Call FHE circuits from other languages

After doing a compilation, we end up with a couple of artifacts, including crypto parameters and a binary file containing the executable circuit. In order to be able to encrypt and run the circuit properly, we need to know how to interpret these artifacts, and there are a couple of utility functions which can be used to load them. These utility functions can be accessed through a variety of languages, including Python and C++.

Demo

We will use a really simple example for a demo, but the same steps can be done for any other circuit. example.mlir will contain the MLIR below:

func.func @main(%arg0: tensor<4x4x!FHE.eint<6>>, %arg1: tensor<4x2xi7>) -> tensor<4x2x!FHE.eint<6>> {
   %0 = "FHELinalg.matmul_eint_int"(%arg0, %arg1): (tensor<4x4x!FHE.eint<6>>, tensor<4x2xi7>) -> (tensor<4x2x!FHE.eint<6>>)
   %tlu = arith.constant dense<[40, 13, 20, 62, 47, 41, 46, 30, 59, 58, 17, 4, 34, 44, 49, 5, 10, 63, 18, 21, 33, 45, 7, 14, 24, 53, 56, 3, 22, 29, 1, 39, 48, 32, 38, 28, 15, 12, 52, 35, 42, 11, 6, 43, 0, 16, 27, 9, 31, 51, 36, 37, 55, 57, 54, 2, 8, 25, 50, 23, 61, 60, 26, 19]> : tensor<64xi64>
   %result = "FHELinalg.apply_lookup_table"(%0, %tlu): (tensor<4x2x!FHE.eint<6>>, tensor<64xi64>) -> (tensor<4x2x!FHE.eint<6>>)
   return %result: tensor<4x2x!FHE.eint<6>>
}

You can use the concretecompiler binary to compile this MLIR program. Same can be done with concrete-python, as we only need the compilation artifacts at the end.

$ concretecompiler --action=compile -o python-demo example.mlir

You should be able to see artifacts listed in the python-demo directory

$ ls python-demo/
client_parameters.concrete.params.json  compilation_feedback.json  fhecircuit-client.h  sharedlib.so  staticlib.a

Now we want to use the Python bindings in order to call the compiled circuit.

from concrete.compiler import (ClientSupport, LambdaArgument, LibrarySupport)

The main struct to manage compilation artifacts is LibrarySupport. You will have to create one with the path you used during compilation, then load the result of the compilation

lib_support = LibrarySupport.new("/path/to/your/python-demo/")
compilation_result = lib_support.reload()

Using the compilation result, you can load the server lambda (the entrypoint to the executable compiled circuit) as well as the client parameters (containing crypto parameters)

server_lambda = lib_support.load_server_lambda(compilation_result)
client_params = lib_support.load_client_parameters(compilation_result)

The client parameters will serve the client to generate keys and encrypt arguments for the circuit

client_support = ClientSupport.new()
key_set = client_support.key_set(client_params)
args = [
	LambdaArgument.from_tensor_u8([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], [4, 4]),
	LambdaArgument.from_tensor_u8([1, 2, 1, 2, 1, 2, 1, 2], [4, 2])
]
encrypted_args = client_support.encrypt_arguments(client_params, key_set, args)

Only evaluation keys are required for the execution of the circuit. You can execute the circuit on the encrypted arguments via server_lambda_call

eval_keys = key_set.get_evaluation_keys()
encrypted_result = lib_support.server_call(server_lambda, encrypted_args, eval_keys)

At this point you have the encrypted result and can decrypt it using the keyset which holds the secret key

result_arg = client_support.decrypt_result(client_params, key_set, encrypted_result)
print("result tensor dims: {}".format(result_arg.n_values()))
print("result tensor data: {}".format(result_arg.get_values()))

There is also a couple of tests in test_compilation.py that can show how to both compile and run a circuit between a client and server using serialization.