mirror of https://github.com/llvm/torch-mlir
116 lines
3.9 KiB
TableGen
116 lines
3.9 KiB
TableGen
//===-------------------------------------------------------*- tablegen -*-===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#ifndef TCP_OPS
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#define TCP_OPS
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include "npcomp/Dialect/TCP/IR/TCPBase.td"
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include "mlir/Dialect/Shape/IR/ShapeBase.td"
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include "mlir/Interfaces/SideEffectInterfaces.td"
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include "mlir/Interfaces/InferTypeOpInterface.td"
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include "mlir/Interfaces/ControlFlowInterfaces.td"
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include "mlir/IR/SymbolInterfaces.td"
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class TCP_Op<string mnemonic, list<OpTrait> traits = []>
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: Op<TCP_Dialect, mnemonic, traits> {
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}
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// TODO: Clarify allowed tensor element types.
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class BinaryArithmeticOp<string mnemonic, list<OpTrait> traits = []> :
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TCP_Op<mnemonic, traits> {
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let arguments = (ins AnyRankedTensor:$lhs, AnyRankedTensor:$rhs);
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let results = (outs AnyRankedTensor:$result);
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let assemblyFormat = "$lhs `,` $rhs attr-dict `:` functional-type(operands, results)";
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}
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def TCP_AddOp : BinaryArithmeticOp<"add"> {
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let summary = "Addition of two tensors";
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let description = [{
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Addition of two tensors.
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}];
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}
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def TCP_MaxOp : BinaryArithmeticOp<"max"> {
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let summary = "Maximum of two tensors";
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let description = [{
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Maximum of two tensors.
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}];
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}
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def TCP_MulOp : BinaryArithmeticOp<"mul"> {
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let summary = "Multiply an input tensor by a scalar tensor.";
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let description = [{
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Multiplies each element of the input `input` with the scalar `other` and returns a new resulting tensor. The tensor types must match and shapes must be broadcastable.
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}];
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}
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class UnaryArithmeticOp<string mnemonic, list<OpTrait> traits = []> :
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TCP_Op<mnemonic,
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!listconcat(traits, [AllTypesMatch<["operand", "result"]>])>,
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AllTypesMatch<["operand", "result"]> {
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let arguments = (ins AnyTensor:$operand);
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let results = (outs AnyTensor:$result);
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let assemblyFormat = "$operand attr-dict `:` type($operand)";
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}
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def TCP_ExpOp : UnaryArithmeticOp<"exp"> {
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let summary = "base-e exponential";
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let description = [{
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See std.exp for more details.
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}];
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}
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def TCP_TanhOp : UnaryArithmeticOp<"tanh"> {
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let summary = "hyperbolic tangent";
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let description = [{
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See std.tanh for more details.
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}];
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}
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// TODO: Generalize this op appropriately and add more verification.
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// For example, should we have a single primitive that does multidimensional
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// contractions? + batching as well in the same op? In fact, if we want to
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// get really general, we can include convolution as well; matmul is the 1x1
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// image and 1x1 kernel special case.
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// It still lowers trivially into linalg.generic even with such generalization
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// -- the main question is what transforms we want to do at the TCP level that
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// would be affected by those design choices.
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def TCP_MatmulOp : TCP_Op<"matmul"> {
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let summary = "Performs a matrix multiplication";
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let description = [{
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Performs a matrix multiplication.
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The tensors have dimensions:
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- lhs: [M, K]
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- rhs: [K, N]
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- result: [M, N]
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If the `K` dimension mismatches between operands, this op has
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undefined behavior.
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}];
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let arguments = (ins 2DTensorOf<[F32]>:$lhs, 2DTensorOf<[F32]>:$rhs);
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let results = (outs 2DTensorOf<[F32]>:$result);
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let assemblyFormat = "$lhs `,` $rhs attr-dict `:` functional-type(operands, results)";
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}
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def TCP_BroadcastToOp : TCP_Op<"broadcast_to"> {
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let summary = "Broadcasts an operand to a given shape.";
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let description = [{
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Broadcasts `operand` to the shape `shape`.
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It is undefined behavior if such a broadcast is not legal.
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}];
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let arguments = (ins AnyRankedTensor:$operand, Shape_ExtentTensorType:$shape);
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let results = (outs AnyRankedTensor:$result);
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let assemblyFormat = "$operand `,` $shape attr-dict `:` functional-type(operands, results)";
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}
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#endif // TCP_OPS
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