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Created October 29, 2021 19:36
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PyTorchOptimizationofBERT_HF
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pytorchoptimizationofbert_hf.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/asw-v4/7d4d1f344b8590474edeb6ca7e7b8080/pytorchoptimizationofbert_hf.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KfGbe_WE5Eq2"
},
"source": [
"Precursor Environmental Installs"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NoPjmai15C3P",
"outputId": "407f9d72-caed-4bf0-8955-21cdbf1f49ac"
},
"source": [
"!pip install transformers"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting transformers\n",
" Downloading transformers-4.11.3-py3-none-any.whl (2.9 MB)\n",
"\u001b[K |████████████████████████████████| 2.9 MB 5.5 MB/s \n",
"\u001b[?25hCollecting huggingface-hub>=0.0.17\n",
" Downloading huggingface_hub-0.0.19-py3-none-any.whl (56 kB)\n",
"\u001b[K |████████████████████████████████| 56 kB 4.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
"Collecting pyyaml>=5.1\n",
" Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
"\u001b[K |████████████████████████████████| 596 kB 58.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
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"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.3.0)\n",
"Collecting tokenizers<0.11,>=0.10.1\n",
" Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
"\u001b[K |████████████████████████████████| 3.3 MB 38.8 MB/s \n",
"\u001b[?25hCollecting sacremoses\n",
" Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
"\u001b[K |████████████████████████████████| 895 kB 37.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.17->transformers) (3.7.4.3)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (2.4.7)\n",
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"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
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"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
"Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
" Attempting uninstall: pyyaml\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
"Successfully installed huggingface-hub-0.0.19 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.11.3\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7mQ7ksvI4e7G"
},
"source": [
"# Optimizing BERT using the PyTorch Framework"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x2o-6PBn4lJI"
},
"source": [
"Import python libs"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Qpt4ypGV4kI2",
"outputId": "59101062-fef1-45a7-f0be-3e7e597b9a2a"
},
"source": [
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"import transformers\n",
"import time\n",
"import torch\n",
"import torch.fx as fx\n",
"import numpy as np\n",
"\n",
"print(transformers.__version__)\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"4.11.3\n"
]
}
]
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},
"id": "of55v0UNlhx_",
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},
"source": [
"# Build tokenizer and model\n",
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased-finetuned-mrpc\")\n",
"\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased-finetuned-mrpc\")\n",
"\n",
"# Setup some example inputs\n",
"sequence_0 = \"The company HuggingFace is based in New York City\"\n",
"sequence_1 = \"Apples are especially bad for your health\"\n",
"sequence_2 = \"HuggingFace's headquarters are situated in Manhattan\"\n",
"\n",
"max_length=128\n",
"paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, max_length=max_length, padding='max_length', truncation=True, return_tensors=\"pt\")\n",
"not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, max_length=max_length, padding='max_length', truncation=True, return_tensors=\"pt\")\n",
"\n",
"# Run the original PyTorch model on compilation exaple\n",
"paraphrase_classification_logits = model(**paraphrase)[0]\n",
"\n",
"# Convert example inputs to a format that is compatible with TorchScript tracing\n",
"example_inputs_paraphrase = paraphrase['input_ids'], paraphrase['attention_mask'], paraphrase['token_type_ids']\n",
"example_inputs_not_paraphrase = not_paraphrase['input_ids'], not_paraphrase['attention_mask'], not_paraphrase['token_type_ids']\n"
],
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],
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},
{
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},
"source": [
"## Define Timer functions\n",
"\n",
"def timer_jit (f, *inputs):\n",
" s = time.perf_counter()\n",
" o = f(*inputs)\n",
" return o, 1000* (time.perf_counter() - s)\n",
"\n",
"def timer (f, enc):\n",
" s = time.perf_counter()\n",
" o = f(**enc)\n",
" return o, 1000* (time.perf_counter() - s)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TvqvBN4BqbDC",
"outputId": "43ca1ca8-903a-44d9-d1ec-61e53cd6e927"
},
"source": [
"iters = 25\n",
"timer_dict = {}\n",
"\n",
"# Get time of normal model on cpu\n",
"output, t = timer(model, paraphrase)\n",
"mn = np.mean([timer(model, paraphrase)[1] for _ in range(iters)])\n",
