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feat(compiler): HLFHELinalg.sub_int_eint

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This commit is contained in:
Quentin Bourgerie
2021-10-21 17:54:12 +02:00
committed by Andi Drebes
parent 3b02a16f7b
commit 64e327209f
4 changed files with 157 additions and 1 deletions
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14
compiler/include/zamalang/Dialect/HLFHELinalg/IR/HLFHELinalgOps.h
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@@ -14,6 +14,7 @@ namespace OpTrait {
namespace impl {
LogicalResult verifyTensorBroadcastingRules(mlir::Operation *op);
LogicalResult verifyTensorBinaryEintInt(mlir::Operation *op);
LogicalResult verifyTensorBinaryIntEint(mlir::Operation *op);
LogicalResult verifyTensorBinaryEint(mlir::Operation *op);
} // namespace impl
@@ -48,6 +49,19 @@ public:
}
};
/// TensorBinaryEintInt verifies that the operation matches the following
/// signature
/// `(tensor<...xi$p'>, tensor<...x!HLFHE.eint<$p>>) ->
/// tensor<...x!HLFHE.eint<$p>>` where `$p <= $p+1`.
template <typename ConcreteType>
class TensorBinaryIntEint
: public mlir::OpTrait::TraitBase<ConcreteType, TensorBinaryEintInt> {
public:
static LogicalResult verifyTrait(Operation *op) {
return impl::verifyTensorBinaryIntEint(op);
}
};
/// TensorBinary verify the operation match the following signature
/// `(tensor<...x!HLFHE.eint<$p>>, tensor<...x!HLFHE.eint<$p>>) ->
/// tensor<...x!HLFHE.eint<$p>>`

54
compiler/include/zamalang/Dialect/HLFHELinalg/IR/HLFHELinalgOps.td
View File

@@ -13,6 +13,7 @@ class HLFHELinalg_Op<string mnemonic, list<OpTrait> traits = []> :
// TensorBroadcastingRules verify that the operands and result verify the broadcasting rules
def TensorBroadcastingRules : NativeOpTrait<"TensorBroadcastingRules">;
def TensorBinaryEintInt : NativeOpTrait<"TensorBinaryEintInt">;
def TensorBinaryIntEint : NativeOpTrait<"TensorBinaryIntEint">;
def TensorBinaryEint : NativeOpTrait<"TensorBinaryEint">;
@@ -121,4 +122,57 @@ def AddEintOp : HLFHELinalg_Op<"add_eint", [TensorBroadcastingRules, TensorBinar
];
}
def SubIntEintOp : HLFHELinalg_Op<"sub_int_eint", [TensorBroadcastingRules, TensorBinaryIntEint]> {
let summary = "Returns a tensor that contains the substraction of a tensor of clear integers and a tensor of encrypted integers.";
let description = [{
Performs a substraction following the broadcasting rules between a tensor of clear integers and a tensor of encrypted integers.
The width of the clear integers should be less or equals than the witdh of encrypted integers.
Examples:
```mlir
// Returns the term to term substraction of `%a0` with `%a1`
"HLFHELinalg.sub_eint_int"(%a0, %a1) : (tensor<4xi5>, tensor<4x!HLFHE.eint<4>>) -> tensor<4x!HLFHE.eint<4>>
// Returns the term to term substraction of `%a0` with `%a1`, where dimensions equal to one are stretched.
"HLFHELinalg.sub_int_eint"(%a0, %a1) : (tensor<4x1x4xi5>, tensor<1x4x4x!HLFHE.eint<4>>) -> tensor<4x4x4x!HLFHE.eint<4>>
// Returns the substraction of a 3x3 matrix of integers and a 3x1 matrix (a column) of encrypted integers.
//
// [1,2,3] [1] [0,2,3]
// [4,5,6] + [2] = [2,3,4]
// [7,8,9] [3] [4,5,6]
//
// The dimension #1 of operand #2 is stretched as it is equals to 1.
"HLFHELinalg.sub_int_eint(%a0, %a1)" : (tensor<3x1xi5>, tensor<3x4x!HLFHE.eint<4>>) -> tensor<3x3x!HLFHE.eint<4>>
// Returns the substraction of a 3x3 matrix of integers and a 1x3 matrix (a line) of encrypted integers.
//
// [1,2,3] [0,0,0]
// [4,5,6] + [1,2,3] = [3,3,3]
// [7,8,9] [6,6,6]
//
// The dimension #2 of operand #2 is stretched as it is equals to 1.
"HLFHELinalg.sub_int_eint(%a0, %a1)" : (tensor<1x3xi5>, tensor<3x4x!HLFHE.eint<4>>) -> tensor<3x3x!HLFHE.eint<4>>
// Same behavior than the previous one, but as the dimension #2 is missing of operand #2.
"HLFHELinalg.sub_int_eint(%a0, %a1)" : (tensor<3xi5>, tensor<3x4x!HLFHE.eint<4>>) -> tensor<4x4x4x!HLFHE.eint<4>>
```
}];
let arguments = (ins
Type<And<[TensorOf<[AnyInteger]>.predicate, HasStaticShapePred]>>:$lhs,
Type<And<[TensorOf<[EncryptedIntegerType]>.predicate, HasStaticShapePred]>>:$rhs
);
let results = (outs Type<And<[TensorOf<[EncryptedIntegerType]>.predicate, HasStaticShapePred]>>);
let builders = [
OpBuilder<(ins "Value":$rhs, "Value":$lhs), [{
build($_builder, $_state, lhs.getType(), rhs, lhs);
}]>
];
}
#endif

