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s-zanella/Opacus bug report (>= 1.0)

Created October 4, 2022 11:01
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Opacus bug report: Length of `BatchSplittingSampler` with Poisson sampling
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Opacus bug report (>= 1.0)
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"cells": [
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"source": [
"# Opacus bug repro notebook\n",
"\n",
"Reproduce a bug you experienced in this notebook. This way, we have a controlled environment and can run and reproduce things so that we can help you much faster.\n",
"\n",
"To help you go faster, let's prefill some things you will always want to have here such as dependencies."
]
},
{
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"metadata": {
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},
"id": "OY0TWxFuXRwt",
"outputId": "fd1454ef-ac4b-4be1-c2e9-6b741adce6dc"
},
"source": [
"%%bash\n",
"apt install git-extras # Yup, Colab supports apt get! git-extras is helpful to eg fetch pull requests\n",
"\n",
"git clone https://github.com/pytorch/opacus.git # This will get you master\n",
"\n",
"# Interested in pulling a specific PR? It can be done like this:\n",
"# cd opacus\n",
"# git pr <PR_NUMBER> # For example, git pr 101 will fetch https://github.com/pytorch/opacus/pull/101\n",
"\n",
"cd opacus\n",
"pip install -e ."
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Reading package lists...\n",
"Building dependency tree...\n",
"Reading state information...\n",
"git-extras is already the newest version (4.5.0-1).\n",
"The following package was automatically installed and is no longer required:\n",
" libnvidia-common-460\n",
"Use 'apt autoremove' to remove it.\n",
"0 upgraded, 0 newly installed, 0 to remove and 20 not upgraded.\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Obtaining file:///content/opacus\n",
"Requirement already satisfied: functorch in /usr/local/lib/python3.7/dist-packages (from opacus==1.2.0) (0.2.1)\n",
"Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from opacus==1.2.0) (1.21.6)\n",
"Requirement already satisfied: torch>=1.8 in /usr/local/lib/python3.7/dist-packages (from opacus==1.2.0) (1.12.1+cu113)\n",
"Requirement already satisfied: scipy>=1.2 in /usr/local/lib/python3.7/dist-packages (from opacus==1.2.0) (1.7.3)\n",
"Requirement already satisfied: opt-einsum>=3.3.0 in /usr/local/lib/python3.7/dist-packages (from opacus==1.2.0) (3.3.0)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.8->opacus==1.2.0) (4.1.1)\n",
"Installing collected packages: opacus\n",
" Attempting uninstall: opacus\n",
" Found existing installation: opacus 1.2.0\n",
" Can't uninstall 'opacus'. No files were found to uninstall.\n",
" Running setup.py develop for opacus\n",
"Successfully installed opacus-1.2.0\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n",
"WARNING: apt does not have a stable CLI interface. Use with caution in scripts.\n",
"\n",
"fatal: destination path 'opacus' already exists and is not an empty directory.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RZOtdlUIYo3_"
},
"source": [
"exit() # Installing a new package requires restarting the environment"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FaFmcQnsYz0f"
},
"source": [
"# Useful utils to speed you up\n",
"\n",
"To show us the behavior of your component, build the smallest possible end to end project.\n",
"\n",
"You can edit this as needed just so you don't start from scratch, but also feel free to ignore if it doesn't apply to your use case."
