mirror of
https://github.com/zebrajr/pytorch.git
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Differential Revision: D12852205 Original commit changeset: 3e0e9218afdf fbshipit-source-id: 114b4873504109394fe9d489200d39764ecc638e
1385 lines
41 KiB
C++
1385 lines
41 KiB
C++
#include "ir.h"
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#include "torch/csrc/jit/operator.h"
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#include "torch/csrc/autograd/function.h"
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#include "torch/csrc/jit/constants.h"
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#include "torch/csrc/jit/assertions.h"
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#include "torch/csrc/jit/script/compiler.h"
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#include "torch/csrc/jit/passes/pretty_print.h"
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#include <iostream>
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#include <unordered_map>
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#include <unordered_set>
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#include <set>
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#include <stack>
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#include <sstream>
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#include <algorithm>
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#include <string>
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namespace torch { namespace jit {
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// Constants relating to maintaining the topological index of nodes.
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//
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// Lower and upper bounds of the index. Inclusive range.
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static constexpr topo_position_t kLowerBound = INT64_MIN;
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static constexpr topo_position_t kUpperBound = INT64_MAX;
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static constexpr topo_position_t kMidPoint = 0;
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// How far away to space nodes that are appended to the graph.
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// should be 2^n, where:
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// - n is the maximum number of repeated insertions without a re-index
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// - 2^(64-n) is the maximum number of appends to the end without reindex
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static constexpr topo_position_t kAppendInterval = 1099511627776ULL /* 2^40 */;
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// Sigh, see https://stackoverflow.com/questions/8016780/undefined-reference-to-static-constexpr-char
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constexpr Symbol PythonOp::Kind;
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void printValueRef(std::ostream & out, const Value * n) {
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out << "%" << n->uniqueName();
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}
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// NB: This overload will become ambiguous with the one Caffe2 provides in its
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// logging, if they ever intersect.
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template <typename T>
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std::ostream& operator<<(std::ostream & out, const std::vector<T> & nodes) {
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out << at::ArrayRef<T>{nodes};
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return out;
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}
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template <typename T>
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std::ostream& printValueRefs(std::ostream & out, const at::ArrayRef<T> & nodes) {
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size_t i = 0;
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for(auto n : nodes) {
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if(i++ > 0)
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out << ", ";
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printValueRef(out, n);
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}
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return out;
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}
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// Can't make these two overloads directly a template, it'll be ambiguous with
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// the global printer for operator<<.
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std::ostream& operator<<(std::ostream & out, const at::ArrayRef<const Value*> & nodes) {
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return printValueRefs(out, nodes);
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}
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std::ostream& operator<<(std::ostream & out, const at::ArrayRef<Value*> & nodes) {
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return printValueRefs(out, nodes);
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}
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struct const_value_list_with_types {
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const ArrayRef<const Value*> values;
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bool use_newlines;
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const_value_list_with_types(ArrayRef<const Value*> values, bool use_newlines = false)
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: values(values), use_newlines(use_newlines) {}
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};
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std::ostream& operator<<(std::ostream & out, const_value_list_with_types l) {
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size_t i = 0;
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for(auto n : l.values) {
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if(i++ > 0) {
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if (l.use_newlines) {
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// TODO: Indent here is hard-coded for "graph(": un-hard-code it
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out << "\n ";
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} else {
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out << ", ";
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}
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}
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printValueRef(out, n);
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out << " : ";
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out << *n->type();
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}
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return out;
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}
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void printAttributes(std::ostream & out, const Node * n, bool ignore_subgraph=false) {
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out << "[";
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auto names = n->attributeNames();
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int i = 0;
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for(auto name : names) {
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if (ignore_subgraph && name == attr::Subgraph)
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continue;
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if(i++ > 0)
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out << ", ";
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// TODO: debugging mode to see the qualifier. We definitely
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// don't want to print the qualifier since it should always
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// be attribute, but you might be able to track down a weird
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// bug by printing it out.
