mlir/examples/toy/Ch4/include/toy/Ops.td - third_party/llvm-project - Git at Google

 //===- 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/Analysis/CallInterfaces.td"
 include "toy/ShapeInferenceInterface.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 = "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<OpTrait> traits = []> :
     Op<Toy_Dialect, mnemonic, traits>;

 //===----------------------------------------------------------------------===//
 // Toy Operations
 //===----------------------------------------------------------------------===//

 // 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 'NoSideEffect' as it is a pure operation
 // and may be removed if dead.
 def ConstantOp : Toy_Op<"constant", [NoSideEffect]> {
   // 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);

   // Specify a parser and printer method.
   let parser = [{ return ::parseConstantOp(parser, result); }];
   let printer = [{ return ::print(p, *this); }];

   // 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<"Builder *builder, OperationState &state, "
               "DenseElementsAttr value", [{
       build(builder, state, value.getType(), value);
     }]>,

     // Build a constant with a given constant floating-point value.
     OpBuilder<"Builder *builder, OperationState &state, double value">
   ];

   // Invoke a static verify method to verify this constant operation.
   let verifier = [{ return ::verify(*this); }];
 }

 def AddOp : Toy_Op<"add",
     [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   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);

   // Specify a parser and printer method.
   let parser = [{ return ::parseBinaryOp(parser, result); }];
   let printer = [{ return ::printBinaryOp(p, *this); }];

   // Allow building an AddOp with from the two input operands.
   let builders = [
     OpBuilder<"Builder *b, OperationState &state, Value lhs, Value rhs">
   ];
 }

 def CastOp : Toy_Op<"cast",
     [DeclareOpInterfaceMethods<ShapeInferenceOpInterface>, NoSideEffect,
      SameOperandsAndResultShape]> {
   let summary = "shape cast operation";
   let description = [{
     The "cast" operation converts a tensor from one type to an equivalent type
     without changing any data elements. The source and destination types
     must both be tensor types with the same element type. If both are ranked
     then the rank should be the same and static dimensions should match. The
     operation is invalid if converting to a mismatching constant dimension.
   }];

   let arguments = (ins F64Tensor:$input);
   let results = (outs F64Tensor:$output);

   let assemblyFormat = "$input attr-dict `:` type($input) `to` type($output)";

   // Set the folder bit so that we can fold redundant cast operations.
   let hasFolder = 1;
 }

 def GenericCallOp : Toy_Op<"generic_call",
     [DeclareOpInterfaceMethods<CallOpInterface>]> {
   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);

   // The return operation only emits the input in the format if it is present.
   let assemblyFormat = [{
     $callee `(` $inputs `)` attr-dict `:` functional-type($inputs, results)
   }];

   // Add custom build methods for the generic call operation.
   let builders = [
     OpBuilder<"Builder *builder, OperationState &state, "
               "StringRef callee, ArrayRef<Value> arguments">
   ];
 }

 def MulOp : Toy_Op<"mul",
     [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   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);

   // Specify a parser and printer method.
   let parser = [{ return ::parseBinaryOp(parser, result); }];
   let printer = [{ return ::printBinaryOp(p, *this); }];

   // Allow building a MulOp with from the two input operands.
   let builders = [
     OpBuilder<"Builder *b, OperationState &state, Value lhs, Value rhs">
   ];
 }

 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)";
 }

 def ReshapeOp : Toy_Op<"reshape", [NoSideEffect]> {
   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;
 }

 def ReturnOp : Toy_Op<"return", [Terminator, HasParent<"FuncOp">]> {
   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
       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<
     "Builder *b, OperationState &state", [{ build(b, state, llvm::None); }]
   >];

   // Provide extra utility definitions on the c++ operation class definition.
   let extraClassDeclaration = [{
     bool hasOperand() { return getNumOperands() != 0; }
   }];

   // Invoke a static verify method to verify this return operation.
   let verifier = [{ return ::verify(*this); }];
 }

 def TransposeOp : Toy_Op<"transpose",
     [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   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<"Builder *b, OperationState &state, Value input">
   ];

   // Invoke a static verify method to verify this transpose operation.
   let verifier = [{ return ::verify(*this); }];
 }

