pytorch/torch/csrc/utils.cpp

#include <torch/csrc/python_headers.h>
#include <cstdarg>
#include <string>
#include <vector>
#include <sstream>
#include <algorithm>
#include <unordered_map>
#include <torch/csrc/THP.h>
#include <torch/csrc/utils/python_strings.h>
#include <torch/csrc/utils/invalid_arguments.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/DynamicTypes.h>

#include <torch/csrc/generic/utils.cpp>
#include <TH/THGenerateAllTypes.h>

#include <torch/csrc/generic/utils.cpp>
#include <TH/THGenerateComplexTypes.h>

#include <torch/csrc/generic/utils.cpp>
#include <TH/THGenerateHalfType.h>

#include <torch/csrc/generic/utils.cpp>
#include <TH/THGenerateBFloat16Type.h>

#include <torch/csrc/WindowsTorchApiMacro.h>
#include <torch/csrc/generic/utils.cpp>
#include <TH/THGenerateBoolType.h>

int THPUtils_getCallable(PyObject *arg, PyObject **result) {
  if (!PyCallable_Check(arg))
    return 0;
  *result = arg;
  return 1;
}

THLongStoragePtr THPUtils_unpackSize(PyObject *arg) {
  THLongStoragePtr result;
  if (!THPUtils_tryUnpackLongs(arg, result)) {
    std::string msg = "THPUtils_unpackSize() expects a torch.Size (got '";
    msg += Py_TYPE(arg)->tp_name;
    msg += "')";
    throw std::runtime_error(msg);
  }
  return result;
}

bool THPUtils_tryUnpackLongs(PyObject *arg, THLongStoragePtr& result) {
  bool tuple = PyTuple_Check(arg);
  bool list = PyList_Check(arg);
  if (tuple || list) {
    int nDim = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
    THLongStoragePtr storage(THLongStorage_newWithSize(nDim));
    for (int i = 0; i != nDim; ++i) {
      PyObject* item = tuple ? PyTuple_GET_ITEM(arg, i) : PyList_GET_ITEM(arg, i);
      if (!THPUtils_checkLong(item)) {
        return false;
      }
      THLongStorage_set(storage, i, THPUtils_unpackLong(item));
    }
    result  = std::move(storage);
    return true;
  }
  return false;
}

std::vector<int64_t> THPUtils_unpackLongs(PyObject *arg) {
  bool tuple = PyTuple_Check(arg);
  bool list = PyList_Check(arg);
  if (tuple || list) {
    int nDim = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
    std::vector<int64_t> sizes(nDim);
    for (int i = 0; i != nDim; ++i) {
      PyObject* item = tuple ? PyTuple_GET_ITEM(arg, i) : PyList_GET_ITEM(arg, i);
      if (!THPUtils_checkLong(item)) {
        std::ostringstream oss;
        oss << "expected int at position " << i << ", but got: " << THPUtils_typename(item);
        throw std::runtime_error(oss.str());
      }
      sizes[i] = THPUtils_unpackLong(item);
    }
    return sizes;
  }
  throw std::runtime_error("Expected tuple or list");
}

bool THPUtils_tryUnpackLongVarArgs(PyObject *args, int ignore_first, THLongStoragePtr& result) {
  Py_ssize_t length = PyTuple_Size(args) - ignore_first;
  if (length < 1) {
    return false;
  }

  PyObject *first_arg = PyTuple_GET_ITEM(args, ignore_first);
  if (length == 1 && THPUtils_tryUnpackLongs(first_arg, result)) {
    return true;
  }

  // Try to parse the numbers
  result = THLongStorage_newWithSize(length);
  for (Py_ssize_t i = 0; i < length; ++i) {
    PyObject *arg = PyTuple_GET_ITEM(args, i + ignore_first);
    if (!THPUtils_checkLong(arg)) {
      return false;
    }
    THLongStorage_set(result, i, THPUtils_unpackLong(arg));
  }
  return true;
}

bool THPUtils_checkIntTuple(PyObject *arg)
{
  if (!PyTuple_Check(arg)) {
    return false;
  }
  for (Py_ssize_t i = 0; i < PyTuple_GET_SIZE(arg); ++i) {
    if (!THPUtils_checkLong(PyTuple_GET_ITEM(arg, i))) {
      return false;
    }
  }
  return true;
}

std::vector<int> THPUtils_unpackIntTuple(PyObject *arg)
{
  if (!THPUtils_checkIntTuple(arg)) {
    throw std::runtime_error("Couldn't unpack int tuple");
  }
  std::vector<int> values(PyTuple_GET_SIZE(arg));
  for (Py_ssize_t i = 0; i < PyTuple_GET_SIZE(arg); ++i) {
    values[i] = (int)THPUtils_unpackLong(PyTuple_GET_ITEM(arg, i));
  }
  return values;
}

void THPUtils_setError(const char *format, ...)
{
  static const size_t ERROR_BUFFER_SIZE = 1000;
  char buffer[ERROR_BUFFER_SIZE];
  va_list fmt_args;

  va_start(fmt_args, format);
  vsnprintf(buffer, ERROR_BUFFER_SIZE, format, fmt_args);
  va_end(fmt_args);
  PyErr_SetString(PyExc_RuntimeError, buffer);
}

void THPUtils_addPyMethodDefs(std::vector<PyMethodDef>& vector, PyMethodDef* methods)
{
  if (!vector.empty()) {
    // remove nullptr terminator
    vector.pop_back();
  }
  while (true) {
    vector.push_back(*methods);
    if (!methods->ml_name) {
      break;
    }
    methods++;
  }
}

static const char* classOrTypename(PyObject* obj) {
  if (PyType_Check(obj)) {
    return ((PyTypeObject*)obj)->tp_name;
  }
  return Py_TYPE(obj)->tp_name;
}

