Optimize cinn_cache_key by replace GraphToProgram to Dot string (#37317)

* optimize cache-key by replace GraphToProgram to Dot string

* fix compile failure bug

paddle/fluid/framework/paddle2cinn/cinn_cache_key.cc

@@ -14,13 +14,16 @@
 
 #include "paddle/fluid/framework/paddle2cinn/cinn_cache_key.h"
 
+#include <algorithm>
+#include <functional>
 #include <map>
+#include <set>
 #include <string>
 
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/inference/analysis/dot.h"
 
 namespace paddle {
 namespace framework {
@@ -39,13 +42,42 @@ CinnCacheKey::CinnCacheKey(const ir::Graph& graph,
   this->SetKey(graph, input_shapes, arch_str);
 }
 
+size_t CinnCacheKey::HashGraph(const ir::Graph& graph) {
+  // using Dot to unqiue graph
+  inference::analysis::Dot dot;
+  std::unordered_map<const ir::Node*, std::string> node2dot;
+  int id = 0;
+  // Create nodes
+  // graph.Nodes() return unordered_set, the same graph may
+  // return different result?
+  for (const ir::Node* n : graph.Nodes()) {
+    std::string node_id = std::to_string(id++);
+    dot.AddNode(node_id, {}, n->Name(), true);
+    node2dot[n] = node_id;
+  }
+
+  // Create edges
+  for (const ir::Node* n : graph.Nodes()) {
+    const auto& src_id = node2dot.at(n);
+    for (auto* out : n->outputs) {
+      const auto& dest_id = node2dot.at(out);
+      dot.AddEdge(src_id, dest_id, {});
+    }
+  }
+
+  const std::string& viz_graph = dot.Build();
+  VLOG(1) << "The hash graph:\n" << viz_graph;
+
+  size_t hash_val = std::hash<std::string>()(viz_graph);
+  VLOG(4) << "The graph's hash value is: " << hash_val;
+  return hash_val;
+}
+
 void CinnCacheKey::SetKey(
     const ir::Graph& graph,
     const std::map<std::string, const LoDTensor*>& input_tensors,
     const std::string& arch_str) {
-  ProgramDesc program;
-  GraphToProgram(graph, &program);
-  program.Proto()->SerializeToString(&graph_serialize_str_);
+  graph_serialize_str_ = std::to_string(HashGraph(graph));
   for (const auto& name_tensor : input_tensors) {
     input_shapes_[name_tensor.first] = name_tensor.second->dims();
   }
@@ -55,9 +87,7 @@ void CinnCacheKey::SetKey(
 void CinnCacheKey::SetKey(const ir::Graph& graph,
                           const std::map<std::string, DDim>& input_shapes,
                           const std::string& arch_str) {
-  ProgramDesc program;
-  GraphToProgram(graph, &program);
-  program.Proto()->SerializeToString(&graph_serialize_str_);
+  graph_serialize_str_ = std::to_string(HashGraph(graph));
   input_shapes_ = input_shapes;
   arch_str_ = arch_str;
 }

paddle/fluid/framework/paddle2cinn/cinn_cache_key.h

@@ -58,6 +58,8 @@ class CinnCacheKey {
   };
 
  private:
+  size_t HashGraph(const ir::Graph& graph);
+
   std::string graph_serialize_str_;
   std::map<std::string, DDim> input_shapes_;
   std::string arch_str_;

paddle/fluid/framework/paddle2cinn/cinn_compiler.cc

@@ -139,7 +139,7 @@ std::string CinnCompiler::VizGraph(const Graph& graph) const {
           node_id,
           {Dot::Attr("shape", "box"), Dot::Attr("style", "rounded,filled,bold"),
            Dot::Attr("color", "#303A3A"), Dot::Attr("fontcolor", "#ffffff")},
-          n->Name());
+          n->Name(), true);
     } else if (n->IsVar()) {
       auto label = n->Name();
       if (n->Var() && n->Var()->GetType() == proto::VarType::LOD_TENSOR) {
@@ -155,7 +155,7 @@ std::string CinnCompiler::VizGraph(const Graph& graph) const {
            Dot::Attr("color", n->Var()->IsParameter() ? "#148b97" : "#dddddd"),
            Dot::Attr("fontcolor",
                      n->Var()->IsParameter() ? "#ffffff" : "#000000")},
-          label);
+          label, true);
     }
     node2dot[n] = node_id;
   }

