c50e53c109
Python bindings currently currently provide a makeScalarType function that constructs one of the predefined types. It was implemented in the bindings directly to circumvent the absence of standalone type parsing function. Now that mlir::parseType has been made available, rely on the core parsing procedure to construct types from strings in the bindings. This changes includes a library reshuffling that splits out "CoreAPIs" implementing the binding helper APIs into a separate library and makes that dependent on the Parser library. PiperOrigin-RevId: 274794516
91 lines
3.2 KiB
C++
91 lines
3.2 KiB
C++
//===- Types.cpp - Implementations of MLIR Core C APIs --------------------===//
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//
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// Copyright 2019 The MLIR Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// =============================================================================
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#include "mlir-c/Core.h"
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#include "mlir/IR/AffineMap.h"
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#include "mlir/IR/Attributes.h"
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#include "mlir/IR/Function.h"
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#include "mlir/IR/MLIRContext.h"
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#include "mlir/IR/StandardTypes.h"
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#include "mlir/IR/Types.h"
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#include "mlir/Support/LLVM.h"
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#include "mlir/Parser.h"
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#include "llvm/ADT/StringSwitch.h"
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using namespace mlir;
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mlir_type_t makeMemRefType(mlir_context_t context, mlir_type_t elemType,
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int64_list_t sizes) {
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auto t = mlir::MemRefType::get(
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llvm::ArrayRef<int64_t>(sizes.values, sizes.n),
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mlir::Type::getFromOpaquePointer(elemType),
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{mlir::AffineMap::getMultiDimIdentityMap(
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sizes.n, reinterpret_cast<mlir::MLIRContext *>(context))},
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0);
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return mlir_type_t{t.getAsOpaquePointer()};
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}
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mlir_type_t makeFunctionType(mlir_context_t context, mlir_type_list_t inputs,
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mlir_type_list_t outputs) {
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llvm::SmallVector<mlir::Type, 8> ins(inputs.n), outs(outputs.n);
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for (unsigned i = 0; i < inputs.n; ++i) {
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ins[i] = mlir::Type::getFromOpaquePointer(inputs.types[i]);
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}
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for (unsigned i = 0; i < outputs.n; ++i) {
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outs[i] = mlir::Type::getFromOpaquePointer(outputs.types[i]);
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}
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auto ft = mlir::FunctionType::get(
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ins, outs, reinterpret_cast<mlir::MLIRContext *>(context));
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return mlir_type_t{ft.getAsOpaquePointer()};
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}
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mlir_type_t makeIndexType(mlir_context_t context) {
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auto *ctx = reinterpret_cast<mlir::MLIRContext *>(context);
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auto type = mlir::IndexType::get(ctx);
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return mlir_type_t{type.getAsOpaquePointer()};
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}
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mlir_attr_t makeIntegerAttr(mlir_type_t type, int64_t value) {
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auto ty = Type::getFromOpaquePointer(reinterpret_cast<const void *>(type));
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auto attr = IntegerAttr::get(ty, value);
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return mlir_attr_t{attr.getAsOpaquePointer()};
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}
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mlir_attr_t makeBoolAttr(mlir_context_t context, bool value) {
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auto *ctx = reinterpret_cast<mlir::MLIRContext *>(context);
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auto attr = BoolAttr::get(value, ctx);
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return mlir_attr_t{attr.getAsOpaquePointer()};
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}
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unsigned getFunctionArity(mlir_func_t function) {
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auto f = mlir::FuncOp::getFromOpaquePointer(function);
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return f.getNumArguments();
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}
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mlir_type_t mlirParseType(const char *type, mlir_context_t context,
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uint64_t *charsRead) {
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auto *ctx = reinterpret_cast<MLIRContext *>(context);
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size_t numRead = 0;
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Type ty = parseType(type, ctx, numRead);
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if (charsRead)
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*charsRead = numRead;
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return mlir_type_t{ty.getAsOpaquePointer()};
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}
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