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This refactor was prompted by challenges handling mixed int/float operations in C++. A previous version of this patch added overloads for each permutation of int/float and was unwieldy https://github.com/pytorch/pytorch/pull/87722/ This PR takes a different approach. The general outline of the patch is to combine the C++ types SymIntNode and SymFloatNode into a single type, SymNode. This is type erased; we no longer know statically at C++ if we have an int/float and have to test it with the is_int()/is_float() virtual methods. This has a number of knock on effects. - We no longer have C++ classes to bind to Python. Instead, we take an entirely new approach to our Python API, where we have a SymInt/SymFloat class defined entirely in Python, which hold a SymNode (which corresponds to the C++ SymNode). However, SymNode is not pybind11-bound; instead, it lives as-is in Python, and is wrapped into C++ SymNode using PythonSymNode when it goes into C++. This implies a userland rename. In principle, it is also possible for the canonical implementation of SymNode to be written in C++, and then bound to Python with pybind11 (we have this code, although it is commented out.) However, I did not implement this as we currently have no C++ implementations of SymNode. Because we do return SymInt/SymFloat from C++ bindings, the C++ binding code needs to know how to find these classes. Currently, this is done just by manually importing torch and getting the attributes. - Because SymInt/SymFloat are easy Python wrappers, __sym_dispatch__ now takes SymInt/SymFloat, rather than SymNode, bringing it in line with how __torch_dispatch__ works. Some miscellaneous improvements: - SymInt now has a constructor that takes SymNode. Note that this constructor is ambiguous if you pass in a subclass of SymNode, so an explicit downcast is necessary. This means toSymFloat/toSymInt are no more. This is a mild optimization as it means rvalue reference works automatically. - We uniformly use the caster for c10::SymInt/SymFloat, rather than going the long way via the SymIntNode/SymFloatNode. - Removed some unnecessary toSymInt/toSymFloat calls in normalize_* functions, pretty sure this doesn't do anything. - guard_int is now a free function, since to guard on an int you cannot assume the method exists. A function can handle both int and SymInt inputs. - We clean up the magic method definition code for SymInt/SymFloat/SymNode. ONLY the user classes (SymInt/SymFloat) get magic methods; SymNode gets plain methods; this is to help avoid confusion between the two types. Signed-off-by: Edward Z. Yang <ezyang@fb.com> cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 Pull Request resolved: https://github.com/pytorch/pytorch/pull/87817 Approved by: https://github.com/albanD, https://github.com/anjali411
280 lines
8.2 KiB
C++
280 lines
8.2 KiB
C++
#include <c10/util/irange.h>
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#include <pybind11/pytypes.h>
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#include <torch/csrc/Size.h>
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#include <torch/csrc/utils/pybind.h>
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#include <torch/csrc/utils/object_ptr.h>
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#include <torch/csrc/utils/python_arg_parser.h>
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#include <torch/csrc/utils/python_numbers.h>
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#include <torch/csrc/utils/python_strings.h>
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#include <torch/csrc/utils/python_tuples.h>
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#include <string>
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#include <torch/csrc/autograd/python_variable.h>
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#include <torch/csrc/jit/frontend/tracer.h>
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#include <torch/csrc/utils/pybind.h>
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struct THPSize {
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PyTupleObject tuple;
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};
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PyObject* THPSize_New(const torch::autograd::Variable& var) {
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if (!torch::jit::tracer::isTracing()) {
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auto sizes = var.sizes();
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return THPSize_NewFromSizes(var.dim(), sizes.data());
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}
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auto self = THPObjectPtr(THPSizeType.tp_alloc(&THPSizeType, var.dim()));
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if (!self)
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throw python_error();
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for (const auto i : c10::irange(var.dim())) {
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PyObject* py_size_tensor =
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THPVariable_Wrap(torch::jit::tracer::getSizeOf(var, i));
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if (!py_size_tensor)
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throw python_error();
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PyTuple_SET_ITEM(self.get(), i, py_size_tensor);
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}
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return self.release();
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}
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PyObject* THPSize_NewFromSizes(int dim, const int64_t* sizes) {
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auto self = THPObjectPtr(THPSizeType.tp_alloc(&THPSizeType, dim));
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if (!self)
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throw python_error();
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THPUtils_packInt64Array(self, dim, sizes);
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return self.release();
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}
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PyObject* THPSize_NewFromSymSizes(const at::Tensor& self_) {
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auto sym_sizes = self_.sym_sizes();
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auto ret = THPObjectPtr(THPSizeType.tp_alloc(&THPSizeType, sym_sizes.size()));
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if (!ret)
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throw python_error();
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for (auto i : c10::irange(sym_sizes.size())) {
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auto si = sym_sizes[i];
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if (si.is_symbolic()) {
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TORCH_CHECK(
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!torch::jit::tracer::isTracing(),
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"JIT Tracing of SymInts isn't supported");
