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c902b84e0b
pytorch/torch/csrc/autograd/python_function.cpp
Jason Ansel c902b84e0b Compiled autograd (#103822) 
This branch:
1) converts the autograd tape into an FX graph
2) caches that conversion using a "shadow" graph
3) compiles and runs the generated FX graph instead of the normal autograd

What works currently:
1) Caching, capture, and initial integration
2) Backwards hooks
3) Inlining AotAutograd generated subgraphs
4) torch.compiling the generated FX graph
5) Auto-detecting dynamic shapes based on changes

Future work
1) Larger scale testing
1) Boxed calling convention, so memory can be freed incrementally
1) Support hooks on SavedTensor
1) Additional testing by running eager autograd tests under compiled_autograd.enable()

Pull Request resolved: https://github.com/pytorch/pytorch/pull/103822
Approved by: https://github.com/ezyang, https://github.com/albanD
2023-07-24 21:12:05 +00:00

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#include <torch/csrc/autograd/python_function.h>
#include <ATen/ATen.h>
#include <ATen/SequenceNumber.h>
#include <c10/util/irange.h>
#include <pybind11/pybind11.h>
#include <structmember.h>
#include <torch/csrc/python_headers.h>
#include <torch/csrc/utils/pybind.h>
#include <ATen/FuncTorchTLS.h>
#include <ATen/functorch/DynamicLayer.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/autograd/functions/accumulate_grad.h>
#include <torch/csrc/autograd/functions/basic_ops.h>
#include <torch/csrc/autograd/functions/utils.h>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/csrc/autograd/graph_task.h>
#include <torch/csrc/autograd/python_anomaly_mode.h>
#include <torch/csrc/autograd/python_cpp_function.h>
#include <torch/csrc/autograd/python_hook.h>
#include <torch/csrc/autograd/saved_variable.h>
#include <torch/csrc/dynamo/compiled_autograd.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/utils/python_strings.h>
#include <exception>
#include <functional>
#include <memory>
#include <stdexcept>
#include <string>
#include <tuple>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
using namespace torch;
using namespace torch::autograd;
using at::Tensor;
PyObject* THPFunctionClass = nullptr;
#define THPFunction_assert(condition, ...) \
if (!(condition)) { \
THPUtils_setError(__VA_ARGS__); \
throw python_error(); \
}
// Anonymous namespace for helpful functions used in this file
namespace {
// Throw a python_error with the PyErr state persisted, so that we
// don't lose the error state if the GIL is released when we don't
// have a PyThreadState created beforehand, this is made so that
// even for pure C++ thread without a pre-created PyThreadState could
// also capture the correct error message.
// TODO: This is a temporary approach to allow C++ thread to correctly
// capture Python Error in autograd, remove this when c10 thread pool
// allow to do one time initialization.
// see discussion in https://github.com/pytorch/pytorch/pull/34845
// Follow up issue: https://github.com/pytorch/pytorch/issues/35006
void throw_python_error() {
python_error err;
err.persist();
throw err;
}
} // namespace
namespace torch {
namespace autograd {
// NOTE: this function is written in a way that assumes it's only called for
// backward; it's used by engine.cpp. This is responsible for forwarding a call
// from C++'s Node::apply to a Python method "apply".
auto PyNode::apply(variable_list&& inputs) -> variable_list {
pybind11::gil_scoped_acquire gil;
at::OptionalDeviceGuard _device_guard;
THPFunction* py_fn = (THPFunction*)obj;
// Massage a C++ variable_list into a Python arguments tuple
auto num_inputs = inputs.size();
THPObjectPtr pyInputs(PyTuple_New(num_inputs));
if (!pyInputs)
throw_python_error();
auto& output_info = py_fn->output_info;
for (const auto i : c10::irange(num_inputs)) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
PyObject* input;
if (inputs[i].defined() || !py_fn->materialize_grads ||
(input_metadata(i).was_default_constructed() &&
!py_fn->materialize_non_diff_grads)) {
input = THPVariable_Wrap(inputs[i]);
} else {
input = THPVariable_Wrap(output_info[i].zeros(_device_guard));
}
if (!input)
throw_python_error();
PyTuple_SET_ITEM(pyInputs.get(), i, input);
}
THPObjectPtr apply_fn(PyObject_GetAttrString(obj, "apply"));
if (!apply_fn)
throw_python_error();
THPObjectPtr r(PyObject_CallObject(apply_fn, pyInputs.get()));
if (!r)
throw_python_error();
ensure_tuple(r);
auto& is_variable_input = py_fn->is_variable_input;
int num_outputs = PyTuple_GET_SIZE(r.get());
int num_forward_inputs = is_variable_input.size();
// Returning too many results is ok, but only as long as they're all None.
// Truncate the result tuple in that case.
if (num_outputs > num_forward_inputs) {
bool all_none = true;
for (const auto i : c10::irange(num_forward_inputs, num_outputs)) {
all_none &= PyTuple_GET_ITEM(r.get(), i) == Py_None;
}
if (all_none) {
num_outputs = num_forward_inputs;
r = PyTuple_GetSlice(r.get(), 0, num_forward_inputs);
if (!r)
throw_python_error();
}
}
// Now the number of gradients should match
if (num_outputs != num_forward_inputs) {
std::string msg("function ");
msg += name() + " returned an incorrect number of gradients (expected ";
msg += std::to_string(num_forward_inputs) + ", got ";
msg += std::to_string(num_outputs) + ")";
throw std::runtime_error(msg);
}
// Massage the Python results tuple back into a C++ variable_list
variable_list results;
results.reserve(num_outputs);
for (int i = 0; i != num_outputs; ++i) {
PyObject* output = PyTuple_GET_ITEM(r.get(), i);
bool was_variable = is_variable_input[i];
if (!was_variable) {
if (output != Py_None) {
std::string msg("function ");
msg += name() + " returned a gradient different than None at position ";
msg += std::to_string(i + 1) +
", but the corresponding forward input was not a Variable";
throw std::runtime_error(msg);
}
continue;
}
if (output == Py_None) {
results.emplace_back();
} else {
if (!THPVariable_Check(output)) {
std::string msg("expected Variable or None (got ");
msg += THPUtils_typename(output);
msg += ")";
throw std::runtime_error(msg);
}
results.emplace_back(THPVariable_Unpack(output));
}
}
return results;
}
auto PyNode::is_traceable() -> bool {
pybind11::gil_scoped_acquire gil;
THPObjectPtr forward_class{PyObject_GetAttrString(obj, "_forward_cls")};
if (!forward_class)
throw_python_error();
THPObjectPtr traceable_py_bool{
PyObject_GetAttrString(forward_class, "is_traceable")};
if (!traceable_py_bool)
throw_python_error();
return traceable_py_bool == Py_True;
}
auto PyNode::release_variables() -> void {
// This function is called as part of the Node destructor!