paddle/fluid/inference/analysis/dot.h

@@ -59,6 +59,9 @@ class Dot {
           attrs(attrs),
           id_("node_" + std::to_string(dot_node_counter++)) {}
 
+    Node(const std::string& name, const std::vector<Attr>& attrs, size_t id)
+        : name(name), attrs(attrs), id_("node_" + std::to_string(id)) {}
+
     std::string id() const { return id_; }
 
     std::string repr() const {
@@ -113,11 +116,15 @@ class Dot {
   explicit Dot(const std::vector<Attr>& attrs) : attrs_(attrs) {}
 
   void AddNode(const std::string& id, const std::vector<Attr>& attrs,
-               std::string label = "") {
+               std::string label = "", bool use_local_id = false) {
     CHECK(!nodes_.count(id)) << "duplicate Node '" << id << "'";
     if (label.empty()) label = id;
+    if (use_local_id) {
+      nodes_.emplace(id, Node{label, attrs, local_node_counter_++});
+    } else {
       nodes_.emplace(id, Node{label, attrs});
     }
+  }
 
   void AddEdge(const std::string& source, const std::string& target,
                const std::vector<Attr>& attrs) {
@@ -154,6 +161,8 @@ class Dot {
   std::unordered_map<std::string, Node> nodes_;
   std::vector<Edge> edges_;
   std::vector<Attr> attrs_;
+
+  size_t local_node_counter_{0};
 };
 
 }  // namespace analysis

paddle/fluid/operators/cinn_launch_op.cc

@@ -98,13 +98,13 @@ CinnTensor CinnLaunchContext::GetCinnTensor(const std::string& var_name) {
   return cinn_scope_->GetTensor(var_name);
 }
 
-std::vector<std::string> CinnLaunchContext::GetInternalVariableNames() {
+std::unordered_set<std::string> CinnLaunchContext::GetInternalVariableNames() {
   std::unordered_set<std::string> all_parameters(cinn_variable_names_);
   std::for_each(name2argument_.begin(), name2argument_.end(),
                 [&all_parameters](const auto& name2arg) {
                   all_parameters.erase(name2arg.first);
                 });
-  return {all_parameters.begin(), all_parameters.end()};
+  return all_parameters;
 }
 
 void CinnLaunchContext::MutableTensorData(const std::string& var_name,

paddle/fluid/operators/cinn_launch_op.h

@@ -62,7 +62,7 @@ class CinnLaunchContext {
 
   // Extract internal variable names from CinnScope
   // by excluding used input and output variables
-  std::vector<std::string> GetInternalVariableNames();
+  std::unordered_set<std::string> GetInternalVariableNames();
 
   // Finalize all execution arguments and return them
   const std::map<std::string, cinn_pod_value_t>& FinalizeArguments() const;

paddle/fluid/operators/cinn_launch_op_test.cc

@@ -223,7 +223,7 @@ TEST(CinnLaunchContextTest, TestGetInternalVariableNames) {
       std::make_unique<CinnLaunchContext>(GetDefaultCompiledObj());
   auto internal_variable_names = launch_context->GetInternalVariableNames();
   ASSERT_EQ(internal_variable_names.size(), 1);
-  EXPECT_EQ(internal_variable_names.front(), "cinn_var2");
+  EXPECT_EQ(*internal_variable_names.begin(), "cinn_var2");
 }
 
 TEST(CinnLaunchContextTest, TestMutableTensorData) {