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#include <ATen/core/PythonFallbackKernel.h>
#include <ATen/core/PythonOpRegistrationTrampoline.h>
#include <torch/csrc/PyInterpreter.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/autograd/generated/VariableType.h>
#include <torch/csrc/utils/python_arg_parser.h>
#include <torch/csrc/utils/python_dispatch.h>
#include <string>
using namespace torch;
using namespace at;
using namespace c10;
namespace torch::detail {
namespace {
// NB: This is a macro and not a template function (like it was before)
// because passing in constexpr char* as template argument breaks some
// versions of MSVC that are being used internally at Meta.
// MSVC 14.16.27023 (vs2017_15.9)
#define CONCRETE_GPU_TRACE(device_type, func_name, ...) \
at::impl::MaybeSetTLSOnEntryGuard guard; \
if (Py_IsInitialized()) { \
pybind11::gil_scoped_acquire gil; \
try { \
/* Masquerade hip as cuda because hip uses `torch.cuda` module. */ \
if (device_type == at::kHIP) { \
device_type = at::kCUDA; \
} \
std::string module_name = "torch." + DeviceTypeName(device_type, true); \
py::module mod = py::module::import(module_name.c_str()); \
py::object hook = \
mod.attr("_gpu_trace").attr(func_name).attr("fire_callbacks"); \
hook(__VA_ARGS__); \
} catch (const std::exception& e) { \
LOG(ERROR) << device_type \
<< " trace hook execution failed: " << e.what(); \
} \
}
struct ConcretePyInterpreterVTable final
: public c10::impl::PyInterpreterVTable {
std::string name() const override;
void incref(PyObject* pyobj) const override;
void decref(PyObject* pyobj, bool has_pyobj_slot) const override;
// TODO: Need to make this work for StorageImpl too. I imagine I'll want to
// operate upon a PyObjectSlot rather than a TensorImpl
c10::intrusive_ptr<c10::TensorImpl> detach(
const c10::TensorImpl* self) const override;
void dispatch(const c10::OperatorHandle& op, torch::jit::Stack* stack)
const override;
void reportErrorCallback(PyObject* callback, DispatchKey key) const override;
void python_dispatcher(
const c10::OperatorHandle& op,
c10::DispatchKeySet,
torch::jit::Stack* stack) const override;
// NB: this is defined in python_dispatch.cpp
void python_op_registration_trampoline(
const c10::OperatorHandle& op,
c10::DispatchKey key,
c10::DispatchKeySet keyset,
torch::jit::Stack* stack,
bool with_keyset,
bool with_op) const override {
torch::impl::dispatch::python_op_registration_trampoline_impl(
op, key, keyset, stack, with_keyset, with_op);
}
void throw_abstract_impl_not_imported_error(
std::string opname,
const char* pymodule,
const char* context) const override {
py::gil_scoped_acquire gil;
pybind11::module::import("torch._utils_internal")
.attr("throw_abstract_impl_not_imported_error")(
opname, pymodule, context);
}
bool is_contiguous(const c10::TensorImpl* self, at::MemoryFormat)
const override;
bool is_strides_like(const c10::TensorImpl* self, at::MemoryFormat)
const override;
bool is_non_overlapping_and_dense(const c10::TensorImpl* self) const override;
c10::Device device(const c10::TensorImpl* self) const override;
int64_t dim(const c10::TensorImpl* self) const override;
c10::IntArrayRef strides(const c10::TensorImpl* self) const override;
c10::IntArrayRef sizes(const c10::TensorImpl* self) const override;
c10::SymIntArrayRef sym_sizes(const c10::TensorImpl* self) const override;
c10::Layout layout(const c10::TensorImpl* self) const override;
int64_t numel(const c10::TensorImpl* self) const override;
c10::SymInt sym_numel(const c10::TensorImpl* self) const override;
