Merge remote-tracking branch 'origin/develop' into fix/scope

paddle/framework/CMakeLists.txt

@@ -44,7 +44,7 @@ cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_co
 
 cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
 set(EXECUTOR_TEST_OP elementwise_add_op gaussian_random_op feed_op fetch_op
-    mul_op sum_op squared_l2_distance_op fill_constant_op sgd_op)
+    mul_op sum_op squared_l2_distance_op fill_constant_op sgd_op mean_op)
 if(WITH_GPU)
     nv_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
 else()

paddle/framework/backward.cc

@@ -273,6 +273,21 @@ static bool AllGradInSet(const std::vector<std::string>& names,
   return true;
 }
 
+static void CreateGradVarInBlock(BlockDescBind* block_desc,
+                                 size_t grad_op_start_index) {
+  auto ops = block_desc->AllOps();
+  for (size_t op_index = grad_op_start_index; op_index < ops.size();
+       ++op_index) {
+    for (const auto& output : ops[op_index]->Outputs()) {
+      for (const auto& real_output : output.second) {
+        if (!block_desc->HasVar(real_output)) {
+          block_desc->Var(real_output);
+        }
+      }
+    }
+  }
+}
+
 std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
     const std::unique_ptr<OpDescBind>& op_desc,
     std::unordered_set<std::string>* no_grad_vars,
@@ -326,15 +341,16 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
   std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
   size_t grad_desc_idx = 0;
   std::vector<std::unique_ptr<OpDescBind>> backward_descs;
+
   for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
     std::vector<std::unique_ptr<OpDescBind>> op_grads =
         MakeOpGrad(*it, no_grad_vars, grad_to_var);
 
     if ((*it)->Type() == "recurrent") {
       PADDLE_ENFORCE_EQ(
-          op_grads.size(), size_t(1),
+          op_grads.size(), static_cast<size_t>(1),
           "rnn_op's gradient process should contain only one op.");
-      int step_block_idx = (*it)->GetBlockAttr("stop_block");
+      int step_block_idx = (*it)->GetBlockAttr("step_block");
       auto backward_block_op_descs = MakeBlockBackward(
           program_desc, step_block_idx, no_grad_vars, grad_to_var);
       BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block);
@@ -380,10 +396,11 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     backward_descs.insert(backward_descs.begin() + p.first + 1,
                           std::move(p.second));
   }
+
   return backward_descs;
 }
 
-void AppendBackward(ProgramDescBind& program_desc,
+void AppendBackward(ProgramDescBind& program_desc, const VarDescBind& target,
                     const std::unordered_set<std::string>& no_grad_vars) {
   std::unordered_set<std::string> no_grad_var_names;
   no_grad_var_names.reserve(no_grad_vars.size() + 1);
@@ -391,13 +408,34 @@ void AppendBackward(ProgramDescBind& program_desc,
   for (auto& name : no_grad_vars) {
     no_grad_var_names.insert(GradVarName(name));
   }
+
   const int root_block_idx = 0;
+  auto root_block = program_desc.Block(root_block_idx);
+  auto& all_ops = root_block->ops_;
+
+  // insert fill one op for target
+  std::string fill_one_op_out = GradVarName(target.Name());
+  std::unique_ptr<OpDescBind> fill_one_op(
+      new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
+                     {{"shape", std::vector<int>{1}},
+                      {"value", static_cast<float>(1.0)},
+                      {"dataType", framework::DataType::FP32}}));
+  all_ops.push_back(std::move(fill_one_op));
+  size_t forward_op_num = all_ops.size();
+  size_t forward_block_num = program_desc.Size();
   std::unordered_map<std::string, std::string> grad_to_var;
   auto backward_op_descs = MakeBlockBackward(program_desc, root_block_idx,
                                              &no_grad_var_names, &grad_to_var);
-  auto& forw_op_descs = program_desc.Block(root_block_idx)->ops_;
   for (auto& ptr : backward_op_descs) {
-    forw_op_descs.push_back(std::move(ptr));
+    all_ops.push_back(std::move(ptr));
+  }
+  root_block->Var(fill_one_op_out);
+
+  // create grad_var for all blocks in this program
+  CreateGradVarInBlock(root_block, forward_op_num);
+  for (size_t block_index = forward_block_num;
+       block_index < program_desc.Size(); ++block_index) {
+    CreateGradVarInBlock(program_desc.Block(block_index), 0);
   }
 }
 

paddle/framework/backward.h

@@ -29,7 +29,7 @@ extern std::unique_ptr<OperatorBase> Backward(
 
 // TODO(jiayi): Add target as parameter and generate backward op
 // according to target.
-void AppendBackward(ProgramDescBind& program_desc,
+void AppendBackward(ProgramDescBind& program_desc, const VarDescBind& target,
                     const std::unordered_set<std::string>& no_grad_vars);
 