// Since this object might be kept alive by C++, it is possible
// that the python interpreter is already dead here. In that case
// we just leak the saved objects.
if (Py_IsInitialized()) {
pybind11::gil_scoped_acquire gil;
auto f = (THPFunction*)obj;
f->saved_variables.clear();
f->has_freed_buffers = 1;
}
}
auto PyNode::name() const -> std::string {
pybind11::gil_scoped_acquire gil;
auto f = (THPFunction*)obj;
auto name = std::string(Py_TYPE(f)->tp_name);
return name;
}
#ifdef TORCH_COMPILED_AUTOGRAD
void PyNode::compiled_args(CompiledNodeArgs& args) {
static PyObject* method_name =
PyUnicode_InternFromString("_compiled_autograd_key");
THPObjectPtr pykey(PyObject_CallMethodNoArgs(obj, method_name));
if (!pykey)
throw_python_error();
TORCH_CHECK(
PyTuple_CheckExact(pykey.get()),
"_compiled_autograd_key shoud return tuple of ints");
auto size = PyTuple_GET_SIZE(pykey.get());
TORCH_INTERNAL_ASSERT(size > 0);
// first value is unique ID of the AotAutograd graph
ssize_t key = PyLong_AsSsize_t(PyTuple_GET_ITEM(pykey.get(), 0));
if (C10_UNLIKELY(key < 0)) {
TORCH_CHECK(PyErr_Occurred(), "key must be positive");
throw_python_error();
}
args.collect_size(static_cast<size_t>(key));
args.collect_size(size);
auto f = (THPFunction*)obj;
f->compiled_autograd_symints.clear();
f->compiled_autograd_symints.reserve(size - 1);
for (const auto i : c10::irange(1, size)) {
auto val = PyLong_AsSsize_t(PyTuple_GET_ITEM(pykey.get(), i));
if (C10_UNLIKELY(val == -1 && PyErr_Occurred()))
throw_python_error();
f->compiled_autograd_symints.emplace_back(val);
}
// AotAutograd symints are all dynamic
auto prior = args.set_default_dyn_type(SizeInput::DYNAMIC);
args.collect(f->compiled_autograd_symints);
args.set_default_dyn_type(prior);
args.collect(f->saved_variables);
args.collect(f->materialize_grads);
args.collect(f->is_variable_input);
args.collect(f->needs_input_grad);
args.collect(f->materialize_non_diff_grads);
args.collect(f->output_info);
args.collect(f->input_info);
}
variable_list PyNode::apply_with_saved(
const variable_list& inputs,
SwapSavedVariables& saved) {
auto f = (THPFunction*)obj;
TORCH_INTERNAL_ASSERT(!f->compiled_autograd_tracing);
saved.before(f->compiled_autograd_symints);
saved.before(f->saved_variables);
saved.before(f->needs_input_grad);
saved.before(f->materialize_non_diff_grads);
saved.before(f->output_info);
saved.before(f->input_info);
f->compiled_autograd_tracing = true;
auto result = apply(variable_list(inputs));
f->compiled_autograd_tracing = false;
saved.after(f->compiled_autograd_symints);
saved.after(f->saved_variables);
saved.after(f->needs_input_grad);
saved.after(f->materialize_non_diff_grads);
saved.after(f->output_info);
saved.after(f->input_info);
return result;
}
#endif
} // namespace autograd
} // namespace torch
// Traverse and clear are required for supporting Python's GC cycle handling.
static int THPFunction_traverse(THPFunction* self, visitproc visit, void* arg) {
// NB: We should not traverse PyObbject stored on PyNode, since we only hold
// as weak reference to the PyNode.
Py_VISIT(self->to_save);
Py_VISIT(self->non_differentiable);
Py_VISIT(self->dirty_tensors);
Py_VISIT(self->saved_for_forward);
return 0;
}
static int THPFunction_clear(THPFunction* self) {
// Note that the cdata might not be expired yet in the case where this
// object is part of a cycle and the GC happens to tp_clear this PyObject
// before the other ones that trigger the de-allocation of the cdata
Py_CLEAR(self->needs_input_grad);
Py_CLEAR(self->to_save);
Py_CLEAR(self->non_differentiable);
Py_CLEAR(self->dirty_tensors);
Py_CLEAR(self->saved_for_forward);
self->output_info.clear();
self->input_info.clear();
self->saved_variables.clear();
self->is_variable_input.clear();
return 0;
}
static void THPFunction_dealloc(THPFunction* self) {
// Why is this guaranteed to be true? Suppose that self->cdata is non-null
// (otherwise the condition is trivially true). Then there is a PyNode
// which contains an owning reference to this object. But we are only
// allowed to clear if all owning references are gone! Contradiction.
//
// However, note that THPFunction_clear is typically called in the shared_ptr
// destructor of PyNode; in that case, per
// https://cplusplus.github.io/LWG/lwg-active.html#2751 it's not currently
// specified in the standard that this is guaranteed. If you see this
// assert triggering in the wild, feel free to comment it out. They're
// likely to standardize that you ARE guaranteed to see the weak pointers
// as expired in the destructor in the future, so we'll keep this for now.
TORCH_INTERNAL_ASSERT(self->cdata.expired());
PyObject_GC_UnTrack(self);
THPFunction_clear(self);
self->cdata.~weak_ptr<PyNode>();
self->output_info.~vector();
self->input_info.~vector();
self->saved_variables.~vector();
self->is_variable_input.~vector();
Py_TYPE(self)->tp_free((PyObject*)self);
}
PyObject* THPFunction_new(
PyTypeObject* type,
PyObject* args,
PyObject* kwargs) {
PyObject* obj = type->tp_alloc(type, 0);
if (!obj)
return nullptr;
// Python zero-initializes the object memory, so there's no need to initialize
// most fields
THPFunction* self = (THPFunction*)obj;
// Setup the PyNode later; we can't keep it live here
new (&self->cdata) std::weak_ptr<PyNode>();
new (&self->output_info) std::vector<VariableInfo>();
new (&self->input_info) std::vector<VariableInfo>();
new (&self->saved_variables) std::vector<SavedVariable>();
new (&self->is_variable_input) std::vector<bool>();
self->materialize_grads = true;
self->materialize_non_diff_grads = true;
self->compiled_autograd_tracing = false;
return obj;
}
////////////////////////////////////////////////////////////////////////////////
// Forward
////////////////////////////////////////////////////////////////////////////////
// Bump the counters of all recorded dirty input tensors, adding each of them
// into dirty_inputs. Also does some sanity checking.
