pytorch/torch/csrc/Generator.cpp
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

186 lines
6.9 KiB
C++

#include <torch/csrc/Generator.h>
#include <structmember.h>
#include <ATen/ATen.h>
#include <TH/TH.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/autograd/generated/VariableType.h>
#include <torch/csrc/utils/tensor_types.h>
#include <torch/csrc/autograd/generated/variable_factories.h>
using namespace at;
using namespace torch;
PyObject *THPGeneratorClass = nullptr;
PyObject * THPGenerator_New()
{
PyObject *args = PyTuple_New(0);
if (!args) {
PyErr_SetString(PyExc_RuntimeError, "Could not create a new generator object - "
"failed to allocate argument tuple");
return nullptr;
}
PyObject *result = PyObject_Call((PyObject*)THPGeneratorClass, args, nullptr);
Py_DECREF(args);
return result;
}
PyObject * THPGenerator_NewWithGenerator(at::Generator& cdata)
{
auto type = (PyTypeObject*)THPGeneratorClass;
auto self = THPObjectPtr{type->tp_alloc(type, 0)};
if (!self) throw python_error();
auto self_ = reinterpret_cast<THPGenerator*>(self.get());
self_->cdata = &cdata;
return self.release();
}
static void THPGenerator_dealloc(THPGenerator* self)
{
if (self->owner) {
delete self->cdata;
}
Py_TYPE(self)->tp_free((PyObject*)self);
}
static PyObject * THPGenerator_pynew(PyTypeObject *type, PyObject *args, PyObject *kwargs)
{
HANDLE_TH_ERRORS
if ((args && PyTuple_Size(args) != 0) || kwargs) {
THPUtils_setError("torch.Generator constructor doesn't accept any arguments");
return nullptr;
}
THPGeneratorPtr self((THPGenerator *)type->tp_alloc(type, 0));
// having to pick a specific type rather than just a backend here is strange,
// but we don't really have fully fledged backend objects.
self->cdata = at::CPU(at::kFloat).generator().release();
self->owner = true;
return (PyObject*)self.release();
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_getState(THPGenerator *self)
{
using namespace torch::autograd;
HANDLE_TH_ERRORS
THGenerator *generator = THPGenerator_TH_CData(self);
Variable var = torch::empty({0}, at::device(at::kCPU).dtype(at::kByte));
THByteTensor_getRNGState(generator, (THByteTensor*)(var.data().unsafeGetTensorImpl()));
return THPVariable_Wrap(std::move(var));
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_setState(THPGenerator *self, PyObject *_new_state)
{
using namespace torch::autograd;
HANDLE_TH_ERRORS
if (!THPVariable_Check(_new_state)) {
throw TypeError("expected a torch.ByteTensor, but got %s", Py_TYPE(_new_state)->tp_name);
}
auto& tensor = ((THPVariable*)_new_state)->cdata.data();
if (tensor.type() != CPU(kByte)) {
auto type_name = torch::utils::type_to_string(tensor.type());
throw TypeError("expected a torch.ByteTensor, but got %s", type_name.c_str());
}
THGenerator *generator = THPGenerator_TH_CData(self);
THByteTensor_setRNGState(generator, (THByteTensor*)tensor.unsafeGetTensorImpl());
Py_INCREF(self);
return (PyObject*)self;
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_manualSeed(THPGenerator *self, PyObject *seed)
{
HANDLE_TH_ERRORS
auto generator = self->cdata;
THPUtils_assert(THPUtils_checkLong(seed), "manual_seed expected a long, "
"but got %s", THPUtils_typename(seed));
generator->manualSeed(THPUtils_unpackLong(seed));
Py_INCREF(self);
return (PyObject*)self;
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_seed(THPGenerator *self)
{
HANDLE_TH_ERRORS
return THPUtils_packUInt64(self->cdata->seed());
END_HANDLE_TH_ERRORS
}
static PyObject * THPGenerator_initialSeed(THPGenerator *self)
{
HANDLE_TH_ERRORS
return THPUtils_packUInt64(self->cdata->initialSeed());
END_HANDLE_TH_ERRORS
}
static PyMethodDef THPGenerator_methods[] = {
{"get_state", (PyCFunction)THPGenerator_getState, METH_NOARGS, nullptr},
{"set_state", (PyCFunction)THPGenerator_setState, METH_O, nullptr},
{"manual_seed", (PyCFunction)THPGenerator_manualSeed, METH_O, nullptr},
{"seed", (PyCFunction)THPGenerator_seed, METH_NOARGS, nullptr},
{"initial_seed", (PyCFunction)THPGenerator_initialSeed, METH_NOARGS, nullptr},
{nullptr}
};
static struct PyMemberDef THPGenerator_members[] = {
{(char*)"_cdata", T_ULONGLONG, offsetof(THPGenerator, cdata), READONLY, nullptr},
{nullptr}
};
PyTypeObject THPGeneratorType = {
PyVarObject_HEAD_INIT(nullptr, 0)
"torch._C.Generator", /* tp_name */
sizeof(THPGenerator), /* tp_basicsize */
0, /* tp_itemsize */
(destructor)THPGenerator_dealloc, /* tp_dealloc */
nullptr, /* tp_print */
nullptr, /* tp_getattr */
nullptr, /* tp_setattr */
nullptr, /* tp_reserved */
nullptr, /* tp_repr */
nullptr, /* tp_as_number */
nullptr, /* tp_as_sequence */
nullptr, /* tp_as_mapping */
nullptr, /* tp_hash */
nullptr, /* tp_call */
nullptr, /* tp_str */
nullptr, /* tp_getattro */
nullptr, /* tp_setattro */
nullptr, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
nullptr, /* tp_doc */
nullptr, /* tp_traverse */
nullptr, /* tp_clear */
nullptr, /* tp_richcompare */
0, /* tp_weaklistoffset */
nullptr, /* tp_iter */
nullptr, /* tp_iternext */
THPGenerator_methods, /* tp_methods */
THPGenerator_members, /* tp_members */
nullptr, /* tp_getset */
nullptr, /* tp_base */
nullptr, /* tp_dict */
nullptr, /* tp_descr_get */
nullptr, /* tp_descr_set */
0, /* tp_dictoffset */
nullptr, /* tp_init */
nullptr, /* tp_alloc */
THPGenerator_pynew, /* tp_new */
};
bool THPGenerator_init(PyObject *module)
{
THPGeneratorClass = (PyObject*)&THPGeneratorType;
if (PyType_Ready(&THPGeneratorType) < 0)
return false;
Py_INCREF(&THPGeneratorType);
PyModule_AddObject(module, "Generator", (PyObject *)&THPGeneratorType);
return true;
}