Enable generating code for a given subgraph.

paddle/fluid/framework/ir/fusion_group/CMakeLists.txt

@@ -1,7 +1,7 @@
 cc_library(code_generator SRCS operation.cc code_generator.cc code_generator_helper.cc DEPS graph)
 if(NOT APPLE AND NOT WIN32)
     if(WITH_GPU)
-        cc_test(test_code_generator SRCS code_generator_tester.cc DEPS code_generator device_code lod_tensor)
+        cc_test(test_code_generator SRCS code_generator_tester.cc DEPS code_generator device_code lod_tensor graph_viz_pass)
     endif()
 endif()
 

paddle/fluid/framework/ir/fusion_group/code_generator.cc

@@ -13,9 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/ir/fusion_group/code_generator.h"
-#include <set>
 #include <sstream>
+#include <unordered_set>
 #include "paddle/fluid/framework/ir/fusion_group/code_generator_helper.h"
+#include "paddle/fluid/framework/ir/fusion_group/operation.h"
 
 namespace paddle {
 namespace framework {
@@ -30,69 +31,205 @@ CodeGenerator::CodeGenerator() {
   code_templates_[0] = elementwise_t;
 }
 
+std::string CodeGenerator::Generate(SubGraph* subgraph) {
+  std::vector<OperationExpression> expressions = ConvertToExpressions(subgraph);
+  return Generate(subgraph->func_name, expressions);
+}
+
+std::vector<OperationExpression> CodeGenerator::ConvertToExpressions(
+    SubGraph* subgraph) {
+  std::unordered_map<std::string, int> var_ids = EncodeVarNodes(subgraph);
+  std::vector<OperationExpression> expressions;
+  for (auto* node : subgraph->SortedNodes()) {
+    if (node && node->IsOp() && node->Op()) {
+      auto* op = node->Op();
+
+      // Input ids should be set in fixed order, like:
+      //  - x, y in forward operations
+      //  - x, y, out, out@GRAD in backward operations
+      std::vector<int> input_ids;
+      std::vector<std::string> input_names =
+          OperationMap::Instance().Get(op->Type()).input_names;
+      for (auto& name : input_names) {
+        // TODO(liuyiqun): support duplicated input.
+        if (op->Input(name).size() >= 1U) {
+          // Some input vars are not used in grad ops, such as
+          // "elementwise_add_grad", where "X", "Y" and "Out" are not used.
+          PADDLE_ENFORCE_NE(var_ids.find(op->Input(name)[0]), var_ids.end(),
+                            "Input(%s) of operation %s should be set.", name,
+                            op->Type());
+          input_ids.push_back(var_ids[op->Input(name)[0]]);
+        } else {
+          input_ids.push_back(-1);
+        }
+      }
+      // Output ids should be set in fixed order, like:
+      //  - dx, dy in backward operations
+      std::vector<int> output_ids;
+      std::vector<std::string> output_names =
+          OperationMap::Instance().Get(op->Type()).output_names;
+      for (auto& name : output_names) {
+        PADDLE_ENFORCE_EQ(op->Output(name).size(), 1U,
+                          "Output(%s) of operation %s should be set.", name,
+                          op->Type());
+        PADDLE_ENFORCE_NE(var_ids.find(op->Output(name)[0]), var_ids.end(),
+                          "Output(%s) of operation %s should be set.", name,
+                          op->Type());
+        output_ids.push_back(var_ids[op->Output(name)[0]]);
+      }
+      expressions.push_back(
+          OperationExpression(node->Name(), input_ids, output_ids));
+    }
+  }
+  return expressions;
+}
+
 // In order to get the right result of expression, we need to calculate and
 // store the expression as suffix Expressions using vector.
-std::string CodeGenerator::GenerateCode(
+std::string CodeGenerator::Generate(
     std::string func_name, std::vector<OperationExpression> expressions) {
-  // Check whether all expressions are elementwise operations.
+  // TODO(liuyiqun): Check whether all expressions are elementwise operations.
+  std::string dtype = "float";
+  std::set<int> input_ids = DistilInputIds(expressions);
+  std::set<int> output_ids = DistilOutputIds(expressions);
+
   TemplateVariable template_var;
   template_var.Add("func_name", func_name);
-  template_var.Add("parameters", EmitParameters(expressions, "float"));
-  template_var.Add("compute_body", EmitComputeBody(expressions));
+  template_var.Add("parameters", EmitParameters(input_ids, output_ids, dtype));
+  template_var.Add("compute_body",
+                   EmitComputeBody(expressions, input_ids, output_ids, dtype));
   return predefined_cuda_functions + code_templates_[0].Format(template_var);
 }
 