static std::unordered_set<at::TensorImpl*> _mark_dirty(THPFunction* self) {
// Increase versions of modified tensors
std::unordered_set<at::TensorImpl*> dirty_inputs;
if (!self->dirty_tensors)
return dirty_inputs;
THPFunction_assert(
PyTuple_Check(self->dirty_tensors),
"autograd "
"internal error: dirty_tensors attribute is expected to be a tuple "
"but is %s",
THPUtils_typename(self->dirty_tensors));
Py_ssize_t num_dirty = PyTuple_GET_SIZE(self->dirty_tensors);
dirty_inputs.reserve(num_dirty);
for (const auto i : c10::irange(num_dirty)) {
PyObject* obj = PyTuple_GET_ITEM(self->dirty_tensors, i);
THPFunction_assert(
THPVariable_Check(obj),
"mark_dirty can "
"only accept variables, but argument %d is of type %s",
i,
THPUtils_typename(obj));
const auto& tensor = THPVariable_Unpack(obj);
dirty_inputs.insert(tensor.unsafeGetTensorImpl());
torch::autograd::impl::bump_version(tensor);
}
// We're not going to ever need this so let's remove references now
Py_CLEAR(self->dirty_tensors);
return dirty_inputs;
}
static std::unordered_set<at::TensorImpl*> _parse_non_differentiable(
THPFunction* self);
// Given a Python tuple of raw output tensors (raw_output), set each of
// the corresponding entries in a different Python tuple (outputs) with
// these tensors wrapped with variables. We save the gradient function (self)
// to the variable if the output requires grad.
//
// There is a considerable amount of complexity to handle if the operation
// that produced these output tensors is inplace. A mapping of *input*
// tensors to variables (t2var) is used to test if this occurred, and
// the set of dirty tensors (dirty_inputs) is used to figure out what to
// do in this case. After this method is run, t2var is extended with
// mappings for output tensors as well.
static void _wrap_outputs(
const std::shared_ptr<PyNode>& cdata,
THPFunction* self,
const variable_list& input_vars,
PyObject* raw_output,
PyObject* outputs,
bool is_executable,
const std::unordered_set<at::TensorImpl*>& to_save_if_setup_context) {
auto cdata_if_executable = is_executable ? cdata : nullptr;
Py_ssize_t num_outputs = PyTuple_GET_SIZE(raw_output);
if (is_executable) {
self->output_info.clear();
self->output_info.reserve(num_outputs);
}
auto non_differentiable = _parse_non_differentiable(self);
auto dirty_inputs = _mark_dirty(self);
std::vector<c10::optional<Variable>> raw_output_vars;
raw_output_vars.reserve(num_outputs);
for (const auto i : c10::irange(num_outputs)) {
PyObject* obj = PyTuple_GET_ITEM(raw_output, i);
// Only process tensors as outputs for autograd purposes.
if (THPVariable_Check(obj)) {
raw_output_vars.emplace_back(THPVariable_Unpack(obj));
} else {
raw_output_vars.emplace_back();
}
}
_jvp_fn_t jvp_user_function = [self](
variable_list inputs,
variable_list grad_inputs) {
pybind11::gil_scoped_acquire gil;
// Massage a C++ variable_list into a Python arguments tuple
// Making sure to introduce the proper None for non-Tensor inputs
auto num_inputs = self->is_variable_input.size();
THPObjectPtr pyInputs(PyTuple_New(num_inputs));
if (!pyInputs)
throw_python_error();
int64_t variable_idx = 0;
for (const auto i : c10::irange(num_inputs)) {
PyObject* input = nullptr;
if (self->is_variable_input[i]) {
if (grad_inputs[variable_idx].defined() || !self->materialize_grads ||
!isDifferentiableType(inputs[variable_idx].scalar_type())) {
input = THPVariable_Wrap(grad_inputs[variable_idx]);
} else {
input = THPVariable_Wrap(at::zeros_like(inputs[variable_idx]));
}
if (!input) {
throw_python_error();
}
variable_idx++;
} else {
Py_INCREF(Py_None);
input = Py_None;
}
PyTuple_SET_ITEM(pyInputs.get(), i, input);
}
THPObjectPtr apply_jvp_fn(
PyObject_GetAttrString((PyObject*)self, "apply_jvp"));
if (!apply_jvp_fn)
throw_python_error();
THPObjectPtr r(PyObject_CallObject(apply_jvp_fn, pyInputs.get()));
if (!r)
throw_python_error();
ensure_tuple(r);
// Massage the Python results tuple back into a C++ variable_list
// Don't do any check on the number of results here as
// it is handled by the caller
const int num_outputs = PyTuple_GET_SIZE(r.get());
variable_list results;
results.reserve(num_outputs);
for (const auto i : c10::irange(num_outputs)) {
PyObject* output = PyTuple_GET_ITEM(r.get(), i);
if (output == Py_None) {
results.emplace_back();
} else {
TORCH_CHECK(
THPVariable_Check(output),
"expected Variable or None (got ",
THPUtils_typename(output),
") for grad output ",
i,
".")
results.emplace_back(THPVariable_Unpack(output));
}
}
return results;
};
// Wrap only the tensor outputs.
auto wrapped_outputs = _wrap_outputs(
input_vars,
non_differentiable,
dirty_inputs,
raw_output_vars,
cdata_if_executable,
std::move(jvp_user_function),
to_save_if_setup_context);
for (const auto i : c10::irange(num_outputs)) {
PyObject* obj = PyTuple_GetItem(raw_output, i);
// Keep the non-tensor outputs as is.
if (!THPVariable_Check(obj)) {
if (is_executable) {
self->output_info.emplace_back();
}
Py_INCREF(obj);
PyTuple_SetItem(outputs, i, obj);
} else {
if (is_executable) {
self->output_info.emplace_back(*wrapped_outputs[i]);
}
PyTuple_SetItem(outputs, i, THPVariable_Wrap(*wrapped_outputs[i]));
}
}
}
static void _get_tensors_to_save(
THPFunction* self,
std::unordered_set<at::TensorImpl*>& to_save_if_setup_context,
std::vector<c10::optional<at::Tensor>>& tensors_to_save,
bool overridden_setup_context,
bool is_executable) {
if (self->saved_for_forward && overridden_setup_context) {
// We look at saved_for_forward here purely for the purpose of populating
// to_save_if_setup_context, the actual saving is not done here.