]
},
{
"cell_type": "code",
"source": [
"from statistics import mean\n",
"\n",
"def train(model, criterion, optimizer, train_loader, device=\"cuda:0\"):\n",
" accs = []\n",
" losses = []\n",
" for x, y in tqdm(train_loader):\n",
" x = x.to(device)\n",
" y = y.to(device)\n",
"\n",
" logits = model(x)\n",
" loss = criterion(logits, y)\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
"\n",
" preds = logits.argmax(-1)\n",
" n_correct = float(preds.eq(y).sum())\n",
" batch_accuracy = n_correct / len(y)\n",
"\n",
" accs.append(batch_accuracy)\n",
" losses.append(float(loss))\n",
"\n",
" print(f\"Train Accuracy: {mean(accs):.6f}. Train Loss: {mean(losses):.6f}\")\n",
"\n",
"def test(model, test_loader, privacy_engine=None, device=\"cuda:0\"):\n",
" accs = []\n",
" with torch.no_grad():\n",
" for x, y in tqdm(test_loader):\n",
" x = x.to(device)\n",
" y = y.to(device)\n",
"\n",
" preds = model(x).argmax(-1)\n",
" n_correct = float(preds.eq(y).sum())\n",
" batch_accuracy = n_correct / len(y)\n",
"\n",
" accs.append(batch_accuracy)\n",
" print(f\"Test Accuracy: {mean(accs):.6f}\")"
],
"metadata": {
"id": "5RllsFY2Yts2"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E64hS_O0Yvqn"
},
"source": [
"# Common imports\n",
"import os\n",
"import sys\n",
"from pathlib import Path\n",
"from typing import *\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"from tqdm.autonotebook import tqdm\n",
"\n",
"import opacus"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Z6DE9tNpZ9pL"
},
"source": [
"# Put here whatever args you have\n",
"\n",
"DEVICE = \"cuda:0\"\n",
"\n",
"BATCH_SZ = 16\n",
"FEATURE_DIM = 16\n",
"N_CLASSES = 3\n",
"\n",
"DATASET_LEN = 50_000"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GVuM3nhpaEaT"
},
"source": [
"class DummyDataset(Dataset):\n",
" def __init__(self):\n",
" pass\n",
"\n",
" def __getitem__(self, i):\n",
" x = torch.randn([FEATURE_DIM]) # for categoricals like nn.Embedding you can do something like `torch.randint(0, VOC_SZ-1, (SEQ_LEN,), dtype=torch.long).to(device)`\n",
" y = 0\n",
" return x, y\n",
"\n",
" def __len__(self):\n",
" return DATASET_LEN"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hJesOjOGaEyP"
},
"source": [
"train_ds = DummyDataset()\n",
"train_loader = DataLoader(train_ds, BATCH_SZ, shuffle=False)\n",
"\n",
"test_ds = DummyDataset()\n",
"test_loader = DataLoader(test_ds, BATCH_SZ, shuffle=False)\n",
"\n",
"x, y = next(iter(train_loader)) # if you want to get a sample batch to play with"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ah2kk1xOaNC1",
"outputId": "834441df-1ca7-4f08-8e62-1da8e8464aaf"
},
"source": [
"model = nn.Linear(FEATURE_DIM, N_CLASSES).to(DEVICE)\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
"\n",
"privacy_engine = opacus.PrivacyEngine()\n",
"\n",
"model, optimizer, train_loader = privacy_engine.make_private(\n",
" module=model, \n",
" optimizer=optimizer,\n",
" data_loader=train_loader,\n",
" noise_multiplier=0.9, \n",
" max_grad_norm=1.0, \n",
")"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/content/opacus/opacus/privacy_engine.py:142: UserWarning: Secure RNG turned off. This is perfectly fine for experimentation as it allows for much faster training performance, but remember to turn it on and retrain one last time before production with ``secure_mode`` turned on.\n",
" \"Secure RNG turned off. This is perfectly fine for experimentation as it allows \"\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121,
"referenced_widgets": [
"1b7264d186234500b74f840f7e5fa086",
"3da4261dcdde4fab801c61e08aadd2ee",
"68b4efba35654e13ba626a33f6e8f350",
"a76e04bb7d654d25b4489888ab41312d",
"94c8811791af403c999c2234ed575914",
