Files
concrete/compiler/include/concretelang/ClientLib/PublicArguments.h
Quentin Bourgerie 8cd3a3a599 feat(compiler): First draft to support FHE.eint up to 16bits
For now what it works are only levelled ops with user parameters. (take a look to the tests)

Done:
- Add parameters to the fhe parameters to support CRT-based large integers
- Add command line options and tests options to allows the user to give those new parameters
- Update the dialects and pipeline to handle new fhe parameters for CRT-based large integers
- Update the client parameters and the client library to handle the CRT-based large integers

Todo:
- Plug the optimizer to compute the CRT-based large interger parameters
- Plug the pbs for the CRT-based large integer
2022-08-12 16:35:11 +02:00

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5.0 KiB
C++

// Part of the Concrete Compiler Project, under the BSD3 License with Zama
// Exceptions. See
// https://github.com/zama-ai/concrete-compiler-internal/blob/main/LICENSE.txt
// for license information.
#ifndef CONCRETELANG_CLIENTLIB_PUBLIC_ARGUMENTS_H
#define CONCRETELANG_CLIENTLIB_PUBLIC_ARGUMENTS_H
#include <iostream>
#include "boost/outcome.h"
#include "concretelang/ClientLib/ClientParameters.h"
#include "concretelang/ClientLib/EncryptedArguments.h"
#include "concretelang/ClientLib/Types.h"
#include "concretelang/Common/Error.h"
#include "concretelang/Runtime/context.h"
namespace concretelang {
namespace serverlib {
class ServerLambda;
}
} // namespace concretelang
namespace mlir {
namespace concretelang {
class JITLambda;
}
} // namespace mlir
namespace concretelang {
namespace clientlib {
using concretelang::error::StringError;
class EncryptedArguments;
/// PublicArguments will be sended to the server. It includes encrypted
/// arguments and public keys.
class PublicArguments {
public:
PublicArguments(const ClientParameters &clientParameters,
std::vector<void *> &&preparedArgs,
std::vector<TensorData> &&ciphertextBuffers);
~PublicArguments();
PublicArguments(PublicArguments &other) = delete;
PublicArguments(PublicArguments &&other) = delete;
static outcome::checked<std::unique_ptr<PublicArguments>, StringError>
unserialize(ClientParameters &expectedParams, std::istream &istream);
outcome::checked<void, StringError> serialize(std::ostream &ostream);
private:
friend class ::concretelang::serverlib::ServerLambda;
friend class ::mlir::concretelang::JITLambda;
outcome::checked<void, StringError> unserializeArgs(std::istream &istream);
ClientParameters clientParameters;
std::vector<void *> preparedArgs;
/// Store buffers of ciphertexts
std::vector<TensorData> ciphertextBuffers;
};
/// PublicResult is a result of a ServerLambda call which contains encrypted
/// results.
struct PublicResult {
PublicResult(const ClientParameters &clientParameters,
std::vector<TensorData> buffers = {})
: clientParameters(clientParameters), buffers(buffers){};
PublicResult(PublicResult &) = delete;
/// Create a public result from buffers.
static std::unique_ptr<PublicResult>
fromBuffers(const ClientParameters &clientParameters,
std::vector<TensorData> buffers) {
return std::make_unique<PublicResult>(clientParameters, buffers);
}
/// Unserialize from an input stream inplace.
outcome::checked<void, StringError> unserialize(std::istream &istream);
/// Unserialize from an input stream returning a new PublicResult.
static outcome::checked<std::unique_ptr<PublicResult>, StringError>
unserialize(ClientParameters &expectedParams, std::istream &istream) {
auto publicResult = std::make_unique<PublicResult>(expectedParams);
OUTCOME_TRYV(publicResult->unserialize(istream));
return std::move(publicResult);
}
/// Serialize into an output stream.
outcome::checked<void, StringError> serialize(std::ostream &ostream);
/// Get the result at `pos` as a vector, if the result is a scalar returns a
/// vector of size 1. Decryption happens if the result is encrypted.
// outcome::checked<std::vector<decrypted_scalar_t>, StringError>
// asClearTextVector(KeySet &keySet, size_t pos);
template <typename T>
outcome::checked<std::vector<T>, StringError>
asClearTextVector(KeySet &keySet, size_t pos) {
OUTCOME_TRY(auto gate, clientParameters.ouput(pos));
if (!gate.isEncrypted()) {
std::vector<T> result;
result.reserve(buffers[pos].values.size());
std::copy(buffers[pos].values.begin(), buffers[pos].values.end(),
std::back_inserter(result));
return result;
}
auto buffer = buffers[pos];
auto lweSize = clientParameters.lweBufferSize(gate);
std::vector<T> decryptedValues(buffer.length() / lweSize);
for (size_t i = 0; i < decryptedValues.size(); i++) {
auto ciphertext = &buffer.values[i * lweSize];
uint64_t decrypted;
OUTCOME_TRYV(keySet.decrypt_lwe(0, ciphertext, decrypted));
decryptedValues[i] = decrypted;
}
return decryptedValues;
}
/// Return the shape of the clear tensor of a result.
outcome::checked<std::vector<int64_t>, StringError>
asClearTextShape(size_t pos) {
OUTCOME_TRY(auto gate, clientParameters.ouput(pos));
return gate.shape.dimensions;
}
// private: TODO tmp
friend class ::concretelang::serverlib::ServerLambda;
ClientParameters clientParameters;
std::vector<TensorData> buffers;
};
/// Helper function to convert from a scalar to TensorData
TensorData tensorDataFromScalar(uint64_t value);
/// Helper function to convert from MemRefDescriptor to
/// TensorData
TensorData tensorDataFromMemRef(size_t memref_rank,
encrypted_scalars_t allocated,
encrypted_scalars_t aligned, size_t offset,
size_t *sizes, size_t *strides);
} // namespace clientlib
} // namespace concretelang
#endif