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{
"cells": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font size=\"1\">\n",
"Supplementary code for \"Build a Large Language Model From Scratch\": <a href=\"https://www.manning.com/books/build-a-large-language-model-from-scratch\">https://www.manning.com/books/build-a-large-language-model-from-scratch</a> by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
"Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
"</font>"
]
},
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{
"cell_type": "markdown",
"metadata": {
"id": "O9i6kzBsZVaZ"
},
"source": [
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"# Appendix A: Introduction to PyTorch (Part 2)"
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ppbG5d-NZezH"
},
"source": [
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"## A.9 Optimizing training performance with GPUs"
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6jH0J_DPZhbn"
},
"source": [
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"### A.9.1 PyTorch computations on GPU devices"
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]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RM7kGhwMF_nO",
"outputId": "ac60b048-b81f-4bb0-90fa-1ca474f04e9a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.1+cu118\n"
]
}
],
"source": [
"import torch\n",
"\n",
"print(torch.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OXLCKXhiUkZt",
"outputId": "39fe5366-287e-47eb-cc34-3508d616c4f9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(torch.cuda.is_available())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MTTlfh53Va-T",
"outputId": "f31d8bbe-577f-4db4-9939-02e66b9f96d1"
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([5., 7., 9.])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tensor_1 = torch.tensor([1., 2., 3.])\n",
"tensor_2 = torch.tensor([4., 5., 6.])\n",
"\n",
"print(tensor_1 + tensor_2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z4LwTNw7Vmmb",
"outputId": "1c025c6a-e3ed-4c7c-f5fd-86c14607036e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([5., 7., 9.], device='cuda:0')\n"
]
}
],
"source": [
"tensor_1 = tensor_1.to(\"cuda\")\n",
"tensor_2 = tensor_2.to(\"cuda\")\n",
"\n",
"print(tensor_1 + tensor_2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 184
},
"id": "tKT6URN1Vuft",
"outputId": "e6f01e7f-d9cf-44cb-cc6d-46fc7907d5c0"
},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-4ff3c4d20fc3>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtensor_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtensor_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!"
]
}
],
"source": [
"tensor_1 = tensor_1.to(\"cpu\")\n",
"print(tensor_1 + tensor_2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c8j1cWDcWAMf"
},
"source": [
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"## A.9.2 Single-GPU training"
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]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "GyY59cjieitv"
},
"outputs": [],
"source": [
"X_train = torch.tensor([\n",
" [-1.2, 3.1],\n",
" [-0.9, 2.9],\n",
" [-0.5, 2.6],\n",
" [2.3, -1.1],\n",
" [2.7, -1.5]\n",
"])\n",
"\n",
"y_train = torch.tensor([0, 0, 0, 1, 1])\n",
"\n",
"X_test = torch.tensor([\n",
" [-0.8, 2.8],\n",
" [2.6, -1.6],\n",
"])\n",
"\n",
"y_test = torch.tensor([0, 1])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "v41gKqEJempa"
},
"outputs": [],
"source": [
"from torch.utils.data import Dataset\n",
"\n",
"\n",
"class ToyDataset(Dataset):\n",
" def __init__(self, X, y):\n",
" self.features = X\n",
" self.labels = y\n",
"\n",
" def __getitem__(self, index):\n",
" one_x = self.features[index]\n",
" one_y = self.labels[index]\n",
" return one_x, one_y\n",
"\n",
" def __len__(self):\n",
" return self.labels.shape[0]\n",
"\n",
"train_ds = ToyDataset(X_train, y_train)\n",
"test_ds = ToyDataset(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "UPGVRuylep8Y"
},
"outputs": [],
"source": [
"from torch.utils.data import DataLoader\n",
"\n",
"torch.manual_seed(123)\n",
"\n",
"train_loader = DataLoader(\n",
" dataset=train_ds,\n",
" batch_size=2,\n",
" shuffle=True,\n",
" num_workers=1,\n",
" drop_last=True\n",
")\n",
"\n",
"test_loader = DataLoader(\n",
" dataset=test_ds,\n",
" batch_size=2,\n",
" shuffle=False,\n",
" num_workers=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "drhg6IXofAXh"
},
"outputs": [],
"source": [
"class NeuralNetwork(torch.nn.Module):\n",
" def __init__(self, num_inputs, num_outputs):\n",
" super().__init__()\n",
"\n",
" self.layers = torch.nn.Sequential(\n",
"\n",
" # 1st hidden layer\n",
" torch.nn.Linear(num_inputs, 30),\n",
" torch.nn.ReLU(),\n",
"\n",
" # 2nd hidden layer\n",
" torch.nn.Linear(30, 20),\n",
" torch.nn.ReLU(),\n",
"\n",
" # output layer\n",
" torch.nn.Linear(20, num_outputs),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" logits = self.layers(x)\n",
" return logits"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7jaS5sqPWCY0",
"outputId": "84c74615-38f2-48b8-eeda-b5912fed1d3a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 001/003 | Batch 000/002 | Train/Val Loss: 0.75\n",
"Epoch: 001/003 | Batch 001/002 | Train/Val Loss: 0.65\n",
"Epoch: 002/003 | Batch 000/002 | Train/Val Loss: 0.44\n",
"Epoch: 002/003 | Batch 001/002 | Train/Val Loss: 0.13\n",
"Epoch: 003/003 | Batch 000/002 | Train/Val Loss: 0.03\n",
"Epoch: 003/003 | Batch 001/002 | Train/Val Loss: 0.00\n"
]
}
],
"source": [
"import torch.nn.functional as F\n",
"\n",
"\n",
"torch.manual_seed(123)\n",
"model = NeuralNetwork(num_inputs=2, num_outputs=2)\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # NEW\n",
"model = model.to(device) # NEW\n",
"\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.5)\n",
"\n",
"num_epochs = 3\n",
"\n",
"for epoch in range(num_epochs):\n",
"\n",
" model.train()\n",
" for batch_idx, (features, labels) in enumerate(train_loader):\n",
"\n",
" features, labels = features.to(device), labels.to(device) # NEW\n",
" logits = model(features)\n",
" loss = F.cross_entropy(logits, labels) # Loss function\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" ### LOGGING\n",
" print(f\"Epoch: {epoch+1:03d}/{num_epochs:03d}\"\n",
" f\" | Batch {batch_idx:03d}/{len(train_loader):03d}\"\n",
" f\" | Train/Val Loss: {loss:.2f}\")\n",
"\n",
" model.eval()\n",
" # Optional model evaluation"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "4qrlmnPPe7FO"
},
"outputs": [],
"source": [
"def compute_accuracy(model, dataloader, device):\n",
"\n",
" model = model.eval()\n",
" correct = 0.0\n",
" total_examples = 0\n",
"\n",
" for idx, (features, labels) in enumerate(dataloader):\n",
"\n",
" features, labels = features.to(device), labels.to(device) # New\n",
"\n",
" with torch.no_grad():\n",
" logits = model(features)\n",
"\n",
" predictions = torch.argmax(logits, dim=1)\n",
" compare = labels == predictions\n",
" correct += torch.sum(compare)\n",
" total_examples += len(compare)\n",
"\n",
" return (correct / total_examples).item()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1_-BfkfEf4HX",
"outputId": "473bf21d-5880-4de3-fc8a-051d75315b94"
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compute_accuracy(model, train_loader, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iYtXKBGEgKss",
"outputId": "508edd84-3fb7-4d04-cb23-9df0c3d24170"
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compute_accuracy(model, test_loader, device=device)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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",
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"version": "3.10.6"
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}
},
"nbformat": 4,
"nbformat_minor": 4
}