THPFunction_assert(
PyTuple_Check(self->saved_for_forward),
"autograd internal "
"error: saved_for_forward attribute is expected to be a tuple but is %s",
THPUtils_typename(self->saved_for_forward));
Py_ssize_t num_saved_for_forward =
PyTuple_GET_SIZE(self->saved_for_forward);
for (const auto i : c10::irange(num_saved_for_forward)) {
PyObject* obj = PyTuple_GET_ITEM(self->saved_for_forward, i);
if (THPVariable_Check(obj)) {
const auto& tensor = THPVariable_Unpack(obj);
to_save_if_setup_context.insert(tensor.unsafeGetTensorImpl());
}
}
}
if (self->to_save) {
THPFunction_assert(
PyTuple_Check(self->to_save),
"autograd internal "
"error: to_save attribute is expected to be a tuple but is %s",
THPUtils_typename(self->to_save));
Py_ssize_t num_saved = PyTuple_GET_SIZE(self->to_save);
for (const auto i : c10::irange(num_saved)) {
PyObject* obj = PyTuple_GET_ITEM(self->to_save, i);
if (obj == Py_None) {
tensors_to_save.push_back(c10::nullopt);
continue;
} else if (THPVariable_Check(obj)) {
const auto& tensor = THPVariable_Unpack(obj);
if (overridden_setup_context) {
to_save_if_setup_context.insert(tensor.unsafeGetTensorImpl());
}
if (is_executable) {
tensors_to_save.push_back(tensor);
}
} else {
if (is_executable) {
// TODO: We should really just ALWAYS throw an error here, but
// doing so will break some internal tests. We should fix those.
throw torch::TypeError(
"save_for_backward can only save variables, but argument %zu is of "
"type %s",
i,
Py_TYPE(obj)->tp_name);
}
}
}
}
}
// Save any variables that requested by to_save
static void _save_variables(
const std::vector<c10::optional<at::Tensor>>& tensors_to_save,
const std::shared_ptr<PyNode>& cdata_ptr,
THPFunction* self) {
if (!self->to_save)
return;
size_t num_saved = tensors_to_save.size();
self->saved_variables.clear();
self->saved_variables.reserve(num_saved);
for (const auto& opt_tensor : tensors_to_save) {
if (!opt_tensor.has_value()) {
self->saved_variables.emplace_back();
} else {
bool is_output = opt_tensor.value().grad_fn().get() == cdata_ptr.get();
self->saved_variables.emplace_back(opt_tensor.value(), is_output);
}
}
// Free .to_save
Py_CLEAR(self->to_save);
}
// Mark requires_grad = 0 on non-differentiable variables (as per
// non_differentiable)
static std::unordered_set<at::TensorImpl*> _parse_non_differentiable(
THPFunction* self) {
std::unordered_set<at::TensorImpl*> set;
if (!self->non_differentiable)
return set;
THPFunction_assert(
PyTuple_Check(self->non_differentiable),
"autograd "
"internal error: non_differentiable attribute is expected to be a "
"tuple but is %s",
THPUtils_typename(self->non_differentiable));
Py_ssize_t num_nondiff = PyTuple_GET_SIZE(self->non_differentiable);
set.reserve(num_nondiff);
for (const auto i : c10::irange(num_nondiff)) {
PyObject* t = PyTuple_GET_ITEM(self->non_differentiable, i);
THPFunction_assert(
THPVariable_Check(t),
"mark_non_differentiable "
"only accepts variable arguments, but got %s",
THPUtils_typename(t));
set.insert(THPVariable_Unpack(t).unsafeGetTensorImpl());
}
Py_CLEAR(self->non_differentiable);
return set;
}
struct UnpackedInput {
THPObjectPtr input_tuple;
variable_list input_vars;
};
struct InputFlags {
bool is_executable = false;
edge_list next_edges;
THPObjectPtr needs_input_grad;
std::vector<bool> is_variable_input;
};
template <bool enforce_variables>
std::pair<UnpackedInput, InputFlags> unpack_input(PyObject* args) {
UnpackedInput unpacked;
InputFlags flags;
auto num_args = PyTuple_GET_SIZE(args);
unpacked.input_tuple = PyTuple_New(num_args);
flags.needs_input_grad = PyTuple_New(num_args);
for (const auto i : c10::irange(num_args)) {
PyObject* arg = PyTuple_GET_ITEM(args, i);
bool is_variable = THPVariable_Check(arg);
flags.is_variable_input.push_back(is_variable);
if (!is_variable) {
// TODO: remove this code path once Variable and Tensor are merged in
// Python
if (enforce_variables) {
THPUtils_setError(
"expected a Tensor argument, but got %s", THPUtils_typename(arg));
throw python_error();
}
Py_INCREF(Py_False);
PyTuple_SET_ITEM(flags.needs_input_grad.get(), i, Py_False);
} else {
const auto& tensor = THPVariable_Unpack(arg);
unpacked.input_vars.push_back(tensor);
PyObject* needs_grad = tensor.requires_grad() ? Py_True : Py_False;
Py_INCREF(needs_grad);
PyTuple_SET_ITEM(flags.needs_input_grad.get(), i, needs_grad);
}
Py_INCREF(arg);
PyTuple_SET_ITEM(unpacked.input_tuple.get(), i, arg);
}
flags.is_executable =
GradMode::is_enabled() && any_variable_requires_grad(unpacked.input_vars);
flags.next_edges =
(flags.is_executable ? collect_next_edges(unpacked.input_vars)
: edge_list());
return std::make_pair(std::move(unpacked), std::move(flags));
}
// Given a prim::PythonOp node, _append_subgraph creates a subgraph such that:
// (1) It has the same inputs as the prim::PythonOp node
// (2) The intermediate nodes used in the PythonOp are cloned and stored in the
// subgraph (3) trace_outputs stores the Value* objects, before a new trace
// value is assigned by the prim::PythonOp node and helps to eventually route
// the outputs of the subgraph correctly This newly created subgraph is then
// added to the prim::PythonOp node as a subgraph attribute
static void _append_subgraph(
torch::jit::Node* node,
torch::jit::Graph* graph,
std::vector<torch::jit::Value*> trace_outputs,
bool unpack_output) {
using Value = torch::jit::Value;
node->g_(
torch::jit::attr::Subgraph,
std::make_shared<torch::jit::Graph>(graph->current_scope()));
auto subgraph = node->g(torch::jit::attr::Subgraph);
std::unordered_map<Value*, Value*> value_map;
auto value_map_func = [&](Value* v) { return value_map.at(v); };
for (size_t i = 0; i < node->inputs().size(); ++i) {
auto subgraph_input = subgraph->addInput();
subgraph_input->copyMetadata(node->inputs().at(i));
value_map[node->inputs().at(i)] = subgraph_input;
}
// Find node position in owning block, all subsequent nodes after are added to
// subgraph
auto owning_block = node->owningBlock();
auto it = std::find(
owning_block->nodes().begin(), owning_block->nodes().end(), node);
// Skip TupleUnpack node if created
if (!unpack_output) {
it++;
}
for (it++; it != owning_block->nodes().end(); ++it) {
torch::jit::Node* node = *it;
auto* clone_node =
subgraph->insertNode(subgraph->createClone(node, value_map_func));
for (size_t i = 0; i < node->outputs().size(); ++i) {
value_map[node->outputs()[i]] = clone_node->outputs()[i];
auto trace_it = std::find(
trace_outputs.begin(), trace_outputs.end(), node->outputs()[i]);
if (trace_it != trace_outputs.end()) {
subgraph->registerOutput(clone_node->outputs()[i]);
}
}
}
}
static torch::jit::Node* _trace_pre_record(
PyObject* op_obj,
PyObject* input_objects,
const variable_list& input_vars) {
if (!jit::tracer::isTracing()) {
return nullptr;
}
// Save scalar args and the calling convention
auto num_args = PyTuple_GET_SIZE(input_objects);
pyobj_list scalar_args;
std::string arg_types;
arg_types.reserve(num_args);
scalar_args.reserve(num_args);
for (const auto i : c10::irange(num_args)) {
PyObject* arg_object = PyTuple_GET_ITEM(input_objects, i);
if (THPVariable_Check(arg_object)) {
arg_types.push_back('d');
} else {
arg_types.push_back('c');
Py_INCREF(arg_object);
scalar_args.emplace_back(arg_object);
}
}
Py_INCREF(op_obj);
auto pyobj = THPObjectPtr(op_obj);
return jit::tracer::preRecordPythonTrace(
std::move(pyobj), arg_types, input_vars, std::move(scalar_args));
}
static void _trace_post_record(
torch::jit::Node* node,
PyObject* op_obj,
const variable_list& input_vars,
PyObject* output_objects,
bool is_inplace,
bool unpack_output) {
if (!jit::tracer::isTracing()) {
return;
}
node->i_(jit::attr::inplace, is_inplace);
if (PyObject* module_name = PyDict_GetItemString(
((PyTypeObject*)op_obj)->tp_dict, "__module__")) {
if (auto ptr = PyUnicode_AsUTF8(module_name)) {
node->s_(jit::attr::module, std::string(ptr));
}
}
// Isolate C variable ptrs in a vector
int num_outputs = PyTuple_GET_SIZE(output_objects);
auto graph = node->owningGraph();
node->addOutput();
auto old_node = node;
if (!unpack_output) {
std::vector<at::TypePtr> tuple_values(num_outputs, at::TensorType::get());
auto tuple_type = at::TupleType::create(std::move(tuple_values));
// Original type is tuple of tensors "without" element type and shape.
