MLP vs SwiGLU.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# MLP vs SwiGLU\n",
        "\n",
        "NOTE: I did not do any tuning or early stopping."
      ],
      "metadata": {
        "id": "XR5Wl7m6bLeM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUMgAzqrNZCS",
        "outputId": "4f540096-488d-4330-819e-09d5d3f128f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchmetrics in /usr/local/lib/python3.10/dist-packages (1.4.3)\n",
            "Requirement already satisfied: numpy>1.20.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.26.4)\n",
            "Requirement already satisfied: packaging>17.1 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (24.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (2.4.1+cu121)\n",
            "Requirement already satisfied: lightning-utilities>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (0.11.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from lightning-utilities>=0.8.0->torchmetrics) (71.0.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from lightning-utilities>=0.8.0->torchmetrics) (4.12.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->torchmetrics) (3.16.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->torchmetrics) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->torchmetrics) (3.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->torchmetrics) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->torchmetrics) (2024.6.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->torchmetrics) (3.0.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->torchmetrics) (1.3.0)\n",
            "Requirement already satisfied: torchview in /usr/local/lib/python3.10/dist-packages (0.2.6)\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "pip install torchmetrics\n",
        "pip install torchview"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from torchmetrics import Accuracy\n",
        "from tqdm.auto import tqdm, trange\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "\n",
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "\n",
        "# Define transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),  # Convert image to tensor\n",
        "])\n",
        "\n",
        "# Define datasets\n",
        "datasets_kwargs = {'download': True, 'transform': transform}\n",
        "train_dataset = datasets.MNIST('data/', train=True, **datasets_kwargs)\n",
        "test_dataset = datasets.MNIST('data/', train=False, **datasets_kwargs)\n",
        "\n",
        "# Define dataloaders\n",
        "dataloader_kwargs = {'batch_size': 256, 'shuffle': True}\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, **dataloader_kwargs)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, **dataloader_kwargs)"
      ],
      "metadata": {
        "id": "i5ANKk6dNiBV"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify models\n",
        "\n",
        "### Convolutional module\n",
        "class ConvModule(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ConvModule, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
        "\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.gelu(self.conv1(x))\n",
        "        x = F.gelu(self.conv2(x))\n",
        "        x = self.pool(x)\n",
        "        return x\n",
        "\n",
        "### MLP module\n",
        "class MLPModule(nn.Module):\n",
        "    def __init__(self, hidden_size=256):\n",
        "        super(MLPModule, self).__init__()\n",
        "        self.fc1 = nn.LazyLinear(hidden_size)\n",
        "        self.fc2 = nn.LazyLinear(hidden_size)\n",
        "        self.output_layer = nn.LazyLinear(10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.gelu(self.fc1(x))\n",
        "        x = F.gelu(self.fc2(x))\n",
        "        x = self.output_layer(x)\n",
        "        return F.softmax(x, dim=1)\n",
        "\n",
        "### SwiGLU module\n",
        "class SwiGLULayer(nn.Module):\n",
        "    def __init__(self, hidden_size, bias=True):\n",
        "        super(SwiGLULayer, self).__init__()\n",
        "        self.gater = nn.LazyLinear(hidden_size, bias=bias)\n",
        "        self.reprojector = nn.LazyLinear(hidden_size, bias=bias)\n",
        "\n",
        "    def forward(self, x):\n",
        "        xw = self.gater(x)\n",
        "        xv = self.reprojector(x)\n",
        "        x_swish = xw * torch.sigmoid(xw)\n",
        "        return x_swish * xv\n",
        "\n",
        "class SwiGLUModule(nn.Module):\n",
        "    def __init__(self, hidden_size=256, num_layers=2):\n",
        "        super(SwiGLUModule, self).__init__()\n",
        "        self.layers = nn.ModuleList([SwiGLULayer(hidden_size) for _ in range(num_layers)])\n",
        "        self.output_layer = nn.LazyLinear(10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        for layer in self.layers:\n",
        "            x = layer(x)\n",
        "        x = self.output_layer(x)\n",
        "        return F.softmax(x, dim=1)\n",
        ""
      ],
      "metadata": {
        "id": "AL2hED8fRz7f"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build models\n",
        "class MLPBasedModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLPBasedModel, self).__init__()\n",
        "        self.conv = ConvModule()\n",
        "        self.mlp = MLPModule()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.mlp(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class SwiGLUBasedModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SwiGLUBasedModel, self).__init__()\n",
        "        self.conv = ConvModule()\n",
        "        self.swiglu = SwiGLUModule()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.swiglu(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "fSh-Hdb3R3hi"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Models"
