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[mlir][NFC] update mlir/examples create APIs (31/n) (#150652)

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See https://github.com/llvm/llvm-project/pull/147168 for more info.
This commit is contained in:
Maksim Levental 2025-07-25 15:14:16 -05:00 committed by GitHub
parent c090ed53fb
commit 284a5c2c0b
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GPG Key ID: B5690EEEBB952194
29 changed files with 235 additions and 228 deletions
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4
mlir/docs/Interfaces.md
View File

@ -563,7 +563,7 @@ def MyInterface : OpInterface<"MyInterface"> {
template <typename ConcreteOp>
struct Model : public Concept {
Operation *create(OpBuilder &builder, Location loc) const override {
return builder.create<ConcreteOp>(loc);
return ConcreteOp::create(builder, loc);
}
}
};
@ -574,7 +574,7 @@ def MyInterface : OpInterface<"MyInterface"> {
}],
"Operation *", "create", (ins "OpBuilder &":$builder, "Location":$loc),
/*methodBody=*/[{
return builder.create<ConcreteOp>(loc);
return ConcreteOp::create(builder, loc);
}]>,
InterfaceMethod<[{

12
mlir/docs/PDLL.md
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@ -1483,7 +1483,7 @@ be defined by specifying a string code block after the rewrite declaration:
```pdll
Rewrite BuildOp(value: Value) -> (foo: Op<my_dialect.foo>, bar: Op<my_dialect.bar>) [{
return {rewriter.create<my_dialect::FooOp>(value), rewriter.create<my_dialect::BarOp>()};
return {my_dialect::FooOp::create(rewriter, value), my_dialect::BarOp::create(rewriter)};
}];
Pattern {
@ -1508,7 +1508,7 @@ translated into:
```c++
std::tuple<my_dialect::FooOp, my_dialect::BarOp> BuildOp(Value value) {
return {rewriter.create<my_dialect::FooOp>(value), rewriter.create<my_dialect::BarOp>()};
return {my_dialect::FooOp::create(rewriter, value), my_dialect::BarOp::create(rewriter)};
}
```
@ -1530,7 +1530,7 @@ below describes the various result translation scenarios:
```pdll
Rewrite createOp() [{
rewriter.create<my_dialect::FooOp>();
my_dialect::FooOp::create(rewriter);
}];
```
@ -1538,7 +1538,7 @@ In the case where a native `Rewrite` has no results, the native function returns
```c++
void createOp(PatternRewriter &rewriter) {
rewriter.create<my_dialect::FooOp>();
my_dialect::FooOp::create(rewriter);
}
```
@ -1546,7 +1546,7 @@ void createOp(PatternRewriter &rewriter) {
```pdll
Rewrite createOp() -> Op<my_dialect.foo> [{
return rewriter.create<my_dialect::FooOp>();
return my_dialect::FooOp::create(rewriter);
}];
```
@ -1555,7 +1555,7 @@ native type for that single result:
```c++
my_dialect::FooOp createOp(PatternRewriter &rewriter) {
return rewriter.create<my_dialect::FooOp>();
return my_dialect::FooOp::create(rewriter);
}
```

6
mlir/docs/Tutorials/QuickstartRewrites.md
View File

@ -130,7 +130,7 @@ def : Pat<(TF_LeakyReluOp:$old_value, $arg, F32Attr:$a),
```c++
static Value createTFLLeakyRelu(PatternRewriter &rewriter, Operation *op,
Value operand, Attribute attr) {
return rewriter.create<mlir::TFL::LeakyReluOp>(
return mlir::TFL::LeakyReluOp::create(rewriter,
op->getLoc(), operands[0].getType(), /*arg=*/operands[0],
/*alpha=*/cast<FloatAttr>(attrs[0]));
}
@ -194,10 +194,10 @@ LogicalResult circt::MulOp::canonicalize(MulOp op, PatternRewriter &rewriter) {
// mul(x, c) -> shl(x, log2(c)), where c is a power of two.
if (inputs.size() == 2 && matchPattern(inputs.back(), m_RConstant(value)) &&
value.isPowerOf2()) {
auto shift = rewriter.create<rtl::ConstantOp>(op.getLoc(), op.getType(),
auto shift = rtl::ConstantOp::create(rewriter, op.getLoc(), op.getType(),
value.exactLogBase2());
auto shlOp =
rewriter.create<comb::ShlOp>(op.getLoc(), inputs[0], shift);
comb::ShlOp::create(rewriter, op.getLoc(), inputs[0], shift);
rewriter.replaceOpWithNewOp<MulOp>(op, op.getType(),
ArrayRef<Value>(shlOp));
return success();

2
mlir/docs/Tutorials/Toy/Ch-2.md
View File

@ -521,7 +521,7 @@ def ConstantOp : Toy_Op<"constant"> {
// Add custom build methods for the constant operation. These methods populate
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

2
mlir/docs/Tutorials/Toy/Ch-4.md
View File

@ -300,7 +300,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
Operation *materializeCallConversion(OpBuilder &builder, Value input,
Type resultType,
Location conversionLoc) const final {
return builder.create<CastOp>(conversionLoc, resultType, input);
return CastOp::create(builder, conversionLoc, resultType, input);
}
};
```

2
mlir/docs/Tutorials/Toy/Ch-5.md
View File

@ -136,7 +136,7 @@ struct TransposeOpLowering : public mlir::ConversionPattern {
// Transpose the elements by generating a load from the reverse
// indices.
SmallVector<mlir::Value, 2> reverseIvs(llvm::reverse(loopIvs));
return rewriter.create<mlir::AffineLoadOp>(loc, input, reverseIvs);
return mlir::AffineLoadOp::create(rewriter, loc, input, reverseIvs);
});
return success();
}

2
mlir/docs/Tutorials/Toy/Ch-6.md
View File

@ -47,7 +47,7 @@ static FlatSymbolRefAttr getOrInsertPrintf(PatternRewriter &rewriter,
// Insert the printf function into the body of the parent module.
PatternRewriter::InsertionGuard insertGuard(rewriter);
rewriter.setInsertionPointToStart(module.getBody());
rewriter.create<LLVM::LLVMFuncOp>(module.getLoc(), "printf", llvmFnType);
LLVM::LLVMFuncOp::create(rewriter, module.getLoc(), "printf", llvmFnType);
return SymbolRefAttr::get("printf", context);
}
```

4
mlir/docs/Tutorials/Toy/Ch-7.md
View File

@ -488,9 +488,9 @@ mlir::Operation *ToyDialect::materializeConstant(mlir::OpBuilder &builder,
mlir::Type type,
mlir::Location loc) {
if (isa<StructType>(type))
return builder.create<StructConstantOp>(loc, type,
return StructConstantOp::create(builder, loc, type,
cast<mlir::ArrayAttr>(value));
return builder.create<ConstantOp>(loc, type,
return ConstantOp::create(builder, loc, type,
cast<mlir::DenseElementsAttr>(value));
}
```

2
mlir/examples/toy/Ch2/include/toy/Ops.td
View File

@ -70,7 +70,7 @@ def ConstantOp : Toy_Op<"constant", [Pure]> {
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

28
mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
View File

@ -121,8 +121,8 @@ private:
llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
getType(VarType{}));
auto funcType = builder.getFunctionType(argTypes, {});
return builder.create<mlir::toy::FuncOp>(location, proto.getName(),
funcType);
return mlir::toy::FuncOp::create(builder, location, proto.getName(),
funcType);
}
/// Emit a new function and add it to the MLIR module.
