mirror of https://github.com/llvm/torch-mlir
76 lines
2.7 KiB
TableGen
76 lines
2.7 KiB
TableGen
//===- NumpyDialect.td - Core numpy dialect ----------------*- tablegen -*-===//
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//
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// This file is licensed 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 NPCOMP_DIALECT_NUMPY_NUMPY_DIALECT
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#define NPCOMP_DIALECT_NUMPY_NUMPY_DIALECT
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include "mlir/IR/OpBase.td"
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include "npcomp/Dialect/Basicpy/BasicpyDialect.td"
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//===----------------------------------------------------------------------===//
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// Dialect definition
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//===----------------------------------------------------------------------===//
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def Numpy_Dialect : Dialect {
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let name = "numpy";
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let summary = "Core numpy dialect";
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let description = [{
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Dialect of types and core numpy ops and abstractions.
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}];
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let cppNamespace = "Numpy";
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}
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//===----------------------------------------------------------------------===//
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// Op templates
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//===----------------------------------------------------------------------===//
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class Numpy_Op<string mnemonic, list<OpTrait> traits = []> :
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Op<Numpy_Dialect, mnemonic, traits> {
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let parser = [{ return parse$cppClass(parser, &result); }];
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let printer = [{ return print$cppClass(p, *this); }];
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}
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//===----------------------------------------------------------------------===//
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// Dialect types
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//===----------------------------------------------------------------------===//
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def Numpy_AnyDtype : DialectType<Numpy_Dialect,
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CPred<"$_self.isa<::mlir::NPCOMP::Numpy::AnyDtypeType>()">, "any dtype">,
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BuildableType<"$_builder.getType<::mlir::NPCOMP::Numpy::AnyDtypeType>()"> {
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let typeDescription = [{
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Placeholder for an unknown dtype in a tensor.
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}];
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}
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//===----------------------------------------------------------------------===//
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// Type predicates
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//===----------------------------------------------------------------------===//
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// Any type, at any stage of analysis that can represent a numpy array.
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def Numpy_AnyArray : TensorOf<[AnyType]>;
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def Numpy_SliceTupleElement : AnyTypeOf<[
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// Supports both "Index Arrays" and "Boolean mask index arrays".
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Numpy_AnyArray,
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// Indicates that an axis should be added (np.newaxis == None).
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Basicpy_NoneType,
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// Indicates that intervening axes should be preserved.
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Basicpy_EllipsisType,
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// A discrete numeric index (represented as IndexType so that a proper
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// width can be target dependent).
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Index,
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// A generalized slice object.
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Basicpy_SliceSlotObjectType,
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], "types that are legal elements of a __getitem__ tuple operating on arrays">;
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#endif // NPCOMP_DIALECT_NUMPY_NUMPY_DIALECT
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