//===- Ops.td - Toy dialect operation definitions ----------*- tablegen -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// Defines the operations of the Toy dialect.
//
//===----------------------------------------------------------------------===//
#ifndef TOY_OPS
#define TOY_OPS
include "mlir/Interfaces/FunctionInterfaces.td"
include "mlir/IR/SymbolInterfaces.td"
include "mlir/Interfaces/SideEffectInterfaces.td"
// Provide a definition of the 'toy' dialect in the ODS framework so that we
// can define our operations.
def Toy_Dialect : Dialect {
let name = "toy";
let cppNamespace = "::mlir::toy";
}
// Base class for toy dialect operations. This operation inherits from the base
// `Op` class in OpBase.td, and provides:
// * The parent dialect of the operation.
// * The mnemonic for the operation, or the name without the dialect prefix.
// * A list of traits for the operation.
class Toy_Op<string mnemonic, list<Trait> traits = []> :
Op<Toy_Dialect, mnemonic, traits>;
//===----------------------------------------------------------------------===//
// Toy Operations
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//
// ConstantOp
//===----------------------------------------------------------------------===//
// We define a toy operation by inheriting from our base 'Toy_Op' class above.
// Here we provide the mnemonic and a list of traits for the operation. The
// constant operation is marked as 'Pure' as it is a pure operation
// and may be removed if dead.
def ConstantOp : Toy_Op<"constant", [Pure]> {
// Provide a summary and description for this operation. This can be used to
// auto-generate documentation of the operations within our dialect.
let summary = "constant";
let description = [{
Constant operation turns a literal into an SSA value. The data is attached
to the operation as an attribute. For example:
```mlir
%0 = toy.constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]>
: tensor<2x3xf64>
```
}];
// The constant operation takes an attribute as the only input.
let arguments = (ins F64ElementsAttr:$value);
// The constant operation returns a single value of TensorType.
let results = (outs F64Tensor);
// Indicate that the operation has a custom parser and printer method.
let hasCustomAssemblyFormat = 1;
// Add custom build methods for the constant operation. These method populates
// the `state` that MLIR uses to create operations, i.e. these are used when
// using `builder.create<ConstantOp>(...)`.
let builders = [
// Build a constant with a given constant tensor value.
OpBuilder<(ins "DenseElementsAttr":$value), [{
build($_builder, $_state, value.getType(), value);
}]>,
// Build a constant with a given constant floating-point value.
OpBuilder<(ins "double":$value)>
];
// Indicate that additional verification for this operation is necessary.
let hasVerifier = 1;
}
//===----------------------------------------------------------------------===//
// AddOp
//===----------------------------------------------------------------------===//
def AddOp : Toy_Op<"add", [Pure]> {
let summary = "element-wise addition operation";
let description = [{
The "add" operation performs element-wise addition between two tensors.
The shapes of the tensor operands are expected to match.
}];
let arguments = (ins F64Tensor:$lhs, F64Tensor:$rhs);
let results = (outs F64Tensor);
// Indicate that the operation has a custom parser and printer method.
let hasCustomAssemblyFormat = 1;
// Allow building an AddOp with from the two input operands.
let builders = [
OpBuilder<(ins "Value":$lhs, "Value":$rhs)>
];
}
//===----------------------------------------------------------------------===//
// FuncOp
//===----------------------------------------------------------------------===//
def FuncOp : Toy_Op<"func", [
FunctionOpInterface, IsolatedFromAbove
]> {
let summary = "user defined function operation";
let description = [{
The "toy.func" operation represents a user defined function. These are
callable SSA-region operations that contain toy computations.
Example:
```mlir
toy.func @main() {
%0 = toy.constant dense<5.500000e+00> : tensor<f64>
%1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
toy.print %1 : tensor<2x2xf64>
toy.return
}
```
}];
let arguments = (ins
SymbolNameAttr:$sym_name,
TypeAttrOf<FunctionType>:$function_type,
OptionalAttr<DictArrayAttr>:$arg_attrs,
OptionalAttr<DictArrayAttr>:$res_attrs
);
let regions = (region AnyRegion:$body);
let builders = [OpBuilder<(ins
"StringRef":$name, "FunctionType":$type,
CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs)
>];
let extraClassDeclaration = [{
//===------------------------------------------------------------------===//
// FunctionOpInterface Methods
//===------------------------------------------------------------------===//
/// Returns the argument types of this function.
ArrayRef<Type> getArgumentTypes() { return getFunctionType().getInputs(); }
/// Returns the result types of this function.
ArrayRef<Type> getResultTypes() { return getFunctionType().getResults(); }
/// Returns the region on the current operation that is callable.
