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python_arg_parser.h
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python_arg_parser.h
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#pragma once
// Parse arguments to Python functions implemented in C++
// This is similar to PyArg_ParseTupleAndKeywords(), but specifically handles
// the types relevant to PyTorch and distinguishes between overloaded function
// signatures.
//
// Example:
//
// static PythonArgParser parser({
// "norm(Scalar p, int64_t dim, bool keepdim=False)",
// "norm(Scalar p=2)",
// });
// ParsedArgs<3> parsed_args;
// auto r = parser.parse(args, kwargs, parsed_args);
// if (r.idx == 0) {
// norm(r.scalar(0), r.int64(1), r.bool(0));
// } else {
// norm(r.scalar(0));
// }
//
// We auto-generate most uses of PythonArgParser; the generated files
// are torch/csrc/autograd/generated/python_*.cpp
//
// Some gotchas that you should watch out for:
//
// - Note [Order of overloads matters]
// Order of overloads matters. A set of input arguments may
// bind to multiple argument specs; we will always pick the
// first one in PythonArgParser. However, when you are writing
// overloads in, e.g., native_functions.yaml, you don't have to
// worry about what order you write them, because the code
// generation logic always gives the overloads a canonical
// order, where Tensor overloads come first, before Scalar overloads.
// This logic is in sort_declarations in
// tools/autograd/gen_python_functions.py
//
// - Zero-dim tensors (e.g., torch.tensor(2)) bind to both
// Scalar and Tensor, UNLESS they require grad (in which case
// they only bind to Tensor).
#include <torch/csrc/python_headers.h>
#include <torch/csrc/Stream.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/Dtype.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/Generator.h>
#include <torch/csrc/MemoryFormat.h>
#include <torch/csrc/QScheme.h>
#include <torch/csrc/Layout.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/python_dimname.h>
#include <torch/csrc/tensor/python_tensor.h>
#include <torch/csrc/utils/numpy_stub.h>
#include <torch/csrc/utils/object_ptr.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_numbers.h>
#include <torch/csrc/utils/python_strings.h>
#include <torch/csrc/utils/disable_torch_function.h>
#include <torch/csrc/utils/six.h>
#include <torch/csrc/autograd/variable.h>
#include <ATen/ATen.h>
#include <c10/util/Exception.h>
#include <array>
#include <cstddef>
#include <memory>
#include <sstream>
#include <string>
#include <vector>
namespace torch {
enum class ParameterType {
TENSOR, SCALAR, INT64, DOUBLE, COMPLEX, TENSOR_LIST, INT_LIST, GENERATOR,
BOOL, STORAGE, PYOBJECT, SCALARTYPE, LAYOUT, MEMORY_FORMAT, DEVICE, STREAM, STRING,
DIMNAME, DIMNAME_LIST, QSCHEME, FLOAT_LIST
};
struct FunctionParameter;
struct FunctionSignature;
struct PythonArgs;
// Contains bound Python arguments in declaration order
template<int N>
struct ParsedArgs {
ParsedArgs() : args() { }
PyObject* args[N];
};
struct PythonArgParser {
explicit PythonArgParser(std::vector<std::string> fmts, bool traceable=false);
// meant only for `torch` functions.