@ -166,7 +166,7 @@ private:
if (!entryBlock.empty())
returnOp = dyn_cast<ReturnOp>(entryBlock.back());
if (!returnOp) {
builder.create<ReturnOp>(loc(funcAST.getProto()->loc()));
ReturnOp::create(builder, loc(funcAST.getProto()->loc()));
} else if (returnOp.hasOperand()) {
// Otherwise, if this return operation has an operand then add a result to
// the function.
@ -202,9 +202,9 @@ private:
// support '+' and '*'.
switch (binop.getOp()) {
case '+':
return builder.create<AddOp>(location, lhs, rhs);
return AddOp::create(builder, location, lhs, rhs);
case '*':
return builder.create<MulOp>(location, lhs, rhs);
return MulOp::create(builder, location, lhs, rhs);
}
emitError(location, "invalid binary operator '") << binop.getOp() << "'";
@ -235,8 +235,8 @@ private:
}
// Otherwise, this return operation has zero operands.
builder.create<ReturnOp>(location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
ReturnOp::create(builder, location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
return mlir::success();
}
@ -280,7 +280,7 @@ private:
// Build the MLIR op `toy.constant`. This invokes the `ConstantOp::build`
// method.
return builder.create<ConstantOp>(loc(lit.loc()), type, dataAttribute);
return ConstantOp::create(builder, loc(lit.loc()), type, dataAttribute);
}
/// Recursive helper function to accumulate the data that compose an array
@ -325,13 +325,13 @@ private:
"does not accept multiple arguments");
return nullptr;
}
return builder.create<TransposeOp>(location, operands[0]);
return TransposeOp::create(builder, location, operands[0]);
}
// Otherwise this is a call to a user-defined function. Calls to
// user-defined functions are mapped to a custom call that takes the callee
// name as an attribute.
return builder.create<GenericCallOp>(location, callee, operands);
return GenericCallOp::create(builder, location, callee, operands);
}
/// Emit a print expression. It emits specific operations for two builtins:
@ -341,13 +341,13 @@ private:
if (!arg)
return mlir::failure();
builder.create<PrintOp>(loc(call.loc()), arg);
PrintOp::create(builder, loc(call.loc()), arg);
return mlir::success();
}
/// Emit a constant for a single number (FIXME: semantic? broadcast?)
mlir::Value mlirGen(NumberExprAST &num) {
return builder.create<ConstantOp>(loc(num.loc()), num.getValue());
return ConstantOp::create(builder, loc(num.loc()), num.getValue());
}
/// Dispatch codegen for the right expression subclass using RTTI.
@ -391,8 +391,8 @@ private:
// with specific shape, we emit a "reshape" operation. It will get
// optimized out later as needed.
if (!vardecl.getType().shape.empty()) {
value = builder.create<ReshapeOp>(loc(vardecl.loc()),
getType(vardecl.getType()), value);
value = ReshapeOp::create(builder, loc(vardecl.loc()),
getType(vardecl.getType()), value);
}
// Register the value in the symbol table.

2
mlir/examples/toy/Ch3/include/toy/Ops.td
View File

@ -69,7 +69,7 @@ def ConstantOp : Toy_Op<"constant", [Pure]> {
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

28
mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
View File

@ -121,8 +121,8 @@ private:
llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
getType(VarType{}));
auto funcType = builder.getFunctionType(argTypes, /*results=*/{});
return builder.create<mlir::toy::FuncOp>(location, proto.getName(),
funcType);
return mlir::toy::FuncOp::create(builder, location, proto.getName(),
funcType);
}
/// Emit a new function and add it to the MLIR module.
@ -166,7 +166,7 @@ private:
if (!entryBlock.empty())
returnOp = dyn_cast<ReturnOp>(entryBlock.back());
if (!returnOp) {
builder.create<ReturnOp>(loc(funcAST.getProto()->loc()));
ReturnOp::create(builder, loc(funcAST.getProto()->loc()));
} else if (returnOp.hasOperand()) {
// Otherwise, if this return operation has an operand then add a result to
// the function.
@ -202,9 +202,9 @@ private:
// support '+' and '*'.
switch (binop.getOp()) {
case '+':
return builder.create<AddOp>(location, lhs, rhs);
return AddOp::create(builder, location, lhs, rhs);
case '*':
return builder.create<MulOp>(location, lhs, rhs);
return MulOp::create(builder, location, lhs, rhs);
}
emitError(location, "invalid binary operator '") << binop.getOp() << "'";
@ -235,8 +235,8 @@ private:
}
// Otherwise, this return operation has zero operands.
builder.create<ReturnOp>(location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
ReturnOp::create(builder, location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
return mlir::success();
}
@ -280,7 +280,7 @@ private:
// Build the MLIR op `toy.constant`. This invokes the `ConstantOp::build`
// method.
return builder.create<ConstantOp>(loc(lit.loc()), type, dataAttribute);
return ConstantOp::create(builder, loc(lit.loc()), type, dataAttribute);
}
/// Recursive helper function to accumulate the data that compose an array
@ -325,13 +325,13 @@ private:
"does not accept multiple arguments");
return nullptr;
}
return builder.create<TransposeOp>(location, operands[0]);
return TransposeOp::create(builder, location, operands[0]);
}
// Otherwise this is a call to a user-defined function. Calls to
// user-defined functions are mapped to a custom call that takes the callee
// name as an attribute.
return builder.create<GenericCallOp>(location, callee, operands);
return GenericCallOp::create(builder, location, callee, operands);
}
/// Emit a print expression. It emits specific operations for two builtins:
@ -341,13 +341,13 @@ private:
if (!arg)
return mlir::failure();
builder.create<PrintOp>(loc(call.loc()), arg);
PrintOp::create(builder, loc(call.loc()), arg);
return mlir::success();
}
/// Emit a constant for a single number (FIXME: semantic? broadcast?)
mlir::Value mlirGen(NumberExprAST &num) {
return builder.create<ConstantOp>(loc(num.loc()), num.getValue());
return ConstantOp::create(builder, loc(num.loc()), num.getValue());
}
/// Dispatch codegen for the right expression subclass using RTTI.
@ -391,8 +391,8 @@ private:
// with specific shape, we emit a "reshape" operation. It will get
// optimized out later as needed.
if (!vardecl.getType().shape.empty()) {
value = builder.create<ReshapeOp>(loc(vardecl.loc()),
getType(vardecl.getType()), value);
value = ReshapeOp::create(builder, loc(vardecl.loc()),
getType(vardecl.getType()), value);
}
// Register the value in the symbol table.

