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init.cpp
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#include <torch/csrc/python_headers.h>
#include <ATen/PythonTorchFunctionTLS.h>
#include <ATen/SavedTensorHooks.h>
#include <ATen/SequenceNumber.h>
#include <ATen/autocast_mode.h>
#include <ATen/core/PythonFallbackKernel.h>
#include <ATen/record_function.h>
#include <c10/core/DeviceType.h>
#include <c10/core/InferenceMode.h>
#include <c10/core/ScalarType.h>
#include <c10/core/impl/PythonDispatcherTLS.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/autograd/VariableTypeUtils.h>
#include <torch/csrc/autograd/autograd.h>
#include <torch/csrc/autograd/autograd_not_implemented_fallback.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/csrc/autograd/profiler.h>
#include <torch/csrc/autograd/profiler_python.h>
#include <torch/csrc/autograd/python_function.h>
#include <torch/csrc/autograd/python_saved_variable_hooks.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/autograd/record_function_ops.h>
#include <torch/csrc/autograd/saved_variable.h>
#include <torch/csrc/autograd/utils/python_arg_parsing.h>
#include <torch/csrc/autograd/utils/wrap_outputs.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/profiler/collection.h>
#include <torch/csrc/profiler/kineto_shim.h>
#include <torch/csrc/utils.h>
#include <torch/csrc/utils/disable_torch_function.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/pycfunction_helpers.h>
#include <torch/csrc/utils/python_raii.h>
#include <torch/csrc/utils/python_torch_function_mode.h>
#ifdef USE_KINETO
#include <libkineto.h>
#endif
#include <set>
#include <unordered_set>
#include <utility>
using torch::impl::py_context_manager;
using torch::impl::py_context_manager_DEPRECATED;
namespace {
struct DisableFuncTorch {
DisableFuncTorch()
: front_guard_(c10::DispatchKey::FuncTorchDynamicLayerFrontMode),
back_guard_(c10::DispatchKey::FuncTorchDynamicLayerBackMode) {}
c10::impl::ExcludeDispatchKeyGuard front_guard_;
c10::impl::ExcludeDispatchKeyGuard back_guard_;
};
struct DisableAutocast {
c10::impl::ExcludeDispatchKeyGuard guard_{c10::autocast_dispatch_keyset};
};
struct EnableTorchFunction {
EnableTorchFunction()
: old_(at::impl::PythonTorchFunctionTLS::get_disabled_state()) {
at::impl::PythonTorchFunctionTLS::set_disabled_state(
at::impl::TorchFunctionDisabledState::ENABLED);
}
~EnableTorchFunction() {
at::impl::PythonTorchFunctionTLS::set_disabled_state(old_);
}
at::impl::TorchFunctionDisabledState old_;
};
struct EnablePythonDispatcher {
EnablePythonDispatcher() : old_(c10::impl::PythonDispatcherTLS::get_state()) {
c10::impl::PythonDispatcherTLS::set_state(getPyInterpreter());
}
~EnablePythonDispatcher() {
c10::impl::PythonDispatcherTLS::set_state(old_);
}
c10::impl::PyInterpreter* old_;
};
struct EnablePreDispatch {
EnablePreDispatch() : guard_(c10::DispatchKey::PreDispatch) {}
c10::impl::IncludeDispatchKeyGuard guard_;
};
} // namespace
PyObject* THPAutograd_initExtension(PyObject* _unused, PyObject* unused) {
using namespace torch::autograd::profiler;
using namespace torch::profiler::impl;
auto tensor_module = THPObjectPtr(PyImport_ImportModule("torch._tensor"));
if (!tensor_module)
return nullptr;
// NOTE: "leaks" THPVariableClass
THPVariableClass = PyObject_GetAttrString(tensor_module, "Tensor");
if (!THPVariableClass)
return nullptr;
auto autograd_module = THPObjectPtr(PyImport_ImportModule("torch.autograd"));
if (!autograd_module)
return nullptr;
// NOTE: "leaks" Function
THPFunctionClass = PyObject_GetAttrString(autograd_module, "Function");
if (!THPFunctionClass)
return nullptr;
// NOTE: "leaks" GradientEdge
auto autograd_graph_mod =
THPObjectPtr(PyImport_ImportModule("torch.autograd.graph"));
THPGradientEdgeClass =
