pytorch/torch/csrc/utils/python_numbers.h
Edward Yang 517c7c9861 Canonicalize all includes in PyTorch. (#14849)
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.

I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.

I used the following script to do the canonicalization:

```
  import subprocess
  import re
  import os.path

  files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
  for fn in files:
      if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
          continue
      if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
          continue
      with open(fn, 'r') as f:
          c = f.read()
      def fmt(p):
          return "#include <{}>".format(p)
      def repl(m):
          p = m.group(1)
          if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
              return fmt(p)
          if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
              return fmt(p)
          for root in ["aten/src", "torch/lib", ""]:
              for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
                  new_p = os.path.relpath(os.path.join(bad_root, p), root)
                  if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
                      return fmt(new_p)
          print("ERROR: ", fn, p)
          return m.group(0)
      new_c = re.sub(r'#include "([^"]+)"', repl, c)
      if new_c != c:
          print(fn)
          with open(fn, 'w') as f:
              f.write(new_c)
```

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849

Reviewed By: dzhulgakov

Differential Revision: D13363445

Pulled By: ezyang

fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
2018-12-08 19:38:30 -08:00

140 lines
3.7 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/tracing_state.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();
}
// NB: This needs to be called before `index` goes out of scope and the
// underlying object's refcount is decremented
return THPUtils_unpackLong(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;
}
inline std::complex<double> THPUtils_unpackComplexDouble(PyObject *obj) {
Py_complex value = PyComplex_AsCComplex(obj);
if (value.real == -1.0 && PyErr_Occurred()) {
throw python_error();
}
return std::complex<double>(value.real, value.imag);
}