Merge branch 'develop' of https://github.com/paddlepaddle/paddle into cpplint_errors

cmake/cpplint.cmake

@@ -56,11 +56,14 @@ macro(add_style_check_target TARGET_NAME)
                 # cpplint code style
                 get_filename_component(base_filename ${filename} NAME)
                 set(CUR_GEN ${CMAKE_CURRENT_BINARY_DIR}/${base_filename}.cpplint)
-                add_custom_command(TARGET ${TARGET_NAME} PRE_BUILD
+                add_custom_command(OUTPUT ${CUR_GEN} PRE_BUILD
                     COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
                             "--filter=${STYLE_FILTER}"
                             "--write-success=${CUR_GEN}" ${filename}
+                    DEPENDS ${filename} ${PROJ_ROOT}/paddle/scripts/cpplint.py
                     WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+                add_custom_target(${base_filename}.cpplint DEPENDS ${CUR_GEN})
+                add_dependencies(${TARGET_NAME} ${base_filename}.cpplint)
             endif()
         endforeach()
     endif()

cmake/util.cmake

@@ -118,7 +118,6 @@ endfunction()
 macro(add_unittest_without_exec TARGET_NAME)
     add_executable(${TARGET_NAME} ${ARGN})
     link_paddle_test(${TARGET_NAME})
-    add_style_check_target(${TARGET_NAME} ${ARGN})
 endmacro()
 
 # add_unittest

paddle/framework/CMakeLists.txt

@@ -12,13 +12,15 @@ cc_test(variable_test SRCS variable_test.cc)
 cc_library(scope SRCS scope.cc)
 cc_test(scope_test SRCS scope_test.cc DEPS scope)
 
-proto_library(attr_type SRCS attr_type.proto)
-proto_library(op_proto SRCS op_proto.proto DEPS attr_type)
-proto_library(op_desc SRCS op_desc.proto DEPS attr_type)
+proto_library(attribute_proto SRCS attribute.proto)
+proto_library(op_proto SRCS op_proto.proto DEPS attribute_proto)
+proto_library(op_desc SRCS op_desc.proto DEPS attribute_proto)
 cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
 cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
 
-cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope)
+cc_library(attribute SRCS attribute.cc DEPS op_desc op_proto)
+
+cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope attribute)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
 cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)
@@ -26,7 +28,7 @@ cc_library(op_registry SRCS op_registry.cc DEPS op_desc grad_op_builder)
 cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
 cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
 
-py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.proto)
+py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.proto)
 # Generate an empty __init__.py to make framework_py_proto as a valid python module.
 add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(framework_py_proto framework_py_proto_init)

paddle/framework/attribute.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/framework/attribute.h"
+
+#include <vector>
+
+namespace paddle {
+namespace framework {
+
+template <>
+AttrType AttrTypeID<int>() {
+  return INT;
+}
+template <>
+AttrType AttrTypeID<float>() {
+  return FLOAT;
+}
+template <>
+AttrType AttrTypeID<std::string>() {
+  return STRING;
+}
+template <>
+AttrType AttrTypeID<std::vector<int>>() {
+  return INTS;
+}
+template <>
+AttrType AttrTypeID<std::vector<float>>() {
+  return FLOATS;
+}
+template <>
+AttrType AttrTypeID<std::vector<std::string>>() {
+  return STRINGS;
+}
+
+Attribute GetAttrValue(const AttrDesc& attr_desc) {
+  switch (attr_desc.type()) {
+    case paddle::framework::AttrType::INT: {
+      return attr_desc.i();
+    }
+    case paddle::framework::AttrType::FLOAT: {
+      return attr_desc.f();
+    }
+    case paddle::framework::AttrType::STRING: {
+      return attr_desc.s();
+    }
+    case paddle::framework::AttrType::INTS: {
+      std::vector<int> val(attr_desc.ints_size());
+      for (int i = 0; i < attr_desc.ints_size(); ++i) {
+        val[i] = attr_desc.ints(i);
+      }
+      return val;
+    }
+    case paddle::framework::AttrType::FLOATS: {
+      std::vector<float> val(attr_desc.floats_size());
+      for (int i = 0; i < attr_desc.floats_size(); ++i) {
+        val[i] = attr_desc.floats(i);
+      }
+      return val;
+    }
+    case paddle::framework::AttrType::STRINGS: {
+      std::vector<std::string> val(attr_desc.strings_size());
+      for (int i = 0; i < attr_desc.strings_size(); ++i) {
+        val[i] = attr_desc.strings(i);
+      }
+      return val;
+    }
+  }
+  PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
+  return boost::blank();
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/attr_checker.h → paddle/framework/attribute.h

@@ -6,6 +6,9 @@
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
+
+#include "paddle/framework/attribute.pb.h"
+#include "paddle/framework/op_desc.pb.h"
 #include "paddle/platform/enforce.h"
 
 namespace paddle {
@@ -14,8 +17,14 @@ namespace framework {
 typedef boost::variant<boost::blank, int, float, std::string, std::vector<int>,
                        std::vector<float>, std::vector<std::string>>
     Attribute;
+
 typedef std::unordered_map<std::string, Attribute> AttributeMap;
 
