├── tests ├── __init__.py ├── test_privacy_accounting.py ├── test_dataloader.py ├── test_deepee.py ├── test_snooper.py ├── test_surgeon.py ├── test_per_sample_wrapper.py ├── test_privacy_wrapper.py └── test_watchdog.py ├── site ├── assets │ ├── javascripts │ │ └── lunr │ │ │ └── min │ │ │ ├── lunr.jp.min.js │ │ │ ├── lunr.vi.min.js │ │ │ ├── lunr.multi.min.js │ │ │ ├── lunr.ja.min.js │ │ │ ├── lunr.stemmer.support.min.js │ │ │ ├── lunr.sv.min.js │ │ │ ├── lunr.da.min.js │ │ │ ├── lunr.no.min.js │ │ │ ├── lunr.nl.min.js │ │ │ ├── lunr.de.min.js │ │ │ ├── lunr.du.min.js │ │ │ ├── lunr.ru.min.js │ │ │ ├── lunr.fi.min.js │ │ │ ├── lunr.hu.min.js │ │ │ ├── lunr.pt.min.js │ │ │ ├── lunr.fr.min.js │ │ │ ├── lunr.ro.min.js │ │ │ ├── lunr.it.min.js │ │ │ ├── lunr.es.min.js │ │ │ └── lunr.ar.min.js │ ├── images │ │ └── favicon.png │ └── stylesheets │ │ └── palette.f1a3b89f.min.css ├── sitemap.xml.gz ├── sitemap.xml └── 404.html ├── mkdocs.yml ├── CI_env.yml ├── environment.yml ├── deepee ├── __init__.py ├── dataloader.py ├── snooper.py ├── surgery.py └── watchdog.py ├── pyproject.toml ├── README.md ├── .github └── workflows │ └── main.yml ├── .gitignore ├── docs ├── index.md └── examples.md └── LICENSE /tests/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.jp.min.js: -------------------------------------------------------------------------------- 1 | module.exports=require("./lunr.ja"); -------------------------------------------------------------------------------- /site/sitemap.xml.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gkaissis/deepee/HEAD/site/sitemap.xml.gz -------------------------------------------------------------------------------- /site/assets/images/favicon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gkaissis/deepee/HEAD/site/assets/images/favicon.png -------------------------------------------------------------------------------- /mkdocs.yml: -------------------------------------------------------------------------------- 1 | site_name: deepee 2 | nav: 3 | - Home: index.md 4 | - Examples: examples.md 5 | theme: 6 | name: material 7 | palette: 8 | primary: black 9 | accent: light blue 10 | font: 11 | text: Fira Sans 12 | mono: Fira Code -------------------------------------------------------------------------------- /CI_env.yml: -------------------------------------------------------------------------------- 1 | name: CI 2 | channels: 3 | - pytorch 4 | - conda-forge 5 | - defaults 6 | dependencies: 7 | - python>=3.7.0 8 | - pytorch>=1.8.0 9 | - torchvision>=0.9.0 10 | - scipy 11 | - torchcsprng 12 | - pytest 13 | - mypy 14 | - black 15 | pip: 16 | - testfixtures 17 | 18 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: deepee2 2 | channels: 3 | - pytorch 4 | - conda-forge 5 | - defaults 6 | dependencies: 7 | - python>=3.7.0 8 | - pytorch>=1.8.0 9 | - torchvision>=0.9.0 10 | - cudatoolkit>=11.1 11 | - scipy 12 | - torchcsprng 13 | - pytest 14 | - mypy 15 | - black 16 | - pip 17 | - pip: 18 | - testfixtures 19 | 20 | -------------------------------------------------------------------------------- /site/sitemap.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | None 4 | 2021-06-02 5 | daily 6 | 7 | None 8 | 2021-06-02 9 | daily 10 | 11 | -------------------------------------------------------------------------------- /deepee/__init__.py: -------------------------------------------------------------------------------- 1 | __version__ = "0.1.9" 2 | 3 | from .wrapper import PrivacyWrapper, PerSampleGradientWrapper 4 | from .snooper import ModelSnooper 5 | from .dataloader import UniformDataLoader 6 | from .surgery import ModelSurgeon, SurgicalProcedures 7 | from .watchdog import PrivacyWatchdog 8 | 9 | __all__ = [ 10 | "PrivacyWrapper", 11 | "PerSampleGradientWrapper", 12 | "UniformDataLoader", 13 | "ModelSurgeon", 14 | "SurgicalProcedures", 15 | "PrivacyWatchdog", 16 | ] 17 | -------------------------------------------------------------------------------- /tests/test_privacy_accounting.py: -------------------------------------------------------------------------------- 1 | from deepee.privacy_accounting import compute_eps_uniform 2 | import pytest 3 | 4 | 5 | def test_epsilon_uniform(): 6 | eps = compute_eps_uniform(10, 1.0, 50_000, 200, 1e-5) 7 | assert 1.29 < eps < 1.31 8 | 9 | 10 | def test_epsilon_float(): 11 | eps = compute_eps_uniform(10.0, 1.0, 50_000, 200, 1e-5) 12 | assert 1.29 < eps < 1.31 13 | 14 | 15 | def test_implausible_values(): 16 | with pytest.raises(ValueError): 17 | compute_eps_uniform(1, 0.001, 60_000, 200, 1e-5) -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "deepee" 3 | version = "0.1.9" 4 | description = "Fast (and cheeky) differentially private gradient-based optimisation in PyTorch" 5 | authors = ["Georgios Kaissis", "Alexander Ziller"] 6 | license = "Apache-2.0" 7 | readme = "README.md" 8 | repository = "https://github.com/gkaissis/deepee" 9 | homepage = "https://github.com/gkaissis/deepee" 10 | 11 | [tool.poetry.dependencies] 12 | python = ">=3.7" 13 | 14 | [build-system] 15 | requires = ["poetry-core>=1.0.0"] 16 | build-backend = "poetry.core.masonry.api" 17 | -------------------------------------------------------------------------------- /tests/test_dataloader.py: -------------------------------------------------------------------------------- 1 | from deepee import UniformDataLoader 2 | from torch.utils.data import Dataset 3 | import torch 4 | 5 | 6 | class SimpleDataset(Dataset): 7 | def __init__(self): 8 | super().__init__() 9 | self.data = torch.arange(1, 100, 1, dtype=torch.int) 10 | 11 | def __getitem__(self, idx: int) -> torch.Tensor: 12 | return self.data[idx] 13 | 14 | def __len__(self) -> int: 15 | return len(self.data) 16 | 17 | 18 | ds = SimpleDataset() 19 | 20 | dl = UniformDataLoader(ds, 50) 21 | 22 | 23 | def test_dataloader(): 24 | for item in dl: 25 | assert ( 26 | len(set(item)) == 50 27 | ) # always returns correct batch size and never the same item twice 28 | -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.vi.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}}); -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # `deepee` 2 | 3 | `deepee` is a library for differentially private deep learning in PyTorch. More precisely, `deepee` implements the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm originally described by [Abadi et al.](https://arxiv.org/pdf/1607.00133.pdf). Despite the name, `deepee` works with any (first order) optimizer, including Adam, AdaGrad, etc. 4 | 5 | It wraps a regular `PyTorch` model and takes care of calculating per-sample gradients, clipping, noising and accumulating gradients with an API which closely mimics the `PyTorch` API of the original model. 