// The missed parts will be added below.
node->output()->setType(std::move(tuple_type));
auto unpacked = graph->createTupleUnpack(node->output())->insertAfter(node);
node = unpacked;
}
std::vector<torch::jit::Value*> trace_outputs;
for (const auto i : c10::irange(num_outputs)) {
PyObject* obj = PyTuple_GET_ITEM(output_objects, i);
if (THPVariable_Check(obj)) {
auto value = node->outputs()[i];
const auto& tensor = THPVariable_Unpack(obj);
if (tensor.defined()) {
value->inferTypeFrom(tensor);
trace_outputs.push_back(jit::tracer::getValueTrace(tensor));
jit::tracer::setValueTrace(tensor, value);
}
}
}
py::object onnx_globals = py::module::import("torch.onnx._globals");
py::bool_ is_in_onnx_export =
py::module::import("torch.onnx.__init__").attr("is_in_onnx_export");
py::bool_ is_autograd_inlining_enabled =
py::cast<bool>(onnx_globals.attr("GLOBALS").attr("autograd_inlining"));
if (py::cast<bool>(is_in_onnx_export) &&
py::cast<bool>(is_autograd_inlining_enabled)) {
_append_subgraph(old_node, graph, std::move(trace_outputs), unpack_output);
}
// If TupleUnpack operator is created, we copy its output type back
// to the original tuple type.
if (!unpack_output) {
std::vector<at::TypePtr> new_tuple_values;
for (const auto i : c10::irange(num_outputs)) {
auto ptr = node->outputs()[i]->type();
new_tuple_values.push_back(ptr);
}
auto tuple_type = at::TupleType::create(std::move(new_tuple_values));
// The i-th tuple element receives a new tensor type with element type and
// shape.
old_node->output()->setType(std::move(tuple_type));
}
}
PyObject* process_outputs(
PyObject* op_obj,
const std::shared_ptr<PyNode>& cdata,
THPFunction* grad_fn,
const UnpackedInput& unpacked,
PyObject* inputs,
THPObjectPtr&& raw_output,
bool is_executable,
torch::jit::Node* node,
bool overridden_setup_context) {
bool unpack_output = ensure_tuple(raw_output);
auto num_outputs = PyTuple_GET_SIZE(raw_output.get());
THPObjectPtr outputs(PyTuple_New(num_outputs));
if (!outputs)
throw python_error();
cdata->clear_input_metadata();
// Record type, device, and size information about inputs
if (is_executable) {
grad_fn->input_info.clear();
grad_fn->input_info.reserve(unpacked.input_vars.size());
for (auto& var : unpacked.input_vars) {
grad_fn->input_info.emplace_back(var);
}
}
std::unordered_set<at::TensorImpl*> to_save_if_setup_context{};
std::vector<c10::optional<at::Tensor>> tensors_to_save{};
_get_tensors_to_save(
grad_fn,
to_save_if_setup_context,
tensors_to_save,
overridden_setup_context,
is_executable);
bool is_inplace = static_cast<bool>(grad_fn->dirty_tensors);
_wrap_outputs(
cdata,
grad_fn,
unpacked.input_vars,
raw_output,
outputs,
is_executable,
to_save_if_setup_context);
_trace_post_record(
node, op_obj, unpacked.input_vars, outputs, is_inplace, unpack_output);
// It is important that creating the SavedVariables happen after the output
// wrapping as the outputs must have their grad_fn/fw_grad properly set before
// we save them.
if (is_executable) {
_save_variables(tensors_to_save, cdata, grad_fn);
} else {
// Remove unnecessary attributes
Py_XDECREF(grad_fn->to_save);
grad_fn->to_save = nullptr;
Py_XDECREF(grad_fn->non_differentiable);
grad_fn->non_differentiable = nullptr;
}
Py_XDECREF(grad_fn->saved_for_forward);
grad_fn->saved_for_forward = nullptr;
// Unpack the output, unless .forward() returned a tuple
if (unpack_output) {
PyObject* output = PyTuple_GET_ITEM(outputs.get(), 0);
Py_INCREF(output);
return output;
}
return outputs.release();
}
PyObject* THPFunction_name(PyObject* self, PyObject* noargs) {
HANDLE_TH_ERRORS
auto cdata = ((THPFunction*)self)->cdata.lock();
TORCH_CHECK(
cdata,
"Attribute 'name' is invalid for this instance of _C._FunctionBase. "
"Accessing this attribute directly on an instance of autograd.Function is a legacy "
"access pattern that is no longer supported. For examples on how to use new-style "
"autograd functions, see "
"https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function ");
return THPUtils_packString(cdata->name());
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_maybe_clear_saved_tensors(
PyObject* self,
PyObject* noargs) {
HANDLE_TH_ERRORS;
auto cdata = ((THPFunction*)self)->cdata.lock();
if (!get_current_graph_task_keep_graph()) {
cdata->release_variables();
}
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
namespace {
THPObjectPtr make_ctx_input_tuple(
THPFunction* ctx,
const UnpackedInput& unpacked_input,
int64_t num_args) {
THPObjectPtr ctx_input_tuple(PyTuple_New(num_args + 1));
if (!ctx_input_tuple)
return {};
Py_INCREF(ctx);
PyTuple_SET_ITEM(ctx_input_tuple.get(), 0, (PyObject*)ctx);
for (const auto i : c10::irange(num_args)) {
PyObject* arg = PyTuple_GET_ITEM(unpacked_input.input_tuple.get(), i);
Py_INCREF(arg);
PyTuple_SET_ITEM(ctx_input_tuple.get(), i + 1, arg);
}
return ctx_input_tuple;
}
THPObjectPtr make_ctx_input_output_tuple(
THPFunction* ctx,
UnpackedInput& unpacked_input,
PyObject* output) {
THPObjectPtr result(PyTuple_New(3));
if (!result)
return {};
Py_INCREF(ctx);
Py_INCREF(unpacked_input.input_tuple.get());
Py_INCREF(output);
PyTuple_SET_ITEM(result.get(), 0, (PyObject*)ctx);
PyTuple_SET_ITEM(result.get(), 1, unpacked_input.input_tuple.get());
PyTuple_SET_ITEM(result.get(), 2, output);
return result;
}
} // namespace
static PyObject* THPFunction_setup_context = nullptr;
static PyObject* get_base_setup_context() {
if (THPFunction_setup_context != nullptr) {
return THPFunction_setup_context;
}
auto module = THPObjectPtr(PyImport_ImportModule("torch.autograd.function"));
if (!module)
return nullptr;
auto function =
THPObjectPtr(PyObject_GetAttrString(module, "_SingleLevelFunction"));
if (!function)
return nullptr;
// setup_context gets "leaked" - we return a new reference and hold onto it
// forever.