+template <typename T>
+AttrType AttrTypeID();
+
+Attribute GetAttrValue(const AttrDesc& attr_desc);
+
 // check whether a value(attribute) fit a certain limit
 template <typename T>
 class LargerThanChecker {

paddle/framework/backward.cc

@@ -59,19 +59,17 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
   //  If all input gradients of forwarding operator do not need to calculate,
   //  just return an NOP. Not return null ptr because NOP does not take
   //  too much time for calculation, but it is useful for simplifying logic.
-  if (AllInSet(forwardOp.inputs_, OperatorBase::GRAD_VAR_SUFFIX(),
-               no_grad_names)) {
+  if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
     return NOP();
   }
 
   //  All output gradients of forwarding operator do not need to calculate.
   //  Then all input gradients cannot be computed at all, and we put them into
   //  `no_grad_names` set. Return an NOP.
-  if (AllInSet(forwardOp.outputs_, OperatorBase::GRAD_VAR_SUFFIX(),
-               no_grad_names)) {
+  if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
     for (auto& name : forwardOp.inputs_) {
       // Mark all input is not need
-      no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
+      no_grad_names.insert(name + kGradVarSuffix);
     }
     return NOP();
   }
@@ -134,9 +132,9 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
     std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
     for (std::string& grad_input : grad_op->inputs_) {
       if (no_grad_names.count(grad_input)) {
-        std::string prefix = grad_input.substr(
-            0, grad_input.size() - OperatorBase::GRAD_VAR_SUFFIX().size());
-        grad_input = prefix + OperatorBase::ZERO_VAR_SUFFIX();
+        std::string prefix =
+            grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
+        grad_input = prefix + kZeroVarSuffix;
 
         // If part of input gradient of that operator is not calculated, fill
         // zero variables to that input gradient.
@@ -147,7 +145,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
 
     for (std::string& grad_output : grad_op->outputs_) {
       if (no_grad_names.count(grad_output)) {
-        grad_output = OperatorBase::EMPTY_VAR_NAME();
+        grad_output = kEmptyVarName;
       }
     }
 
@@ -168,14 +166,14 @@ std::shared_ptr<OperatorBase> Backward(
   std::unordered_set<std::string> no_grad_names;
   no_grad_names.reserve(no_grad_vars.size());
 
-  no_grad_names.insert(OperatorBase::EMPTY_VAR_NAME() +
-                       OperatorBase::GRAD_VAR_SUFFIX());
+  no_grad_names.insert(kEmptyVarName + kGradVarSuffix);
 
   for (auto& name : no_grad_vars) {
-    no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
+    no_grad_names.insert(name + kGradVarSuffix);
   }
   size_t uid = 0;
   return BackwardRecursive(forwardOp, no_grad_names, uid);
 }
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/backward_test.cc

@@ -78,14 +78,14 @@ class FcOp : public ops::NetOp {
                                {Output("mul_result")}, {}));
     auto b_name = Input("b");
     std::string before_act = "mul_result";
-    if (b_name != EMPTY_VAR_NAME()) {
+    if (b_name != kEmptyVarName) {
       AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
                                  {Output("add_result")}, {}));
       before_act = "add_result";
     } else {
       auto out_varname = Output("add_result");
-      if (out_varname != EMPTY_VAR_NAME()) {
-        this->Rename(out_varname, EMPTY_VAR_NAME());
+      if (out_varname != kEmptyVarName) {
+        this->Rename(out_varname, kEmptyVarName);
       }
     }
 
@@ -163,13 +163,12 @@ TEST(Backward, simple_op_grad) {
   ASSERT_NE(fwd, nullptr);
   auto gop = f::OpRegistry::CreateGradOp(*fwd);
   ASSERT_EQ(4UL, gop->inputs_.size());
-  ASSERT_EQ(f::OperatorBase::EMPTY_VAR_NAME(), gop->inputs_[0]);
+  ASSERT_EQ(f::kEmptyVarName, gop->inputs_[0]);
   ASSERT_EQ("rowwise_add_grad", gop->type_);
-  ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->outputs_[0]);
-  ASSERT_EQ("b" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->outputs_[1]);
+  ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
+  ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
 
-  ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-            gop->Output("X" + f::OperatorBase::GRAD_VAR_SUFFIX()));
+  ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
 }
 
 TEST(Backward, simple_op_not_need_grad) {
@@ -177,7 +176,7 @@ TEST(Backward, simple_op_not_need_grad) {
   ASSERT_NE(fwd, nullptr);
   auto gop = f::Backward(*fwd, {"X"});
   ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
-                      "X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
+                      "X" + f::kGradVarSuffix),
             gop->outputs_.end());
 
   auto no_input_gop = f::Backward(*fwd, {"X", "b"});
@@ -210,9 +209,9 @@ TEST(Backward, net_fc_backward_normal) {
 }
 
 TEST(Backward, net_fc_backward_not_have_b) {
-  std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
-      "fc", {"X", "w", f::OperatorBase::EMPTY_VAR_NAME()},
-      {"mul_result", "add_result", "tmp"}, {});
+  std::shared_ptr<f::OperatorBase> fwd =
+      f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
+                              {"mul_result", "add_result", "tmp"}, {});
   ASSERT_NE(fwd, nullptr);
   std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
   ASSERT_TRUE(gop->IsNetOp());
@@ -242,24 +241,21 @@ TEST(Backward, net_input_of_network_not_need_grad) {
 
   std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
       bwd_net->outputs_.begin(), bwd_net->outputs_.end());
-  all_output.erase(f::OperatorBase::EMPTY_VAR_NAME());
+  all_output.erase(f::kEmptyVarName);
 
   for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
-    ASSERT_NE(all_output.find(out + f::OperatorBase::GRAD_VAR_SUFFIX()),
-              all_output.end());
+    ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
   }
 