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out << name.toUnqualString() << "=";
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n->printValue(out, name);
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}
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out << "]";
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}
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static std::ostream & indent(std::ostream & out, size_t level) {
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for(size_t i = 0; i < level; ++i)
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out << " ";
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return out;
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}
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std::ostream& printNode(std::ostream & out, size_t level, const Node * n, std::vector<const Node*> * groups) {
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auto outputs = n->outputs();
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indent(out, level) << const_value_list_with_types(outputs);
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out << " = ";
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IR_IFM_CONST(n,PythonOp)
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out << "^" << value->name();
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value->writeScalars(out);
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IR_ELSE()
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if(n->hasAttribute(attr::Subgraph) && groups) {
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out << n->kind().toQualString() << "_" << groups->size();
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if (n->numAttributes() > 1 && n->kind() != prim::DifferentiableGraph) {
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printAttributes(out, n, /*ignore_subgraph=*/true);
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}
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groups->push_back(n);
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} else {
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out << n->kind().toQualString();
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if(n->hasAttributes()) {
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printAttributes(out,n);
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}
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}
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IR_END()
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out << "(" << n->inputs() << ")";
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std::string scopeName = n->scopeName();
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if (scopeName.empty()) {
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out << "\n";
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}
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else {
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out << ", ";
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out << "scope: " << scopeName << "\n";
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}
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for(size_t i = 0; i < n->blocks().size(); ++i) {
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auto b = n->blocks()[i];
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indent(out, level + 1) << "block" << i << "(" << const_value_list_with_types(b->inputs(), false) << ") {\n";
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for(auto n : b->nodes()) {
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printNode(out, level + 2, n, groups);
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}
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indent(out, level + 2) << "-> (" << b->outputs() << ")\n";
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indent(out, level + 1) << "}\n";
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}
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return out;
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}
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std::ostream& operator<<(std::ostream & out, const Node & n) {
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return printNode(out, 0, &n, nullptr);
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}
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std::ostream& operator<<(std::ostream & out, const Graph & g) {
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out << "graph(" << const_value_list_with_types(g.inputs(), true) << ") {\n";
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std::vector<const Node*> groups;
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for(auto n : g.nodes()) {
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printNode(out, 1, n, &groups);
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}
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out << " return (" << g.outputs() << ");\n}\n";
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size_t i = 0;
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for(auto fg : groups) {
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out << "with " << fg->kind().toQualString() << "_" <<i++ << " = " << *fg->g(attr::Subgraph);
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}
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/*
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// Uncomment this to debug all_nodes issues
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{
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out << "\n";
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out << "all_nodes:\n";
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for (auto& n : g.all_nodes) {
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printNode(out, const_cast<Node*>(n), nullptr);
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}
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}
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*/
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return out;
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}
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std::ostream& Graph::prettyPrint(std::ostream & out) {
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PrettyPrint(out, *this);
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return out;
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}
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void Graph::dumpPretty() {
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PrettyPrint(std::cout, *this);
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}
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static void checkSameDevice(const Node* node) {
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bool has_device = false;
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int device;
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auto checkValue = [&](const Value* v) {
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if(CompleteTensorTypePtr type = v->type()->cast<CompleteTensorType>()) {
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if(!has_device) {
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has_device = true;
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device = type->device();
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} else {
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JIT_ASSERT(device == type->device());
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}
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}
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};
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for(auto input : node->inputs()) {
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checkValue(input);
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}
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for(auto output : node->outputs()) {
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checkValue(output);
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}
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}
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using node_set = std::set<const Node*>;
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#define ALL_OF(container) container.begin(), container.end()
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// These functions purposely operate on the internal members directly, to force
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// you to think about how the invariants change if you change the data
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// representation (even if the external API does not change.)
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// NB: This assert is written to assume you don't have any unattached
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// nodes. Unattached nodes can occur while manipulations to the
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// graph are occurring.
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void Node::lint() const {
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// Node invariants
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// - if node should live in list, nodes_iter is consistent
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// - Inputs are all marked as a use by the nodes they refer to
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// - Owning graph is non-null and consistent
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// - The "Select" invariant, when the node is MultiReturn
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//
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// The handle invariant:
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// If a node takes a handle as an input, it is always the
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// LAST input of the node. There is at most one handle input.