6 | 7 | Check out the documentation [here](http://g-k.ai/deepee/) 8 | 9 | # For paper readers 10 | If you would like to reproduce the results from our paper, please go [here](https://github.com/gkaissis/deepee/tree/results) -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.multi.min.js: -------------------------------------------------------------------------------- 1 | !function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p=0;n--)if(/\S/.test(o.charAt(n))){o=o.substring(0,n+1);break}for(a=[],s=o.length,c=0,l=0;c<=s;c++)if(u=o.charAt(c),m=c-l,u.match(/\s/)||c==s){if(m>0)for(p=t.segment(o.slice(l,c)).filter(function(e){return!!e}),f=l,n=0;n Generator: 53 | for _ in range(len(self)): 54 | yield np.random.choice(self.sample_size, self.batch_size, replace=False) 55 | 56 | def __len__(self) -> int: 57 | return self.sample_size // self.batch_size 58 | -------------------------------------------------------------------------------- /tests/test_surgeon.py: -------------------------------------------------------------------------------- 1 | from deepee.wrapper import PrivacyWrapper 2 | from torchvision.models import resnet18 3 | from torch import nn 4 | from deepee import ModelSurgeon, SurgicalProcedures 5 | from functools import partial 6 | import torch 7 | import pytest 8 | 9 | 10 | def test_bn_to_bn_nostats(): 11 | model = resnet18() 12 | surgeon = ModelSurgeon(SurgicalProcedures.BN_to_BN_nostats) 13 | converted_model = surgeon.operate(model) 14 | assert converted_model.bn1.track_running_stats == False 15 | wrapped = PrivacyWrapper( 16 | converted_model, 17 | 2, 18 | 1.0, 19 | 1.0, 20 | ) 21 | data = torch.rand(2, 3, 224, 224) 22 | output = wrapped(data) 23 | 24 | 25 | def test_bn_to_bn_nostats_small_input(): 26 | model = resnet18() 27 | surgeon = ModelSurgeon(SurgicalProcedures.BN_to_BN_nostats) 28 | converted_model = surgeon.operate(model) 29 | assert converted_model.bn1.track_running_stats == False 30 | wrapped = PrivacyWrapper( 31 | converted_model, 32 | 2, 33 | 1.0, 34 | 1.0, 35 | ) 36 | data = torch.rand(2, 3, 32, 32) 37 | with pytest.raises(RuntimeError): 38 | output = wrapped(data) 39 | 40 | 41 | def test_bn_to_gn_default(): 42 | model = resnet18() 43 | surgeon = ModelSurgeon(SurgicalProcedures.BN_to_GN) 44 | converted_model = surgeon.operate(model) 45 | assert isinstance(converted_model.bn1, nn.modules.normalization.GroupNorm) 46 | assert converted_model.bn1.num_groups == 32 47 | wrapped = PrivacyWrapper( 48 | converted_model, 49 | 2, 50 | 1.0, 51 | 1.0, 52 | ) 53 | data = torch.rand(2, 3, 224, 224) 54 | output = wrapped(data) 55 | 56 | 57 | def test_bn_to_gn_96(): 58 | model = resnet18() 59 | surgeon = ModelSurgeon(partial(SurgicalProcedures.BN_to_GN, num_groups=96)) 60 | converted_model = surgeon.operate(model) 61 | assert isinstance(converted_model.bn1, nn.modules.normalization.GroupNorm) 62 | assert converted_model.bn1.num_groups == 96 63 | wrapped = PrivacyWrapper( 64 | converted_model, 65 | 2, 66 | 1.0, 67 | 1.0, 68 | ) 69 | data = torch.rand(2, 3, 224, 224) 70 | with pytest.raises(RuntimeError): 71 | output = wrapped(data) 72 | 73 | 74 | def test_bn_to_in(): 75 | model = resnet18() 76 | surgeon = ModelSurgeon(SurgicalProcedures.BN_to_IN) 77 | converted_model = surgeon.operate(model) 78 | assert isinstance(converted_model.bn1, nn.modules.instancenorm._InstanceNorm) 79 | wrapped = PrivacyWrapper( 80 | converted_model, 81 | 2, 82 | 1.0, 83 | 1.0, 84 | ) 85 | data = torch.rand(2, 3, 224, 224) 86 | output = wrapped(data) 87 | 88 | 89 | def test_bn_to_ln(): 90 | model = resnet18() 91 | surgeon = ModelSurgeon(partial(SurgicalProcedures.BN_to_LN, normalized_shape=16)) 92 | converted_model = surgeon.operate(model) 93 | assert isinstance(converted_model.bn1, nn.modules.normalization.LayerNorm) 94 | assert converted_model.bn1.normalized_shape == (16,) 95 | -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.stemmer.support.min.js: -------------------------------------------------------------------------------- 1 | !function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new 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a=s+(e-s>>1),f=0,l=o=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}}); -------------------------------------------------------------------------------- /deepee/snooper.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from typing import Callable 3 | 4 | 5 | class BadModuleError(Exception): 6 | """Informs the user that the module has some kinda problem.""" 7 | 8 | pass 9 | 10 | 11 | class ModelSnooper: 12 | def __init__(self) -> None: 13 | """The ModelSnooper checks the model for common problems in DP deep learning. 14 | In general, all layers which maintain state based on multiple samples from a batch 15 | are not natively supported since the state is calculated non-privately. 16 | These include: 17 | - Having vanilla BatchNorm layers in the model unless the track_running_stats 18 | attribute is deactivated. In PyTorch, this turns them into InstanceNorm layers. 19 | - Having InstanceNorm layers which track running statistics. 20 | """ 21 | self.validators = [ 22 | Validator( 23 | "InstanceNorm Running Stats Off", 24 | IN_running_stats_off, 25 | "InstanceNorm Layers must have track_running_stats turned off. ", 26 | ), 27 | Validator( 28 | "BatchNorm Running Stats Off", 29 | BN_running_stats_off, 30 | "BatchNorm Layers must have track_running_stats turned off, otherwise be" 31 | " replaced with InstanceNorm, LayerNorm or GroupNorm.", 32 | ), 33 | ] 34 | 35 | def snoop(self, model: torch.nn.Module) -> None: 36 | msg = "" 37 | for validator in self.validators: 38 | msg += validator.validate(model) 39 | if msg != "": 40 | raise BadModuleError(msg) 41 | 42 | 43 | class Validator: 44 | def __init__(self, name: str, val_func: Callable, message: str) -> None: 45 | """Private class for use in the ModelSnooper. Takes a validator function 46 | and applies it to the model's modules. If the function fails, it returns 47 | an exception message for the user. 48 | 49 | Args: 50 | name (str): Human-readable description. Not used elsewhere. 51 | val_func (Callable): The function which validates the model. Should 52 | return False if it fails 53 | message (str): The error message which is passed to the ModelSnooper 54 | to be shown to the user. 55 | """ 56 | self.name = name 57 | self.val_func = val_func 58 | self.message = message 59 | 60 | def validate(self, model: torch.nn.Module) -> str: 61 | """Run the validators on the model. Returns an error 62 | message if one of the validators fails or an empty string otherwise. 63 | """ 64 | if not self.val_func(model): 65 | return self.message 66 | return "" 67 | 68 | 69 | def IN_running_stats_off(model: torch.nn.Module) -> bool: 70 | """Module has InstanceNorm layer with running stats 71 | activated. 72 | """ 73 | for module in model.modules(): 74 | if isinstance( 75 | module, 76 | (torch.nn.InstanceNorm1d, torch.nn.InstanceNorm2d, torch.nn.InstanceNorm3d), 77 | ): 78 | if module.track_running_stats: 79 | return False 80 | return True 81 | 82 | 83 | def BN_running_stats_off(model: torch.nn.Module) -> bool: 84 | """Module contains BatchNorm layer with running stats 85 | activated. 86 | """ 87 | for module in model.modules(): 88 | if isinstance( 89 | module, 90 | (torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d), 91 | ): 92 | if module.track_running_stats: 93 | return False 94 | return True 95 | -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | # About 2 | 3 | `deepee` is a library for differentially private deep learning in PyTorch. More precisely, `deepee` implements the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm originally described by [Abadi et al.](https://arxiv.org/pdf/1607.00133.pdf). Despite the name, `deepee` works with any (first order) optimizer, including Adam, AdaGrad, etc. 4 | 5 | It wraps a regular `PyTorch` model and takes care of calculating per-sample gradients, clipping, noising and accumulating gradients with an API which closely mimics the `PyTorch` API of the original model. 6 | 7 | # Design principles 8 | 9 | The DP-SGD algorithm requires two key steps, which differ from "normal" neural network training. For every minibatch, the algorithm requires to: 10 | 11 | 1. Obtain the gradients of each individual sample in the batch to calculate their L2-norm and _clip_ it to a pre-set threshold 12 | 2. Average the gradients and add Gaussian noise to the average before taking an optimisation step. 13 | 14 | The first of these two steps is complicated, as deep learning frameworks like `PyTorch` are not designed to provide per-sample gradients by default. `deepee` works around this limitation by creating a "copy" of the network for every sample in the batch and then doing a parallel forward and backward pass on each sample simultaneously. This technique has two benefits: 15 | 16 | 1. `deepee` works with *any* neural network architecture that can be defined in `PyTorch` without any user modification 17 | 2. The process is very efficient as it happens in parallel and doesn't create significant memory overhead. The copies of the models are kept as references to the original weights and thus it's not required to create "real" model copies. The only memory overhead thus results from keeping the per-sample gradients in memory for a short time during each batch. 