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pytorch/torch/csrc/autograd/python_function.cpp

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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/PyInterpreter.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/autograd/utils/wrap_outputs.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/profiler/api.h>
#include <torch/csrc/utils/python_numbers.h>
#include <torch/csrc/utils/python_strings.h>
#include <torch/csrc/utils/tensor_dtypes.h>
#include <torch/csrc/autograd/function.h>
#include <functional>
#include <memory>
#include <stdexcept>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
using namespace torch;
using namespace torch::autograd;
using at::Tensor;
PyObject* THPFunctionClass = nullptr;
PyObject* THPGradientEdgeClass = nullptr;
#define THPFunction_assert(condition, ...) \
if (!(condition)) { \
THPUtils_setError(__VA_ARGS__); \
throw python_error(); \
}
// Anonymous namespace for helpful functions used in this file
namespace {
inline void check_legacy_fn_attr_access(
const std::shared_ptr<torch::autograd::Node>& cdata,
const char* attr) {
TORCH_CHECK(
cdata,
"Attribute '",
attr,
"' 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 newstyle autograd functions, see "
"https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function ");
}
// TODO: We shouldn't need to call this function because the engine
// can already persist the errors for us. This still seems to be
// needed for the DistEngine however.
//
// python test/distributed/rpc/test_tensorpipe_agent.py -k
// test_backward_autograd_engine_error
//
// See Note [ Persisting PyErr state across autograd engine threads ]
void throw_python_error() {
python_error err;
err.persist();
throw std::move(err);
}
static PyObject* unpack_saved_variables(
THPFunction* self,
const std::function<PyObject*(const Variable&)>& unpack_fn) {
HANDLE_TH_ERRORS
TORCH_CHECK(!self->has_freed_buffers, ERR_BACKWARD_TWICE);
auto& saved_variables = self->saved_variables;
if (saved_variables.empty())
return PyTuple_New(0);
auto num_saved = saved_variables.size();
THPObjectPtr saved(PyTuple_New(static_cast<Py_ssize_t>(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();
END_HANDLE_TH_ERRORS
}
PyObject* to_py_size(const std::vector<c10::SymInt>& size) {
c10::SymIntArrayRef sym_sizes(size);
auto ret = THPObjectPtr(THPSizeType.tp_alloc(
&THPSizeType, static_cast<Py_ssize_t>(sym_sizes.size())));
if (!ret)
throw python_error();
for (auto i : c10::irange(sym_sizes.size())) {
auto symint = sym_sizes[i];
if (auto maybe_int = symint.maybe_as_int(); maybe_int.has_value()) {
PyTuple_SET_ITEM(ret.get(), i, THPUtils_packInt64(*maybe_int));
} else {
auto py_symint = py::cast(symint).release().ptr();
PyTuple_SET_ITEM(ret.get(), i, py_symint);
}
}
return ret.release();
}
} // namespace
namespace torch::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".
// NOLINTNEXTLINE(*-rvalue-reference*)
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
THPObjectPtr pyInputs(to_py_args(inputs, &_device_guard));
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;
auto num_outputs = PyTuple_GET_SIZE(r.get());
auto num_forward_inputs = static_cast<Py_ssize_t>(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
TORCH_CHECK(
num_outputs == num_forward_inputs,
"function ",
name(),
" returned an incorrect number of gradients (expected ",
num_forward_inputs,
", got ",
num_outputs,
")");
// Massage the Python results tuple back into a C++ variable_list
return to_variable_list(r.get(), is_variable_input);
}
auto PyNode::apply_with_saved_impl(
const variable_list& inputs,
const SwapSavedVariables& saved) -> 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
THPObjectPtr pyInputs(to_py_args(inputs, &_device_guard));
const auto& is_variable_input = py_fn->is_variable_input;
const auto& input_infos = py_fn->input_info;
// input_info only contains info from variable inputs and should be a subset
