github/concrete
1
1
Fork 0
mirror of https://github.com/zama-ai/concrete.git synced 2026-02-09 12:15:09 -05:00
Code Issues Packages Projects Releases Wiki Activity

feat(compiler): Lowering of HLFHELinalg.mul_eint_int

Browse Source
This commit is contained in:
Quentin Bourgerie
2021-10-25 20:50:39 +02:00
committed by Andi Drebes
parent 0b5ee3497a
commit a135d05e4d
3 changed files with 225 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
compiler/include/zamalang/Dialect/HLFHELinalg/IR/HLFHELinalgOps.td
View File

@@ -197,7 +197,7 @@ def MulEintIntOp : HLFHELinalg_Op<"mul_eint_int", [TensorBroadcastingRules, Tens
// [7,8,9] [3] [21,24,27]
//
// The dimension #1 of operand #2 is stretched as it is equals to 1.
"HLFHELinalg.mul_eint_int(%a0, %a1)" : (tensor<3x4x!HLFHE.eint<4>>, tensor<3x1xi5>) -> tensor<3x3x!HLFHE.eint<4>>
"HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!HLFHE.eint<4>>, tensor<3x1xi5>) -> tensor<3x3x!HLFHE.eint<4>>
// Returns the multiplication of a 3x3 matrix of encrypted integers and a 1x3 matrix (a line) of integers.
//
@@ -206,10 +206,10 @@ def MulEintIntOp : HLFHELinalg_Op<"mul_eint_int", [TensorBroadcastingRules, Tens
// [7,8,9] [8,10,12]
//
// The dimension #2 of operand #2 is stretched as it is equals to 1.
"HLFHELinalg.mul_eint_int(%a0, %a1)" : (tensor<3x4x!HLFHE.eint<4>>, tensor<1x3xi5>) -> tensor<3x3x!HLFHE.eint<4>>
"HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!HLFHE.eint<4>>, tensor<1x3xi5>) -> tensor<3x3x!HLFHE.eint<4>>
// Same behavior than the previous one, but as the dimension #2 is missing of operand #2.
"HLFHELinalg.mul_eint_int(%a0, %a1)" : (tensor<3x4x!HLFHE.eint<4>>, tensor<3xi5>) -> tensor<4x4x4x!HLFHE.eint<4>>
"HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!HLFHE.eint<4>>, tensor<3xi5>) -> tensor<3x3x!HLFHE.eint<4>>
```
}];

4
compiler/lib/Conversion/HLFHETensorOpsToLinalg/TensorOpsToLinalg.cpp
View File

@@ -276,6 +276,10 @@ void HLFHETensorOpsToLinalg::runOnFunction() {
HLFHELinalgOpToLinalgGeneric<mlir::zamalang::HLFHELinalg::SubIntEintOp,
mlir::zamalang::HLFHE::SubIntEintOp>>(
&getContext());
patterns.insert<
HLFHELinalgOpToLinalgGeneric<mlir::zamalang::HLFHELinalg::MulEintIntOp,
mlir::zamalang::HLFHE::MulEintIntOp>>(
&getContext());
if (mlir::applyPartialConversion(function, target, std::move(patterns))
.failed())

218
compiler/tests/unittest/end_to_end_jit_encrypted_tensor.cc
View File

@@ -823,4 +823,222 @@ TEST(End2EndJit_HLFHELinalg, sub_int_eint_matrix_line_missing_dim) {
<< a0[i][j] - a1[0][j] << " got " << result[i][j] << "\n";
}
}
}
///////////////////////////////////////////////////////////////////////////////
// HLFHELinalg mul_eint_int ///////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////
TEST(End2EndJit_HLFHELinalg, mul_eint_int_term_to_term) {
mlir::zamalang::CompilerEngine engine;
auto mlirStr = R"XXX(
// Returns the term to term multiplication of `%a0` with `%a1`
func @main(%a0: tensor<4x!HLFHE.eint<4>>, %a1: tensor<4xi5>) -> tensor<4x!HLFHE.eint<4>> {
%res = "HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<4x!HLFHE.eint<4>>, tensor<4xi5>) -> tensor<4x!HLFHE.eint<4>>
return %res : tensor<4x!HLFHE.eint<4>>
}
)XXX";
const uint8_t a0[4]{31, 6, 12, 9};
const uint8_t a1[4]{2, 3, 2, 3};
ASSERT_LLVM_ERROR(engine.compile(mlirStr, defaultV0Constraints()));
auto maybeArgument = engine.buildArgument();
ASSERT_LLVM_ERROR(maybeArgument.takeError());
auto argument = std::move(maybeArgument.get());
// Set the %a0 and %a1 argument
ASSERT_LLVM_ERROR(argument->setArg(0, (uint8_t *)a0, 4));
ASSERT_LLVM_ERROR(argument->setArg(1, (uint8_t *)a1, 4));
// Invoke the function
ASSERT_LLVM_ERROR(engine.invoke(*argument));
// Get and assert the result
uint64_t result[4];
ASSERT_LLVM_ERROR(argument->getResult(0, (uint64_t *)result, 4));
for (size_t i = 0; i < 4; i++) {
EXPECT_EQ(result[i], a0[i] * a1[i])
<< "result differ at pos " << i << ", expect " << a0[i] * a1[i]
<< " got " << result[i];
}
}
TEST(End2EndJit_HLFHELinalg, mul_eint_int_term_to_term_broadcast) {
mlir::zamalang::CompilerEngine engine;
auto mlirStr = R"XXX(
// Returns the term to term multiplication of `%a0` with `%a1`, where dimensions equals to one are stretched.
