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2025-07-25 21:05:08 +02:00

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"source": [
"# Quantizing Phi-1.5 with GPTQ Algorithm\n",
"\n",
"We first setup our environment by checking whether all libraries and dependencies are installed or in the correct versions.\n",
"\n",
"We will first apply AutoGPTQ. Documentation can be found in this [Github Repo](https://github.com/AutoGPTQ/AutoGPTQ).\n",
"\n"
],
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},
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"outputId": "6182e24c-7585-40b5-9a6a-fb244ec567b0"
},
"outputs": [
{
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"name": "stdout",
"text": [
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2024 NVIDIA Corporation\n",
"Built on Thu_Jun__6_02:18:23_PDT_2024\n",
"Cuda compilation tools, release 12.5, V12.5.82\n",
"Build cuda_12.5.r12.5/compiler.34385749_0\n"
]
}
],
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]
},
{
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"We can see that CUDA 12 is used."
],
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}
},
{
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],
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},
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"outputId": "34f9b2d2-291a-49bb-b7bf-8ab5f30907b9"
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]
}
]
},
{
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"source": [
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],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "pT_OLWHwtq1S",
"outputId": "2420e54e-1d24-414c-8035-738f41f1ed7b"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"\u001b[?25hInstalling collected packages: onnx, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, humanfriendly, nvidia-cusparse-cu12, nvidia-cudnn-cu12, coloredlogs, onnxruntime, nvidia-cusolver-cu12, transformers, optimum\n",
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" Attempting uninstall: nvidia-cufft-cu12\n",
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" Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
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" Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-cupti-cu12\n",
" Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cublas-cu12\n",
" Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
" Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
" Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
" Attempting uninstall: nvidia-cusparse-cu12\n",
" Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
" Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
" Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
" Attempting uninstall: nvidia-cudnn-cu12\n",
" Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
" Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
" Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
" Attempting uninstall: nvidia-cusolver-cu12\n",
" Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
" Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
" Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.53.3\n",
" Uninstalling transformers-4.53.3:\n",
" Successfully uninstalled transformers-4.53.3\n",
"Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 onnx-1.18.0 onnxruntime-1.22.1 optimum-1.26.1 transformers-4.52.4\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"nvidia",
"transformers"
]
},
"id": "e76d48369e0d4298a6dca850e5f24875"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"!pip install auto-gptq"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rce-C5fCtqzW",
"outputId": "9af1ba10-85da-465e-dc73-7b94c631013b"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting auto-gptq\n",
" Downloading auto_gptq-0.7.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n",
"Requirement already satisfied: accelerate>=0.26.0 in /usr/local/lib/python3.11/dist-packages (from auto-gptq) (1.9.0)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from auto-gptq) (2.0.2)\n",
"Collecting rouge (from auto-gptq)\n",
" Downloading rouge-1.0.1-py3-none-any.whl.metadata (4.1 kB)\n",
"Collecting gekko (from auto-gptq)\n",
" Downloading gekko-1.3.0-py3-none-any.whl.metadata (3.0 kB)\n",
"Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.11/dist-packages (from auto-gptq) (2.6.0+cu124)\n",
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"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.4.127)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (10.3.5.147)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (11.6.1.9)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.3.1.170)\n",
"Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (0.6.2)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.4.127)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (12.4.127)\n",
"Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->auto-gptq) (3.2.0)\n",
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"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.13.0->auto-gptq) (1.3.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers>=4.31.0->auto-gptq) (2024.11.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers>=4.31.0->auto-gptq) (2.32.3)\n",
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"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets->auto-gptq) (18.1.0)\n",
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"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets->auto-gptq) (2.2.2)\n",
"Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets->auto-gptq) (3.5.0)\n",
"Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets->auto-gptq) (0.70.16)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from rouge->auto-gptq) (1.17.0)\n",
"Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (3.12.14)\n",
"Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub>=0.21.0->accelerate>=0.26.0->auto-gptq) (1.1.5)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers>=4.31.0->auto-gptq) (3.4.2)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers>=4.31.0->auto-gptq) (2025.7.14)\n",
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"Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (1.4.0)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (25.3.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (1.7.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (6.6.3)\n",
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (0.3.2)\n",
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->auto-gptq) (1.20.1)\n",
"Downloading auto_gptq-0.7.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.5/23.5 MB\u001b[0m \u001b[31m41.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading gekko-1.3.0-py3-none-any.whl (13.2 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m76.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
"Installing collected packages: rouge, gekko, auto-gptq\n",
"Successfully installed auto-gptq-0.7.1 gekko-1.3.0 rouge-1.0.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"print(torch.__version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bQLiEhWktqwl",
"outputId": "46852ab6-c262-45b5-b7d4-daded513c516"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.6.0+cu124\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
"48d808460ce74631a9c723a98f104eee",
"c3f3a3cd5d9049feb44b20afdf4b4558",
"f299c8a9ec9c4f72adddaf27527524aa",
"0bee3ad123754767be6b82da79b252b3",
"410e43c953764390841016a737d11628",
"58127c12b9e441a4a99ebae327478bde",
"59af6ceeacd343c089ca47f13daeaf3c",
"cb3e4fd1f5814b33ac6dd3f20ed7829c",
"81ff126389994532baaf1e50a425fdbf",
"bb7a39dc49dd4d919f9f3d587c38497d",
"da13bb101ed142df872f04c46d03336c",
"ed408510e1984bb7807e159cc22e389e",
"46c7728f2a6544b380a36971037fbc85",
"fd0783bbb1a3440093f6f40ebae775a9",
"ed27fd8b40c642059f3c44572254f0f7",
"370291a74ab34ff194bf19da884ca18f",
"7fbde9afdf864ac9a8ae5a22f0dd346c",
"8f3987b7258746e9bc1eaa9090fea965",
"d8d99dcfcab44b388b5e54b3e96dab3d",
"067bcd4e85cf4716a4c6afcb9718f690"
]
},
"id": "hgC4NH3zx15W",
"outputId": "701b36d8-0289-429d-b390-430bc7a64d34"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "48d808460ce74631a9c723a98f104eee"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModelForCausalLM, GPTQConfig, AutoTokenizer\n",
"from huggingface_hub import snapshot_download\n",
"import torch\n",
"import time\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"model_id = \"microsoft/phi-1_5\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(model_id).half().to(device)\n",
"model_memory = model.get_memory_footprint()/(1024**3)\n",
"print(f\"Model memory: {model_memory:.2f} GB\")\n",
"\n",
"\n",
"#gptq_config = GPTQConfig(bits=4, dataset=\"c4\", tokenizer=tokenizer)\n",
"\n",
"#model_name_or_path = local_dir\n",
"\n",
"#tokenizer= AutoTokenizer.from_pretrained(model_name_or_path)\n",
"#quant_model = AutoModelForCausalLM.from_pretrained(model_name_or_path,\n",
"# quantization_config=quantization_config,\n",
"# device_map='auto')"
],
"metadata": {