TORCH_INTERNAL_ASSERT(is_variable_input.size() >= input_infos.size());
// The gradients returned in the backwards need to match the number of inputs
// to the forward, and their metadata, so we pass the fwdInputs
THPObjectPtr fwdInputMetadatas(
PyTuple_New(static_cast<Py_ssize_t>(is_variable_input.size())));
if (!fwdInputMetadatas)
throw python_error();
int offset = 0;
for (const auto i : c10::irange(is_variable_input.size())) {
if (!is_variable_input[i]) {
// input at i is not a variable, skip index
PyTuple_SET_ITEM(fwdInputMetadatas.get(), i, Py_None);
offset++;
continue;
}
const auto& input_info = input_infos[i - offset];
PyObject* device(THPDevice_New(input_info.device));
if (!device)
throw_python_error();
// Metadata is a tuple of 4 elements: (layout, device, dtype, size)
PyObject* fwdInputMetadata = PyTuple_Pack(
4,
autograd::utils::wrap(input_info.layout),
device,
autograd::utils::wrap(input_info.scalar_type),
to_py_size(input_info.size));
if (!fwdInputMetadata)
throw python_error();
PyTuple_SET_ITEM(fwdInputMetadatas.get(), i, fwdInputMetadata);
}
THPObjectPtr saved_tensors(unpack_saved_variables(
py_fn, [](const Variable& var) { return THPVariable_Wrap(var); }));
auto [bwd_idx, maybe_bwd_state_idx] = saved.retrieve_pynode_objs(this);
PyObject* backward_state_idx = Py_None;
if (maybe_bwd_state_idx.has_value()) {
backward_state_idx = THPUtils_packUInt64(maybe_bwd_state_idx.value());
// this might be simplifiable now that we no longer inline
Py_CLEAR(py_fn->compiled_autograd_backward_state);
}
THPObjectPtr r(PyObject_CallMethod(
saved.get_py_compiler(),
"proxy_call_backward",
"OOOiOO",
pyInputs.get(),
fwdInputMetadatas.get(),
saved_tensors.get(),
bwd_idx,
obj,
backward_state_idx));
if (!r)
throw_python_error();
ensure_tuple(r);
// Massage the Python results tuple back into a C++ variable_list
return to_variable_list(r.get(), is_variable_input);
}
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;
}
bool PyNode::is_aot_backward() const {
py::handle handle(obj);
return py::hasattr(py::getattr(handle, "_forward_cls"), "_aot_id");
}
void PyNode::compiled_args(CompiledNodeArgs& args) const {
static PyObject* method_name =
PyUnicode_InternFromString("_compiled_autograd_key");
THPObjectPtr pykey(PyObject_CallMethodObjArgs(obj, method_name, nullptr));
if (!pykey)
throw_python_error();
TORCH_CHECK(
PyTuple_CheckExact(pykey.get()),
"_compiled_autograd_key should return tuple of ints");
auto size = PyTuple_GET_SIZE(pykey.get());
TORCH_INTERNAL_ASSERT(size > 0);
// first value is unique id managed by AUTOGRAD_FUNCTION_COUNTER
auto 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(static_cast<size_t>(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(torch::dynamo::autograd::SizeInput::DYNAMIC);
args.collect(f->compiled_autograd_symints);
args.set_default_dyn_type(prior);
args.collect(f->saved_variables, true); // always unpacked as output in eager
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);
Py_INCREF(obj);
c10::SafePyObject backward_obj(obj, getPyInterpreter());
std::optional<c10::SafePyObject> backward_state_obj;
PyObject* bw_state = f->compiled_autograd_backward_state;
if (args.cond(bw_state != nullptr)) {
Py_INCREF(bw_state);
backward_state_obj = c10::SafePyObject(bw_state, getPyInterpreter());
}
args.collect_pynode_objs(
this, std::move(backward_obj), std::move(backward_state_obj));
}
variable_list PyNode::apply_with_saved(
const variable_list& inputs,
SwapSavedVariables& saved) {
auto f = (THPFunction*)obj;
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);
variable_list result = apply_with_saved_impl(variable_list(inputs), saved);
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;
}
PyObject* PyNode::to_py_args(
const variable_list& inputs,
at::OptionalDeviceGuard* device_guard) {
THPFunction* py_fn = (THPFunction*)obj;
auto zeros_without_gil = [](const VariableInfo& variable,
at::OptionalDeviceGuard& dg) {
pybind11::gil_scoped_release gil;
return variable.zeros(dg);
};
auto num_inputs = inputs.size();