PyObject_GetAttrString(autograd_graph_mod, "GradientEdge");
if (!THPGradientEdgeClass)
return nullptr;
auto torch_C_module = THPObjectPtr(PyImport_ImportModule("torch._C"));
if (!torch_C_module)
return nullptr;
auto _C_m = py::handle(torch_C_module).cast<py::module>();
auto m = _C_m.def_submodule("_autograd", "autograd bindings");
auto parameter_module =
THPObjectPtr(PyImport_ImportModule("torch.nn.parameter"));
if (!parameter_module)
return nullptr;
// NOTE: "leaks" ParameterClass
ParameterClass = PyObject_GetAttrString(parameter_module, "Parameter");
if (!ParameterClass)
return nullptr;
#if defined(USE_KINETO) && defined(__linux__) && !defined(USE_ROCM)
// Initialize the Kineto profilers, if they have not already.
// DO NOT REMOVE, this is needed for on-demand profiling.
if (!libkineto::api().isProfilerRegistered()) {
libkineto_init(
/*cpuOnly=*/!(at::hasCUDA() || at::hasXPU() || at::hasMTIA()),
/*logOnError=*/true);
libkineto::api().suppressLogMessages();
}
libkineto::api().initProfilerIfRegistered();
// Used for unit test to check profiler was initialized.
m.def("_isProfilerInitialized", []() {
return libkineto::api().isProfilerInitialized();
});
#endif
m.def("_is_use_kineto_defined", []() -> bool {
#ifdef USE_KINETO
return true;
#endif
return false;
});
py::class_<LegacyEvent>(m, "ProfilerEvent")
.def("kind", &LegacyEvent::kindStr)
.def("name", [](const LegacyEvent& e) { return e.name(); })
.def("thread_id", &LegacyEvent::threadId)
.def("fwd_thread_id", &LegacyEvent::fwdThreadId)
.def("device", &LegacyEvent::device)
.def("cpu_elapsed_us", &LegacyEvent::cpuElapsedUs)
.def("cuda_elapsed_us", &LegacyEvent::cudaElapsedUs)
.def("has_cuda", &LegacyEvent::hasCuda)
.def("shapes", &LegacyEvent::shapes)
.def("cpu_memory_usage", &LegacyEvent::cpuMemoryUsage)
.def("cuda_memory_usage", &LegacyEvent::cudaMemoryUsage)
.def("handle", &LegacyEvent::handle)
.def("node_id", &LegacyEvent::nodeId)
.def("is_remote", &LegacyEvent::isRemote)
.def("sequence_nr", &LegacyEvent::sequenceNr)
.def("stack", &LegacyEvent::stack)
.def("scope", &LegacyEvent::scope)
.def("correlation_id", &LegacyEvent::correlationId)
.def("start_us", &LegacyEvent::cpuUs)
.def("flops", &LegacyEvent::flops)
.def("is_async", &LegacyEvent::isAsync);
py::enum_<c10::DeviceType>(m, "DeviceType")
.value("CPU", c10::DeviceType::CPU)
.value("CUDA", c10::DeviceType::CUDA)
.value("MKLDNN", c10::DeviceType::MKLDNN)
.value("OPENGL", c10::DeviceType::OPENGL)
.value("OPENCL", c10::DeviceType::OPENCL)
.value("IDEEP", c10::DeviceType::IDEEP)
.value("HIP", c10::DeviceType::HIP)
.value("FPGA", c10::DeviceType::FPGA)
.value("ORT", c10::DeviceType::ORT)
.value("XLA", c10::DeviceType::XLA)
.value("Vulkan", c10::DeviceType::Vulkan)
.value("Metal", c10::DeviceType::Metal)
.value("XPU", c10::DeviceType::XPU)
.value("MPS", c10::DeviceType::MPS)
.value("MTIA", c10::DeviceType::MTIA)
.value("Meta", c10::DeviceType::Meta)
.value("HPU", c10::DeviceType::HPU)
.value("VE", c10::DeviceType::VE)
.value("Lazy", c10::DeviceType::Lazy)
.value("IPU", c10::DeviceType::IPU)
.value("PrivateUse1", c10::DeviceType::PrivateUse1);
using torch::autograd::CreationMeta;
py::enum_<CreationMeta>(m, "CreationMeta")
.value("DEFAULT", CreationMeta::DEFAULT)
.value("IN_CUSTOM_FUNCTION", CreationMeta::IN_CUSTOM_FUNCTION)
.value("MULTI_OUTPUT_NODE", CreationMeta::MULTI_OUTPUT_NODE)
.value("NO_GRAD_MODE", CreationMeta::NO_GRAD_MODE)
.value("INFERENCE_MODE", CreationMeta::INFERENCE_MODE);
py::class_<KinetoEvent>(m, "_KinetoEvent")
// name of the event
.def("name", [](const KinetoEvent& e) { return e.name(); })
// PyTorch thread id of the start callback
.def(
"start_thread_id",
[](const KinetoEvent& e) { return e.startThreadId(); })
// PyTorch thread id of the end callback
.def(