2
mlir/examples/toy/Ch4/include/toy/Ops.td
View File

@ -72,7 +72,7 @@ def ConstantOp : Toy_Op<"constant", [Pure]> {
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

8
mlir/examples/toy/Ch4/mlir/Dialect.cpp
View File

@ -91,7 +91,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
Operation *materializeCallConversion(OpBuilder &builder, Value input,
Type resultType,
Location conversionLoc) const final {
return builder.create<CastOp>(conversionLoc, resultType, input);
return CastOp::create(builder, conversionLoc, resultType, input);
}
};
@ -206,7 +206,8 @@ void ConstantOp::print(mlir::OpAsmPrinter &printer) {
llvm::LogicalResult ConstantOp::verify() {
// If the return type of the constant is not an unranked tensor, the shape
// must match the shape of the attribute holding the data.
auto resultType = llvm::dyn_cast<mlir::RankedTensorType>(getResult().getType());
auto resultType =
llvm::dyn_cast<mlir::RankedTensorType>(getResult().getType());
if (!resultType)
return success();
@ -395,7 +396,8 @@ llvm::LogicalResult ReturnOp::verify() {
auto resultType = results.front();
// Check that the result type of the function matches the operand type.
if (inputType == resultType || llvm::isa<mlir::UnrankedTensorType>(inputType) ||
if (inputType == resultType ||
llvm::isa<mlir::UnrankedTensorType>(inputType) ||
llvm::isa<mlir::UnrankedTensorType>(resultType))
return mlir::success();

28
mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
View File

@ -121,8 +121,8 @@ private:
llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
getType(VarType{}));
auto funcType = builder.getFunctionType(argTypes, /*results=*/{});
return builder.create<mlir::toy::FuncOp>(location, proto.getName(),
funcType);
return mlir::toy::FuncOp::create(builder, location, proto.getName(),
funcType);
}
/// Emit a new function and add it to the MLIR module.
@ -166,7 +166,7 @@ private:
if (!entryBlock.empty())
returnOp = dyn_cast<ReturnOp>(entryBlock.back());
if (!returnOp) {
builder.create<ReturnOp>(loc(funcAST.getProto()->loc()));
ReturnOp::create(builder, loc(funcAST.getProto()->loc()));
} else if (returnOp.hasOperand()) {
// Otherwise, if this return operation has an operand then add a result to
// the function.
@ -206,9 +206,9 @@ private:
// support '+' and '*'.
switch (binop.getOp()) {
case '+':
return builder.create<AddOp>(location, lhs, rhs);
return AddOp::create(builder, location, lhs, rhs);
case '*':
return builder.create<MulOp>(location, lhs, rhs);
return MulOp::create(builder, location, lhs, rhs);
}
emitError(location, "invalid binary operator '") << binop.getOp() << "'";
@ -239,8 +239,8 @@ private:
}
// Otherwise, this return operation has zero operands.
builder.create<ReturnOp>(location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
ReturnOp::create(builder, location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
return mlir::success();
}
@ -284,7 +284,7 @@ private:
// Build the MLIR op `toy.constant`. This invokes the `ConstantOp::build`
// method.
return builder.create<ConstantOp>(loc(lit.loc()), type, dataAttribute);
return ConstantOp::create(builder, loc(lit.loc()), type, dataAttribute);
}
/// Recursive helper function to accumulate the data that compose an array
@ -329,13 +329,13 @@ private:
"does not accept multiple arguments");
return nullptr;
}
return builder.create<TransposeOp>(location, operands[0]);
return TransposeOp::create(builder, location, operands[0]);
}
// Otherwise this is a call to a user-defined function. Calls to
// user-defined functions are mapped to a custom call that takes the callee
// name as an attribute.
return builder.create<GenericCallOp>(location, callee, operands);
return GenericCallOp::create(builder, location, callee, operands);
}
/// Emit a print expression. It emits specific operations for two builtins:
@ -345,13 +345,13 @@ private:
if (!arg)
return mlir::failure();
builder.create<PrintOp>(loc(call.loc()), arg);
PrintOp::create(builder, loc(call.loc()), arg);
return mlir::success();
}
/// Emit a constant for a single number (FIXME: semantic? broadcast?)
mlir::Value mlirGen(NumberExprAST &num) {
return builder.create<ConstantOp>(loc(num.loc()), num.getValue());
return ConstantOp::create(builder, loc(num.loc()), num.getValue());
}
/// Dispatch codegen for the right expression subclass using RTTI.
@ -395,8 +395,8 @@ private:
// with specific shape, we emit a "reshape" operation. It will get
// optimized out later as needed.
if (!vardecl.getType().shape.empty()) {
value = builder.create<ReshapeOp>(loc(vardecl.loc()),
getType(vardecl.getType()), value);
value = ReshapeOp::create(builder, loc(vardecl.loc()),
getType(vardecl.getType()), value);
}
// Register the value in the symbol table.

2
mlir/examples/toy/Ch5/include/toy/Ops.td
View File

@ -72,7 +72,7 @@ def ConstantOp : Toy_Op<"constant", [Pure]> {
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

8
mlir/examples/toy/Ch5/mlir/Dialect.cpp
View File

@ -91,7 +91,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
Operation *materializeCallConversion(OpBuilder &builder, Value input,
Type resultType,
Location conversionLoc) const final {
return builder.create<CastOp>(conversionLoc, resultType, input);
return CastOp::create(builder, conversionLoc, resultType, input);
}
};
@ -206,7 +206,8 @@ void ConstantOp::print(mlir::OpAsmPrinter &printer) {
llvm::LogicalResult ConstantOp::verify() {
// If the return type of the constant is not an unranked tensor, the shape
// must match the shape of the attribute holding the data.
auto resultType = llvm::dyn_cast<mlir::RankedTensorType>(getResult().getType());
auto resultType =
llvm::dyn_cast<mlir::RankedTensorType>(getResult().getType());
if (!resultType)
return success();
@ -395,7 +396,8 @@ llvm::LogicalResult ReturnOp::verify() {
auto resultType = results.front();
// Check that the result type of the function matches the operand type.
if (inputType == resultType || llvm::isa<mlir::UnrankedTensorType>(inputType) ||
if (inputType == resultType ||
llvm::isa<mlir::UnrankedTensorType>(inputType) ||
llvm::isa<mlir::UnrankedTensorType>(resultType))
return mlir::success();

38
mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp
View File

@ -55,7 +55,7 @@ static MemRefType convertTensorToMemRef(RankedTensorType type) {
/// Insert an allocation and deallocation for the given MemRefType.
static Value insertAllocAndDealloc(MemRefType type, Location loc,
PatternRewriter &rewriter) {
auto alloc = rewriter.create<memref::AllocOp>(loc, type);
auto alloc = memref::AllocOp::create(rewriter, loc, type);
// Make sure to allocate at the beginning of the block.
auto *parentBlock = alloc->getBlock();
@ -63,7 +63,7 @@ static Value insertAllocAndDealloc(MemRefType type, Location loc,
// Make sure to deallocate this alloc at the end of the block. This is fine
// as toy functions have no control flow.
auto dealloc = rewriter.create<memref::DeallocOp>(loc, alloc);
auto dealloc = memref::DeallocOp::create(rewriter, loc, alloc);
dealloc->moveBefore(&parentBlock->back());
return alloc;
}
@ -99,8 +99,8 @@ static void lowerOpToLoops(Operation *op, ValueRange operands,
// and the loop induction variables. This function will return the value
// to store at the current index.