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auto py_symint = py::cast(si).release().ptr();
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if (!py_symint)
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throw python_error();
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PyTuple_SET_ITEM(ret.get(), i, py_symint);
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} else {
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if (torch::jit::tracer::isTracing()) {
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PyObject* py_size_tensor =
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THPVariable_Wrap(torch::jit::tracer::getSizeOf(self_, i));
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if (!py_size_tensor)
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throw python_error();
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PyTuple_SET_ITEM(ret.get(), i, py_size_tensor);
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} else {
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PyTuple_SET_ITEM(
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ret.get(), i, THPUtils_packInt64(si.as_int_unchecked()));
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}
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}
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}
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return ret.release();
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}
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static bool isTracedZeroDimVar(PyObject* item) {
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if (!THPVariable_Check(item))
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return false;
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auto& var = THPVariable_Unpack(item);
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return var.dim() == 0 && torch::jit::tracer::getValueTrace(var);
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}
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static PyObject* THPSize_pynew(
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PyTypeObject* type,
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PyObject* args,
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PyObject* kwargs) {
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HANDLE_TH_ERRORS
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THPObjectPtr self(PyTuple_Type.tp_new(type, args, kwargs));
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if (self) {
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for (Py_ssize_t i = 0; i < PyTuple_Size(self); ++i) {
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PyObject* item = PyTuple_GET_ITEM(self.get(), i);
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if (THPUtils_checkLong(item)) {
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continue;
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}
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if (torch::is_symint(item)) {
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continue;
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}
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if (torch::jit::tracer::isTracing() && isTracedZeroDimVar(item)) {
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continue;
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}
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// item.__index__() works with 0-dim tensors and tensors with one element
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THPObjectPtr number(PyNumber_Index(item));
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if (number && THPUtils_checkLong(number.get())) {
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Py_INCREF(number.get());
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auto status = PyTuple_SetItem(self, i, number.get());
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if (status != 0) {
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throw python_error();
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}
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continue;
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}
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return PyErr_Format(
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PyExc_TypeError,
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"torch.Size() takes an iterable of 'int' (item %zd is '%s')",
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i,
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Py_TYPE(item)->tp_name);
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}
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}
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return self.release();
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END_HANDLE_TH_ERRORS
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}
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static PyObject* THPSize_repr(THPSize* self) {
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HANDLE_TH_ERRORS
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std::string repr("torch.Size([");
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for (Py_ssize_t i = 0; i < PyTuple_Size((PyObject*)self); ++i) {
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if (i != 0) {
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repr += ", ";
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}
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auto item = PyTuple_GET_ITEM(self, i);
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auto ih = py::handle(item);
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repr += torch::is_symint(ih)
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? std::string(py::str(ih))
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: std::to_string(THPUtils_unpackLong(PyTuple_GET_ITEM(self, i)));
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}
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repr += "])";
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return THPUtils_packString(repr);
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END_HANDLE_TH_ERRORS
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}
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extern PyTypeObject THPSizeType;
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template <typename FnType, FnType fn, typename... Args>
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static PyObject* wrap_tuple_fn(Args... args) {
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THPObjectPtr result((*fn)(std::forward<Args>(args)...));
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if (!result)
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return nullptr;
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if (PyTuple_Check(result.get())) {
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return PyObject_CallFunctionObjArgs(
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(PyObject*)&THPSizeType, result.get(), nullptr);
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}
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return result.release();
