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#include <c10/util/irange.h>
#include <torch/csrc/jit/python/python_arg_flatten.h>
#include <torch/csrc/utils/python_strings.h>
#include <torch/csrc/utils/six.h>
#include <torch/csrc/autograd/grad_mode.h>
namespace torch {
namespace jit {
namespace python {
using namespace torch::autograd;
using namespace at;
// Alphabet used to describe structure of inputs/outputs (D for desc)
namespace D {
static constexpr char DictOpen = '<';
static constexpr char DictClose = '>';
static constexpr char ListOpen = '[';
static constexpr char ListClose = ']';
static constexpr char TupleOpen = '(';
static constexpr char TupleClose = ')';
static constexpr char Variable = 'v';
static constexpr char Bool = 'b';
static constexpr char Long = 'l';
static constexpr char Double = 'd';
static constexpr char String = 's';
static constexpr char NoneType = 'n';
} // namespace D
namespace {
inline bool PyNone_Check(PyObject* o) {
return o == Py_None;
}
template <typename T>
py::object cast_handle_sequence(std::vector<py::handle> objs) {
auto num_objs = objs.size();
T sequence{num_objs};
for (const auto i : c10::irange(num_objs)) {
sequence[i] = py::reinterpret_borrow<py::object>(objs[i]);
}
return sequence;
}
void flatten_rec(PyObject* obj, ParsedArgs& args) {
auto& structure = args.desc.structure;
if (six::isTuple(obj)) {
structure.push_back(D::TupleOpen);
for (auto item : py::reinterpret_borrow<py::tuple>(obj))
flatten_rec(item.ptr(), args);
structure.push_back(D::TupleClose);
} else if (PyList_Check(obj)) {
structure.push_back(D::ListOpen);
for (auto item : py::reinterpret_borrow<py::list>(obj))
flatten_rec(item.ptr(), args);
structure.push_back(D::ListClose);
} else if (PyDict_Check(obj)) {
auto dict_items = PyDict_Items(obj);
structure.push_back(D::DictOpen);
for (auto item : py::reinterpret_borrow<py::list>(dict_items)) {
flatten_rec(item.ptr(), args);
}
structure.push_back(D::DictClose);
} else if (THPUtils_checkString(obj)) {
string str = THPUtils_unpackString(obj);
args.desc.strings.emplace_back(str);
args.desc.structure.push_back(D::String);
} else if (THPVariable_Check(obj)) {
auto& var = THPVariable_Unpack(obj);
args.vars.push_back(var);
args.desc.metadata.emplace_back(var);
args.desc.structure.push_back(D::Variable);
} else if (PyNone_Check(obj)) {
args.desc.structure.push_back(D::NoneType);
} else if (PyBool_Check(obj)) { // Wrap bools in Bool tensors
at::Tensor var = scalar_to_tensor(at::Scalar(THPUtils_unpackBool(obj)));
args.vars.push_back(var);
args.desc.metadata.emplace_back(var);
args.desc.structure.push_back(D::Bool);
} else if (PyLong_Check(obj)) { // Wrap longs in Long tensors
at::Tensor var = scalar_to_tensor(
at::Scalar(static_cast<int64_t>(THPUtils_unpackLong(obj))));
args.vars.push_back(var);
args.desc.metadata.emplace_back(var);
args.desc.structure.push_back(D::Long);
} else if (PyFloat_Check(obj)) { // Wrap floats in Double tensors
at::Tensor var = scalar_to_tensor(THPUtils_unpackDouble(obj));
args.vars.push_back(var);
args.desc.metadata.emplace_back(var);
args.desc.structure.push_back(D::Double);
} else {
std::string msg =
"Only tuples, lists and Variables are supported as JIT inputs/outputs. "
"Dictionaries and strings are also accepted, but their usage is not "
"recommended. Here, received an input of unsupported type: ";
msg += THPUtils_typename(obj);
throw std::runtime_error(msg);
}
}
} // anonymous namespace
ParsedArgs flatten(py::handle obj) {
ParsedArgs args;
args.desc.grad_enabled = autograd::GradMode::is_enabled();
flatten_rec(obj.ptr(), args);
return args;
}
namespace {
template <typename T>
py::object cast_sequence(std::vector<py::object> objs) {
auto num_objs = objs.size();
T sequence{num_objs};
for (const auto i : c10::irange(num_objs)) {
sequence[i] = std::move(objs[i]);
}
return std::move(sequence);
}
py::object cast_dict(std::vector<py::object> objs) {
auto num_objs = objs.size();
py::dict sequence = {};
for (const auto i : c10::irange(num_objs)) {
py::tuple obj = py::reinterpret_borrow<py::tuple>(objs[i]);
sequence[obj[0]] = obj[1];
}
return std::move(sequence);
}
py::object unflatten_rec(
ArrayRef<Variable>::iterator& var_it,
ArrayRef<Variable>::iterator& var_it_end,
std::string::const_iterator& desc_it,
std::vector<string>::const_iterator& str_it,
std::vector<string>::const_iterator& str_it_end) {
char type = *desc_it++;
if (type == D::TupleOpen) {
std::vector<py::object> objs;
while (*desc_it != D::TupleClose)
objs.push_back(
unflatten_rec(var_it, var_it_end, desc_it, str_it, str_it_end));
++desc_it;
return cast_sequence<py::tuple>(objs);
} else if (type == D::ListOpen) {
std::vector<py::object> objs;
while (*desc_it != D::ListClose)
objs.push_back(
unflatten_rec(var_it, var_it_end, desc_it, str_it, str_it_end));
++desc_it;
return cast_sequence<py::list>(objs);
} else if (type == D::DictOpen) {
std::vector<py::object> objs;
while (*desc_it != D::DictClose) {
objs.push_back(
unflatten_rec(var_it, var_it_end, desc_it, str_it, str_it_end));
}
++desc_it;
return cast_dict(objs);
} else if (type == D::String) {
if (str_it == str_it_end)
throw std::runtime_error("Not enough Variables given to unflatten");
auto str = *str_it++;
return py::reinterpret_borrow<py::object>(THPUtils_packString(str));
} else if (type == D::NoneType) {
return py::reinterpret_borrow<py::object>(py::none());
} else {
// if (type == D::Long || type == D::Double || type == D::Bool ||
// D::Variable) unwrap variables (D::Variable), or unwrap primitive types
// (Long, Double, Bool) as variables for tracer.
if (var_it == var_it_end)
throw std::runtime_error("Not enough Variables given to unflatten");
auto var = *var_it++;
return py::reinterpret_steal<py::object>(THPVariable_Wrap(var));
}
}
} // anonymous namespace
PyObject* unflatten(ArrayRef<Variable> vars, const IODescriptor& desc) {
// NB: We don't do correctness checking on descriptor.
// It has to be a correct bytes object produced by unflatten.
auto vars_it = vars.begin();
auto vars_it_end = vars.end();
auto desc_it = desc.structure.begin();
std::vector<std::string>::const_iterator str_it = desc.strings.begin();
std::vector<std::string>::const_iterator str_end = desc.strings.end();
auto output = unflatten_rec(vars_it, vars_it_end, desc_it, str_it, str_end);
if (vars_it != vars_it_end)
throw std::runtime_error("Too many Variables given to unflatten");
return output.release().ptr();
}
} // namespace python
} // namespace jit
} // namespace torch
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