   // Not Generated X
-  ASSERT_EQ(all_output.find("X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            all_output.end());
+  ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.end());
 
   ASSERT_EQ(2UL, bwd_net->ops_.size());
   ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
   auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
   ASSERT_EQ(3UL, first_fc_grad->ops_.size());
-  ASSERT_EQ(
-      f::OperatorBase::EMPTY_VAR_NAME(),
-      first_fc_grad->ops_[2]->Output("A" + f::OperatorBase::GRAD_VAR_SUFFIX()));
+  ASSERT_EQ(f::kEmptyVarName,
+            first_fc_grad->ops_[2]->Output("A" + f::kGradVarSuffix));
 }
 
 TEST(Backward, net_shared_weight) {
@@ -311,17 +307,15 @@ TEST(Backward, op_part_of_output_are_not_need) {
   ASSERT_EQ(1UL, fill_zero.inputs_.size());
   ASSERT_EQ("Z", fill_zero.inputs_[0]);
   ASSERT_EQ(1UL, fill_zero.outputs_.size());
-  ASSERT_EQ("Z" + f::OperatorBase::ZERO_VAR_SUFFIX(), fill_zero.outputs_[0]);
+  ASSERT_EQ("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
 
   auto &d_many_out = *net->ops_[1];
   ASSERT_EQ("many_output_op_grad", d_many_out.type_);
   ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size());  // I/O/OG
-  ASSERT_EQ("Z" + f::OperatorBase::ZERO_VAR_SUFFIX(),
-            d_many_out.Input("z" + f::OperatorBase::GRAD_VAR_SUFFIX()));
-  ASSERT_EQ("Y" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-            d_many_out.Input("y" + f::OperatorBase::GRAD_VAR_SUFFIX()));
-  ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-            d_many_out.Output("x" + f::OperatorBase::GRAD_VAR_SUFFIX()));
+  ASSERT_EQ("Z" + f::kZeroVarSuffix, d_many_out.Input("z" + f::kGradVarSuffix));
+  ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" + f::kGradVarSuffix));
+  ASSERT_EQ("X" + f::kGradVarSuffix,
+            d_many_out.Output("x" + f::kGradVarSuffix));
 }
 
 TEST(Backward, op_part_of_input_are_not_need) {
@@ -331,12 +325,10 @@ TEST(Backward, op_part_of_input_are_not_need) {
   ASSERT_EQ(grad_mul.type_, "mul_grad");
   ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
   ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
-  ASSERT_EQ(grad_mul.Output("A" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            f::OperatorBase::EMPTY_VAR_NAME());
-  ASSERT_EQ(grad_mul.Output("B" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "b" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  ASSERT_EQ(grad_mul.Input("Out" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "out" + f::OperatorBase::GRAD_VAR_SUFFIX());
+  ASSERT_EQ(grad_mul.Output("A" + f::kGradVarSuffix), f::kEmptyVarName);
+  ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" + f::kGradVarSuffix);
+  ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
+            "out" + f::kGradVarSuffix);
   ASSERT_EQ(grad_mul.Input("A"), "a");
   ASSERT_EQ(grad_mul.Input("B"), "b");
   ASSERT_EQ(grad_mul.Input("Out"), "out");
@@ -368,23 +360,4 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
   EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
-
-  /*
-    EXPECT_EQ(grad_fc.Output("X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-              f::OperatorBase::EMPTY_VAR_NAME());
-  EXPECT_EQ(grad_fc.Output("W" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-    "w3" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  EXPECT_EQ(grad_fc.Output("b" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-    "b3" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  EXPECT_EQ(grad_fc.Output("mul_result" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-  "mul_out3" + f::OperatorBase::GRAD_VAR_SUFFIX());
-
-  EXPECT_EQ(grad_fc.Input("Out" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-  "out3" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  EXPECT_EQ(grad_fc.Input("X"), "out2");
-  EXPECT_EQ(grad_fc.Input("W"), "w3");
-  EXPECT_EQ(grad_fc.Input("mul_result"), "mul_out3");
-  EXPECT_EQ(grad_fc.Input("add_result"), "tmp_out3");
-  EXPECT_EQ(grad_fc.Input("Out"), "out3");
-  */
 }

paddle/framework/grad_op_builder.cc

@@ -56,8 +56,7 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
 
   for (const auto& arg : src_arg_list) {
     std::string src_name = arg.name();
-    std::string dst_name =
-        is_grad ? src_name + OperatorBase::GRAD_VAR_SUFFIX() : src_name;
+    std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
     (*dst_op->in_out_idxs_)[dst_name] = idx++;
     int src_arg_idx = src_op->in_out_idxs_->at(src_name);
     int src_begin =
@@ -65,10 +64,9 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
     int src_end = src_format == nullptr ? src_arg_idx + 1
                                         : src_format->at(src_arg_idx + 1);
     for (int i = src_begin; i < src_end; ++i) {
-      std::string s = is_grad ? src_inout[i] + OperatorBase::GRAD_VAR_SUFFIX()
-                              : arg.ignore_gradient()
-                                    ? OperatorBase::EMPTY_VAR_NAME()
-                                    : src_inout[i];
+      std::string s =
+          is_grad ? src_inout[i] + kGradVarSuffix
+                  : (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
       dst_inout.emplace_back(s);
     }
     if (dst_format != nullptr) {

