mirror of
https://github.com/zebrajr/pytorch.git
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* Bind cauchy_, exponential_, normal_, uniform_ functions to THPVariable. Also changes the error messages around Generator parser; previously, you'd get an error like: torch._C.Generator is not a torch.Generator; now the check is proper but returns that only None is supported. * Support passing Generators to ATen Variable-bound methods. This involves changing THPGenerator to have an at::Generator rather than a THGenerator. TH getRNGState, setRNGState are still called directly because they are not bound from ATen yet; they should probably be on the Generators and return (opaque) GenerateState objects. * Fix default values. * Properly use THRandom_initialSeed. * update standard gamma to use new default generator.
171 lines
6.1 KiB
C++
171 lines
6.1 KiB
C++
#include <Python.h>
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#include <structmember.h>
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#include <stdbool.h>
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#include <TH/TH.h>
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#include "THP.h"
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PyObject *THPGeneratorClass = NULL;
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PyObject * THPGenerator_New()
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{
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PyObject *args = PyTuple_New(0);
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if (!args) {
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PyErr_SetString(PyExc_RuntimeError, "Could not create a new generator object - "
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"failed to allocate argument tuple");
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return NULL;
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}
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PyObject *result = PyObject_Call((PyObject*)THPGeneratorClass, args, NULL);
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Py_DECREF(args);
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return result;
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}
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PyObject * THPGenerator_NewWithGenerator(at::Generator& cdata)
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{
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auto type = (PyTypeObject*)THPGeneratorClass;
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auto self = THPObjectPtr{type->tp_alloc(type, 0)};
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if (!self) throw python_error();
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auto self_ = reinterpret_cast<THPGenerator*>(self.get());
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self_->cdata = &cdata;
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return self.release();
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}
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static void THPGenerator_dealloc(THPGenerator* self)
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{
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if (self->owner) {
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delete self->cdata;
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}
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Py_TYPE(self)->tp_free((PyObject*)self);
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}
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static PyObject * THPGenerator_pynew(PyTypeObject *type, PyObject *args, PyObject *kwargs)
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{
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HANDLE_TH_ERRORS
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if ((args && PyTuple_Size(args) != 0) || kwargs) {
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THPUtils_setError("torch.Generator constructor doesn't accept any arguments");
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return NULL;
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}
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THPGeneratorPtr self((THPGenerator *)type->tp_alloc(type, 0));
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// having to pick a specific type rather than just a backend here is strange,
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// but we don't really have fully fledged backend objects.
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self->cdata = at::CPU(at::kFloat).generator().release();
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self->owner = true;
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return (PyObject*)self.release();
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_getState(THPGenerator *self)
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{
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HANDLE_TH_ERRORS
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THGenerator *generator = THPGenerator_TH_CData(self);
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THPByteTensorPtr res((THPByteTensor *)THPByteTensor_NewEmpty());
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if (!res) return NULL;
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THByteTensor_getRNGState(generator, res->cdata);
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return (PyObject *)res.release();
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_setState(THPGenerator *self, PyObject *_new_state)
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{
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HANDLE_TH_ERRORS
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THGenerator *generator = THPGenerator_TH_CData(self);
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THPUtils_assert(THPByteTensor_Check(_new_state), "set_state expects a "
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"torch.ByteTensor, but got %s", THPUtils_typename(_new_state));
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THByteTensor *new_state = ((THPByteTensor*)_new_state)->cdata;
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THByteTensor_setRNGState(generator, new_state);
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Py_INCREF(self);
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return (PyObject*)self;
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_manualSeed(THPGenerator *self, PyObject *seed)
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{
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HANDLE_TH_ERRORS
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auto generator = self->cdata;
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THPUtils_assert(THPUtils_checkLong(seed), "manual_seed expected a long, "
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"but got %s", THPUtils_typename(seed));
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generator->manualSeed(THPUtils_unpackLong(seed));
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Py_INCREF(self);
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return (PyObject*)self;
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_seed(THPGenerator *self)
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{
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HANDLE_TH_ERRORS
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return PyLong_FromUnsignedLong(self->cdata->seed());
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_initialSeed(THPGenerator *self)
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{
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HANDLE_TH_ERRORS
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return PyLong_FromUnsignedLong(self->cdata->initialSeed());
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END_HANDLE_TH_ERRORS
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}
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static PyMethodDef THPGenerator_methods[] = {
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{"get_state", (PyCFunction)THPGenerator_getState, METH_NOARGS, NULL},
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{"set_state", (PyCFunction)THPGenerator_setState, METH_O, NULL},
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{"manual_seed", (PyCFunction)THPGenerator_manualSeed, METH_O, NULL},
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{"seed", (PyCFunction)THPGenerator_seed, METH_NOARGS, NULL},
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{"initial_seed", (PyCFunction)THPGenerator_initialSeed, METH_NOARGS, NULL},
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{NULL}
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};
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static struct PyMemberDef THPGenerator_members[] = {
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{(char*)"_cdata", T_ULONGLONG, offsetof(THPGenerator, cdata), READONLY, NULL},
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{NULL}
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};
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PyTypeObject THPGeneratorType = {
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PyVarObject_HEAD_INIT(NULL, 0)
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"torch._C.Generator", /* tp_name */
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sizeof(THPGenerator), /* tp_basicsize */
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0, /* tp_itemsize */
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(destructor)THPGenerator_dealloc, /* tp_dealloc */
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0, /* tp_print */
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0, /* tp_getattr */
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0, /* tp_setattr */
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0, /* tp_reserved */
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0, /* tp_repr */
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0, /* tp_as_number */
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0, /* tp_as_sequence */
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0, /* tp_as_mapping */
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0, /* tp_hash */
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0, /* tp_call */
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0, /* tp_str */
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0, /* tp_getattro */
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0, /* tp_setattro */
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0, /* tp_as_buffer */
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Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
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NULL, /* tp_doc */
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0, /* tp_traverse */
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0, /* tp_clear */
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0, /* tp_richcompare */
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0, /* tp_weaklistoffset */
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0, /* tp_iter */
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0, /* tp_iternext */
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THPGenerator_methods, /* tp_methods */
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THPGenerator_members, /* tp_members */
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0, /* tp_getset */
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0, /* tp_base */
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0, /* tp_dict */
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0, /* tp_descr_get */
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0, /* tp_descr_set */
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0, /* tp_dictoffset */
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0, /* tp_init */
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0, /* tp_alloc */
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THPGenerator_pynew, /* tp_new */
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};
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bool THPGenerator_init(PyObject *module)
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{
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THPGeneratorClass = (PyObject*)&THPGeneratorType;
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if (PyType_Ready(&THPGeneratorType) < 0)
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return false;
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Py_INCREF(&THPGeneratorType);
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PyModule_AddObject(module, "Generator", (PyObject *)&THPGeneratorType);
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return true;
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}
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