      ],
      "metadata": {
        "id": "6GyKrOBgSxSN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, name):\n",
        "    with torch.inference_mode():\n",
        "        accuracy = Accuracy(task='multiclass', num_classes=10)\n",
        "        for x_test, y_test in tqdm(test_loader, desc=f\"Evaluating {name} model\", total=len(test_loader)):\n",
        "            # Move data to device\n",
        "            x_test, y_test = x_test.to(device), y_test.to(device)\n",
        "\n",
        "            # Forward pass\n",
        "            y_hat = model.forward(x_test)\n",
        "            accuracy.update(y_hat.cpu(), y_test.cpu())\n",
        "\n",
        "        # Print accuracy\n",
        "        computed_accuracy = accuracy.compute()\n",
        "        print(f\"Accuracy: {computed_accuracy:.2%}\")\n",
        "        accuracy.reset()\n",
        "    return computed_accuracy"
      ],
      "metadata": {
        "id": "wqKnGC0mYkH7"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MLP Based CNN"
      ],
      "metadata": {
        "id": "Ja-HVEYpVSJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_based_model = MLPBasedModel().to(device)\n",
        "mlp_optimizer = torch.optim.Adam(mlp_based_model.parameters(), lr=1e-4)\n",
        "mlp_criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Training loop\n",
        "hist = []\n",
        "n_epochs = 10\n",
        "best_epoch = 0\n",
        "best_loss = float('inf')\n",
        "iterator = trange(n_epochs, desc=\"Training MLP-based model\")\n",
        "for epoch in iterator:\n",
        "    loss_i = 0.0\n",
        "    for i, (x_train, y_train) in tqdm(enumerate(train_loader, start=0), total=len(train_loader)):\n",
        "        # Move data to device\n",
        "        x_train, y_train = x_train.to(device), y_train.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        mlp_optimizer.zero_grad()\n",
        "        y_hat = mlp_based_model.forward(x_train)\n",
        "        loss = mlp_criterion(y_hat, y_train)\n",
        "\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        mlp_optimizer.step()\n",
        "\n",
        "        # Update loss\n",
        "        loss_i += loss.item()\n",
        "    iterator.set_postfix({'loss': loss_i, 'test_acc': evaluate_model(mlp_based_model, 'MLP-based (training)')})\n",
        "\n"
      ],
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      "outputs": [
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            "text/plain": [
              "Training MLP-based model:   0%|          | 0/10 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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            "Accuracy: 93.29%\n"
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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            "Accuracy: 96.51%\n"
          ]
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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            "Accuracy: 97.29%\n"
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
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              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c1beb0b7cd047b8b2f775235ab11577"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f761c9ff18d9450ab51d71e77bd9be9c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.99%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fd75bc1992a4a79ba31086cc92d327f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6585597f5ee0439c83a55af0a8bbca73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 98.10%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5585d9577ed04d66bbf68515809b9e5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating MLP-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dd55006e051490a9b850e9d1fce78ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 98.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_accuracy = evaluate_model(mlp_based_model, \"MLP-based\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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            "3ac0535ca880499c932ed875742b514b",
            "edb9fc09c91e4b1a9aceec2087210051"
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        },
        "id": "jFEI2uybT7jw",
        "outputId": "1c3c7f56-fd3f-4d07-91b8-19450421475d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating MLP-based model:   0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36f13af9d2d946d09a7acca859a6d94f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 98.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SwiGLU based CNN"
      ],
      "metadata": {
        "id": "KhyxiSKsVVlk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "swish_based_model = SwiGLUBasedModel().to(device)\n",
        "swish_optimizer = torch.optim.Adam(swish_based_model.parameters(), lr=1e-4)\n",
        "swish_criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Training loop\n",
        "hist = []\n",
        "n_epochs = 10\n",
        "best_epoch = 0\n",
        "best_loss = float('inf')\n",
        "iterator = trange(n_epochs, desc=\"Training SwiGLU-based model\")\n",
        "for epoch in iterator:\n",
        "    loss_i = 0.0\n",
        "    for i, (x_train, y_train) in tqdm(enumerate(train_loader, start=0), total=len(train_loader)):\n",
        "        # Move data to device\n",
        "        x_train, y_train = x_train.to(device), y_train.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        swish_optimizer.zero_grad()\n",
        "        y_hat = swish_based_model.forward(x_train)\n",
        "        loss = swish_criterion(y_hat, y_train)\n",