Value valueToStore = processIteration(nestedBuilder, operands, ivs);
nestedBuilder.create<affine::AffineStoreOp>(loc, valueToStore, alloc,
ivs);
affine::AffineStoreOp::create(nestedBuilder, loc, valueToStore, alloc,
ivs);
});
// Replace this operation with the generated alloc.
@ -131,15 +131,15 @@ struct BinaryOpLowering : public ConversionPattern {
// Generate loads for the element of 'lhs' and 'rhs' at the
// inner loop.
auto loadedLhs = builder.create<affine::AffineLoadOp>(
loc, binaryAdaptor.getLhs(), loopIvs);
auto loadedRhs = builder.create<affine::AffineLoadOp>(
loc, binaryAdaptor.getRhs(), loopIvs);
auto loadedLhs = affine::AffineLoadOp::create(
builder, loc, binaryAdaptor.getLhs(), loopIvs);
auto loadedRhs = affine::AffineLoadOp::create(
builder, loc, binaryAdaptor.getRhs(), loopIvs);
// Create the binary operation performed on the loaded
// values.
return builder.create<LoweredBinaryOp>(loc, loadedLhs,
loadedRhs);
return LoweredBinaryOp::create(builder, loc, loadedLhs,
loadedRhs);
});
return success();
}
@ -174,11 +174,11 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
if (!valueShape.empty()) {
for (auto i : llvm::seq<int64_t>(0, *llvm::max_element(valueShape)))
constantIndices.push_back(
rewriter.create<arith::ConstantIndexOp>(loc, i));
arith::ConstantIndexOp::create(rewriter, loc, i));
} else {
// This is the case of a tensor of rank 0.
constantIndices.push_back(
rewriter.create<arith::ConstantIndexOp>(loc, 0));
arith::ConstantIndexOp::create(rewriter, loc, 0));
}
// The constant operation represents a multi-dimensional constant, so we
@ -191,9 +191,9 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
// The last dimension is the base case of the recursion, at this point
// we store the element at the given index.
if (dimension == valueShape.size()) {
rewriter.create<affine::AffineStoreOp>(
loc, rewriter.create<arith::ConstantOp>(loc, *valueIt++), alloc,
llvm::ArrayRef(indices));
affine::AffineStoreOp::create(
rewriter, loc, arith::ConstantOp::create(rewriter, loc, *valueIt++),
alloc, llvm::ArrayRef(indices));
return;
}
@ -238,8 +238,8 @@ struct FuncOpLowering : public OpConversionPattern<toy::FuncOp> {
}
// Create a new non-toy function, with the same region.
auto func = rewriter.create<mlir::func::FuncOp>(op.getLoc(), op.getName(),
op.getFunctionType());
auto func = mlir::func::FuncOp::create(rewriter, op.getLoc(), op.getName(),
op.getFunctionType());
rewriter.inlineRegionBefore(op.getRegion(), func.getBody(), func.end());
rewriter.eraseOp(op);
return success();
@ -308,8 +308,8 @@ struct TransposeOpLowering : public ConversionPattern {
// Transpose the elements by generating a load from the
// reverse indices.
SmallVector<Value, 2> reverseIvs(llvm::reverse(loopIvs));
return builder.create<affine::AffineLoadOp>(loc, input,
reverseIvs);
return affine::AffineLoadOp::create(builder, loc, input,
reverseIvs);
});
return success();
}

28
mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
View File

@ -121,8 +121,8 @@ private:
llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
getType(VarType{}));
auto funcType = builder.getFunctionType(argTypes, /*results=*/{});
return builder.create<mlir::toy::FuncOp>(location, proto.getName(),
funcType);
return mlir::toy::FuncOp::create(builder, location, proto.getName(),
funcType);
}
/// Emit a new function and add it to the MLIR module.
@ -166,7 +166,7 @@ private:
if (!entryBlock.empty())
returnOp = dyn_cast<ReturnOp>(entryBlock.back());
if (!returnOp) {
builder.create<ReturnOp>(loc(funcAST.getProto()->loc()));
ReturnOp::create(builder, loc(funcAST.getProto()->loc()));
} else if (returnOp.hasOperand()) {
// Otherwise, if this return operation has an operand then add a result to
// the function.
@ -206,9 +206,9 @@ private:
// support '+' and '*'.
switch (binop.getOp()) {
case '+':
return builder.create<AddOp>(location, lhs, rhs);
return AddOp::create(builder, location, lhs, rhs);
case '*':
return builder.create<MulOp>(location, lhs, rhs);
return MulOp::create(builder, location, lhs, rhs);
}
emitError(location, "invalid binary operator '") << binop.getOp() << "'";
@ -239,8 +239,8 @@ private:
}
// Otherwise, this return operation has zero operands.
builder.create<ReturnOp>(location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
ReturnOp::create(builder, location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
return mlir::success();
}
@ -284,7 +284,7 @@ private:
// Build the MLIR op `toy.constant`. This invokes the `ConstantOp::build`
// method.
return builder.create<ConstantOp>(loc(lit.loc()), type, dataAttribute);
return ConstantOp::create(builder, loc(lit.loc()), type, dataAttribute);
}
/// Recursive helper function to accumulate the data that compose an array
@ -329,13 +329,13 @@ private:
"does not accept multiple arguments");
return nullptr;
}
return builder.create<TransposeOp>(location, operands[0]);
return TransposeOp::create(builder, location, operands[0]);
}
// Otherwise this is a call to a user-defined function. Calls to
// user-defined functions are mapped to a custom call that takes the callee
// name as an attribute.
return builder.create<GenericCallOp>(location, callee, operands);
return GenericCallOp::create(builder, location, callee, operands);
}
/// Emit a print expression. It emits specific operations for two builtins:
@ -345,13 +345,13 @@ private:
if (!arg)
return mlir::failure();
builder.create<PrintOp>(loc(call.loc()), arg);
PrintOp::create(builder, loc(call.loc()), arg);
return mlir::success();
}
/// Emit a constant for a single number (FIXME: semantic? broadcast?)
mlir::Value mlirGen(NumberExprAST &num) {
return builder.create<ConstantOp>(loc(num.loc()), num.getValue());
return ConstantOp::create(builder, loc(num.loc()), num.getValue());
}
/// Dispatch codegen for the right expression subclass using RTTI.
@ -395,8 +395,8 @@ private:
// with specific shape, we emit a "reshape" operation. It will get
// optimized out later as needed.
if (!vardecl.getType().shape.empty()) {
value = builder.create<ReshapeOp>(loc(vardecl.loc()),
getType(vardecl.getType()), value);
value = ReshapeOp::create(builder, loc(vardecl.loc()),
getType(vardecl.getType()), value);
}
// Register the value in the symbol table.

