Andi Drebes c8c969773e Rebase onto llvm-project 465ee9bfb26d with local changes
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
2023-03-09 17:47:16 +01:00
2022-01-31 17:17:24 +01:00
2022-12-05 12:35:26 +01:00
2023-03-08 11:23:21 +01:00

Concrete

The concrete project is a set of crates that implements Zama's variant of TFHE and make it easy to use. In a nutshell, fully homomorphic encryption (FHE), allows you to perform computations over encrypted data, allowing you to implement Zero Trust services.

Concrete is based on the Learning With Errors (LWE) and the Ring Learning With Errors (RLWE) problems, which are well studied cryptographic hardness assumptions believed to be secure even against quantum computers.

Project layout

The concrete project is a set of several modules which are high-level frontends, compilers, backends and side tools.

  • The frontends directory contains a python frontend.
  • The compilers directory contains the concrete-compiler and concrete-optimizer modules. The concrete-compiler is a compiler that synthetize a FHE computation dag expressed as a MLIR dialect, compile to a set of artifacts, and provide tools to manipulate those artifacts at runtime. The concrete-optimizer is a specific module used by the compiler to find the best, secure and accurate set of crypto parameters for a given dag.
  • The backends directory contains implementations of cryptographic primitives on different computation unit, used by the concrete-compiler runtime. The concrete-cpu module provides CPU implementation, while concrete-cuda module provides GPU implementation using the CUDA platform.
  • The tools directory contains side tools used by the rest of the project.
Description
No description provided
Readme 148 MiB
Languages
C++ 34.3%
Python 23.1%
MLIR 22.9%
Rust 14.6%
C 2.2%
Other 2.8%