        "\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        swish_optimizer.step()\n",
        "\n",
        "        # Update loss\n",
        "        loss_i += loss.item()\n",
        "    iterator.set_postfix({'loss': loss_i, 'test_acc': evaluate_model(swish_based_model, 'SwiGLU-based (training)')})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863,
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        },
        "id": "ht3sE16oVYiF",
        "outputId": "b213421e-8493-4e08-b551-878acf991f5e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "model_id": "a7fbb31271f24ca58e42cfeee849f470"
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              "version_major": 2,
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              "model_id": "6ea554fc74284c5bbf4cbe6b7b6a847a"
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          "output_type": "display_data",
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        {
          "output_type": "stream",
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          "text": [
            "Accuracy: 83.34%\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "70e00422710c46adbbe1b36d198b4171"
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "936d3f6e93b343e9b36c8c531bb8813b"
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          },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 85.67%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "5641e7d57bbd441d8f81ce05b1f28719"
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        {
          "output_type": "display_data",
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          "output_type": "stream",
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          "text": [
            "Accuracy: 86.09%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d37c5686cf4a4fd5850a119dbff3ba25"
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "74813516d44543209fa02df6148547fe"
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        {
          "output_type": "stream",
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          "text": [
            "Accuracy: 87.48%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e5751d4b24f4e588fb9bb0eca17a639"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "796a56cc29024cf2a1a0052ebe9e727c"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 95.39%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bd2d77902824cb4a5bcd0768b7251c7"
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        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "680014f53ca24b819b0155634d3cf0a7"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 96.85%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82234f3d30a044b0915c42b5f5eaeb0f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "da34707cdbad4ae6ad451603a2cb3e7f"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.20%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2679f6e146ff44dd9fd7835823c6b6c1"
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ca3a33e9997493da1d789eca0e6dd8f"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.31%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d78270bd946425db079dbbfc58255dc"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "107d1bcf499845b798b84ca1646016d1"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.62%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/235 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "23b50a05208f44bbbb0008439a3d044f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating SwiGLU-based (training) model:   0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5cf0a5a98764c69a8c07f4a0d968892"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.50%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "swish_accuracy = evaluate_model(swish_based_model, \"SwiGLU-based\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "4b51198d75244113963b0fff4f8a376b",
            "75c411fae86548d8b8d8d70831a86da0",
            "a3379ecd46634238ab65a80b24a7305f",
            "fe2f116d63114beb80bd6e8ab204d6cc",
            "6caaf03079fe4deaa270a6e20ab01d26",
            "fafc9a40d8084fb889d05388094876ab",
            "677488d81ef24e4fa0065a5b0b0495c0",
            "96f7edbbd7864dcd8e1da4b09eb5b397",
            "f06e73facdbb4471a49bb5a207b174ae",
            "073b02d643d34a12abfbec7f4435afe0",
            "e7dc057f9b73405cae48de81cdbbbbc1"
          ]
        },
        "id": "ohecwxyFWozJ",
        "outputId": "34b32d14-ff7b-4581-fe11-8d4c5df1fa64"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating SwiGLU-based model:   0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b51198d75244113963b0fff4f8a376b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.50%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Complexity ratio = {count_parameters(swish_based_model) / count_parameters(mlp_based_model):.1f}:1')\n",
        "print(f'    SwiGLU based model parameters = {count_parameters(swish_based_model):,}')\n",
        "print(f'    MLP based model parameters    = {count_parameters(mlp_based_model):,}')\n",
        "print()\n",
        "print(f'SwiGLU based model Accuracy = {swish_accuracy:.2%}')\n",
        "print(f'MLP based model Accuracy    = {mlp_accuracy:.2%}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "losxRyNJaPL4",
        "outputId": "970cbef0-f59d-4315-91d8-a1f859a7eecf"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Complexity ratio = 2.0:1\n",
            "    SwiGLU based model parameters = 6,576,010\n",
            "    MLP based model parameters    = 3,298,698\n",
            "\n",
            "SwiGLU based model Accuracy = 97.50%\n",
            "MLP based model Accuracy    = 98.13%\n"
          ]
        }
      ]
    }
  ]
}