ee3338d135
This PR ports all in-tree dialect extensions to use the `PyConcreteType`, `PyConcreteAttribute` CRTPs instead of `mlir_pure_subclass`. After this PR we can soft deprecate `mlir_pure_subclass`. Also API signatures are updated to use `Py*` instead of `Mlir*` so that type "inference" and hints are improved.
546 lines
21 KiB
C++
546 lines
21 KiB
C++
//===- Rewrite.cpp - Rewrite ----------------------------------------------===//
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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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#include "Rewrite.h"
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#include "mlir-c/IR.h"
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#include "mlir-c/Rewrite.h"
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#include "mlir-c/Support.h"
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#include "mlir/Bindings/Python/IRCore.h"
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// clang-format off
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#include "mlir/Bindings/Python/Nanobind.h"
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#include "mlir-c/Bindings/Python/Interop.h" // This is expected after nanobind.
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// clang-format on
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#include "mlir/Config/mlir-config.h"
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#include "nanobind/nanobind.h"
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namespace nb = nanobind;
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using namespace mlir;
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using namespace nb::literals;
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using namespace mlir::python::MLIR_BINDINGS_PYTHON_DOMAIN;
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namespace mlir {
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namespace python {
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namespace MLIR_BINDINGS_PYTHON_DOMAIN {
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class PyPatternRewriter {
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public:
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PyPatternRewriter(MlirPatternRewriter rewriter)
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: base(mlirPatternRewriterAsBase(rewriter)),
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ctx(PyMlirContext::forContext(mlirRewriterBaseGetContext(base))) {}
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PyInsertionPoint getInsertionPoint() const {
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MlirBlock block = mlirRewriterBaseGetInsertionBlock(base);
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MlirOperation op = mlirRewriterBaseGetOperationAfterInsertion(base);
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if (mlirOperationIsNull(op)) {
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MlirOperation owner = mlirBlockGetParentOperation(block);
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auto parent = PyOperation::forOperation(ctx, owner);
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return PyInsertionPoint(PyBlock(parent, block));
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}
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return PyInsertionPoint(PyOperation::forOperation(ctx, op));
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}
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void replaceOp(MlirOperation op, MlirOperation newOp) {
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mlirRewriterBaseReplaceOpWithOperation(base, op, newOp);
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}
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void replaceOp(MlirOperation op, const std::vector<MlirValue> &values) {
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mlirRewriterBaseReplaceOpWithValues(base, op, values.size(), values.data());
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}
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void eraseOp(const PyOperation &op) { mlirRewriterBaseEraseOp(base, op); }
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private:
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MlirRewriterBase base;
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PyMlirContextRef ctx;
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};
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struct PyMlirPDLResultList : MlirPDLResultList {};
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#if MLIR_ENABLE_PDL_IN_PATTERNMATCH
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static nb::object objectFromPDLValue(MlirPDLValue value) {
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if (MlirValue v = mlirPDLValueAsValue(value); !mlirValueIsNull(v))
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return nb::cast(v);
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if (MlirOperation v = mlirPDLValueAsOperation(value); !mlirOperationIsNull(v))
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return nb::cast(v);
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if (MlirAttribute v = mlirPDLValueAsAttribute(value); !mlirAttributeIsNull(v))
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return nb::cast(v);
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if (MlirType v = mlirPDLValueAsType(value); !mlirTypeIsNull(v))
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return nb::cast(v);
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throw std::runtime_error("unsupported PDL value type");
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}
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static std::vector<nb::object> objectsFromPDLValues(size_t nValues,
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MlirPDLValue *values) {
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std::vector<nb::object> args;
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args.reserve(nValues);
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for (size_t i = 0; i < nValues; ++i)
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args.push_back(objectFromPDLValue(values[i]));
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return args;
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}
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// Convert the Python object to a boolean.
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// If it evaluates to False, treat it as success;
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// otherwise, treat it as failure.
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// Note that None is considered success.
