mirror of https://github.com/llvm/torch-mlir
111 lines
3.2 KiB
C++
111 lines
3.2 KiB
C++
//===- NumpyDialect.cpp - Core numpy dialect --------------------*- C++ -*-===//
|
|
//
|
|
// This file is licensed 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
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "npcomp/Dialect/Numpy/IR/NumpyDialect.h"
|
|
#include "mlir/IR/DialectImplementation.h"
|
|
#include "npcomp/Dialect/Basicpy/IR/BasicpyDialect.h"
|
|
#include "npcomp/Dialect/Numpy/IR/NumpyOps.h"
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::NPCOMP::Numpy;
|
|
|
|
NumpyDialect::NumpyDialect(MLIRContext *context)
|
|
: Dialect(getDialectNamespace(), context) {
|
|
addOperations<
|
|
#define GET_OP_LIST
|
|
#include "npcomp/Dialect/Numpy/IR/NumpyOps.cpp.inc"
|
|
>();
|
|
addTypes<AnyDtypeType, NdArrayType>();
|
|
}
|
|
|
|
Type NumpyDialect::parseType(DialectAsmParser &parser) const {
|
|
StringRef keyword;
|
|
if (parser.parseKeyword(&keyword))
|
|
return Type();
|
|
|
|
if (keyword == "any_dtype")
|
|
return AnyDtypeType::get(getContext());
|
|
if (keyword == "ndarray") {
|
|
// Parse:
|
|
// ndarray<?>
|
|
// ndarray<i32>
|
|
Type dtype = Basicpy::UnknownType::get(getContext());
|
|
if (parser.parseLess())
|
|
return Type();
|
|
if (failed(parser.parseOptionalQuestion())) {
|
|
// Specified dtype.
|
|
if (parser.parseType(dtype))
|
|
return Type();
|
|
}
|
|
if (parser.parseGreater())
|
|
return Type();
|
|
return NdArrayType::get(dtype);
|
|
}
|
|
|
|
parser.emitError(parser.getNameLoc(), "unknown numpy type: ") << keyword;
|
|
return Type();
|
|
}
|
|
|
|
void NumpyDialect::printType(Type type, DialectAsmPrinter &os) const {
|
|
switch (type.getKind()) {
|
|
case NumpyTypes::AnyDtypeType:
|
|
os << "any_dtype";
|
|
return;
|
|
case NumpyTypes::NdArray: {
|
|
auto unknownType = Basicpy::UnknownType::get(getContext());
|
|
auto ndarray = type.cast<NdArrayType>();
|
|
auto dtype = ndarray.getDtype();
|
|
os << "ndarray<";
|
|
if (dtype != unknownType)
|
|
os.printType(dtype);
|
|
else
|
|
os << "?";
|
|
os << ">";
|
|
return;
|
|
}
|
|
default:
|
|
llvm_unreachable("unexpected 'numpy' type kind");
|
|
}
|
|
}
|
|
|
|
//----------------------------------------------------------------------------//
|
|
// Type and attribute detail
|
|
//----------------------------------------------------------------------------//
|
|
namespace mlir {
|
|
namespace NPCOMP {
|
|
namespace Numpy {
|
|
namespace detail {
|
|
|
|
struct NdArrayTypeStorage : public TypeStorage {
|
|
using KeyTy = Type;
|
|
NdArrayTypeStorage(Type optionalDtype) : optionalDtype(optionalDtype) {}
|
|
bool operator==(const KeyTy &other) const { return optionalDtype == other; }
|
|
static llvm::hash_code hashKey(const KeyTy &key) {
|
|
return llvm::hash_combine(key);
|
|
}
|
|
static NdArrayTypeStorage *construct(TypeStorageAllocator &allocator,
|
|
const KeyTy &key) {
|
|
return new (allocator.allocate<NdArrayTypeStorage>())
|
|
NdArrayTypeStorage(key);
|
|
}
|
|
|
|
Type optionalDtype;
|
|
};
|
|
|
|
} // namespace detail
|
|
} // namespace Numpy
|
|
} // namespace NPCOMP
|
|
} // namespace mlir
|
|
|
|
NdArrayType NdArrayType::get(Type dtype) {
|
|
assert(dtype && "dtype cannot be null");
|
|
return Base::get(dtype.getContext(), NumpyTypes::NdArray, dtype);
|
|
}
|
|
|
|
Type NdArrayType::getDtype() { return getImpl()->optionalDtype; }
|