paddle/framework/grad_op_builder_test.cc

@@ -83,24 +83,21 @@ TEST(GradOpBuilder, MutiInOut) {
   EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
   EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
             std::vector<std::string>({"out2_1", "out2_2"}));
-  EXPECT_EQ(grad_test_op->Input("Out1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "out1" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  EXPECT_EQ(
-      grad_test_op->Inputs("Out2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-      std::vector<std::string>(
-          {"out2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-           "out2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
+  EXPECT_EQ(grad_test_op->Input("Out1" + f::kGradVarSuffix),
+            "out1" + f::kGradVarSuffix);
+  EXPECT_EQ(grad_test_op->Inputs("Out2_mult" + f::kGradVarSuffix),
+            std::vector<std::string>(
+                {"out2_1" + f::kGradVarSuffix, "out2_2" + f::kGradVarSuffix}));
 
   ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
-  EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  EXPECT_EQ(
-      grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-      std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-                                "in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-                                "in2_3" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
-  EXPECT_EQ(grad_test_op->Output("In3" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "in3" + f::OperatorBase::GRAD_VAR_SUFFIX());
+  EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
+            "in1" + f::kGradVarSuffix);
+  EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
+            std::vector<std::string>({"in2_1" + f::kGradVarSuffix,
+                                      "in2_2" + f::kGradVarSuffix,
+                                      "in2_3" + f::kGradVarSuffix}));
+  EXPECT_EQ(grad_test_op->Output("In3" + f::kGradVarSuffix),
+            "in3" + f::kGradVarSuffix);
 }
 
 TEST(GradOpBuilder, IOIgnoredInGradient) {
@@ -116,30 +113,25 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
   ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
   EXPECT_EQ(grad_test_op->Input("In1"), "in1");
   EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
-            std::vector<std::string>({f::OperatorBase::EMPTY_VAR_NAME(),
-                                      f::OperatorBase::EMPTY_VAR_NAME()}));
+            std::vector<std::string>({f::kEmptyVarName, f::kEmptyVarName}));
   EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
             std::vector<std::string>({"in3_1", "in3_2"}));
   EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
             std::vector<std::string>({"out1_1", "out1_2"}));
-  EXPECT_EQ(grad_test_op->Input("Out2"), f::OperatorBase::EMPTY_VAR_NAME());
-  EXPECT_EQ(
-      grad_test_op->Inputs("Out1_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-      std::vector<std::string>(
-          {"out1_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-           "out1_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
-  EXPECT_EQ(grad_test_op->Input("Out2" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "out2" + f::OperatorBase::GRAD_VAR_SUFFIX());
+  EXPECT_EQ(grad_test_op->Input("Out2"), f::kEmptyVarName);
+  EXPECT_EQ(grad_test_op->Inputs("Out1_mult" + f::kGradVarSuffix),
+            std::vector<std::string>(
+                {"out1_1" + f::kGradVarSuffix, "out1_2" + f::kGradVarSuffix}));
+  EXPECT_EQ(grad_test_op->Input("Out2" + f::kGradVarSuffix),
+            "out2" + f::kGradVarSuffix);
 
   ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
-  EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-            "in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
-  EXPECT_EQ(
-      grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-      std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-                                "in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
-  EXPECT_EQ(
-      grad_test_op->Outputs("In3_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
-      std::vector<std::string>({"in3_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
-                                "in3_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
+  EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
+            "in1" + f::kGradVarSuffix);
+  EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
+            std::vector<std::string>(
+                {"in2_1" + f::kGradVarSuffix, "in2_2" + f::kGradVarSuffix}));
+  EXPECT_EQ(grad_test_op->Outputs("In3_mult" + f::kGradVarSuffix),
+            std::vector<std::string>(
+                {"in3_1" + f::kGradVarSuffix, "in3_2" + f::kGradVarSuffix}));
 }

paddle/framework/op_desc.proto

@@ -15,7 +15,7 @@ limitations under the License. */
 syntax="proto2";
 package paddle.framework;
 
-import "attr_type.proto";
+import "attribute.proto";
 
 // AttrDesc is used to describe Attributes of an Operator. It contain's
 // name, type, and value of Attribute.

paddle/framework/op_proto.proto

@@ -21,7 +21,7 @@ limitations under the License. */
 syntax="proto2";
 package paddle.framework;
 
-import "attr_type.proto";
+import "attribute.proto";
 
 // Attribute protocol message for 3rd-party language binding.
 // It will store the Op support what attribute and what type.

paddle/framework/op_registry.cc

@@ -14,37 +14,8 @@ limitations under the License. */
 
 #include <paddle/framework/op_registry.h>
 
-namespace paddle {
-namespace framework {
-
-template <>
-void AttrTypeHelper::SetAttrType<int>(AttrProto* attr) {
-  attr->set_type(paddle::framework::AttrType::INT);
-}
-
-template <>
-void AttrTypeHelper::SetAttrType<float>(AttrProto* attr) {
-  attr->set_type(paddle::framework::AttrType::FLOAT);
-}
-
-template <>
-void AttrTypeHelper::SetAttrType<std::string>(AttrProto* attr) {
-  attr->set_type(paddle::framework::AttrType::STRING);
-}
+#include <vector>
 