35
compiler/lib/Dialect/HLFHELinalg/IR/HLFHELinalgOps.cpp
View File

@@ -118,7 +118,40 @@ LogicalResult verifyTensorBinaryEintInt(mlir::Operation *op) {
"tensor of operand #1";
return mlir::failure();
}
// llvm::errs() << width << "";
if (el1Ty.getWidth() > el0Ty.getWidth() + 1) {
op->emitOpError()
<< "should have the width of integer values less or equals "
"than the width of encrypted values + 1";
return mlir::failure();
}
return mlir::success();
}
LogicalResult verifyTensorBinaryIntEint(mlir::Operation *op) {
if (op->getNumOperands() != 2) {
op->emitOpError() << "should have exactly 2 operands";
return mlir::failure();
}
auto op0Ty = op->getOperand(0).getType().dyn_cast_or_null<mlir::TensorType>();
auto op1Ty = op->getOperand(1).getType().dyn_cast_or_null<mlir::TensorType>();
if (op0Ty == nullptr || op1Ty == nullptr) {
op->emitOpError() << "should have both operands as tensor";
return mlir::failure();
}
auto el0Ty = op0Ty.getElementType().dyn_cast_or_null<mlir::IntegerType>();
if (el0Ty == nullptr) {
op->emitOpError() << "should have an integer as the element type of the "
"tensor of operand #0";
return mlir::failure();
}
auto el1Ty =
op1Ty.getElementType()
.dyn_cast_or_null<mlir::zamalang::HLFHE::EncryptedIntegerType>();
if (el1Ty == nullptr) {
op->emitOpError() << "should have a !HLFHE.eint as the element type of the "
"tensor of operand #1";
return mlir::failure();
}
if (el1Ty.getWidth() > el0Ty.getWidth() + 1) {
op->emitOpError()
<< "should have the width of integer values less or equals "

