pytorch/torch/csrc/jit/ir.cpp
Michael Suo defe96eb6c add topology index check in Graph::lint() (#13037)
Summary:
just a sanity check to make sure everything is in order
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13037

Differential Revision: D10854563

Pulled By: michaelsuo

fbshipit-source-id: 409303c4cbf058b75e24bf2213b49e9d79cb862e
2018-10-25 17:02:38 -07:00

1169 lines
34 KiB
C++

#include "ir.h"
#include "torch/csrc/jit/operator.h"
#include "torch/csrc/autograd/function.h"
#include "torch/csrc/jit/constants.h"
#include "torch/csrc/jit/assertions.h"
#include "torch/csrc/jit/script/compiler.h"
#include "torch/csrc/jit/passes/pretty_print.h"
#include <iostream>
#include <unordered_map>
#include <unordered_set>
#include <set>
#include <stack>
#include <sstream>
#include <algorithm>
#include <string>
namespace torch { namespace jit {
// Constants relating to maintaining the topological index of nodes.
//
// Lower and upper bounds of the index. Inclusive range.
static constexpr topo_position_t kLowerBound = INT64_MIN;
static constexpr topo_position_t kUpperBound = INT64_MAX;
static constexpr topo_position_t kMidPoint = 0;
// How far away to space nodes that are appended to the graph.
// should be 2^n, where:
// - n is the maximum number of repeated insertions without a re-index
// - 2^(64-n) is the maximum number of appends to the end without reindex
static constexpr topo_position_t kAppendInterval = 1099511627776ULL /* 2^40 */;
// Sigh, see https://stackoverflow.com/questions/8016780/undefined-reference-to-static-constexpr-char
constexpr Symbol PythonOp::Kind;
void printValueRef(std::ostream & out, const Value * n) {
out << "%" << n->uniqueName();
}
// NB: This overload will become ambiguous with the one Caffe2 provides in its
// logging, if they ever intersect.
template <typename T>
std::ostream& operator<<(std::ostream & out, const std::vector<T> & nodes) {
out << at::ArrayRef<T>{nodes};
return out;
}
template <typename T>
std::ostream& printValueRefs(std::ostream & out, const at::ArrayRef<T> & nodes) {
size_t i = 0;
for(auto n : nodes) {
if(i++ > 0)
out << ", ";
printValueRef(out, n);
}
return out;
}
// Can't make these two overloads directly a template, it'll be ambiguous with
// the global printer for operator<<.
std::ostream& operator<<(std::ostream & out, const at::ArrayRef<const Value*> & nodes) {
return printValueRefs(out, nodes);
}
std::ostream& operator<<(std::ostream & out, const at::ArrayRef<Value*> & nodes) {
return printValueRefs(out, nodes);
}
struct const_value_list_with_types {
const ArrayRef<const Value*> values;
bool use_newlines;
const_value_list_with_types(ArrayRef<const Value*> values, bool use_newlines = false)
: values(values), use_newlines(use_newlines) {}
};
std::ostream& operator<<(std::ostream & out, const_value_list_with_types l) {
size_t i = 0;
for(auto n : l.values) {
if(i++ > 0) {
if (l.use_newlines) {
// TODO: Indent here is hard-coded for "graph(": un-hard-code it
out << "\n ";
} else {
out << ", ";
}
}
printValueRef(out, n);
out << " : ";
out << *n->type();
}
return out;
}
void printAttributes(std::ostream & out, const Node * n, bool ignore_subgraph=false) {
out << "[";
auto names = n->attributeNames();
int i = 0;
for(auto name : names) {
if (ignore_subgraph && name == attr::Subgraph)
continue;
if(i++ > 0)
out << ", ";
// TODO: debugging mode to see the qualifier. We definitely
// don't want to print the qualifier since it should always
// be attribute, but you might be able to track down a weird
// bug by printing it out.