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{
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size_t i = 0;
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for (auto input : inputs_) {
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// WARNING: O(n^2)
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
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JIT_ASSERT(std::find(ALL_OF(input->uses_), Use(const_cast<Node*>(this), i)) != input->uses_.end());
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JIT_ASSERT(graph_->all_nodes.count(this) == 1);
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i++;
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}
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}
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for(auto o : outputs()) {
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size_t i = 0;
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for (auto use : o->uses()) {
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// Use invariants
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// - Use is consistent with inputs
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// - Every user node is live (checked in Graph)
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JIT_ASSERT(use.user->inputs_[use.offset] == o);
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i++;
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}
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}
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// Node subclass invariants
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IR_IF(this,Constant)
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JIT_ASSERT(inputs_.size() == 0);
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IR_ELSEIF(LoadWorld)
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JIT_ASSERT(inputs_.size() == 0);
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JIT_ASSERT(outputs_.size() == 1);
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IR_ELSEIF(StoreWorld)
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JIT_ASSERT(inputs_.size() == 1);
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JIT_ASSERT(outputs_.size() == 0);
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IR_ELSEIF(Return)
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// Return uses is zero
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JIT_ASSERT(outputs().size() == 0);
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IR_ELSEIF(Param)
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// Param inputs is zero
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JIT_ASSERT(inputs_.size() == 0);
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IR_ELSEIFM_CONST(PythonOp)
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// Python operator cconv is correct
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size_t n_scalars = 0, n_tensors = 0;
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for (auto c : value->cconv) {
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if (c == 'c') {
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n_scalars++;
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} else if (c == 'd') {
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n_tensors++;
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} else {
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JIT_ASSERT(0);
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}
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JIT_ASSERT(static_cast<bool>(value->pyobj));
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}
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JIT_ASSERT(n_scalars == value->scalar_args.size());
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JIT_ASSERT(n_tensors == inputs_.size());
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IR_ELSEIF(Eval)
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// TODO: add invariants
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// TODO: It's not good for these ops to be top-level, it makes cases longer.
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IR_ELSEIF(FusionGroup)
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checkSameDevice(value);
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// TODO: Typecheck the parameters
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value->g(attr::Subgraph)->lint();
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IR_END()
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}
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// TODO: When lint fails, give better indication about which
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// instruction triggered the failure.
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void Graph::lint() const {
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// Graph invariants
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// Uncomment the following to see the graph
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// std::cout << *const_cast<Graph*>(this);
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// nodes
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// - nodes_ is a valid topological ordering for inputs
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// - No repeated nodes
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// - Params and return do NOT occur in nodes
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// - next_unique_ is greater than all uniques in graph
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// - uniques in all_nodes are unique
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// - every use will occur later in the topsort
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struct LintScope {
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LintScope() = default;
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LintScope(std::unique_ptr<LintScope> parent)
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: parent(std::move(parent)) {}
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bool contains(const Value * v) {
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return values.count(v) > 0 || (parent && parent->contains(v));
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}
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bool contains(const Node * n) {
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return nodes.count(n) > 0 || (parent && parent->contains(n));
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}
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void insert(const Value * v) {
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JIT_ASSERT(!contains(v));
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values.insert(v);
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}
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void insert(const Node * n) {
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JIT_ASSERT(!contains(n));
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nodes.insert(n);
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}
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std::unique_ptr<LintScope> parent;
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private:
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std::unordered_set<const Value*> values;
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std::unordered_set<const Node*> nodes;
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};
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// Struct enables mutual recursion in linting methods.
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// Putting it inside Graph::lint enables access to private Graph members
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struct LintImpl {
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LintImpl(const Graph & g)
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: g(g)
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, scope(new LintScope())
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, all_nodes_set(ALL_OF(g.all_nodes)) {} // NB: all_nodes is *unordered*
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const Graph & g;
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std::unique_ptr<LintScope> scope;
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std::unordered_set<size_t> seen_uniques;
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std::unordered_map<const Node*, int64_t> anticipated_uses;
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node_set all_nodes_set;
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node_set sum_set;
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void check_value(const Value* v) {
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scope->insert(v);
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auto b2 = seen_uniques.insert(v->unique());
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JIT_ASSERT(b2.second); // insertion took place
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JIT_ASSERT(v->unique() < g.next_unique_);
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for (auto use : v->uses()) {
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JIT_ASSERT(!scope->contains(use.user));
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JIT_ASSERT(g.all_nodes.count(use.user) == 1);
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anticipated_uses[use.user]++; // int default constructs to 0
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}
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}
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void check_node(const Node* n) {
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for (auto input : n->inputs_) {
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if (!scope->contains(input)) {
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JIT_ASSERTM(0, input->unique(), " not in scope");
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}
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}
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JIT_ASSERT(anticipated_uses[n] == static_cast<int64_t>(n->inputs_.size()));
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anticipated_uses[n] = -1; // we saw the anticipated user!
