1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276
|
#include <torch/csrc/Generator.h>
#include <ATen/ATen.h>
#include <ATen/CPUGeneratorImpl.h>
#include <structmember.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/autograd/generated/VariableType.h>
#include <torch/csrc/autograd/generated/variable_factories.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/utils/python_arg_parser.h>
#include <torch/csrc/utils/tensor_types.h>
#ifdef USE_CUDA
#include <ATen/cuda/CUDAGeneratorImpl.h>
#endif
using namespace at;
using namespace torch;
PyObject* THPGeneratorClass = nullptr;
PyObject* THPGenerator_initDefaultGenerator(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(PyObject* _self) {
auto self = reinterpret_cast<THPGenerator*>(_self);
if (self->cdata.defined()) {
self->cdata.set_pyobj(nullptr);
self->cdata.~Generator();
}
Py_TYPE(_self)->tp_free(_self);
}
static PyObject* THPGenerator_pynew(
PyTypeObject* type,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static torch::PythonArgParser parser({"Generator(Device device=None)"});
torch::ParsedArgs<1> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
auto device = r.deviceWithDefault(0, at::Device(at::kCPU));
THPGeneratorPtr self((THPGenerator*)type->tp_alloc(type, 0));
#ifdef USE_CUDA
if (device.type() == at::kCPU) {
self->cdata = make_generator<CPUGeneratorImpl>();
} else if (device.type() == at::kCUDA) {
self->cdata = make_generator<CUDAGeneratorImpl>(device.index());
} else {
AT_ERROR(
"Device type ",
c10::DeviceTypeName(device.type()),
" is not supported for torch.Generator() api.");
}
#else
TORCH_CHECK(
device.type() == at::kCPU,
"Device type ",
c10::DeviceTypeName(device.type()),
" is not supported for torch.Generator() api.");
self->cdata = make_generator<CPUGeneratorImpl>();
#endif
return (PyObject*)self.release();
END_HANDLE_TH_ERRORS
}
static PyObject* THPGenerator_getState(PyObject* _self, PyObject* noargs) {
using namespace torch::autograd;
HANDLE_TH_ERRORS
auto& gen = ((THPGenerator*)_self)->cdata;
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(gen.mutex());
auto state_tensor = gen.get_state();
return THPVariable_Wrap(std::move(state_tensor));
END_HANDLE_TH_ERRORS
}
static PyObject* THPGenerator_setState(PyObject* _self, PyObject* _new_state) {
using namespace torch::autograd;
HANDLE_TH_ERRORS
if (!THPVariable_Check(_new_state)) {
throw torch::TypeError(
"expected a torch.ByteTensor, but got %s",
Py_TYPE(_new_state)->tp_name);
}
auto self = (THPGenerator*)_self;
auto& gen = self->cdata;
const auto& new_state_tensor = THPVariable_Unpack(_new_state);
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(gen.mutex());
gen.set_state(new_state_tensor);
Py_INCREF(self);
return (PyObject*)self;
END_HANDLE_TH_ERRORS
}
static PyObject* THPGenerator_manualSeed(PyObject* _self, PyObject* seed) {
HANDLE_TH_ERRORS
auto self = (THPGenerator*)_self;
auto generator = self->cdata;
THPUtils_assert(
THPUtils_checkLong(seed),
"manual_seed expected a long, "
"but got %s",
THPUtils_typename(seed));
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(generator.mutex());
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
uint64_t seed_unpacked;
try {
// First try to interpret as unsigned long
seed_unpacked = THPUtils_unpackUInt64(seed);
} catch (...) {
if (PyErr_ExceptionMatches(PyExc_OverflowError)) {
// If an overflow happened, then the seed could be negative,
// so try to interpret it as signed long
PyErr_Clear();
int64_t seed_unpacked_signed = THPUtils_unpackLong(seed);
seed_unpacked = *(reinterpret_cast<uint64_t*>(&seed_unpacked_signed));
} else {
// If any other type of exception happened, rethrow it
throw;
}
}
generator.set_current_seed(seed_unpacked);
Py_INCREF(self);
return (PyObject*)self;
END_HANDLE_TH_ERRORS
}
static PyObject* THPGenerator_seed(PyObject* _self, PyObject* noargs) {
HANDLE_TH_ERRORS
// See Note [Acquire lock when using random generators]
auto self = (THPGenerator*)_self;
std::lock_guard<std::mutex> lock(self->cdata.mutex());
uint64_t seed_val = self->cdata.seed();
return THPUtils_packUInt64(seed_val);
END_HANDLE_TH_ERRORS
}
static PyObject* THPGenerator_initialSeed(PyObject* _self, PyObject* noargs) {
HANDLE_TH_ERRORS
auto self = (THPGenerator*)_self;
return THPUtils_packUInt64(self->cdata.current_seed());
END_HANDLE_TH_ERRORS
}
static PyObject* THPGenerator_get_device(THPGenerator* self, void* unused) {
HANDLE_TH_ERRORS
return THPDevice_New(self->cdata.device());
END_HANDLE_TH_ERRORS
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables)
static struct PyGetSetDef THPGenerator_properties[] = {
{"device", (getter)THPGenerator_get_device, nullptr, nullptr, nullptr},
{nullptr}};
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables)
static PyMethodDef THPGenerator_methods[] = {
{"get_state", THPGenerator_getState, METH_NOARGS, nullptr},
{"set_state", THPGenerator_setState, METH_O, nullptr},
{"manual_seed", THPGenerator_manualSeed, METH_O, nullptr},
{"seed", THPGenerator_seed, METH_NOARGS, nullptr},
{"initial_seed", THPGenerator_initialSeed, METH_NOARGS, nullptr},
{nullptr}};
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables)
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 */
THPGenerator_dealloc, /* tp_dealloc */
0, /* tp_vectorcall_offset */
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 */
THPGenerator_properties, /* 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;
}
void set_pyobj(const Generator& self, PyObject* pyobj) {
TORCH_CHECK(self.defined(), "cannot call set_pyobj() on undefined generator");
self.set_pyobj(pyobj);
}
PyObject* pyobj(const Generator& self) {
TORCH_CHECK(self.defined(), "cannot call pyobj() on undefined generator");
return self.pyobj();
}
PyObject* THPGenerator_Wrap(Generator gen) {
if (!gen.defined()) {
Py_RETURN_NONE;
}
if (auto obj = pyobj(gen)) {
Py_INCREF(obj);
return obj;
}
return THPGenerator_NewWithVar(
(PyTypeObject*)THPGeneratorClass, std::move(gen));
}
// Creates a new Python object for a Generator. The Generator must not already
// have a PyObject* associated with it.
PyObject* THPGenerator_NewWithVar(PyTypeObject* type, Generator gen) {
PyObject* obj = type->tp_alloc(type, 0);
if (obj) {
auto g = (THPGenerator*)obj;
new (&g->cdata) Generator(std::move(gen));
set_pyobj(g->cdata, obj);
}
return obj;
}
|