c10::SymIntArrayRef sym_strides(const c10::TensorImpl* self) const override;
c10::SymInt sym_storage_offset(const c10::TensorImpl* self) const override;
void trace_gpu_event_creation(at::DeviceType device_type, uintptr_t event)
const override {
CONCRETE_GPU_TRACE(device_type, "EventCreationCallbacks", event);
}
void trace_gpu_event_deletion(at::DeviceType device_type, uintptr_t event)
const override {
CONCRETE_GPU_TRACE(device_type, "EventDeletionCallbacks", event);
}
void trace_gpu_event_record(
at::DeviceType device_type,
uintptr_t event,
uintptr_t stream) const override {
CONCRETE_GPU_TRACE(device_type, "EventRecordCallbacks", event, stream);
}
void trace_gpu_event_wait(
at::DeviceType device_type,
uintptr_t event,
uintptr_t stream) const override {
CONCRETE_GPU_TRACE(device_type, "EventWaitCallbacks", event, stream);
}
void trace_gpu_memory_allocation(at::DeviceType device_type, uintptr_t ptr)
const override {
CONCRETE_GPU_TRACE(device_type, "MemoryAllocationCallbacks", ptr);
}
void trace_gpu_memory_deallocation(at::DeviceType device_type, uintptr_t ptr)
const override {
CONCRETE_GPU_TRACE(device_type, "MemoryDeallocationCallbacks", ptr);
}
void trace_gpu_stream_creation(at::DeviceType device_type, uintptr_t stream)
const override {
CONCRETE_GPU_TRACE(device_type, "StreamCreationCallbacks", stream);
}
void trace_gpu_device_synchronization(
at::DeviceType device_type) const override {
CONCRETE_GPU_TRACE(device_type, "DeviceSynchronizationCallbacks");
}
void trace_gpu_stream_synchronization(
at::DeviceType device_type,
uintptr_t stream) const override {
CONCRETE_GPU_TRACE(device_type, "StreamSynchronizationCallbacks", stream);
}
void trace_gpu_event_synchronization(
at::DeviceType device_type,
uintptr_t event) const override {
CONCRETE_GPU_TRACE(device_type, "EventSynchronizationCallbacks", event);
}
void reset_backward_hooks(const c10::TensorImpl* self) const override;
static ConcretePyInterpreterVTable* instance() {
static ConcretePyInterpreterVTable s;
return &s;
}
};
class PyInterpreterHolder {
public:
PyInterpreterHolder()
: impl_(new c10::impl::PyInterpreter(
ConcretePyInterpreterVTable::instance())),
is_main_interpreter_(
at::impl::PythonOpRegistrationTrampoline::registerInterpreter(
impl_)) {}
// NB: intentionally leaks the PyInterpreter, as there may still be
// references to it that are live, living in objects that aren't being
// destructed while Python is being cleaned up.
~PyInterpreterHolder() {
impl_->disarm();
}
c10::impl::PyInterpreter* get() const noexcept {
return impl_;
}
bool is_main_interpreter() const noexcept {
return is_main_interpreter_;
}
private:
c10::impl::PyInterpreter* impl_;
bool is_main_interpreter_;
};
py::object torchDispatchFromTensorImpl(
const c10::TensorImpl* self,
const char* func_name,
PyObject* torch_api_function,
const char* module_name,
// WARNING: MUST NOT BE TENSOR ARGS
c10::SmallVector<py::object, 1> extra_args = {}) {
if (torch_api_function == nullptr) {
throw python_error();
}
TORCH_CHECK(
PyGILState_Check(),
"GIL must be held before you call parseIValuesToPyArgsKwargs");
std::vector<PyObject*> overloaded_args;
// TODO: there should be a shorter way to spell this
// TODO: fix the constness of target
at::Tensor self_t = at::Tensor(
c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
unsafe_reclaim_from_nonowning(const_cast<c10::TensorImpl*>(self)));
auto self_p = py::reinterpret_steal<py::object>(THPVariable_Wrap(self_t));
// NB: this may not be a python tensor if you got here from a mode!