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scope->insert(n);
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for(auto block : n->blocks()) {
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std::unique_ptr<LintScope> new_scope(new LintScope(std::move(scope)));
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scope = std::move(new_scope);
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check_block(block);
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scope = std::move(scope->parent);
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}
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size_t i = 0;
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for(auto o : n->outputs()) {
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JIT_ASSERT(o->node() == n);
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JIT_ASSERT(i++ == o->offset_);
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check_value(o);
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}
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n->lint();
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}
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void check_block(const Block *b) {
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// Check topological ordering
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JIT_ASSERT(b->param_node()->isBefore(*b->nodes().begin()));
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auto curNode = *b->nodes().begin();
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while (curNode != b->return_node()) {
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JIT_ASSERT(curNode->isBefore(curNode->next()));
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curNode = curNode->next();
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}
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for (auto input : b->inputs()) {
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check_value(input);
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JIT_ASSERT(input->node()->kind_ == prim::Param);
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}
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for (auto n : b->nodes()) {
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JIT_ASSERT(n->kind_ != prim::Param);
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JIT_ASSERT(n->kind_ != prim::Return);
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JIT_ASSERT(n->kind_ != prim::DummyWorld);
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check_node(n);
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}
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JIT_ASSERT(b->output_->kind() == prim::Return);
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check_node(b->output_);
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// all_nodes
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// - inputs_, output_ and nodes_ are all included in all_nodes
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// - all_nodes does not contain dead nodes??? (likely to be temporarily
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// suspended). Weaker: all_nodes contains all inputs and returns
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// - only one return node???
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node_set nodes_set(ALL_OF(b->nodes()));
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node_set inputs_set {b->input_};
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node_set output_set {b->output_};
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// TODO: Make a more type safe std::includes wrapper which disallows use on
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// non-ordered containers
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JIT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(nodes_set)));
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JIT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(inputs_set)));
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JIT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(output_set)));
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sum_set.insert(ALL_OF(nodes_set));
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sum_set.insert(ALL_OF(inputs_set));
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sum_set.insert(ALL_OF(output_set));
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}
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void check_graph() {
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node_set all_nodes_set(ALL_OF(g.all_nodes)); // NB: all_nodes is *unordered*
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check_block(g.block_);
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for (auto kv : anticipated_uses) {
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JIT_ASSERT(kv.second == -1);
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}
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JIT_ASSERT(std::includes(ALL_OF(sum_set), ALL_OF(all_nodes_set)));
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}
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};
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LintImpl(*this).check_graph();
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}
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void Graph::dump() const {
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std::cout << *this << "\n";
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}
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void LintGraph(std::shared_ptr<Graph>& graph) {
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graph->lint();
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}
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Block::Block(Graph* graph_, Node* node_)
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: graph_(graph_),
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output_(initOutput(graph_->create(prim::Return, 0))),
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input_(graph_->create(prim::Param, 0)),
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owning_node_(node_) {
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graph_->all_blocks.emplace(this);
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output_->owning_block_ = this;
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output_->topo_position_ = kUpperBound;
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input_->owning_block_ = this;
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input_->topo_position_ = kLowerBound;
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}
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void Block::reIndexTopology() {
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auto curPos = kLowerBound;
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for (auto node : nodes()) {
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AT_ASSERT(curPos <= (kUpperBound - kAppendInterval));
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curPos += kAppendInterval;
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node->topo_position_ = curPos;
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}
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}
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void Block::cloneFrom(Block * src, std::function<Value*(Value*)> value_map) {
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std::unordered_map<Value*, Value*> local_map;
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auto env = [&](Value * v) {
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auto it = local_map.find(v);
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if(it != local_map.end())
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return it->second;
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return value_map(v);
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};
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auto graph = owningGraph();
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for(auto input : src->inputs()) {
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local_map[input] = this->addInput()->copyMetadata(input);
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}
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|
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();
|
|
}
|
|
|
|
const FunctionSchema* Node::maybeSchema() const {
|
|
if(!schema_) {
|
|
if(auto op = findOperatorFor(this)) {
|
|
schema_ = &op->schema();
|
|
}
|
|
}
|
|
return 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;
|
|
}
|
|
|
|
bool Node::moveAfterTopologicallyValid(Node* n) {
|
|
return tryMove(n, MoveSide::AFTER);
|
|
}
|
|
|
|
bool Node::moveBeforeTopologicallyValid(Node* n) {
|
|
// We have to distinguish the move side (instead of just moving after
|
|
// n->prev()). Consider the following example:
|
|
// If the dependency graph looks like this -> n -> o then moveBefore(o) will
|
|
// end up with [this, o, n], but moveAfter(n) will return false.
