactly ONE true doc, aligned by row index.\n",
        "  Searches the whole corpus (size == len(docs)).\n",
        "  \"\"\"\n",
        "  model.eval()\n",
        "  q_dev = q.to(device)\n",
        "  docs_dev = docs.to(device)\n",
        "\n",
        "  logger.info(f'\\n--- Calculating Recall@{k} ---')\n",
        "  logger.info(f'Encoding {len(docs_dev)} documents...')\n",
        "  doc_vecs = []\n",
        "  encode_start = time.time()\n",
        "  for i in range(0, len(docs_dev), batch_size_eval):\n",
        "    batch_docs = docs_dev[i : i + batch_size_eval]\n",
        "    doc_vecs.append(model.encode(batch_docs))\n",
        "  doc_vecs = torch.cat(doc_vecs, 0)  # (M, dim)\n",
        "  doc_mat = doc_vecs.t()  # (dim, M) transpose for efficient matmul\n",
        "  encode_end = time.time()\n",
        "  logger.info(f'Document encoding took {encode_end - encode_start:.2f}s')\n",
        "\n",
        "  logger.info(f'Performing search for {len(q_dev)} queries...')\n",
        "  hits = 0\n",
        "  search_start = time.time()\n",
        "  for i in range(0, len(q_dev), batch_size_eval):\n",
        "    batch_q = q_dev[i : i + batch_size_eval]\n",
        "    qv = model.encode(batch_q)  # (B, dim)\n",
        "    # Calculate similarity scores (dot product since vectors are normalized)\n",
        "    sim = qv @ doc_mat  # (B, M)\n",
        "    # Get top K indices for each query\n",
        "    topk_indices = sim.topk(k, dim=1).indices  # (B, k)\n",
        "    # Create target indices corresponding to the\n",
        "    # true positive document for each query in the batch\n",
        "    # The true positive doc for query `j` (absolute index) is `docs[j]`,\n",
        "    # so the target index in the `doc_mat` is `j`.\n",
        "    # For a batch starting at index `i`, the targets are `i, i+1, ..., i+B-1`.\n",
        "    target_indices = torch.arange(i, i + qv.size(0), device=device).unsqueeze(\n",
        "      1\n",
        "    )  # (B, 1)\n",
        "    # Check if the target index is present in the top K indices for each query\n",
        "    # Broadcasting (topk_indices == target_indices) compares each element of\n",
        "    # topk_indices with the target_index for that row\n",
        "    hits += (topk_indices == target_indices).any(dim=1).sum().item()\n",
        "  search_end = time.time()\n",
        "  logger.info(f'Search took {search_end - search_start:.2f}s')\n",
        "\n",
        "  recall = hits / len(q_dev)\n",
        "  return recall\n",
        "\n",
        "\n",
        "# Use the device-specific tensors for recall calculation\n",
        "rec10 = recall_at_k_corpus(\n",
        "  model, queries_dev, docs_pos_dev, k=10, batch_size_eval=64\n",
        ")  # Adjusted batch size for recall\n",
        "logger.info(f'\\nRecall@10 (full corpus): {rec10:.2%}')  # Formatted as percentage"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TG8azPaLnJ0v",
        "outputId": "2f604670-c158-4114-b709-0064bf839051"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2025-05-05 18:14:34 [info     ] Using device                   device=device(type='cpu')\n",
            "2025-05-05 18:14:34 [info     ] Building Synthetic Data\n",
            "2025-05-05 18:14:34 [info     ] Creating DataLoader\n",
            "2025-05-05 18:14:34 [info     ] Initializing Model and Optimizer\n",
            "2025-05-05 18:14:34 [info     ] Starting Training\n",
            "2025-05-05 18:14:34 [info     ] epoch_summary                  epoch=1 loss=0.07298048480879515 lr=0.0003 time=0.1070857048034668\n",
            "2025-05-05 18:14:34 [info     ] epoch_summary                  epoch=2 loss=0.06174305998138152 lr=0.0003 time=0.10353636741638184\n",
            "2025-05-05 18:14:34 [info     ] epoch_summary                  epoch=3 loss=0.05704956880072132 lr=0.0003 time=0.11271166801452637\n",
            "2025-05-05 18:14:34 [info     ] epoch_summary                  epoch=4 loss=0.04646842536749318 lr=0.0003 time=0.10748982429504395\n",
            "2025-05-05 18:14:35 [info     ] epoch_summary                  epoch=5 loss=0.042200435855193064 lr=0.0003 time=0.10848402976989746\n",
            "2025-05-05 18:14:35 [info     ] epoch_summary                  epoch=6 loss=0.035808030545013025 lr=0.0003 time=0.11028051376342773\n",
            "2025-05-05 18:14:35 [info     ] epoch_summary                  epoch=7 loss=0.029849466984160244 lr=0.0003 time=0.1286604404449463\n",
            "2025-05-05 18:14:35 [info     ] epoch_summary                  epoch=8 loss=0.02677890786435455 lr=0.0003 time=0.11213111877441406\n",
            "2025-05-05 18:14:35 [info     ] epoch_summary                  epoch=9 loss=0.02544243846205063 lr=0.0003 time=0.10509753227233887\n",
            "2025-05-05 18:14:35 [info     ] epoch_summary                  epoch=10 loss=0.020474434189964086 lr=0.0003 time=0.1045992374420166\n",
            "2025-05-05 18:14:35 [info     ] Training Finished              total_training_time=1.1107416152954102\n",
            "2025-05-05 18:14:35 [info     ] Running Diagnostics\n",
            "2025-05-05 18:14:35 [info     ] pos>neg accuracy               accuracy=0.972000002861023\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2025-05-05 18:14:36 [info     ] \n",
            "--- Calculating Recall@10 ---\n",
            "2025-05-05 18:14:36 [info     ] Encoding 500 documents...\n",
            "2025-05-05 18:14:36 [info     ] Document encoding took 0.00s\n",
            "2025-05-05 18:14:36 [info     ] Performing search for 500 queries...\n",
            "2025-05-05 18:14:36 [info     ] Search took 0.01s\n",
            "2025-05-05 18:14:36 [info     ] \n",
            "Recall@10 (full corpus): 52.20%\n"
          ]
        }
      ]
    }
  ]
}