out << name.toUnqualString() << "=";
n->printValue(out, name);
}
out << "]";
}
static std::ostream & indent(std::ostream & out, size_t level) {
for(size_t i = 0; i < level; ++i)
out << " ";
return out;
}
std::ostream& printNode(std::ostream & out, size_t level, const Node * n, std::vector<const Node*> * groups) {
auto outputs = n->outputs();
indent(out, level) << const_value_list_with_types(outputs);
out << " = ";
IR_IFM_CONST(n,PythonOp)
out << "^" << value->name();
value->writeScalars(out);
IR_ELSE()
if(n->hasAttribute(attr::Subgraph) && groups) {
out << n->kind().toQualString() << "_" << groups->size();
if (n->numAttributes() > 1 && n->kind() != prim::DifferentiableGraph) {
printAttributes(out, n, /*ignore_subgraph=*/true);
}
groups->push_back(n);
} else {
out << n->kind().toQualString();
if(n->hasAttributes()) {
printAttributes(out,n);
}
}
IR_END()
out << "(" << n->inputs() << ")";
std::string scopeName = n->scopeName();
if (scopeName.empty()) {
out << "\n";
}
else {
out << ", ";
out << "scope: " << scopeName << "\n";
}
for(size_t i = 0; i < n->blocks().size(); ++i) {
auto b = n->blocks()[i];
indent(out, level + 1) << "block" << i << "(" << const_value_list_with_types(b->inputs(), false) << ") {\n";
for(auto n : b->nodes()) {
printNode(out, level + 2, n, groups);
}
indent(out, level + 2) << "-> (" << b->outputs() << ")\n";
indent(out, level + 1) << "}\n";
}
return out;
}
std::ostream& operator<<(std::ostream & out, const Node & n) {
return printNode(out, 0, &n, nullptr);
}
std::ostream& operator<<(std::ostream & out, const Graph & g) {
out << "graph(" << const_value_list_with_types(g.inputs(), true) << ") {\n";
std::vector<const Node*> groups;
for(auto n : g.nodes()) {
printNode(out, 1, n, &groups);
}
out << " return (" << g.outputs() << ");\n}\n";
size_t i = 0;
for(auto fg : groups) {
out << "with " << fg->kind().toQualString() << "_" <<i++ << " = " << *fg->g(attr::Subgraph);
}
/*
// Uncomment this to debug all_nodes issues
{
out << "\n";
out << "all_nodes:\n";
for (auto& n : g.all_nodes) {
printNode(out, const_cast<Node*>(n), nullptr);
}
}
*/
return out;
}
std::ostream& Graph::prettyPrint(std::ostream & out) {
PrettyPrint(out, *this);
return out;
}
void Graph::dumpPretty() {
PrettyPrint(std::cout, *this);
}
static void checkSameDevice(const Node* node) {
bool has_device = false;
int device;
auto checkValue = [&](const Value* v) {
if(CompleteTensorTypePtr type = v->type()->cast<CompleteTensorType>()) {
if(!has_device) {
has_device = true;
device = type->device();
} else {
JIT_ASSERT(device == type->device());
}
}
};
for(auto input : node->inputs()) {
checkValue(input);
}
for(auto output : node->outputs()) {
checkValue(output);
}
}
using node_set = std::set<const Node*>;
#define ALL_OF(container) container.begin(), container.end()
// These functions purposely operate on the internal members directly, to force
// you to think about how the invariants change if you change the data
// representation (even if the external API does not change.)
// NB: This assert is written to assume you don't have any unattached
// nodes. Unattached nodes can occur while manipulations to the
// graph are occurring.
void Node::lint() const {
// Node invariants
// - if node should live in list, nodes_iter is consistent
// - Inputs are all marked as a use by the nodes they refer to
// - Owning graph is non-null and consistent
// - The "Select" invariant, when the node is MultiReturn
//
// The handle invariant:
// If a node takes a handle as an input, it is always the
// LAST input of the node. There is at most one handle input.
{
size_t i = 0;
for (auto input : inputs_) {
// WARNING: O(n^2)
JIT_ASSERT(std::find(ALL_OF(input->uses_), Use(const_cast<Node*>(this), i)) != input->uses_.end());
JIT_ASSERT(graph_->all_nodes.count(this) == 1);
i++;
}
}
for(auto o : outputs()) {
size_t i = 0;
for (auto use : o->uses()) {
// Use invariants
// - Use is consistent with inputs
// - Every user node is live (checked in Graph)
JIT_ASSERT(use.user->inputs_[use.offset] == o);
i++;
}
}
// Node subclass invariants
IR_IF(this,Constant)
JIT_ASSERT(inputs_.size() == 0);
IR_ELSEIF(LoadWorld)
JIT_ASSERT(inputs_.size() == 0);
JIT_ASSERT(outputs_.size() == 1);
IR_ELSEIF(StoreWorld)
JIT_ASSERT(inputs_.size() == 1);
JIT_ASSERT(outputs_.size() == 0);
IR_ELSEIF(Return)
// Return uses is zero
JIT_ASSERT(outputs().size() == 0);
IR_ELSEIF(Param)
// Param inputs is zero
JIT_ASSERT(inputs_.size() == 0);
IR_ELSEIFM_CONST(PythonOp)
// Python operator cconv is correct
size_t n_scalars = 0, n_tensors = 0;
for (auto c : value->cconv) {
if (c == 'c') {
n_scalars++;
} else if (c == 'd') {
n_tensors++;
} else {
JIT_ASSERT(0);
}
JIT_ASSERT(static_cast<bool>(value->pyobj));
}
JIT_ASSERT(n_scalars == value->scalar_args.size());
JIT_ASSERT(n_tensors == inputs_.size());
IR_ELSEIF(Eval)
// TODO: add invariants
// TODO: It's not good for these ops to be top-level, it makes cases longer.