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static MlirLogicalResult logicalResultFromObject(const nb::object &obj) {
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if (obj.is_none())
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return mlirLogicalResultSuccess();
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return nb::cast<bool>(obj) ? mlirLogicalResultFailure()
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: mlirLogicalResultSuccess();
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}
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/// Owning Wrapper around a PDLPatternModule.
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class PyPDLPatternModule {
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public:
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PyPDLPatternModule(MlirPDLPatternModule module) : module(module) {}
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PyPDLPatternModule(PyPDLPatternModule &&other) noexcept
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: module(other.module) {
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other.module.ptr = nullptr;
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}
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~PyPDLPatternModule() {
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if (module.ptr != nullptr)
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mlirPDLPatternModuleDestroy(module);
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}
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MlirPDLPatternModule get() { return module; }
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void registerRewriteFunction(const std::string &name,
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const nb::callable &fn) {
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mlirPDLPatternModuleRegisterRewriteFunction(
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get(), mlirStringRefCreate(name.data(), name.size()),
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[](MlirPatternRewriter rewriter, MlirPDLResultList results,
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size_t nValues, MlirPDLValue *values,
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void *userData) -> MlirLogicalResult {
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nb::handle f = nb::handle(static_cast<PyObject *>(userData));
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return logicalResultFromObject(
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f(PyPatternRewriter(rewriter), PyMlirPDLResultList{results.ptr},
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objectsFromPDLValues(nValues, values)));
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},
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fn.ptr());
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}
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void registerConstraintFunction(const std::string &name,
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const nb::callable &fn) {
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mlirPDLPatternModuleRegisterConstraintFunction(
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get(), mlirStringRefCreate(name.data(), name.size()),
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[](MlirPatternRewriter rewriter, MlirPDLResultList results,
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size_t nValues, MlirPDLValue *values,
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void *userData) -> MlirLogicalResult {
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nb::handle f = nb::handle(static_cast<PyObject *>(userData));
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return logicalResultFromObject(
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f(PyPatternRewriter(rewriter), PyMlirPDLResultList{results.ptr},
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objectsFromPDLValues(nValues, values)));
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},
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fn.ptr());
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}
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private:
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MlirPDLPatternModule module;
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};
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#endif // MLIR_ENABLE_PDL_IN_PATTERNMATCH
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/// Owning Wrapper around a FrozenRewritePatternSet.
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class PyFrozenRewritePatternSet {
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public:
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PyFrozenRewritePatternSet(MlirFrozenRewritePatternSet set) : set(set) {}
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PyFrozenRewritePatternSet(PyFrozenRewritePatternSet &&other) noexcept
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: set(other.set) {
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other.set.ptr = nullptr;
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}
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~PyFrozenRewritePatternSet() {
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if (set.ptr != nullptr)
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mlirFrozenRewritePatternSetDestroy(set);
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}
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MlirFrozenRewritePatternSet get() { return set; }
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nb::object getCapsule() {
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return nb::steal<nb::object>(
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mlirPythonFrozenRewritePatternSetToCapsule(get()));
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}
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static nb::object createFromCapsule(const nb::object &capsule) {
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MlirFrozenRewritePatternSet rawPm =
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mlirPythonCapsuleToFrozenRewritePatternSet(capsule.ptr());
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if (rawPm.ptr == nullptr)
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throw nb::python_error();
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return nb::cast(PyFrozenRewritePatternSet(rawPm), nb::rv_policy::move);
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}
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private:
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MlirFrozenRewritePatternSet set;
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};
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class PyRewritePatternSet {
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public:
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PyRewritePatternSet(MlirContext ctx)
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: set(mlirRewritePatternSetCreate(ctx)), ctx(ctx) {}
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~PyRewritePatternSet() {
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if (set.ptr)
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mlirRewritePatternSetDestroy(set);
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}
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void add(MlirStringRef rootName, unsigned benefit,
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const nb::callable &matchAndRewrite) {
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MlirRewritePatternCallbacks callbacks;
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callbacks.construct = [](void *userData) {
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nb::handle(static_cast<PyObject *>(userData)).inc_ref();
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};
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callbacks.destruct = [](void *userData) {
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nb::handle(static_cast<PyObject *>(userData)).dec_ref();