template<int N>
inline PythonArgs parse(PyObject* self, PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst);
template<int N>
inline PythonArgs parse(PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst);
inline PythonArgs parse(PyObject* self, ParsedArgs<0>& dst);
// Formatted strings of non-hidden signatures
std::vector<std::string> get_signatures() const;
private:
[[noreturn]]
void print_error(PyObject* self, PyObject* args, PyObject* kwargs, PyObject* parsed_args[]);
void check_deprecated(const FunctionSignature & signature);
PythonArgs raw_parse(PyObject* self, PyObject* args, PyObject* kwargs, PyObject* parsed_args[]);
std::vector<FunctionSignature> signatures_;
std::string function_name;
ssize_t max_args;
bool traceable;
};
struct PYBIND11_EXPORT FunctionSignature {
explicit FunctionSignature(const std::string& fmt, int index);
bool parse(PyObject* self, PyObject* args, PyObject* kwargs, PyObject* dst[], bool raise_exception);
std::string toString() const;
std::string name;
std::vector<FunctionParameter> params;
std::vector<py::handle> overloaded_args;
ssize_t min_args;
ssize_t max_args;
ssize_t max_pos_args;
int index;
bool hidden;
bool deprecated;
bool disable_torch_function;
};
struct PythonArgs {
PythonArgs(bool traceable, const FunctionSignature& signature, PyObject** args)
: idx(signature.index)
, traceable(traceable)
, signature(signature)
, args(args) {}
int idx;
bool traceable;
const FunctionSignature& signature;
PyObject** args;
inline bool has_torch_function();
inline std::string get_func_name();
inline at::Tensor tensor(int i);
inline c10::optional<at::Tensor> optionalTensor(int i);
inline at::Scalar scalar(int i);
inline at::Scalar scalarWithDefault(int i, at::Scalar default_scalar);
inline std::vector<at::Tensor> tensorlist(int i);
template<int N>
inline std::array<at::Tensor, N> tensorlist_n(int i);
inline std::vector<int64_t> intlist(int i);
inline c10::OptionalArray<int64_t> intlistOptional(int i);
inline std::vector<int64_t> intlistWithDefault(int i, std::vector<int64_t> default_intlist);
inline c10::optional<at::Generator> generator(int i);
inline at::Storage storage(int i);
inline c10::Stream stream(int i);
inline at::ScalarType scalartype(int i);
inline at::ScalarType scalartypeWithDefault(int i, at::ScalarType default_scalartype);
inline c10::optional<at::ScalarType> scalartypeOptional(int i);
inline c10::optional<at::Scalar> scalarOptional(int i);
inline c10::optional<int64_t> toInt64Optional(int i);
inline c10::optional<bool> toBoolOptional(int i);
inline c10::optional<double> toDoubleOptional(int i);
inline c10::OptionalArray<double> doublelistOptional(int i);
inline std::vector<double> doublelist(int i);
inline std::vector<double> getDoublelist(int i);
inline at::Layout layout(int i);
inline at::Layout layoutWithDefault(int i, at::Layout default_layout);
inline c10::optional<at::Layout> layoutOptional(int i);
inline at::Device device(int i);
inline at::Device deviceWithDefault(int i, const at::Device& default_device);
inline c10::optional<at::Device> deviceOptional(int i);
inline at::Dimname dimname(int i);
inline std::vector<at::Dimname> dimnamelist(int i);
inline c10::optional<std::vector<at::Dimname>> toDimnameListOptional(int i);
inline at::MemoryFormat memoryformat(int i);
inline c10::optional<at::MemoryFormat> memoryformatOptional(int i);
inline at::QScheme toQScheme(int i);
inline std::string string(int i);
inline std::string stringWithDefault(int i, const std::string& default_str);
inline c10::optional<std::string> stringOptional(int i);
inline PyObject* pyobject(int i);
inline int64_t toInt64(int i);
inline int64_t toInt64WithDefault(int i, int64_t default_int);
inline double toDouble(int i);
inline double toDoubleWithDefault(int i, double default_double);
inline c10::complex<double> toComplex(int i);
inline c10::complex<double> toComplexWithDefault(int i, c10::complex<double> default_complex);
inline bool toBool(int i);
inline bool toBoolWithDefault(int i, bool default_bool);
inline bool isNone(int i);
private:
at::Tensor tensor_slow(int i);
at::Scalar scalar_slow(int i);
};
struct FunctionParameter {
FunctionParameter(const std::string& fmt, bool keyword_only);
bool check(PyObject* obj, std::vector<py::handle> &overloaded_args, int argnum);
void set_default_str(const std::string& str);
std::string type_name() const;
ParameterType type_;
bool optional;
bool allow_none;
bool keyword_only;
bool allow_numbers_as_tensors = false;
int size;
std::string name;
// having this as a raw PyObject * will presumably leak it, but these are only held by static objects
// anyway, and Py_Finalize can already be called when this is destructed.
