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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 <torch/csrc/python_headers.h>
#include <structmember.h>
#include <unordered_map>
#include <unordered_set>
#include <exception>
#include <ATen/ATen.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/autograd/grad_mode.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/python_cpp_function.h>
#include <torch/csrc/autograd/python_hook.h>
#include <torch/csrc/autograd/saved_variable.h>
#include <torch/csrc/autograd/python_anomaly_mode.h>
#include <torch/csrc/jit/tracer.h>
#include <torch/csrc/jit/ir.h>
#include <torch/csrc/jit/python_tracer.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/utils/auto_gil.h>
#include <torch/csrc/Exceptions.h>
#include <exception>
#include <functional>
#include <memory>
#include <stdexcept>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
using namespace torch;
using namespace torch::autograd;
using namespace torch::jit;
using at::Tensor;
PyObject *THPFunctionClass = nullptr;
#define THPFunction_assert(condition, ...) \
if (!(condition)) { THPUtils_setError(__VA_ARGS__); throw python_error(); }
namespace torch { namespace autograd {
auto PyNode::legacy_apply(const variable_list& inputs) -> variable_list {
AutoGIL gil;
THPObjectPtr pyInputs(PyTuple_New(inputs.size()));
if (!pyInputs) throw python_error();
for (size_t i = 0; i != inputs.size(); ++i) {
PyTuple_SET_ITEM(pyInputs.get(), i, THPVariable_Wrap(inputs[i]));
}
THPObjectPtr r(PyObject_CallMethod(
obj, "_do_backward", "OO", pyInputs.get(), Py_True));
if (!r) throw python_error();
auto num_outputs = PyTuple_GET_SIZE(r.get());
tensor_list tensor_results(num_outputs);
for (int i = 0; i != num_outputs; ++i) {
PyObject* obj = PyTuple_GET_ITEM(r.get(), i);
if (obj != Py_None) {
if (!THPVariable_Check(obj)) {
std::string msg("expected Variable (got '");
msg += THPUtils_typename(obj);
msg += "')'";
throw std::runtime_error(msg);
}
tensor_results[i] = ((THPVariable*)obj)->cdata.tensor_data();
}
}
// XXX: this might get requires_grad wrong - there's no way to figure out
// if _do_backward didn't use ctx.saved_tensors and as a result some
// Variables might require grad, even if no args do. Unfortunately, this
// leads to unexpected error messages ("no nodes require computing gradients"),
// but I don't have a better idea. These functions would raise an error
// in backward anyway.
return wrap_outputs(
inputs,
std::move(tensor_results),
[this](edge_list&& next_edges) {
return std::make_shared<Error>(
name() + " is not differentiable twice", std::move(next_edges));
});
}
// 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 {
AutoGIL gil;
at::OptionalDeviceGuard _device_guard;
THPFunction* py_fn = (THPFunction*)obj;
THPObjectPtr _legacy(PyObject_GetAttrString(obj, "_is_legacy"));
if (_legacy == Py_True) {
return legacy_apply(inputs);
}
// 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 (size_t i = 0; i < num_inputs; ++i) {
PyObject* input;
if (inputs[i].defined()) {
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 (int i = num_forward_inputs; i < num_outputs; i++) {
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);
auto& input_info = py_fn->input_info;
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) {
auto& info = input_info[results.size()];
if (info.requires_grad) {
results.emplace_back(info.zeros(_device_guard));
} else {
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*)output)->cdata);
}
}
return results;
}
auto PyNode::is_traceable() -> bool {
AutoGIL 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 {
AutoGIL gil;
auto f = (THPFunction*) obj;
f->saved_variables.clear();
f->has_freed_buffers = 1;
}
auto PyNode::name() const -> std::string {
AutoGIL gil;
auto f = (THPFunction*) obj;
auto name = std::string(Py_TYPE(f)->tp_name);
// Python API functions are not const-correct
THPObjectPtr _legacy(PyObject_GetAttrString(const_cast<PyObject*>(obj), "_is_legacy")); // NOLINT
if (_legacy == Py_True) {
name += "LegacyBackward";
}
return name;
}
}} // 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)
{
// cdata could be null if someone constructed a legacy function but haven't
// actually called backward() on it yet, or if the PyNode has already
// gone out of scope by the time we're GC'ing this THPFunction (e.g., the
// user saved grad_fn only).