 }  // namespace framework

paddle/framework/backward_test.cc

@@ -18,6 +18,7 @@
 #include "paddle/framework/block_desc.h"
 #include "paddle/framework/op_desc.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/var_desc.h"
 #include "paddle/operators/net_op.h"
 
 namespace paddle {
@@ -468,10 +469,14 @@ TEST(Backward, simple_single_op) {
   op->SetInput("b", {"b"});
   op->SetOutput("Out", {"out"});
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out");
+  AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 2UL);
-  f::OpDescBind *grad_op = block->AllOps()[1];
+  ASSERT_EQ(block->AllOps().size(), 3UL);
+  f::OpDescBind *fill_op = block->AllOps()[1];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op = block->AllOps()[2];
   EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
@@ -494,13 +499,17 @@ TEST(Backward, default_attribute) {
   op->SetOutput("Out", {"out"});
   op->CheckAttrs();
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out");
+  AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 2UL);
+  ASSERT_EQ(block->AllOps().size(), 3UL);
   EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
   EXPECT_EQ(boost::get<int>(op->GetAttr("y_num_col_dims")), 1);
 
-  f::OpDescBind *grad_op = block->AllOps()[1];
+  f::OpDescBind *fill_op = block->AllOps()[1];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op = block->AllOps()[2];
   ASSERT_EQ(grad_op->Type(), "mul_grad");
   EXPECT_EQ(boost::get<int>(grad_op->GetAttr("x_num_col_dims")), 1);
   EXPECT_EQ(boost::get<int>(grad_op->GetAttr("y_num_col_dims")), 1);
@@ -528,10 +537,15 @@ TEST(Backward, simple_mult_op) {
   op3->SetInput("b", {"b3"});
   op3->SetOutput("Out", {"out3"});
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out3");
+  size_t forward_len = block->AllOps().size();
+  AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 6UL);
-  f::OpDescBind *grad_op1 = block->AllOps()[5];
+  ASSERT_EQ(block->AllOps().size(), 6UL + 1);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op1 = block->AllOps()[6];
   EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -542,7 +556,7 @@ TEST(Backward, simple_mult_op) {
   EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b1")}));
 
-  f::OpDescBind *grad_op2 = block->AllOps()[4];
+  f::OpDescBind *grad_op2 = block->AllOps()[5];
   EXPECT_EQ(grad_op2->Type(), "mul_grad");
   ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
   ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
@@ -556,7 +570,7 @@ TEST(Backward, simple_mult_op) {
   EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
             std::vector<std::string>({f::GradVarName("y2")}));
 
-  f::OpDescBind *grad_op3 = block->AllOps()[3];
+  f::OpDescBind *grad_op3 = block->AllOps()[4];
   EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
@@ -596,10 +610,15 @@ TEST(Backward, intermedia_var_no_grad) {
   op4->SetInput("Y", {"out3"});
   op4->SetOutput("Out", {"out4"});
 
-  AppendBackward(program, {"out3"});
+  auto target = f::VarDescBind("out4");
+  size_t forward_len = block->AllOps().size();
+  AppendBackward(program, target, {"out3"});
 
-  ASSERT_EQ(block->AllOps().size(), 6UL);
-  f::OpDescBind *grad_op1 = block->AllOps()[5];
+  ASSERT_EQ(block->AllOps().size(), 7UL);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op1 = block->AllOps()[6];
   EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -610,7 +629,7 @@ TEST(Backward, intermedia_var_no_grad) {
   EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b1")}));
 
-  f::OpDescBind *grad_op4 = block->AllOps()[4];
+  f::OpDescBind *grad_op4 = block->AllOps()[5];
   EXPECT_EQ(grad_op4->Type(), "mul_grad");
   ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
   ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
@@ -642,10 +661,15 @@ TEST(Backward, var_no_grad) {
   op2->SetOutput("Y", {"y2"});
   op2->SetOutput("Z", {"z2"});
 
-  AppendBackward(program, {"z1"});
+  auto target = f::VarDescBind("z2");
+  size_t forward_len = block->AllOps().size();
+  AppendBackward(program, target, {"z1"});
 
