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llvm/mlir/lib/Bindings/Python/IRInterfaces.cpp

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//===- IRInterfaces.cpp - MLIR IR interfaces pybind -----------------------===//
//
// Part of the LLVM Project, 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 <cstdint>
#include <optional>
#include <pybind11/cast.h>
#include <pybind11/detail/common.h>
#include <pybind11/pybind11.h>
#include <pybind11/pytypes.h>
#include <string>
#include <utility>
#include <vector>
#include "IRModule.h"
#include "mlir-c/BuiltinAttributes.h"
#include "mlir-c/IR.h"
#include "mlir-c/Interfaces.h"
#include "mlir-c/Support.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
namespace py = pybind11;
namespace mlir {
namespace python {
constexpr static const char *constructorDoc =
R"(Creates an interface from a given operation/opview object or from a
subclass of OpView. Raises ValueError if the operation does not implement the
interface.)";
constexpr static const char *operationDoc =
R"(Returns an Operation for which the interface was constructed.)";
constexpr static const char *opviewDoc =
R"(Returns an OpView subclass _instance_ for which the interface was
constructed)";
constexpr static const char *inferReturnTypesDoc =
R"(Given the arguments required to build an operation, attempts to infer
its return types. Raises ValueError on failure.)";
constexpr static const char *inferReturnTypeComponentsDoc =
R"(Given the arguments required to build an operation, attempts to infer
its return shaped type components. Raises ValueError on failure.)";
namespace {
/// Takes in an optional ist of operands and converts them into a SmallVector
/// of MlirVlaues. Returns an empty SmallVector if the list is empty.
llvm::SmallVector<MlirValue> wrapOperands(std::optional<py::list> operandList) {
llvm::SmallVector<MlirValue> mlirOperands;
if (!operandList || operandList->empty()) {
return mlirOperands;
}
// Note: as the list may contain other lists this may not be final size.
mlirOperands.reserve(operandList->size());
for (const auto &&it : llvm::enumerate(*operandList)) {
if (it.value().is_none())
continue;
PyValue *val;
try {
val = py::cast<PyValue *>(it.value());
if (!val)
throw py::cast_error();
mlirOperands.push_back(val->get());
continue;
} catch (py::cast_error &err) {
// Intentionally unhandled to try sequence below first.
(void)err;
}
try {
auto vals = py::cast<py::sequence>(it.value());
for (py::object v : vals) {
try {
val = py::cast<PyValue *>(v);
if (!val)
throw py::cast_error();
mlirOperands.push_back(val->get());
} catch (py::cast_error &err) {
throw py::value_error(
(llvm::Twine("Operand ") + llvm::Twine(it.index()) +
" must be a Value or Sequence of Values (" + err.what() + ")")
.str());
}
}
continue;
} catch (py::cast_error &err) {
throw py::value_error((llvm::Twine("Operand ") + llvm::Twine(it.index()) +
" must be a Value or Sequence of Values (" +
err.what() + ")")
.str());
}
throw py::cast_error();
}
return mlirOperands;
}
/// Takes in an optional vector of PyRegions and returns a SmallVector of
/// MlirRegion. Returns an empty SmallVector if the list is empty.
llvm::SmallVector<MlirRegion>
wrapRegions(std::optional<std::vector<PyRegion>> regions) {
llvm::SmallVector<MlirRegion> mlirRegions;
if (regions) {
mlirRegions.reserve(regions->size());
for (PyRegion &region : *regions) {
mlirRegions.push_back(region);
}
}
return mlirRegions;
}
} // namespace
/// CRTP base class for Python classes representing MLIR Op interfaces.
/// Interface hierarchies are flat so no base class is expected here. The
/// derived class is expected to define the following static fields:
/// - `const char *pyClassName` - the name of the Python class to create;
/// - `GetTypeIDFunctionTy getInterfaceID` - the function producing the TypeID
/// of the interface.
/// Derived classes may redefine the `bindDerived(ClassTy &)` method to bind
/// interface-specific methods.
