mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:34:18 +08:00
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26060 This PR enables BUILD_NAMEDTENSOR by default. This is done via including a header, `c10/core/EnableNamedTensor`, that sets `BUILD_NAMEDTENSOR`. In the future, the plan is to get rid of the flag entirely: we can incrementally delete usages after this PR goes in. This PR also maintains the namedtensor ci vs regular ci distinction. `test/test_namedtensor.py` only runs if TEST_NAMEDTENSOR=1 is specified. TEST_NAMEDTENSOR=1 is set on the namedtensor ci. I'll remove this distinction later and send out an announcement about it; devs will be responsible for named tensor failures after that. The initial reason why we had the BUILD_NAMEDTENSOR flag was so that we could quickly prototype named tensor features without worrying about adding overhead to the framework. The overheads can be categorized as memory overhead and performance overhead. Memory overhead: named tensors adds 1 additional word per Tensor. This is because TensorImpl stores a `unique_ptr<NamedTensorMetaInterface>` field. This is not a lot of overhead. Performance overhead: At all entry points to name inference, we check if inputs to an op are named. If inputs are not named, we short-circuit and don't do name inference. These calls should therefore be as efficient as error-checking code and not take up a lot of time. My plan is to benchmark a few functions and then post the results in a comment to this PR. Test Plan: - [namedtensor ci] Differential Revision: D17331635 Pulled By: zou3519 fbshipit-source-id: deed901347448ae2c26066c1fa432e3dc0cadb92
104 lines
3.3 KiB
C++
104 lines
3.3 KiB
C++
#include <torch/csrc/python_dimname.h>
|
|
#include <torch/csrc/Exceptions.h>
|
|
#include <torch/csrc/utils/python_strings.h>
|
|
#include <c10/util/flat_hash_map.h>
|
|
#include <ATen/core/EnableNamedTensor.h>
|
|
|
|
#ifdef BUILD_NAMEDTENSOR
|
|
namespace torch {
|
|
|
|
struct InternedStringsTable {
|
|
InternedStringsTable() = default;
|
|
~InternedStringsTable();
|
|
InternedStringsTable(const InternedStringsTable &) = delete;
|
|
InternedStringsTable& operator =(InternedStringsTable const&) = delete;
|
|
InternedStringsTable(InternedStringsTable&&) = delete;
|
|
InternedStringsTable& operator=(InternedStringsTable&&) = delete;
|
|
|
|
at::optional<at::Dimname> lookup(PyObject* obj);
|
|
// Precondition: obj is an interned python string.
|
|
void addMapping(PyObject* obj, at::Dimname dimname);
|
|
private:
|
|
ska::flat_hash_map<PyObject*,at::Dimname> py_interned_string_to_dimname_;
|
|
};
|
|
|
|
InternedStringsTable kPyInternedStringToDimname;
|
|
|
|
InternedStringsTable::~InternedStringsTable() {
|
|
for (auto it = py_interned_string_to_dimname_.begin();
|
|
it != py_interned_string_to_dimname_.end(); ++it) {
|
|
// See Note [References to python interned strings]
|
|
Py_DECREF(it->first);
|
|
}
|
|
}
|
|
|
|
at::optional<at::Dimname> InternedStringsTable::lookup(PyObject* obj) {
|
|
auto it = py_interned_string_to_dimname_.find(obj);
|
|
if (it == py_interned_string_to_dimname_.end()) {
|
|
return at::nullopt;
|
|
}
|
|
return it->second;
|
|
}
|
|
|
|
|
|
void InternedStringsTable::addMapping(PyObject* obj, at::Dimname dimname) {
|
|
// Note [References to python interned strings]
|
|
// If a Python interned string has no references to it, then it gets
|
|
// deallocated, invalidating this mapping. Let's immortalize the string by
|
|
// holding a refcount to it and releasing it in the destructor
|
|
Py_INCREF(obj);
|
|
py_interned_string_to_dimname_.emplace(obj, dimname);
|
|
}
|
|
|
|
} // namespace torch
|
|
|
|
bool THPUtils_checkDimname(PyObject* obj) {
|
|
return obj == Py_None || THPUtils_checkString(obj);
|
|
}
|
|
|
|
// To avoid ambiguity with IntArrayRef, we parse obj as a DimnameList if
|
|
// it is a list or tuple and its first elt is a Dimname
|
|
bool THPUtils_checkDimnameList(PyObject* obj) {
|
|
auto tuple = PyTuple_Check(obj);
|
|
if (!tuple && !PyList_Check(obj)) {
|
|
return false;
|
|
}
|
|
auto size = tuple ? PyTuple_GET_SIZE(obj) : PyList_GET_SIZE(obj);
|
|
if (size == 0) {
|
|
return true;
|
|
}
|
|
PyObject* first_elt = tuple ? PyTuple_GET_ITEM(obj, 0) : PyList_GET_ITEM(obj, 0);
|
|
return THPUtils_checkDimname(first_elt);
|
|
}
|
|
|
|
at::Dimname THPDimname_parse(PyObject* obj) {
|
|
if (obj == Py_None) {
|
|
return at::Dimname::wildcard();
|
|
}
|
|
|
|
if (!THPUtils_checkString(obj)) {
|
|
throw torch::TypeError("expected None or string for Dimname but got %s", Py_TYPE(obj)->tp_name);
|
|
}
|
|
|
|
if (!THPUtils_isInterned(obj)) {
|
|
// internStringInPlace decrefs obj and increfs the result. Because we're
|
|
// not actually returning the result to the user, we need to undo these.
|
|
// See https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_InternInPlace
|
|
Py_INCREF(obj);
|
|
THPUtils_internStringInPlace(&obj);
|
|
Py_DECREF(obj);
|
|
}
|
|
|
|
auto maybeDimname = torch::kPyInternedStringToDimname.lookup(obj);
|
|
if (maybeDimname) {
|
|
return *maybeDimname;
|
|
}
|
|
|
|
const auto name = THPUtils_unpackString(obj);
|
|
auto dimname = at::Dimname::fromSymbol(at::Symbol::dimname(name));
|
|
torch::kPyInternedStringToDimname.addMapping(obj, dimname);
|
|
return dimname;
|
|
}
|
|
|
|
#endif
|