18 | 19 | 20 | # Basic usage 21 | The key component of the framework is the `PrivacyWrapper` class, which wraps a `PyTorch module` and takes care of the training process behind the scenes. The clipping norm and noise multiplier parameters can be set here. `deepee` will automatically check if a model contains incompatible layers (such as Batch Normalisation) and throw an error. If such layers exist in the network, the `ModelSurgeon` can be used to replace them with compatible layers such as Group Normalisation, Instance Normalisation etc.. `deepee` also offers automatic privacy accounting and will interrupt the training (and optionally save the last model) when the privacy budget is exhausted. This process is abstracted using the `PrivacyWatchdog` class. 22 | 23 | An example for the use of `deepee` showcasing all these concepts can be found [here](examples.md). 24 | 25 | # For paper readers 26 | If you would like to reproduce the experiments from our paper, please switch to the `results` branch. Instructions for reproduction can be found in the README of the branch. 27 | # Installation 28 | You can install deepee with: 29 | 30 | `pip install deepee` 31 | 32 | `deepee` does not come with any hard-coded dependencies to maintain high compatibility, so these must be installed separately. `deepee` is tested with `pytorch>1.8`(CPU and GPU) on Ubuntu Linux (min. 20.04) and MacOS >11.3. No GPU support is available for MacOS. 33 | 34 | The cryptographically secure random number generator (CSPRNG) used by `deepee` must also be installed separately. The framework will function without the CSPRNG, however we stress that it should **not** be used in production environments without this feature. 35 | 36 | To install `torchcsprng`, follow the instructions on [this](https://pypi.org/project/torchcsprng/) page. 37 | 38 | Lastly, `SciPy` is required, which can be installed with `pip install scipy`. 39 | 40 | For Linux, the full installation can therefore look something like this: 41 | 42 | ``` 43 | pip3 install torchcsprng==0.2.0 torch==1.8.0+cu101 torchvision==0.9.0 -f https://download.pytorch.org/whl/cu101/torch_stable.html 44 | pip3 install scipy deepee 45 | ``` 46 | 47 | 48 | # Contributing 49 | `deepee` is licensed under the Apache 2.0. license. Contributions are welcome via PR. To contribute, please install the following additional dependencies: `mypy, black, pytest, testfixtures`. Packaging is carried out using `poetry`. 50 | 51 | -------------------------------------------------------------------------------- /tests/test_per_sample_wrapper.py: -------------------------------------------------------------------------------- 1 | from deepee import PerSampleGradientWrapper 2 | import torch 3 | import pytest 4 | 5 | 6 | class MiniModel(torch.nn.Module): 7 | def __init__(self): 8 | super().__init__() 9 | self.lin = torch.nn.Linear(10, 1) 10 | 11 | def forward(self, x): 12 | return self.lin(x) 13 | 14 | 15 | def test_wrap(): 16 | wrapped = PerSampleGradientWrapper(MiniModel(), 2) 17 | 18 | 19 | def test_forward(): 20 | data = torch.randn(2, 1, 10) 21 | wrapped = PerSampleGradientWrapper(MiniModel(), 2) 22 | output = wrapped(data) 23 | assert output.shape == (2, 1, 1) 24 | 25 | 26 | def test_raises_param_error(): 27 | wrapped = PerSampleGradientWrapper(MiniModel(), 2) 28 | with pytest.raises(ValueError): 29 | params = wrapped.parameters() 30 | 31 | 32 | def test_check_device_cpu(): 33 | wrapped = PerSampleGradientWrapper(MiniModel(), 2).to("cpu") 34 | assert ( 35 | next( 36 | iter( 37 | set([param.device.type for param in wrapped.wrapped_model.parameters()]) 38 | ) 39 | ) 40 | == "cpu" 41 | ) 42 | for model in wrapped.models: 43 | assert ( 44 | next(iter(set([param.device.type for param in model.parameters()]))) 45 | == "cpu" 46 | ) 47 | 48 | 49 | def test_check_device_gpu(): 50 | if torch.cuda.is_available(): 51 | wrapped = PerSampleGradientWrapper(MiniModel(), 2).to("cuda") 52 | assert "cuda" in next( 53 | iter( 54 | set([param.device.type for param in wrapped.wrapped_model.parameters()]) 55 | ) 56 | ) 57 | for model in wrapped.models: 58 | assert "cuda" in next( 59 | iter(set([param.device.type for param in model.parameters()])) 60 | ) 61 | else: 62 | pass 63 | 64 | 65 | def test_per_sample_grads(): 66 | torch.manual_seed(42) 67 | data = torch.randn(2, 1, 10) 68 | torch.manual_seed(42) 69 | wrapped = PerSampleGradientWrapper(MiniModel(), 2) 70 | torch.manual_seed(42) 71 | model = MiniModel() # single copy 72 | output_single = model(data) 73 | output_wrapped = wrapped(data) 74 | loss_single = output_single.mean() 75 | loss_wrapped = output_wrapped.mean() 76 | 77 | loss_single.backward() 78 | loss_wrapped.backward() 79 | wrapped.calculate_per_sample_gradients() 80 | single_grads = torch.cat([param.grad.flatten() for param in model.parameters()]) 81 | accumulated_grads = torch.cat( 82 | [ 83 | param.accumulated_gradients.sum(dim=0).flatten() 84 | for param in wrapped.wrapped_model.parameters() 85 | ] 86 | ) 87 | assert torch.allclose(single_grads, accumulated_grads) 88 | 89 | 90 | def test_per_sample_grads_transfer_learning(): 91 | class MiniModel(torch.nn.Module): 92 | def __init__(self): 93 | super().__init__() 94 | self.lin = torch.nn.Linear(10, 1) 95 | list(self.lin.parameters())[0].requires_grad_(False) 96 | 97 | def forward(self, x): 98 | return self.lin(x) 99 | 100 | "One model parameter does not requires_grad" 101 | torch.manual_seed(42) 102 | data = torch.randn(2, 1, 10) 103 | torch.manual_seed(42) 104 | wrapped = PerSampleGradientWrapper(MiniModel(), 2) 105 | torch.manual_seed(42) 106 | model = MiniModel() # single copy 107 | output_single = model(data) 108 | output_wrapped = wrapped(data) 109 | loss_single = output_single.mean() 110 | loss_wrapped = output_wrapped.mean() 111 | 112 | loss_single.backward() 113 | loss_wrapped.backward() 114 | wrapped.calculate_per_sample_gradients() 115 | single_grads = torch.cat( 116 | [param.grad.flatten() for param in model.parameters() if param.requires_grad] 117 | ) 118 | accumulated_grads = torch.cat( 119 | [ 120 | param.accumulated_gradients.sum(dim=0).flatten() 121 | for param in wrapped.wrapped_model.parameters() 122 | if hasattr(param, "accumulated_gradients") 123 | ] 124 | ) 125 | assert torch.allclose(single_grads, accumulated_grads) -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.sv.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Swedish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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-------------------------------------------------------------------------------- /deepee/surgery.py: -------------------------------------------------------------------------------- 1 | r""" 2 | Several functions in this module reused directly from 3 | https://github.com/pytorch/opacus/blob/master/opacus/utils/module_modification.py 4 | under Apache-2.0 License terms. 5 | """ 6 | from typing import Callable, Type, Union 7 | import torch 8 | from torch import nn 9 | 10 | 11 | def _replace_child( 12 | root: nn.Module, child_name: str, converter: Callable[[nn.Module], nn.Module] 13 | ) -> None: 14 | parent = root 15 | nameList = child_name.split(".") 