///
/// An interface class may be constructed from either an Operation/OpView object
/// or from a subclass of OpView. In the latter case, only the static interface
/// methods are available, similarly to calling ConcereteOp::staticMethod on the
/// C++ side. Implementations of concrete interfaces can use the `isStatic`
/// method to check whether the interface object was constructed from a class or
/// an operation/opview instance. The `getOpName` always succeeds and returns a
/// canonical name of the operation suitable for lookups.
template <typename ConcreteIface>
class PyConcreteOpInterface {
protected:
using ClassTy = py::class_<ConcreteIface>;
using GetTypeIDFunctionTy = MlirTypeID (*)();
public:
/// Constructs an interface instance from an object that is either an
/// operation or a subclass of OpView. In the latter case, only the static
/// methods of the interface are accessible to the caller.
PyConcreteOpInterface(py::object object, DefaultingPyMlirContext context)
: obj(std::move(object)) {
try {
operation = &py::cast<PyOperation &>(obj);
} catch (py::cast_error &) {
// Do nothing.
}
try {
operation = &py::cast<PyOpView &>(obj).getOperation();
} catch (py::cast_error &) {
// Do nothing.
}
if (operation != nullptr) {
if (!mlirOperationImplementsInterface(*operation,
ConcreteIface::getInterfaceID())) {
std::string msg = "the operation does not implement ";
throw py::value_error(msg + ConcreteIface::pyClassName);
}
MlirIdentifier identifier = mlirOperationGetName(*operation);
MlirStringRef stringRef = mlirIdentifierStr(identifier);
opName = std::string(stringRef.data, stringRef.length);
} else {
try {
opName = obj.attr("OPERATION_NAME").template cast<std::string>();
} catch (py::cast_error &) {
throw py::type_error(
"Op interface does not refer to an operation or OpView class");
}
if (!mlirOperationImplementsInterfaceStatic(
mlirStringRefCreate(opName.data(), opName.length()),
context.resolve().get(), ConcreteIface::getInterfaceID())) {
std::string msg = "the operation does not implement ";
throw py::value_error(msg + ConcreteIface::pyClassName);
}
}
}
/// Creates the Python bindings for this class in the given module.
static void bind(py::module &m) {
py::class_<ConcreteIface> cls(m, ConcreteIface::pyClassName,
py::module_local());
cls.def(py::init<py::object, DefaultingPyMlirContext>(), py::arg("object"),
py::arg("context") = py::none(), constructorDoc)
.def_property_readonly("operation",
&PyConcreteOpInterface::getOperationObject,
operationDoc)
.def_property_readonly("opview", &PyConcreteOpInterface::getOpView,
opviewDoc);
ConcreteIface::bindDerived(cls);
}
/// Hook for derived classes to add class-specific bindings.
static void bindDerived(ClassTy &cls) {}
/// Returns `true` if this object was constructed from a subclass of OpView
/// rather than from an operation instance.
bool isStatic() { return operation == nullptr; }
/// Returns the operation instance from which this object was constructed.
/// Throws a type error if this object was constructed from a subclass of
/// OpView.
py::object getOperationObject() {
if (operation == nullptr) {
throw py::type_error("Cannot get an operation from a static interface");
}
return operation->getRef().releaseObject();
}
/// Returns the opview of the operation instance from which this object was
/// constructed. Throws a type error if this object was constructed form a
/// subclass of OpView.
py::object getOpView() {
if (operation == nullptr) {
throw py::type_error("Cannot get an opview from a static interface");
}
return operation->createOpView();
}
/// Returns the canonical name of the operation this interface is constructed
/// from.
const std::string &getOpName() { return opName; }
private:
PyOperation *operation = nullptr;
std::string opName;
py::object obj;
};
/// Python wrapper for InferTypeOpInterface. This interface has only static
/// methods.
class PyInferTypeOpInterface
: public PyConcreteOpInterface<PyInferTypeOpInterface> {
public:
using PyConcreteOpInterface<PyInferTypeOpInterface>::PyConcreteOpInterface;
constexpr static const char *pyClassName = "InferTypeOpInterface";
constexpr static GetTypeIDFunctionTy getInterfaceID =
&mlirInferTypeOpInterfaceTypeID;
/// C-style user-data structure for type appending callback.
struct AppendResultsCallbackData {
std::vector<PyType> &inferredTypes;
PyMlirContext &pyMlirContext;
};
/// Appends the types provided as the two first arguments to the user-data
/// structure (expects AppendResultsCallbackData).
