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Hybrid models

FHE enables cloud applications to process private user data without running the risk of data leaks. Furthermore, deploying ML models in the cloud is advantageous as it eases model updates, allows to scale to large numbers of users by using large amounts of compute power, and protects model IP by keeping the model on a trusted server instead of the client device.
However, not all applications can be easily converted to FHE computation and the computation cost of FHE may make a full conversion exceed latency requirements.
Hybrid models provide a balance between on-device deployment and cloud-based deployment. This approach entails executing parts of the model directly on the client side, while other parts are securely processed with FHE on the server side. Concrete ML facilitates the hybrid deployment of various neural network models, including MLP (multilayer perceptron), CNN (convolutional neural network), and Large Language Models.
If model IP protection is important, care must be taken in choosing the parts of a model to be executed on the cloud. Some black-box model stealing attacks rely on knowledge distillation or on differential methods. As a general rule, the difficulty to steal a machine learning model is proportional to the size of the model, in terms of numbers of parameters and model depth.
The hybrid model deployment API provides an easy way to integrate the standard deployment procedure into neural network style models that are compiled with compile_brevitas_qat_model or compile_torch_model.

Compilation

To use hybrid model deployment, the first step is to define what part of the PyTorch neural network model must be executed in FHE. The model part must be a nn.Module and is identified by its key in the original model's .named_modules().
import numpy as np
import os
import torch
from pathlib import Path
from torch import nn
from concrete.ml.torch.hybrid_model import HybridFHEModel, tuple_to_underscore_str
from concrete.ml.deployment import FHEModelServer
class FCSmall(nn.Module):
"""Torch model for the tests."""
def __init__(self, dim):
super().__init__()
self.seq = nn.Sequential(nn.Linear(dim, dim), nn.ReLU(), nn.Linear(dim, dim))
def forward(self, x):
return self.seq(x)
model = FCSmall(10)
model_name = "FCSmall"
submodule_name = "seq.0"
inputs = torch.Tensor(np.random.uniform(size=(10, 10)))
# Prints ['', 'seq', 'seq.0', 'seq.1', 'seq.2']
print([k for (k, _) in model.named_modules()])
# Create a hybrid model
hybrid_model = HybridFHEModel(model, [submodule_name])
hybrid_model.compile_model(
inputs,
n_bits=8,
)
models_dir = Path(os.path.abspath('')) / "compiled_models"
models_dir.mkdir(exist_ok=True)
model_dir = models_dir / model_name
hybrid_model.save_and_clear_private_info(model_dir, via_mlir=True)

Server Side Deployment

The save_and_clear_private_info function serializes the FHE circuits corresponding to the various parts of the model that were chosen to be moved server-side. It also saves the client-side model, removing the weights of the layers that are transferred server-side. Furthermore it saves all necessary information required to serve these sub-models with FHE, using the FHEModelDev class.
The FHEModelServer class should be used to create a server application that creates end-points to serve these sub-models:
input_shape_subdir = tuple_to_underscore_str( (1,) + inputs.shape[1:] )
MODULES = { model_name: { submodule_name: {"path": model_dir / submodule_name / input_shape_subdir }}}
server = FHEModelServer(str(MODULES[model_name][submodule_name]["path"]))
For more information about serving FHE models, see the client/server section.

Client Side

A client application that deploys a model with hybrid deployment can be developed in a very similar manner to on-premise deployment: the model is loaded normally with PyTorch, but an extra step is required to specify the remote endpoint and the model parts that are to be executed remotely.
# Modify model to use remote FHE server instead of local weights
hybrid_model = HybridFHEModel(
model,
submodule_name,
server_remote_address="http://0.0.0.0:8000",
model_name=f"{model_name}",
verbose=False,
)
Next, the client application must obtain the parameters necessary to encrypt and quantize data, as detailed in the client/server documentation.
path_to_clients = Path(__file__).parent / "clients"
hybrid_model.init_client(path_to_clients=path_to_clients)
When the client application is ready to make inference requests to the server, it must set the operation mode of the HybridFHEModel instance to HybridFHEMode.REMOTE:
for module in hybrid_model.remote_modules.values():
module.fhe_local_mode = HybridFHEMode.REMOTE
When performing inference with the HybridFHEModel instance, hybrid_model, only the regular forward method is called, as if the model was fully deployed locally:
hybrid_model.forward(torch.randn((dim, )))
When calling forward, the HybridFHEModel handles, for each model part that is deployed remotely, all the necessary intermediate steps: quantizing the data, encrypting it, makes the request to the server using requests Python module, decrypting and de-quantizing the result.