|
|
return tryMove(n, MoveSide::BEFORE);
|
|
}
|
|
|
|
// Helper for topologically-safe node moves. See `tryMove()` for details.
|
|
namespace {
|
|
struct WorkingSet {
|
|
public:
|
|
explicit WorkingSet(Node* mover) {
|
|
add(mover);
|
|
}
|
|
|
|
// Add `n` to the working set
|
|
void add(Node* n) {
|
|
nodes_.push_back(n);
|
|
for (const auto user : getUsersSameBlock(n)) {
|
|
users_[user]++;
|
|
}
|
|
}
|
|
|
|
void eraseMover() {
|
|
auto mover = nodes_.front();
|
|
for (const auto user : getUsersSameBlock(mover)) {
|
|
// If this user node only uses the mover, we can remove it
|
|
if (users_[user] == 1) {
|
|
users_.erase(user);
|
|
}
|
|
}
|
|
nodes_.pop_front();
|
|
}
|
|
|
|
const std::list<Node*>& nodes() {
|
|
return nodes_;
|
|
}
|
|
|
|
// Does the working set depend on `n`?
|
|
bool dependsOn(Node* n) const {
|
|
if (nodes_.empty()) {
|
|
return false;
|
|
}
|
|
|
|
if (n->isAfter(nodes_.front())) {
|
|
return producesFor(n);
|
|
} else {
|
|
return consumesFrom(n);
|
|
}
|
|
}
|
|
|
|
// Does the working set produce any values consumed by `n`?
|
|
bool producesFor(Node* n) const {
|
|
// This equivalent to asking: does the total use-set of all the nodes in the
|
|
// working set include `n`?
|
|
return users_.count(n) != 0;
|
|
}
|
|
|
|
// Does the working set consume any values produced by `n`?
|
|
bool consumesFrom(Node* n) const {
|
|
const auto users = getUsersSameBlock(n);
|
|
return std::any_of(nodes_.begin(), nodes_.end(), [&](Node* node) {
|
|
return users.count(node) != 0;
|
|
});
|
|
}
|
|
|
|
private:
|
|
// Get all users of outputs of `n`, in the same block as `n`.
|
|
// This means if there is an `if` node that uses an output of `n` in some
|
|
// inner sub-block, we will consider the whole `if` node a user of `n`.
|
|
std::unordered_set<Node*> getUsersSameBlock(Node* n) const {
|
|
std::unordered_set<Node*> users;
|
|
for (const auto output : n->outputs()) {
|
|
for (const auto& use : output->uses()) {
|
|
if (use.user->owningBlock() == n->owningBlock()) {
|
|
users.insert(use.user);
|
|
} else {
|
|
// This user is in a sub-block. Traverse the blockchain upward until
|
|
// we arrive at a node that shares a block with `this`
|
|
auto curNode = use.user;
|
|
while (curNode->owningBlock() != n->owningBlock()) {
|
|
curNode = curNode->owningBlock()->owningNode();
|
|
JIT_ASSERT(curNode);
|
|
}
|
|
users.insert(curNode);
|
|
}
|
|
}
|
|
}
|
|
|
|
return users;
|
|
}
|
|
|
|
std::list<Node*> nodes_;
|
|
// users => # of working set nodes it uses
|
|
std::unordered_map<Node*, size_t> users_;
|
|
};
|
|
} // namespace
|
|
|
|
// Try to move `this` before/after `movePoint` while preserving value
|
|
// dependencies. Returns false iff such a move could not be made
|
|
//
|
|
// The basic approach is: have a "working set" that we are moving forward, one
|
|
// node at a time. When we can't move past a node (because it depends on the
|
|
// working set), then add it to the working set and keep moving until we hit
|
|
// `moveAfter`.