IR_ELSEIF(FusionGroup)
checkSameDevice(value);
// TODO: Typecheck the parameters
value->g(attr::Subgraph)->lint();
IR_END()
}
// TODO: When lint fails, give better indication about which
// instruction triggered the failure.
void Graph::lint() const {
// Graph invariants
// Uncomment the following to see the graph
// std::cout << *const_cast<Graph*>(this);
// nodes
// - nodes_ is a valid topological ordering for inputs
// - No repeated nodes
// - Params and return do NOT occur in nodes
// - next_unique_ is greater than all uniques in graph
// - uniques in all_nodes are unique
// - every use will occur later in the topsort
struct LintScope {
LintScope() = default;
LintScope(std::unique_ptr<LintScope> parent)
: parent(std::move(parent)) {}
bool contains(const Value * v) {
return values.count(v) > 0 || (parent && parent->contains(v));
}
bool contains(const Node * n) {
return nodes.count(n) > 0 || (parent && parent->contains(n));
}
void insert(const Value * v) {
JIT_ASSERT(!contains(v));
values.insert(v);
}
void insert(const Node * n) {
JIT_ASSERT(!contains(n));
nodes.insert(n);
}
std::unique_ptr<LintScope> parent;
private:
std::unordered_set<const Value*> values;
std::unordered_set<const Node*> nodes;
};
// Struct enables mutual recursion in linting methods.
// Putting it inside Graph::lint enables access to private Graph members
struct LintImpl {
LintImpl(const Graph & g)
: g(g)
, scope(new LintScope())
, all_nodes_set(ALL_OF(g.all_nodes)) {} // NB: all_nodes is *unordered*
const Graph & g;
std::unique_ptr<LintScope> scope;
std::unordered_set<size_t> seen_uniques;
std::unordered_map<const Node*, int64_t> anticipated_uses;
node_set all_nodes_set;
node_set sum_set;
void check_value(const Value* v) {
scope->insert(v);
auto b2 = seen_uniques.insert(v->unique());
JIT_ASSERT(b2.second); // insertion took place
JIT_ASSERT(v->unique() < g.next_unique_);
for (auto use : v->uses()) {
JIT_ASSERT(!scope->contains(use.user));
JIT_ASSERT(g.all_nodes.count(use.user) == 1);
anticipated_uses[use.user]++; // int default constructs to 0
}
}
void check_node(const Node* n) {
for (auto input : n->inputs_) {
if (!scope->contains(input)) {
JIT_ASSERTM(0, input->unique(), " not in scope");
}
}
JIT_ASSERT(anticipated_uses[n] == static_cast<int64_t>(n->inputs_.size()));
anticipated_uses[n] = -1; // we saw the anticipated user!
scope->insert(n);
for(auto block : n->blocks()) {
std::unique_ptr<LintScope> new_scope(new LintScope(std::move(scope)));
scope = std::move(new_scope);
check_block(block);
scope = std::move(scope->parent);
}
size_t i = 0;
for(auto o : n->outputs()) {
JIT_ASSERT(o->node() == n);
JIT_ASSERT(i++ == o->offset_);
check_value(o);
}
n->lint();
}
void check_block(const Block *b) {
// Check topological ordering
JIT_ASSERT(b->param_node()->isBefore(*b->nodes().begin()));
auto curNode = *b->nodes().begin();
while (curNode != b->return_node()) {
JIT_ASSERT(curNode->isBefore(curNode->next()));
curNode = curNode->next();
}
for (auto input : b->inputs()) {
check_value(input);
JIT_ASSERT(input->node()->kind_ == prim::Param);
}
for (auto n : b->nodes()) {
JIT_ASSERT(n->kind_ != prim::Param);
JIT_ASSERT(n->kind_ != prim::Return);
JIT_ASSERT(n->kind_ != prim::DummyWorld);
check_node(n);
}
JIT_ASSERT(b->output_->kind() == prim::Return);
check_node(b->output_);
// all_nodes
// - inputs_, output_ and nodes_ are all included in all_nodes
// - all_nodes does not contain dead nodes??? (likely to be temporarily
// suspended). Weaker: all_nodes contains all inputs and returns
// - only one return node???