//
// TODO: I'm not really sure if we're actually obligated to traverse PyObject
// that is stored in PyNode, since we don't really own that C++ object.
if (auto cdata = self->cdata.lock()) {
for (const auto& hook : cdata->pre_hooks()) {
if (auto pyhook = dynamic_cast<PyFunctionPreHook*>(hook.get())) {
Py_VISIT(pyhook->dict);
}
}
for (const auto& hook : cdata->post_hooks()) {
if (auto pyhook = dynamic_cast<PyFunctionPostHook*>(hook.get())) {
Py_VISIT(pyhook->dict);
}
}
}
Py_VISIT(self->to_save);
Py_VISIT(self->non_differentiable);
Py_VISIT(self->dirty_tensors);
return 0;
}
static int THPFunction_clear(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());
Py_CLEAR(self->needs_input_grad);
Py_CLEAR(self->to_save);
Py_CLEAR(self->non_differentiable);
Py_CLEAR(self->dirty_tensors);
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)
{
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>();
return obj;
}
////////////////////////////////////////////////////////////////////////////////
// Forward
////////////////////////////////////////////////////////////////////////////////
using t2var_type = std::unordered_map<PyObject *, THPVariable *>;
// 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 (int i = 0; i < num_dirty; i++) {
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));
dirty_inputs.insert(((THPVariable*)obj)->cdata.unsafeGetTensorImpl());
auto variable = (THPVariable*)obj;
variable->cdata.bump_version();
}
// 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)
{
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 as_variable = [&](PyObject* obj, int i) -> Variable {
if (THPVariable_Check(obj)) {
return ((THPVariable*)obj)->cdata;
}
throw TypeError("%s.forward: expected Variable (got %s) for return value %d",
Py_TYPE(self)->tp_name, Py_TYPE(obj)->tp_name, i);
};
auto non_differentiable = _parse_non_differentiable(self);
auto dirty_inputs = _mark_dirty(self);
std::vector<Variable> raw_output_vars;
raw_output_vars.reserve(num_outputs);
for(int i = 0; i < num_outputs; ++i){
PyObject* obj = PyTuple_GET_ITEM(raw_output, i);
raw_output_vars.push_back(as_variable(obj,i));
}
auto wrapped_outputs = _wrap_outputs(input_vars, non_differentiable, dirty_inputs, raw_output_vars, cdata_if_executable);
for (int i = 0; i < num_outputs; i++) {
if (is_executable) {
self->output_info.emplace_back(wrapped_outputs[i]);
}
PyTuple_SET_ITEM(outputs, i, THPVariable_Wrap(wrapped_outputs[i]));
}
}
// Save any variables that requested by to_save
static void _save_variables(const std::shared_ptr<PyNode>& cdata_ptr, THPFunction* self)
{
if (!self->to_save) return;
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);
self->saved_variables.clear();
self->saved_variables.reserve(num_saved);
for (int i = 0; i < num_saved; i++) {
PyObject *obj = PyTuple_GET_ITEM(self->to_save, i);
if (obj == Py_None) {
self->saved_variables.emplace_back();
continue;
} else if (THPVariable_Check(obj)) {
auto variable = (THPVariable*)obj;
bool is_output = variable->cdata.grad_fn().get() == cdata_ptr.get();
self->saved_variables.emplace_back(variable->cdata, is_output);
} else {
throw TypeError(
"save_for_backward can only save variables, but argument %d is of "
"type %s", i, Py_TYPE(obj)->tp_name);
}
}
// 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 (int i = 0; i < num_nondiff; i++) {
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*)t)->cdata.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 (int i = 0; i < num_args; i++) {
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 Variable 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 {
THPVariable* variable = (THPVariable*)arg;
unpacked.input_vars.push_back(variable->cdata);
PyObject* needs_grad = variable->cdata.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 = collect_next_edges(unpacked.input_vars);
return std::make_pair(std::move(unpacked), std::move(flags));
}
static void _assert_not_tracing(const char* name, const variable_list& input_vars) {
if (tracer::isTracing()) {
std::ostringstream oss;