16 | for name in nameList[:-1]: 17 | parent = parent._modules[name] 18 | # set to identity 19 | parent._modules[nameList[-1]] = converter(parent._modules[nameList[-1]]) 20 | 21 | 22 | def replace_all_modules( 23 | root: nn.Module, 24 | target_class: Type[nn.Module], 25 | converter: Callable[[nn.Module], nn.Module], 26 | ) -> nn.Module: 27 | if isinstance(root, target_class): 28 | return converter(root) 29 | for name, obj in root.named_modules(): 30 | if isinstance(obj, target_class): 31 | _replace_child(root, name, converter) 32 | return root 33 | 34 | 35 | bn_to_in = { 36 | nn.BatchNorm1d: nn.InstanceNorm1d, 37 | nn.BatchNorm2d: nn.InstanceNorm2d, 38 | nn.BatchNorm3d: nn.InstanceNorm3d, 39 | } 40 | 41 | in_to_in = { 42 | nn.InstanceNorm1d: nn.InstanceNorm1d, 43 | nn.InstanceNorm2d: nn.InstanceNorm2d, 44 | nn.InstanceNorm3d: nn.InstanceNorm3d, 45 | } 46 | 47 | bn_to_bn_nostats = { 48 | nn.BatchNorm1d: nn.BatchNorm1d, 49 | nn.BatchNorm2d: nn.BatchNorm2d, 50 | nn.BatchNorm3d: nn.BatchNorm3d, 51 | } 52 | 53 | 54 | class SurgicalProcedures: 55 | @staticmethod 56 | def BN_to_IN(module: nn.modules.batchnorm._BatchNorm) -> nn.Module: 57 | """BatchNorm to InstanceNorm""" 58 | return bn_to_in.get(type(module))( 59 | module.num_features, track_running_stats=False 60 | ) 61 | 62 | @staticmethod 63 | def BN_to_BN_nostats(module: nn.modules.batchnorm._BatchNorm) -> nn.Module: 64 | """BatchNorm to BatchNorm without running stats (=InstanceNorm)""" 65 | return bn_to_bn_nostats.get(type(module))( 66 | module.num_features, track_running_stats=False 67 | ) 68 | 69 | @staticmethod 70 | def IN_to_IN_nostats(module: nn.modules.instancenorm._InstanceNorm) -> nn.Module: 71 | """InstanceNorm to InstanceNorm (without running stats)""" 72 | return in_to_in.get(type(module))( 73 | module.num_features, track_running_stats=False 74 | ) 75 | 76 | @staticmethod 77 | def BN_to_GN( 78 | module: nn.modules.batchnorm._BatchNorm, num_groups: Union[str, int] = "default" 79 | ) -> nn.Module: 80 | """BatchNorm to GroupNorm""" 81 | if num_groups == "default": 82 | return nn.GroupNorm(min(32, module.num_features), module.num_features) 83 | elif isinstance(num_groups, int): 84 | return nn.GroupNorm(num_groups, module.num_features) 85 | else: 86 | raise ValueError( 87 | "num_groups must either be set to 'default' or the number of groups" 88 | ) 89 | 90 | @staticmethod 91 | def BN_to_LN( 92 | module: nn.Module, 93 | normalized_shape: Union[int, list, torch.Size], 94 | ) -> nn.Module: 95 | """BatchNorm to LayerNorm""" 96 | return nn.LayerNorm(normalized_shape=normalized_shape) 97 | 98 | 99 | class ModelSurgeon: 100 | def __init__(self, converter: Callable): 101 | """Convenience class to replace privacy-incompatible 102 | normalisation layers (most commonly BatchNorm) in the model. 103 | Can be used to remedy BadModelError exceptions thrown by the 104 | ModelSnooper. 105 | 106 | Args: 107 | converter (Callable): The converter to be used. Options: 108 | - BN_to_BN_nostats : BatchNorm to BatchNorm without running stats. 109 | This is equivalent to InstanceNorm. 110 | - BN_to_IN : BatchNorm to InstanceNorm 111 | - BN_to_GN : BatchNorm to GroupNorm. By default will use min(32, model.num_features) 112 | groups. A different value can be bound to the function using functools.partial. 113 | - BN_to_LN : BatchNorm to LayerNorm. Requires a normalized_shape value to be passed 114 | using functools partial. 115 | - IN_to_IN_nostats : InstanceNorm with running stats to InstanceNorm without running stats. 116 | 117 | Example use: 118 | >> from functools import partial 119 | >> from deepee.surgery import BN_to_GN 120 | >> model = ... [Throws BadModuleError when the PrivacyWrapper is attached] 121 | >> surgeon = ModelSurgeon(partial(BN_to_GN, num_channels=32)) 122 | >> converted_model = surgeon.operate(model) 123 | """ 124 | self.converter = converter 125 | 126 | def operate(self, model: nn.Module) -> nn.Module: 127 | """Convert the model based on the defined converter.""" 128 | return replace_all_modules( 129 | model, 130 | target_class=nn.modules.batchnorm._BatchNorm, 131 | converter=self.converter, 132 | ) 133 | -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.nl.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Dutch` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. 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r=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-r,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var r;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.slice_del(),w=!0,a())))}function m(){var r;u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.eq_s_b(3,"gem")||(C.cursor=C.limit-r,C.slice_del(),a())))}function f(){var r,e,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,r=C.find_among_b(h,5))switch(C.bra=C.cursor,r){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 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e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],p=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],g=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],h=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],k=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],v=[new e("aa",-1,-1),new e("ee",-1,-1),new e("oo",-1,-1),new e("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(r){C.setCurrent(r)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var e=C.cursor;return r(),C.cursor=e,o(),C.limit_backward=e,C.cursor=C.limit,f(),C.cursor=C.limit_backward,s(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.nl.stemmer,"stemmer-nl"),r.nl.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.nl.stopWordFilter,"stopWordFilter-nl")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.de.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `German` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.du.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Dutch` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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e=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-e,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var e;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.slice_del(),w=!0,a())))}function m(){var e;u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.eq_s_b(3,"gem")||(C.cursor=C.limit-e,C.slice_del(),a())))}function d(){var e,r,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,e=C.find_among_b(h,5))switch(C.bra=C.cursor,e){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 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r("",-1,6),new r("á",0,1),new r("ä",0,1),new r("é",0,2),new r("ë",0,2),new r("í",0,3),new r("ï",0,3),new r("ó",0,4),new r("ö",0,4),new r("ú",0,5),new r("ü",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursor=C.limit,d(),C.cursor=C.limit_backward,s(),!0}};return function(e){return"function"==typeof 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utilisation of `deepee` in a step by step MNIST example. 4 | 5 | Being by importing the relevant libraries: 6 | 7 | ```py 8 | from deepee import (PrivacyWrapper, PrivacyWatchdog, UniformDataLoader, 9 | ModelSurgeon, SurgicalProcedures) 10 | import torch 11 | from torch import nn 12 | from torchvision import datasets, transforms 13 | 14 | class args: 15 | batch_size = 200 16 | test_batch_size = 200 17 | log_interval = 1000 18 | num_epochs = 5 19 | device = "cuda" if torch.cuda.is_available() else "cpu" 20 | device = args.device 21 | ``` 22 | 23 | To train with DP guarantees, a special DataLoader is required. `deepee` provides this DataLoader with sensible presets: 24 | 25 | ```py 26 | train_loader = UniformDataLoader( 27 | datasets.MNIST( 28 | "./data", 29 | train=True, 30 | download=True, 31 | transform=transforms.Compose( 32 | [ 33 | transforms.ToTensor(), 34 | transforms.Normalize((0.1307,), (0.3081,)), 35 | ] 36 | ), 37 | ), 38 | batch_size=args.batch_size, 39 | ) 40 | test_loader = torch.utils.data.DataLoader( 41 | datasets.MNIST( 42 | "./data", 43 | train=False, 44 | transform=transforms.Compose( 45 | [ 46 | transforms.ToTensor(), 47 | transforms.Normalize((0.1307,), (0.3081,)), 48 | ] 49 | ), 50 | ), 51 | batch_size=args.test_batch_size, 52 | shuffle=True, 53 | ) 54 | ``` 55 | 56 | Next, define the network architecture: 57 | 58 | ```py 59 | class SimpleNet(nn.Module): 60 | def __init__(self): 61 | super().