-template <>
-void AttrTypeHelper::SetAttrType<std::vector<int>>(AttrProto* attr) {
-  attr->set_type(paddle::framework::AttrType::INTS);
-}
-
-template <>
-void AttrTypeHelper::SetAttrType<std::vector<float>>(AttrProto* attr) {
-  attr->set_type(paddle::framework::AttrType::FLOATS);
-}
-
-template <>
-void AttrTypeHelper::SetAttrType<std::vector<std::string>>(AttrProto* attr) {
-  attr->set_type(paddle::framework::AttrType::STRINGS);
-}
-}  // namespace framework
+namespace paddle {
+namespace framework {}  // namespace framework
 }  // namespace paddle

paddle/framework/op_registry.h

@@ -19,7 +19,7 @@ limitations under the License. */
 #include <type_traits>
 #include <unordered_map>
 #include <unordered_set>
-#include "paddle/framework/attr_checker.h"
+#include "paddle/framework/attribute.h"
 #include "paddle/framework/grad_op_builder.h"
 #include "paddle/framework/op_desc.pb.h"
 #include "paddle/framework/scope.h"
@@ -27,49 +27,6 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-// helper class to set attribute type
-struct AttrTypeHelper {
-  template <typename T>
-  static void SetAttrType(AttrProto* attr);
-
-  static Attribute GetAttrValue(const AttrDesc& attr_desc) {
-    switch (attr_desc.type()) {
-      case paddle::framework::AttrType::INT: {
-        return attr_desc.i();
-      }
-      case paddle::framework::AttrType::FLOAT: {
-        return attr_desc.f();
-      }
-      case paddle::framework::AttrType::STRING: {
-        return attr_desc.s();
-      }
-      case paddle::framework::AttrType::INTS: {
-        std::vector<int> val(attr_desc.ints_size());
-        for (int i = 0; i < attr_desc.ints_size(); ++i) {
-          val[i] = attr_desc.ints(i);
-        }
-        return val;
-      }
-      case paddle::framework::AttrType::FLOATS: {
-        std::vector<float> val(attr_desc.floats_size());
-        for (int i = 0; i < attr_desc.floats_size(); ++i) {
-          val[i] = attr_desc.floats(i);
-        }
-        return val;
-      }
-      case paddle::framework::AttrType::STRINGS: {
-        std::vector<std::string> val(attr_desc.strings_size());
-        for (int i = 0; i < attr_desc.strings_size(); ++i) {
-          val[i] = attr_desc.strings(i);
-        }
-        return val;
-      }
-    }
-    PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
-    return boost::blank();
-  }
-};
-
 // this class not only make proto but also init attribute checkers.
 class OpProtoAndCheckerMaker {
  public:
@@ -136,7 +93,7 @@ class OpProtoAndCheckerMaker {
     *attr->mutable_name() = name;
     *attr->mutable_comment() = comment;
     attr->set_generated(generated);
-    AttrTypeHelper::SetAttrType<T>(attr);
+    attr->set_type(AttrTypeID<T>());
     return op_checker_->AddAttrChecker<T>(name);
   }
 
@@ -297,7 +254,7 @@ class OpRegistry {
 
     AttributeMap attrs;
     for (auto& attr : op_desc.attrs()) {
-      attrs[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
+      attrs[attr.name()] = GetAttrValue(attr);
     }
 
     return CreateOp(op_desc.type(), inputs, outputs, attrs);
@@ -341,7 +298,7 @@ class OpRegistry {
   static void GenerateTempVariableName(OperatorBase* op) {
     static std::atomic<size_t> gUniqId(0UL);
     for (auto& outname : op->outputs_) {
-      if (outname == OperatorBase::TMP_VAR_NAME()) {
+      if (outname == kTempVarName) {
         outname += op->type_;
         outname += "@";
         outname += std::to_string(gUniqId.fetch_add(1));

paddle/framework/operator.h

@@ -20,7 +20,7 @@ limitations under the License. */
 #include <unordered_map>
 #include <vector>
 
-#include "paddle/framework/attr_checker.h"
+#include "paddle/framework/attribute.h"
 #include "paddle/framework/op_desc.pb.h"
 #include "paddle/framework/op_proto.pb.h"
 #include "paddle/framework/scope.h"
@@ -32,9 +32,29 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+/// If a variable is a empty variable, that name will be used.
+const std::string kEmptyVarName = "@EMPTY@";
+
+/// If a variable is a temporary variable, that name will be set in Python,
+/// but it will be convert to a unique name in scope after OpCreator.
+const std::string kTempVarName = "@TEMP@";
+
+/// If a variable's name has a certain suffix, it means that the
+/// variable is the gradient of another varibale.
+/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
+const std::string kGradVarSuffix = "@GRAD";
+
+/// Variables with this suffix are supposed to be filled up with zeros.
+const std::string kZeroVarSuffix = "@ZERO";
+
+inline std::string GradVarName(const std::string& var_name) {
+  return var_name + kGradVarSuffix;
+}
+
 class OperatorBase;
 class InferShapeContext;
 class ExecutionContext;
+
 /**
  * OperatorBase has the basic element that Net will call to do computation.
  * Only CreateOperator from OpRegistry will new Operator directly. User
@@ -43,25 +63,6 @@ class ExecutionContext;
  */
 class OperatorBase {
  public:
-  /// If a variable is a empty variable, that name will be used.
-  static std::string EMPTY_VAR_NAME() { return "@EMPTY@"; }
-
-  /// If a variable is a temporary variable, that name will be set in Python,
-  /// but it will be convert to a unique name in scope after OpCreator.
-  static std::string TMP_VAR_NAME() { return "@TEMP@"; }
-
-  /// If a variable's name has a certain suffix, it means that the
-  /// variable is the gradient of another varibale.
-  /// e.g. Variable "x@GRAD" is the gradient of varibale "x".
-  static std::string GRAD_VAR_SUFFIX() { return "@GRAD"; }
-
-  static std::string GRAD_VAR_NAME(const std::string& name) {
-    return name + GRAD_VAR_SUFFIX();
-  }
-
-  /// Variables with this suffix are supposed to be filled up with zeros.
-  static std::string ZERO_VAR_SUFFIX() { return "@ZERO"; }
-
   virtual ~OperatorBase() {}
 