static void appendResultsCallback(intptr_t nTypes, MlirType *types,
void *userData) {
auto *data = static_cast<AppendResultsCallbackData *>(userData);
data->inferredTypes.reserve(data->inferredTypes.size() + nTypes);
for (intptr_t i = 0; i < nTypes; ++i) {
data->inferredTypes.emplace_back(data->pyMlirContext.getRef(), types[i]);
}
}
/// Given the arguments required to build an operation, attempts to infer its
/// return types. Throws value_error on failure.
std::vector<PyType>
inferReturnTypes(std::optional<py::list> operandList,
Introduce MLIR Op Properties This new features enabled to dedicate custom storage inline within operations. This storage can be used as an alternative to attributes to store data that is specific to an operation. Attribute can also be stored inside the properties storage if desired, but any kind of data can be present as well. This offers a way to store and mutate data without uniquing in the Context like Attribute. See the OpPropertiesTest.cpp for an example where a struct with a std::vector<> is attached to an operation and mutated in-place: struct TestProperties { int a = -1; float b = -1.; std::vector<int64_t> array = {-33}; }; More complex scheme (including reference-counting) are also possible. The only constraint to enable storing a C++ object as "properties" on an operation is to implement three functions: - convert from the candidate object to an Attribute - convert from the Attribute to the candidate object - hash the object Optional the parsing and printing can also be customized with 2 extra functions. A new options is introduced to ODS to allow dialects to specify: let usePropertiesForAttributes = 1; When set to true, the inherent attributes for all the ops in this dialect will be using properties instead of being stored alongside discardable attributes. The TestDialect showcases this feature. Another change is that we introduce new APIs on the Operation class to access separately the inherent attributes from the discardable ones. We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`, and other similar method which don't make the distinction explicit, leading to an entirely separate namespace for discardable attributes. Recommit d572cd1b067f after fixing python bindings build. Differential Revision: https://reviews.llvm.org/D141742
2023-02-26 10:46:01 -05:00
std::optional<PyAttribute> attributes, void *properties,
std::optional<std::vector<PyRegion>> regions,
DefaultingPyMlirContext context,
DefaultingPyLocation location) {
llvm::SmallVector<MlirValue> mlirOperands = wrapOperands(operandList);
llvm::SmallVector<MlirRegion> mlirRegions = wrapRegions(regions);
std::vector<PyType> inferredTypes;
PyMlirContext &pyContext = context.resolve();
AppendResultsCallbackData data{inferredTypes, pyContext};
MlirStringRef opNameRef =
mlirStringRefCreate(getOpName().data(), getOpName().length());
MlirAttribute attributeDict =
attributes ? attributes->get() : mlirAttributeGetNull();
MlirLogicalResult result = mlirInferTypeOpInterfaceInferReturnTypes(
opNameRef, pyContext.get(), location.resolve(), mlirOperands.size(),
Introduce MLIR Op Properties This new features enabled to dedicate custom storage inline within operations. This storage can be used as an alternative to attributes to store data that is specific to an operation. Attribute can also be stored inside the properties storage if desired, but any kind of data can be present as well. This offers a way to store and mutate data without uniquing in the Context like Attribute. See the OpPropertiesTest.cpp for an example where a struct with a std::vector<> is attached to an operation and mutated in-place: struct TestProperties { int a = -1; float b = -1.; std::vector<int64_t> array = {-33}; }; More complex scheme (including reference-counting) are also possible. The only constraint to enable storing a C++ object as "properties" on an operation is to implement three functions: - convert from the candidate object to an Attribute - convert from the Attribute to the candidate object - hash the object Optional the parsing and printing can also be customized with 2 extra functions. A new options is introduced to ODS to allow dialects to specify: let usePropertiesForAttributes = 1; When set to true, the inherent attributes for all the ops in this dialect will be using properties instead of being stored alongside discardable attributes. The TestDialect showcases this feature. Another change is that we introduce new APIs on the Operation class to access separately the inherent attributes from the discardable ones. We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`, and other similar method which don't make the distinction explicit, leading to an entirely separate namespace for discardable attributes. Recommit d572cd1b067f after fixing python bindings build. Differential Revision: https://reviews.llvm.org/D141742