"end_thread_id", [](const KinetoEvent& e) { return e.endThreadId(); })
// for events of scope BACKWARD_FUNCTION - PyTorch thread id
// of the corresponding forward op
.def(
"fwd_thread_id", [](const KinetoEvent& e) { return e.fwdThreadId(); })
// together with fwd_thread_id, used to uniquely identify
// the forward op
.def("sequence_nr", [](const KinetoEvent& e) { return e.sequenceNr(); })
// absolute start time (since unix epoch) in us
.def("start_us", [](const KinetoEvent& e) { return e.startUs(); })
// duration in us
.def("duration_us", [](const KinetoEvent& e) { return e.durationUs(); })
// used for correlation between high-level PyTorch events
// and low-level device events
.def(
"correlation_id",
[](const KinetoEvent& e) { return e.correlationId(); })
// shapes of input tensors
.def("shapes", [](const KinetoEvent& e) { return e.shapes().vec(); })
.def("dtypes", [](const KinetoEvent& e) { return e.dtypes().vec(); })
.def(
"concrete_inputs",
[](const KinetoEvent& e) {
std::vector<py::object> as_pyobj;
std::transform(
e.concreteInputs().begin(),
e.concreteInputs().end(),
std::back_inserter(as_pyobj),
[](const c10::IValue& val) {
return torch::jit::toPyObject(val);
});
return as_pyobj;
})
// stack traces of the PyTorch CPU events
.def("stack", [](const KinetoEvent& e) { return e.stack().vec(); })
// type of the RecordFunction that generated a PyTorch CPU event
// (op, torchscript function, user label, etc)
.def("scope", [](const KinetoEvent& e) { return e.scope(); })
// device number, for CPU - process id
.def("device_index", [](const KinetoEvent& e) { return e.deviceIndex(); })
// for CUDA - stream id, for CPU - start thread id
.def(
"device_resource_id",
[](const KinetoEvent& e) { return e.deviceResourceId(); })
// device type
.def("device_type", [](const KinetoEvent& e) { return e.deviceType(); })
// correlation id of a linked event
.def(
"linked_correlation_id",
[](const KinetoEvent& e) { return e.linkedCorrelationId(); })
// compute flops
.def("flops", [](const KinetoEvent& e) { return e.flops(); })
// Whether this is async event or not
.def("is_async", [](const KinetoEvent& e) { return e.isAsync(); })
.def("cuda_elapsed_us", &KinetoEvent::cudaElapsedUs)
.def("privateuse1_elapsed_us", &KinetoEvent::privateuse1ElapsedUs)
.def("nbytes", [](const KinetoEvent& e) { return e.nBytes(); });
m.def("_soft_assert_raises", &setSoftAssertRaises);
m.def("_get_sequence_nr", &at::sequence_number::peek);
py::class_<ProfilerResult>(m, "_ProfilerResult")
.def("trace_start_us", &ProfilerResult::trace_start_us)
.def("events", &ProfilerResult::events)
.def("experimental_event_tree", &ProfilerResult::event_tree)
#ifdef USE_KINETO
.def("save", &ProfilerResult::save)
#endif // USE_KINETO
;
m.def(
"_enable_profiler",
&enableProfiler,
py::arg("config"),
py::arg("activities"),
py::arg("scopes") = std::unordered_set<at::RecordScope>());
m.def("_disable_profiler", disableProfiler);
m.def("_prepare_profiler", prepareProfiler);
m.def("_add_metadata_json", addMetadataJson); // Only if `USE_KINETO` is set
m.def("_kineto_step", profilerStep); // Only if `USE_KINETO` is set
m.def("kineto_available", []() { return torch::profiler::kKinetoAvailable; });
// NOTICE: These record functions are not torch operators and may not show up
// in TorchScript tracing, FX transforms, or operator serialization. For these
// use cases, please use `torch.profiler.record_function`.
// Creates a new profiling scope using RecordFunction and invokes its starting
// callbacks.
m.def(
"_record_function_with_args_enter",
[](const std::string& name, const py::args& args) {
using torch::autograd::profiler::PythonRecordFunction;
auto python_rec = c10::make_intrusive<PythonRecordFunction>(
at::RecordScope::USER_SCOPE);