auto setup_context = PyObject_GetAttrString(function, "setup_context");
if (!setup_context)
return nullptr;
THPFunction_setup_context = setup_context;
return THPFunction_setup_context;
}
PyObject* THPFunction_apply(PyObject* cls, PyObject* inputs) {
HANDLE_TH_ERRORS
// save a local copy of seq_id before it gets incremented
int seq_id = at::sequence_number::peek();
auto info_pair = unpack_input<false>(inputs);
UnpackedInput& unpacked_input = info_pair.first;
InputFlags& input_info = info_pair.second;
// Call record function after all the inputs have been decoded, but
// before context has been allocated.
RECORD_FUNCTION(
((PyTypeObject*)cls)->tp_name,
std::vector<c10::IValue>(
unpacked_input.input_vars.begin(), unpacked_input.input_vars.end()),
seq_id);
const auto& functorch_tls = at::functorch::functorchTLSAccessor();
if (functorch_tls) {
// autograd.Function support for functorch is handled in Python.
// If we have gotten here, then either we are dealing with a
// torch.autograd.function._SingleLevelFunction, or something in
// the implementation went wrong.
// The following code is useful for debugging when something goes wrong
// because it'll raise a loud error (instead of being silently incorrect).
functorch_tls->checkSupportsSingleLevelAutogradFunction();
}
THPObjectPtr backward_cls(PyObject_GetAttrString(cls, "_backward_cls"));
if (!backward_cls)
return nullptr;
THPObjectPtr ctx_obj(PyObject_CallFunctionObjArgs(backward_cls, nullptr));
if (!ctx_obj)
return nullptr;
THPFunction* ctx = (THPFunction*)ctx_obj.get();
auto cdata =
std::shared_ptr<PyNode>(new PyNode(std::move(ctx_obj)), deleteNode);
ctx->cdata = cdata;
// Record input nodes if tracing
auto* node = _trace_pre_record(cls, inputs, unpacked_input.input_vars);
// Initialize backward function (and ctx)
bool is_executable = input_info.is_executable;
cdata->set_next_edges(std::move(input_info.next_edges));
ctx->needs_input_grad = input_info.needs_input_grad.release();
ctx->is_variable_input = std::move(input_info.is_variable_input);
// autograd.Function may optionally override a setup_context staticmethod.
// In this case, autograd.Function.forward does NOT accept a ctx object.
// Determine if this is the case.
auto cls_setup_context =
THPObjectPtr(PyObject_GetAttrString(cls, "setup_context"));
if (!cls_setup_context) {
return nullptr;
}
auto orig_setup_context = get_base_setup_context();
if (!orig_setup_context) {
return nullptr;
}
auto overridden_setup_context = cls_setup_context.get() != orig_setup_context;
auto num_args = PyTuple_GET_SIZE(inputs);
// Call forward
THPObjectPtr output;
{
AutoGradMode grad_mode(false);
at::AutoFwGradMode fw_grad_mode(false);
THPObjectPtr forward_fn(PyObject_GetAttrString(cls, "forward"));
if (!forward_fn)
return nullptr;
if (overridden_setup_context) {
// call forward followed by setup_context
output = PyObject_CallObject(forward_fn, unpacked_input.input_tuple);
if (!output) {
return nullptr;
}
// signature is setup_context(ctx, inputs, output)
auto ctx_input_output_tuple =
make_ctx_input_output_tuple(ctx, unpacked_input, output);
if (!ctx_input_output_tuple) {
return nullptr;
}
THPObjectPtr setup_context_fn(
PyObject_GetAttrString(cls, "setup_context"));
auto result =
PyObject_CallObject(setup_context_fn, ctx_input_output_tuple);
if (!result) {
return nullptr;
}
} else {
// call forward
auto ctx_input_tuple =
make_ctx_input_tuple(ctx, unpacked_input, num_args);
if (!ctx_input_tuple) {
return nullptr;
}
output = PyObject_CallObject(forward_fn, ctx_input_tuple);
}
if (!output)
return nullptr;
}
return process_outputs(
cls,
cdata,
ctx,
unpacked_input,
inputs,
std::move(output),
is_executable,
node,
overridden_setup_context);
END_HANDLE_TH_ERRORS
}
////////////////////////////////////////////////////////////////////////////////
// Other methods / attributes
////////////////////////////////////////////////////////////////////////////////
PyObject* THPFunction__register_hook_dict(PyObject* _self, PyObject* _var) {
HANDLE_TH_ERRORS
THPUtils_assert(
THPVariable_Check(_var), "_register_hook_dict expected a Tensor");
THPVariable* var = reinterpret_cast<THPVariable*>(_var);
const auto& tensor = THPVariable_Unpack(var);
std::unique_ptr<FunctionPreHook> hook(
new PyFunctionTensorPreHook(var->backward_hooks, tensor.output_nr()));
auto self = (THPFunction*)_self;
auto cdata = self->cdata.lock();
TORCH_CHECK(
cdata,
"Attribute '_register_hook_dict' is invalid for this instance of _C._FunctionBase. "
"Accessing this attribute directly on an instance of autograd.Function is a legacy "
"access pattern that is no longer supported. For examples on how to use new-style "
"autograd functions, see "
"https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function ");
cdata->add_tensor_pre_hook(std::move(hook));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_register_hook(PyObject* _self, PyObject* hook) {
HANDLE_TH_ERRORS
auto self = (THPFunction*)_self;
auto cdata = self->cdata.lock();
TORCH_CHECK(
cdata,
"Attribute 'register_hook' is invalid for this instance of _C._FunctionBase. "
"Accessing this attribute directly on an instance of autograd.Function is a legacy "
"access pattern that is no longer supported. For examples on how to use new-style "
"autograd functions, see "
"https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function ");
return torch::autograd::registerFunctionHook(*cdata, hook);
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_register_prehook(PyObject* _self, PyObject* hook) {
HANDLE_TH_ERRORS
auto self = (THPFunction*)_self;
auto cdata = self->cdata.lock();
TORCH_CHECK(
cdata,
"Attribute 'register_prehook' is invalid for this instance of _C._FunctionBase. "
"Accessing this attribute directly on an instance of autograd.Function is a legacy "
"access pattern that is no longer supported. For examples on how to use new-style "
"autograd functions, see "