PyObject *python_name;
at::SmallVector<PyObject *, 5> numpy_python_names;
at::Scalar default_scalar;
std::vector<int64_t> default_intlist;
std::string default_string;
union {
bool default_bool;
int64_t default_int;
double default_double;
double default_complex[2]; // see Scalar
at::ScalarType default_scalartype;
at::Layout default_layout;
};
};
template<int N>
inline PythonArgs PythonArgParser::parse(PyObject* self, PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst) {
if (N < max_args) {
throw ValueError("PythonArgParser: dst ParsedArgs buffer does not have enough capacity, expected %d (got %d)",
(int)max_args, N);
}
return raw_parse(self, args, kwargs, dst.args);
}
template<int N>
inline PythonArgs PythonArgParser::parse(PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst) {
return parse(nullptr, args, kwargs, dst);
}
inline PythonArgs PythonArgParser::parse(PyObject* self, ParsedArgs<0>& dst) {
return parse(self, nullptr, nullptr, dst);
}
inline bool PythonArgs::has_torch_function(){
return !this->signature.overloaded_args.empty();
}
inline std::string PythonArgs::get_func_name(){
return signature.name;
}
inline at::Tensor PythonArgs::tensor(int i) {
if (args[i] && THPVariable_CheckExact(args[i])) {
return reinterpret_cast<THPVariable*>(args[i])->cdata;
}
return tensor_slow(i);
}
inline c10::optional<at::Tensor> PythonArgs::optionalTensor(int i) {
at::Tensor t = tensor(i);
if (t.defined()) {
return t;
} else {
return c10::nullopt;
}
}
inline at::Scalar PythonArgs::scalar(int i) {
if (!args[i]) return signature.params[i].default_scalar;
return scalar_slow(i);
}
inline at::Scalar PythonArgs::scalarWithDefault(int i, at::Scalar default_scalar) {
if (!args[i]) return default_scalar;
return scalar_slow(i);
}
inline c10::optional<at::Scalar> PythonArgs::scalarOptional(int i) {
if (!args[i]) return c10::nullopt;
return scalar_slow(i);
}
inline std::vector<at::Tensor> PythonArgs::tensorlist(int i) {
if (!args[i]) return std::vector<at::Tensor>();
auto tuple = six::isTuple(args[i]);
THPObjectPtr arg = six::maybeAsTuple(args[i]);
auto size = tuple ? PyTuple_GET_SIZE(arg.get()) : PyList_GET_SIZE(arg.get());
std::vector<at::Tensor> res(size);
for (int idx = 0; idx < size; idx++) {
PyObject* obj = tuple ? PyTuple_GET_ITEM(arg.get(), idx) : PyList_GET_ITEM(arg.get(), idx);
// This is checked by the argument parser so it's safe to cast without checking
// if this is a tensor first
res[idx] = reinterpret_cast<THPVariable*>(obj)->cdata;
}
return res;
}
template<int N>
inline std::array<at::Tensor, N> PythonArgs::tensorlist_n(int i) {
auto res = std::array<at::Tensor, N>();
if (!args[i]) return res;
auto tuple = six::isTuple(args[i]);
THPObjectPtr arg = six::maybeAsTuple(args[i]);
auto size = tuple ? PyTuple_GET_SIZE(arg.get()) : PyList_GET_SIZE(arg.get());
if (size != N) {
throw TypeError("expected tuple of %d elements but got %d", N, (int)size);
}
for (int idx = 0; idx < size; idx++) {
PyObject* obj = tuple ? PyTuple_GET_ITEM(arg.get(), idx) : PyList_GET_ITEM(arg.get(), idx);
// This is checked by the argument parser so it's safe to cast without checking
// if this is a tensor first
res[idx] = reinterpret_cast<THPVariable*>(obj)->cdata;
}
return res;
}
inline std::vector<int64_t> PythonArgs::intlist(int i) {
return intlistWithDefault(i, signature.params[i].default_intlist);
}
inline std::vector<int64_t> PythonArgs::intlistWithDefault(int i, std::vector<int64_t> default_intlist) {
if (!args[i]) return default_intlist;
PyObject* arg = args[i];
auto size = signature.params[i].size;
if (size > 0 && THPUtils_checkLong(arg)) {
return std::vector<int64_t>(size, THPUtils_unpackIndex(arg));
}
auto tuple = PyTuple_Check(arg);
size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
std::vector<int64_t> res(size);
for (int idx = 0; idx < size; idx++) {
PyObject* obj = tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
try {
// Elements of torch.Size are tensors during tracing, and we need to record extra
// information before they are turned into an IntArrayRef
if (traceable && jit::tracer::isTracing() && THPVariable_Check(obj)) {
auto & var = THPVariable_Unpack(obj);
jit::tracer::ArgumentStash::stashIntArrayRefElem(
signature.params[i].name, size, idx, var);
res[idx] = var.item<int64_t>();
continue;
