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python_arg_flatten.h
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python_arg_flatten.h
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#pragma once
#include <c10/util/hash.h>
#include <c10/util/irange.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/jit/python/pybind.h>
#include <ATen/ATen.h>
#include <functional>
#include <tuple>
#include <vector>
namespace torch::jit::python {
struct IODescriptor {
struct VariableMetadata {
VariableMetadata(const autograd::Variable& var)
: sizes(var.sizes().vec()),
type(var.scalar_type()),
device(var.device()),
requires_grad(var.requires_grad()) {}
bool operator==(const VariableMetadata& o) const {
return std::tie(device, requires_grad, type, sizes) ==
std::tie(o.device, o.requires_grad, o.type, o.sizes);
}
static size_t hash(const VariableMetadata& m) {
return c10::get_hash(m.sizes, m.device, m.requires_grad, m.type);
}
std::vector<int64_t> sizes;
at::ScalarType type;
at::Device device;
bool requires_grad;
};
bool operator==(const IODescriptor& o) const {
return std::tie(structure, metadata, grad_enabled) ==
std::tie(o.structure, o.metadata, o.grad_enabled);
}
static size_t hash(const IODescriptor& o) {
return c10::get_hash(o.structure, o.metadata, o.grad_enabled);
}
void extend(const autograd::variable_list& list) {
metadata.reserve(metadata.size() + list.size());
for (auto& var : list)
metadata.emplace_back(var);
}
// Description of argument structure. Variables are replaced with
// different characters, depending on their flags, beginnings and
// ends of tuples and lists are denoted by a pair of parenthesis
// of their corresponding kind. They should always be paired.
// Example desc: (vv[v(v)v])
// NOTE: if extend() was ever called then metadata.size() can be
// different than the number of 'v's in structure.
std::string structure;
std::vector<std::string> strings;
std::vector<VariableMetadata> metadata;
bool grad_enabled = false;
};
static inline std::ostream& operator<<(
std::ostream& out,
const IODescriptor::VariableMetadata& meta) {
at::Device meta_device = meta.device;
auto& t = at::getDeprecatedTypeProperties(
meta_device.is_cpu() ? at::Backend::CPU : at::Backend::CUDA, meta.type);
out << t << "(requires_grad=" << meta.requires_grad;
if (meta_device.is_cuda()) {
out << ", device=" << meta_device.index();
}
out << ") {";
for (const auto i : c10::irange(meta.sizes.size())) {
if (i > 0)
out << ", ";
out << meta.sizes[i];
}
out << "}";
return out;
}
static inline std::ostream& operator<<(
std::ostream& out,
const IODescriptor& desc) {
out << desc.structure << "\n";
out << " with grad_enabled=" << desc.grad_enabled << "\n";
for (const auto i : c10::irange(desc.metadata.size())) {
out << " with v" << i << " having type " << desc.metadata[i] << "\n";
}
return out;
}
struct ParsedArgs {
// Flat vector of Variables found in arguments
autograd::variable_list vars;
// Metadata describing nesting of objects received from Python and
// metadata of vars and whether grad is enabled.
IODescriptor desc;
void extend(const autograd::variable_list& list) {
if (list.empty())
return;
vars.reserve(vars.size() + list.size());
for (auto& var : list)
vars.emplace_back(var);
desc.extend(list);
}
};
ParsedArgs flatten(py::handle obj);
PyObject* unflatten(
at::ArrayRef<autograd::Variable> vars,
const IODescriptor& structure);
} // namespace torch::jit::python