::mlir::Region *getCallableRegion() { return &getBody(); }
}];
let hasCustomAssemblyFormat = 1;
let skipDefaultBuilders = 1;
}
//===----------------------------------------------------------------------===//
// GenericCallOp
//===----------------------------------------------------------------------===//
def GenericCallOp : Toy_Op<"generic_call"> {
let summary = "generic call operation";
let description = [{
Generic calls represent calls to a user defined function that needs to
be specialized for the shape of its arguments. The callee name is attached
as a symbol reference via an attribute. The arguments list must match the
arguments expected by the callee. For example:
```mlir
%4 = toy.generic_call @my_func(%1, %3)
: (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
```
This is only valid if a function named "my_func" exists and takes two
arguments.
}];
// The generic call operation takes a symbol reference attribute as the
// callee, and inputs for the call.
let arguments = (ins FlatSymbolRefAttr:$callee, Variadic<F64Tensor>:$inputs);
// The generic call operation returns a single value of TensorType.
let results = (outs F64Tensor);
// Specialize assembly printing and parsing using a declarative format.
let assemblyFormat = [{
$callee `(` $inputs `)` attr-dict `:` functional-type($inputs, results)
}];
// Add custom build methods for the generic call operation.
let builders = [
OpBuilder<(ins "StringRef":$callee, "ArrayRef<Value>":$arguments)>
];
}
//===----------------------------------------------------------------------===//
// MulOp
//===----------------------------------------------------------------------===//
def MulOp : Toy_Op<"mul", [Pure]> {
let summary = "element-wise multiplication operation";
let description = [{
The "mul" operation performs element-wise multiplication between two
tensors. The shapes of the tensor operands are expected to match.
}];
let arguments = (ins F64Tensor:$lhs, F64Tensor:$rhs);
let results = (outs F64Tensor);
// Indicate that the operation has a custom parser and printer method.
let hasCustomAssemblyFormat = 1;
// Allow building a MulOp with from the two input operands.
let builders = [
OpBuilder<(ins "Value":$lhs, "Value":$rhs)>
];
}
//===----------------------------------------------------------------------===//
// PrintOp
//===----------------------------------------------------------------------===//
def PrintOp : Toy_Op<"print"> {
let summary = "print operation";
let description = [{
The "print" builtin operation prints a given input tensor, and produces
no results.
}];
// The print operation takes an input tensor to print.
let arguments = (ins F64Tensor:$input);
let assemblyFormat = "$input attr-dict `:` type($input)";
}
//===----------------------------------------------------------------------===//
// ReshapeOp
//===----------------------------------------------------------------------===//
def ReshapeOp : Toy_Op<"reshape", [Pure]> {
let summary = "tensor reshape operation";
let description = [{
Reshape operation is transforming its input tensor into a new tensor with
the same number of elements but different shapes. For example:
```mlir
%0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
```
}];
let arguments = (ins F64Tensor:$input);
// We expect that the reshape operation returns a statically shaped tensor.
let results = (outs StaticShapeTensorOf<[F64]>);
let assemblyFormat = [{
`(` $input `:` type($input) `)` attr-dict `to` type(results)
}];
// Enable registering canonicalization patterns with this operation.
let hasCanonicalizer = 1;
}
//===----------------------------------------------------------------------===//
// ReturnOp
//===----------------------------------------------------------------------===//
def ReturnOp : Toy_Op<"return", [Pure, HasParent<"FuncOp">,
Terminator]> {
let summary = "return operation";
let description = [{
The "return" operation represents a return operation within a function.
The operation takes an optional tensor operand and produces no results.
The operand type must match the signature of the function that contains
the operation. For example:
```mlir
toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
```
}];
// The return operation takes an optional input operand to return. This
// value must match the return type of the enclosing function.
let arguments = (ins Variadic<F64Tensor>:$input);
// The return operation only emits the input in the format if it is present.
let assemblyFormat = "($input^ `:` type($input))? attr-dict ";
// Allow building a ReturnOp with no return operand.
let builders = [
OpBuilder<(ins), [{ build($_builder, $_state, std::nullopt); }]>
];
// Provide extra utility definitions on the c++ operation class definition.
let extraClassDeclaration = [{
bool hasOperand() { return getNumOperands() != 0; }
}];
// Indicate that additional verification for this operation is necessary.
let hasVerifier = 1;
}
//===----------------------------------------------------------------------===//
// TransposeOp
//===----------------------------------------------------------------------===//
def TransposeOp : Toy_Op<"transpose", [Pure]> {
let summary = "transpose operation";
let arguments = (ins F64Tensor:$input);
let results = (outs F64Tensor);
let assemblyFormat = [{
`(` $input `:` type($input) `)` attr-dict `to` type(results)
}];
// Enable registering canonicalization patterns with this operation.
let hasCanonicalizer = 1;
// Allow building a TransposeOp with from the input operand.
let builders = [
OpBuilder<(ins "Value":$input)>
];
// Indicate that additional verification for this operation is necessary.
let hasVerifier = 1;
}
#endif // TOY_OPS
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