} else {
res[idx] = THPUtils_unpackIndex(obj);
}
} catch (const std::exception &e) {
throw TypeError("%s(): argument '%s' must be %s, but found element of type %s at pos %d",
signature.name.c_str(), signature.params[i].name.c_str(),
signature.params[i].type_name().c_str(), Py_TYPE(obj)->tp_name, idx + 1);
}
}
return res;
}
inline c10::OptionalArray<int64_t> PythonArgs::intlistOptional(int i) {
if (!args[i]) {
return {};
}
return intlist(i);
}
inline std::vector<double> PythonArgs::getDoublelist(int i) {
PyObject* arg = args[i];
auto tuple = PyTuple_Check(arg);
auto size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
std::vector<double> res(size);
for (int idx = 0; idx < size; idx++) {
PyObject* obj = tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
try {
res[idx] = THPUtils_unpackDouble(obj);
} catch (const std::exception &e) {
throw TypeError("%s(): argument '%s' must be %s, but found element of type %s at pos %d",
signature.name.c_str(), signature.params[i].name.c_str(),
signature.params[i].type_name().c_str(), Py_TYPE(obj)->tp_name, idx + 1);
}
}
return res;
}
inline c10::OptionalArray<double> PythonArgs::doublelistOptional(int i) {
if (!args[i]) {
return {};
}
return this->getDoublelist(i);
}
inline std::vector<double> PythonArgs::doublelist(int i) {
if (!args[i]) {
return {};
}
return this->getDoublelist(i);
}
inline at::ScalarType PythonArgs::scalartypeWithDefault(int i, at::ScalarType default_scalartype) {
if (!args[i]) return default_scalartype;
return scalartype(i);
}
inline at::ScalarType PythonArgs::scalartype(int i) {
if (!args[i]) {
auto scalartype = signature.params[i].default_scalartype;
return (scalartype == at::ScalarType::Undefined) ?
torch::tensors::get_default_scalar_type() : scalartype;
}
PyObject *obj = args[i];
if (obj == (PyObject*)&PyFloat_Type) {
return at::ScalarType::Double;
}
if (obj == (PyObject*)&PyBool_Type) {
return at::ScalarType::Bool;
}
if (obj == (PyObject*)&PyLong_Type) {
return at::ScalarType::Long;
}
return reinterpret_cast<THPDtype*>(obj)->scalar_type;
}
inline c10::optional<at::ScalarType> PythonArgs::scalartypeOptional(int i) {
if (!args[i])
return c10::nullopt;
return scalartype(i);
}
inline at::Layout PythonArgs::layout(int i) {
if (!args[i]) return signature.params[i].default_layout;
return reinterpret_cast<THPLayout*>(args[i])->layout;
}
inline at::Layout PythonArgs::layoutWithDefault(int i, at::Layout default_layout) {
if (!args[i]) return default_layout;
return layout(i);
}
inline c10::optional<at::Layout> PythonArgs::layoutOptional(int i) {
if (!args[i]) return c10::nullopt;
return layout(i);
}
inline at::Device PythonArgs::device(int i) {
if (!args[i]) {
return at::Device(backendToDeviceType(dispatchKeyToBackend(torch::tensors::get_default_dispatch_key())));
}
if (THPDevice_Check(args[i])) {
const auto device = reinterpret_cast<THPDevice*>(args[i]);
return device->device;
}
if (THPUtils_checkLong(args[i])) {
const auto device_index = THPUtils_unpackLong(args[i]);
TORCH_CHECK(device_index >= 0, "Device index must not be negative");
return at::Device(DeviceType::CUDA, device_index);
}
const std::string &device_str = THPUtils_unpackString(args[i]);
return at::Device(device_str);
}
inline at::Device PythonArgs::deviceWithDefault(int i, const at::Device& default_device) {
if (!args[i]) return default_device;
return device(i);
}
inline c10::optional<at::Device> PythonArgs::deviceOptional(int i) {
if (!args[i])
return c10::nullopt;
return device(i);
}
inline at::Dimname PythonArgs::dimname(int i) {
TORCH_INTERNAL_ASSERT(args[i] != nullptr);
return THPDimname_parse(args[i]);
}
inline std::vector<at::Dimname> parseDimnameList(PyObject* arg) {
auto tuple = PyTuple_Check(arg);
auto size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
std::vector<at::Dimname> res;
res.reserve(size);
for (int idx = 0; idx < size; idx++) {
PyObject* obj = tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
res.push_back(THPDimname_parse(obj));
}
return res;
}
inline c10::optional<std::vector<at::Dimname>> PythonArgs::toDimnameListOptional(int i) {
if (!args[i]) return c10::nullopt;
return parseDimnameList(args[i]);
}
inline std::vector<at::Dimname> PythonArgs::dimnamelist(int i) {
TORCH_INTERNAL_ASSERT(args[i]);
PyObject* arg = args[i];