node_set nodes_set(ALL_OF(b->nodes()));
node_set inputs_set {b->input_};
node_set output_set {b->output_};
// TODO: Make a more type safe std::includes wrapper which disallows use on
// non-ordered containers
JIT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(nodes_set)));
JIT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(inputs_set)));
JIT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(output_set)));
sum_set.insert(ALL_OF(nodes_set));
sum_set.insert(ALL_OF(inputs_set));
sum_set.insert(ALL_OF(output_set));
}
void check_graph() {
node_set all_nodes_set(ALL_OF(g.all_nodes)); // NB: all_nodes is *unordered*
check_block(g.block_);
for (auto kv : anticipated_uses) {
JIT_ASSERT(kv.second == -1);
}
JIT_ASSERT(std::includes(ALL_OF(sum_set), ALL_OF(all_nodes_set)));
}
};
LintImpl(*this).check_graph();
}
void Graph::dump() const {
std::cout << *this << "\n";
}
void LintGraph(std::shared_ptr<Graph>& graph) {
graph->lint();
}
Block::Block(Graph* graph_, Node* node_)
: graph_(graph_),
output_(initOutput(graph_->create(prim::Return, 0))),
input_(graph_->create(prim::Param, 0)),
owning_node_(node_) {
graph_->all_blocks.emplace(this);
output_->owning_block_ = this;
output_->topo_position_ = kUpperBound;
input_->owning_block_ = this;
input_->topo_position_ = kLowerBound;
}
void Block::reIndexTopology() {
auto curPos = kLowerBound;
for (auto node : nodes()) {
AT_ASSERT(curPos <= (kUpperBound - kAppendInterval));
curPos += kAppendInterval;
node->topo_position_ = curPos;
}
}
void Block::cloneFrom(Block * src, std::function<Value*(Value*)> value_map) {
std::unordered_map<Value*, Value*> local_map;
auto env = [&](Value * v) {
auto it = local_map.find(v);
if(it != local_map.end())
return it->second;
return value_map(v);
};
auto graph = owningGraph();
for(auto input : src->inputs()) {
local_map[input] = this->addInput()->copyMetadata(input);
}
for(auto node : src->nodes()) {
auto new_node = this->appendNode(graph->createClone(node, env));
for(size_t i = 0; i < node->outputs().size(); ++i) {
auto oo = node->outputs()[i];
auto no = new_node->outputs()[i];
local_map[oo] = no;
no->copyMetadata(oo);
}
}
for(auto output : src->outputs()) {
this->registerOutput(env(output));
}
}
void Block::destroy() {
// we cannot destroy the output because it is used as the sentinel
// for the nodes() list and has to remain valid for the loop
output_->removeAllInputs();
for(auto it = this->nodes().reverse().begin(),
end = this->nodes().reverse().end();
it != end; ++it) {
it.destroyCurrent();
}
output_->destroy();
input_->destroy();
graph_->freeBlock(this);
}
std::shared_ptr<Graph> Graph::copy() {
auto new_g = std::make_shared<Graph>();
auto env = [](Value* v) -> Value* {
AT_ERROR(
"Graph::copy() encountered a use of a value not in scope. Run lint!");
};
new_g->block()->cloneFrom(this->block(), env);
return new_g;
}
Value* Value::setUniqueName(const std::string & name) {
if (name.size() > 0 && name.find_first_not_of("0123456789") == std::string::npos) {
throw std::runtime_error("names may not be integers: " + name);
}
auto & names = node()->owningGraph()->unique_names_;
// clear any old name from the map
if(hasUniqueName()) {
names.erase(unique_name_);
unique_name_ = "";
}
// allow "" to clear the uniquename
if(name == "")
return this;
// if someone else has this name, then rename the other value
auto old_owner_of_name = names.find(name);
if(old_owner_of_name != names.end()) {
size_t suffix = 1;
std::string name_base = name;
auto last_dot_pos = name.find_last_of('.');
if (last_dot_pos != std::string::npos && last_dot_pos + 1 != name.size()) {
if (name.find_first_not_of("0123456789", last_dot_pos + 1) == std::string::npos) {
suffix = std::stoll(name.substr(last_dot_pos + 1));
name_base = name.substr(0, last_dot_pos);
}
}
std::string replacement_name;
do {
std::stringstream ss;
ss << name_base << "." << suffix++;
replacement_name = ss.str();
} while(names.count(replacement_name) > 0);
old_owner_of_name->second->setUniqueName(replacement_name);
}
names[name] = this;
unique_name_ = name;
return this;
}
Value* Value::copyMetadata(Value * from) {
setType(from->type());
if (from->hasUniqueName())
setUniqueName(from->uniqueName());