-// we get the parameter list code for the expression information
-std::string CodeGenerator::EmitParameters(
-    std::vector<OperationExpression> expressions, std::string dtype) {
+std::set<int> CodeGenerator::DistilInputIds(
+    const std::vector<OperationExpression>& expressions) {
   std::set<int> input_ids;
-  std::set<int> output_ids;
-  // Remove the reptead id and get a ordered list.
+  // Use std::set to remove the reptead id and get a ordered list.
   for (size_t i = 0; i < expressions.size(); i++) {
     for (auto id : expressions[i].GetInputIds()) {
-      input_ids.insert(id);
+      if (id >= 0) {
+        input_ids.insert(id);
+      }
     }
+  }
+  return input_ids;
+}
+
+std::set<int> CodeGenerator::DistilOutputIds(
+    const std::vector<OperationExpression>& expressions) {
+  std::set<int> output_ids;
+  // Use std::set to remove the reptead id and get a ordered list.
+  for (size_t i = 0; i < expressions.size(); i++) {
     for (auto id : expressions[i].GetOutputIds()) {
       output_ids.insert(id);
     }
   }
+  return output_ids;
+}
+
+// we get the parameter list code for the expression information
+std::string CodeGenerator::EmitParameters(const std::set<int>& input_ids,
+                                          const std::set<int>& output_ids,
+                                          std::string dtype) {
+  std::stringstream ret;
+  ret << "int N, ";
 
   // If a id is in the input and output list at the same time, then remove it
   // from the input list.
-  for (auto iter = input_ids.begin(); iter != input_ids.end();) {
-    if (output_ids.find(*iter) != output_ids.end()) {
-      input_ids.erase(iter++);
-    } else {
-      iter++;
+  for (auto id : input_ids) {
+    if (output_ids.find(id) == output_ids.end()) {
+      ret << dtype << "* " << ArgName(id) << ", ";
     }
   }
 
-  std::stringstream ret;
-  ret << "int N, ";
-  for (auto iter = input_ids.begin(); iter != input_ids.end(); iter++) {
-    ret << dtype << "* " << VarName(*iter) << ", ";
-  }
-
-  size_t count_index = 0;
-  for (auto iter = output_ids.begin(); iter != output_ids.end(); iter++) {
-    ret << dtype << "* " << VarName(*iter);
-    if (count_index != output_ids.size() - 1) {
+  size_t index = 0;
+  for (auto id : output_ids) {
+    ret << dtype << "* " << ArgName(id);
+    if (index != output_ids.size() - 1) {
       ret << ", ";
     }
-    count_index++;
+    index++;
   }
 
   return ret.str();
 }
 
 std::string CodeGenerator::EmitComputeBody(
-    std::vector<OperationExpression> expressions) {
-  // get the right experssion code using suffix expression
-  std::stringstream ret;
+    const std::vector<OperationExpression>& expressions,
+    const std::set<int>& input_ids, const std::set<int>& output_ids,
+    std::string dtype) {
+  std::ostringstream compute;
+  std::unordered_set<int> used;
   for (size_t i = 0; i < expressions.size(); i++) {
-    ret << expressions[i].GetExpression();
+    VLOG(3) << DebugString(expressions[i]);
+    compute << expressions[i].GetExpression(dtype, &used);
   }
-  return ret.str();
+
+  // Load input to temporal variables.
+  std::ostringstream load;
+  for (auto id : input_ids) {
+    if (output_ids.find(id) == output_ids.end() &&
+        used.find(id) != used.end()) {
+      load << dtype << " " << TmpName(id) << " = " << ArgName(id) << "[idx];";
+    }
+  }
+
+  // Store temporal variables to memory.
+  std::ostringstream store;
+  for (auto id : output_ids) {
+    store << ArgName(id) << "[idx] = " << TmpName(id) << ";";
+  }
+
+  return load.str() + compute.str() + store.str();
+}
+
+std::unordered_map<std::string, int> CodeGenerator::EncodeVarNodes(
+    SubGraph* subgraph) {
+  const auto& input_var_nodes = subgraph->GetInputVarNodes();
+  const auto& output_var_nodes = subgraph->GetOutputVarNodes();
+
+  int id = 0;
+  std::unordered_map<std::string, int> var_ids;
+  // Numbering input vars.
+  for (auto* in : input_var_nodes) {
+    VLOG(3) << "Encoding input names:" << in->Name() << ", id:" << id;
+    if (var_ids.find(in->Name()) == var_ids.end()) {
+      var_ids[in->Name()] = id++;
+    }
+  }
+  // Numbering internal vars.
+  for (auto* node : subgraph->SortedNodes()) {
+    if (node && node->IsVar() && node->Var()) {
+      bool is_found = false;
+      for (auto* in : input_var_nodes) {
+        if (node == in) {
+          is_found = true;
+          break;
+        }
+      }
+      if (is_found) {
+        continue;
+      }
+      for (auto* out : output_var_nodes) {
+        if (node == out) {
+          is_found = true;
+          break;
+        }
+      }
+      PADDLE_ENFORCE_EQ(
+          is_found, true,
+          "Subgraph with internal var nodes (%s) is not supported yet.",
+          node->Name());
+    }
+  }
+  // Encoding output vars.
+  for (auto* out : output_var_nodes) {
+    VLOG(3) << "Ecoding output names:" << out->Name() << ", id:" << id;
+    if (var_ids.find(out->Name()) == var_ids.end()) {
+      var_ids[out->Name()] = id++;
+    }
+  }
+  return var_ids;
 }
 