auto size = signature.params[i].size;
TORCH_INTERNAL_ASSERT(size == 0 || size == 1);
if (size == 1 && THPUtils_checkDimname(arg)) {
return { THPDimname_parse(arg) };
}
return parseDimnameList(arg);
}
inline at::MemoryFormat PythonArgs::memoryformat(int i) {
if (!args[i]) return at::MemoryFormat::Contiguous;
TORCH_CHECK(THPMemoryFormat_Check(args[i]), "memory_format arg must be an instance of the torch.memory_format");
const auto memory_format = reinterpret_cast<THPMemoryFormat*>(args[i]);
return memory_format->memory_format;
}
inline c10::optional<at::MemoryFormat> PythonArgs::memoryformatOptional(int i) {
if (!args[i])
return c10::nullopt;
return memoryformat(i);
}
inline at::QScheme PythonArgs::toQScheme(int i) {
if (!args[i]) return at::kPerTensorAffine;
TORCH_CHECK(THPQScheme_Check(args[i]), "qscheme arg must be an instance of the torch.qscheme");
const auto qscheme = reinterpret_cast<THPQScheme*>(args[i]);
return qscheme->qscheme;
}
inline std::string PythonArgs::string(int i) {
return stringWithDefault(i, signature.params[i].default_string);
}
inline std::string PythonArgs::stringWithDefault(int i, const std::string& default_str) {
if (!args[i]) return default_str;
return THPUtils_unpackString(args[i]);
}
inline c10::optional<std::string> PythonArgs::stringOptional(int i) {
if (!args[i]) return c10::nullopt;
return THPUtils_unpackString(args[i]);
}
inline int64_t PythonArgs::toInt64(int i) {
if (!args[i]) return signature.params[i].default_int;
if (traceable && jit::tracer::isTracing() && THPVariable_Check(args[i])) {
auto & var = THPVariable_Unpack(args[i]);
jit::tracer::ArgumentStash::stashValue(
signature.params[i].name, idx, var, jit::IntType::get());
}
return THPUtils_unpackLong(args[i]);
}
inline int64_t PythonArgs::toInt64WithDefault(int i, int64_t default_int) {
if (!args[i]) return default_int;
return toInt64(i);
}
inline c10::optional<int64_t> PythonArgs::toInt64Optional(int i) {
if (!args[i])
return c10::nullopt;
return toInt64(i);
}
inline c10::optional<bool> PythonArgs::toBoolOptional(int i) {
if (!args[i]) {
return c10::nullopt;
}
return toBool(i);
}
inline c10::optional<double> PythonArgs::toDoubleOptional(int i) {
if (!args[i]) {
return c10::nullopt;
}
return toDouble(i);
}
inline double PythonArgs::toDouble(int i) {
if (!args[i]) return signature.params[i].default_double;
return THPUtils_unpackDouble(args[i]);
}
inline double PythonArgs::toDoubleWithDefault(int i, double default_double) {
if (!args[i]) return default_double;
return toDouble(i);
}
inline c10::complex<double> PythonArgs::toComplex(int i) {
c10::complex<double> default_value = *const_cast<c10::complex<double> *>(
reinterpret_cast<const c10::complex<double> *>(signature.params[i].default_complex));
if (!args[i]) return default_value;
return THPUtils_unpackComplexDouble(args[i]);
}
inline c10::complex<double> PythonArgs::toComplexWithDefault(int i, c10::complex<double> default_value) {
if (!args[i]) return default_value;
return toComplex(i);
}
inline bool PythonArgs::toBool(int i) {
if (!args[i]) return signature.params[i].default_bool;
return args[i] == Py_True;
}
inline bool PythonArgs::toBoolWithDefault(int i, bool default_bool) {
if (!args[i]) return default_bool;
return toBool(i);
}
inline bool PythonArgs::isNone(int i) {
return args[i] == nullptr;
}
inline c10::optional<at::Generator> PythonArgs::generator(int i) {
if (!args[i]) return c10::nullopt;
return reinterpret_cast<THPGenerator*>(args[i])->cdata;
}
inline at::Storage PythonArgs::storage(int i) {
if (!args[i]) return at::Storage();
return createStorage(args[i]);
}
inline c10::Stream PythonArgs::stream(int i) {
if (!args[i]) return c10::Stream(c10::Stream::Default::DEFAULT, c10::Device(DeviceType::CPU, -1));
if (!THPStream_Check(args[i])) {
throw TypeError("expected Stream object. Got '%s'", Py_TYPE(args[i])->tp_name);
}
return c10::Stream::unpack(((THPStream*)args[i])->cdata);
}
inline PyObject* PythonArgs::pyobject(int i) {
if (!args[i]) return Py_None;
return args[i];
}
/*
* Reference: https://github.com/numpy/numpy/blob/f4c497c768e0646df740b647782df463825bfd27/numpy/core/src/common/get_attr_string.h#L42
*
* Stripped down version of PyObject_GetAttrString,
* avoids lookups for None, tuple, and List objects,
* and doesn't create a PyErr since this code ignores it.