auto* rec = &python_rec->record;
if (rec->isActive()) {
if (rec->needsInputs()) {
auto iv_inputs = std::vector<c10::IValue>();
for (const auto& arg : args) {
iv_inputs.push_back(torch::jit::toTypeInferredIValue(arg));
}
rec->before(
name,
c10::ArrayRef<const c10::IValue>(
iv_inputs.data(), iv_inputs.size()));
} else {
rec->before(name);
}
}
return torch::jit::toPyObject(std::move(python_rec));
});
// Ends the profiling scope created with record_function_with_param_enter.
m.def("_record_function_with_args_exit", [](const py::object& obj) {
using torch::autograd::profiler::PythonRecordFunction;
auto python_record = torch::jit::toCustomClass<PythonRecordFunction>(obj);
// We don't actually need to do anything with handle just need to persist
// the lifetime until now.
python_record->record.end();
});
m.def("_supported_activities", []() {
std::set<torch::profiler::impl::ActivityType> activities{
torch::profiler::impl::ActivityType::CPU};
#if defined(USE_KINETO) && \
(!defined(LIBKINETO_NOCUPTI) || !defined(LIBKINETO_NOROCTRACER))
if (at::getNumGPUs() > 0) {
activities.insert(torch::profiler::impl::ActivityType::CUDA);
}
#elif defined(USE_KINETO)
if (at::hasXPU()) {
activities.insert(torch::profiler::impl::ActivityType::XPU);
}
if (at::hasMTIA()) {
activities.insert(torch::profiler::impl::ActivityType::MTIA);
}
#endif
return activities;
});
m.def("_unsafe_set_version_counter", [](const at::Tensor& t, int64_t i) {
auto vc = torch::autograd::impl::version_counter(t);
vc.set_version(i);
});
m.def("_enable_profiler_legacy", enableProfilerLegacy);
py::class_<ProfilerDisableOptions>(m, "_ProfilerDisableOptions")
.def(py::init<bool, bool>());
m.def(
"_disable_profiler_legacy",
disableProfilerLegacy,
py::arg("profiler_disable_options") = ProfilerDisableOptions());
m.def("_profiler_enabled", profilerEnabled);
m.def("_profiler_type", torch::profiler::impl::profilerType);
m.def("_enable_record_function", [](bool enable) {
at::enableRecordFunction(enable);
});
m.def("_set_empty_test_observer", [](bool is_global, double sampling_prob) {
auto cb =
at::RecordFunctionCallback(nullptr).needsInputs(true).samplingProb(
sampling_prob);
if (is_global) {
at::addGlobalCallback(cb);
} else {
at::addThreadLocalCallback(cb);
}
});
m.def("_clear_callbacks", []() { at::clearCallbacks(); });
m.def(
"_saved_tensors_hooks_is_enabled",
at::SavedTensorDefaultHooks::is_enabled);
m.def("_saved_tensors_hooks_enable", at::SavedTensorDefaultHooks::enable);
m.def("_saved_tensors_hooks_disable", at::SavedTensorDefaultHooks::disable);
m.def(
"_saved_tensors_hooks_get_disabled_error_message",
at::SavedTensorDefaultHooks::get_disabled_error_message);
m.def(
"_push_saved_tensors_default_hooks",
[](py::function& pack_hook, py::function& unpack_hook) {
torch::autograd::PyDefaultSavedVariableHooks::push_hooks(
pack_hook, unpack_hook);
});
m.def("_pop_saved_tensors_default_hooks", []() {
torch::autograd::PyDefaultSavedVariableHooks::pop_hooks();
});
m.def("_get_creation_meta", [](const at::Tensor& t) {
auto* meta = torch::autograd::impl::get_view_autograd_meta(t);
TORCH_CHECK(meta != nullptr);
return meta->get_creation_meta();
});
m.def(
"_set_creation_meta",
[](const at::Tensor& t, CreationMeta new_creation_meta) {
auto* meta = torch::autograd::impl::get_view_autograd_meta(t);
TORCH_CHECK(meta != nullptr);
meta->set_creation_meta(new_creation_meta);
});
_C_m.def(
"_register_py_class_for_device",
[](const std::string& device, py::object python_type_class) {
auto cls = python_type_class.ptr();
registerPythonTensorClass(device, cls);
});
_C_m.def("_set_autograd_fallback_mode", [](const std::string& mode) {
if (mode == "nothing") {
torch::autograd::setAutogradFallbackMode(
torch::autograd::AutogradFallbackMode::Nothing);
return;
}