|
|
bool Node::tryMove(Node* movePoint, MoveSide moveSide) {
|
|
JIT_ASSERT(this->inBlockList() && movePoint->inBlockList());
|
|
JIT_ASSERT(this->owningBlock() == movePoint->owningBlock());
|
|
if (this == movePoint) {
|
|
return true;
|
|
}
|
|
|
|
// 1. Move from `this` toward movePoint, building up the working set of
|
|
// dependencies
|
|
WorkingSet workingSet(this);
|
|
|
|
int direction;
|
|
if (this->isAfter(movePoint)) {
|
|
direction = kPrevDirection;
|
|
} else {
|
|
direction = kNextDirection;
|
|
}
|
|
|
|
auto curNode = this->next_in_graph[direction];
|
|
// Move forward one node at a time
|
|
while (curNode != movePoint) {
|
|
if (workingSet.dependsOn(curNode)) {
|
|
// If we can't move past this node, add it to the working set
|
|
workingSet.add(curNode);
|
|
}
|
|
curNode = curNode->next_in_graph[direction];
|
|
}
|
|
|
|
// 2. Decide whether we can move it all to `movePoint`.
|
|
|
|
// Say we are moving directly before movePoint and `this` starts before
|
|
// movePoint in the graph. The move looks like
|
|
//
|
|
// `this` `this` |
|
|
// <dependencies> -> `movePoint` | `this` and deps are split
|
|
// `movePoint` <dependencies> |
|
|
//
|
|
// Contrast with the case where `this` starts AFTER movePoint:
|
|
//
|
|
// `movePoint` <dependencies> |
|
|
// <dependencies> -> `this` | `this` and deps are together
|
|
// `this` `movePoint` |
|
|
//
|
|
// In the first case, we need to split `this` off from its dependencies, so we
|
|
// can move the dependencies below `movePoint` and keep `this` above.
|
|
const bool splitThisAndDeps =
|
|
(moveSide == MoveSide::BEFORE && this->isBefore(movePoint)) ||
|
|
(moveSide == MoveSide::AFTER && this->isAfter(movePoint));
|
|
|
|
if (splitThisAndDeps) {
|
|
// remove `this` from dependencies to be moved past `movePoint`
|
|
workingSet.eraseMover();
|
|
}
|
|
|
|
// Check if we can move the working set past the move point
|
|
if (workingSet.dependsOn(movePoint)) {
|
|
// if we can't, then there are intermediate dependencies between the
|
|
// `this` and `movePoint`, so we can't do the move
|
|
return false;
|
|
}
|
|
|
|
// 3. Execute the move
|
|
JIT_ASSERT(curNode == movePoint);
|
|
if (splitThisAndDeps) {
|
|
// Move `this`
|
|
this->move(movePoint, moveSide);
|
|
|
|
// Then move all of its dependencies on the other side of `movePoint`
|
|
const auto reversed =
|
|
moveSide == MoveSide::BEFORE ? MoveSide::AFTER : MoveSide::BEFORE;
|
|
for (auto toMove : workingSet.nodes()) {
|
|
toMove->move(curNode, reversed);
|
|
curNode = toMove;
|
|
}
|
|
} else {
|
|
// Just append/prepend everything to `movePoint`
|
|
for (auto toMove : workingSet.nodes()) {
|
|
toMove->move(curNode, moveSide);
|
|
curNode = toMove;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
// Helper function so we can generalize `tryMove`
|
|
void Node::move(Node* movePoint, MoveSide moveSide) {
|
|
switch (moveSide) {
|
|
case MoveSide::BEFORE:
|
|
this->moveBefore(movePoint);
|
|
break;
|
|
case MoveSide::AFTER:
|
|
this->moveAfter(movePoint);
|
|
break;
|
|
}
|
|
}
|
|
|
|
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
|