-  ASSERT_EQ(block->AllOps().size(), 5UL);
-  f::OpDescBind *grad_op2 = block->AllOps()[2];
+  ASSERT_EQ(block->AllOps().size(), 6UL);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op2 = block->AllOps()[3];
   ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad");
   ASSERT_EQ(grad_op2->InputNames().size(), 6UL);
   ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
@@ -661,7 +685,7 @@ TEST(Backward, var_no_grad) {
             std::vector<std::string>({f::GradVarName("y1")}));
   EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), std::vector<std::string>());
 
-  f::OpDescBind *fill_zero_op = block->AllOps()[3];
+  f::OpDescBind *fill_zero_op = block->AllOps()[4];
   ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like");
   ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL);
   ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL);
@@ -669,7 +693,7 @@ TEST(Backward, var_no_grad) {
   EXPECT_EQ(fill_zero_op->Output("Y"),
             std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
 
-  f::OpDescBind *grad_op1 = block->AllOps()[4];
+  f::OpDescBind *grad_op1 = block->AllOps()[5];
   ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 6UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -709,10 +733,15 @@ TEST(Backward, shared_var) {
   op3->SetInput("b", {"b3"});
   op3->SetOutput("Out", {"out3"});
 
-  AppendBackward(program, {});
+  auto target = f::VarDescBind("out3");
+  size_t forward_len = block->AllOps().size();
+  AppendBackward(program, target, {});
 
-  ASSERT_EQ(block->AllOps().size(), 7UL);
-  f::OpDescBind *grad_op3 = block->AllOps()[3];
+  ASSERT_EQ(block->AllOps().size(), 8UL);
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
+
+  f::OpDescBind *grad_op3 = block->AllOps()[4];
   ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
@@ -723,7 +752,7 @@ TEST(Backward, shared_var) {
   EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
             std::vector<std::string>({f::GradVarName("b3")}));
 
-  f::OpDescBind *grad_op4 = block->AllOps()[4];
+  f::OpDescBind *grad_op4 = block->AllOps()[5];
   ASSERT_EQ(grad_op4->Type(), "mul_grad");
   ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
   ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
@@ -737,7 +766,7 @@ TEST(Backward, shared_var) {
   EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
             std::vector<std::string>({f::GradVarName("y2")}));
 
-  f::OpDescBind *sum_op = block->AllOps()[5];
+  f::OpDescBind *sum_op = block->AllOps()[6];
   ASSERT_EQ(sum_op->Type(), "sum");
   ASSERT_EQ(sum_op->InputNames().size(), 1UL);
   ASSERT_EQ(sum_op->OutputNames().size(), 1UL);
@@ -747,7 +776,7 @@ TEST(Backward, shared_var) {
   EXPECT_EQ(sum_op->Output("Out"),
             std::vector<std::string>({f::GradVarName("out1")}));
 
-  f::OpDescBind *grad_op1 = block->AllOps()[6];
+  f::OpDescBind *grad_op1 = block->AllOps()[7];
   ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad");
   ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
   ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
@@ -769,7 +798,11 @@ TEST(Backward, half_backward) {
   op1->SetInput("Y", {"b"});
   op1->SetOutput("Out", {"out"});
 
-  AppendBackward(program, {"b"});
+  auto target = f::VarDescBind("out");
+  size_t forward_len = block->AllOps().size();
+  AppendBackward(program, target, {"b"});
+  f::OpDescBind *fill_op = block->AllOps()[forward_len];
+  EXPECT_EQ(fill_op->Type(), "fill_constant");
   auto ops = block->AllOps();
-  ASSERT_EQ(2UL, ops.size());
-}
+  ASSERT_EQ(3UL, ops.size());
+}
\ No newline at end of file

paddle/framework/block_desc.h

@@ -39,7 +39,7 @@ class BlockDescBind {
       std::unordered_map<std::string, std::string> *grad_to_var);
 
   friend void AppendBackward(
-      ProgramDescBind &program_desc,
+      ProgramDescBind &program_desc, const VarDescBind &target,
       const std::unordered_set<std::string> &no_grad_vars);
 
   BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)

paddle/framework/executor_test.cc

@@ -34,6 +34,7 @@ USE_OP(mul);
 USE_OP(sum);
 USE_OP(squared_l2_distance);
 USE_OP(fill_constant);
+USE_OP(mean);
 USE_OP(sgd);
 
 using namespace paddle::platform;
@@ -45,9 +46,16 @@ void AddOp(const std::string& type, const VariableNameMap& inputs,
   // insert output
   for (auto kv : outputs) {
     for (auto v : kv.second) {
-      auto var = block->Var(v);
-      var->SetType(VarDesc::LOD_TENSOR);
-      var->SetDataType(paddle::framework::DataType::FP32);
+      // <<<<<<< HEAD
+      //       auto var = block->Var(v);
+      //       var->SetType(VarDesc::LOD_TENSOR);
+      //       var->SetDataType(paddle::framework::DataType::FP32);
+      // =======
+      if (!block->HasVar(v)) {
+        auto var = block->Var(v);
+        var->SetDataType(paddle::framework::DataType::FP32);
+      }
+      // >>>>>>> origin/develop
     }
   }
 