"f6ad0aa471af425fb15d686d6a746724",
"7d57435c4eb04f52af7daa73a023dbfb",
"c519a423d9384b5ab8ceb6d00dd9f93b",
"f8506a6294a340a7a4c4a228f054d5a8",
"90021be0ab754c8383d2208cf31abdbb",
"bba7c3d19e5a4c47aacba3f4c00d9679"
]
},
"id": "fmYOuCfJa467",
"outputId": "56aa09eb-7cfe-46af-fcc4-73d4f87b2861"
},
"source": [
"train(model, criterion, optimizer, train_loader)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/3124 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1b7264d186234500b74f840f7e5fa086"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py:1053: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
" warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Accuracy: 0.997487. Train Loss: 0.022327\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 66,
"referenced_widgets": [
"9525fbd73a2240059bd81e4630f60638",
"c7127e86f93d45c5a8ee385d9b4b0517",
"51221454d11441f28e596f6d580a8255",
"75de3546e2c840f29629a8a6795f091f",
"24344d06f5bd4b538586b907598daa9f",
"fd4fbd95549a44f4b8522b586f6b30ea",
"a48285d3a26b496ba3ca14248f7f70dc",
"5787f126a763411f9ac7fd7adf5c5c37",
"4825b35f525e44e396a176069c0ce66e",
"39734c9a75e34a2b857dc0000709b2a3",
"adf696a5a49749bea392bffe7577997e"
]
},
"id": "pkggnjwra_uT",
"outputId": "c91b0700-900f-4dd8-b93e-5f303a18ae8c"
},
"source": [
"test(model, test_loader)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/3125 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9525fbd73a2240059bd81e4630f60638"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test Accuracy: 1.000000\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from opacus.utils.batch_memory_manager import BatchMemoryManager, wrap_data_loader\n",
"\n",
"def memory_safe_train(model, criterion, optimizer, train_loader, \n",
" max_physical_batch_size, total=None, device=\"cuda:0\"):\n",
" accs = []\n",
" losses = []\n",
"\n",
" with BatchMemoryManager(\n",
" data_loader=train_loader, \n",
" max_physical_batch_size=max_physical_batch_size, \n",
" optimizer=optimizer\n",
" ) as memory_safe_train_loader:\n",
" for x, y in tqdm(memory_safe_train_loader, total=total):\n",
" x = x.to(device)\n",
" y = y.to(device)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" logits = model(x)\n",
" loss = criterion(logits, y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" preds = logits.argmax(-1)\n",
" n_correct = float(preds.eq(y).sum())\n",
" batch_accuracy = n_correct / len(y)\n",
"\n",
" accs.append(batch_accuracy)\n",
" losses.append(float(loss))\n",
"\n",
" print(f\"Train Accuracy: {mean(accs):.6f}. Train Loss: {mean(losses):.6f}\")"
],
"metadata": {
"id": "KJA-PUnzGlOI"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = nn.Linear(FEATURE_DIM, N_CLASSES).to(DEVICE)\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
"\n",
"privacy_engine = opacus.PrivacyEngine()\n",
"\n",
"model, optimizer, train_loader = privacy_engine.make_private(\n",
" module=model, \n",
" optimizer=optimizer,\n",
" data_loader=train_loader,\n",
" noise_multiplier=0.9, \n",
" max_grad_norm=1.0, \n",
")"
],
"metadata": {
"id": "UeOaNwoRndBE"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"max_physical_batch_size = 8\n",
"\n",
"memory_safe_train_loader = wrap_data_loader(\n",
" data_loader=train_loader,\n",
" max_batch_size=max_physical_batch_size,\n",
" optimizer=optimizer,\n",
")\n",
"\n",
"print(f\"len(memory_safe_train_loader) = {len(memory_safe_train_loader)} largely underapproximates the expected length.\\n\"\n",
" \"The progress bar overflows\")\n",
"\n",
"memory_safe_train(model, criterion, optimizer, train_loader, \n",
" max_physical_batch_size=max_physical_batch_size)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101,