2
mlir/examples/toy/Ch6/include/toy/Ops.td
View File

@ -72,7 +72,7 @@ def ConstantOp : Toy_Op<"constant", [Pure]> {
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

8
mlir/examples/toy/Ch6/mlir/Dialect.cpp
View File

@ -91,7 +91,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
Operation *materializeCallConversion(OpBuilder &builder, Value input,
Type resultType,
Location conversionLoc) const final {
return builder.create<CastOp>(conversionLoc, resultType, input);
return CastOp::create(builder, conversionLoc, resultType, input);
}
};
@ -206,7 +206,8 @@ void ConstantOp::print(mlir::OpAsmPrinter &printer) {
llvm::LogicalResult ConstantOp::verify() {
// If the return type of the constant is not an unranked tensor, the shape
// must match the shape of the attribute holding the data.
auto resultType = llvm::dyn_cast<mlir::RankedTensorType>(getResult().getType());
auto resultType =
llvm::dyn_cast<mlir::RankedTensorType>(getResult().getType());
if (!resultType)
return success();
@ -395,7 +396,8 @@ llvm::LogicalResult ReturnOp::verify() {
auto resultType = results.front();
// Check that the result type of the function matches the operand type.
if (inputType == resultType || llvm::isa<mlir::UnrankedTensorType>(inputType) ||
if (inputType == resultType ||
llvm::isa<mlir::UnrankedTensorType>(inputType) ||
llvm::isa<mlir::UnrankedTensorType>(resultType))
return mlir::success();

38
mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp
View File

@ -55,7 +55,7 @@ static MemRefType convertTensorToMemRef(RankedTensorType type) {
/// Insert an allocation and deallocation for the given MemRefType.
static Value insertAllocAndDealloc(MemRefType type, Location loc,
PatternRewriter &rewriter) {
auto alloc = rewriter.create<memref::AllocOp>(loc, type);
auto alloc = memref::AllocOp::create(rewriter, loc, type);
// Make sure to allocate at the beginning of the block.
auto *parentBlock = alloc->getBlock();
@ -63,7 +63,7 @@ static Value insertAllocAndDealloc(MemRefType type, Location loc,
// Make sure to deallocate this alloc at the end of the block. This is fine
// as toy functions have no control flow.
auto dealloc = rewriter.create<memref::DeallocOp>(loc, alloc);
auto dealloc = memref::DeallocOp::create(rewriter, loc, alloc);
dealloc->moveBefore(&parentBlock->back());
return alloc;
}
@ -99,8 +99,8 @@ static void lowerOpToLoops(Operation *op, ValueRange operands,
// and the loop induction variables. This function will return the value
// to store at the current index.
Value valueToStore = processIteration(nestedBuilder, operands, ivs);
nestedBuilder.create<affine::AffineStoreOp>(loc, valueToStore, alloc,
ivs);
affine::AffineStoreOp::create(nestedBuilder, loc, valueToStore, alloc,
ivs);
});
// Replace this operation with the generated alloc.
@ -131,15 +131,15 @@ struct BinaryOpLowering : public ConversionPattern {
// Generate loads for the element of 'lhs' and 'rhs' at the
// inner loop.
auto loadedLhs = builder.create<affine::AffineLoadOp>(
loc, binaryAdaptor.getLhs(), loopIvs);
auto loadedRhs = builder.create<affine::AffineLoadOp>(
loc, binaryAdaptor.getRhs(), loopIvs);
auto loadedLhs = affine::AffineLoadOp::create(
builder, loc, binaryAdaptor.getLhs(), loopIvs);
auto loadedRhs = affine::AffineLoadOp::create(
builder, loc, binaryAdaptor.getRhs(), loopIvs);
// Create the binary operation performed on the loaded
// values.
return builder.create<LoweredBinaryOp>(loc, loadedLhs,
loadedRhs);
return LoweredBinaryOp::create(builder, loc, loadedLhs,
loadedRhs);
});
return success();
}
@ -174,11 +174,11 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
if (!valueShape.empty()) {
for (auto i : llvm::seq<int64_t>(0, *llvm::max_element(valueShape)))
constantIndices.push_back(
rewriter.create<arith::ConstantIndexOp>(loc, i));
arith::ConstantIndexOp::create(rewriter, loc, i));
} else {
// This is the case of a tensor of rank 0.
constantIndices.push_back(
rewriter.create<arith::ConstantIndexOp>(loc, 0));
arith::ConstantIndexOp::create(rewriter, loc, 0));
}
// The constant operation represents a multi-dimensional constant, so we
@ -191,9 +191,9 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
// The last dimension is the base case of the recursion, at this point
// we store the element at the given index.
if (dimension == valueShape.size()) {
rewriter.create<affine::AffineStoreOp>(
loc, rewriter.create<arith::ConstantOp>(loc, *valueIt++), alloc,
llvm::ArrayRef(indices));
affine::AffineStoreOp::create(
rewriter, loc, arith::ConstantOp::create(rewriter, loc, *valueIt++),
alloc, llvm::ArrayRef(indices));
return;
}
@ -238,8 +238,8 @@ struct FuncOpLowering : public OpConversionPattern<toy::FuncOp> {
}
// Create a new non-toy function, with the same region.
auto func = rewriter.create<mlir::func::FuncOp>(op.getLoc(), op.getName(),
op.getFunctionType());
auto func = mlir::func::FuncOp::create(rewriter, op.getLoc(), op.getName(),
op.getFunctionType());
rewriter.inlineRegionBefore(op.getRegion(), func.getBody(), func.end());
rewriter.eraseOp(op);
return success();
@ -308,8 +308,8 @@ struct TransposeOpLowering : public ConversionPattern {
// Transpose the elements by generating a load from the
// reverse indices.
SmallVector<Value, 2> reverseIvs(llvm::reverse(loopIvs));
return builder.create<affine::AffineLoadOp>(loc, input,
reverseIvs);
return affine::AffineLoadOp::create(builder, loc, input,
reverseIvs);
});
return success();
}

45
mlir/examples/toy/Ch6/mlir/LowerToLLVM.cpp
View File

@ -86,12 +86,12 @@ public:
// Create a loop for each of the dimensions within the shape.
