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291 lines
11 KiB
C++
291 lines
11 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 <iostream>
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#include <mlir/Dialect/Func/IR/FuncOps.h>
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#include <mlir/Dialect/Linalg/IR/Linalg.h>
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#include <mlir/IR/Operation.h>
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#include "concretelang/Dialect/TFHE/IR/TFHEOps.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "concretelang/Conversion/FHEToTFHE/Patterns.h"
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#include "concretelang/Conversion/Passes.h"
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#include "concretelang/Conversion/Utils/RegionOpTypeConverterPattern.h"
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#include "concretelang/Conversion/Utils/TensorOpTypeConversion.h"
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#include "concretelang/Dialect/FHE/IR/FHEDialect.h"
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#include "concretelang/Dialect/FHE/IR/FHETypes.h"
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#include "concretelang/Dialect/RT/IR/RTOps.h"
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#include "concretelang/Dialect/TFHE/IR/TFHEDialect.h"
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#include "concretelang/Dialect/TFHE/IR/TFHETypes.h"
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namespace FHE = mlir::concretelang::FHE;
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namespace TFHE = mlir::concretelang::TFHE;
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namespace {
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struct FHEToTFHEPass : public FHEToTFHEBase<FHEToTFHEPass> {
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void runOnOperation() final;
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};
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} // namespace
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using mlir::concretelang::FHE::EncryptedIntegerType;
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using mlir::concretelang::TFHE::GLWECipherTextType;
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/// FHEToTFHETypeConverter is a TypeConverter that transform
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/// `FHE.eint<p>` to `TFHE.glwe<{_,_,_}{p}>`
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class FHEToTFHETypeConverter : public mlir::TypeConverter {
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public:
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FHEToTFHETypeConverter() {
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addConversion([](mlir::Type type) { return type; });
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addConversion([](EncryptedIntegerType type) {
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return mlir::concretelang::convertTypeEncryptedIntegerToGLWE(
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type.getContext(), type);
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});
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addConversion([](mlir::RankedTensorType type) {
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auto eint =
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type.getElementType().dyn_cast_or_null<EncryptedIntegerType>();
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if (eint == nullptr) {
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return (mlir::Type)(type);
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}
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mlir::Type r = mlir::RankedTensorType::get(
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type.getShape(),
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mlir::concretelang::convertTypeEncryptedIntegerToGLWE(
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eint.getContext(), eint));
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return r;
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});
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}
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};
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// This rewrite pattern transforms any instance of `FHE.apply_lookup_table`
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// operators.
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//
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// Example:
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//
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// ```mlir
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// %0 = "FHE.apply_lookup_table"(%ct, %lut): (!FHE.eint<2>, tensor<4xi64>)
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// ->(!FHE.eint<2>)
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// ```
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//
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// becomes:
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//
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// ```mlir
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// %glwe_lut = "TFHE.glwe_from_table"(%lut)
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// : (tensor<4xi64>) -> !TFHE.glwe<{_,_,_}{2}>
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// %glwe_ks = "TFHE.keyswitch_glwe"(%ct)
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// {baseLog = -1 : i32, level = -1 : i32}
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// : (!TFHE.glwe<{_,_,_}{2}>) -> !TFHE.glwe<{_,_,_}{2}>
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// %0 = "TFHE.bootstrap_glwe"(%glwe_ks, %glwe_lut)
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// {baseLog = -1 : i32, glweDimension = -1 : i32, level = -1 : i32,
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// polynomialSize = -1 : i32}
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// : (!TFHE.glwe<{_,_,_}{2}>, !TFHE.glwe<{_,_,_}{2}>) ->
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// !TFHE.glwe<{_,_,_}{2}>
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// ```
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struct ApplyLookupTableEintOpPattern
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: public mlir::OpRewritePattern<FHE::ApplyLookupTableEintOp> {
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ApplyLookupTableEintOpPattern(mlir::MLIRContext *context,
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mlir::PatternBenefit benefit = 1)
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: ::mlir::OpRewritePattern<FHE::ApplyLookupTableEintOp>(context,
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benefit) {}
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::mlir::LogicalResult
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matchAndRewrite(FHE::ApplyLookupTableEintOp lutOp,
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mlir::PatternRewriter &rewriter) const override {
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FHEToTFHETypeConverter converter;
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auto inputTy = converter.convertType(lutOp.a().getType())
