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This commit rebases the compiler onto commit 465ee9bfb26d from
llvm-project with locally maintained patches on top, i.e.:
* 5d8669d669ee: Fix the element alignment (size) for memrefCopy
* 4239163ea337: fix: Do not fold the memref.subview if the offset are
!= 0 and strides != 1
* 72c5decfcc21: remove github stuff from llvm
* 8d0ce8f9eca1: Support arbitrary element types in named operations
via attributes
* 94f64805c38c: Copy attributes of scf.for on bufferization and make
it an allocation hoisting barrier
Main upstream changes from llvm-project that required modification of
concretecompiler:
* Switch to C++17
* Various changes in the interfaces for linalg named operations
* Transition from `llvm::Optional` to `std::optional`
* Use of enums instead of string values for iterator types in linalg
* Changed default naming convention of getter methods in
ODS-generated operation classes from `some_value()` to
`getSomeValue()`
* Renaming of Arithmetic dialect to Arith
* Refactoring of side effect interfaces (i.e., renaming from
`NoSideEffect` to `Pure`)
* Re-design of the data flow analysis framework
* Refactoring of build targets for Python bindings
* Refactoring of array attributes with integer values
* Renaming of `linalg.init_tensor` to `tensor.empty`
* Emission of `linalg.map` operations in bufferization of the Tensor
dialect requiring another linalg conversion pass and registration
of the bufferization op interfaces for linalg operations
* Refactoring of the one-shot bufferizer
* Necessity to run the expand-strided-metadata, affine-to-std and
finalize-memref-to-llvm passes before converson to the LLVM
dialect
* Renaming of `BlockAndValueMapping` to `IRMapping`
* Changes in the build function of `LLVM::CallOp`
* Refactoring of the construction of `llvm::ArrayRef` and
`llvm::MutableArrayRef` (direct invocation of constructor instead
of builder functions for some cases)
* New naming conventions for generated SSA values requiring rewrite
of some check tests
* Refactoring of `mlir::LLVM::lookupOrCreateMallocFn()`
* Interface changes in generated type parsers
* New dependencies for to mlir_float16_utils and
MLIRSparseTensorRuntime for the runtime
* Overhaul of MLIR-c deleting `mlir-c/Registration.h`
* Deletion of library MLIRLinalgToSPIRV
* Deletion of library MLIRLinalgAnalysis
* Deletion of library MLIRMemRefUtils
* Deletion of library MLIRQuantTransforms
* Deletion of library MLIRVectorToROCDL
111 lines
3.8 KiB
C++
111 lines
3.8 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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#include "concretelang/ClientLib/EncryptedArguments.h"
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#include "concretelang/ClientLib/PublicArguments.h"
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namespace concretelang {
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namespace clientlib {
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using StringError = concretelang::error::StringError;
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outcome::checked<std::unique_ptr<PublicArguments>, StringError>
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EncryptedArguments::exportPublicArguments(ClientParameters clientParameters) {
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return std::make_unique<PublicArguments>(
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clientParameters, std::move(preparedArgs), std::move(ciphertextBuffers));
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}
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/// Split the input integer into `size` chunks of `chunkWidth` bits each
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std::vector<uint64_t> chunkInput(uint64_t value, size_t size,
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unsigned int chunkWidth) {
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std::vector<uint64_t> chunks;
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chunks.reserve(size);
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uint64_t mask = (1 << chunkWidth) - 1;
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for (size_t i = 0; i < size; i++) {
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auto chunk = value & mask;
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chunks.push_back((uint64_t)chunk);
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value >>= chunkWidth;
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}
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return chunks;
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}
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outcome::checked<void, StringError>
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EncryptedArguments::pushArg(uint64_t arg, KeySet &keySet) {
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OUTCOME_TRYV(checkPushTooManyArgs(keySet));
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OUTCOME_TRY(CircuitGate input, keySet.clientParameters().input(currentPos));
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// a chunked input is represented as a tensor in lower levels, and need to to
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// splitted into chunks and encrypted as such
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if (input.chunkInfo.has_value()) {
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std::vector<uint64_t> chunks =
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chunkInput(arg, input.shape.size, input.chunkInfo.value().width);
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return this->pushArg(chunks.data(), input.shape.size, keySet);
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}
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// we only increment if we don't forward the call to another pushArg method
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auto pos = currentPos++;
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if (input.shape.size != 0) {
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return StringError("argument #") << pos << " is not a scalar";
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}
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if (!input.encryption.has_value()) {
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// clear scalar: just push the argument
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preparedArgs.push_back((void *)arg);
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return outcome::success();
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}
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std::vector<int64_t> shape = keySet.clientParameters().bufferShape(input);
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// Allocate empty
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ciphertextBuffers.emplace_back(
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TensorData(shape, clientlib::EncryptedScalarElementType,
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clientlib::EncryptedScalarElementWidth));
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TensorData &values_and_sizes = ciphertextBuffers.back().getTensor();
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OUTCOME_TRYV(keySet.encrypt_lwe(
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pos, values_and_sizes.getElementPointer<decrypted_scalar_t>(0), arg));
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// Note: Since we bufferized lwe ciphertext take care of memref calling
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// convention
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// allocated
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preparedArgs.push_back(nullptr);
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// aligned
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preparedArgs.push_back((void *)values_and_sizes.getValuesAsOpaquePointer());
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// offset
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preparedArgs.push_back((void *)0);
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// sizes
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for (auto size : values_and_sizes.getDimensions()) {
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preparedArgs.push_back((void *)size);
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}
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// strides
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int64_t stride = TensorData::getNumElements(shape);
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for (size_t size : values_and_sizes.getDimensions()) {
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stride = (size == 0 ? 0 : (stride / size));
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preparedArgs.push_back((void *)stride);
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}
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return outcome::success();
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}
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outcome::checked<void, StringError>
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EncryptedArguments::checkPushTooManyArgs(KeySet &keySet) {
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size_t arity = keySet.numInputs();
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if (currentPos < arity) {
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return outcome::success();
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}
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return StringError("function has arity ")
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<< arity << " but is applied to too many arguments";
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}
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outcome::checked<void, StringError>
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EncryptedArguments::checkAllArgs(KeySet &keySet) {
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size_t arity = keySet.numInputs();
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if (currentPos == arity) {
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return outcome::success();
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}
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return StringError("function expects ")
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<< arity << " arguments but has been called with " << currentPos
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<< " arguments";
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}
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} // namespace clientlib
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} // namespace concretelang
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