"referenced_widgets": [
"b8858d7a1cac47b6941ef9eb4d68b0b3",
"b5e7b298d0064f79acebc8d72efe7797",
"fc28b377d66e481480d0b966961a5f26",
"49206dfa3fdf4e33943bf32994ac7626",
"aa6627c92a834daa8d3aebf39ec7e433",
"2b828d7bd7484070923ac20f7432e864",
"60b4a9c6279f4b09806dc59e1fbbd475",
"00fca4c4f8dc43d396beb7e2d40f061e",
"b261fec9c4c64aae923cd58e1e05ff62",
"1c9982b46eb44ed296e2454aa5817140",
"6dc7ef0c6bd44eea9ecdf6b293722ebe"
]
},
"id": "kX8ttlZRGnjT",
"outputId": "4d323728-125d-4728-ce78-3cc335458ffb"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"len(memory_safe_train_loader) = 6249 largely underapproximates the expected length.\n",
"The progress bar overflows\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/6249 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b8858d7a1cac47b6941ef9eb4d68b0b3"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Accuracy: 0.994346. Train Loss: 0.026313\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from scipy.stats import binom\n",
"\n",
"def approximate_length(memory_safe_train_loader):\n",
" self = memory_safe_train_loader.batch_sampler\n",
"\n",
" def F(k):\n",
" return binom(self.sampler.num_samples, self.sampler.sample_rate).cdf(k * self.max_batch_size) - \\\n",
" binom(self.sampler.num_samples, self.sampler.sample_rate).cdf((k - 1) * self.max_batch_size)\n",
"\n",
" expected_physical_batches = int(self.sampler.num_samples * self.sampler.sample_rate / self.max_batch_size)\n",
"\n",
" return int(\n",
" len(self.sampler) *\n",
" sum([i * F(i) for i in range(expected_physical_batches - 4, expected_physical_batches + 4)])\n",
" )"
],
"metadata": {
"id": "QYkedjFAG8Jx"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = nn.Linear(FEATURE_DIM, N_CLASSES).to(DEVICE)\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
"\n",
"privacy_engine = opacus.PrivacyEngine()\n",
"\n",
"model, optimizer, train_loader = privacy_engine.make_private(\n",
" module=model, \n",
" optimizer=optimizer,\n",
" data_loader=train_loader,\n",
" noise_multiplier=0.9, \n",
" max_grad_norm=1.0, \n",
")"
],
"metadata": {
"id": "LtOevw6zngxp"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"memory_safe_train_loader = wrap_data_loader(\n",
" data_loader=train_loader,\n",
" max_batch_size=max_physical_batch_size,\n",
" optimizer=optimizer,\n",
")\n",
"\n",
"print(f\"approximate_length(memory_safe_train_loader) = {approximate_length(memory_safe_train_loader)} better approximates the expected length.\")\n",
"\n",
"memory_safe_train(model, criterion, optimizer, train_loader, \n",
" max_physical_batch_size=max_physical_batch_size, total=approximate_length(memory_safe_train_loader))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84,
"referenced_widgets": [
"d4f526c345644f8b84b3e98610764fb6",
"80fe9504dec249778039e524f674a708",
"4730c06cbc474335aa4d57c87f656c7f",
"806cd519c6cd4ac0bd11098f12284b8b",
"9d42428444aa451bbda5c8f85ae43646",
"18b12caf51af41bc99ad786621db54f0",
"133c2697d25040bc84c5c10dee102335",
"1b1e8c58877b44779485494fc0955ee3",
"aa26e68eb69c4360bedb297b0c91e611",
"3625a111cb8247e6896c5e1c607ada55",
"9b5f24d1dc2348eba591e723f72f0e30"
]
},
"id": "DSE9KX87Hr3_",
"outputId": "b1ad56b4-5add-45ca-b88b-fff2f9f279e0"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"approximate_length(memory_safe_train_loader) = 7607 better approximates the expected length.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/7607 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d4f526c345644f8b84b3e98610764fb6"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Accuracy: 0.995118. Train Loss: 0.028032\n"
]
}
]
}
]
}
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