// TORCH_INTERNAL_ASSERT(isPythonTensor(self_t));
append_overloaded_tensor(&overloaded_args, self_p.ptr());
auto args = py::reinterpret_steal<py::object>(
PyTuple_New(static_cast<Py_ssize_t>(1 + extra_args.size())));
PyTuple_SET_ITEM(args.ptr(), 0, self_p.release().ptr());
int64_t i = 1;
for (auto& a : extra_args) {
if (a.ptr() == nullptr)
throw python_error();
PyTuple_SET_ITEM(args.ptr(), i, std::move(a).release().ptr());
i++;
}
py::dict kwargs;
return py::reinterpret_steal<py::object>(
handle_torch_function_no_python_arg_parser(
overloaded_args,
args.ptr(),
kwargs.ptr(),
func_name,
torch_api_function,
module_name,
TorchFunctionName::TorchDispatch));
}
// NOTE [PyInterpreter::decref takes a `has_pyobj_slot` arg]
// Before calling PyInterpreter::decref, we must statically know if the
// pyobj has a PyObjectSlot or not.
// - If it has a PyObjectSlot, we need to be careful about PyObject resurrection
// - If it does not have a PyObjectSlot, we can freely decref
// One alternative to this is using PyObject_IsInstance
// to get at this information. However, we don't want to risk an incorrect
// `__instancecheck__` changing the semantics here.
void ConcretePyInterpreterVTable::decref(PyObject* pyobj, bool has_pyobj_slot)
const {
// Leak the pyobj if not initialized. This can happen if we are running
// exit handlers that are destructing tensors with residual (owned)
// PyObjects stored in them.
if (!Py_IsInitialized())
return;
pybind11::gil_scoped_acquire gil;
// Two possibilities:
// 1. We are decref-ing an object that has a PyObjectSlot, like a Tensor or
// Storage. Then we must be careful about PyObject resurrection (see
// THPVariable_clear).
// 2. We are decref-ing some other Python object. We don't do
// PyObject resurrection on non-Tensors, so we just carry on as usual
if (has_pyobj_slot && Py_REFCNT(pyobj) > 1) {
if (THPVariable_Check(pyobj)) {
// It's still alive! This can happen if a weak ref resurrected
// the PyObject without flipping ownership. At this point it is
// too late to rescue the object, so just stub out the PyObject
// so that it fails on subsequent uses. Don't raise an error here;
// you're probably in a destructor.
TORCH_WARN(
"Deallocating Tensor that still has live PyObject references. "
"This probably happened because you took out a weak reference to "
"Tensor and didn't call _fix_weakref() after dereferencing it. "
"Subsequent accesses to this tensor via the PyObject will now fail.");
((THPVariable*)pyobj)->cdata =
c10::MaybeOwned<torch::autograd::Variable>();
} else if (THPStorage_Check(pyobj)) {
TORCH_WARN(
"Deallocating UntypedStorage that still has live PyObject references. "
"This probably happened because you took out a weak reference to "
"UntypedStorage and didn't call _fix_weakref() after dereferencing it. "
"Subsequent accesses to this storage via the PyObject will now fail.");
((THPStorage*)pyobj)->cdata = c10::MaybeOwned<c10::Storage>();
}
}
Py_DECREF(pyobj);
}
void ConcretePyInterpreterVTable::incref(PyObject* pyobj) const {
if (!Py_IsInitialized())
return;
pybind11::gil_scoped_acquire gil;
Py_INCREF(pyobj);
}
bool isPythonTensor(const at::Tensor& tensor) {
return tensor.unsafeGetTensorImpl()->key_set().has(c10::DispatchKey::Python);
}
void ConcretePyInterpreterVTable::reportErrorCallback(
PyObject* callback,
DispatchKey key) const {
py::gil_scoped_acquire g;
auto func = py::reinterpret_borrow<py::object>(callback);
// Not all DispatchKeys are pybind'ed into Python and we do not have infra
// to ensure this, so just pass a string back to Python.