__init__() 62 | self.fc1 = nn.Linear(784, 256) 63 | self.bn1 = nn.BatchNorm1d(256) 64 | self.fc2 = nn.Linear(256, 64) 65 | self.fc3 = nn.Linear(64, 10) 66 | 67 | def forward(self, x): 68 | x = torch.flatten(x, 1) 69 | x = torch.sigmoid(self.fc1(x)) 70 | x = self.bn1(x) 71 | x = torch.sigmoid(self.fc2(x)) 72 | x = self.fc3(x) 73 | return x 74 | ``` 75 | 76 | To train with DP, we now attach the `PrivacyWrapper` to the model and set up a `PrivacyWatchDog` to monitor the privacy loss: 77 | 78 | ```py 79 | watchdog = PrivacyWatchdog( 80 | train_loader, 81 | target_epsilon=1.0, 82 | abort=False, 83 | target_delta=1e-5, 84 | fallback_to_rdp=False, 85 | ) 86 | 87 | model = PrivacyWrapper(SimpleNet(), args.batch_size, 1.0, 1.0, watchdog=watchdog).to( 88 | args.device 89 | ) 90 | optimizer = torch.optim.SGD(model.wrapped_model.parameters(), lr=0.1) 91 | ``` 92 | 93 | The `PrivacyWrapper` will throw an error now: 94 | 95 | ```py 96 | --------------------------------------------------------------------------- 97 | BadModuleError Traceback (most recent call last) 98 | in () 99 | 7 ) 100 | 8 101 | ----> 9 model = PrivacyWrapper(SimpleNet(), args.batch_size, 1.0, 1.0, watchdog=watchdog).to( 102 | 10 args.device 103 | 11 ) 104 | 105 | 1 frames 106 | /usr/local/lib/python3.7/dist-packages/deepee/snooper.py in snoop(self, model) 107 | 38 msg += validator.validate(model) 108 | 39 if msg != "": 109 | ---> 40 raise BadModuleError(msg) 110 | 41 111 | 42 112 | 113 | BadModuleError: BatchNorm Layers must have track_running_stats turned off, otherwise be replaced with InstanceNorm, LayerNorm or GroupNorm. 114 | ``` 115 | 116 | Luckily, this modification can be easily done using the `ModelSurgeon` (add this code after the definition of the watchdog above): 117 | 118 | ```py 119 | # change BatchNorm to GroupNorm 120 | surgeon = ModelSurgeon(SurgicalProcedures.BN_to_GN) 121 | model = surgeon.operate(SimpleNet()) 122 | 123 | # now wrap the model 124 | model = PrivacyWrapper(model, args.batch_size, 1.0, 1.0, watchdog=watchdog).to( 125 | args.device 126 | ) 127 | optimizer = torch.optim.SGD(model.wrapped_model.parameters(), lr=0.1) 128 | ``` 129 | 130 | We can now proceed with training as usual: 131 | 132 | ```py 133 | # Train 134 | for epoch in range(args.num_epochs): 135 | model.train() 136 | for batch_idx, (data, target) in enumerate(train_loader): 137 | data, target = data.to(device), target.to(device) 138 | optimizer.zero_grad() 139 | output = model(data) 140 | loss = torch.nn.CrossEntropyLoss()(output, target) 141 | loss.backward() 142 | model.clip_and_accumulate() 143 | model.noise_gradient() 144 | optimizer.step() 145 | model.prepare_next_batch() 146 | if batch_idx % args.log_interval == 0: 147 | print( 148 | "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( 149 | epoch, 150 | batch_idx * len(data), 151 | len(train_loader.dataset), 152 | 100.0 * batch_idx / len(train_loader), 153 | loss.item(), 154 | ) 155 | ) 156 | 157 | # Test 158 | model.eval() 159 | test_loss = 0 160 | correct = 0 161 | with torch.no_grad(): 162 | for data, target in test_loader: 163 | data, target = data.to(device), target.to(device) 164 | output = model(data) 165 | test_loss += torch.nn.CrossEntropyLoss(reduction="sum")( 166 | output, target 167 | ).item() # sum up batch loss 168 | pred = output.argmax( 169 | dim=1, keepdim=True 170 | ) # get the index of the max log-probability 171 | correct += pred.eq(target.view_as(pred)).sum().item() 172 | 173 | test_loss /= len(test_loader.dataset) 174 | 175 | print( 176 | "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format( 177 | test_loss, 178 | correct, 179 | len(test_loader.dataset), 180 | 100.0 * correct / len(test_loader.dataset), 181 | ) 182 | ) 183 | ``` 184 | 185 | ``` 186 | Train Epoch: 0 [0/60000 (0%)] Loss: 2.317095 187 | INFO:root:Privacy spent at 200 steps: 0.27 188 | INFO:root:Privacy spent at 300 steps: 0.34 189 | INFO:root:Privacy spent at 400 steps: 0.39 190 | 191 | Test set: Average loss: 1.6950, Accuracy: 5639/10000 (56%) 192 | Train Epoch: 1 [0/60000 (0%)] Loss: 1.667842 193 | INFO:root:Privacy spent at 500 steps: 0.44 194 | INFO:root:Privacy spent at 600 steps: 0.49 195 | ``` 196 | -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.ru.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Russian` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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If an epsilon and delta are provided, it can either track 37 | the privacy spent during model training or abort the training when the privacy 38 | budget is exhausted. Optionally, it can preserve the final model weights before 39 | aborting. 40 | 41 | Args: 42 | target_epsilon (float): The target epsilon to warn or abort 43 | the training at. Must be a positive float. 44 | target_delta (float): The corresponding delta value. Must be positive 45 | and between 0 and 1. 46 | report_every_n_steps (int, optional): Outputs the privacy spent to STDERR 47 | every n steps. Defaults to 100. 48 | abort (Optional[bool], optional): Whether to abort the training at the set 49 | epsilon level. Defaults to False. 50 | save (Optional[bool], optional): Whether to save the last model before 51 | aborting training. Ignored if abort is set to False. Defaults to False. 52 | path (Optional[Union[str, Path]], optional): The path to save the final 53 | model state dictionary before aborting training. Ignored if abort or save 54 | are set to False. Defaults to None. 55 | fallback_to_rdp (Optional[bool], optional): Whether to fall back to Renyi 56 | DP accounting in case Gaussian DP accounting (default) fails. This is for 57 | convenience and will likely return a worse privacy guarantee which may also 58 | be incorrect. It should not be used for mission-critical work. If False, 59 | the PrivacyWatchdog will raise an error if privacy cannot be calculated with 60 | the given settings. 61 | 62 | """ 63 | self.dataloader = dataloader 64 | if not ( 65 | isinstance(self.dataloader, UniformDataLoader) 66 | or isinstance(self.dataloader.batch_sampler, UniformWORSubsampler) 67 | ): 68 | logging.critical( 69 | "Privacy accounting is only correct when using the UniformDataLoader or" 70 | " a custom DataLoader with a batch_sampler implementing uniform sampling" 71 | " without replacement." 72 | ) 73 | self.target_epsilon = target_epsilon 74 | if self.target_epsilon is None or (not self.target_epsilon >= 0.0): # type: ignore 75 | raise ValueError("Epsilon must be a positive float.") 76 | self.target_delta = target_delta 77 | if self.target_delta is None or (not (0 <= self.target_delta <= 1.0)): 78 | raise ValueError("Delta must be between 0.0 and 1.0") 79 | self.report_every_n_steps = report_every_n_steps 80 | self.abort = abort 81 | self.save = save 82 | self.path = path 83 | self.wrapper = None 84 | self.fallback_to_rdp = fallback_to_rdp 85 | 86 | if self.fallback_to_rdp: 87 | logging.critical( 88 | "Privacy accounting is set to fall back to RDP if privacy " 89 | " accounting using GDP fails. This estimate is potentially inaccurate and" 90 | " should not be used for mission-critical work!", 91 | ) 92 | 93 | if (not self.abort) and (self.save or self.path): 94 | logging.warning( 95 | "When setting 'save' or 'path' without setting 'abort=True', the" 96 | " settings are ignored." 97 | ) 98 | 99 | if (self.abort and self.save) and not self.path: 100 | raise ValueError("When setting 'save', a path to save to must be provided.") 101 | 102 | def calc_epsilon(self, steps_taken: int) -> float: 103 | if not self.wrapper: 104 | raise RuntimeError("WatchDog must be attached to a PrivacyWrapper.") 