*
* This can be much faster then PyObject_GetAttrString where
* exceptions are not used by caller.
*
* 'obj' is the object to search for attribute.
*
* 'name' is the attribute to search for.
*
* Returns a py::object wrapping the return value. If the attribute lookup failed
* the value will be NULL.
*
*/
static py::object PyObject_FastGetAttrString(PyObject *obj, char *name)
{
PyTypeObject *tp = Py_TYPE(obj);
PyObject *res = (PyObject *)NULL;
/* Attribute referenced by (char *)name */
if (tp->tp_getattr != NULL) {
res = (*tp->tp_getattr)(obj, name);
if (res == NULL) {
PyErr_Clear();
}
}
/* Attribute referenced by (PyObject *)name */
else if (tp->tp_getattro != NULL) {
PyObject *w = THPUtils_internString(name);
if (w == NULL) {
return py::object();
}
res = (*tp->tp_getattro)(obj, w);
Py_DECREF(w);
if (res == NULL) {
PyErr_Clear();
}
}
return py::reinterpret_steal<py::object>(res);
}
// Makes sure that we don't check for __torch_function__ on basic Python types
static bool _is_basic_python_type(PyTypeObject *tp)
{
return (
/* Basic number types */
tp == &PyBool_Type ||
tp == &PyLong_Type ||
tp == &PyFloat_Type ||
tp == &PyComplex_Type ||
/* Basic sequence types */
tp == &PyList_Type ||
tp == &PyTuple_Type ||
tp == &PyDict_Type ||
tp == &PySet_Type ||
tp == &PyFrozenSet_Type ||
tp == &PyUnicode_Type ||
tp == &PyBytes_Type ||
/* other builtins */
tp == &PySlice_Type ||
tp == Py_TYPE(Py_None) ||
tp == Py_TYPE(Py_Ellipsis) ||
tp == Py_TYPE(Py_NotImplemented) ||
PyModule_Check(tp) ||
/* sentinel to swallow trailing || */
false
);
}
/*
* Lookup a special method, following the python approach of looking up
* on the type object, rather than on the instance itself.
*
* Assumes that the special method is a torch-specific one, so does not
* look at builtin types, nor does it look at a base Tensor.
*
* If no special method is found, return NULL, otherwise returns a new
* reference to the function object
*
* In future, could be made more like _Py_LookupSpecial
*/
static py::object PyTorch_LookupSpecial(PyObject *obj, char* name)
{
if (THPVariable_CheckExact(obj)) {
return py::object();
}
PyTypeObject *tp = Py_TYPE(obj);
if (_is_basic_python_type(tp)) {
return py::object();
}
return PyObject_FastGetAttrString((PyObject *)tp, name);
}
/*
* Checks if obj has a __torch_function__ implementation
*
* Returns true if an implementation is found and false otherwise
*
*/
static auto check_has_torch_function(PyObject* obj) -> bool
{
if (!torch_function_enabled()) {
return false;
}
py::object method = PyTorch_LookupSpecial(obj, "__torch_function__");
if(method.ptr() != nullptr && method.ptr() != disabled_torch_function_impl()){
return true;
}
return false;
}
/*
*
* Handle __torch_function__ overrides if we know that there are overloaded
* arguments. All objects stored in r.overloaded_args must have a
* __torch_function__ implementation and the arguments must be ordered in order
* of precedence. Precedence goes from left to right in the order of the
* signature of the function the overloaded arguments were passed to, except
* subclasses are always considered before superclasses.