if (mode == "warn") {
torch::autograd::setAutogradFallbackMode(
torch::autograd::AutogradFallbackMode::Warn);
return;
}
if (mode == "error") {
torch::autograd::setAutogradFallbackMode(
torch::autograd::AutogradFallbackMode::Error);
return;
}
TORCH_INTERNAL_ASSERT(false, "Unsupported AutogradFallbackMode: ", mode);
});
_C_m.def("_get_autograd_fallback_mode", []() {
auto mode = torch::autograd::getAutogradFallbackMode();
switch (mode) {
case torch::autograd::AutogradFallbackMode::Nothing:
return "nothing";
case torch::autograd::AutogradFallbackMode::Warn:
return "warn";
case torch::autograd::AutogradFallbackMode::Error:
return "error";
default:
TORCH_INTERNAL_ASSERT(false, "Unsupported AutogradFallbackMode");
}
});
_C_m.def("_activate_cuda_trace", []() { activateCUDATrace(); });
py_context_manager_DEPRECATED<c10::InferenceMode, bool>(
_C_m, "_InferenceMode");
py_context_manager<at::impl::RestorePythonTLSSnapshot>(
_C_m, "_RestorePythonTLSSnapshot");
py_context_manager_DEPRECATED<torch::DisableTorchDispatch>(
_C_m, "_DisableTorchDispatch");
py_context_manager_DEPRECATED<EnableTorchFunction>(
_C_m, "_EnableTorchFunction");
py_context_manager_DEPRECATED<EnablePythonDispatcher>(
_C_m, "_EnablePythonDispatcher");
py_context_manager<c10::impl::DisablePythonDispatcher>(
_C_m, "_DisablePythonDispatcher");
py_context_manager<EnablePreDispatch>(_C_m, "_EnablePreDispatch");
py_context_manager_DEPRECATED<DisableFuncTorch>(_C_m, "_DisableFuncTorch");
py_context_manager<DisableAutocast>(_C_m, "_DisableAutocast");
py::class_<torch::autograd::SavedVariable>(std::move(m), "SavedTensor")
.def(py::init([]() -> torch::autograd::SavedVariable {
TORCH_CHECK(
false,
"Trying to create a SavedTensor object from Python is forbidden.");
}))
.def(
"register_hooks",
[](torch::autograd::SavedVariable& s,
py::function& pack_hook,
py::function& unpack_hook) {
// Because we use a py::object, pybind will increment the refcount
// of the hook functions for us
s.register_hooks(
std::make_unique<torch::autograd::PySavedVariableHooks>(
pack_hook, unpack_hook));
});
torch::autograd::profiler::python_tracer::init();
Py_RETURN_TRUE;
}
namespace torch {
namespace autograd {
static PyObject* set_autocast_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!PyBool_Check(arg)) {
throw TypeError("enabled must be a bool (got %s)", Py_TYPE(arg)->tp_name);
}
at::autocast::set_enabled(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_autocast_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (at::autocast::is_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* is_any_autocast_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (at::autocast::is_enabled() || at::autocast::is_cpu_enabled() ||
at::autocast::is_xpu_enabled() || at::autocast::is_ipu_enabled() ||
at::autocast::is_xla_enabled() || at::autocast::is_hpu_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_cpu_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!PyBool_Check(arg)) {
throw TypeError("enabled must be a bool (got %s)", Py_TYPE(arg)->tp_name);
}
at::autocast::set_cpu_enabled(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_autocast_cpu_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (at::autocast::is_cpu_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_ipu_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!PyBool_Check(arg)) {
throw TypeError("enabled must be a bool (got %s)", Py_TYPE(arg)->tp_name);
}
at::autocast::set_ipu_enabled(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_autocast_ipu_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (at::autocast::is_ipu_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_xla_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!PyBool_Check(arg)) {