105 | batch_size = ( 106 | self.dataloader.batch_size or self.dataloader.batch_sampler.batch_size # type: ignore 107 | ) 108 | epoch = (steps_taken * batch_size) / len( # type: ignore 109 | self.dataloader.dataset # type: ignore 110 | ) 111 | try: 112 | spent = compute_eps_uniform( 113 | epoch=epoch, 114 | noise_multi=self.wrapper.noise_multiplier, # type: ignore 115 | n=len(self.dataloader.dataset), # type: ignore 116 | batch_size=batch_size, # type: ignore 117 | delta=self.target_delta, # type: ignore 118 | ) 119 | except ValueError as e: 120 | if self.fallback_to_rdp: 121 | approx_sample_rate = batch_size / len(self.dataloader.dataset) 122 | orders = [1 + x / 10.0 for x in range(1, 100)] + list(range(12, 64)) 123 | rdp = compute_rdp( 124 | q=approx_sample_rate, 125 | noise_multiplier=self.wrapper.noise_multiplier, 126 | steps=steps_taken, 127 | orders=orders, 128 | ) 129 | spent, _ = rdp_privacy_spent( 130 | orders=orders, rdp=rdp, delta=self.target_delta 131 | ) 132 | else: 133 | raise RuntimeError( 134 | "Epsilon could not be determined, likely because of implausible values" 135 | " for the L2 clip ratio and/or the noise multiplier. Try decreasing the " 136 | " L2 clip ratio or increasing the noise multiplier. If you are trying to" 137 | " 'disable' DP by setting these values, errors can be avoided by not" 138 | " attaching a WatchDog to your PrivacyWrapper." 139 | ) from e 140 | return spent 141 | 142 | def inform(self, steps_taken: int) -> None: 143 | spent = self.calc_epsilon(steps_taken) 144 | if steps_taken % self.report_every_n_steps == 0: 145 | logging.info(f"Privacy spent at {steps_taken} steps: {spent:.2f}") 146 | 147 | if spent >= self.target_epsilon: # type: ignore 148 | if self.abort: # type: ignore 149 | self.abort_training(epsilon_spent=spent, save=self.save, path=self.path) 150 | else: 151 | logging.warning( 152 | f"Privacy budget exhausted. Epsilon spent is {spent}, epsilon" 153 | f"allowed is {self.target_epsilon:.2f} at delta {self.target_delta:.2e}" 154 | ) 155 | 156 | def abort_training( 157 | self, 158 | epsilon_spent: float, 159 | save: Optional[bool] = False, 160 | path: Optional[Union[str, Path]] = None, 161 | ): 162 | error_message = f"Privacy budget exhausted. Epsilon spent is {epsilon_spent}," 163 | f"epsilon allowed is {self.target_epsilon:.2f} at delta {self.target_delta:e}" 164 | if not save: 165 | raise PrivacyBudgetExhausted(error_message) 166 | else: 167 | torch.save(self.wrapper.wrapped_model, path) # type: ignore 168 | raise PrivacyBudgetExhausted(error_message) 169 | -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.fi.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Finnish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(i,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(i.lunr)}(this,function(){return function(i){if(void 0===i)throw new Error("Lunr is not present. 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i(),k=!1,A.limit_backward=e,A.cursor=A.limit,s(),A.cursor=A.limit,o(),A.cursor=A.limit,u(),A.cursor=A.limit,c(),A.cursor=A.limit,k?(m(),A.cursor=A.limit):(A.cursor=A.limit,w(),A.cursor=A.limit),_(),!0}};return function(i){return"function"==typeof i.update?i.update(function(i){return n.setCurrent(i),n.stem(),n.getCurrent()}):(n.setCurrent(i),n.stem(),n.getCurrent())}}(),i.Pipeline.registerFunction(i.fi.stemmer,"stemmer-fi"),i.fi.stopWordFilter=i.generateStopWordFilter("ei eivät emme en et ette että he heidän heidät heihin heille heillä heiltä heissä heistä heitä hän häneen hänelle hänellä häneltä hänen hänessä hänestä hänet häntä itse ja johon joiden joihin joiksi joilla joille joilta joina joissa joista joita joka joksi jolla jolle jolta jona jonka jos jossa josta jota jotka kanssa keiden keihin keiksi keille keillä keiltä keinä keissä keistä keitä keneen keneksi kenelle kenellä keneltä kenen kenenä kenessä kenestä kenet ketkä ketkä ketä koska kuin kuka kun me meidän meidät meihin meille meillä meiltä meissä meistä meitä mihin miksi mikä mille millä miltä minkä minkä minua minulla minulle minulta minun minussa minusta minut minuun minä minä missä mistä mitkä mitä mukaan mutta ne niiden niihin niiksi niille niillä niiltä niin niin niinä niissä niistä niitä noiden noihin noiksi noilla noille noilta noin noina noissa noista noita nuo nyt näiden näihin näiksi näille näillä näiltä näinä näissä näistä näitä nämä ole olemme olen olet olette oli olimme olin olisi olisimme olisin olisit olisitte olisivat olit olitte olivat olla olleet ollut on ovat poikki se sekä sen siihen siinä siitä siksi sille sillä sillä siltä sinua sinulla sinulle sinulta sinun sinussa sinusta sinut sinuun sinä sinä sitä tai te teidän teidät teihin teille teillä teiltä teissä teistä teitä tuo tuohon tuoksi tuolla tuolle tuolta tuon tuona tuossa tuosta tuota tähän täksi tälle tällä tältä tämä tämän tänä tässä tästä tätä vaan vai vaikka yli".split(" ")),i.Pipeline.registerFunction(i.fi.stopWordFilter,"stopWordFilter-fi")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.hu.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Hungarian` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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e(),L.limit_backward=n,L.cursor=L.limit,c(),L.cursor=L.limit,o(),L.cursor=L.limit,w(),L.cursor=L.limit,l(),L.cursor=L.limit,u(),L.cursor=L.limit,k(),L.cursor=L.limit,f(),L.cursor=L.limit,b(),L.cursor=L.limit,m(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.hu.stemmer,"stemmer-hu"),e.hu.stopWordFilter=e.generateStopWordFilter("a abban ahhoz ahogy ahol aki akik akkor alatt amely amelyek amelyekben amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az azok azon azonban azt aztán azután azzal azért be belül benne bár cikk cikkek cikkeket csak de e ebben eddig egy egyes egyetlen egyik egyre egyéb egész ehhez ekkor el ellen elsõ elég elõ elõször elõtt emilyen ennek erre ez ezek ezen ezt ezzel ezért fel felé hanem hiszen hogy hogyan igen ill ill. illetve ilyen ilyenkor ismét ison itt jobban jó jól kell kellett keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}}); -------------------------------------------------------------------------------- /site/404.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | deepee 18 | 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235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | -------------------------------------------------------------------------------- /tests/test_privacy_wrapper.py: -------------------------------------------------------------------------------- 1 | from deepee import PrivacyWrapper 2 | import torch 3 | import pytest 4 | 5 | 6 | class MiniModel(torch.nn.Module): 7 | def __init__(self): 8 | super().