return this;
}
void Value::replaceFirstUseWith(Value * newValue) {
JIT_ASSERT(owningGraph() == newValue->owningGraph());
auto u = uses()[0];
u.user->inputs_[u.offset] = newValue;
newValue->uses_.push_back(u);
uses_.erase(uses_.begin());
}
void Value::replaceAllUsesWith(Value * newValue) {
while (!uses().empty()) {
replaceFirstUseWith(newValue);
}
}
size_t findArgument(const FunctionSchema& the_schema, Symbol name) {
auto name_str = name.toUnqualString();
for (size_t i = 0; i < the_schema.arguments().size(); ++i) {
const Argument* arg = &the_schema.arguments()[i];
if (arg->name() == name_str) {
return i;
}
}
throw std::runtime_error(std::string("Couldn't find an argument called ") + name.toQualString());
}
c10::optional<IValue> Node::get(Symbol name) const {
return toIValue(namedInput(name));
}
Value* Node::namedInput(Symbol name) const {
return input(findArgument(schema(), name));
}
bool Node::matches(const char *signature_literal, at::ArrayRef<Symbol> const_inputs) const {
if (!sig(signature_literal).matches(this)) return false;
for (Symbol s : const_inputs) {
if (!is_constant(s)) return false;
}
return true;
}
void Node::dump() const {
std::cout << *this << "\n";
}
void Node::findSchema() const {
schema_ = &getOperatorFor(this).schema();
}
bool Node::isNondeterministic() const {
static const OperatorSet nondeterministic_ops = {
"aten::dropout(Tensor input, float p, bool train) -> Tensor",
"aten::_fused_dropout(Tensor self, float p, Generator generator) -> (Tensor, Tensor)",
"aten::_standard_gamma(Tensor self, Generator generator) -> Tensor",
"aten::bernoulli(Tensor self, *, Generator generator) -> Tensor",
"aten::bernoulli(Tensor self, float p, *, Generator generator) -> Tensor",
"aten::multinomial(Tensor self, int num_samples, bool replacement, *, Generator generator) -> Tensor",
"aten::normal(Tensor mean, Tensor std, *, Generator generator) -> Tensor",
"aten::normal(float mean, Tensor std, *, Generator generator) -> Tensor",
"aten::normal(Tensor mean, float std, *, Generator generator) -> Tensor",
"aten::poisson(Tensor self, Generator generator) -> Tensor",
"aten::rrelu(Tensor self, Scalar lower, Scalar upper, bool training, Generator generator) -> Tensor",
"aten::rrelu_with_noise(Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, Generator generator) -> Tensor",
"aten::rand(int[] size, *, int dtype, int layout, int[] device) -> Tensor",
"aten::rand_like(Tensor self) -> Tensor",
"aten::rand_like(Tensor self, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randint(int high, int[] size, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randint(int low, int high, int[] size, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randint_like(Tensor self, int high) -> Tensor",
"aten::randint_like(Tensor self, int low, int high) -> Tensor",
"aten::randint_like(Tensor self, int high, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randint_like(Tensor self, int low, int high, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randn(int[] size, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randn_like(Tensor self) -> Tensor",
"aten::randn_like(Tensor self, *, int dtype, int layout, int[] device) -> Tensor",
"aten::randperm(int n, *, int dtype, int layout, int[] device) -> Tensor"
};
if (nondeterministic_ops.find(this) == nullptr) {
return false;
}
// Dropout with train = False is deterministic
if (matches("aten::dropout(Tensor input, float p, bool train) -> Tensor") && is_constant(attr::train) && !get<bool>(attr::train).value()) {
return false;
}
return true;
}
// Assign this node a topological position, to facilitate fast isBefore() and
// isAfter() queries. Must be called right after a node is inserted into the
// node list.
//
// The basic scheme is: assign every node a position (uint64_t). The common
// case (appending to the end of the graph) is made more efficient by advancing
// a fixed interval past the previous node and placing `this` there. Otherwise,
// assign `this` a position at the midpoint between its prev() and next()
// nodes.
//
// If we ever run out of space (by, e.g. inserting too much in place), we
// reindex by spreading out all the nodes again.
