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Run generate example in ch06 optionally on GPU (#352)

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* model.to("cuda")

model.to("cuda")

* update device placement

---------

Co-authored-by: rasbt <mail@sebastianraschka.com>
This commit is contained in:
Mingyuan Xu 2024-09-13 21:01:52 +08:00 committed by GitHub
parent 0da32f3976
commit 21e6971b11
1 changed files with 52 additions and 12 deletions
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64
ch06/01_main-chapter-code/load-finetuned-model.ipynb
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@ -3,7 +3,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d", "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d",
"metadata": {}, "metadata": {
"id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d"
},
"source": [ "source": [
"<table style=\"width:100%\">\n", "<table style=\"width:100%\">\n",
"<tr>\n", "<tr>\n",
@ -23,7 +25,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "f3f83194-82b9-4478-9550-5ad793467bd0", "id": "f3f83194-82b9-4478-9550-5ad793467bd0",
"metadata": {}, "metadata": {
"id": "f3f83194-82b9-4478-9550-5ad793467bd0"
},
"source": [ "source": [
"# Load And Use Finetuned Model" "# Load And Use Finetuned Model"
] ]
@ -31,7 +35,9 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e", "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e",
"metadata": {}, "metadata": {
"id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e"
},
"source": [ "source": [
"This notebook contains minimal code to load the finetuned model that was created and saved in chapter 6 via [ch06.ipynb](ch06.ipynb)." "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", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52", "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": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
@ -66,7 +78,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201", "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201",
"metadata": {}, "metadata": {
"id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from pathlib import Path\n", "from pathlib import Path\n",
@ -83,7 +97,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"id": "fb02584a-5e31-45d5-8377-794876907bc6", "id": "fb02584a-5e31-45d5-8377-794876907bc6",
"metadata": {}, "metadata": {
"id": "fb02584a-5e31-45d5-8377-794876907bc6"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from previous_chapters import GPTModel\n", "from previous_chapters import GPTModel\n",
@ -116,7 +132,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94", "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94",
"metadata": {}, "metadata": {
"id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import torch\n", "import torch\n",
@ -128,6 +146,7 @@
"# Then load pretrained weights\n", "# Then load pretrained weights\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\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.load_state_dict(torch.load(\"review_classifier.pth\", map_location=device, weights_only=True))\n",
"model.to(device)\n",
"model.eval();" "model.eval();"
] ]
}, },
@ -135,7 +154,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,
"id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5", "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5",
"metadata": {}, "metadata": {
"id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import tiktoken\n", "import tiktoken\n",
@ -147,7 +168,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 6,
"id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2", "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2",
"metadata": {}, "metadata": {
"id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# This function was implemented in ch06.ipynb\n", "# This function was implemented in ch06.ipynb\n",
@ -167,7 +190,7 @@
"\n", "\n",
" # Model inference\n", " # Model inference\n",
" with torch.no_grad():\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", " predicted_label = torch.argmax(logits, dim=-1).item()\n",
"\n", "\n",
" # Return the classified result\n", " # Return the classified result\n",
@ -178,7 +201,13 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f", "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": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
@ -203,7 +232,13 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8", "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": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
@ -226,6 +261,11 @@
} }
], ],
"metadata": { "metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "L4",
"provenance": []
},
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "Python 3 (ipykernel)",
"language": "python", "language": "python",