__init__() 9 | self.lin = torch.nn.Linear(10, 1) 10 | self.lin2 = torch.nn.Linear(1, 1) 11 | 12 | def forward(self, x): 13 | return self.lin2(self.lin(x)) 14 | 15 | 16 | def test_wrap(): 17 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 18 | 19 | 20 | def test_forward(): 21 | data = torch.randn(2, 1, 10) 22 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 23 | output = wrapped(data) 24 | assert output.shape == (2, 1, 1) 25 | 26 | 27 | def test_clip_accum(): 28 | data = torch.randn(2, 1, 10) 29 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 30 | output = wrapped(data) 31 | loss = output.mean() 32 | loss.backward() 33 | wrapped.clip_and_accumulate() 34 | 35 | 36 | def test_noise_insecure(): 37 | data = torch.randn(2, 1, 10) 38 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0, secure_rng=False, seed=None) 39 | output = wrapped(data) 40 | loss = output.mean() 41 | loss.backward() 42 | wrapped.clip_and_accumulate() 43 | wrapped.noise_gradient() 44 | 45 | 46 | def test_noise_insecure_seed(): 47 | data = torch.randn(2, 1, 10) 48 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0, secure_rng=False, seed=42) 49 | output = wrapped(data) 50 | loss = output.mean() 51 | loss.backward() 52 | wrapped.clip_and_accumulate() 53 | wrapped.noise_gradient() 54 | 55 | 56 | def test_noise_secure(): 57 | data = torch.randn(2, 1, 10) 58 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0, secure_rng=True) 59 | output = wrapped(data) 60 | loss = output.mean() 61 | loss.backward() 62 | wrapped.clip_and_accumulate() 63 | wrapped.noise_gradient() 64 | 65 | 66 | def test_raise_reduce_error(): 67 | data = torch.randn(2, 1, 10) 68 | wrapped = PrivacyWrapper(MiniModel(), 2, 1000, 1e-12, secure_rng=True) 69 | output = wrapped(data) 70 | loss = output.mean() 71 | loss.backward() 72 | with pytest.raises(ValueError): 73 | wrapped.clip_and_accumulate(reduce="foo") 74 | 75 | 76 | def test_next_batch(): 77 | data = torch.randn(2, 1, 10) 78 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 79 | output = wrapped(data) 80 | loss = output.mean() 81 | loss.backward() 82 | wrapped.clip_and_accumulate() 83 | wrapped.noise_gradient() 84 | wrapped.prepare_next_batch() 85 | main_params = list(wrapped.wrapped_model.parameters()) 86 | copy_1_params = list(wrapped.models[0].parameters()) 87 | copy_2_params = list(wrapped.models[1].parameters()) 88 | for mp, c1, c2 in zip(main_params, copy_1_params, copy_2_params): 89 | assert (mp == c1).all() and (mp == c2).all() and (c1 == c2).all() 90 | 91 | 92 | def test_verification_and_steps_taken(): 93 | data = torch.randn(2, 1, 10) 94 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 95 | assert wrapped._steps_taken == 0 96 | assert ( 97 | wrapped._forward_succesful 98 | == wrapped._noise_succesful 99 | == wrapped._clip_succesful 100 | == False 101 | ) 102 | output = wrapped(data) 103 | assert wrapped._forward_succesful == True 104 | loss = output.mean() 105 | loss.backward() 106 | wrapped.clip_and_accumulate() 107 | assert wrapped._clip_succesful == True 108 | wrapped.noise_gradient() 109 | assert wrapped._noise_succesful == True 110 | wrapped.prepare_next_batch() 111 | assert wrapped._steps_taken == 1 112 | with pytest.raises(RuntimeError): 113 | wrapped.prepare_next_batch() # call a second time to raise error 114 | assert wrapped._steps_taken == 1 # steps should still be 1 115 | 116 | 117 | def test_steps_taken(): 118 | data = torch.randn(2, 1, 10) 119 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 120 | for _ in range(5): 121 | output = wrapped(data) 122 | loss = output.mean() 123 | loss.backward() 124 | wrapped.clip_and_accumulate() 125 | wrapped.noise_gradient() 126 | wrapped.prepare_next_batch() 127 | assert wrapped._steps_taken == 5 128 | 129 | 130 | def test_in_order(): 131 | """Case 1: forward not called before clip""" 132 | data = torch.randn(2, 1, 10) 133 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 134 | with pytest.raises(RuntimeError): 135 | wrapped.clip_and_accumulate() 136 | 137 | """Case 2: clip not called before noise""" 138 | data = torch.randn(2, 1, 10) 139 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 140 | output = wrapped(data) 141 | with pytest.raises(RuntimeError): 142 | wrapped.noise_gradient() 143 | 144 | """Case 3: noise not called before prepare """ 145 | data = torch.randn(2, 1, 10) 146 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 147 | output = wrapped(data) 148 | loss = output.mean() 149 | loss.backward() 150 | wrapped.clip_and_accumulate() 151 | with pytest.raises(RuntimeError): 152 | wrapped.prepare_next_batch() 153 | 154 | """Case 3: forward called without prepare """ 155 | data = torch.randn(2, 1, 10) 156 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 157 | output = wrapped(data) 158 | loss = output.mean() 159 | loss.backward() 160 | wrapped.clip_and_accumulate() 161 | wrapped.noise_gradient() 162 | with pytest.raises(RuntimeError): 163 | output = wrapped(data) 164 | 165 | 166 | def test_parameters(): 167 | m = MiniModel() 168 | for param in m.lin2.parameters(): 169 | param.requires_grad = False 170 | wrapped = PrivacyWrapper(m, 2, 1.0, 1.0) 171 | optim = torch.optim.SGD(wrapped.parameters(), lr=1) 172 | params = wrapped.parameters() 173 | assert all([a.shape == b.shape for a, b in zip(params, MiniModel().parameters())]) 174 | data = torch.randn(2, 1, 10) 175 | optim.zero_grad() 176 | output = wrapped(data) 177 | loss = output.mean() 178 | loss.backward() 179 | wrapped.clip_and_accumulate() 180 | wrapped.noise_gradient() 181 | optim.step() 182 | wrapped.prepare_next_batch() 183 | for param in wrapped.wrapped_model.parameters(): 184 | param.requires_grad = True 185 | optim.zero_grad() 186 | data = torch.randn(2, 1, 10) 187 | output = wrapped(data) 188 | loss = output.mean() 189 | loss.backward() 190 | with pytest.raises(RuntimeError): 191 | wrapped.clip_and_accumulate() 192 | 193 | # same procedure as last year miss sophie? 194 | m = MiniModel() 195 | for param in m.lin2.parameters(): 196 | param.requires_grad = False 197 | wrapped = PrivacyWrapper(m, 2, 1.0, 1.0) 198 | optim = torch.optim.SGD(wrapped.parameters(), lr=1) 199 | params = wrapped.parameters() 200 | assert all([a.shape == b.shape for a, b in zip(params, MiniModel().parameters())]) 201 | data = torch.randn(2, 1, 10) 202 | optim.zero_grad() 203 | output = wrapped(data) 204 | loss = output.mean() 205 | loss.backward() 206 | wrapped.clip_and_accumulate() 207 | wrapped.noise_gradient() 208 | optim.step() 209 | wrapped.prepare_next_batch() 210 | for param in wrapped.wrapped_model.parameters(): 211 | param.requires_grad = True 212 | wrapped.update_clones() 213 | optim.zero_grad() 214 | data = torch.randn(2, 1, 10) 215 | output = wrapped(data) 216 | loss = output.mean() 217 | loss.backward() 218 | wrapped.clip_and_accumulate() 219 | 220 | 221 | def test_update_error(): 222 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 223 | 224 | 225 | def test_check_device_cpu(): 226 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0).to("cpu") 227 | assert ( 228 | next( 229 | iter( 230 | set([param.device.type for param in wrapped.wrapped_model.parameters()]) 231 | ) 232 | ) 233 | == "cpu" 234 | ) 235 | for model in wrapped.models: 236 | assert ( 237 | next(iter(set([param.device.type for param in model.parameters()]))) 238 | == "cpu" 239 | ) 240 | 241 | 242 | def test_check_device_gpu(): 243 | if torch.cuda.is_available(): 244 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0).to("cuda") 245 | assert "cuda" in next( 246 | iter( 247 | set([param.device.type for param in wrapped.wrapped_model.parameters()]) 248 | ) 249 | ) 250 | for model in wrapped.models: 251 | assert "cuda" in next( 252 | iter(set([param.device.type for param in model.parameters()])) 253 | ) 254 | else: 255 | pass 256 | 257 | 258 | def test_raises_rng_collision(): 259 | with pytest.raises(ValueError): 260 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0, secure_rng=True, seed=42) 261 | 262 | 263 | def test_transfer_learning(): 264 | """Some model parameters do not require gradients""" 265 | 266 | class MiniModel(torch.nn.Module): 267 | def __init__(self): 268 | super().__init__() 269 | self.lin = torch.nn.Linear(10, 1) 270 | list(self.lin.parameters())[0].requires_grad_(False) 271 | 272 | def forward(self, x): 273 | return self.lin(x) 274 | 275 | data = torch.randn(2, 1, 10) 276 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0) 277 | output = wrapped(data) 278 | loss = output.mean() 279 | loss.backward() 280 | wrapped.clip_and_accumulate() 281 | wrapped.noise_gradient() 282 | wrapped.prepare_next_batch() -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.pt.