SmallVector<Value, 4> loopIvs;
for (unsigned i = 0, e = memRefShape.size(); i != e; ++i) {
auto lowerBound = rewriter.create<arith::ConstantIndexOp>(loc, 0);
auto lowerBound = arith::ConstantIndexOp::create(rewriter, loc, 0);
auto upperBound =
rewriter.create<arith::ConstantIndexOp>(loc, memRefShape[i]);
auto step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
arith::ConstantIndexOp::create(rewriter, loc, memRefShape[i]);
auto step = arith::ConstantIndexOp::create(rewriter, loc, 1);
auto loop =
rewriter.create<scf::ForOp>(loc, lowerBound, upperBound, step);
scf::ForOp::create(rewriter, loc, lowerBound, upperBound, step);
for (Operation &nested : make_early_inc_range(*loop.getBody()))
rewriter.eraseOp(&nested);
loopIvs.push_back(loop.getInductionVar());
@ -101,19 +101,18 @@ public:
// Insert a newline after each of the inner dimensions of the shape.
if (i != e - 1)
rewriter.create<LLVM::CallOp>(loc, getPrintfType(context), printfRef,
newLineCst);
rewriter.create<scf::YieldOp>(loc);
LLVM::CallOp::create(rewriter, loc, getPrintfType(context), printfRef,
newLineCst);
scf::YieldOp::create(rewriter, loc);
rewriter.setInsertionPointToStart(loop.getBody());
}
// Generate a call to printf for the current element of the loop.
auto printOp = cast<toy::PrintOp>(op);
auto elementLoad =
rewriter.create<memref::LoadOp>(loc, printOp.getInput(), loopIvs);
rewriter.create<LLVM::CallOp>(
loc, getPrintfType(context), printfRef,
ArrayRef<Value>({formatSpecifierCst, elementLoad}));
memref::LoadOp::create(rewriter, loc, printOp.getInput(), loopIvs);
LLVM::CallOp::create(rewriter, loc, getPrintfType(context), printfRef,
ArrayRef<Value>({formatSpecifierCst, elementLoad}));
// Notify the rewriter that this operation has been removed.
rewriter.eraseOp(op);
@ -142,8 +141,8 @@ private:
// Insert the printf function into the body of the parent module.
PatternRewriter::InsertionGuard insertGuard(rewriter);
rewriter.setInsertionPointToStart(module.getBody());
rewriter.create<LLVM::LLVMFuncOp>(module.getLoc(), "printf",
getPrintfType(context));
LLVM::LLVMFuncOp::create(rewriter, module.getLoc(), "printf",
getPrintfType(context));
return SymbolRefAttr::get(context, "printf");
}
@ -159,19 +158,19 @@ private:
builder.setInsertionPointToStart(module.getBody());
auto type = LLVM::LLVMArrayType::get(
IntegerType::get(builder.getContext(), 8), value.size());
global = builder.create<LLVM::GlobalOp>(loc, type, /*isConstant=*/true,
LLVM::Linkage::Internal, name,
builder.getStringAttr(value),
/*alignment=*/0);
global = LLVM::GlobalOp::create(builder, loc, type, /*isConstant=*/true,
LLVM::Linkage::Internal, name,
builder.getStringAttr(value),
/*alignment=*/0);
}
// Get the pointer to the first character in the global string.
Value globalPtr = builder.create<LLVM::AddressOfOp>(loc, global);
Value cst0 = builder.create<LLVM::ConstantOp>(loc, builder.getI64Type(),
builder.getIndexAttr(0));
return builder.create<LLVM::GEPOp>(
loc, LLVM::LLVMPointerType::get(builder.getContext()), global.getType(),
globalPtr, ArrayRef<Value>({cst0, cst0}));
Value globalPtr = LLVM::AddressOfOp::create(builder, loc, global);
Value cst0 = LLVM::ConstantOp::create(builder, loc, builder.getI64Type(),
builder.getIndexAttr(0));
return LLVM::GEPOp::create(
builder, loc, LLVM::LLVMPointerType::get(builder.getContext()),
global.getType(), globalPtr, ArrayRef<Value>({cst0, cst0}));
}
};
} // namespace

28
mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
View File

@ -121,8 +121,8 @@ private:
llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
getType(VarType{}));
auto funcType = builder.getFunctionType(argTypes, /*results=*/{});
return builder.create<mlir::toy::FuncOp>(location, proto.getName(),
funcType);
return mlir::toy::FuncOp::create(builder, location, proto.getName(),
funcType);
}
/// Emit a new function and add it to the MLIR module.
@ -166,7 +166,7 @@ private:
if (!entryBlock.empty())
returnOp = dyn_cast<ReturnOp>(entryBlock.back());
if (!returnOp) {
builder.create<ReturnOp>(loc(funcAST.getProto()->loc()));
ReturnOp::create(builder, loc(funcAST.getProto()->loc()));
} else if (returnOp.hasOperand()) {
// Otherwise, if this return operation has an operand then add a result to
// the function.
@ -206,9 +206,9 @@ private:
// support '+' and '*'.
switch (binop.getOp()) {
case '+':
return builder.create<AddOp>(location, lhs, rhs);
return AddOp::create(builder, location, lhs, rhs);
case '*':
return builder.create<MulOp>(location, lhs, rhs);
return MulOp::create(builder, location, lhs, rhs);
}
emitError(location, "invalid binary operator '") << binop.getOp() << "'";
@ -239,8 +239,8 @@ private:
}
// Otherwise, this return operation has zero operands.
builder.create<ReturnOp>(location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
ReturnOp::create(builder, location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
return mlir::success();
}
@ -284,7 +284,7 @@ private:
// Build the MLIR op `toy.constant`. This invokes the `ConstantOp::build`
// method.
return builder.create<ConstantOp>(loc(lit.loc()), type, dataAttribute);
return ConstantOp::create(builder, loc(lit.loc()), type, dataAttribute);
}
/// Recursive helper function to accumulate the data that compose an array
@ -329,13 +329,13 @@ private:
"does not accept multiple arguments");
return nullptr;
}
return builder.create<TransposeOp>(location, operands[0]);
return TransposeOp::create(builder, location, operands[0]);
}
// Otherwise this is a call to a user-defined function. Calls to
// user-defined functions are mapped to a custom call that takes the callee
// name as an attribute.
return builder.create<GenericCallOp>(location, callee, operands);
return GenericCallOp::create(builder, location, callee, operands);
}
/// Emit a print expression. It emits specific operations for two builtins:
@ -345,13 +345,13 @@ private:
if (!arg)
return mlir::failure();
builder.create<PrintOp>(loc(call.loc()), arg);
PrintOp::create(builder, loc(call.loc()), arg);
return mlir::success();
}
/// Emit a constant for a single number (FIXME: semantic? broadcast?)
mlir::Value mlirGen(NumberExprAST &num) {
return builder.create<ConstantOp>(loc(num.loc()), num.getValue());
return ConstantOp::create(builder, loc(num.loc()), num.getValue());
}
/// Dispatch codegen for the right expression subclass using RTTI.
@ -395,8 +395,8 @@ private:
// with specific shape, we emit a "reshape" operation. It will get
// optimized out later as needed.
if (!vardecl.getType().shape.empty()) {
value = builder.create<ReshapeOp>(loc(vardecl.loc()),
getType(vardecl.getType()), value);
value = ReshapeOp::create(builder, loc(vardecl.loc()),
getType(vardecl.getType()), value);
}
// Register the value in the symbol table.