"https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function ");
return torch::autograd::registerFunctionPreHook(*cdata, hook);
END_HANDLE_TH_ERRORS
}
int THPFunction_set_materialize_grads(
THPFunction* self,
PyObject* value,
void* unused) {
HANDLE_TH_ERRORS
if (!PyBool_Check(value)) {
THPUtils_invalidArguments(
value, nullptr, "set_materialize_grads", 1, "(bool)");
return -1;
}
self->materialize_grads = (value == Py_True);
return 0;
END_HANDLE_TH_ERRORS_RET(-1)
}
PyObject* THPFunction_get_materialize_non_diff_grads(
THPFunction* self,
void* _unused) {
HANDLE_TH_ERRORS
if (self->materialize_non_diff_grads) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
int THPFunction_set_materialize_non_diff_grads(
THPFunction* self,
PyObject* value,
void* unused) {
HANDLE_TH_ERRORS
if (!PyBool_Check(value)) {
THPUtils_invalidArguments(
value, nullptr, "set_materialize_non_diff_grads", 1, "(bool)");
return -1;
}
self->materialize_non_diff_grads = (value == Py_True);
return 0;
END_HANDLE_TH_ERRORS_RET(-1)
}
static PyObject* unpack_saved_variables(
THPFunction* self,
const std::function<PyObject*(const Variable&)>& unpack_fn) {
THPUtils_assert(!self->has_freed_buffers, ERR_BACKWARD_TWICE);
auto& saved_variables = self->saved_variables;
if (saved_variables.empty())
return PyTuple_New(0);
int num_saved = saved_variables.size();
THPObjectPtr saved(PyTuple_New(num_saved));
if (!saved)
return nullptr;
auto saved_for = self->cdata.lock();
// This is really a true assert, because we've already tested for the
// self->has_freed_buffers case at the beginning of this function:
// buffers are freed when PyNode dies; if the buffers are not freed,
// PyNode must be live. (Note that the buffers could be freed
// even though the PyNode is live, but that doesn't matter here
// because we will never hit this line of code if the buffers are freed--
// and in any case saved_for will be non-NULL.)
TORCH_INTERNAL_ASSERT(saved_for);
for (const auto i : c10::irange(num_saved)) {
auto unpacked_var = saved_variables[i].unpack(saved_for);
THPObjectPtr value;
if (!unpacked_var.defined()) {
Py_INCREF(Py_None);
value = Py_None;
} else {
value = unpack_fn(unpacked_var);
}
PyTuple_SET_ITEM(saved.get(), i, value.release());
}
return saved.release();
}
PyObject* THPFunction_saved_tensors(THPFunction* self, void* _unused) {
HANDLE_TH_ERRORS
if (self->saved_for_forward) {
Py_INCREF(self->saved_for_forward);
return self->saved_for_forward;
} else {
return unpack_saved_variables(
self, [](const Variable& var) { return THPVariable_Wrap(var); });
}
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_saved_variables(THPFunction* self, void* _unused) {
HANDLE_TH_ERRORS
auto r = PyErr_WarnEx(
PyExc_DeprecationWarning,
"'saved_variables' is deprecated; use 'saved_tensors'",
0);
if (r != 0)
throw python_error();
return unpack_saved_variables(
self, [](const Variable& var) { return THPVariable_Wrap(var); });
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_is_compiled_autograd_tracing(
PyObject* self,
PyObject* _unused) {
HANDLE_TH_ERRORS
if (((THPFunction*)self)->compiled_autograd_tracing) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_get_compiled_autograd_symints(
PyObject* _self,
PyObject* _unused) {
HANDLE_TH_ERRORS
auto self = (THPFunction*)_self;
auto size = self->compiled_autograd_symints.size();
PyObject* result = PyTuple_New(size);
if (!result) {
throw python_error();
}
for (const auto i : c10::irange(size)) {
PyTuple_SET_ITEM(
result,
i,
py::cast(self->compiled_autograd_symints[i]).release().ptr());
}
return result;
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_raw_saved_tensors(THPFunction* self, void* _unused) {
HANDLE_TH_ERRORS
// User tries to access saved variables after they have been freed
THPUtils_assert(!self->has_freed_buffers, ERR_BACKWARD_TWICE);
const auto& saved_variables = self->saved_variables;
if (saved_variables.empty())
return PyTuple_New(0);
size_t num_saved = saved_variables.size();
THPObjectPtr saved(PyTuple_New(num_saved));
if (!saved) {
return nullptr;
}
for (const auto i : c10::irange(num_saved)) {
py::object obj =
py::cast(saved_variables[i], py::return_value_policy::reference);
PyTuple_SET_ITEM(saved.get(), i, obj.release().ptr());
}
return saved.release();
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_next_functions(THPFunction* self, void* _unused) {
HANDLE_TH_ERRORS
auto cdata = self->cdata.lock();
TORCH_CHECK(
cdata,
"Attribute 'next_functions' is invalid for this instance of _C._FunctionBase. "
"Accessing this attribute directly on an instance of autograd.Function is a legacy "
"access pattern that is no longer supported. For examples on how to use new-style "
"autograd functions, see "
"https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function ");
const auto num_outputs = cdata->num_outputs();
THPObjectPtr result(PyTuple_New(num_outputs));
if (!result)
return nullptr;
for (const auto i : c10::irange(num_outputs)) {
THPObjectPtr fn_tuple(PyTuple_New(2));
if (!fn_tuple)
return nullptr;
const auto& edge = cdata->next_edge(i);
PyObject* fn = functionToPyObject(edge.function);
if (!fn)
return nullptr;
PyTuple_SET_ITEM(fn_tuple.get(), 0, fn);
PyTuple_SET_ITEM(fn_tuple.get(), 1, THPUtils_packInt64(edge.input_nr));
PyTuple_SET_ITEM(result.get(), i, fn_tuple.release());
}
return result.release();
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_metadata(THPFunction* self, void* _unused) {
HANDLE_TH_ERRORS
auto cdata = self->cdata.lock();
// The correct way to solve this problem is to stop exposing grad_fn
// of PyFunctions as THPFunction; instead, we should use THPCppFunction
// like everyone else. But this is a BC-breaking change as it would
// mean that you no longer get the property that grad_fn is a subclass
// of the autograd function class that you defined in the custom case,
// so I didn't fix it here.