"print(\"Paraphrase - Norm: \",output[0], \"in avg\", mn, \"ms\")\n",
"timer_dict[\"Paraphrase - Norm\"] = mn\n",
"output, t = timer(model, not_paraphrase)\n",
"mn = np.mean([timer(model, not_paraphrase)[1] for _ in range(iters)])\n",
"print(\"NotParaphr - Norm: \",output[0], \"in avg\", mn, \"ms\")\n",
"timer_dict[\"NotParaphr - Norm\"] = mn\n",
"\n",
"# Get time of JIT model on cpu\n",
"output, t = timer_jit(model_jit, *example_inputs_paraphrase)\n",
"mn = np.mean([timer_jit(model_jit, *example_inputs_paraphrase)[1] for _ in range(iters)])\n",
"print(\"Paraphrase - JIT: \",output[\"logits\"], \"in avg\", mn, \"ms\")\n",
"timer_dict[\"Paraphrase - JIT\"] = mn\n",
"output, t = timer_jit(model_jit, *example_inputs_not_paraphrase)\n",
"mn = np.mean([timer_jit(model_jit, *example_inputs_not_paraphrase)[1] for _ in range(iters)])\n",
"print(\"NotParaphr - JIT: \",output[\"logits\"], \"in avg\", t, \"ms\")\n",
"timer_dict[\"NotParaphr - JIT\"] = mn\n",
"\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Paraphrase - Norm: tensor([[-0.3495, 1.9004]], grad_fn=<AddmmBackward>) in avg 466.87474180000436 ms\n",
"NotParaphr - Norm: tensor([[ 0.5386, -2.2197]], grad_fn=<AddmmBackward>) in avg 454.10800460000695 ms\n",
"Paraphrase - JIT: tensor([[-0.3495, 1.9004]], grad_fn=<AddmmBackward>) in avg 442.25114480000457 ms\n",
"NotParaphr - JIT: tensor([[ 0.5386, -2.2197]], grad_fn=<AddmmBackward>) in avg 445.52506099995526 ms\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "l8bLYqBptULm"
},
"source": [
"# Quantize Linear modules in bert model\n",
"\n",
"quantized_model = torch.quantization.quantize_dynamic(\n",
" model, {torch.nn.Linear}, dtype=torch.qint8\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MyC5RgOFuUea",
"outputId": "0364f78e-16bc-4457-8ba8-ccbffe24f1f3"
},
"source": [
"# Get times of Quantized base model on cpu\n",
"output, t = timer_jit(quantized_model, *example_inputs_paraphrase)\n",
"mn = np.mean([timer_jit(quantized_model, *example_inputs_paraphrase)[1] for _ in range(iters)])\n",
"print(\"Paraphrase - NormQuant: \",output[\"logits\"], \"in avg\", mn, \"ms\")\n",
"timer_dict[\"Paraphrase - NormQuant\"] = mn\n",
"output, t = timer_jit(quantized_model, *example_inputs_not_paraphrase)\n",
"mn = np.mean([timer_jit(quantized_model, *example_inputs_not_paraphrase)[1] for _ in range(iters)])\n",
"print(\"NotParaphr - NormQuant: \",output[\"logits\"], \"in avg\", t, \"ms\")\n",
"timer_dict[\"NotParaphr - NormQuant\"] = mn"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Paraphrase - NormQuant: tensor([[-0.2978, 1.8203]]) in avg 389.149349679999 ms\n",
"NotParaphr - NormQuant: tensor([[ 0.7058, -2.2485]]) in avg 399.11502200004634 ms\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dsIN3qdsxn53"
},
"source": [
"# Trace Quantized model with JIT\n",
"quantized_model_jit = torch.jit.trace(quantized_model, example_inputs_paraphrase, strict=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "N1gJbXYbxzlk",
"outputId": "993f4e4f-d99a-4139-d979-b41c299f6a5b"
},
"source": [
"# Get times of Quantized JIT Tracedbase model on cpu\n",
"output, t = timer_jit(quantized_model_jit, *example_inputs_paraphrase)\n",
"mn = np.mean([timer_jit(quantized_model_jit, *example_inputs_paraphrase)[1] for _ in range(iters)])\n",
"print(\"Paraphrase - JITQuant: \",output[\"logits\"], \"in avg\", mn, \"ms\")\n",
"timer_dict[\"Paraphrase - JITQuant\"] = mn\n",
"output, t = timer_jit(quantized_model_jit, *example_inputs_not_paraphrase)\n",
"mn = np.mean([timer_jit(quantized_model_jit, *example_inputs_not_paraphrase)[1] for _ in range(iters)])\n",
"print(\"NotParaphr - JITQuant: \",output[\"logits\"], \"in avg\", t, \"ms\")\n",
"timer_dict[\"NotParaphr - JITQuant\"] = mn\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Paraphrase - JITQuant: tensor([[-0.2978, 1.8203]]) in avg 381.3574273999939 ms\n",
"NotParaphr - JITQuant: tensor([[ 0.7058, -2.2485]]) in avg 399.7605430000135 ms\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "26CZcheD3DKP",
"outputId": "b84f8136-799c-4ad9-d7db-c8b587f7292a"
},
"source": [
"import os\n",
"def print_size_of_model(model):\n",
" torch.save(model.state_dict(), \"temp.p\")\n",
" print('Size (MB):', os.path.getsize(\"temp.p\")/1e6)\n",
" os.remove('temp.p')\n",
"\n",
"for m , name in [[model, \"Model\"],[model_jit,\"JIT\"], [quantized_model,\"Quant\"],[quantized_model_jit,\"QuantJIT\"]]:\n",
" print(name)\n",
" print(\"-\"*10)\n",
" print_size_of_model(m)\n",
" output, t = timer_jit(m, *example_inputs_not_paraphrase)\n",
" print(\"Runtime (ms):\", t)\n",
" print(\"-\"*10)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model\n",
"----------\n",
"Size (MB): 433.328557\n",
"Runtime (ms): 487.1507699999711\n",
"----------\n",
"JIT\n",
"----------\n",
"Size (MB): 433.332971\n",
"Runtime (ms): 462.55940900005044\n",
"----------\n",
"Quant\n",
"----------\n",
"Size (MB): 176.806533\n",
"Runtime (ms): 395.5846989999827\n",
"----------\n",
"QuantJIT\n",
"----------\n",
"Size (MB): 90.852096\n",
"Runtime (ms): 377.12325799998325\n",
"----------\n"
]
}
]
}
]
}
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