*
* If the result of calling __torch_function__ is NotImplemented, the
* next implementation in the precedence order is called. If all
* arguments return NotImplemented from their __torch_function__
* implementation, a TypeError is raised in Python.
*
* Assumes overloaded_args has at least one entry. All entries must have
* a __torch_function__ attribute that resolves to a callable that
* accepts a torch API function, a tuple of arguments, and a dict of
* keyword arguments for the torch API function.
*
* It is sufficient to call PythonArgs::has_torch_function before
* calling this function to verify that there are valid arguments
* present. If that is not done then special care must be taken to
* ensure there are arguments that are overloaded with
* __torch_function__.
*
* See torch._overrides.handle_torch_function for the equivalent
* code in the pure-python implementation.
*
* 'r' is a parsed PythonArgs instance, returned from
* PythonArgParser::parse.
*
* 'args' is a reference to the python tuple of arguments to the torch
* API function.
*
* 'kwargs' is a reference to the python dict of keyword arguments to
* the torch API function.
*
* 'torch_api' is a reference to a python torch API namespace.
*
* 'torch_api_function' is the reference to the original torch method, usually,
* we can use torch_api and func_name to get torch_api_function. In some cases,
* e.g., torch custom op, we create the function in C++, if we still use
* torch_api and func_name to fetch original api, a cyclic call will happen.
*
* 'overloaded_args' is the args which have overloaded __torch_function__.
*
* 'func_name' is the named of the original torch method.
*
* TODO: we could use different names for the following 'handle_torch_function'
* instead of overloading.
*
*/
// Used for Tensor methods with arguments.
auto handle_torch_function(PythonArgs &r, PyObject* self, PyObject* args, PyObject* kwargs, PyObject* torch_api, const char* module_name) -> PyObject*;
// Used for functions which needs to parse python args.
auto handle_torch_function(PythonArgs &r, PyObject* args, PyObject* kwargs, PyObject* torch_api, const char* module_name) -> PyObject*;
// Used for functions that have no argument parsing.
auto handle_torch_function(PyObject* self, const std::string& func_name, PyObject* args=nullptr, PyObject* kwargs=nullptr, PyObject* torch_api=THPVariableClass, const std::string& module_name="torch.Tensor") -> PyObject*;
// Used for functions created in C++, e.g., C++ custom op, which doesn't use PythonArgParser to get overloaded_args.
auto handle_torch_function_no_python_arg_parser(const std::vector<py::handle> &overloaded_args, PyObject* args, PyObject* kwargs, const char* func_name, PyObject* torch_api_function, const char* module_name) -> PyObject*;
// Used for getters of Tensor properties
auto handle_torch_function_getter(THPVariable* self, const std::string& property_name) -> PyObject*;
// Used for setters of Tensor properties.
auto handle_torch_function_setter(THPVariable* self, const std::string& property_name, PyObject* value) -> int;
/*
* Check if the input obj is Tensor type, including its subclass, or overloaded
* type. If the type defines __torch_function__, it also returns true.
* Otherwise returns flase. If the class is not torch.Tensor, and it defines
* __torch_function__, we append obj to overloaded_args.
*
* 'obj': the input argument to be checked
* 'overloaded_args': the vector to append the overloaded args.
*/
bool is_tensor_and_append_overloaded(PyObject* obj, std::vector<py::handle>* overloaded_args);
/*
* Check if the input obj is Tensor List or Tensor Tuple type. First check
* whether obj is Tuple or List type, if true, iterate over each element and
* check whether it is Tensor type, including its subclass or overloaded type.
* At the same time, the overloaded arg is appended to the overloaded_args.
*
* 'obj': the input argument to be checked
* 'overloaded_args': the vector to append the overloaded args.
* 'argnum': the number of total arguments of the function being checked.
* 'throw_error': whether throw error if any element in the list or tuple is
* not tensor type or overloaded.
*/
bool is_tensor_list_and_append_overloaded(PyObject* obj, std::vector<py::handle>* overloaded_args, int argnum, bool throw_error);
} // namespace torch