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Portuguese` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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LogCapture 8 | 9 | 10 | class DS(Dataset): 11 | def __getitem__(self, idx): 12 | return torch.rand( 13 | 1, 14 | ) 15 | 16 | def __len__(self): 17 | return 5 18 | 19 | 20 | dl = DataLoader(DS()) 21 | udl = UniformDataLoader(DS(), 1) 22 | bsdl = DataLoader(DS(), batch_sampler=UniformWORSubsampler(DS(), 5)) 23 | 24 | 25 | def test_uniform_dl(): 26 | with LogCapture() as l: 27 | watchdog = PrivacyWatchdog(udl, target_delta=1e-5, target_epsilon=1.0) 28 | watchdog2 = PrivacyWatchdog(bsdl, target_delta=1e-5, target_epsilon=1.0) 29 | watchdog2 = PrivacyWatchdog(dl, target_delta=1e-5, target_epsilon=1.0) 30 | assert "CRITICAL" and "replacement" in str(l) 31 | 32 | 33 | def test_epsilon_delta_positive(): 34 | with pytest.raises(ValueError): 35 | watchdog = PrivacyWatchdog(udl, target_delta=1e-5, target_epsilon=None) 36 | with pytest.raises(ValueError): 37 | watchdog = PrivacyWatchdog(udl, target_delta=None, target_epsilon=1.0) 38 | with pytest.raises(ValueError): 39 | watchdog = PrivacyWatchdog(udl, target_delta=1.2, target_epsilon=1.0) 40 | with pytest.raises(ValueError): 41 | watchdog = PrivacyWatchdog(udl, target_delta=1.2, target_epsilon=1.0) 42 | with pytest.raises(ValueError): 43 | watchdog = PrivacyWatchdog(udl, target_delta=1e-5, target_epsilon=-4) 44 | 45 | 46 | def test_warn_without_save_or_path(): 47 | with LogCapture() as l: 48 | watchdog = PrivacyWatchdog( 49 | udl, 50 | target_delta=1e-5, 51 | target_epsilon=1.0, 52 | abort=False, 53 | save=True, 54 | ) 55 | assert "WARNING" and "ignored" in str(l) 56 | 57 | with LogCapture() as l: 58 | watchdog = PrivacyWatchdog( 59 | udl, 60 | target_delta=1e-5, 61 | target_epsilon=1.0, 62 | abort=False, 63 | save=False, 64 | path="somepath", 65 | ) 66 | assert "WARNING" and "ignored" in str(l) 67 | 68 | 69 | def test_save_fails_without_path(): 70 | """User asked for save without specifying path""" 71 | with pytest.raises(ValueError): 72 | watchdog = PrivacyWatchdog( 73 | udl, target_delta=1e-5, target_epsilon=1.0, abort=True, save=True, path=None 74 | ) 75 | 76 | 77 | def test_inform(): 78 | """Test reporting of epsilon""" 79 | 80 | class BigDS(Dataset): 81 | def __getitem__(self, idx): 82 | return torch.rand( 83 | 1, 84 | ) 85 | 86 | def __len__(self): 87 | return 50_000 88 | 89 | dl = UniformDataLoader(BigDS(), batch_size=200) 90 | watchdog = PrivacyWatchdog( 91 | dl, 92 | report_every_n_steps=1, 93 | target_delta=1e-5, 94 | target_epsilon=1.0, 95 | ) 96 | 97 | class FakeWrapper: 98 | noise_multiplier = 1.0 99 | 100 | watchdog.wrapper = FakeWrapper 101 | with LogCapture() as l: 102 | watchdog.inform(1) 103 | assert "Privacy spent at 1 steps" in str(l) 104 | 105 | 106 | def test_orphan_watchdog(): 107 | """Watchdog not attached""" 108 | dl = UniformDataLoader(udl, batch_size=200) 109 | watchdog = PrivacyWatchdog( 110 | dl, 111 | report_every_n_steps=1, 112 | target_delta=1e-5, 113 | target_epsilon=1.0, 114 | ) 115 | with pytest.raises(RuntimeError): 116 | watchdog.inform(1) 117 | 118 | 119 | def test_abort_training(): 120 | class BigDS(Dataset): 121 | def __getitem__(self, idx): 122 | return torch.rand( 123 | 1, 124 | ) 125 | 126 | def __len__(self): 127 | return 50_000 128 | 129 | dl = UniformDataLoader(BigDS(), batch_size=200) 130 | watchdog = PrivacyWatchdog( 131 | dl, report_every_n_steps=1, target_delta=1e-5, target_epsilon=1.0, abort=True 132 | ) 133 | 134 | class FakeWrapper: 135 | noise_multiplier = 1.0 136 | 137 | watchdog.wrapper = FakeWrapper 138 | with pytest.raises(PrivacyBudgetExhausted): 139 | watchdog.inform(50000) 140 | 141 | 142 | def test_log_exhausted(): 143 | class BigDS(Dataset): 144 | def __getitem__(self, idx): 145 | return torch.rand( 146 | 1, 147 | ) 148 | 149 | def __len__(self): 150 | return 50_000 151 | 152 | dl = UniformDataLoader(BigDS(), batch_size=200) 153 | watchdog = PrivacyWatchdog( 154 | dl, report_every_n_steps=1, target_delta=1e-5, target_epsilon=1.0, abort=False 155 | ) 156 | 157 | class FakeWrapper: 158 | noise_multiplier = 1.0 159 | 160 | watchdog.wrapper = FakeWrapper 161 | with LogCapture() as l: 162 | watchdog.inform(50000) 163 | assert "WARNING" and "exhausted" in str(l) 164 | 165 | 166 | def test_wrapper_returns_epsilon(): 167 | class MiniModel(torch.nn.Module): 168 | def __init__(self): 169 | super().__init__() 170 | self.lin = torch.nn.Linear(10, 1) 171 | 172 | def forward(self, x): 173 | return self.lin(x) 174 | 175 | class BigDS(Dataset): 176 | def __getitem__(self, idx): 177 | return torch.rand( 178 | 1, 179 | ) 180 | 181 | def __len__(self): 182 | return 50_000 183 | 184 | dl = UniformDataLoader(BigDS(), batch_size=200) 185 | watchdog = PrivacyWatchdog( 186 | dl, report_every_n_steps=1, target_delta=1e-5, target_epsilon=1.0, abort=False 187 | ) 188 | 189 | data = torch.randn(2, 1, 10) 190 | wrapped = PrivacyWrapper(MiniModel(), 2, 1.0, 1.0, watchdog=watchdog) 191 | epsila = [] # this one's for you @a1302z 192 | for _ in range(5): 193 | output = wrapped(data) 194 | loss = output.mean() 195 | loss.backward() 196 | wrapped.clip_and_accumulate() 197 | wrapped.noise_gradient() 198 | wrapped.prepare_next_batch() 199 | epsila.append(wrapped.current_epsilon) 200 | assert len(epsila) == 5 and None not in epsila 201 | 202 | 203 | def test_fallback_warning(): 204 | class MiniModel(torch.nn.Module): 205 | def __init__(self): 206 | super().__init__() 207 | self.lin = torch.nn.Linear(10, 1) 208 | 209 | def forward(self, x): 210 | return self.lin(x) 211 | 212 | class BigDS(Dataset): 213 | def __getitem__(self, idx): 214 | return torch.rand( 215 | 1, 216 | ) 217 | 218 | def __len__(self): 219 | return 50_000 220 | 221 | dl = UniformDataLoader(BigDS(), batch_size=200) 222 | with LogCapture() as l: 223 | watchdog = PrivacyWatchdog( 224 | dl, 225 | report_every_n_steps=1, 226 | target_delta=1e-5, 227 | target_epsilon=1.0, 228 | abort=False, 229 | fallback_to_rdp=True, 230 | ) 231 | assert "CRITICAL" and "RDP" in str(l) 232 | 233 | 234 | def test_fallback_works(): 235 | class MiniModel(torch.nn.Module): 236 | def __init__(self): 237 | super().__init__() 238 | self.lin = torch.nn.Linear(10, 1) 239 | 240 | def forward(self, x): 241 | return self.lin(x) 242 | 243 | class BigDS(Dataset): 244 | def __getitem__(self, idx): 245 | return torch.rand( 246 | 1, 247 | ) 248 | 249 | def __len__(self): 250 | return 50_000 251 | 252 | dl = UniformDataLoader(BigDS(), batch_size=200) 253 | watchdog = PrivacyWatchdog( 254 | dl, 255 | report_every_n_steps=1, 256 | target_delta=1e-5, 257 | target_epsilon=1.0, 258 | abort=False, 259 | fallback_to_rdp=True, 260 | ) 261 | data = torch.randn(2, 1, 10) 262 | wrapped = PrivacyWrapper(MiniModel(), 2, 10, 0.001, watchdog=watchdog) 263 | epsila = [] # this one's for you @a1302z 264 | for _ in range(5): 265 | output = wrapped(data) 266 | loss = output.mean() 267 | loss.backward() 268 | wrapped.clip_and_accumulate() 269 | wrapped.noise_gradient() 270 | epsilon = wrapped.prepare_next_batch() 271 | epsila.append(epsilon) 272 | assert len(epsila) == 5 273 | 274 | 275 | def test_no_fallback_crashes(): 276 | class MiniModel(torch.nn.Module): 277 | def __init__(self): 278 | super().__init__() 279 | self.lin = torch.nn.Linear(10, 1) 280 | 281 | def forward(self, x): 282 | return self.lin(x) 283 | 284 | class BigDS(Dataset): 285 | def __getitem__(self, idx): 286 | return torch.rand( 287 | 1, 288 | ) 289 | 290 | def __len__(self): 291 | return 50_000 292 | 293 | dl = UniformDataLoader(BigDS(), batch_size=200) 294 | watchdog = PrivacyWatchdog( 295 | dl, 296 | report_every_n_steps=1, 297 | target_delta=1e-5, 298 | target_epsilon=1.0, 299 | abort=False, 300 | fallback_to_rdp=False, 301 | ) 302 | data = torch.randn(2, 1, 10) 303 | wrapped = PrivacyWrapper(MiniModel(), 2, 10, 0.001, watchdog=watchdog) 304 | with pytest.raises(RuntimeError): 305 | output = wrapped(data) 306 | loss = output.mean() 307 | loss.backward() 308 | wrapped.clip_and_accumulate() 309 | wrapped.noise_gradient() 310 | wrapped.prepare_next_batch() 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