void Node::assignTopoPosition() {
auto returnNode = owningBlock()->return_node();
const auto prevPos = prev()->topo_position_;
const auto nextPos = next()->topo_position_;
// Append to the end of the graph
if (next() == returnNode) {
if (next() == prev()) {
// the node list is empty, assign the first position
topo_position_ = kMidPoint;
return;
}
if (prevPos >= (kUpperBound - kAppendInterval)) {
// we're running off the edge
owningBlock()->reIndexTopology();
return;
}
topo_position_ = prevPos + kAppendInterval;
// Prepend to the graph
} else if (prev() == returnNode) {
// next() is the first element in the block list
if (nextPos <= (kLowerBound + kAppendInterval)) {
// we're running off the edge
owningBlock()->reIndexTopology();
return;
}
topo_position_ = nextPos - kAppendInterval;
// insert between two existing nodes
} else {
const auto posBetween = prevPos + (nextPos - prevPos) / 2;
if (posBetween == prevPos) {
// There was no room
owningBlock()->reIndexTopology();
return;
}
topo_position_ = posBetween;
}
}
Node::Node(Graph * graph_, NodeKind kind_) :
kind_(kind_),
graph_(graph_),
owning_block_(nullptr),
scope_(graph_->current_scope_),
schema_(nullptr) {
graph_->all_nodes.emplace(this);
}
void Node::eraseOutput(size_t i) {
JIT_ASSERT(i < outputs_.size());
JIT_ASSERT(outputs_[i]->uses().empty());
schema_ = nullptr;
Value * n = outputs_[i];
outputs_.erase(outputs_.begin() + i);
owningGraph()->freeValue(n);
for(size_t j = i; j < outputs_.size(); j++) {
outputs_[j]->offset_--;
}
}
Block * Node::addBlock() {
schema_ = nullptr;
blocks_.push_back(new Block(owningGraph(), this));
return blocks_.back();
}
void Node::eraseBlock(size_t i) {
JIT_ASSERT(i < blocks_.size());
schema_ = nullptr;
Block * n = blocks_[i];
blocks_.erase(blocks_.begin() + i);
n->destroy();
}
void Node::destroy() {
while(!outputs().empty())
eraseOutput(outputs().size() - 1);
while(!blocks().empty())
eraseBlock(blocks().size() - 1);
removeAllInputs();
if(inBlockList())
removeFromList();
graph_->freeNode(this);
}
void Node::cloneFrom(Node * s) {
setSourceLocation(s->getSourceLocation());
if(s->scope_ && !s->scope_->isBlank())
scope_ = s->scope_;
copyAttributes(*s);
}
void Node::replaceAllUsesWith(Node * n) {
JIT_ASSERT(outputs().size() == n->outputs().size());
size_t nOutputs = outputs().size();
for(size_t i = 0; i < nOutputs; i++) {
outputs()[i]->replaceAllUsesWith(n->outputs()[i]);
}
}
Value* Node::insertInput(size_t i, Value* value) {
JIT_ASSERT(graph_ == value->owningGraph());
schema_ = nullptr;
// First we update the offsets for all existing inputs that will reside
// after the one we're inserting. Concretely, these are the inputs at
// indices [i, # input). Since we're inserting one input before all of
// these inputs, increment their use offsets for this value by 1
for (size_t use_itr = i; use_itr < inputs_.size(); ++use_itr) {
// See Note [User node does not uniquely identify use]
auto use = findUseForInput(use_itr);
use->offset += 1;
}
// Insert the actual input at the specified index
inputs_.insert(inputs_.begin() + i, value);
// Register the new use of the value we're inserted as an input.
value->uses_.emplace_back(this, i);
return value;
}
Value* Node::addInput(Value * value) {
JIT_ASSERT(graph_ == value->owningGraph());
schema_ = nullptr;
value->uses_.emplace_back(this, inputs_.size());
inputs_.push_back(value);
return value;
}
Value* Node::replaceInput(size_t i, Value * newValue) {
JIT_ASSERT(newValue->owningGraph() == graph_);
schema_ = nullptr;
Value * old = dropInput(i);
inputs_[i] = newValue;
newValue->uses_.emplace_back(this, i);
return old;
}
void Node::replaceInputWith(Value * from, Value * to) {
JIT_ASSERT(from->owningGraph() == graph_);
JIT_ASSERT(to->owningGraph() == graph_);
schema_ = nullptr;
size_t i = 0;
for(auto input : inputs()) {
if(input == from)
replaceInput(i, to);
i++;
}
}
Value* Node::addOutput() {
outputs_.push_back(new Value(this, outputs_.size()));
schema_ = nullptr;
return outputs_.back();
}
Value* Node::insertOutput(size_t i) {
schema_ = nullptr;
outputs_.insert(outputs_.begin() + i, new Value(this, i));
for (size_t itr = i + 1; itr < outputs_.size(); ++itr) {
outputs_[itr]->setOffset(outputs_[itr]->offset() + 1);