@@ -147,12 +155,12 @@ class ExecutorTesterRandom : public ::testing::Test {
     AddOp("squared_l2_distance", {{"X", {"a"}}, {"Y", {"a_out"}}},
           {{"Out", {"l2_distance"}}, {"sub_result", {"l2_distance_sub"}}}, {},
           root_block);
+    AddOp("mean", {{"X", {"l2_distance"}}}, {{"Out", {"mean_out"}}}, {},
+          root_block);
 
     // backward
-    AddOp("fill_constant", {}, {{"Out", {"l2_distance@GRAD"}}},
-          {{"shape", std::vector<int>{batch_size, 1}}, {"value", float(1.0)}},
-          root_block);
-    AppendBackward(program, {});
+    auto target = VarDescBind("mean_out");
+    AppendBackward(program, target, {});
 
     // update
     AddOp("fill_constant", {}, {{"Out", {"learning_rate"}}},

paddle/framework/var_desc.h

@@ -54,7 +54,10 @@ inline void VectorToRepeated(const std::vector<bool> &vec,
 
 class VarDescBind {
  public:
-  explicit VarDescBind(const std::string &name) { desc_.set_name(name); }
+  explicit VarDescBind(const std::string &name) {
+    desc_.set_name(name);
+    desc_.set_type(VarDesc::LOD_TENSOR);
+  }
 
   VarDesc *Proto() { return &desc_; }
 

paddle/pybind/protobuf.cc

@@ -118,9 +118,9 @@ void BindProgramDesc(py::module &m) {
       .def("append_block", &ProgramDescBind::AppendBlock,
            py::return_value_policy::reference)
       .def("append_backward",
-           [](ProgramDescBind &program_desc,
+           [](ProgramDescBind &program_desc, const VarDescBind &target,
               const std::unordered_set<std::string> &no_grad_vars) {
-             AppendBackward(program_desc, no_grad_vars);
+             AppendBackward(program_desc, target, no_grad_vars);
            })
       .def("block", &ProgramDescBind::Block, py::return_value_policy::reference)
       .def("num_blocks", &ProgramDescBind::Size)

python/paddle/v2/framework/framework.py

@@ -176,6 +176,18 @@ class Operator(object):
         proto = OpProtoHolder.instance().get_op_proto(type)
 
         if inputs is not None:
+            given = set()
+            need = set()
+            for n in inputs:
+                given.add(n)
+            for m in proto.inputs:
+                need.add(m.name)
+            if not given == need:
+                raise ValueError(
+                    "Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]"
+                    % (type, ", ".join(str(e) for e in need), ", ".join(
+                        str(e) for e in given)))
+
             for in_proto in proto.inputs:
                 in_argus = inputs[in_proto.name]
                 if not isinstance(in_argus, list):
@@ -190,6 +202,18 @@ class Operator(object):
                 self.desc.set_input(in_proto.name, in_argu_names)
 
         if outputs is not None:
+            given = set()
+            need = set()
+            for n in outputs:
+                given.add(n)
+            for m in proto.outputs:
+                need.add(m.name)
+            if not given == need:
+                raise ValueError(
+                    "Incorrect setting for output(s) of operator \"%s\". Need: [%s] Given: [%s]"
+                    % (type, ", ".join(str(e) for e in need), ", ".join(
+                        str(e) for e in given)))
+
             for out_proto in proto.outputs:
                 out_argus = outputs[out_proto.name]
                 if not isinstance(out_argus, list):

python/paddle/v2/framework/tests/test_program.py

@@ -51,11 +51,14 @@ class TestProgram(unittest.TestCase):
         sum_op_desc.set_input("Y", ["b1"])
         sum_op_desc.set_output("Out", ["out2"])
 
+        target = block.new_var("out2")
+
         expect_ops = [
-            "mul", "elementwise_add", "elementwise_add_grad", "mul_grad"
+            "mul", "elementwise_add", "fill_constant", "elementwise_add_grad",
+            "mul_grad"
         ]
         actual_ops = []
-        prog.append_backward(set())
+        prog.append_backward(target, set())
         for op in block.all_ops():
             actual_ops.append(op.type())
         print(actual_ops)