 }  // namespace fusion_group

paddle/fluid/framework/ir/fusion_group/code_generator.h

@@ -14,9 +14,12 @@ limitations under the License. */
 
 #pragma once
 
+#include <set>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/ir/fusion_group/code_generator_helper.h"
+#include "paddle/fluid/framework/ir/fusion_group/subgraph.h"
 
 namespace paddle {
 namespace framework {
@@ -27,18 +30,31 @@ class CodeGenerator {
  public:
   CodeGenerator();
 
-  std::string GenerateCode(std::string func_name,
-                           std::vector<OperationExpression> expressions);
+  std::string Generate(std::string func_name,
+                       std::vector<OperationExpression> expressions);
 
-  // TODO(wangchao): add a more general interface
-  // std::string Generate(const std::string name, const SubGraph& subgraph);
+  std::string Generate(SubGraph* subgraph);
+
+  std::vector<OperationExpression> ConvertToExpressions(SubGraph* subgraph);
 
  private:
+  std::set<int> DistilInputIds(
+      const std::vector<OperationExpression>& expressions);
+  std::set<int> DistilOutputIds(
+      const std::vector<OperationExpression>& expressions);
+
   // we get the parameter list code for the expression information
-  std::string EmitParameters(std::vector<OperationExpression> expressions,
+  std::string EmitParameters(const std::set<int>& input_ids,
+                             const std::set<int>& output_ids,
                              std::string dtype);
 
-  std::string EmitComputeBody(std::vector<OperationExpression> expressions);
+  std::string EmitComputeBody(
+      const std::vector<OperationExpression>& expressions,
+      const std::set<int>& input_ids, const std::set<int>& output_ids,
+      std::string dtype);
+
+  // Encode all var nodes in the subgraph with an unique number.
+  std::unordered_map<std::string, int> EncodeVarNodes(SubGraph* subgraph);
 
  private:
   std::vector<CodeTemplate> code_templates_;

paddle/fluid/framework/ir/fusion_group/code_generator_helper.cc

@@ -33,8 +33,9 @@ static T StringTo(const std::string& str) {
   return value;
 }
 
-std::string OperationExpression::GetRHS(size_t i) {
-  auto rhs = OperationMap::Instance().Get(op_).exprs[i];
+std::string OperationExpression::GetRHS(std::unordered_set<int>* used,
+                                        size_t i) const {
+  auto rhs = OperationMap::Instance().Get(op_type_).exprs[i];
   for (size_t i = 0; i < rhs.size(); i++) {
     size_t pos = i;
     if (rhs[pos] == '$' && rhs[pos + 1] == '{') {
@@ -47,29 +48,33 @@ std::string OperationExpression::GetRHS(size_t i) {
       PADDLE_ENFORCE_LT(index, input_ids_.size(),
                         "Only %d inputs are provided, but need %d.",
                         input_ids_.size(), index + 1);
-      rhs.replace(pos, length + 3, VarName(input_ids_[index]) + R"([idx])");
+      PADDLE_ENFORCE_GE(input_ids_[index], 0,
+                        "Input id should be no less than 0.");
+      rhs.replace(pos, length + 3, TmpName(input_ids_[index]));
+      used->insert(input_ids_[index]);
     }
   }
   return rhs;
 }
 
-std::string OperationExpression::GetLHS(size_t i) {
+std::string OperationExpression::GetLHS(size_t i) const {
   std::stringstream ret;
-  ret << VarName(output_ids_[i]) << R"([idx])";
+  ret << TmpName(output_ids_[i]);
   return ret.str();
 }
 
-bool OperationExpression::IsSupport() {
-  return OperationMap::Instance().Has(op_);
+bool OperationExpression::IsSupport() const {
+  return OperationMap::Instance().Has(op_type_);
 }
 
 // we Traverse the graph and get the group , all input id and output id is
 // unique for the node which belong the group
-std::string OperationExpression::GetExpression() {
+std::string OperationExpression::GetExpression(
+    std::string dtype, std::unordered_set<int>* used) const {
   std::stringstream ret;
   if (IsSupport()) {
     for (size_t i = 0; i < output_ids_.size(); ++i) {
-      ret << GetLHS(i) << " = " << GetRHS(i) << ";";
+      ret << dtype << " " << GetLHS(i) << " = " << GetRHS(used, i) << ";";
     }
   }