}
return outputs_.at(i);
}
bool Node::isBefore(const Node * n) const {
if (this == n) {
return false;
}
return !isAfter(n);
}
bool Node::isAfter(const Node * n) const {
JIT_ASSERT(this->owningBlock() == n->owningBlock());
return this->topo_position_ > n->topo_position_;
}
Node* Node::insertBefore(Node * n) {
JIT_ASSERT(n->inBlockList());
insertAfter(n->prev());
return this;
}
Node* Node::insertAfter(Node * n) {
JIT_ASSERT(!inBlockList() && n->inBlockList());
JIT_ASSERT(n->owningBlock());
this->owning_block_ = n->owningBlock();
Node * next = n->next();
n->next() = this;
this->prev() = n;
this->next() = next;
next->prev() = this;
assignTopoPosition();
return this;
}
void Node::moveAfter(Node * n) {
removeFromList();
insertAfter(n);
}
void Node::moveBefore(Node * n) {
removeFromList();
insertBefore(n);
}
void Node::removeInput(size_t i) {
schema_ = nullptr;
dropInput(i);
// everything after this input shifts left,
// so we need to update their use offsets to match
for(size_t j = i+1; j < inputs_.size(); j++) {
auto it = findUseForInput(j);
it->offset--;
}
inputs_.erase(inputs_.begin() + i);
}
void Node::removeAllInputs() {
schema_ = nullptr;
for(size_t i = 0; i < inputs().size(); ++i)
dropInput(i);
inputs_.clear();
}
use_list::iterator Node::findUseForInput(size_t i) {
auto & input_uses = inputs_[i]->uses_;
// O(N) on the use list, but unless we get nodes with +100 uses
// vector traversal still is probably faster than linked list
auto use_it = std::find(input_uses.begin(), input_uses.end(), Use(this, i));
JIT_ASSERT(use_it != input_uses.end());
return use_it;
}
Value* Node::dropInput(size_t i) {
JIT_ASSERT(i < inputs_.size());
auto input_node = inputs_[i];
auto use_it = findUseForInput(i);
input_node->uses_.erase(use_it);
inputs_[i] = nullptr;
return input_node;
}
void Node::removeFromList() {
JIT_ASSERT(inBlockList());
this->owning_block_ = nullptr;
Node * next = this->next();
Node * prev = this->prev();
prev->next() = next;
next->prev() = prev;
this->next() = nullptr;
this->prev() = nullptr;
}
inline const SourceRange& fakeRange() {
static SourceRange range(std::make_shared<std::string>("<internally-created-node>"), 0, 1);
return range;
}
Value* Graph::insert(Symbol opname, at::ArrayRef<NamedValue> args, at::ArrayRef<NamedValue> kwargs) {
return script::emitBuiltinCall(fakeRange(), *this, opname, c10::nullopt, args, kwargs, /*required=*/true);
}
Node* Graph::create(NodeKind kind, size_t num_outputs) {
// NB: Node constructor adds node to all_nodes
auto n = new Node(this, kind);
for(size_t i = 0; i < num_outputs; i++)
n->addOutput();
return n;
}
Node* Graph::create(NodeKind kind, ArrayRef<Value*> inputs, size_t num_outputs) {
auto n = create(kind, num_outputs);
for(auto i : inputs)
n->addInput(i);
return n;
}
Node* Graph::createUndefined() {
return create(prim::Undefined);
}
Node * Graph::createNoneGenerator() {
auto n = create(prim::NoneGenerator);
n->output()->setType(GeneratorType::get());
return n;
}
Node * Graph::createFusionGroup(int device) {
auto n = create(prim::FusionGroup, 0);
n->g_(attr::Subgraph,std::make_shared<Graph>(current_scope()));
n->i_(attr::device, device);
return n;
}
Node* Graph::createTuple(at::ArrayRef<Value*> values) {
auto types = fmap(values, [](Value* v) { return v->type(); });
auto tt = TupleType::create(std::move(types));
auto n = create(prim::TupleConstruct, values);
n->output()->setType(tt);
return n;
}
Node* Graph::createTupleUnpack(Value * v) {
TupleTypePtr tt = v->type()->expect<TupleType>();
auto n = create(prim::TupleUnpack, {v}, 0);
for(auto & element : tt->elements()) {
n->addOutput()->setType(element);
}
return n;
}
Node* Graph::createTupleIndex(Value * tup, int64_t index) {
auto n = create(prim::TupleIndex, {tup});
n->i_(attr::index, index);
auto tuple_type = tup->type()->expect<TupleType>();
n->output()->setType(tuple_type->elements().at(index));
return n;
}
Node* Graph::createTupleSlice(Value * tup, int64_t beg, int64_t end) {
auto n = create(prim::TupleSlice, {tup});
auto tuple_type = tup->type()->expect<TupleType>();
n->i_(attr::beg, beg);
n->i_(attr::end, end);
std::vector<TypePtr> output_types;
for (auto i = beg; i < end; ++i) {