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}
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// We use an anonymous namespace instead of static to work around
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// (what @peterjc123 think is) a bug in Visual Studio
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namespace {
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auto sq_concat = PyTuple_Type.tp_as_sequence -> sq_concat;
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auto sq_repeat = PyTuple_Type.tp_as_sequence -> sq_repeat;
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binaryfunc mp_subscript = PyTuple_Type.tp_as_mapping->mp_subscript;
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} // namespace
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static PySequenceMethods THPSize_as_sequence = {
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nullptr, /* sq_length */
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wrap_tuple_fn<decltype(&sq_concat), &sq_concat>,
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wrap_tuple_fn<decltype(&sq_repeat), &sq_repeat>,
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nullptr, /* sq_item */
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nullptr, /* sq_slice */
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nullptr, /* sq_ass_item */
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nullptr, /* sq_ass_slice */
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nullptr /* sq_contains */
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};
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static PyMappingMethods THPSize_as_mapping = {
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nullptr, /* mp_length */
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wrap_tuple_fn<decltype(&mp_subscript), &mp_subscript>,
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nullptr};
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static PyObject* THPSize_numel(PyObject* _self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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auto self = (THPSize*)_self;
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int64_t numel = 1;
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for (Py_ssize_t i = 0; i < PyTuple_Size((PyObject*)self); ++i) {
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numel *= THPUtils_unpackLong(PyTuple_GET_ITEM(self, i));
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}
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return THPUtils_packInt64(numel);
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END_HANDLE_TH_ERRORS
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}
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static PyObject* THPSize_reduce(PyObject* _self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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auto self = (THPSize*)_self;
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auto ret = THPObjectPtr{PyTuple_New(2)};
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if (!ret)
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throw python_error();
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auto obj = (PyObject*)(&THPSizeType);
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Py_INCREF(&THPSizeType);
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PyTuple_SET_ITEM(ret.get(), 0, obj);
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THPObjectPtr t(PyTuple_New(PyTuple_Size((PyObject*)self)));
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if (!t)
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throw python_error();
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for (Py_ssize_t i = 0; i < PyTuple_Size((PyObject*)self); ++i) {
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auto d = PyTuple_GET_ITEM(self, i);
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Py_INCREF(d);
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PyTuple_SET_ITEM(t.get(), i, d);
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}
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THPObjectPtr dims(Py_BuildValue("(O)", t.get()));
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if (!dims)
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throw python_error();
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PyTuple_SET_ITEM(ret.get(), 1, dims.release());
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return ret.release();
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END_HANDLE_TH_ERRORS
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
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static PyMethodDef THPSize_methods[] = {
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{"numel", THPSize_numel, METH_NOARGS, nullptr},
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{"__reduce__", THPSize_reduce, METH_NOARGS, nullptr},
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{nullptr}};
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PyTypeObject THPSizeType = {
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PyVarObject_HEAD_INIT(nullptr, 0) "torch.Size", /* tp_name */
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sizeof(THPSize), /* tp_basicsize */
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0, /* tp_itemsize */
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nullptr, /* 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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(reprfunc)THPSize_repr, /* tp_repr */
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nullptr, /* tp_as_number */
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&THPSize_as_sequence, /* tp_as_sequence */
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&THPSize_as_mapping, /* 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, /* 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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THPSize_methods, /* tp_methods */
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nullptr, /* tp_members */
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nullptr, /* tp_getset */
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&PyTuple_Type, /* 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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THPSize_pynew, /* tp_new */
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};
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void THPSize_init(PyObject* module) {
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if (PyType_Ready(&THPSizeType) < 0) {
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throw python_error();
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}
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Py_INCREF(&THPSizeType);
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if (PyModule_AddObject(module, "Size", (PyObject*)&THPSizeType) < 0) {
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throw python_error();
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}
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}
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