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};
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callbacks.matchAndRewrite = [](MlirRewritePattern, MlirOperation op,
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MlirPatternRewriter rewriter,
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void *userData) -> MlirLogicalResult {
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nb::handle f(static_cast<PyObject *>(userData));
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PyMlirContextRef ctx =
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PyMlirContext::forContext(mlirOperationGetContext(op));
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nb::object opView = PyOperation::forOperation(ctx, op)->createOpView();
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nb::object res = f(opView, PyPatternRewriter(rewriter));
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return logicalResultFromObject(res);
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};
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MlirRewritePattern pattern = mlirOpRewritePatternCreate(
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rootName, benefit, ctx, callbacks, matchAndRewrite.ptr(),
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/* nGeneratedNames */ 0,
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/* generatedNames */ nullptr);
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mlirRewritePatternSetAdd(set, pattern);
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}
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PyFrozenRewritePatternSet freeze() {
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MlirRewritePatternSet s = set;
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set.ptr = nullptr;
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return mlirFreezeRewritePattern(s);
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}
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private:
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MlirRewritePatternSet set;
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MlirContext ctx;
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};
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enum PyGreedyRewriteStrictness : std::underlying_type_t<
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MlirGreedyRewriteStrictness> {
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MLIR_GREEDY_REWRITE_STRICTNESS_ANY_OP = MLIR_GREEDY_REWRITE_STRICTNESS_ANY_OP,
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MLIR_GREEDY_REWRITE_STRICTNESS_EXISTING_AND_NEW_OPS =
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MLIR_GREEDY_REWRITE_STRICTNESS_EXISTING_AND_NEW_OPS,
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MLIR_GREEDY_REWRITE_STRICTNESS_EXISTING_OPS =
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MLIR_GREEDY_REWRITE_STRICTNESS_EXISTING_OPS,
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};
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enum PyGreedySimplifyRegionLevel : std::underlying_type_t<
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MlirGreedySimplifyRegionLevel> {
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MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_DISABLED =
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MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_DISABLED,
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MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_NORMAL =
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MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_NORMAL,
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MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_AGGRESSIVE =
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MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_AGGRESSIVE
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};
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/// Owning Wrapper around a GreedyRewriteDriverConfig.
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class PyGreedyRewriteDriverConfig {
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public:
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PyGreedyRewriteDriverConfig()
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: config(mlirGreedyRewriteDriverConfigCreate()) {}
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PyGreedyRewriteDriverConfig(PyGreedyRewriteDriverConfig &&other) noexcept
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: config(other.config) {
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other.config.ptr = nullptr;
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}
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~PyGreedyRewriteDriverConfig() {
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if (config.ptr != nullptr)
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mlirGreedyRewriteDriverConfigDestroy(config);
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}
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MlirGreedyRewriteDriverConfig get() { return config; }
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void setMaxIterations(int64_t maxIterations) {
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mlirGreedyRewriteDriverConfigSetMaxIterations(config, maxIterations);
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}
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void setMaxNumRewrites(int64_t maxNumRewrites) {
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mlirGreedyRewriteDriverConfigSetMaxNumRewrites(config, maxNumRewrites);
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}
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void setUseTopDownTraversal(bool useTopDownTraversal) {
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mlirGreedyRewriteDriverConfigSetUseTopDownTraversal(config,
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useTopDownTraversal);
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}
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void enableFolding(bool enable) {
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mlirGreedyRewriteDriverConfigEnableFolding(config, enable);
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}
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void setStrictness(PyGreedyRewriteStrictness strictness) {
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mlirGreedyRewriteDriverConfigSetStrictness(
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config, static_cast<MlirGreedyRewriteStrictness>(strictness));
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}
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void setRegionSimplificationLevel(PyGreedySimplifyRegionLevel level) {
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mlirGreedyRewriteDriverConfigSetRegionSimplificationLevel(
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config, static_cast<MlirGreedySimplifyRegionLevel>(level));
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}
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void enableConstantCSE(bool enable) {
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mlirGreedyRewriteDriverConfigEnableConstantCSE(config, enable);
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}
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int64_t getMaxIterations() {
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return mlirGreedyRewriteDriverConfigGetMaxIterations(config);
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}
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int64_t getMaxNumRewrites() {
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return mlirGreedyRewriteDriverConfigGetMaxNumRewrites(config);
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}
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bool getUseTopDownTraversal() {
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return mlirGreedyRewriteDriverConfigGetUseTopDownTraversal(config);
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}
