Hummingbird Usage

Why Hummingbird?

Hummingbird contains an interesting feature for Concrete-ML: it converts many algorithms (see supported algorithms) to tensor computations using a specific backend (torch, torchscript, ONNX and TVM).

Concrete-ML allows the conversion of an ONNX inference to NumPy inference (note that NumPy is always our entry point to run models in FHE).

Usage

We use a simple functionnality of Hummingbird, which is the convert function that can be imported as follows from the hummingbird.ml package:

# Disable Hummingbird warnings for pytest.
import warnings
warnings.filterwarnings("ignore")
from hummingbird.ml import convert

This function can be used to convert a machine learning model to an ONNX as follows:

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression

# Instantiate the logistic regression from sklearn
lr = LogisticRegression()

# Create synthetic data
X, y = make_classification(
    n_samples=100, n_features=20, n_classes=2
)

# Fit the model
lr.fit(X, y)

# Convert the model to ONNX
onnx_model = convert(lr, backend="onnx", test_input=X).model

In theory, we can directly use this onnx_model within our get_equivalent_numpy_forward (as long as all operators present in the ONNX model are implemented in NumPy) and get the NumPy inference.

In practice, we have some steps to clean the ONNX and make the graph compatible with our framework such as:

• applying quantization where needed

• deleting non-FHE friendly ONNX operators, such as Softmax and ArgMax