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JeffCarpenter/quant-fp8.ipynb

Created October 15, 2024 14:59
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"cells": [
{
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"metadata": {
"id": "view-in-github",
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},
"source": [
"<a href=\"https://colab.research.google.com/gist/JeffCarpenter/8b94bfd4d5af548cd1c650fbc2591995/quant-fp8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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"source": [
"%pip install llmcompressor"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y0AJP0FRoo_X",
"outputId": "e94f54e9-c137-4ca0-9f86-cdb92e6427e7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting llmcompressor\n",
" Downloading llmcompressor-0.2.0-py3-none-any.whl.metadata (5.8 kB)\n",
"Collecting loguru (from llmcompressor)\n",
" Downloading loguru-0.7.2-py3-none-any.whl.metadata (23 kB)\n",
"Requirement already satisfied: pyyaml>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from llmcompressor) (6.0.2)\n",
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"Requirement already satisfied: transformers<5.0,>4.0 in /usr/local/lib/python3.10/dist-packages (from llmcompressor) (4.44.2)\n",
"Collecting datasets (from llmcompressor)\n",
" Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n",
"Requirement already satisfied: accelerate>=0.20.3 in /usr/local/lib/python3.10/dist-packages (from llmcompressor) (0.34.2)\n",
"Collecting pynvml==11.5.3 (from llmcompressor)\n",
" Downloading pynvml-11.5.3-py3-none-any.whl.metadata (8.8 kB)\n",
"Collecting compressed-tensors (from llmcompressor)\n",
" Downloading compressed_tensors-0.7.0-py3-none-any.whl.metadata (6.8 kB)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->llmcompressor) (24.1)\n",
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"Collecting dill<0.3.9,>=0.3.0 (from datasets->llmcompressor)\n",
" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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" Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
"Collecting multiprocess (from datasets->llmcompressor)\n",
" Downloading multiprocess-0.70.17-py310-none-any.whl.metadata (7.2 kB)\n",
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"INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
" Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
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"\u001b[?25hInstalling collected packages: xxhash, pynvml, loguru, dill, multiprocess, datasets, compressed-tensors, llmcompressor\n",
"Successfully installed compressed-tensors-0.7.0 datasets-3.0.1 dill-0.3.8 llmcompressor-0.2.0 loguru-0.7.2 multiprocess-0.70.16 pynvml-11.5.3 xxhash-3.5.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%pip install hf_transfer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m9j3rMfCpSbl",
"outputId": "dc8432c7-2e0a-4198-fc4f-9b4f8d20a48b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting hf_transfer\n",
" Downloading hf_transfer-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB)\n",
"Downloading hf_transfer-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
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"\u001b[?25hInstalling collected packages: hf_transfer\n",
"Successfully installed hf_transfer-0.1.8\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%env HF_HUB_ENABLE_HF_TRANSFER 1\n",
"%env HF_HUB_DISABLE_TELEMETRY 1\n",
"import huggingface_hub\n",
"huggingface_hub.login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 181,
"referenced_widgets": [
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},
"id": "sxTnjeBVpVpR",
"outputId": "f232a830-2596-49a7-a210-80287c7404ee"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"env: HF_HUB_ENABLE_HF_TRANSFER=1\n",
"env: HF_HUB_DISABLE_TELEMETRY=1\n"
]
},
{
"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": "a04ac1c2a0bc4eb99667bf1841b0a8f5"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from datasets import load_dataset\n",
"from transformers import AutoTokenizer\n",
"\n",
"from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot\n",
"from llmcompressor.transformers.compression.helpers import (\n",
" calculate_offload_device_map,\n",
" custom_offload_device_map,\n",
")\n",
"\n",
"recipe = \"\"\"\n",
"quant_stage:\n",
" quant_modifiers:\n",
" QuantizationModifier:\n",
" ignore: [\"lm_head\"]\n",
" config_groups:\n",
" group_0:\n",
" weights:\n",
" num_bits: 8\n",
" type: float\n",
" strategy: tensor\n",
" dynamic: false\n",
" symmetric: true\n",
" input_activations:\n",
" num_bits: 8\n",
" type: float\n",
" strategy: tensor\n",
" dynamic: false\n",
" symmetric: true\n",
" targets: [\"Linear\"]\n",
"\"\"\"\n",
"\n",
"model_stub = \"aminabbasi/PsychoLexLLaMA-8B\"\n",
"model_name = model_stub.split(\"/\")[-1]\n",
"\n",
"device_map = calculate_offload_device_map(\n",
" model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype=\"auto\"\n",
")\n",
"\n",
"model = SparseAutoModelForCausalLM.from_pretrained(\n",
" model_stub, torch_dtype=\"auto\", device_map=device_map\n",
")\n",
"tokenizer = AutoTokenizer.from_pretrained(model_stub)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
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},
"id": "w7tng95Fohmx",
"outputId": "27e97ff6-2188-422d-d2bf-6e7915d9ce34"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/855 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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{
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"data": {
"text/plain": [
"model.safetensors.index.json: 0%| | 0.00/23.9k [00:00<?, ?B/s]"
],
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{
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"data": {
"text/plain": [
"Downloading shards: 0%| | 0/7 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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{
"output_type": "display_data",
"data": {
"text/plain": [
"model-00001-of-00007.safetensors: 0%| | 0.00/4.89G [00:00<?, ?B/s]"
],
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"version_major": 2,
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}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uC7OZiESoak3"
},
"outputs": [],
"source": [
"output_dir = f\"./{model_name}-FP8\"\n",
"\n",
"DATASET_ID = \"HuggingFaceH4/ultrachat_200k\"\n",
"DATASET_SPLIT = \"train_sft\"\n",
"NUM_CALIBRATION_SAMPLES = 512\n",
"MAX_SEQUENCE_LENGTH = 4096\n",
"\n",
"ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)\n",
"ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))\n",
"\n",
"def preprocess(example):\n",
" return {\n",
" \"text\": tokenizer.apply_chat_template(\n",
" example[\"messages\"],\n",
" tokenize=False,\n",
" )\n",
" }\n",
"\n",
"ds = ds.map(preprocess)\n",
"\n",
"def tokenize(sample):\n",
" return tokenizer(\n",
" sample[\"text\"],\n",
" padding=False,\n",
" max_length=MAX_SEQUENCE_LENGTH,\n",
" truncation=True,\n",
" add_special_tokens=False,\n",
" )\n",
"\n",
"ds = ds.map(tokenize, remove_columns=ds.column_names)\n",
"\n",
"oneshot(\n",
" model=model,\n",
" output_dir=output_dir,\n",
" dataset=ds,\n",
" recipe=recipe,\n",
" max_seq_length=MAX_SEQUENCE_LENGTH,\n",
" num_calibration_samples=NUM_CALIBRATION_SAMPLES,\n",
" save_compressed=True,\n",
")\n"
]
}
]
}
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