2
mlir/examples/toy/Ch7/include/toy/Ops.td
View File

@ -93,7 +93,7 @@ def ConstantOp : Toy_Op<"constant",
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
// using `ConstantOp::create(builder, ...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{

13
mlir/examples/toy/Ch7/mlir/Dialect.cpp
View File

@ -97,7 +97,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
Operation *materializeCallConversion(OpBuilder &builder, Value input,
Type resultType,
Location conversionLoc) const final {
return builder.create<CastOp>(conversionLoc, resultType, input);
return CastOp::create(builder, conversionLoc, resultType, input);
}
};
@ -429,7 +429,8 @@ llvm::LogicalResult ReturnOp::verify() {
auto resultType = results.front();
// Check that the result type of the function matches the operand type.
if (inputType == resultType || llvm::isa<mlir::UnrankedTensorType>(inputType) ||
if (inputType == resultType ||
llvm::isa<mlir::UnrankedTensorType>(inputType) ||
llvm::isa<mlir::UnrankedTensorType>(resultType))
return mlir::success();
@ -657,8 +658,8 @@ mlir::Operation *ToyDialect::materializeConstant(mlir::OpBuilder &builder,
mlir::Type type,
mlir::Location loc) {
if (llvm::isa<StructType>(type))
return builder.create<StructConstantOp>(loc, type,
llvm::cast<mlir::ArrayAttr>(value));
return builder.create<ConstantOp>(loc, type,
llvm::cast<mlir::DenseElementsAttr>(value));
return StructConstantOp::create(builder, loc, type,
llvm::cast<mlir::ArrayAttr>(value));
return ConstantOp::create(builder, loc, type,
llvm::cast<mlir::DenseElementsAttr>(value));
}

38
mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp
View File

@ -55,7 +55,7 @@ static MemRefType convertTensorToMemRef(RankedTensorType type) {
/// Insert an allocation and deallocation for the given MemRefType.
static Value insertAllocAndDealloc(MemRefType type, Location loc,
PatternRewriter &rewriter) {
auto alloc = rewriter.create<memref::AllocOp>(loc, type);
auto alloc = memref::AllocOp::create(rewriter, loc, type);
// Make sure to allocate at the beginning of the block.
auto *parentBlock = alloc->getBlock();
@ -63,7 +63,7 @@ static Value insertAllocAndDealloc(MemRefType type, Location loc,
// Make sure to deallocate this alloc at the end of the block. This is fine
// as toy functions have no control flow.
auto dealloc = rewriter.create<memref::DeallocOp>(loc, alloc);
auto dealloc = memref::DeallocOp::create(rewriter, loc, alloc);
dealloc->moveBefore(&parentBlock->back());
return alloc;
}
@ -99,8 +99,8 @@ static void lowerOpToLoops(Operation *op, ValueRange operands,
// and the loop induction variables. This function will return the value
// to store at the current index.
Value valueToStore = processIteration(nestedBuilder, operands, ivs);
nestedBuilder.create<affine::AffineStoreOp>(loc, valueToStore, alloc,
ivs);
affine::AffineStoreOp::create(nestedBuilder, loc, valueToStore, alloc,
ivs);
});
// Replace this operation with the generated alloc.
@ -131,15 +131,15 @@ struct BinaryOpLowering : public ConversionPattern {
// Generate loads for the element of 'lhs' and 'rhs' at the
// inner loop.
auto loadedLhs = builder.create<affine::AffineLoadOp>(
loc, binaryAdaptor.getLhs(), loopIvs);
auto loadedRhs = builder.create<affine::AffineLoadOp>(
loc, binaryAdaptor.getRhs(), loopIvs);
auto loadedLhs = affine::AffineLoadOp::create(
builder, loc, binaryAdaptor.getLhs(), loopIvs);
auto loadedRhs = affine::AffineLoadOp::create(
builder, loc, binaryAdaptor.getRhs(), loopIvs);
// Create the binary operation performed on the loaded
// values.
return builder.create<LoweredBinaryOp>(loc, loadedLhs,
loadedRhs);
return LoweredBinaryOp::create(builder, loc, loadedLhs,
loadedRhs);
});
return success();
}
@ -174,11 +174,11 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
if (!valueShape.empty()) {
for (auto i : llvm::seq<int64_t>(0, *llvm::max_element(valueShape)))
constantIndices.push_back(
rewriter.create<arith::ConstantIndexOp>(loc, i));
arith::ConstantIndexOp::create(rewriter, loc, i));
} else {
// This is the case of a tensor of rank 0.
constantIndices.push_back(
rewriter.create<arith::ConstantIndexOp>(loc, 0));
arith::ConstantIndexOp::create(rewriter, loc, 0));
}
// The constant operation represents a multi-dimensional constant, so we
@ -191,9 +191,9 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
// The last dimension is the base case of the recursion, at this point
// we store the element at the given index.
if (dimension == valueShape.size()) {
rewriter.create<affine::AffineStoreOp>(
loc, rewriter.create<arith::ConstantOp>(loc, *valueIt++), alloc,
llvm::ArrayRef(indices));
affine::AffineStoreOp::create(
rewriter, loc, arith::ConstantOp::create(rewriter, loc, *valueIt++),
alloc, llvm::ArrayRef(indices));
return;
}
@ -238,8 +238,8 @@ struct FuncOpLowering : public OpConversionPattern<toy::FuncOp> {
}
// Create a new non-toy function, with the same region.
auto func = rewriter.create<mlir::func::FuncOp>(op.getLoc(), op.getName(),
op.getFunctionType());
auto func = mlir::func::FuncOp::create(rewriter, op.getLoc(), op.getName(),
op.getFunctionType());
rewriter.inlineRegionBefore(op.getRegion(), func.getBody(), func.end());
rewriter.eraseOp(op);
return success();
@ -308,8 +308,8 @@ struct TransposeOpLowering : public ConversionPattern {
// Transpose the elements by generating a load from the
// reverse indices.
SmallVector<Value, 2> reverseIvs(llvm::reverse(loopIvs));
return builder.create<affine::AffineLoadOp>(loc, input,
reverseIvs);
return affine::AffineLoadOp::create(builder, loc, input,
reverseIvs);
});
return success();
}

45
mlir/examples/toy/Ch7/mlir/LowerToLLVM.cpp
View File

@ -86,12 +86,12 @@ public:
// Create a loop for each of the dimensions within the shape.
SmallVector<Value, 4> loopIvs;
for (unsigned i = 0, e = memRefShape.size(); i != e; ++i) {
auto lowerBound = rewriter.create<arith::ConstantIndexOp>(loc, 0);
auto lowerBound = arith::ConstantIndexOp::create(rewriter, loc, 0);
auto upperBound =
rewriter.create<arith::ConstantIndexOp>(loc, memRefShape[i]);
auto step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
arith::ConstantIndexOp::create(rewriter, loc, memRefShape[i]);
auto step = arith::ConstantIndexOp::create(rewriter, loc, 1);
auto loop =
rewriter.create<scf::ForOp>(loc, lowerBound, upperBound, step);
scf::ForOp::create(rewriter, loc, lowerBound, upperBound, step);
for (Operation &nested : make_early_inc_range(*loop.getBody()))
rewriter.eraseOp(&nested);
loopIvs.push_back(loop.getInductionVar());
@ -101,19 +101,18 @@ public:
// Insert a newline after each of the inner dimensions of the shape.
if (i != e - 1)
rewriter.create<LLVM::CallOp>(loc, getPrintfType(context), printfRef,
newLineCst);
rewriter.create<scf::YieldOp>(loc);
LLVM::CallOp::create(rewriter, loc, getPrintfType(context), printfRef,
newLineCst);
scf::YieldOp::create(rewriter, loc);
rewriter.setInsertionPointToStart(loop.getBody());
}
// Generate a call to printf for the current element of the loop.
auto printOp = cast<toy::PrintOp>(op);
auto elementLoad =
rewriter.create<memref::LoadOp>(loc, printOp.getInput(), loopIvs);
rewriter.create<LLVM::CallOp>(
loc, getPrintfType(context), printfRef,
ArrayRef<Value>({formatSpecifierCst, elementLoad}));
memref::LoadOp::create(rewriter, loc, printOp.getInput(), loopIvs);
LLVM::CallOp::create(rewriter, loc, getPrintfType(context), printfRef,
ArrayRef<Value>({formatSpecifierCst, elementLoad}));
// Notify the rewriter that this operation has been removed.
rewriter.eraseOp(op);
@ -142,8 +141,8 @@ private:
// Insert the printf function into the body of the parent module.