func(c10::toString(key));
}
void ConcretePyInterpreterVTable::dispatch(
const c10::OperatorHandle& op,
torch::jit::Stack* stack) const {
const auto& schema = op.schema();
const auto num_arguments = schema.arguments().size();
auto arguments = torch::jit::pop(*stack, num_arguments);
// The plan: convert all the arguments back into PyObjects,
// extracting out the tensor handles, then call
// handle_torch_function_no_python_arg_parser
// NB: at the point arguments are pushed to the stack, ALL defaults
// are already present
py::gil_scoped_acquire g;
std::vector<PyObject*> overloaded_args;
py::handle torch_api_function_overload = getTorchApiFunction(op);
// Find overloaded tensors
for (const auto idx : c10::irange(arguments.size())) {
const auto& ivalue = arguments[idx];
if (ivalue.isTensor()) {
const auto& tensor = ivalue.toTensor();
if (isPythonTensor(tensor)) {
append_overloaded_tensor(&overloaded_args, py::cast(tensor).ptr());
}
} else if (ivalue.isList()) {
const auto& list = ivalue.toListRef();
for (const auto jdx : c10::irange(list.size())) {
const auto& nv = list[jdx];
if (nv.isTensor()) {
const auto& tensor = nv.toTensor();
if (isPythonTensor(tensor)) {
append_overloaded_tensor(&overloaded_args, py::cast(tensor).ptr());
}
}
}
}
}
auto args_kwargs = parseIValuesToPyArgsKwargs(op, arguments);
auto args = std::move(args_kwargs.first);
auto kwargs = std::move(args_kwargs.second);
PyObject* obj = handle_torch_function_no_python_arg_parser(
overloaded_args,
args.ptr(),
kwargs.ptr(),
nullptr,
torch_api_function_overload.ptr(),
nullptr,
TorchFunctionName::TorchDispatch);
pushPyOutToStack(
op, stack, py::reinterpret_steal<py::object>(obj), "__torch_dispatch__");
}
void ConcretePyInterpreterVTable::python_dispatcher(
const c10::OperatorHandle& op,
c10::DispatchKeySet ks,
torch::jit::Stack* stack) const {
py::gil_scoped_acquire g;
py::handle torch_api_function_overload = getTorchApiFunction(op);
// TODO: if necessary, can optimize to cache the cache lookup
// TODO: if necessary, can optimize OpOverload to have slots
auto cache = py::dict(torch_api_function_overload.attr("_dispatch_cache"));
if (cache.ptr() == nullptr) {
throw python_error();
}
c10::DispatchKey k = ks.highestPriorityTypeId();
PyObject* raw_handler = nullptr;
if (PyDict_GetItemRef(cache.ptr(), py::cast(k).ptr(), &raw_handler) < 0) {
// There was an error that is not missing key (which would return 0)
throw python_error();
}
auto handler = py::reinterpret_steal<py::object>(raw_handler);
if (handler.ptr() == nullptr) {
// Slow path
handler = torch_api_function_overload.attr("_get_dispatch")(k);
}
if (py::isinstance<c10::DispatchKey>(handler)) {
// NB: not redispatch, as that will permanently remove the python
// dispatcher for subsequent redispatches
op.callBoxedForDispatchKey(py::cast<c10::DispatchKey>(handler), *stack);
return;
}
const auto& schema = op.schema();
const auto num_arguments = schema.arguments().size();
auto arguments = torch::jit::pop(*stack, num_arguments);
auto args_kwargs = parseIValuesToPyArgsKwargs(op, arguments);
auto args = std::move(args_kwargs.first);
auto kwargs = std::move(args_kwargs.second);
py::object obj = py::reinterpret_steal<py::object>(
PyObject_Call(handler.ptr(), args.ptr(), kwargs.ptr()));
if (obj.ptr() == nullptr) {
throw python_error();
}
pushPyOutToStack(op, stack, std::move(obj), "Python dispatcher");
}
c10::intrusive_ptr<c10::TensorImpl> ConcretePyInterpreterVTable::detach(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"detach",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("detach")
.attr("default")
.ptr(),
"torch.ops.aten");
TORCH_CHECK(
THPVariable_Check(out.ptr()),
"detach returned invalid type ",
py::detail::get_fully_qualified_tp_name(Py_TYPE(out.ptr())),
", expected Tensor");
const at::Tensor& res_t = THPVariable_Unpack(out.ptr());