PyObject * THPUtils_dispatchStateless(
    PyObject *tensor, const char *name, PyObject *args, PyObject *kwargs)
{
  THPObjectPtr methods(PyObject_GetAttrString(tensor, THP_STATELESS_ATTRIBUTE_NAME));
  if (!methods) {
    return PyErr_Format(
        PyExc_TypeError,
        "Type %s doesn't implement stateless methods",
        classOrTypename(tensor));
  }
  THPObjectPtr method(PyObject_GetAttrString(methods, name));
  if (!method) {
    return PyErr_Format(
        PyExc_TypeError,
        "Type %s doesn't implement stateless method %s",
        classOrTypename(tensor),
        name);
  }
  return PyObject_Call(method.get(), args, kwargs);
}

void THPUtils_invalidArguments(PyObject *given_args, PyObject *given_kwargs,
        const char *function_name, size_t num_options, ...) {
  std::vector<std::string> option_strings;
  va_list option_list;
  va_start(option_list, num_options);
  for (size_t i = 0; i < num_options; i++)
    option_strings.emplace_back(va_arg(option_list, const char*));
  va_end(option_list);

  PyErr_SetString(PyExc_TypeError, torch::format_invalid_args(
      given_args, given_kwargs, function_name, option_strings).c_str());
}

template<>
void THPPointer<THPGenerator>::free() {
  if (ptr)
    Py_DECREF(ptr);
}

template class THPPointer<THPGenerator>;

static bool backCompatBroadcastWarn = false;

void setBackCompatBroadcastWarn(bool warn) {
  backCompatBroadcastWarn = warn;
}

bool getBackCompatBroadcastWarn() {
  return backCompatBroadcastWarn;
}

static bool backCompatKeepdimWarn = false;

void setBackCompatKeepdimWarn(bool warn) {
  backCompatKeepdimWarn = warn;
}

bool getBackCompatKeepdimWarn() {
  return backCompatKeepdimWarn;
}

bool maybeThrowBackCompatKeepdimWarn(char *func) {
  if(getBackCompatKeepdimWarn()) {
     std::ostringstream ss;
     ss << "backwards compatibility: call to \"" << func
        << "\" uses default value for keepdim which has changed default to False.  Consider passing as kwarg.",
    PyErr_WarnEx(PyExc_UserWarning, ss.str().c_str(), 1);
  }
  return true;
}

template<>
void THPPointer<THTensor>::free() {
  if (ptr) {
    THTensor_free(LIBRARY_STATE ptr);
  }
}

template<>
void THPPointer<THPStorage>::free() {
  if (ptr)
    Py_DECREF(ptr);
}

template class THPPointer<THPStorage>;

namespace torch { namespace gdb {
/* ~~~ misc debugging utilities ~~~
 *
 * torch::gdb::* functions are NOT meant to be called by general pytorch code,
 * but only from within a gdb session. As such, utils.h does not contain any
 * declaration for those.
 */

// This is a helper needed by the torch-tensor-repr gdb command.
// Return an human-readable representation of the given Tensor. The resulting
// string is stored into a malloc()ed buffer. The caller is responsible to
// free() it. We use malloc() instead of new[] because it's much easier to
// call free than delete[] from withing gdb.
// Currently the code for computing the repr of a tensor is written in Python,
// so we need to wrap the Tensor into a Python object first.
char *tensor_repr(at::Tensor tensor) {
  PyGILState_STATE gil = PyGILState_Ensure();
  PyObject *pytensor = NULL;
  PyObject *repr = NULL;
  Py_ssize_t bufsize;
  const char *buf = NULL;
  char *result = NULL;

  pytensor = THPVariable_Wrap(at::Tensor(tensor));
  if (!pytensor)
    goto error;
  repr = PyObject_Repr(pytensor);
  if (!repr)
    goto error;
  buf = PyUnicode_AsUTF8AndSize(repr, &bufsize);
  if (!buf)
    goto error;
  result = static_cast<char*>(malloc(bufsize + 1)); // account for the trailing \0
  if (!result) {
    fprintf(stderr, "cannot allocate memory for the result\n");
    goto error;
  }
  strcpy(result, buf);
  Py_XDECREF(pytensor);
  Py_XDECREF(repr);
  PyGILState_Release(gil);
  return result;

error:
  fprintf(stderr, "torch::gdb::tensor_repr: unexpected error\n");
  if (PyErr_Occurred())
    PyErr_Print();
  Py_XDECREF(pytensor);
  Py_XDECREF(repr);
  free(result);
  PyGILState_Release(gil);
  return NULL;
}

}} // namespace torch::gdb