PyObject* pyInputs = PyTuple_New(static_cast<Py_ssize_t>(num_inputs));
if (!pyInputs)
throw_python_error();
auto& output_info = py_fn->output_info;
for (const auto i : c10::irange(num_inputs)) {
PyObject* input = nullptr;
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(zeros_without_gil(output_info[i], *device_guard));
}
if (!input)
throw_python_error();
PyTuple_SET_ITEM(pyInputs, i, input);
}
return pyInputs;
}
variable_list PyNode::to_variable_list(
const PyObject* outputs,
const std::vector<bool>& is_variable_input) {
auto num_outputs = PyTuple_GET_SIZE(outputs);
variable_list results;
results.reserve(num_outputs);
for (int i = 0; i != num_outputs; ++i) {
PyObject* output = PyTuple_GET_ITEM(outputs, i);
bool was_variable = is_variable_input[i];
if (!was_variable) {
TORCH_CHECK(
output == Py_None,
"function ",
name(),
" returned a gradient different than None at position ",
i + 1,
", but the corresponding forward input was not a Variable");
continue;
}
if (output == Py_None) {
results.emplace_back();
} else {
TORCH_CHECK(
THPVariable_Check(output),
"expected Variable or None (got ",
THPUtils_typename(output),
")");
results.emplace_back(THPVariable_Unpack(output));
}
}
return results;
}
} // namespace torch::autograd
// 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->compiled_autograd_backward_state);
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->compiled_autograd_backward_state);
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);
}
static 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;
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 ",
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 ",
i,
" is of type ",
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<std::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(static_cast<Py_ssize_t>(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;
};
auto view_as_self_fn = [](const at::Tensor& x) -> at::Tensor {
pybind11::gil_scoped_acquire gil;
THPObjectPtr py_x(THPVariable_Wrap(x));
THPObjectPtr py_view_as_method(PyObject_GetAttrString(py_x, "view_as"));
if (!py_view_as_method)
throw python_error();
THPObjectPtr args(PyTuple_Pack(1, py_x.get()));
if (!args)
throw python_error();
THPObjectPtr result(PyObject_CallObject(py_view_as_method, args));
if (!result)
throw python_error();
return THPVariable_Unpack(result);
};
// Wrap only the tensor outputs.
auto wrapped_outputs = _wrap_outputs(
input_vars,
non_differentiable,
dirty_inputs,
raw_output_vars,
cdata_if_executable,
jvp_user_function,
to_save_if_setup_context,
view_as_self_fn);
for (const auto i : c10::irange(num_outputs)) {
PyObject* obj = PyTuple_GetItem(raw_output, i);
const auto& wrapped_output = wrapped_outputs[i];
// Keep the non-tensor outputs as is.
if (!THPVariable_Check(obj) || !wrapped_output.has_value()) {
if (is_executable) {
self->output_info.emplace_back();
}
Py_INCREF(obj);
PyTuple_SetItem(outputs, i, obj);
} else {
if (is_executable) {
// If one of the grad outputs is undefined, a correctly-shaped zeros
// should be used instead. To construct these for NJT, zeros_like() must
// be used until we have factory function support.
bool is_differentiable =
(non_differentiable.count(wrapped_output->unsafeGetTensorImpl()) ==
0 &&
isDifferentiableType(wrapped_output->scalar_type()));
bool use_zeros_like =
is_differentiable && num_outputs > 1 && wrapped_output->is_nested();
self->output_info.emplace_back(wrapped_output.value(), use_zeros_like);
}
PyTuple_SetItem(outputs, i, THPVariable_Wrap(wrapped_output.value()));
}
}
}
static void _get_tensors_to_save(
THPFunction* self,
std::unordered_set<at::TensorImpl*>& to_save_if_setup_context,
std::vector<std::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 ",
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 ",
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.emplace_back(std::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.emplace_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.
