forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Generator.cpp
279 lines (248 loc) · 9.9 KB
/
Generator.cpp
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
277
278
279
#include <torch/csrc/Generator.h>
#include <structmember.h>
#include <ATen/ATen.h>
#include <ATen/CPUGeneratorImpl.h>
#include <TH/TH.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/Device.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/utils/python_arg_parser.h>
#include <torch/csrc/autograd/generated/variable_factories.h>
#ifdef USE_CUDA
#include <ATen/CUDAGeneratorImpl.h>
#endif
using namespace at;
using namespace torch;
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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}
};
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
PyTypeObject THPGeneratorType = {
PyVarObject_HEAD_INIT(nullptr, 0)
"torch._C.Generator", /* tp_name */
sizeof(THPGenerator), /* tp_basicsize */
0, /* tp_itemsize */
THPGenerator_dealloc, /* tp_dealloc */
// NOLINTNEXTLINE(modernize-use-nullptr)
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;
}