 #endif // TOY_OPS
	//===- 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/Analysis/CallInterfaces.td"
	include "toy/ShapeInferenceInterface.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 = "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<OpTrait> traits = []> :
	Op<Toy_Dialect, mnemonic, traits>;

	//===----------------------------------------------------------------------===//
	// Toy Operations
	//===----------------------------------------------------------------------===//

	// 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 'NoSideEffect' as it is a pure operation
	// and may be removed if dead.
	def ConstantOp : Toy_Op<"constant", [NoSideEffect]> {
	// 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);

	// Specify a parser and printer method.
	let parser = [{ return ::parseConstantOp(parser, result); }];
	let printer = [{ return ::print(p, *this); }];

	// 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<"Builder *builder, OperationState &state, "
	"DenseElementsAttr value", [{
	build(builder, state, value.getType(), value);
	}]>,

	// Build a constant with a given constant floating-point value.
	OpBuilder<"Builder *builder, OperationState &state, double value">
	];

	// Invoke a static verify method to verify this constant operation.
	let verifier = [{ return ::verify(*this); }];
	}

	def AddOp : Toy_Op<"add",
	[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
	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);

	// Specify a parser and printer method.
	let parser = [{ return ::parseBinaryOp(parser, result); }];
	let printer = [{ return ::printBinaryOp(p, *this); }];

	// Allow building an AddOp with from the two input operands.
	let builders = [
	OpBuilder<"Builder *b, OperationState &state, Value lhs, Value rhs">
	];
	}

	def CastOp : Toy_Op<"cast",
	[DeclareOpInterfaceMethods<ShapeInferenceOpInterface>, NoSideEffect,
	SameOperandsAndResultShape]> {
	let summary = "shape cast operation";
	let description = [{
	The "cast" operation converts a tensor from one type to an equivalent type
	without changing any data elements. The source and destination types
	must both be tensor types with the same element type. If both are ranked
	then the rank should be the same and static dimensions should match. The
	operation is invalid if converting to a mismatching constant dimension.
	}];

	let arguments = (ins F64Tensor:$input);
	let results = (outs F64Tensor:$output);

	let assemblyFormat = "$input attr-dict `:` type($input) `to` type($output)";

	// Set the folder bit so that we can fold redundant cast operations.
	let hasFolder = 1;
	}

	def GenericCallOp : Toy_Op<"generic_call",
	[DeclareOpInterfaceMethods<CallOpInterface>]> {
	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);

	// The return operation only emits the input in the format if it is present.
	let assemblyFormat = [{
	$callee `(` $inputs `)` attr-dict `:` functional-type($inputs, results)
	}];

	// Add custom build methods for the generic call operation.
	let builders = [
	OpBuilder<"Builder *builder, OperationState &state, "
	"StringRef callee, ArrayRef<Value> arguments">
	];
	}

	def MulOp : Toy_Op<"mul",
	[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
	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);

	// Specify a parser and printer method.
	let parser = [{ return ::parseBinaryOp(parser, result); }];
	let printer = [{ return ::printBinaryOp(p, *this); }];

	// Allow building a MulOp with from the two input operands.
	let builders = [
	OpBuilder<"Builder *b, OperationState &state, Value lhs, Value rhs">
	];
	}

	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)";
	}

	def ReshapeOp : Toy_Op<"reshape", [NoSideEffect]> {
	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;
	}

	def ReturnOp : Toy_Op<"return", [Terminator, HasParent<"FuncOp">]> {
	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
	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<
	"Builder *b, OperationState &state", [{ build(b, state, llvm::None); }]
	>];

	// Provide extra utility definitions on the c++ operation class definition.
	let extraClassDeclaration = [{
	bool hasOperand() { return getNumOperands() != 0; }
	}];

	// Invoke a static verify method to verify this return operation.
	let verifier = [{ return ::verify(*this); }];
	}

	def TransposeOp : Toy_Op<"transpose",
	[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
	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<"Builder *b, OperationState &state, Value input">
	];

	// Invoke a static verify method to verify this transpose operation.
	let verifier = [{ return ::verify(*this); }];
	}

	#endif // TOY_OPS