2023-02-26 10:46:01 -05:00
mlirOperands.data(), attributeDict, properties, mlirRegions.size(),
mlirRegions.data(), &appendResultsCallback, &data);
if (mlirLogicalResultIsFailure(result)) {
throw py::value_error("Failed to infer result types");
}
return inferredTypes;
}
static void bindDerived(ClassTy &cls) {
cls.def("inferReturnTypes", &PyInferTypeOpInterface::inferReturnTypes,
py::arg("operands") = py::none(),
Introduce MLIR Op Properties This new features enabled to dedicate custom storage inline within operations. This storage can be used as an alternative to attributes to store data that is specific to an operation. Attribute can also be stored inside the properties storage if desired, but any kind of data can be present as well. This offers a way to store and mutate data without uniquing in the Context like Attribute. See the OpPropertiesTest.cpp for an example where a struct with a std::vector<> is attached to an operation and mutated in-place: struct TestProperties { int a = -1; float b = -1.; std::vector<int64_t> array = {-33}; }; More complex scheme (including reference-counting) are also possible. The only constraint to enable storing a C++ object as "properties" on an operation is to implement three functions: - convert from the candidate object to an Attribute - convert from the Attribute to the candidate object - hash the object Optional the parsing and printing can also be customized with 2 extra functions. A new options is introduced to ODS to allow dialects to specify: let usePropertiesForAttributes = 1; When set to true, the inherent attributes for all the ops in this dialect will be using properties instead of being stored alongside discardable attributes. The TestDialect showcases this feature. Another change is that we introduce new APIs on the Operation class to access separately the inherent attributes from the discardable ones. We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`, and other similar method which don't make the distinction explicit, leading to an entirely separate namespace for discardable attributes. Recommit d572cd1b067f after fixing python bindings build. Differential Revision: https://reviews.llvm.org/D141742
2023-02-26 10:46:01 -05:00
py::arg("attributes") = py::none(),
py::arg("properties") = py::none(), py::arg("regions") = py::none(),
py::arg("context") = py::none(), py::arg("loc") = py::none(),
inferReturnTypesDoc);
}
};
/// Wrapper around an shaped type components.
class PyShapedTypeComponents {
public:
PyShapedTypeComponents(MlirType elementType) : elementType(elementType) {}
PyShapedTypeComponents(py::list shape, MlirType elementType)
: shape(shape), elementType(elementType), ranked(true) {}
PyShapedTypeComponents(py::list shape, MlirType elementType,
MlirAttribute attribute)
: shape(shape), elementType(elementType), attribute(attribute),
ranked(true) {}
PyShapedTypeComponents(PyShapedTypeComponents &) = delete;
PyShapedTypeComponents(PyShapedTypeComponents &&other)
: shape(other.shape), elementType(other.elementType),
attribute(other.attribute), ranked(other.ranked) {}
static void bind(py::module &m) {
py::class_<PyShapedTypeComponents>(m, "ShapedTypeComponents",
py::module_local())
.def_property_readonly(
"element_type",
[](PyShapedTypeComponents &self) { return self.elementType; },
"Returns the element type of the shaped type components.")
.def_static(
"get",
[](PyType &elementType) {
return PyShapedTypeComponents(elementType);
},
py::arg("element_type"),
"Create an shaped type components object with only the element "
"type.")
.def_static(
"get",
[](py::list shape, PyType &elementType) {
return PyShapedTypeComponents(shape, elementType);
},
py::arg("shape"), py::arg("element_type"),
"Create a ranked shaped type components object.")
.def_static(
"get",
[](py::list shape, PyType &elementType, PyAttribute &attribute) {
return PyShapedTypeComponents(shape, elementType, attribute);
},
py::arg("shape"), py::arg("element_type"), py::arg("attribute"),
"Create a ranked shaped type components object with attribute.")
.def_property_readonly(
"has_rank",
[](PyShapedTypeComponents &self) -> bool { return self.ranked; },
"Returns whether the given shaped type component is ranked.")
