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Summary: Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857 These are the only hand-written parts of this diff: - the addition to `.github/workflows/lint.yml` - the file endings changed in these four files (to appease FB-internal land-blocking lints): - `GLOSSARY.md` - `aten/src/ATen/core/op_registration/README.md` - `scripts/README.md` - `torch/csrc/jit/codegen/fuser/README.md` The rest was generated by running this command (on macOS): ``` git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//' ``` I looked over the auto-generated changes and didn't see anything that looked problematic. Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406 Test Plan: This run (after adding the lint but before removing existing trailing spaces) failed: - https://github.com/pytorch/pytorch/runs/2043032377 This run (on the tip of this PR) succeeded: - https://github.com/pytorch/pytorch/runs/2043296348 Reviewed By: walterddr, seemethere Differential Revision: D26856620 Pulled By: samestep fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
273 lines
9.3 KiB
C++
273 lines
9.3 KiB
C++
#include <torch/csrc/Generator.h>
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#include <structmember.h>
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#include <ATen/ATen.h>
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#include <ATen/CPUGeneratorImpl.h>
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#include <TH/TH.h>
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#include <torch/csrc/THP.h>
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#include <torch/csrc/Device.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/python_variable.h>
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#include <torch/csrc/autograd/generated/VariableType.h>
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#include <torch/csrc/utils/tensor_types.h>
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#include <torch/csrc/utils/python_arg_parser.h>
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#include <torch/csrc/autograd/generated/variable_factories.h>
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#ifdef USE_CUDA
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#include <ATen/CUDAGeneratorImpl.h>
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#endif
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using namespace at;
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using namespace torch;
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PyObject *THPGeneratorClass = nullptr;
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PyObject * THPGenerator_initDefaultGenerator(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(PyObject* _self)
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{
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auto self = reinterpret_cast<THPGenerator*>(_self);
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if (self->cdata.defined()) {
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self->cdata.set_pyobj(nullptr);
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self->cdata.~Generator();
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}
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Py_TYPE(_self)->tp_free(_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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static torch::PythonArgParser parser({
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"Generator(Device device=None)"
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});
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torch::ParsedArgs<1> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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auto device = r.deviceWithDefault(0, at::Device(at::kCPU));
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THPGeneratorPtr self((THPGenerator *)type->tp_alloc(type, 0));
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#ifdef USE_CUDA
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if (device.type() == at::kCPU) {
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self->cdata = make_generator<CPUGeneratorImpl>();
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} else if (device.type() == at::kCUDA){
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self->cdata = make_generator<CUDAGeneratorImpl>(device.index());
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} else {
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AT_ERROR("Device type ", c10::DeviceTypeName(device.type()),
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" is not supported for torch.Generator() api.");
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}
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#else
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TORCH_CHECK(device.type() == at::kCPU,
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"Device type ", c10::DeviceTypeName(device.type()),
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" is not supported for torch.Generator() api.");
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self->cdata = make_generator<CPUGeneratorImpl>();
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#endif
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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(PyObject *_self, PyObject *noargs)
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{
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using namespace torch::autograd;
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HANDLE_TH_ERRORS
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auto& gen = ((THPGenerator*)_self)->cdata;
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// See Note [Acquire lock when using random generators]
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std::lock_guard<std::mutex> lock(gen.mutex());
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auto state_tensor = gen.get_state();
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return THPVariable_Wrap(std::move(state_tensor));
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_setState(PyObject *_self, PyObject *_new_state)
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{
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using namespace torch::autograd;
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HANDLE_TH_ERRORS
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if (!THPVariable_Check(_new_state)) {
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throw torch::TypeError("expected a torch.ByteTensor, but got %s", Py_TYPE(_new_state)->tp_name);
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}
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auto self = (THPGenerator*)_self;
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auto& gen = self->cdata;
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auto& new_state_tensor = ((THPVariable*)_new_state)->cdata;
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// See Note [Acquire lock when using random generators]
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std::lock_guard<std::mutex> lock(gen.mutex());
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gen.set_state(new_state_tensor);
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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(PyObject *_self, PyObject *seed)
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{
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HANDLE_TH_ERRORS
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auto self = (THPGenerator*)_self;
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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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// See Note [Acquire lock when using random generators]
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std::lock_guard<std::mutex> lock(generator.mutex());
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uint64_t seed_unpacked;
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try {
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// First try to interpret as unsigned long
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seed_unpacked = THPUtils_unpackUInt64(seed);
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} catch(...) {
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if (PyErr_ExceptionMatches(PyExc_OverflowError)) {
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// If an overflow happened, then the seed could be negative,
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// so try to interpret it as signed long
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PyErr_Clear();
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int64_t seed_unpacked_signed = THPUtils_unpackLong(seed);
