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Generator.cpp
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Generator.cpp
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#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;
}