oss << "Attempted to trace " << name;
oss << ", but tracing of legacy functions is not supported";
throw std::runtime_error(oss.str());
}
}
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 (int i = 0; i < num_args; i++) {
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_(attr::inplace, is_inplace);
// Isolate C variable ptrs in a vector
int num_outputs = PyTuple_GET_SIZE(output_objects);
variable_list output_vars(num_outputs);
auto graph = node->owningGraph();
node->addOutput();
if (!unpack_output) {
std::vector<TypePtr> tuple_values(num_outputs, TensorType::get());
TypePtr tuple_type = TupleType::create(std::move(tuple_values));
node->output()->setType(tuple_type);
auto unpacked = graph->createTupleUnpack(node->output())->insertAfter(node);
node = unpacked;
}
for (int i = 0; i < num_outputs; ++i) {
auto var = (THPVariable*)PyTuple_GET_ITEM(output_objects, i);
Value* value = node->outputs()[i];
if (var->cdata.defined()) {
value->inferTypeFrom(var->cdata);
jit::tracer::setValueTrace(autograd::as_variable_ref(var->cdata), value);
}
}
}
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 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);
}
}
bool is_inplace = static_cast<bool>(grad_fn->dirty_tensors);
_wrap_outputs(cdata, grad_fn, unpacked.input_vars, raw_output, outputs, is_executable);
_trace_post_record(node, op_obj, unpacked.input_vars, outputs, is_inplace, unpack_output);
if (is_executable) {
_save_variables(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;
}
// 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();
}
// Legacy codepath
PyObject *THPFunction_do_forward(THPFunction *self, PyObject *_inputs)
{
HANDLE_TH_ERRORS
RECORD_FUNCTION(
Py_TYPE(self)->tp_name,
std::vector<c10::IValue>(),
autograd::Node::peek_at_next_sequence_nr());
TORCH_WARN("Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. ",
"Please use new-style autograd function with static forward method. ",
"(Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)");
auto info_pair = unpack_input<true>(_inputs);
auto& unpacked_input = info_pair.first;
auto& input_info = info_pair.second;
bool is_executable = input_info.is_executable;
std::shared_ptr<PyNode> cdata = self->cdata.lock();
if (cdata) {
// In some pathological cases, self->cdata can already be set on entry to
// this function. This occurs on misuse of the legacy autograd API in the
// following way:
//
// f = MyFunction()
// y1 = f(x1)
// y2 = f(x2) # bad!!
//
// Historically, we did something very nutty: we set y1.grad_fn ==
// y2.grad_fn (even though these variables really have nothing to do with
// each other.) At least now we have a warning. All of this hoo-ha will
// go away when we delete the implementation of legacy autograd.
TORCH_WARN(
"Legacy autograd function object was called twice. You will probably "
"get incorrect gradients from this computation, as the saved tensors "
"from the second invocation will clobber the saved tensors from the "
"first invocation. Please consider rewriting your autograd function "
"in the modern style; for information on the new format, please see: "
"https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd");
} else {
Py_INCREF(self);
cdata = std::shared_ptr<PyNode>(new PyNode(THPObjectPtr((PyObject*)self)), deleteNode);
self->cdata = cdata;
}
cdata->set_next_edges(std::move(input_info.next_edges));
self->needs_input_grad = input_info.needs_input_grad.release();
// We don't support tracing in the legacy code path
_assert_not_tracing(Py_TYPE(self)->tp_name, unpacked_input.input_vars);
// Now we're ready to call a forward (implemented in Python)
THPObjectPtr raw_output;
{
AutoGradMode grad_mode(false);
THPObjectPtr forward_fn(PyObject_GetAttrString((PyObject*)self, "forward"));
if (!forward_fn) return nullptr;
raw_output = PyObject_CallObject(forward_fn, unpacked_input.input_tuple);
if (!raw_output) return nullptr;
}
return process_outputs(nullptr, cdata, self, unpacked_input, _inputs, std::move(raw_output),
is_executable, nullptr);
END_HANDLE_TH_ERRORS