throw TypeError("enabled must be a bool (got %s)", Py_TYPE(arg)->tp_name);
}
at::autocast::set_xla_enabled(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_autocast_xla_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (at::autocast::is_xla_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_gpu_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!THPDtype_Check(arg)) {
throw TypeError(
"dtype must be a torch.dtype (got %s)", Py_TYPE(arg)->tp_name);
}
at::ScalarType targetType = reinterpret_cast<THPDtype*>(arg)->scalar_type;
at::autocast::set_autocast_gpu_dtype(targetType);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_cpu_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!THPDtype_Check(arg)) {
throw TypeError(
"dtype must be a torch.dtype (got %s)", Py_TYPE(arg)->tp_name);
}
at::ScalarType targetType = reinterpret_cast<THPDtype*>(arg)->scalar_type;
at::autocast::set_autocast_cpu_dtype(targetType);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_ipu_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!THPDtype_Check(arg)) {
throw TypeError(
"dtype must be a torch.dtype (got %s)", Py_TYPE(arg)->tp_name);
}
at::ScalarType targetType = reinterpret_cast<THPDtype*>(arg)->scalar_type;
at::autocast::set_autocast_ipu_dtype(targetType);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_xla_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!THPDtype_Check(arg)) {
throw TypeError(
"dtype must be a torch.dtype (got %s)", Py_TYPE(arg)->tp_name);
}
at::ScalarType targetType = reinterpret_cast<THPDtype*>(arg)->scalar_type;
at::autocast::set_autocast_xla_dtype(targetType);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* get_autocast_gpu_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
at::ScalarType current_dtype = at::autocast::get_autocast_gpu_dtype();
auto dtype = (PyObject*)torch::getTHPDtype(current_dtype);
Py_INCREF(dtype);
return dtype;
END_HANDLE_TH_ERRORS
}
static PyObject* get_autocast_cpu_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
at::ScalarType current_dtype = at::autocast::get_autocast_cpu_dtype();
auto dtype = (PyObject*)torch::getTHPDtype(current_dtype);
Py_INCREF(dtype);
return dtype;
END_HANDLE_TH_ERRORS
}
static PyObject* get_autocast_ipu_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
at::ScalarType current_dtype = at::autocast::get_autocast_ipu_dtype();
auto dtype = (PyObject*)torch::getTHPDtype(current_dtype);
Py_INCREF(dtype);
return dtype;
END_HANDLE_TH_ERRORS
}
static PyObject* get_autocast_xla_dtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
at::ScalarType current_dtype = at::autocast::get_autocast_xla_dtype();
auto dtype = (PyObject*)torch::getTHPDtype(current_dtype);
Py_INCREF(dtype);
return dtype;
END_HANDLE_TH_ERRORS
}
static PyObject* clear_autocast_cache(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
at::autocast::clear_cache();
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* autocast_increment_nesting(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
return THPUtils_packInt64(at::autocast::increment_nesting());
END_HANDLE_TH_ERRORS
}
static PyObject* autocast_decrement_nesting(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
return THPUtils_packInt64(at::autocast::decrement_nesting());
END_HANDLE_TH_ERRORS
}
static PyObject* is_autocast_cache_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (at::autocast::is_autocast_cache_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_autocast_cache_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!PyBool_Check(arg)) {
throw TypeError("enabled must be a bool (got %s)", Py_TYPE(arg)->tp_name);
}
at::autocast::set_autocast_cache_enabled(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* set_grad_enabled(