return res_t.getIntrusivePtr();
}
bool ConcretePyInterpreterVTable::is_contiguous(
const c10::TensorImpl* self,
at::MemoryFormat memory_format) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
py::object out;
if (memory_format == at::MemoryFormat::Contiguous) {
// For backwards compatibility
out = torchDispatchFromTensorImpl(
self,
"is_contiguous",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("is_contiguous")
.attr("default")
.ptr(),
"torch.ops.aten");
} else {
out = torchDispatchFromTensorImpl(
self,
"is_contiguous",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("is_contiguous")
.attr("memory_format")
.ptr(),
"torch.ops.aten",
{py::cast(memory_format)});
}
if (out.is_none()) {
return self->is_contiguous_default(memory_format);
}
TORCH_CHECK(
PyBool_Check(out.ptr()),
"is_contiguous returned invalid type ",
py::detail::get_fully_qualified_tp_name(Py_TYPE(out.ptr())),
", expected bool");
return PyObject_IsTrue(out.ptr());
}
bool ConcretePyInterpreterVTable::is_strides_like(
const c10::TensorImpl* self,
at::MemoryFormat memory_format) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"is_strides_like",
py::module::import("torch")
.attr("ops")
.attr("aten")
// NB: intentionally suffixed with _format to avoid
// triggering matches against "_like" suffix
.attr("is_strides_like_format")
.attr("default")
.ptr(),
"torch.ops.aten",
{py::cast(memory_format)});
if (out.is_none()) {
return self->is_strides_like_default(memory_format);
}
TORCH_CHECK(
PyBool_Check(out.ptr()),
"is_strides_like_format returned invalid type ",
py::detail::get_fully_qualified_tp_name(Py_TYPE(out.ptr())),
", expected bool");
return PyObject_IsTrue(out.ptr());
}
bool ConcretePyInterpreterVTable::is_non_overlapping_and_dense(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"is_non_overlapping_and_dense",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("is_non_overlapping_and_dense")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
return self->is_non_overlapping_and_dense_default();
}
TORCH_CHECK(
PyBool_Check(out.ptr()),
"is_non_overlapping_and_dense returned invalid type ",
py::detail::get_fully_qualified_tp_name(Py_TYPE(out.ptr())),
", expected bool");
return PyObject_IsTrue(out.ptr());
}
int64_t ConcretePyInterpreterVTable::dim(const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"dim",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("dim")
.attr("default")
.ptr(),
"torch.ops.aten");
TORCH_CHECK(
PyLong_Check(out.ptr()),
"dim returned invalid type ",
py::detail::get_fully_qualified_tp_name(Py_TYPE(out.ptr())),
", expected int");
return THPUtils_unpackLong(out.ptr());
}
c10::Device ConcretePyInterpreterVTable::device(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"device",
py::module::import("torch")
.attr("ops")
.attr("prim")
.attr("device")
.attr("default")
.ptr(),
"torch.ops.prim");
return toDevice(out.ptr());
}
static void set_tensor_attr_with_capsule(
const c10::TensorImpl* tensor,
py::capsule& capsule,
const char* attr_name) {
std::optional<PyObject*> mb_obj = tensor->pyobj_slot()->check_pyobj(
getPyInterpreter(), /*ignore_hermetic_tls=*/false);
TORCH_CHECK(
mb_obj.has_value(), "Tensor subclass's PyInterpreter has no value");
auto obj = mb_obj.value();
py::handle(obj).attr(attr_name) = capsule;
}
// Note [Tensor Subclass custom size/stride caching strategy]
// Tensor subclasses can use __torch_dispatch__ to override size/stride calls.
// However, this presents a problem:
// (1) When you return a custom (maybe symbolic) size/stride
// from python, we need to stash this fresh vector of ints/symints
// somewhere so that it has the same lifetime as the tensor.
// (2) If the subclass experiences a metadata mutation,
// this stashed vector is no longer valid, so we need to allocate a fresh
// buffer to store the new sizes the next time someone asks for them.