func @main(%a0: tensor<4x1x4x!HLFHE.eint<4>>, %a1: tensor<1x4x4xi5>) -> tensor<4x4x4x!HLFHE.eint<4>> {
%res = "HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<4x1x4x!HLFHE.eint<4>>, tensor<1x4x4xi5>) -> tensor<4x4x4x!HLFHE.eint<4>>
return %res : tensor<4x4x4x!HLFHE.eint<4>>
}
)XXX";
const uint8_t a0[4][1][4]{
{{1, 2, 3, 4}},
{{5, 6, 7, 8}},
{{9, 10, 11, 12}},
{{13, 14, 15, 16}},
};
const uint8_t a1[1][4][4]{
{
{1, 2, 0, 1},
{2, 0, 1, 2},
{0, 1, 2, 0},
{1, 2, 0, 1},
},
};
ASSERT_LLVM_ERROR(engine.compile(mlirStr, defaultV0Constraints()));
auto maybeArgument = engine.buildArgument();
ASSERT_LLVM_ERROR(maybeArgument.takeError());
auto argument = std::move(maybeArgument.get());
// Set the %a0 and %a1 argument
ASSERT_LLVM_ERROR(argument->setArg(0, (uint8_t *)a0, {4, 1, 4}));
ASSERT_LLVM_ERROR(argument->setArg(1, (uint8_t *)a1, {1, 4, 4}));
// Invoke the function
ASSERT_LLVM_ERROR(engine.invoke(*argument));
// Get and assert the result
uint64_t result[4][4][4];
ASSERT_LLVM_ERROR(argument->getResult(0, (uint64_t *)result, 4 * 4 * 4));
for (size_t i = 0; i < 4; i++) {
for (size_t j = 0; j < 4; j++) {
for (size_t k = 0; k < 4; k++) {
EXPECT_EQ(result[i][j][k], a0[i][0][k] * a1[0][j][k])
<< "result differ at pos " << i << ", expect "
<< a0[i][0][k] * a1[0][j][k] << " got " << result[i];
}
}
}
}
TEST(End2EndJit_HLFHELinalg, mul_eint_int_matrix_column) {
mlir::zamalang::CompilerEngine engine;
auto mlirStr = R"XXX(
// Returns the multiplication of a 3x3 matrix of encrypted integers and a 3x1 matrix (a column) of integers.
//
// [1,2,3] [1] [1,2,3]
// [4,5,6] * [2] = [8,10,18]
// [7,8,9] [3] [21,24,27]
//
// The dimension #1 of operand #2 is stretched as it is equals to 1.
func @main(%a0: tensor<3x3x!HLFHE.eint<4>>, %a1: tensor<3x1xi5>) -> tensor<3x3x!HLFHE.eint<4>> {
%res = "HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!HLFHE.eint<4>>, tensor<3x1xi5>) -> tensor<3x3x!HLFHE.eint<4>>
return %res : tensor<3x3x!HLFHE.eint<4>>
}
)XXX";
const uint8_t a0[3][3]{
{1, 2, 3},
{4, 5, 6},
{7, 8, 9},
};
const uint8_t a1[3][1]{
{1},
{2},
{3},
};
ASSERT_LLVM_ERROR(engine.compile(mlirStr, defaultV0Constraints()));
auto maybeArgument = engine.buildArgument();
ASSERT_LLVM_ERROR(maybeArgument.takeError());
auto argument = std::move(maybeArgument.get());
// Set the %a0 and %a1 argument
ASSERT_LLVM_ERROR(argument->setArg(0, (uint8_t *)a0, {3, 3}));
ASSERT_LLVM_ERROR(argument->setArg(1, (uint8_t *)a1, {3, 1}));
// Invoke the function
ASSERT_LLVM_ERROR(engine.invoke(*argument));
// Get and assert the result
uint64_t result[3][3];
ASSERT_LLVM_ERROR(argument->getResult(0, (uint64_t *)result, 3 * 3));
for (size_t i = 0; i < 3; i++) {
for (size_t j = 0; j < 3; j++) {
EXPECT_EQ(result[i][j], a0[i][j] * a1[i][0])
<< "result differ at pos " << i << ", expect " << a0[i][j] * a1[i][0]
<< " got " << result[i];
}
}
}
TEST(End2EndJit_HLFHELinalg, mul_eint_int_matrix_line) {
mlir::zamalang::CompilerEngine engine;
auto mlirStr = R"XXX(
// Returns the multiplication of a 3x3 matrix of encrypted integers and a 1x3 matrix (a line) of integers.