}
PyObject *THPFunction_apply(PyObject *cls, PyObject *inputs)
{
HANDLE_TH_ERRORS
RECORD_FUNCTION(
((PyTypeObject*)cls)->tp_name,
std::vector<c10::IValue>(),
autograd::Node::peek_at_next_sequence_nr());
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;
// Prepare inputs and allocate context (grad fn)
auto info_pair = unpack_input<false>(inputs);
UnpackedInput& unpacked_input = info_pair.first;
InputFlags& input_info = info_pair.second;
// 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);
// Prepend ctx to input_tuple, in preparation for static method call
auto num_args = PyTuple_GET_SIZE(inputs);
THPObjectPtr ctx_input_tuple(PyTuple_New(num_args + 1));
if (!ctx_input_tuple) return nullptr;
Py_INCREF(ctx);
PyTuple_SET_ITEM(ctx_input_tuple.get(), 0, (PyObject*)ctx);
for (int i = 0; i < num_args; ++i) {
PyObject *arg = PyTuple_GET_ITEM(unpacked_input.input_tuple.get(), i);
Py_INCREF(arg);
PyTuple_SET_ITEM(ctx_input_tuple.get(), i + 1, arg);
}
// Call forward
THPObjectPtr tensor_outputs;
{
AutoGradMode grad_mode(false);
THPObjectPtr forward_fn(PyObject_GetAttrString(cls, "forward"));
if (!forward_fn) return nullptr;
tensor_outputs = PyObject_CallObject(forward_fn, ctx_input_tuple);
if (!tensor_outputs) return nullptr;
}
return process_outputs(cls, cdata, ctx, unpacked_input, inputs, std::move(tensor_outputs),
is_executable, node);
END_HANDLE_TH_ERRORS
}
////////////////////////////////////////////////////////////////////////////////
// Backward
////////////////////////////////////////////////////////////////////////////////
static void _prepare_grads(THPFunction *self, THPObjectPtr& raw_grads, bool is_grad_output)
{
at::OptionalDeviceGuard device_guard;
int num_grads = PyTuple_GET_SIZE(raw_grads.get());
// First, check if any of grads is None. If not, there's nothing to do
bool has_none = false;
for (int i = 0; i < num_grads; i++) {
has_none |= PyTuple_GET_ITEM(raw_grads.get(), i) == Py_None;
}
if (!has_none)
return;
THPObjectPtr grads;
grads = PyTuple_New(num_grads);
if (!grads) throw python_error();
// Look for Nones and replace them with new buffers
auto& grads_info = is_grad_output ? self->output_info : self->input_info;
AT_ASSERT(grads_info.size() == (size_t)num_grads);
for (int i = 0; i < num_grads; i++) {
PyObject *grad = PyTuple_GET_ITEM(raw_grads.get(), i);
if (grad == Py_None) {
grad = THPVariable_Wrap(grads_info[i].zeros(device_guard));
if (!grad) throw python_error();
} else {
Py_INCREF(grad);
}
PyTuple_SET_ITEM(grads.get(), i, grad);
}
raw_grads = grads.release();
}
static void _trim_grad_input(const std::shared_ptr<PyNode>& cdata, THPFunction *self, THPObjectPtr& grad_input)
{
int num_grads = PyTuple_GET_SIZE(grad_input.get());
const int num_outputs = cdata->num_outputs();
if (num_grads > num_outputs) {
// Check that all extra grads are none
bool all_none = true;
for (int i = num_outputs; i < num_grads; i++) {
all_none = (PyTuple_GET_ITEM(grad_input.get(), i) == Py_None);
if (!all_none) break;
}
// If yes, slice the tuple
if (all_none) {
num_grads = num_outputs;
grad_input = PyTuple_GetSlice(grad_input.get(), 0, num_grads);
if (!grad_input) throw python_error();
}
}
}
PyObject * THPFunction_do_backward(THPFunction *self, PyObject *args)
{
try {
Py_ssize_t num_args = args ? PyTuple_GET_SIZE(args) : 0;
THPUtils_assert(num_args == 2, "_do_backward expects exactly two arguments");
PyObject *raw_grad_output = PyTuple_GET_ITEM(args, 0);
PyObject *retain_variables = PyTuple_GET_ITEM(args, 1);
if (!PyTuple_Check(raw_grad_output) || !PyBool_Check(retain_variables)) {
THPUtils_invalidArguments(args, nullptr, "_do_backward", 1, "(tuple, bool)");
return nullptr;
}
auto cdata = self->cdata.lock();
// In obscure situations, cdata might be nullptr because it's expired. THAT
// is an internal error and I'd like to know about it, but since this is
// all dead soon I didn't bother implementing a sanity check here. See
// https://stackoverflow.com/questions/45507041/how-to-check-if-weak-ptr-is-empty-non-assigned
// for how to do it.
