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