//
// We handle this in the same way that `TensorImpl::sizes_default()`
// handles its buffer: we simply reallocate the buffer whenever
// the number of dimensions changes due to a resize.
// Notable, we do *not* reallocate the buffer if the values changed,
// but the number of dimensions stayed the same (e.g. `.transpose_()`).
template <typename T>
static c10::ArrayRef<T> get_set_cached_attr(
const c10::TensorImpl* tensor,
const char* base_attr_name,
const py::object& obj) {
std::optional<PyObject*> mb_obj =
tensor->pyobj_slot()->check_pyobj(getPyInterpreter());
TORCH_CHECK(
mb_obj.has_value(), "Tensor subclass's PyInterpreter has no value");
auto tensor_obj = mb_obj.value();
auto buffer_len_attr_name = std::string(base_attr_name) + std::string("_len");
bool is_buffer_allocated = false;
size_t curr_size = 0;
if (PyObject_HasAttrString(tensor_obj, buffer_len_attr_name.c_str())) {
auto len_pyobj = py::handle(tensor_obj).attr(buffer_len_attr_name.c_str());
curr_size = py::cast<size_t>(len_pyobj);
is_buffer_allocated = true;
}
size_t new_size = py::len(obj);
// We do the smallvector optimization here: any time the new_size is <=5,
// we always allocate our buffer to size 5, so that if the next resize
// is also to <=5 elements, we don't need to reallocate.
// Note: I tried removing this optimization and tripped ASAN
// in a batchnorm kernel here:
// https://pipelinesghubeus21.actions.githubusercontent.com/mBh68xKhi8LyM7tp3vECvYXNFvuV4gyVGgmYCteuEZP9JH92QN/_apis/pipelines/1/runs/3373307/signedlogcontent/790?urlExpires=2023-09-15T21%3A13%3A51.4327798Z&urlSigningMethod=HMACV1&urlSignature=tDeX7ZqaARVU5NNwyr5yYqqkWq3A2j4z8FFdqYwGr0Q%3D
// We should fix this instead.
bool needs_resize = false;
// We need to resize if:
// (1) we haven't allocated our buffer at all yet
// (2) Our buffer size is different from the new size
// (note: we use the small vector optimization, where our buffer
// is always allocated to at least size 5, and any resizes
// within the <= 5 regime to not require a reallocation).
auto is_smallvector = curr_size <= 5;
needs_resize = !is_buffer_allocated || (is_smallvector && new_size > 5) ||
(!is_smallvector && curr_size != new_size);
if (needs_resize) {
// If our current buffer is not the right size (either because we haven't
// allocated it yet, or there was a metadata mutation that changed the
// number of dims of the tensor), allocate a fresh buffer. Note that this
// will trash the previous buffer if there already was one, invalidating any
// existing SymIntArrayRef's from an old .sym_size() call.
auto new_buffer_size = new_size;
if (new_size <= 5) {
// This is the smallvector optimization
new_buffer_size = 5;
}
T* ptr = new T[new_buffer_size];
auto capsule =
py::capsule(ptr, [](void* p) { delete[] reinterpret_cast<T*>(p); });
int64_t idx = 0;
for (auto it = obj.begin(); it != obj.end(); ++it, ++idx) {
ptr[idx] = py::cast<T>(*it);
}
// Set the buffer
set_tensor_attr_with_capsule(tensor, capsule, base_attr_name);
// Set the len buffer
py::handle(tensor_obj).attr(buffer_len_attr_name.c_str()) = new_size;
} else {
TORCH_INTERNAL_ASSERT(PyObject_HasAttrString(tensor_obj, base_attr_name));
auto curr_buffer_pyobj = py::handle(tensor_obj).attr(base_attr_name);
void* buffer_pycapsule =
PyCapsule_GetPointer(curr_buffer_pyobj.ptr(), nullptr);
auto curr_buffer = reinterpret_cast<T*>(buffer_pycapsule);
// Overwrite the buffer with our new values, but only if any of them changed
// (due to a metadata mutation).
// This is technically not thread safe, because the update happens lazily.