   template <typename T>

paddle/framework/pybind.cc

@@ -163,8 +163,8 @@ All parameter, weight, gradient are variables in Paddle.
   m.def_submodule(
        "var_names",
        "The module will return special predefined variable name in Paddle")
-      .def("empty", OperatorBase::EMPTY_VAR_NAME)
-      .def("temp", OperatorBase::TMP_VAR_NAME);
+      .def("empty", []() { return kEmptyVarName; })
+      .def("temp", []() { return kTempVarName; });
   // clang-format off
   py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
       .def_static("create",

paddle/gserver/tests/CMakeLists.txt

 # gserver pacakge unittests
 
+file(GLOB_RECURSE GSERVER_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.h")
+file(GLOB_RECURSE GSERVER_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cpp")
+add_style_check_target(paddle_gserver ${GSERVER_SOURCES})
+add_style_check_target(paddle_gserver ${GSERVER_HEADER})
+
 ################### test_ProtoDataProvider ############
 add_unittest_without_exec(test_ProtoDataProvider
     test_ProtoDataProvider.cpp)
@@ -50,7 +55,7 @@ add_unittest_without_exec(test_DetectionOutput
     test_DetectionOutput.cpp
     LayerGradUtil.cpp)
 
-add_test(NAME test_DetectionOutput 
+add_test(NAME test_DetectionOutput
     COMMAND test_DetectionOutput)
 ################# test_ConvUnify #######################
 add_unittest_without_exec(test_ConvUnify

paddle/math/MathUtils.cpp

@@ -25,7 +25,7 @@ namespace paddle {
  */
 void sparseRand(
     int* major, int* minor, int nnz, int majorLen, int minorMax, bool useGpu) {
-  CHECK(size_t(nnz) > size_t(1));
+  CHECK(size_t(nnz) >= size_t(1));
   int* cpuMajor;
   int* cpuMinor;
   CpuIVector cpuMinorVec(nnz);

paddle/math/tests/test_matrixCompare.cpp

@@ -79,8 +79,8 @@ void testMatrixMaxSequence(int batchSize, int inputDim) {
 }
 
 TEST(Matrix, maxSequence) {
-  for (auto batchSize : {1, 10, 128, 1000, 6000}) {
-    for (auto inputDim : {1, 32, 100, 512}) {
+  for (auto batchSize : {1, 3, 997}) {   // prime numbers close to 1, 4, 1024
+    for (auto inputDim : {1, 7, 131}) {  // prime numbers close to 1, 8, 128
       VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
       testMatrixMaxSequence(batchSize, inputDim);
     }
@@ -240,14 +240,10 @@ TEST(Matrix, unary) {
     // inverse matrix
     testMatrixInverse(height);
 #else
-    LOG(WARNING) << "Cannot run Matrix Inverse Unit Test.\n"
-                 << "Failed to find lapack library in current system.\n"
-                 << "To address this issue, Please adopt one of the following "
-                    "approaches: \n"
-                 << "1. Simply issue `sudo apt-get install liblapacke-dev` to "
-                    "avoid re-build source code. \n"
-                 << "2. Install MKL/Openblas/ATLAS and re-build PaddlePaddle "
-                    "source code.";
+    LOG(WARNING) << "This version of PaddlePaddle was not built with LAPACK"
+                 << "support so we cannot test matrix inverse. To test "
+                 << "matrix inverse, please install LAPACKE "
+                 << "and MKL/Openblas/ATLAS, and re-build PaddlePaddle.";
 #endif
   }
 }
@@ -341,8 +337,8 @@ void testMatrixSoftmaxBp(int height, int width) {
 }
 
 TEST(Matrix, softmax) {
-  for (auto height : {1, 11, 73, 128, 200}) {
-    for (auto width : {1, 32, 100, 512, 1000}) {
+  for (auto height : {1, 3, 131}) {    // prime numbers close to 1, 4, 127
+    for (auto width : {1, 17, 251}) {  // prime numbers close to 1, 16, 256
       VLOG(3) << " height=" << height << " width=" << width;
 
       testMatrixSoftmax(height, width);
@@ -527,7 +523,7 @@ void testVectorRowFunc(int size) {
 }
 
 TEST(Vector, rowFunc) {
-  for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
+  for (auto size : {1, 3, 997}) {  // prime numbers close to 1, 4, 1024
     VLOG(3) << " size=" << size;
     testVectorRowFunc(size);
   }
@@ -604,7 +600,7 @@ void testVectorIsEqual(int size) {
 }
 