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seed_unpacked = *(reinterpret_cast<uint64_t*>(&seed_unpacked_signed));
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} else {
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// If any other type of exception happened, rethrow it
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throw;
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}
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}
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generator.set_current_seed(seed_unpacked);
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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(PyObject *_self, PyObject *noargs)
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{
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HANDLE_TH_ERRORS
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// See Note [Acquire lock when using random generators]
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auto self = (THPGenerator*)_self;
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std::lock_guard<std::mutex> lock(self->cdata.mutex());
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uint64_t seed_val = self->cdata.seed();
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return THPUtils_packUInt64(seed_val);
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_initialSeed(PyObject *_self, PyObject *noargs)
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{
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HANDLE_TH_ERRORS
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auto self = (THPGenerator*)_self;
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return THPUtils_packUInt64(self->cdata.current_seed());
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_get_device(THPGenerator *self, void *unused) {
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HANDLE_TH_ERRORS
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return THPDevice_New(self->cdata.device());
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END_HANDLE_TH_ERRORS
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}
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static struct PyGetSetDef THPGenerator_properties[] = {
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{"device", (getter)THPGenerator_get_device, nullptr, nullptr, nullptr},
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{nullptr}
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};
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static PyMethodDef THPGenerator_methods[] = {
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{"get_state", THPGenerator_getState, METH_NOARGS, nullptr},
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{"set_state", THPGenerator_setState, METH_O, nullptr},
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{"manual_seed", THPGenerator_manualSeed, METH_O, nullptr},
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{"seed", THPGenerator_seed, METH_NOARGS, nullptr},
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{"initial_seed", THPGenerator_initialSeed, METH_NOARGS, nullptr},
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{nullptr}
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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, nullptr},
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{nullptr}
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};
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PyTypeObject THPGeneratorType = {
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PyVarObject_HEAD_INIT(nullptr, 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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THPGenerator_dealloc, /* tp_dealloc */
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0, /* tp_vectorcall_offset */
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nullptr, /* tp_getattr */
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nullptr, /* tp_setattr */
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nullptr, /* tp_reserved */
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nullptr, /* tp_repr */
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nullptr, /* tp_as_number */
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nullptr, /* tp_as_sequence */
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nullptr, /* tp_as_mapping */
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nullptr, /* tp_hash */
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nullptr, /* tp_call */
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nullptr, /* tp_str */
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nullptr, /* tp_getattro */
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nullptr, /* tp_setattro */
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nullptr, /* tp_as_buffer */
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Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
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nullptr, /* tp_doc */
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nullptr, /* tp_traverse */
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nullptr, /* tp_clear */
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nullptr, /* tp_richcompare */
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0, /* tp_weaklistoffset */
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nullptr, /* tp_iter */
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nullptr, /* tp_iternext */
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THPGenerator_methods, /* tp_methods */
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THPGenerator_members, /* tp_members */
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THPGenerator_properties, /* tp_getset */
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nullptr, /* tp_base */
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nullptr, /* tp_dict */
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nullptr, /* tp_descr_get */
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nullptr, /* tp_descr_set */
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0, /* tp_dictoffset */
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nullptr, /* tp_init */
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nullptr, /* 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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void set_pyobj(const Generator& self, PyObject* pyobj) {
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TORCH_CHECK(self.defined(), "cannot call set_pyobj() on undefined generator");
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self.set_pyobj(pyobj);
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}
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PyObject* pyobj(const Generator& self) {
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TORCH_CHECK(self.defined(), "cannot call pyobj() on undefined generator");
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return self.pyobj();
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}
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PyObject * THPGenerator_Wrap(Generator gen)
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{
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if (!gen.defined()) {
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Py_RETURN_NONE;
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}
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if (auto obj = pyobj(gen)) {
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Py_INCREF(obj);
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return obj;
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}
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return THPGenerator_NewWithVar((PyTypeObject *)THPGeneratorClass, std::move(gen));
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}
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// Creates a new Python object for a Generator. The Generator must not already
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// have a PyObject* associated with it.
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PyObject* THPGenerator_NewWithVar(PyTypeObject* type, Generator gen)
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{
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PyObject* obj = type->tp_alloc(type, 0);
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if (obj) {
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auto g = (THPGenerator*) obj;
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new (&g->cdata) Generator(std::move(gen));
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set_pyobj(g->cdata, obj);
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}
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return obj;
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}
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