Test code in pytorch 2.4 (#285)

* test code in pytorch 2.4

* update

appendix-A/01_main-chapter-code/code-part2.ipynb

@@ -2,7 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "AAAnDw04iAm4"
+   },
    "source": [
     "<table style=\"width:100%\">\n",
     "<tr>\n",
@@ -54,14 +56,14 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "RM7kGhwMF_nO",
-    "outputId": "ac60b048-b81f-4bb0-90fa-1ca474f04e9a"
+    "outputId": "b1872617-aacd-46fa-e5f3-f130fd81b246"
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.0.1+cu118\n"
+      "2.4.0+cu121\n"
      ]
     }
    ],
@@ -79,7 +81,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "OXLCKXhiUkZt",
-    "outputId": "39fe5366-287e-47eb-cc34-3508d616c4f9"
+    "outputId": "e9ca3c58-d92c-4c8b-a9c9-cd7fcc1fedb4"
    },
    "outputs": [
     {
@@ -102,18 +104,15 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "MTTlfh53Va-T",
-    "outputId": "f31d8bbe-577f-4db4-9939-02e66b9f96d1"
+    "outputId": "bae76cb5-d1d3-441f-a7c5-93a161e2e86a"
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "tensor([5., 7., 9.])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([5., 7., 9.])\n"
+     ]
     }
    ],
    "source": [
@@ -125,13 +124,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "Z4LwTNw7Vmmb",
-    "outputId": "1c025c6a-e3ed-4c7c-f5fd-86c14607036e"
+    "outputId": "9ad97923-bc8e-4c49-88bf-48dc1de56804"
    },
    "outputs": [
     {
@@ -151,24 +150,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 184
+     "height": 158
     },
     "id": "tKT6URN1Vuft",
-    "outputId": "e6f01e7f-d9cf-44cb-cc6d-46fc7907d5c0"
+    "outputId": "8396eb18-47c8-47a1-c1b6-8bcb9480fb52"
    },
    "outputs": [
     {
      "ename": "RuntimeError",
-     "evalue": "ignored",
+     "evalue": "Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!",
      "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;32m/tmp/ipykernel_2321/2079609735.py\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!"
      ]
     }
@@ -189,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "id": "GyY59cjieitv"
    },
@@ -215,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "id": "v41gKqEJempa"
    },
@@ -243,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {
     "id": "UPGVRuylep8Y"
    },
@@ -271,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "metadata": {
     "id": "drhg6IXofAXh"
    },
@@ -302,13 +301,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "7jaS5sqPWCY0",
-    "outputId": "84c74615-38f2-48b8-eeda-b5912fed1d3a"
+    "outputId": "8a5cd93d-671c-4abf-d5cd-97845f300ffd"
    },
    "outputs": [
     {
@@ -362,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {
     "id": "4qrlmnPPe7FO"
    },
@@ -391,13 +390,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "1_-BfkfEf4HX",
-    "outputId": "473bf21d-5880-4de3-fc8a-051d75315b94"
+    "outputId": "9453154f-0a5b-4a44-a3c9-f010e08d5a2c"
    },
    "outputs": [
     {
@@ -406,7 +405,7 @@
        "1.0"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,13 +416,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "iYtXKBGEgKss",
-    "outputId": "508edd84-3fb7-4d04-cb23-9df0c3d24170"
+    "outputId": "d6cc870a-34de-490e-e5d3-23e6956744bd"
    },
    "outputs": [
     {
@@ -432,7 +431,7 @@
        "1.0"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -443,21 +442,27 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "nc2LGFVbiAnB"
+   },
    "source": [
     "### A.9.3 Training with multiple GPUs"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "cOUza9iQiAnC"
+   },
    "source": [
     "See [DDP-script.py](DDP-script.py)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "YOYk5Fh7iAnC"
+   },
    "source": [
     "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/appendix-a_compressed/12.webp\" width=\"600px\">\n",
     "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/appendix-a_compressed/13.webp\" width=\"600px\">"
@@ -485,7 +490,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,