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bool isFoldingEnabled() {
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return mlirGreedyRewriteDriverConfigIsFoldingEnabled(config);
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}
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PyGreedyRewriteStrictness getStrictness() {
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return static_cast<PyGreedyRewriteStrictness>(
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mlirGreedyRewriteDriverConfigGetStrictness(config));
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}
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PyGreedySimplifyRegionLevel getRegionSimplificationLevel() {
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return static_cast<PyGreedySimplifyRegionLevel>(
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mlirGreedyRewriteDriverConfigGetRegionSimplificationLevel(config));
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}
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bool isConstantCSEEnabled() {
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return mlirGreedyRewriteDriverConfigIsConstantCSEEnabled(config);
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}
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private:
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MlirGreedyRewriteDriverConfig config;
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};
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/// Create the `mlir.rewrite` here.
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void populateRewriteSubmodule(nb::module_ &m) {
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// Enum definitions
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nb::enum_<PyGreedyRewriteStrictness>(m, "GreedyRewriteStrictness")
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.value("ANY_OP", MLIR_GREEDY_REWRITE_STRICTNESS_ANY_OP)
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.value("EXISTING_AND_NEW_OPS",
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MLIR_GREEDY_REWRITE_STRICTNESS_EXISTING_AND_NEW_OPS)
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.value("EXISTING_OPS", MLIR_GREEDY_REWRITE_STRICTNESS_EXISTING_OPS);
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nb::enum_<PyGreedySimplifyRegionLevel>(m, "GreedySimplifyRegionLevel")
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.value("DISABLED", MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_DISABLED)
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.value("NORMAL", MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_NORMAL)
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.value("AGGRESSIVE", MLIR_GREEDY_SIMPLIFY_REGION_LEVEL_AGGRESSIVE);
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//----------------------------------------------------------------------------
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// Mapping of the PatternRewriter
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//----------------------------------------------------------------------------
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nb::class_<PyPatternRewriter>(m, "PatternRewriter")
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.def_prop_ro("ip", &PyPatternRewriter::getInsertionPoint,
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"The current insertion point of the PatternRewriter.")
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.def(
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"replace_op",
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[](PyPatternRewriter &self, PyOperationBase &op,
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PyOperationBase &newOp) {
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self.replaceOp(op.getOperation(), newOp.getOperation());
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},
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"Replace an operation with a new operation.", nb::arg("op"),
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nb::arg("new_op"))
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.def(
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"replace_op",
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[](PyPatternRewriter &self, PyOperationBase &op,
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const std::vector<PyValue> &values) {
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std::vector<MlirValue> values_(values.size());
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std::copy(values.begin(), values.end(), values_.begin());
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self.replaceOp(op.getOperation(), values_);
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},
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"Replace an operation with a list of values.", nb::arg("op"),
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nb::arg("values"))
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.def("erase_op", &PyPatternRewriter::eraseOp, "Erase an operation.",
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nb::arg("op"));
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//----------------------------------------------------------------------------
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// Mapping of the RewritePatternSet
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//----------------------------------------------------------------------------
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nb::class_<PyRewritePatternSet>(m, "RewritePatternSet")
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.def(
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"__init__",
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[](PyRewritePatternSet &self, DefaultingPyMlirContext context) {
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new (&self) PyRewritePatternSet(context.get()->get());
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},
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"context"_a = nb::none())
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.def(
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"add",
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[](PyRewritePatternSet &self, nb::handle root, const nb::callable &fn,
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unsigned benefit) {
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std::string opName;
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if (root.is_type()) {
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opName = nb::cast<std::string>(root.attr("OPERATION_NAME"));
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} else if (nb::isinstance<nb::str>(root)) {
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opName = nb::cast<std::string>(root);
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} else {
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throw nb::type_error(
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"the root argument must be a type or a string");
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}
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self.add(mlirStringRefCreate(opName.data(), opName.size()), benefit,
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fn);
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},
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"root"_a, "fn"_a, "benefit"_a = 1,
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// clang-format off
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nb::sig("def add(self, root: type | str, fn: typing.Callable[[" MAKE_MLIR_PYTHON_QUALNAME("ir.Operation") ", PatternRewriter], typing.Any], benefit: int = 1) -> None"),
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// clang-format on
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R"(
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Add a new rewrite pattern on the specified root operation, using the provided callable
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for matching and rewriting, and assign it the given benefit.