// The original metadata mutation call on the tensor might have been thread
// safe (e.g. a .resize_() call), but we won't actually mutate the size
// buffer until the first call to .sizes() which the user might not access
// in a thread-safe way. For now we are not explicitly locking, but maybe we
// should.
int64_t idx = 0;
// Quick sanity assert that our buffer size is large enough
// to compare against all the elements in the new buffer.
size_t curr_buffer_size = 5;
if (curr_buffer_size < curr_size) {
curr_buffer_size = curr_size;
}
TORCH_INTERNAL_ASSERT(curr_buffer_size >= new_size);
for (auto it = obj.begin(); it != obj.end(); ++it, ++idx) {
auto actual_val = py::cast<T>(*it);
if constexpr (std::is_same_v<T, c10::SymInt>) {
// if our SymInts are symbolic, we are *not* doing an equality check on
// the symints. we just want to see if the nodes are the same. this is
// because we don't want to introduce any guards here.
if (!curr_buffer[idx].is_same(actual_val)) {
curr_buffer[idx] = actual_val;
}
} else {
if (curr_buffer[idx] != actual_val) {
curr_buffer[idx] = actual_val;
}
}
}
}
// The correct data is now stored at the buffer - read and return it.
auto curr_buffer_pyobj = py::handle(tensor_obj).attr(base_attr_name);
void* buffer_pycapsule =
PyCapsule_GetPointer(curr_buffer_pyobj.ptr(), nullptr);
auto curr_buffer = reinterpret_cast<T*>(buffer_pycapsule);
return c10::ArrayRef<T>(curr_buffer, new_size);
}
c10::IntArrayRef ConcretePyInterpreterVTable::strides(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"stride",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("stride")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
TORCH_CHECK(
!self->has_symbolic_sizes_strides(),
"Cannot call strides on a tensor with symbolic shapes/strides");
return self->strides_default();
}
TORCH_CHECK(
py::isinstance<py::tuple>(out) || py::isinstance<py::list>(out),
"strides must be a list or a tuple");
auto updated_strides =
get_set_cached_attr<int64_t>(self, "_strides_capsule", out);
return updated_strides;
}
c10::IntArrayRef ConcretePyInterpreterVTable::sizes(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
HANDLE_TH_ERRORS
auto out = torchDispatchFromTensorImpl(
self,
"size",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("size")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
TORCH_CHECK(
!self->has_symbolic_sizes_strides(),
"Cannot call sizes on a tensor with symbolic shapes/strides");
return self->sizes_default();
}
TORCH_CHECK(
py::isinstance<py::tuple>(out) || py::isinstance<py::list>(out),
"sizes must be a list or a tuple");
auto updated_sizes =
get_set_cached_attr<int64_t>(self, "_sizes_capsule", out);
return updated_sizes;
END_HANDLE_TH_ERRORS_PYBIND
}
c10::SymIntArrayRef ConcretePyInterpreterVTable::sym_sizes(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
HANDLE_TH_ERRORS
auto out = torchDispatchFromTensorImpl(
self,
"sym_size",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("sym_size")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
return self->sym_sizes_default();
}
TORCH_CHECK(
py::isinstance<py::tuple>(out) || py::isinstance<py::list>(out),
"sym_size must be a list or a tuple");
// See Note [Tensor Subclass custom size/stride caching strategy]
auto updated_sym_sizes =
get_set_cached_attr<c10::SymInt>(self, "_sym_sizes_capsule", out);
return updated_sym_sizes;
END_HANDLE_TH_ERRORS_PYBIND
}
c10::Layout ConcretePyInterpreterVTable::layout(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"layout",
py::module::import("torch")
.attr("ops")
.attr("prim")
.attr("layout")
.attr("default")
.ptr(),
"torch.ops.prim");
TORCH_CHECK(
THPLayout_Check(out.ptr()) || PyLong_Check(out.ptr()),
"layout returned invalid type ",
py::detail::get_fully_qualified_tp_name(Py_TYPE(out.ptr())),
", expected Layout");
if (THPLayout_Check(out.ptr())) {
return toLayout(out.ptr());
} else {