TORCH_CHECK(cdata,
"Legacy autograd function attempted to call backward before forward "
"was called. This could occur if you manually called _do_backward on Function. "
"In any case, this is very naughty! If you absolutely need this to work, "
"try porting your code to use non-legacy autograd function, see: "
"https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd");
THPUtils_assert(PyTuple_GET_SIZE(raw_grad_output) == cdata->num_inputs(),
"%s got an invalid number of gradients (expected %d got %d)",
THPUtils_typename(self), cdata->num_inputs(),
PyTuple_GET_SIZE(raw_grad_output));
// Some of the output might have been unused, so we have to allocate
// zero-filled buffers instead
Py_INCREF(raw_grad_output);
THPObjectPtr grad_output(raw_grad_output);
_prepare_grads(self, grad_output, true);
// self.backward(*grad_output)
THPObjectPtr backward_fn(PyObject_GetAttrString((PyObject*)self, "backward"));
THPUtils_assert(backward_fn.get(), "function %s doesn't implement a required "
"'backward' method", THPUtils_typename((PyObject*)self));
THPObjectPtr grad_input(PyObject_CallObject(backward_fn, grad_output.get()));
if (!grad_input) return nullptr;
ensure_tuple(grad_input);
// We allow functions to return more gradients, than there were outputs,
// if and only if the additional ones are all None
_trim_grad_input(cdata, self, grad_input);
int num_grads = PyTuple_GET_SIZE(grad_input.get());
int num_outputs = cdata->num_outputs();
THPUtils_assert(num_grads == num_outputs, "%s returned an invalid number of "
"gradient tensors (expected %d, but got %d)", THPUtils_typename(self),
num_outputs, num_grads);
// If any of the remaining grad_inputs are None, zero them.
_prepare_grads(self, grad_input, false);
return grad_input.release();
} catch (python_error& e) {
return nullptr;
} catch (std::exception& e) {
THPUtils_setError(e.what());
return nullptr;
}
}
////////////////////////////////////////////////////////////////////////////////
// Other methods / attributes
////////////////////////////////////////////////////////////////////////////////
PyObject* THPFunction__register_hook_dict(THPFunction *self, PyObject *_var)
{
HANDLE_TH_ERRORS
THPUtils_assert(THPVariable_Check(_var), "_register_hook_dict expected a variable");
THPVariable *var = (THPVariable*)_var;
std::unique_ptr<FunctionPreHook> hook(new PyFunctionPreHook(
var->backward_hooks, var->cdata.output_nr()));
auto cdata = self->cdata.lock();
TORCH_CHECK(cdata,
"Legacy autograd function had register_hook called before the function was "
"invoked. This usage pattern is no longer supported: please call register_hook "
"AFTER calling your function, or port your code to use non-legacy autograd function, see: "
"https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd")
cdata->add_pre_hook(std::move(hook));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
PyObject* THPFunction_register_hook(THPFunction *self, PyObject *hook)
{
HANDLE_TH_ERRORS
auto cdata = self->cdata.lock();
TORCH_CHECK(cdata,
"Legacy autograd function had _register_hook called before the function was "
"invoked. This usage pattern is no longer supported: please call _register_hook "
"AFTER calling your function, or port your code to use non-legacy autograd function, see: "
"https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd")
return torch::autograd::registerFunctionHook(*cdata, hook);
END_HANDLE_TH_ERRORS
}
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 (int i = 0; i < num_saved; i++) {
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
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_next_functions(THPFunction *self, void *_unused)
{
HANDLE_TH_ERRORS
auto cdata = self->cdata.lock();
TORCH_CHECK(cdata,
"Legacy autograd function had next_functions accessed before the function was "
"invoked. This doesn't make any sense: we have no idea what the next "
"functions are, because you haven't actually inserted this grad_fn inside "
"a graph. Try invoking your function first before accessing this field.")
const auto num_outputs = cdata->num_outputs();
THPObjectPtr result(PyTuple_New(num_outputs));
if (!result)
return nullptr;
for (uint32_t i = 0; i < num_outputs; i++) {
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 {