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Args:
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root: The root operation to which this pattern applies.
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This may be either an OpView subclass (e.g., ``arith.AddIOp``) or
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an operation name string (e.g., ``"arith.addi"``).
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fn: The callable to use for matching and rewriting,
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which takes an operation and a pattern rewriter as arguments.
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The match is considered successful iff the callable returns
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a value where ``bool(value)`` is ``False`` (e.g. ``None``).
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If possible, the operation is cast to its corresponding OpView subclass
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before being passed to the callable.
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benefit: The benefit of the pattern, defaulting to 1.)")
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.def("freeze", &PyRewritePatternSet::freeze,
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"Freeze the pattern set into a frozen one.");
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//----------------------------------------------------------------------------
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// Mapping of the PDLResultList and PDLModule
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//----------------------------------------------------------------------------
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#if MLIR_ENABLE_PDL_IN_PATTERNMATCH
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nb::class_<PyMlirPDLResultList>(m, "PDLResultList")
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.def("append",
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[](PyMlirPDLResultList results, const PyValue &value) {
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mlirPDLResultListPushBackValue(results, value);
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})
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.def("append",
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[](PyMlirPDLResultList results, const PyOperation &op) {
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mlirPDLResultListPushBackOperation(results, op);
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})
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.def("append",
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[](PyMlirPDLResultList results, const PyType &type) {
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mlirPDLResultListPushBackType(results, type);
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})
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.def("append", [](PyMlirPDLResultList results, const PyAttribute &attr) {
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mlirPDLResultListPushBackAttribute(results, attr);
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});
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nb::class_<PyPDLPatternModule>(m, "PDLModule")
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.def(
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"__init__",
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[](PyPDLPatternModule &self, PyModule &module) {
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new (&self) PyPDLPatternModule(
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mlirPDLPatternModuleFromModule(module.get()));
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},
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"module"_a, "Create a PDL module from the given module.")
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.def(
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"__init__",
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[](PyPDLPatternModule &self, PyModule &module) {
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new (&self) PyPDLPatternModule(
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mlirPDLPatternModuleFromModule(module.get()));
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},
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"module"_a, "Create a PDL module from the given module.")