output_types.push_back(tuple_type->elements().at(i));
}
auto tt = TupleType::create(std::move(output_types));
n->output()->setType(tt);
return n;
}
Node* Graph::createList(const TypePtr& elem_type, at::ArrayRef<Value*> values) {
auto n = create(prim::ListConstruct, values);
for(const auto & v : values) {
JIT_ASSERT(v->type()->isSubtypeOf(elem_type));
}
n->output()->setType(ListType::create(elem_type));
return n;
}
Node* Graph::createListUnpack(Value *v, size_t size) {
ListTypePtr list_type = v->type()->expect<ListType>();
TypePtr elem_type = list_type->getElementType();
auto n = create(prim::ListUnpack, {v}, 0);
for (size_t i = 0; i < size; ++i) {
n->addOutput()->setType(elem_type);
}
return n;
}
Node* Graph::createNumToTensor(Value* value) {
auto typ = value->type();
Node * result = create(prim::NumToTensor, {value});
result->output()->setType(CompleteTensorType::fromNumberType(typ));
return result;
}
Node* Graph::createBoolToTensor(Value* value) {
auto typ = value->type();
Node * result = create(prim::BoolToTensor, {value});
if (!typ->isSubtypeOf(BoolType::get())) {
AT_ERROR("Cannot create bool type from ", typ->str());
}
result->output()->setType(CompleteTensorType::fromBoolType());
return result;
}
Node* Graph::createTensorToNum(const TypePtr& type, Value* value) {
auto* result = create(prim::TensorToNum, {value});
result->output()->setType(type);
return result;
}
Node* Graph::createImplicitTensorToNum(const TypePtr& type, Value* value) {
auto* result = create(prim::ImplicitTensorToNum, {value});
result->output()->setType(type);
return result;
}
Node* Graph::createTensorToBool(Value* value) {
auto* result = create(prim::TensorToBool, {value});
result->output()->setType(BoolType::get());
return result;
}
Node* Graph::createIntToFloat(Value* value) {
JIT_ASSERT(*value->type() == *IntType::get());
auto* result = create(prim::IntToFloat, {value});
result->output()->setType(FloatType::get());
return result;
}
Node* Graph::createFloatToInt(Value* value) {
JIT_ASSERT(*value->type() == *FloatType::get());
auto* result = create(prim::FloatToInt, {value});
result->output()->setType(IntType::get());
return result;
}
Node* Graph::createStringToFloat(Value* value) {
JIT_ASSERT(*value->type() == *StringType::get());
auto* result = create(prim::StringToFloat, {value});
result->output()->setType(FloatType::get());
return result;
}
Node* Graph::createClone(Node * n, std::function<Value*(Value*)> value_map, bool copy_blocks) {
//n can be from a different graph
Node * r = n->allocNewInstance(this);
for(auto o : n->outputs()) {
r->addOutput()->copyMetadata(o);
}
r->cloneFrom(n);
for(auto i : n->inputs()) {
r->addInput(value_map(i));
}
if(copy_blocks) {
for(auto b : n->blocks()) {
r->addBlock()->cloneFrom(b, value_map);
}
}
return r;
}
Value* Graph::insertConstant(
IValue val,
c10::optional<SourceRange> loc,
c10::optional<ScopePtr> scope) {
return jit::insertConstant(*this, std::move(val), loc, scope);
}
Value* Graph::insertDummyWorld() {
auto node = create(prim::DummyWorld, 1);
node->output()->setType(WorldType::get());
return insertNode(node)->output();
}
std::string Graph::toString() const {
std::ostringstream oss;
oss << *this;
return oss.str();
}
Graph::~Graph() {
for (const Node * n : all_nodes)
delete n;
for (const Value * v : all_values)
delete v;
for (const Block * b : all_blocks)
delete b;
}
void Graph::freeNode(Node * n) {
auto it = all_nodes.find(n);
JIT_ASSERT(it != all_nodes.end());
delete *it;
all_nodes.erase(it);
}
void Graph::freeValue(Value * v) {
v->setUniqueName("");
auto it = all_values.find(v);
JIT_ASSERT(it != all_values.end());
delete *it;
all_values.erase(it);
}
void Graph::freeBlock(Block * b) {
auto it = all_blocks.find(b);
JIT_ASSERT(it != all_blocks.end());
delete *it;
all_blocks.erase(it);
}
PythonOp* defaultAllocPythonOp(Graph*g) {
throw std::runtime_error("Trying to allocate a Python object without python bindings loaded");
}
std::atomic<decltype(&defaultAllocPythonOp)> alloc_python_op;
// patched in when python bindings are loaded
PythonOp* allocPythonOp(Graph* g) {
return alloc_python_op.load()(g);
}
void setAllocPythonOp(PythonOp* (*v)(Graph* g)) {
alloc_python_op.store(v);
}
}} // namespace torch::jit