"id": "pXbESESrtqp5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e44f7753-62ea-447c-e38f-9c4cb15d1b5c"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model memory: 2.64 GB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import time\n",
"\n",
"quantization_config = GPTQConfig(bits=4,\n",
" group_size=128,\n",
" dataset=\"c4\",\n",
" tokenizer=tokenizer,\n",
" desc_act=False)\n",
"\n",
"print(\"Starting Model Quantization...\")\n",
"\n",
"t0 = time.time()\n",
"bit_quant = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\", quantization_config=quantization_config)\n",
"t_quant = time.time() - t0\n",
"\n",
"print(f\"Quantization time: {t_quantization:.2f} s = {t_quantization/60:.2f} min\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 560,
"referenced_widgets": [
"b858ebd2dd4345a6b6e09a8f41c0e754",
"3488c4ae6335479cad957d5725cf4d73",
"cffe2d3dea3f4db29ce6c7da772ed2f7",
"9b703e6a6f48418da47d41752a6965fa",
"0619bafedd3342c4b147d929c903165b",
"0677da1369ba4e1d884a6217f31e4c80",
"28d2604ed1754be3a43d2146d3f7f477",
"8fa4ac50498b4ac28111fbc35e11e398",
"adbd6c93f6ee44709bb7a1164d43a216",
"860cd51afa924d1ea11bbd041d426e70",
"f8aaf333d3b04f4fa164264c7edeb40e",
"29f8ec4b24324c7980a67f5be2112d02",
"f68b28aae07a485181c0d3e2348fcd4e",
"e5b9838e6cd9438fb848eb615fa6b09f",
"7021348d013b4fac8f8ce08f03124543",
"773382fa3a2946adb0bde97007c56203",
"92fe384bed5845279c39b254d15afdbb",
"e2a583187ee9477a96b72490ed8ff4e4",
"b9fd8a5319004a9aab8c7cdc368f5871",
"9ffbdb5962d147e7b2154c4d32efb5c9",
"c8182966a6d642c3a65804de49c1426d",
"2737de0a9a69429abcb2b8b579889bc7",
"426625005005441eaa099d488c6a8227",
"20ad9117f8ae463cb73d2b5280d27c5b",
"675bb398199e4a568b7d5216ba123c1d",
"6c119c389e8e4001b0a474a10160d3a3",
"7fe1862984d94346a7e25783ba072ce4",
"8315c4f458894fe3bcf902ce3e057c53",
"5bd541acd17b428ea61007d2afc9b391",
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},
"id": "SCXCVB4StquB",
"outputId": "cda75f4e-f1b8-4ba2-fd79-3f125a56bc18"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Starting Model Quantization...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/auto_gptq/nn_modules/triton_utils/kernels.py:410: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
" @custom_fwd\n",
"/usr/local/lib/python3.11/dist-packages/auto_gptq/nn_modules/triton_utils/kernels.py:418: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n",
" @custom_bwd\n",
"/usr/local/lib/python3.11/dist-packages/auto_gptq/nn_modules/triton_utils/kernels.py:461: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
" @custom_fwd(cast_inputs=torch.float16)\n",
"WARNING:auto_gptq.nn_modules.qlinear.qlinear_cuda:CUDA extension not installed.\n",
"WARNING:auto_gptq.nn_modules.qlinear.qlinear_cuda_old:CUDA extension not installed.\n"
]
},
{
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"data": {
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"Generating train split: 0 examples [00:00, ? examples/s]"
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"text": [
"Token indices sequence length is longer than the specified maximum sequence length for this model (3403 > 2048). Running this sequence through the model will result in indexing errors\n"
]
},
{
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"data": {
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"Quantizing model.layers blocks : 0%| | 0/24 [00:00<?, ?it/s]"
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{
"output_type": "stream",
"name": "stderr",
"text": [
"`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.\n"
]
},
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 't_quantization' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipython-input-5-1944319448.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mt_quant\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Quantization time: {t_quantization:.2f} s = {t_quantization/60:.2f} min\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 't_quantization' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"model_reqs_4_bit = bit_quant.get_memory_footprint() / (1024**3)\n",
"print(f\"Model Memory After Quantization: {model_reqs_4_bit:.2f} GB\")"
],
"metadata": {
"id": "ps7PwaQQJGrF",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "95377dd1-88fa-42b6-ef9c-922b9f42ef1c"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Memory After Quantization: 0.98 GB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"torch.save(bit_quant.state_dict(), \"quantized_gptq_phi.pt\")\n"
],
"metadata": {
"id": "bWZGMpaStqlm"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "rpNNqMm0tbkO"
},
"execution_count": null,
"outputs": []
}
]
}