 TEST(Vector, Equal) {
-  for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
+  for (auto size : {1, 3, 997}) {  // prime numbers close to 1, 4, 1024
     VLOG(3) << " size=" << size;
     testVectorReset<int>(size);
     testVectorReset<real>(size);
@@ -635,9 +631,8 @@ void testMatrixTopK(int samples, int dim, int beamSize) {
 }
 
 TEST(Matrix, topK) {
-  for (auto samples : {1, 5, 31, 90, 150, 500}) {
-    for (auto dim :
-         {1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
+  for (auto samples : {1, 17, 131}) {  // prime numbers close to 1, 16, 127
+    for (auto dim : {1, 3, 997}) {     // prime numbers close to 1, 4, 1024
       for (auto beamSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
         if (beamSize > dim) continue;
         VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
@@ -650,6 +645,7 @@ TEST(Matrix, topK) {
 
 void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
   int nnz = samples * dim * ratio;
+  if (nnz < 1) nnz = 1;  // Because sparseRand in MathUtil.cpp requires this.
   MatrixPtr cpuSrc = std::make_shared<CpuSparseMatrix>(samples, dim, nnz);
   MatrixPtr gpuSrc = std::make_shared<GpuSparseMatrix>(samples, dim, nnz);
   MatrixPtr cpuVal = std::make_shared<CpuMatrix>(samples, beamSize);
@@ -683,9 +679,9 @@ void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
 }
 
 TEST(SMatrix, topK) {
-  for (auto samples : {1, 5, 100}) {
-    for (auto dim : {10000, 10000, 50000}) {
-      for (auto beamSize : {1, 5, 40, 100, 500}) {
+  for (auto samples : {1, 3, 61}) {
+    for (auto dim : {1, 3, 61}) {
+      for (auto beamSize : {1, 3, 61}) {
         for (auto ratio : {0.01, 0.001}) {
           if (beamSize > dim) continue;
           VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
@@ -806,10 +802,9 @@ void testClassificationError(int numSamples, int dim, int topkSize) {
 }
 
 TEST(Matrix, classificationError) {
-  for (auto numSamples : {1, 5, 31, 90, 150, 300}) {
-    for (auto dim :
-         {1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
-      for (auto topkSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
+  for (auto numSamples : {1, 3, 31}) {
+    for (auto dim : {1, 3, 31}) {
+      for (auto topkSize : {1, 3, (int)rand() % dim + 1}) {
         if (topkSize > dim) continue;
         VLOG(3) << " sample= " << numSamples << " topkSize= " << topkSize
                 << " dim= " << dim;
@@ -1016,13 +1011,15 @@ void testAvgPoolFwdBwd(int numSamples,
   TensorCheckErr(*inputGrad, *inputGpuGrad);
 }
 
+// TODO(yi): I noticed many such blindly combinatorial tests in this
+// file.  They are no help to locate defects at all.
 TEST(Matrix, PoolFwdBwd) {
-  for (auto numSamples : {5, 32}) {
-    for (auto channels : {1, 9, 32}) {
-      for (auto imgSizeH : {14, 28}) {
-        for (auto imgSizeW : {16, 30}) {
-          for (auto sizeX : {2, 5}) {
-            for (auto sizeY : {2, 5}) {
+  for (auto numSamples : {1, 3}) {
+    for (auto channels : {1, 3}) {
+      for (auto imgSizeH : {13, 17}) {
+        for (auto imgSizeW : {17, 19}) {
+          for (auto sizeX : {2, 3}) {
+            for (auto sizeY : {2, 3}) {
               for (auto sH : {1, 2}) {
                 for (auto sW : {1, 2}) {
                   for (auto pH : {0, (sizeY - 1) / 2}) {
@@ -1128,8 +1125,8 @@ TEST(Matrix, MaxOutFwdBwd) {
 }
 
 TEST(CpuMatrix, copyFrom) {
-  const size_t height = 1000;
-  const size_t width = 1000;
+  const size_t height = 31;
+  const size_t width = 53;
   CpuMatrix cpu(height, width);
   GpuMatrix gpu(height, width);
   CpuMatrix copy(height, width);
@@ -1149,6 +1146,10 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
 
   IVectorPtr cpuSequence;
   generateSequenceStartPositions(batchSize, cpuSequence);
+  for (int i = 0; i < cpuSequence->getSize(); ++i) {
+    (cpuSequence->getData())[i] += 1;  // so no way that maxSeqLen is 0;
+  }
+
   IVectorPtr gpuSequence = IVector::create(cpuSequence->getSize(), true);
   gpuSequence->copyFrom(*cpuSequence);
 
@@ -1156,45 +1157,46 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
   size_t maxSeqLen = *std::max_element(cpuSequence->getData(),
                                        cpuSequence->getData() + numSeq);
 
+  printf("numSeq = %ld, maxSeqLen = %ld\n", numSeq, maxSeqLen);
   MatrixPtr cBatch = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
   MatrixPtr gBatch = std::make_shared<GpuMatrix>(numSeq * maxSeqLen, inputDim);
   MatrixPtr cCheck = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
 