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.cast<TFHE::GLWECipherTextType>();
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auto resultTy = converter.convertType(lutOp.getType());
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// %glwe_lut = "TFHE.glwe_from_table"(%lut)
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auto glweLut = rewriter.create<TFHE::GLWEFromTableOp>(lutOp.getLoc(),
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inputTy, lutOp.lut());
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// %glwe_ks = "TFHE.keyswitch_glwe"(%ct)
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auto glweKs = rewriter.create<TFHE::KeySwitchGLWEOp>(
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lutOp.getLoc(), inputTy, lutOp.a(), -1, -1);
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mlir::concretelang::convertOperandAndResultTypes(
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rewriter, glweKs, [&](mlir::MLIRContext *, mlir::Type t) {
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return converter.convertType(t);
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});
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// %0 = "TFHE.bootstrap_glwe"(%glwe_ks, %glwe_lut)
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rewriter.replaceOpWithNewOp<TFHE::BootstrapGLWEOp>(lutOp, resultTy, glweKs,
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glweLut, -1, -1);
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return ::mlir::success();
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};
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};
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// This rewrite pattern transforms any instance of `FHE.sub_eint_int`
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// operators to a negation and an addition.
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struct SubEintIntOpPattern : public mlir::OpRewritePattern<FHE::SubEintIntOp> {
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SubEintIntOpPattern(mlir::MLIRContext *context,
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mlir::PatternBenefit benefit = 1)
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: ::mlir::OpRewritePattern<FHE::SubEintIntOp>(context, benefit) {}
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::mlir::LogicalResult
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matchAndRewrite(FHE::SubEintIntOp op,
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mlir::PatternRewriter &rewriter) const override {
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mlir::Location location = op.getLoc();
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mlir::Value lhs = op.getOperand(0);
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mlir::Value rhs = op.getOperand(1);
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mlir::Type rhsType = rhs.getType();
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mlir::Attribute minusOneAttr = mlir::IntegerAttr::get(rhsType, -1);
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mlir::Value minusOne =
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rewriter.create<mlir::arith::ConstantOp>(location, minusOneAttr)
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.getResult();
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mlir::Value negative =
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rewriter.create<mlir::arith::MulIOp>(location, rhs, minusOne)
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.getResult();
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FHEToTFHETypeConverter converter;
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auto resultTy = converter.convertType(op.getType());
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auto addition =
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rewriter.create<TFHE::AddGLWEIntOp>(location, resultTy, lhs, negative);
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mlir::concretelang::convertOperandAndResultTypes(
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rewriter, addition, [&](mlir::MLIRContext *, mlir::Type t) {
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return converter.convertType(t);
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});
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rewriter.replaceOp(op, {addition.getResult()});
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return mlir::success();
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};
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};
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// This rewrite pattern transforms any instance of `FHE.sub_eint`
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// operators to a negation and an addition.
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struct SubEintOpPattern : public mlir::OpRewritePattern<FHE::SubEintOp> {
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SubEintOpPattern(mlir::MLIRContext *context, mlir::PatternBenefit benefit = 1)
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: ::mlir::OpRewritePattern<FHE::SubEintOp>(context, benefit) {}
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::mlir::LogicalResult
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matchAndRewrite(FHE::SubEintOp op,
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mlir::PatternRewriter &rewriter) const override {
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mlir::Location location = op.getLoc();
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mlir::Value lhs = op.getOperand(0);
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mlir::Value rhs = op.getOperand(1);
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FHEToTFHETypeConverter converter;
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auto rhsTy = converter.convertType(rhs.getType());
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auto negative = rewriter.create<TFHE::NegGLWEOp>(location, rhsTy, rhs);
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mlir::concretelang::convertOperandAndResultTypes(
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rewriter, negative, [&](mlir::MLIRContext *, mlir::Type t) {
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return converter.convertType(t);
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});
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auto resultTy = converter.convertType(op.getType());
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auto addition = rewriter.create<TFHE::AddGLWEOp>(location, resultTy, lhs,
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negative.getResult());
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mlir::concretelang::convertOperandAndResultTypes(
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rewriter, addition, [&](mlir::MLIRContext *, mlir::Type t) {
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return converter.convertType(t);
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});
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rewriter.replaceOp(op, {addition.getResult()});
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return mlir::success();
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};