PyObject* _unused,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static PythonArgParser parser({
"set_grad_enabled(bool enabled)",
});
ParsedArgs<1> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
if (at::impl::torch_function_mode_enabled()) {
auto torch_C_module = THPObjectPtr(PyImport_ImportModule("torch._C"));
return handle_torch_function(
r, args, kwargs, torch_C_module, "torch._C", "_set_grad_enabled");
}
auto grad_enabled = r.toBool(0);
GradMode::set_enabled(grad_enabled);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_grad_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (GradMode::is_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_fwd_grad_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (!PyBool_Check(arg)) {
throw TypeError("enabled must be a bool (got %s)", Py_TYPE(arg)->tp_name);
}
c10::AutogradState::get_tls_state().set_fw_grad_mode(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_fwd_grad_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (c10::AutogradState::get_tls_state().get_fw_grad_mode()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_multithreading_enabled(
PyObject* self,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static PythonArgParser parser({
"set_multithreading_enabled(bool enabled)",
});
ParsedArgs<1> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
if (at::impl::torch_function_mode_enabled()) {
auto torch_C_module = THPObjectPtr(PyImport_ImportModule("torch._C"));
return handle_torch_function(
r,
args,
kwargs,
torch_C_module,
"torch._C",
"_set_multithreading_enabled");
}
auto multithreading_enabled = r.toBool(0);
c10::AutogradState::get_tls_state().set_multithreading_enabled(
multithreading_enabled);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_multithreading_enabled(PyObject* self, PyObject* args) {
HANDLE_TH_ERRORS
if (c10::AutogradState::get_tls_state().get_multithreading_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_view_replay_enabled(
PyObject* self,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static PythonArgParser parser({
"set_view_replay_enabled(bool enabled)",
});
ParsedArgs<1> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
if (at::impl::torch_function_mode_enabled()) {
auto torch_C_module = THPObjectPtr(PyImport_ImportModule("torch._C"));
return handle_torch_function(
r,
args,
kwargs,
torch_C_module,
"torch._C",
"_set_view_replay_enabled");
}
auto view_replay_enabled = r.toBool(0);
c10::AutogradState::get_tls_state().set_view_replay_enabled(
view_replay_enabled);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_view_replay_enabled(PyObject* self, PyObject* args) {
HANDLE_TH_ERRORS
if (c10::AutogradState::get_tls_state().get_view_replay_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* is_inference_mode_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (c10::InferenceMode::is_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* set_anomaly_mode_enabled(
PyObject* _unused,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static PythonArgParser parser({
"set_anomaly_enabled(bool enabled, bool check_nan=True)",
});
ParsedArgs<2> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
AnomalyMode::set_enabled(r.toBool(0), r.toBool(1));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_anomaly_mode_enabled(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
if (AnomalyMode::is_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* is_anomaly_check_nan_enabled(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
if (AnomalyMode::should_check_nan()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* python_enter_dual_level(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
// It is unlikely that the depth of forward nesting will overflow int64_t so
// we just static cast here.