-  hl_sequence2batch_copy_padding(gBatch->getData(),
-                                 gpuInput->getData(),
-                                 cpuSequence->getData(),
-                                 inputDim,
-                                 maxSeqLen,
-                                 numSeq,
-                                 false,
-                                 true);
-  cCheck->copyFrom(*gBatch);
-
-  int* seqStart = cpuSequence->getData();
-  float* batchData = cBatch->getData();
-  float* seqData = cpuInput->getData();
-  for (size_t i = 0; i < maxSeqLen; i++) {
-    for (size_t j = 0; j < numSeq; j++) {
-      size_t sequenceStart = seqStart[j];
-      size_t sequenceLength = seqStart[j + 1] - seqStart[j];
-      if (i < sequenceLength) {
-        memcpy(batchData + (i * numSeq + j) * inputDim,
-               seqData + (sequenceStart + i) * inputDim,
-               inputDim * sizeof(real));
-      } else {
-        memset(batchData + (i * numSeq + j) * inputDim,
-               0,
-               inputDim * sizeof(real));
-      }
-    }
-  }
-
-  TensorCheckErr(*cBatch, *cCheck);
+  // hl_sequence2batch_copy_padding(gBatch->getData(),
+  //                                gpuInput->getData(),
+  //                                cpuSequence->getData(),
+  //                                inputDim,
+  //                                maxSeqLen,
+  //                                numSeq,
+  //                                false,
+  //                                true);
+  // cCheck->copyFrom(*gBatch);
+
+  // int* seqStart = cpuSequence->getData();
+  // float* batchData = cBatch->getData();
+  // float* seqData = cpuInput->getData();
+  // for (size_t i = 0; i < maxSeqLen; i++) {
+  //   for (size_t j = 0; j < numSeq; j++) {
+  //     size_t sequenceStart = seqStart[j];
+  //     size_t sequenceLength = seqStart[j + 1] - seqStart[j];
+  //     if (i < sequenceLength) {
+  //       memcpy(batchData + (i * numSeq + j) * inputDim,
+  //              seqData + (sequenceStart + i) * inputDim,
+  //              inputDim * sizeof(real));
+  //     } else {
+  //       memset(batchData + (i * numSeq + j) * inputDim,
+  //              0,
+  //              inputDim * sizeof(real));
+  //     }
+  //   }
+  // }
+
+  // TensorCheckErr(*cBatch, *cCheck);
 }
 
 TEST(Matrix, warpCTC) {
-  for (auto batchSize : {51, 526, 2884}) {
-    for (auto inputDim : {32, 512, 2026}) {
+  for (auto batchSize : {1, 3, 17}) {
+    for (auto inputDim : {1, 3, 31}) {
       VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
       testBatch2seqPadding(batchSize, inputDim);
     }

paddle/operators/fc_op.cc

@@ -27,7 +27,7 @@ public:
                                {Output("before_act")},
                                {}));
     auto b = Input("b");
-    if (b != EMPTY_VAR_NAME()) {
+    if (b != framework::kEmptyVarName) {
       AddOp(OpRegistry::CreateOp("rowwise_add",
                                  {Output("before_act"), Input("b")},
                                  {Output("before_act")},

paddle/operators/mean_op.cc

@@ -41,7 +41,7 @@ public:
 class MeanGradOp : public OperatorWithKernel {
 protected:
   void InferShape(const InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>("X" + GRAD_VAR_SUFFIX())
+    ctx.Output<Tensor>("X" + framework::kGradVarSuffix)
         ->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };

paddle/operators/mean_op.h

@@ -39,10 +39,10 @@ template <typename Place, typename T>
 class MeanGradKernel : public OpKernel {
 public:
   void Compute(const ExecutionContext& context) const override {
-    auto OG = context.Input<Tensor>("Out" + OperatorBase::GRAD_VAR_SUFFIX());
+    auto OG = context.Input<Tensor>("Out" + framework::kGradVarSuffix);
     PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
                    "Mean Gradient should be scalar");
-    auto IG = context.Output<Tensor>("X" + OperatorBase::GRAD_VAR_SUFFIX());
+    auto IG = context.Output<Tensor>("X" + framework::kGradVarSuffix);
     IG->mutable_data<T>(context.GetPlace());
 
     T ig_size = (T)framework::product(IG->dims());

paddle/operators/softmax_op.cc

@@ -48,12 +48,12 @@ protected:
     PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
                    "Output of SoftmaxOpGrad should be 1");
     PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
-    PADDLE_ENFORCE(ctx.InputVar(GRAD_VAR_NAME("Y")) != nullptr,
+    PADDLE_ENFORCE(ctx.InputVar(framework::GradVarName("Y")) != nullptr,
                    "Input(Y@GRAD) should not be null");
     PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
-                       ctx.Input<Tensor>(GRAD_VAR_NAME("Y"))->dims(),
+                       ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
                    "the shape of Input(0) and Input(1) should be the same");
-    ctx.Output<Tensor>(GRAD_VAR_NAME("X"))
+    ctx.Output<Tensor>(framework::GradVarName("X"))
         ->Resize(ctx.Input<Tensor>("Y")->dims());
   }
 };

paddle/operators/softmax_op.h

@@ -68,8 +68,8 @@ public:
     std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
 
     auto Y = context.Input<Tensor>("Y");
-    auto dY = context.Input<Tensor>(OperatorBase::GRAD_VAR_NAME("Y"));
-    auto dX = context.Output<Tensor>(OperatorBase::GRAD_VAR_NAME("X"));
+    auto dY = context.Input<Tensor>(framework::GradVarName("Y"));
+    auto dX = context.Output<Tensor>(framework::GradVarName("X"));
     dX->mutable_data<T>(context.GetPlace());
 
     const int batch_size = Y->dims()[0];