From f77c376b0524398ca4f23e34de34c622185dd709 Mon Sep 17 00:00:00 2001
From: Mingyuan Xu <mix16@pitt.edu>
Date: Fri, 13 Sep 2024 21:01:52 +0800
Subject: [PATCH] Run generate example in ch06 optionally on GPU (#352)

* model.to("cuda")

model.to("cuda")

* update device placement

---------

Co-authored-by: rasbt <mail@sebastianraschka.com>
---
 .../load-finetuned-model.ipynb                | 64 +++++++++++++++----
 1 file changed, 52 insertions(+), 12 deletions(-)

diff --git a/ch06/01_main-chapter-code/load-finetuned-model.ipynb b/ch06/01_main-chapter-code/load-finetuned-model.ipynb
index 7ac6f5e6..4e931695 100644
--- a/ch06/01_main-chapter-code/load-finetuned-model.ipynb
+++ b/ch06/01_main-chapter-code/load-finetuned-model.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "markdown",
    "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d",
-   "metadata": {},
+   "metadata": {
+    "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d"
+   },
    "source": [
     "<table style=\"width:100%\">\n",
     "<tr>\n",
@@ -23,7 +25,9 @@
   {
    "cell_type": "markdown",
    "id": "f3f83194-82b9-4478-9550-5ad793467bd0",
-   "metadata": {},
+   "metadata": {
+    "id": "f3f83194-82b9-4478-9550-5ad793467bd0"
+   },
    "source": [
     "# Load And Use Finetuned Model"
    ]
@@ -31,7 +35,9 @@
   {
    "cell_type": "markdown",
    "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e",
-   "metadata": {},
+   "metadata": {
+    "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e"
+   },
    "source": [
     "This notebook contains minimal code to load the finetuned model that was created and saved in chapter 6 via [ch06.ipynb](ch06.ipynb)."
    ]
@@ -40,7 +46,13 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
+    "outputId": "9eeefb8e-a7eb-4d62-cf78-c797b3ed4e2e"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -66,7 +78,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201",
-   "metadata": {},
+   "metadata": {
+    "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201"
+   },
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
@@ -83,7 +97,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "fb02584a-5e31-45d5-8377-794876907bc6",
-   "metadata": {},
+   "metadata": {
+    "id": "fb02584a-5e31-45d5-8377-794876907bc6"
+   },
    "outputs": [],
    "source": [
     "from previous_chapters import GPTModel\n",
@@ -116,7 +132,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94",
-   "metadata": {},
+   "metadata": {
+    "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94"
+   },
    "outputs": [],
    "source": [
     "import torch\n",
@@ -128,6 +146,7 @@
     "# Then load pretrained weights\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model.load_state_dict(torch.load(\"review_classifier.pth\", map_location=device, weights_only=True))\n",
+    "model.to(device)\n",
     "model.eval();"
    ]
   },
@@ -135,7 +154,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5",
-   "metadata": {},
+   "metadata": {
+    "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5"
+   },
    "outputs": [],
    "source": [
     "import tiktoken\n",
@@ -147,7 +168,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2",
-   "metadata": {},
+   "metadata": {
+    "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2"
+   },
    "outputs": [],
    "source": [
     "# This function was implemented in ch06.ipynb\n",
@@ -167,7 +190,7 @@
     "\n",
     "    # Model inference\n",
     "    with torch.no_grad():\n",
-    "        logits = model(input_tensor)[:, -1, :]  # Logits of the last output token\n",
+    "        logits = model(input_tensor.to(device))[:, -1, :]  # Logits of the last output token\n",
     "    predicted_label = torch.argmax(logits, dim=-1).item()\n",
     "\n",
     "    # Return the classified result\n",
@@ -178,7 +201,13 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
+    "outputId": "28eb2c02-0e38-4356-b2a3-2bf6accb5316"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -203,7 +232,13 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
+    "outputId": "0cd3cd62-f407-45f3-fa4f-51ff665355eb"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -226,6 +261,11 @@
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "L4",
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",