Files
pytorch/torch/csrc/utils/python_numbers.h
Peter Goldsborough dccd0f2de6 Bag of clang tidy fixes for torch/csrc/ and torch/csrc/autograd (#11050)
Summary:
Linting `torch/csrc/` (non-recursive) and `torch/csrc/autograd` (non-recursive).

Fixed things like:
- `typedef` vs `using`
- Use `.empty()` instead of comparing with empty string/using `.size() == 0`
- Use range for loops instead of old style loops (`modernize-`)
- Remove some `virtual` + `override`
- Replace `stdint.h` with `cstdint`
- Replace `return Type(x, y)` with `return {x, y}`
- Use boolean values (`true`/`false`)  instead of numbers (1/0)
- More ...

ezyang apaszke cpuhrsch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11050

Differential Revision: D9597505

Pulled By: goldsborough

fbshipit-source-id: cb0fb4793ade885a8dbf4b10484487b84c64c7f2
2018-09-05 19:55:50 -07:00

129 lines
3.3 KiB
C++

#pragma once
#include "torch/csrc/python_headers.h"
#include <cstdint>
#include <stdexcept>
#include "torch/csrc/Exceptions.h"
#include "torch/csrc/utils/tensor_numpy.h"
#include "torch/csrc/jit/tracer.h"
// largest integer that can be represented consecutively in a double
const int64_t DOUBLE_INT_MAX = 9007199254740992;
inline PyObject* THPUtils_packInt64(int64_t value) {
#if PY_MAJOR_VERSION == 2
if (sizeof(long) == sizeof(int64_t)) {
return PyInt_FromLong(static_cast<long>(value));
} else if (value <= INT32_MAX && value >= INT32_MIN) {
return PyInt_FromLong(static_cast<long>(value));
}
#endif
return PyLong_FromLongLong(value);
}
inline PyObject* THPUtils_packUInt64(uint64_t value) {
#if PY_MAJOR_VERSION == 2
if (value <= INT32_MAX) {
return PyInt_FromLong(static_cast<long>(value));
}
#endif
return PyLong_FromUnsignedLongLong(value);
}
inline PyObject* THPUtils_packDoubleAsInt(double value) {
#if PY_MAJOR_VERSION == 2
if (value <= INT32_MAX && value >= INT32_MIN) {
return PyInt_FromLong(static_cast<long>(value));
}
#endif
return PyLong_FromDouble(value);
}
inline bool THPUtils_checkLong(PyObject* obj) {
#if PY_MAJOR_VERSION == 2
return (PyLong_Check(obj) || PyInt_Check(obj)) && !PyBool_Check(obj);
#else
return PyLong_Check(obj) && !PyBool_Check(obj);
#endif
}
inline int64_t THPUtils_unpackLong(PyObject* obj) {
int overflow;
long long value = PyLong_AsLongLongAndOverflow(obj, &overflow);
if (value == -1 && PyErr_Occurred()) {
throw python_error();
}
if (overflow != 0) {
throw std::runtime_error("Overflow when unpacking long");
}
return (int64_t)value;
}
inline bool THPUtils_checkIndex(PyObject *obj) {
if (PyBool_Check(obj)) {
return false;
}
if (THPUtils_checkLong(obj)) {
return true;
}
torch::jit::tracer::NoWarn no_warn_guard;
auto index = THPObjectPtr(PyNumber_Index(obj));
if (!index) {
PyErr_Clear();
return false;
}
return true;
}
inline int64_t THPUtils_unpackIndex(PyObject* obj) {
if (!THPUtils_checkLong(obj)) {
auto index = THPObjectPtr(PyNumber_Index(obj));
if (index == nullptr) {
throw python_error();
}
obj = index.get();
}
return THPUtils_unpackLong(obj);
}
inline bool THPUtils_checkDouble(PyObject* obj) {
bool is_numpy_scalar;
#ifdef USE_NUMPY
is_numpy_scalar = torch::utils::is_numpy_scalar(obj);
#else
is_numpy_scalar = false;
#endif
#if PY_MAJOR_VERSION == 2
return PyFloat_Check(obj) || PyLong_Check(obj) || PyInt_Check(obj) || is_numpy_scalar;
#else
return PyFloat_Check(obj) || PyLong_Check(obj) || is_numpy_scalar;
#endif
}
inline double THPUtils_unpackDouble(PyObject* obj) {
if (PyFloat_Check(obj)) {
return PyFloat_AS_DOUBLE(obj);
}
if (PyLong_Check(obj)) {
int overflow;
long long value = PyLong_AsLongLongAndOverflow(obj, &overflow);
if (overflow != 0) {
throw std::runtime_error("Overflow when unpacking double");
}
if (value > DOUBLE_INT_MAX || value < -DOUBLE_INT_MAX) {
throw std::runtime_error("Precision loss when unpacking double");
}
return (double)value;
}
#if PY_MAJOR_VERSION == 2
if (PyInt_Check(obj)) {
return (double)PyInt_AS_LONG(obj);
}
#endif
double value = PyFloat_AsDouble(obj);
if (value == -1 && PyErr_Occurred()) {
throw python_error();
}
return value;
}