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python_function.cpp
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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/utils/python_strings.h>
#include <torch/csrc/utils/tensor_dtypes.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;
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 {
// 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 {
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
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
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
return to_variable_list(r.get(), is_variable_input);
}
auto PyNode::compiled_apply(
variable_list&& inputs,
std::optional<PyObject*> compiler) -> 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); }));
TORCH_INTERNAL_ASSERT(
_backward_idx.has_value(),
"indices should already be set by compiled_args, called before apply_with_saved");
THPObjectPtr r(PyObject_CallMethod(
*compiler,
"proxy_call_backward",
"OOOi",
pyInputs.get(),
fwdInputMetadatas.get(),
saved_tensors.get(),
*_backward_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;
}
auto PyNode::compiled_autograd_should_lift() const -> bool {
pybind11::gil_scoped_acquire gil;
static PyObject* attr_name =
PyUnicode_InternFromString("_compiled_autograd_should_lift");
THPObjectPtr should_lift(PyObject_GetAttr(obj, attr_name));
return PyObject_IsTrue(should_lift.get()) == 1;
}
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
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(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);
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);
static PyObject* forward_cls_name =
PyUnicode_InternFromString("_forward_cls");
THPObjectPtr forward_cls(PyObject_GetAttr(obj, forward_cls_name));
static PyObject* backward_name = PyUnicode_InternFromString("backward");
PyObject* backward(PyObject_GetAttr(forward_cls.get(), backward_name));
_backward_idx =
args.add_backward(c10::SafePyObject(backward, getPyInterpreter()));
}
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;
variable_list result;
if (!compiled_autograd_should_lift()) {
result = apply(variable_list(inputs));
} else {
result = compiled_apply(variable_list(inputs), saved.get_py_compiler());
}
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;
}
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)) {
// 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(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) {
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;
}
} // 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 ",
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<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(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);
// 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) {
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
self->output_info.emplace_back(*wrapped_outputs[i]);
}
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
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 ",
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(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.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.
throw torch::TypeError(
"save_for_backward can only save variables, but argument %ld 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 ",
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;
};
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 ", 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;