return c10::Layout(py::cast<int64_t>(out));
}
}
int64_t ConcretePyInterpreterVTable::numel(const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"numel",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("numel")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
TORCH_CHECK(
!self->has_symbolic_sizes_strides(),
"Cannot call sizes on a tensor with symbolic shapes/strides");
return self->numel_default();
}
return py::cast<int64_t>(out);
}
c10::SymInt ConcretePyInterpreterVTable::sym_numel(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"sym_numel",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("sym_numel")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
return self->sym_numel_default();
}
return torch::is_symint(out) ? out.cast<c10::SymInt>()
: c10::SymInt{py::cast<int64_t>(out)};
}
c10::SymInt ConcretePyInterpreterVTable::sym_storage_offset(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
auto out = torchDispatchFromTensorImpl(
self,
"sym_storage_offset",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("sym_storage_offset")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
return self->sym_storage_offset_default();
}
return torch::is_symint(out) ? out.cast<c10::SymInt>()
: c10::SymInt{py::cast<int64_t>(out)};
}
c10::SymIntArrayRef ConcretePyInterpreterVTable::sym_strides(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
HANDLE_TH_ERRORS
auto out = torchDispatchFromTensorImpl(
self,
"sym_stride",
py::module::import("torch")
.attr("ops")
.attr("aten")
.attr("sym_stride")
.attr("default")
.ptr(),
"torch.ops.aten");
if (out.is_none()) {
return self->sym_strides_default();
}
// We need to squeeze SymIntNodes and ints into `SymInts`
// since it's a format `sym_strides()` are stored in
TORCH_CHECK(
py::isinstance<py::tuple>(out) || py::isinstance<py::list>(out),
"sym_strides must be a list or a tuple");
auto updated_sym_strides =
get_set_cached_attr<c10::SymInt>(self, "_sym_strides_capsule", out);
return updated_sym_strides;
END_HANDLE_TH_ERRORS_PYBIND
}
void ConcretePyInterpreterVTable::reset_backward_hooks(
const c10::TensorImpl* self) const {
pybind11::gil_scoped_acquire gil;
at::impl::MaybeSetTLSOnEntryGuard guard;
HANDLE_TH_ERRORS
Tensor self_t =
Tensor(c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
unsafe_reclaim_from_nonowning(const_cast<c10::TensorImpl*>(self)));
auto self_p = py::reinterpret_steal<py::object>(THPVariable_Wrap(self_t));
PyObject_SetAttrString(self_p.ptr(), "_backward_hooks", Py_None);
END_HANDLE_TH_ERRORS_PYBIND
}
std::string ConcretePyInterpreterVTable::name() const {
std::stringstream ss;
ss << getPyInterpreter();
return ss.str();
}
PyInterpreterHolder self_interpreter;
} // anonymous namespace
py::handle getTorchApiFunction(const c10::OperatorHandle& op) {
return op.getPythonOp(getPyInterpreter(), [&]() -> PyObject* {
// Parse the name into namespace and name (no overload_name)
// TODO: put this into the library
const auto& schema = op.schema();
const auto& qualified_name = op.operator_name().name;
const auto& overload_name = schema.overload_name();
auto pos = qualified_name.find("::");
TORCH_INTERNAL_ASSERT(pos != std::string::npos, qualified_name);
// Make me some null terminated strings
std::string ns_str = qualified_name.substr(0, pos);
const char* ns = ns_str.c_str();
const char* func_name = qualified_name.c_str() + pos + strlen("::");
py::handle torch_api_function =
py::module::import("torch").attr("ops").attr(ns).attr(func_name);
if (overload_name.empty()) {
return torch_api_function.attr("default").ptr();
} else {
return torch_api_function.attr(overload_name.c_str()).ptr();
}
});
}
} // namespace torch::detail
c10::impl::PyInterpreter* getPyInterpreter() {
return torch::detail::self_interpreter.get();
}
bool isMainPyInterpreter() {
return torch::detail::self_interpreter.is_main_interpreter();
}
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