TORCH_CHECK(
cdata,
"You attempted to access the anomaly metadata of a custom autograd function "
"but the underlying PyNode has already been deallocated. The most likely "
"reason this occurred is because you assigned x.grad_fn to a local variable "
"and then let the original variable get deallocated. Don't do that! If "
"you really have no way of restructuring your code so this is the case, "
"please file an issue reporting that you are affected by this.");
auto metadata = static_cast<PyAnomalyMetadata*>(cdata->metadata())->dict();
Py_INCREF(metadata);
return metadata;
END_HANDLE_TH_ERRORS
}
typedef PyObject* (*getter)(PyObject*, void*);
typedef int (*setter)(PyObject*, PyObject*, void*);
namespace {
template <PyObject* THPFunction::*ptr>
PyObject* getObject(PyObject* obj, void* _unused) {
auto self = (THPFunction*)obj;
PyObject* value = self->*ptr;
if (!value) {
Py_RETURN_NONE;
}
Py_INCREF(value);
return value;
}
template <PyObject* THPFunction::*ptr>
int setObject(PyObject* obj, PyObject* value, void* _unused) {
auto self = (THPFunction*)obj;
if (value == Py_None) {
value = nullptr;
}
Py_XDECREF((self->*ptr));
Py_XINCREF(value);
self->*ptr = value;
return 0;
}
template <typename M, M THPFunction::*ptr, PyObject* (*Convert)(long)>
PyObject* getMember(PyObject* obj, void* _unused) {
auto self = (THPFunction*)obj;
return Convert(self->*ptr);
}
template <typename M, M autograd::Node::*ptr, PyObject* (*Convert)(long)>
PyObject* getImplMember(PyObject* obj, void* _unused) {
auto self = (THPFunction*)obj;
return Convert(self->cdata.*ptr);
}
PyObject* getRequiresGrad(PyObject* obj, void* _unused) {
Py_RETURN_TRUE;
}
} // namespace
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables)
static struct PyGetSetDef THPFunction_properties[] = {
{"saved_tensors",
(getter)THPFunction_saved_tensors,
nullptr,
nullptr,
nullptr},
{"saved_variables",
(getter)THPFunction_saved_variables,
nullptr,
nullptr,
nullptr},
{"_raw_saved_tensors",
(getter)THPFunction_raw_saved_tensors,
nullptr,
nullptr,
nullptr},
{"next_functions",
(getter)THPFunction_next_functions,
nullptr,
nullptr,
nullptr},
{"to_save",
&getObject<&THPFunction::to_save>,
&setObject<&THPFunction::to_save>,
nullptr,
nullptr},
{"non_differentiable",
&getObject<&THPFunction::non_differentiable>,
&setObject<&THPFunction::non_differentiable>,
nullptr,
nullptr},
{"dirty_tensors",
&getObject<&THPFunction::dirty_tensors>,
&setObject<&THPFunction::dirty_tensors>,
nullptr,
nullptr},
{"saved_for_forward",
&getObject<&THPFunction::saved_for_forward>,
&setObject<&THPFunction::saved_for_forward>,
nullptr,
nullptr},
{"needs_input_grad",
&getObject<&THPFunction::needs_input_grad>,
nullptr,
nullptr,
nullptr},
{"requires_grad", getRequiresGrad, nullptr, nullptr, nullptr},
{"metadata", (getter)THPFunction_metadata, nullptr, nullptr, nullptr},
{"materialize_grads",
nullptr,
(setter)THPFunction_set_materialize_grads,
nullptr,
nullptr},
{"_materialize_non_diff_grads",
(getter)THPFunction_get_materialize_non_diff_grads,
(setter)THPFunction_set_materialize_non_diff_grads,
nullptr,
nullptr},
{nullptr}};
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables)
static struct PyMethodDef THPFunction_methods[] = {
{(char*)"name", THPFunction_name, METH_NOARGS, nullptr},
{(char*)"maybe_clear_saved_tensors",
THPFunction_maybe_clear_saved_tensors,
METH_NOARGS,
nullptr},
{(char*)"apply", THPFunction_apply, METH_CLASS | METH_VARARGS, nullptr},
{(char*)"_register_hook_dict",
THPFunction__register_hook_dict,
METH_O,
nullptr},
{(char*)"register_hook", THPFunction_register_hook, METH_O, nullptr},
{(char*)"register_prehook", THPFunction_register_prehook, METH_O, nullptr},
{(char*)"_is_compiled_autograd_tracing",
THPFunction_is_compiled_autograd_tracing,
METH_NOARGS,
nullptr},
{(char*)"_get_compiled_autograd_symints",
THPFunction_get_compiled_autograd_symints,
METH_NOARGS,
nullptr},
{nullptr}};
PyTypeObject THPFunctionType = {
PyVarObject_HEAD_INIT(nullptr, 0) "torch._C._FunctionBase", /* tp_name */
sizeof(THPFunction), /* tp_basicsize */
0, /* tp_itemsize */
(destructor)THPFunction_dealloc, /* tp_dealloc */
0, /* tp_vectorcall_offset */
nullptr, /* tp_getattr */
nullptr, /* tp_setattr */
nullptr, /* tp_reserved */
nullptr, /* tp_repr */
nullptr, /* tp_as_number */
nullptr, /* tp_as_sequence */
nullptr, /* tp_as_mapping */
nullptr, /* tp_hash */
nullptr, /* tp_call */
nullptr, /* tp_str */
nullptr, /* tp_getattro */
nullptr, /* tp_setattro */
nullptr, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE |
Py_TPFLAGS_HAVE_GC, /* tp_flags */
nullptr, /* tp_doc */
(traverseproc)THPFunction_traverse, /* tp_traverse */
(inquiry)THPFunction_clear, /* tp_clear */
nullptr, /* tp_richcompare */
0, /* tp_weaklistoffset */
nullptr, /* tp_iter */
nullptr, /* tp_iternext */
THPFunction_methods, /* tp_methods */
nullptr, /* tp_members */
THPFunction_properties, /* tp_getset */
nullptr, /* tp_base */
nullptr, /* tp_dict */
nullptr, /* tp_descr_get */
nullptr, /* tp_descr_set */
0, /* tp_dictoffset */
nullptr, /* tp_init */
nullptr, /* tp_alloc */
THPFunction_new /* tp_new */
};
bool THPFunction_initModule(PyObject* module) {
if (PyType_Ready(&THPFunctionType) < 0)
return false;
Py_INCREF(&THPFunctionType);
PyModule_AddObject(module, "_FunctionBase", (PyObject*)&THPFunctionType);
return true;
}
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