PatternRewriter::InsertionGuard insertGuard(rewriter);
rewriter.setInsertionPointToStart(module.getBody());
rewriter.create<LLVM::LLVMFuncOp>(module.getLoc(), "printf",
getPrintfType(context));
LLVM::LLVMFuncOp::create(rewriter, module.getLoc(), "printf",
getPrintfType(context));
return SymbolRefAttr::get(context, "printf");
}
@ -159,19 +158,19 @@ private:
builder.setInsertionPointToStart(module.getBody());
auto type = LLVM::LLVMArrayType::get(
IntegerType::get(builder.getContext(), 8), value.size());
global = builder.create<LLVM::GlobalOp>(loc, type, /*isConstant=*/true,
LLVM::Linkage::Internal, name,
builder.getStringAttr(value),
/*alignment=*/0);
global = LLVM::GlobalOp::create(builder, loc, type, /*isConstant=*/true,
LLVM::Linkage::Internal, name,
builder.getStringAttr(value),
/*alignment=*/0);
}
// Get the pointer to the first character in the global string.
Value globalPtr = builder.create<LLVM::AddressOfOp>(loc, global);
Value cst0 = builder.create<LLVM::ConstantOp>(loc, builder.getI64Type(),
builder.getIndexAttr(0));
return builder.create<LLVM::GEPOp>(
loc, LLVM::LLVMPointerType::get(builder.getContext()), global.getType(),
globalPtr, ArrayRef<Value>({cst0, cst0}));
Value globalPtr = LLVM::AddressOfOp::create(builder, loc, global);
Value cst0 = LLVM::ConstantOp::create(builder, loc, builder.getI64Type(),
builder.getIndexAttr(0));
return LLVM::GEPOp::create(
builder, loc, LLVM::LLVMPointerType::get(builder.getContext()),
global.getType(), globalPtr, ArrayRef<Value>({cst0, cst0}));
}
};
} // namespace

36
mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
View File

@ -183,8 +183,8 @@ private:
argTypes.push_back(type);
}
auto funcType = builder.getFunctionType(argTypes, /*results=*/{});
return builder.create<mlir::toy::FuncOp>(location, proto.getName(),
funcType);
return mlir::toy::FuncOp::create(builder, location, proto.getName(),
funcType);
}
/// Emit a new function and add it to the MLIR module.
@ -227,7 +227,7 @@ private:
if (!entryBlock.empty())
returnOp = dyn_cast<ReturnOp>(entryBlock.back());
if (!returnOp) {
builder.create<ReturnOp>(loc(funcAST.getProto()->loc()));
ReturnOp::create(builder, loc(funcAST.getProto()->loc()));
} else if (returnOp.hasOperand()) {
// Otherwise, if this return operation has an operand then add a result to
// the function.
@ -333,7 +333,7 @@ private:
emitError(location, "invalid access into struct expression");
return nullptr;
}
return builder.create<StructAccessOp>(location, lhs, *accessIndex);
return StructAccessOp::create(builder, location, lhs, *accessIndex);
}
// Otherwise, this is a normal binary op.
@ -345,9 +345,9 @@ private:
// support '+' and '*'.
switch (binop.getOp()) {
case '+':
return builder.create<AddOp>(location, lhs, rhs);
return AddOp::create(builder, location, lhs, rhs);
case '*':
return builder.create<MulOp>(location, lhs, rhs);
return MulOp::create(builder, location, lhs, rhs);
}
emitError(location, "invalid binary operator '") << binop.getOp() << "'";
@ -378,8 +378,8 @@ private:
}
// Otherwise, this return operation has zero operands.
builder.create<ReturnOp>(location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
ReturnOp::create(builder, location,
expr ? ArrayRef(expr) : ArrayRef<mlir::Value>());
return mlir::success();
}
@ -464,7 +464,7 @@ private:
// Build the MLIR op `toy.constant`. This invokes the `ConstantOp::build`
// method.
return builder.create<ConstantOp>(loc(lit.loc()), type, dataAttribute);
return ConstantOp::create(builder, loc(lit.loc()), type, dataAttribute);
}
/// Emit a struct literal. It will be emitted as an array of
@ -477,7 +477,8 @@ private:
// Build the MLIR op `toy.struct_constant`. This invokes the
// `StructConstantOp::build` method.
return builder.create<StructConstantOp>(loc(lit.loc()), dataType, dataAttr);
return StructConstantOp::create(builder, loc(lit.loc()), dataType,
dataAttr);
}
/// Recursive helper function to accumulate the data that compose an array
@ -522,7 +523,7 @@ private:
"does not accept multiple arguments");
return nullptr;
}
return builder.create<TransposeOp>(location, operands[0]);
return TransposeOp::create(builder, location, operands[0]);
}
// Otherwise this is a call to a user-defined function. Calls to
@ -534,8 +535,9 @@ private:
return nullptr;
}
mlir::toy::FuncOp calledFunc = calledFuncIt->second;
return builder.create<GenericCallOp>(
location, calledFunc.getFunctionType().getResult(0), callee, operands);
return GenericCallOp::create(builder, location,
calledFunc.getFunctionType().getResult(0),
callee, operands);
}
/// Emit a print expression. It emits specific operations for two builtins:
@ -545,13 +547,13 @@ private:
if (!arg)
return mlir::failure();
builder.create<PrintOp>(loc(call.loc()), arg);
PrintOp::create(builder, loc(call.loc()), arg);
return mlir::success();
}
/// Emit a constant for a single number (FIXME: semantic? broadcast?)
mlir::Value mlirGen(NumberExprAST &num) {
return builder.create<ConstantOp>(loc(num.loc()), num.getValue());
return ConstantOp::create(builder, loc(num.loc()), num.getValue());
}
/// Dispatch codegen for the right expression subclass using RTTI.
@ -613,8 +615,8 @@ private:
// declared with specific shape, we emit a "reshape" operation. It will
// get optimized out later as needed.
} else if (!varType.shape.empty()) {
value = builder.create<ReshapeOp>(loc(vardecl.loc()),
getType(varType.shape), value);
value = ReshapeOp::create(builder, loc(vardecl.loc()),
getType(varType.shape), value);
}
// Register the value in the symbol table.