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};
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void FHEToTFHEPass::runOnOperation() {
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auto op = this->getOperation();
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mlir::ConversionTarget target(getContext());
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FHEToTFHETypeConverter converter;
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// Mark ops from the target dialect as legal operations
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target.addLegalDialect<mlir::concretelang::TFHE::TFHEDialect>();
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target.addLegalDialect<mlir::arith::ArithmeticDialect>();
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// Make sure that no ops from `FHE` remain after the lowering
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target.addIllegalDialect<mlir::concretelang::FHE::FHEDialect>();
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// Make sure that no ops `linalg.generic` that have illegal types
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target
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.addDynamicallyLegalOp<mlir::linalg::GenericOp, mlir::tensor::GenerateOp>(
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[&](mlir::Operation *op) {
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return (
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converter.isLegal(op->getOperandTypes()) &&
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converter.isLegal(op->getResultTypes()) &&
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converter.isLegal(op->getRegion(0).front().getArgumentTypes()));
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});
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// Make sure that func has legal signature
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target.addDynamicallyLegalOp<mlir::func::FuncOp>(
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[&](mlir::func::FuncOp funcOp) {
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return converter.isSignatureLegal(funcOp.getFunctionType()) &&
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converter.isLegal(&funcOp.getBody());
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});
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// Add all patterns required to lower all ops from `FHE` to
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// `TFHE`
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mlir::RewritePatternSet patterns(&getContext());
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populateWithGeneratedFHEToTFHE(patterns);
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patterns.add<
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mlir::concretelang::GenericTypeConverterPattern<mlir::func::ReturnOp>>(
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patterns.getContext(), converter);
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patterns.add<ApplyLookupTableEintOpPattern>(&getContext());
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patterns.add<SubEintOpPattern>(&getContext());
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patterns.add<SubEintIntOpPattern>(&getContext());
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patterns.add<RegionOpTypeConverterPattern<mlir::linalg::GenericOp,
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FHEToTFHETypeConverter>>(
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&getContext(), converter);
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patterns.add<
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mlir::concretelang::GenericTypeConverterPattern<mlir::linalg::YieldOp>>(
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patterns.getContext(), converter);
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patterns.add<RegionOpTypeConverterPattern<mlir::tensor::GenerateOp,
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FHEToTFHETypeConverter>>(
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&getContext(), converter);
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patterns.add<
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RegionOpTypeConverterPattern<mlir::scf::ForOp, FHEToTFHETypeConverter>>(
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&getContext(), converter);
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patterns.add<mlir::concretelang::GenericTypeAndOpConverterPattern<
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mlir::concretelang::FHE::ZeroTensorOp,
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mlir::concretelang::TFHE::ZeroTensorGLWEOp>>(&getContext(), converter);
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mlir::concretelang::populateWithTensorTypeConverterPatterns(patterns, target,
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converter);
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mlir::populateFunctionOpInterfaceTypeConversionPattern<mlir::func::FuncOp>(
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patterns, converter);
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// Conversion of RT Dialect Ops
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patterns.add<mlir::concretelang::GenericTypeConverterPattern<
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mlir::concretelang::RT::DataflowTaskOp>>(patterns.getContext(),
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converter);
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mlir::concretelang::addDynamicallyLegalTypeOp<
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mlir::concretelang::RT::DataflowTaskOp>(target, converter);
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patterns.add<mlir::concretelang::GenericTypeConverterPattern<
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mlir::concretelang::RT::DataflowYieldOp>>(patterns.getContext(),
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converter);
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mlir::concretelang::addDynamicallyLegalTypeOp<
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mlir::concretelang::RT::DataflowYieldOp>(target, converter);
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// Apply conversion
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if (mlir::applyPartialConversion(op, target, std::move(patterns)).failed()) {
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this->signalPassFailure();
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}
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}
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namespace mlir {
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namespace concretelang {
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std::unique_ptr<OperationPass<ModuleOp>> createConvertFHEToTFHEPass() {
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return std::make_unique<FHEToTFHEPass>();
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}
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} // namespace concretelang
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} // namespace mlir
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