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.def(
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"freeze",
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[](PyPDLPatternModule &self) {
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return PyFrozenRewritePatternSet(mlirFreezeRewritePattern(
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mlirRewritePatternSetFromPDLPatternModule(self.get())));
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},
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nb::keep_alive<0, 1>())
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.def(
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"register_rewrite_function",
|
|
[](PyPDLPatternModule &self, const std::string &name,
|
|
const nb::callable &fn) {
|
|
self.registerRewriteFunction(name, fn);
|
|
},
|
|
nb::keep_alive<1, 3>())
|
|
.def(
|
|
"register_constraint_function",
|
|
[](PyPDLPatternModule &self, const std::string &name,
|
|
const nb::callable &fn) {
|
|
self.registerConstraintFunction(name, fn);
|
|
},
|
|
nb::keep_alive<1, 3>());
|
|
#endif // MLIR_ENABLE_PDL_IN_PATTERNMATCH
|
|
|
|
nb::class_<PyGreedyRewriteDriverConfig>(m, "GreedyRewriteDriverConfig")
|
|
.def(nb::init<>(), "Create a greedy rewrite driver config with defaults")
|
|
.def_prop_rw("max_iterations",
|
|
&PyGreedyRewriteDriverConfig::getMaxIterations,
|
|
&PyGreedyRewriteDriverConfig::setMaxIterations,
|
|
"Maximum number of iterations")
|
|
.def_prop_rw("max_num_rewrites",
|
|
&PyGreedyRewriteDriverConfig::getMaxNumRewrites,
|
|
&PyGreedyRewriteDriverConfig::setMaxNumRewrites,
|
|
"Maximum number of rewrites per iteration")
|
|
.def_prop_rw("use_top_down_traversal",
|
|
&PyGreedyRewriteDriverConfig::getUseTopDownTraversal,
|
|
&PyGreedyRewriteDriverConfig::setUseTopDownTraversal,
|
|
"Whether to use top-down traversal")
|
|
.def_prop_rw("enable_folding",
|
|
&PyGreedyRewriteDriverConfig::isFoldingEnabled,
|
|
&PyGreedyRewriteDriverConfig::enableFolding,
|
|
"Enable or disable folding")
|
|
.def_prop_rw("strictness", &PyGreedyRewriteDriverConfig::getStrictness,
|
|
&PyGreedyRewriteDriverConfig::setStrictness,
|
|
"Rewrite strictness level")
|
|
.def_prop_rw("region_simplification_level",
|
|
&PyGreedyRewriteDriverConfig::getRegionSimplificationLevel,
|
|
&PyGreedyRewriteDriverConfig::setRegionSimplificationLevel,
|
|
"Region simplification level")
|
|
.def_prop_rw("enable_constant_cse",
|
|
&PyGreedyRewriteDriverConfig::isConstantCSEEnabled,
|
|
&PyGreedyRewriteDriverConfig::enableConstantCSE,
|
|
"Enable or disable constant CSE");
|
|
|
|
nb::class_<PyFrozenRewritePatternSet>(m, "FrozenRewritePatternSet")
|
|
.def_prop_ro(MLIR_PYTHON_CAPI_PTR_ATTR,
|
|
&PyFrozenRewritePatternSet::getCapsule)
|
|
.def(MLIR_PYTHON_CAPI_FACTORY_ATTR,
|
|
&PyFrozenRewritePatternSet::createFromCapsule);
|
|
m.def(
|
|
"apply_patterns_and_fold_greedily",
|
|
[](PyModule &module, PyFrozenRewritePatternSet &set) {
|
|
auto status = mlirApplyPatternsAndFoldGreedily(
|
|
module.get(), set.get(), mlirGreedyRewriteDriverConfigCreate());
|
|
if (mlirLogicalResultIsFailure(status))
|
|
throw std::runtime_error("pattern application failed to converge");
|
|
},
|
|
"module"_a, "set"_a,
|
|
"Applys the given patterns to the given module greedily while folding "
|
|
"results.")
|
|
.def(
|
|
"apply_patterns_and_fold_greedily",
|
|
[](PyOperationBase &op, PyFrozenRewritePatternSet &set) {
|
|
auto status = mlirApplyPatternsAndFoldGreedilyWithOp(
|
|
op.getOperation(), set.get(),
|
|
mlirGreedyRewriteDriverConfigCreate());
|
|
if (mlirLogicalResultIsFailure(status))
|
|
throw std::runtime_error(
|
|
"pattern application failed to converge");
|
|
},
|
|
"op"_a, "set"_a,
|
|
"Applys the given patterns to the given op greedily while folding "
|
|
"results.")
|
|
.def(
|
|
"walk_and_apply_patterns",
|
|
[](PyOperationBase &op, PyFrozenRewritePatternSet &set) {
|
|
mlirWalkAndApplyPatterns(op.getOperation(), set.get());
|
|
},
|
|
"op"_a, "set"_a,
|
|
"Applies the given patterns to the given op by a fast walk-based "
|
|
"driver.");
|
|
}
|
|
} // namespace MLIR_BINDINGS_PYTHON_DOMAIN
|
|
} // namespace python
|
|
} // namespace mlir
|