//
// [1,2,3] [2,4,6]
// [4,5,6] * [1,2,3] = [5,7,9]
// [7,8,9] [8,10,12]
//
// The dimension #2 of operand #2 is stretched as it is equals to 1.
func @main(%a0: tensor<3x3x!HLFHE.eint<4>>, %a1: tensor<1x3xi5>) -> tensor<3x3x!HLFHE.eint<4>> {
%res = "HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!HLFHE.eint<4>>, tensor<1x3xi5>) -> tensor<3x3x!HLFHE.eint<4>>
return %res : tensor<3x3x!HLFHE.eint<4>>
}
)XXX";
const uint8_t a0[3][3]{
{1, 2, 3},
{4, 5, 6},
{7, 8, 9},
};
const uint8_t a1[1][3]{
{1, 2, 3},
};
ASSERT_LLVM_ERROR(engine.compile(mlirStr, defaultV0Constraints()));
auto maybeArgument = engine.buildArgument();
ASSERT_LLVM_ERROR(maybeArgument.takeError());
auto argument = std::move(maybeArgument.get());
// Set the %a0 and %a1 argument
ASSERT_LLVM_ERROR(argument->setArg(0, (uint8_t *)a0, {3, 3}));
ASSERT_LLVM_ERROR(argument->setArg(1, (uint8_t *)a1, {1, 3}));
// Invoke the function
ASSERT_LLVM_ERROR(engine.invoke(*argument));
// Get and assert the result
uint64_t result[3][3];
ASSERT_LLVM_ERROR(argument->getResult(0, (uint64_t *)result, 3 * 3));
for (size_t i = 0; i < 3; i++) {
for (size_t j = 0; j < 3; j++) {
EXPECT_EQ(result[i][j], a0[i][j] * a1[0][j])
<< "result differ at pos (" << i << "," << j << "), expect "
<< a0[i][j] * a1[0][j] << " got " << result[i][j] << "\n";
}
}
}
TEST(End2EndJit_HLFHELinalg, mul_eint_int_matrix_line_missing_dim) {
mlir::zamalang::CompilerEngine engine;
auto mlirStr = R"XXX(
// Same behavior than the previous one, but as the dimension #2 of operand #2 is missing.
func @main(%a0: tensor<3x3x!HLFHE.eint<4>>, %a1: tensor<3xi5>) -> tensor<3x3x!HLFHE.eint<4>> {
%res = "HLFHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!HLFHE.eint<4>>, tensor<3xi5>) -> tensor<3x3x!HLFHE.eint<4>>
return %res : tensor<3x3x!HLFHE.eint<4>>
}
)XXX";
const uint8_t a0[3][3]{
{1, 2, 3},
{4, 5, 6},
{7, 8, 9},
};
const uint8_t a1[1][3]{
{1, 2, 3},
};
ASSERT_LLVM_ERROR(engine.compile(mlirStr, defaultV0Constraints()));
auto maybeArgument = engine.buildArgument();
ASSERT_LLVM_ERROR(maybeArgument.takeError());
auto argument = std::move(maybeArgument.get());
// Set the %a0 and %a1 argument
ASSERT_LLVM_ERROR(argument->setArg(0, (uint8_t *)a0, {3, 3}));
ASSERT_LLVM_ERROR(argument->setArg(1, (uint8_t *)a1, {3}));
// Invoke the function
ASSERT_LLVM_ERROR(engine.invoke(*argument));
// Get and assert the result
uint64_t result[3][3];
ASSERT_LLVM_ERROR(argument->getResult(0, (uint64_t *)result, 3 * 3));
for (size_t i = 0; i < 3; i++) {
for (size_t j = 0; j < 3; j++) {
EXPECT_EQ(result[i][j], a0[i][j] * a1[0][j])
<< "result differ at pos (" << i << "," << j << "), expect "
<< a0[i][j] * a1[0][j] << " got " << result[i][j] << "\n";
}
}
}