.def_property_readonly(
"rank",
[](PyShapedTypeComponents &self) -> py::object {
if (!self.ranked) {
return py::none();
}
return py::int_(self.shape.size());
},
"Returns the rank of the given ranked shaped type components. If "
"the shaped type components does not have a rank, None is "
"returned.")
.def_property_readonly(
"shape",
[](PyShapedTypeComponents &self) -> py::object {
if (!self.ranked) {
return py::none();
}
return py::list(self.shape);
},
"Returns the shape of the ranked shaped type components as a list "
"of integers. Returns none if the shaped type component does not "
"have a rank.");
}
pybind11::object getCapsule();
static PyShapedTypeComponents createFromCapsule(pybind11::object capsule);
private:
py::list shape;
MlirType elementType;
MlirAttribute attribute;
bool ranked{false};
};
/// Python wrapper for InferShapedTypeOpInterface. This interface has only
/// static methods.
class PyInferShapedTypeOpInterface
: public PyConcreteOpInterface<PyInferShapedTypeOpInterface> {
public:
using PyConcreteOpInterface<
PyInferShapedTypeOpInterface>::PyConcreteOpInterface;
constexpr static const char *pyClassName = "InferShapedTypeOpInterface";
constexpr static GetTypeIDFunctionTy getInterfaceID =
&mlirInferShapedTypeOpInterfaceTypeID;
/// C-style user-data structure for type appending callback.
struct AppendResultsCallbackData {
std::vector<PyShapedTypeComponents> &inferredShapedTypeComponents;
};
/// Appends the shaped type components provided as unpacked shape, element
/// type, attribute to the user-data.
static void appendResultsCallback(bool hasRank, intptr_t rank,
const int64_t *shape, MlirType elementType,
MlirAttribute attribute, void *userData) {
auto *data = static_cast<AppendResultsCallbackData *>(userData);
if (!hasRank) {
data->inferredShapedTypeComponents.emplace_back(elementType);
} else {
py::list shapeList;
for (intptr_t i = 0; i < rank; ++i) {
shapeList.append(shape[i]);
}
data->inferredShapedTypeComponents.emplace_back(shapeList, elementType,
attribute);
}
}
/// Given the arguments required to build an operation, attempts to infer the
/// shaped type components. Throws value_error on failure.
std::vector<PyShapedTypeComponents> inferReturnTypeComponents(
std::optional<py::list> operandList,
std::optional<PyAttribute> attributes, void *properties,
std::optional<std::vector<PyRegion>> regions,
DefaultingPyMlirContext context, DefaultingPyLocation location) {
llvm::SmallVector<MlirValue> mlirOperands = wrapOperands(operandList);
llvm::SmallVector<MlirRegion> mlirRegions = wrapRegions(regions);
std::vector<PyShapedTypeComponents> inferredShapedTypeComponents;
PyMlirContext &pyContext = context.resolve();
AppendResultsCallbackData data{inferredShapedTypeComponents};
MlirStringRef opNameRef =
mlirStringRefCreate(getOpName().data(), getOpName().length());
MlirAttribute attributeDict =
attributes ? attributes->get() : mlirAttributeGetNull();
MlirLogicalResult result = mlirInferShapedTypeOpInterfaceInferReturnTypes(
opNameRef, pyContext.get(), location.resolve(), mlirOperands.size(),
mlirOperands.data(), attributeDict, properties, mlirRegions.size(),
mlirRegions.data(), &appendResultsCallback, &data);
if (mlirLogicalResultIsFailure(result)) {
throw py::value_error("Failed to infer result shape type components");
}
return inferredShapedTypeComponents;
}
static void bindDerived(ClassTy &cls) {
cls.def("inferReturnTypeComponents",
&PyInferShapedTypeOpInterface::inferReturnTypeComponents,
py::arg("operands") = py::none(),
py::arg("attributes") = py::none(), py::arg("regions") = py::none(),
py::arg("properties") = py::none(), py::arg("context") = py::none(),
py::arg("loc") = py::none(), inferReturnTypeComponentsDoc);
}
};
void populateIRInterfaces(py::module &m) {
PyInferTypeOpInterface::bind(m);
PyShapedTypeComponents::bind(m);
PyInferShapedTypeOpInterface::bind(m);
}
} // namespace python
} // namespace mlir