55
compiler/tests/Dialect/HLFHELinalg/ops.mlir
View File

@@ -106,4 +106,59 @@ func @add_eint_broadcast_1(%a0: tensor<1x4x5x!HLFHE.eint<2>>, %a1: tensor<3x4x1x
func @add_eint_broadcast_2(%a0: tensor<4x!HLFHE.eint<2>>, %a1: tensor<3x4x!HLFHE.eint<2>>) -> tensor<3x4x!HLFHE.eint<2>> {
%1 ="HLFHELinalg.add_eint"(%a0, %a1) : (tensor<4x!HLFHE.eint<2>>, tensor<3x4x!HLFHE.eint<2>>) -> tensor<3x4x!HLFHE.eint<2>>
return %1: tensor<3x4x!HLFHE.eint<2>>
}
/////////////////////////////////////////////////
// HLFHELinalg.sub_eint_int
/////////////////////////////////////////////////
// 1D tensor
// CHECK: func @sub_int_eint_1D(%[[a0:.*]]: tensor<4xi3>, %[[a1:.*]]: tensor<4x!HLFHE.eint<2>>) -> tensor<4x!HLFHE.eint<2>> {
// CHECK-NEXT: %[[V0:.*]] = "HLFHELinalg.sub_int_eint"(%[[a0]], %[[a1]]) : (tensor<4xi3>, tensor<4x!HLFHE.eint<2>>) -> tensor<4x!HLFHE.eint<2>>
// CHECK-NEXT: return %[[V0]] : tensor<4x!HLFHE.eint<2>>
// CHECK-NEXT: }
func @sub_int_eint_1D(%a0: tensor<4xi3>, %a1: tensor<4x!HLFHE.eint<2>>) -> tensor<4x!HLFHE.eint<2>> {
%1 = "HLFHELinalg.sub_int_eint"(%a0, %a1) : (tensor<4xi3>, tensor<4x!HLFHE.eint<2>>) -> tensor<4x!HLFHE.eint<2>>
return %1: tensor<4x!HLFHE.eint<2>>
}
// 2D tensor
// CHECK: func @sub_int_eint_2D(%[[a0:.*]]: tensor<2x4xi3>, %[[a1:.*]]: tensor<2x4x!HLFHE.eint<2>>) -> tensor<2x4x!HLFHE.eint<2>> {
// CHECK-NEXT: %[[V0:.*]] = "HLFHELinalg.sub_int_eint"(%[[a0]], %[[a1]]) : (tensor<2x4xi3>, tensor<2x4x!HLFHE.eint<2>>) -> tensor<2x4x!HLFHE.eint<2>>
// CHECK-NEXT: return %[[V0]] : tensor<2x4x!HLFHE.eint<2>>
// CHECK-NEXT: }
func @sub_int_eint_2D(%a0: tensor<2x4xi3>, %a1: tensor<2x4x!HLFHE.eint<2>>) -> tensor<2x4x!HLFHE.eint<2>> {
%1 = "HLFHELinalg.sub_int_eint"(%a0, %a1) : (tensor<2x4xi3>, tensor<2x4x!HLFHE.eint<2>>) -> tensor<2x4x!HLFHE.eint<2>>
return %1: tensor<2x4x!HLFHE.eint<2>>
}
// 10D tensor
// CHECK: func @sub_int_eint_10D(%[[a0:.*]]: tensor<1x2x3x4x5x6x7x8x9x10xi3>, %[[a1:.*]]: tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>) -> tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>> {
// CHECK-NEXT: %[[V0:.*]] = "HLFHELinalg.sub_int_eint"(%[[a0]], %[[a1]]) : (tensor<1x2x3x4x5x6x7x8x9x10xi3>, tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>) -> tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>
// CHECK-NEXT: return %[[V0]] : tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>
// CHECK-NEXT: }
func @sub_int_eint_10D(%a0: tensor<1x2x3x4x5x6x7x8x9x10xi3>, %a1: tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>) -> tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>> {
%1 = "HLFHELinalg.sub_int_eint"(%a0, %a1) : (tensor<1x2x3x4x5x6x7x8x9x10xi3>, tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>) -> tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>
return %1: tensor<1x2x3x4x5x6x7x8x9x10x!HLFHE.eint<2>>
}
// Broadcasting with tensor with dimensions equals to one
// CHECK: func @sub_int_eint_broadcast_1(%[[a0:.*]]: tensor<3x4x1xi3>, %[[a1:.*]]: tensor<1x4x5x!HLFHE.eint<2>>) -> tensor<3x4x5x!HLFHE.eint<2>> {
// CHECK-NEXT: %[[V0:.*]] = "HLFHELinalg.sub_int_eint"(%[[a0]], %[[a1]]) : (tensor<3x4x1xi3>, tensor<1x4x5x!HLFHE.eint<2>>) -> tensor<3x4x5x!HLFHE.eint<2>>
// CHECK-NEXT: return %[[V0]] : tensor<3x4x5x!HLFHE.eint<2>>
// CHECK-NEXT: }
func @sub_int_eint_broadcast_1(%a0: tensor<3x4x1xi3>, %a1: tensor<1x4x5x!HLFHE.eint<2>>) -> tensor<3x4x5x!HLFHE.eint<2>> {
%1 = "HLFHELinalg.sub_int_eint"(%a0, %a1) : (tensor<3x4x1xi3>, tensor<1x4x5x!HLFHE.eint<2>>) -> tensor<3x4x5x!HLFHE.eint<2>>
return %1: tensor<3x4x5x!HLFHE.eint<2>>
}
// Broadcasting with a tensor less dimensions of another
// CHECK: func @sub_int_eint_broadcast_2(%[[a0:.*]]: tensor<3x4xi3>, %[[a1:.*]]: tensor<4x!HLFHE.eint<2>>) -> tensor<3x4x!HLFHE.eint<2>> {
// CHECK-NEXT: %[[V0:.*]] = "HLFHELinalg.sub_int_eint"(%[[a0]], %[[a1]]) : (tensor<3x4xi3>, tensor<4x!HLFHE.eint<2>>) -> tensor<3x4x!HLFHE.eint<2>>
// CHECK-NEXT: return %[[V0]] : tensor<3x4x!HLFHE.eint<2>>
// CHECK-NEXT: }
func @sub_int_eint_broadcast_2(%a0: tensor<3x4xi3>, %a1: tensor<4x!HLFHE.eint<2>>) -> tensor<3x4x!HLFHE.eint<2>> {
%1 ="HLFHELinalg.sub_int_eint"(%a0, %a1) : (tensor<3x4xi3>, tensor<4x!HLFHE.eint<2>>) -> tensor<3x4x!HLFHE.eint<2>>
return %1: tensor<3x4x!HLFHE.eint<2>>
}