TORCH_CHECK_TYPE(
false,
fmt::format(
"save_for_backward can only save variables, but argument {} is of "
"type {}",
i,
Py_TYPE(obj)->tp_name));
}
}
}
Py_CLEAR(self->to_save);
}
}
// Save any variables that requested by to_save
static void _save_variables(
const std::vector<std::optional<at::Tensor>>& tensors_to_save,
const std::shared_ptr<PyNode>& cdata_ptr,
THPFunction* self) {
if (tensors_to_save.size() == 0)
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);
}
}
}
// 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 ",
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 ",
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;
// record_function_inputs is for RECORD_FUNCTION only
std::vector<c10::IValue> record_function_inputs;
};
struct InputFlags {
bool is_executable = false;
edge_list next_edges;
THPObjectPtr needs_input_grad;
std::vector<bool> is_variable_input;
};
namespace {
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);
bool profiler_need_input = torch::autograd::profiler::profilerEnabled() &&
torch::autograd::profiler::getProfilerConfig().report_input_shapes;
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 ", THPUtils_typename(arg));
throw python_error();
}
Py_INCREF(Py_False);
PyTuple_SET_ITEM(flags.needs_input_grad.get(), i, Py_False);
if (profiler_need_input) {
// The following conversion from PyObject to IValue is expensive
// Only do it if profiler is enabled and needs input shapes
auto match = torch::jit::tryToInferPrimitiveType(arg);
if (match.success()) {
unpacked.record_function_inputs.push_back(
torch::jit::toIValue(arg, match.type()));
}
}
} 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);
unpacked.record_function_inputs.emplace_back(tensor);
}
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
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]);
}
}
}
}
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));
}
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._internal.torchscript_exporter._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,
// NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
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<std::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_CLEAR(grad_fn->to_save);
Py_CLEAR(grad_fn->non_differentiable);
}
Py_CLEAR(grad_fn->saved_for_forward);
// 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();
check_legacy_fn_attr_access(cdata, "name");
return THPUtils_packString(cdata->name());
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_sequence_nr(PyObject* self, PyObject* noargs) {
HANDLE_TH_ERRORS;
auto cdata = ((THPFunction*)self)->cdata.lock();
check_legacy_fn_attr_access(cdata, "_sequence_nr");
return THPUtils_packUInt64(cdata->sequence_nr());
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_set_sequence_nr(PyObject* self, PyObject* sequence_nr) {
HANDLE_TH_ERRORS;
auto cdata = ((THPFunction*)self)->cdata.lock();
check_legacy_fn_attr_access(cdata, "_set_sequence_nr");
cdata->set_sequence_nr(THPUtils_unpackUInt64(sequence_nr));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_input_metadata(PyObject* self, void* unused) {
HANDLE_TH_ERRORS;
auto cdata = ((THPFunction*)self)->cdata.lock();
check_legacy_fn_attr_access(cdata, "_input_metadata");
const auto num_inputs = cdata->num_inputs();
THPObjectPtr list(PyTuple_New(num_inputs));
if (!list) {
return nullptr;
}
for (size_t i = 0; i < num_inputs; ++i) {
const auto& metadata = cdata->input_metadata(i);
THPObjectPtr item(py::cast(metadata).release().ptr());
if (!item) {
return nullptr;
}
PyTuple_SET_ITEM(list.get(), i, item.release());
}
return list.release();
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
}
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;
}
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
auto 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,
unpacked_input.record_function_inputs,
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
TORCH_CHECK(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();
check_legacy_fn_attr_access(cdata, "_register_hook_dict");
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();
check_legacy_fn_attr_access(cdata, "register_hook");
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();
check_legacy_fn_attr_access(cdata, "register_prehook");
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)
}
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_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(static_cast<Py_ssize_t>(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_get_compiled_autograd_backward_state(
PyObject* _self,
void* _unused) {
HANDLE_TH_ERRORS
auto self = (THPFunction*)_self;
PyObject* bw_state = self->compiled_autograd_backward_state;
if (bw_state == nullptr) {
bw_state = Py_None;
}
Py_INCREF(bw_state);
return bw_state;
END_HANDLE_TH_ERRORS
}
int THPFunction_set_compiled_autograd_backward_state(
PyObject* _self,
PyObject* bw_state,
void* _unused) {
HANDLE_TH_ERRORS
auto self = (THPFunction*)_self;
TORCH_INTERNAL_ASSERT(self->compiled_autograd_backward_state == nullptr);
Py_INCREF(bw_state);
self->compiled_autograd_backward_state = bw_state;
return 0;
END_HANDLE_TH_ERRORS_RET(-1)
}
PyObject* THPFunction_raw_saved_tensors(THPFunction* self, void* _unused) {
HANDLE_TH_ERRORS
// User tries to access saved variables after they have been freed
TORCH_CHECK(!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(static_cast<Py_ssize_t>(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();
check_legacy_fn_attr_access(cdata, "next_functions");
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
}
} // namespace
using getter = PyObject* (*)(PyObject*, void*);
using setter = int (*)(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>,
&setObject<&THPFunction::needs_input_grad>,
nullptr,
nullptr},
{"requires_grad", getRequiresGrad, nullptr, nullptr, nullptr},
{"metadata", (getter)THPFunction_metadata, nullptr, nullptr, nullptr},
{"_input_metadata",
(getter)THPFunction_input_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},
{"_compiled_autograd_backward_state",
(getter)THPFunction_get_compiled_autograd_backward_state,
(setter)THPFunction_set_compiled_autograd_backward_state,
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*)"_sequence_nr", THPFunction_sequence_nr, METH_NOARGS, nullptr},
{(char*)"_set_sequence_nr", THPFunction_set_sequence_nr, METH_O, 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*)"_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 */
// NOLINTNEXTLINE(misc-redundant-expression)
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;
}