return utils::wrap(static_cast<int64_t>(forward_ad::enter_dual_level()));
END_HANDLE_TH_ERRORS
}
static PyObject* python_exit_dual_level(
PyObject* _unused,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static PythonArgParser parser({"exit_dual_level(int64_t level)"});
ParsedArgs<1> parsed_args;
auto _r = parser.parse(args, kwargs, parsed_args);
auto idx = _r.toInt64(0);
// Make sure the given index is valid before casting it
TORCH_CHECK(idx >= 0, "Dual level must be a positive number.");
forward_ad::exit_dual_level(static_cast<uint64_t>(idx));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* is_torch_function_mode_enabled(
PyObject* _unused,
PyObject* _unused2) {
HANDLE_TH_ERRORS
if (at::impl::torch_function_mode_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* push_on_torch_function_stack(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
if (arg != Py_None) {
Py_INCREF(arg);
at::impl::PythonTorchFunctionTLS::push_onto_stack(
std::make_shared<c10::SafePyObject>(arg, getPyInterpreter()));
}
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* pop_torch_function_stack(
PyObject* _unused,
PyObject* _unused2) {
HANDLE_TH_ERRORS
const auto& mode = at::impl::PythonTorchFunctionTLS::pop_stack();
auto* r = mode->ptr(getPyInterpreter());
Py_INCREF(r);
return r;
END_HANDLE_TH_ERRORS
}
static PyObject* get_function_stack_at(
PyObject* _unused,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static PythonArgParser parser({"get_stack_at(int64_t level)"});
ParsedArgs<1> parsed_args;
auto _r = parser.parse(args, kwargs, parsed_args);
auto idx = _r.toInt64(0);
const auto& mode = at::impl::PythonTorchFunctionTLS::get_stack_at(idx);
auto* r = mode->ptr(getPyInterpreter());
Py_INCREF(r);
return r;
END_HANDLE_TH_ERRORS
}
static PyObject* len_torch_function_stack(
PyObject* _unused,
PyObject* _unused2) {
HANDLE_TH_ERRORS
const auto len = at::impl::PythonTorchFunctionTLS::stack_len();
return utils::wrap(static_cast<int64_t>(len));
END_HANDLE_TH_ERRORS
}
static PyObject* push_on_torch_dispatch_stack(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
if (arg != Py_None) {
using c10::impl::TorchDispatchModeKey;
// When we push a mode onto the mode stack, we need to
// check if it's an "infra" mode, by checking its _mode_key attribute.
c10::optional<c10::impl::TorchDispatchModeKey> mode_key = c10::nullopt;
py::object maybe_mode_key_obj =
PyObject_FastGetAttrString(arg, "_mode_key");
if (maybe_mode_key_obj) {
mode_key = py::cast<c10::impl::TorchDispatchModeKey>(maybe_mode_key_obj);
c10::impl::TorchDispatchModeTLS::set_mode(
std::make_shared<c10::SafePyObject>(arg, getPyInterpreter()),
mode_key.value());
} else {