Merge remote-tracking branch 'upstream/develop' into mklso

.travis.yml

@@ -37,8 +37,8 @@ before_install:
   - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
   # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
   # protobuf version.
-  - pip install numpy wheel 'protobuf==3.1' sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit requests==2.9.2 LinkChecker
-  - pip install rarfile nltk==3.2.2 scipy==0.19.0 recordio matplotlib Pillow
+  - pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt
+  - pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker
   - curl https://glide.sh/get | bash
   - eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
   - go get -u github.com/alecthomas/gometalinter

doc/design/auto_gradient_check.md

+## Auto Gradient Checker Design
+
+## Backgraound：
+- Operator forward computing is easy to check if the result is right because it has a clear definition. **But** backpropagation is a notoriously difficult algorithm to debug and get right:
+  - 1. you should get the right backpropagation formula according to the forward computation.
+  - 2. you should implement it right in CPP.
+  - 3. it's difficult to prepare test data.
+
+- Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
+  - 1. numeric gradient checker only need forward operator.
+  - 2. user only need to prepare the input data for forward Operator.
+
+## Mathematical Theory
+The following two document from stanford has a detailed explanation of how to get numeric gradient and why it's useful.
+
+- [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
+- [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
+
+
+## Numeric Gradient Implementation
+### Python Interface
+```python
+def get_numeric_gradient(op,
+                         input_values,
+                         output_name,
+                         input_to_check,
+                         delta=0.005,
+                         local_scope=None):
+    """
+    Get Numeric Gradient for an operator's input.
+
+    :param op: C++ operator instance, could be an network
+    :param input_values: The input variables. Should be an dictionary, key is
+    variable name. Value is numpy array.
+    :param output_name: The final output variable name.
+    :param input_to_check: The input variable need to get gradient.
+    :param delta: The perturbation value for numeric gradient method. The
+    smaller delta is, the more accurate result will get. But if that delta is
+     too small, it could occur numerical stability problem.
+    :param local_scope: The local scope used for get_numeric_gradient.
+    :return: The gradient array in numpy format.
+    """
+```
+
+### Explaination:
+
+- Why need `output_name`
+  - One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.
+
+- Why need `input_to_check`
+  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
+
+
+### Core Algorithm Implementation
+
+
+```python
+    # we only compute gradient of one element each time.
+    # we use a for loop to compute the gradient of every element.
+    for i in xrange(tensor_size):
+        # get one input element throw it's index i.
+        origin = tensor_to_check.get_float_element(i)
+
+        # add delta to it, run op and then get the sum of the result tensor.
+        x_pos = origin + delta
+        tensor_to_check.set_float_element(i, x_pos)
+        y_pos = get_output()
+
+        # plus delta to this element, run op and get the sum of the result tensor.
+        x_neg = origin - delta
+        tensor_to_check.set_float_element(i, x_neg)
+        y_neg = get_output()
+
+        # restore old value
+        tensor_to_check.set_float_element(i, origin)
+
+        # compute the gradient of this element and store it into a numpy array.
+        gradient_flat[i] = (y_pos - y_neg) / delta / 2
+
+    # reshape the gradient result to the shape of the source tensor.
+    return gradient_flat.reshape(tensor_to_check.get_dims())
+```
+
+## Auto Graident Checker Framework
+
+Each Operator Kernel has three kinds of Gradient:
+
+- 1. Numeric Gradient
+- 2. CPU Operator Gradient
+- 3. GPU Operator Gradient(if supported)
+
+Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.
+
+- 1. calculate the numeric gradient.
+- 2. calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.
+- 3. calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)
+
+#### Python Interface
+
+```python
+    def check_grad(self,
+                   forward_op,
+                   input_vars,
+                   inputs_to_check,
+                   output_name,
+                   no_grad_set=None,
+                   only_cpu=False,
+                   max_relative_error=0.005):
+        """
+        :param forward_op: used to create backward_op
+        :param input_vars: numpy value of input variable. The following
+            computation will use these variables.
+        :param inputs_to_check: inputs var names that should check gradient.
+        :param output_name: output name that used to
+        :param max_relative_error: The relative tolerance parameter.
+        :param no_grad_set: used when create backward ops
+        :param only_cpu: only compute and check gradient on cpu kernel.
+        :return:
+        """
+```
+
+### How to check if two numpy array is close enough?
+if `abs_numeric_grad` is nearly zero, then use abs error for numeric_grad, not relative
+
+```python
+numeric_grad = ...
+operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
+
+abs_numeric_grad = numpy.abs(numeric_grad)
+# if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative
+# error.
+abs_numeric_grad[abs_numeric_grad < 1e-3] = 1
+
+diff_mat = numpy.abs(abs_numeric_grad - operator_grad) / abs_numeric_grad
+max_diff = numpy.max(diff_mat)
+```
+
+
+#### Notes：
+1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.
+
+
+#### Refs:
+
+- [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
+- [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)

go/pserver/client/c/test/test_train.py

@@ -17,12 +17,10 @@ def main():
     # network config
     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
     y_predict = paddle.layer.fc(input=x,
-                                param_attr=paddle.attr.Param(
-                                    name='w', learning_rate=1e-3),
+                                param_attr=paddle.attr.Param(name='w'),
                                 size=1,
                                 act=paddle.activation.Linear(),
-                                bias_attr=paddle.attr.Param(
-                                    name='b', learning_rate=1e-3))
+                                bias_attr=paddle.attr.Param(name='b'))
     y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
     cost = paddle.layer.mse_cost(input=y_predict, label=y)
 

paddle/api/ParameterUpdater.cpp

@@ -41,7 +41,7 @@ ParameterUpdater *ParameterUpdater::createNewRemoteUpdater(
       config->m->getConfig(), pserverSpec, useEtcd));
   return updater;
 #else
-  throw UnsupportError();
+  throw UnsupportError("not compiled with WITH_GOLANG");
 #endif
 }
 

paddle/framework/CMakeLists.txt

@@ -36,8 +36,8 @@ py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.
 add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(framework_py_proto framework_py_proto_init)
 add_custom_command(TARGET framework_py_proto POST_BUILD
-    COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJ_ROOT}/python/paddle/v2/framework/proto
-    COMMAND cp *.py ${PROJ_ROOT}/python/paddle/v2/framework/proto/
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto
+    COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto/
     COMMENT "Copy generated python proto into directory paddle/v2/framework/proto."
     WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
@@ -48,9 +48,12 @@ if(WITH_PYTHON)
 cc_library(paddle_pybind SHARED
     SRCS pybind.cc
     DEPS pybind python backward
-    fc_op
     sgd_op
     add_op
+    mul_op
+    rowwise_add_op
+    sigmoid_op
+    softmax_op
     mean_op
     cross_entropy_op
     recurrent_op

paddle/framework/backward_test.cc

@@ -30,6 +30,8 @@ using DeviceContext = platform::DeviceContext;
 
 class EmptyOp : public OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(EmptyOp, OperatorBase)
+
   void InferShape(const Scope &scope) const override {}
   void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
 };

paddle/framework/ddim.h

@@ -20,7 +20,6 @@ limitations under the License. */
 #include "paddle/framework/dim.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/variant.h"
-#include "unsupported/Eigen/CXX11/Tensor"
 
 namespace paddle {
 namespace framework {

paddle/framework/grad_op_builder.cc

@@ -19,45 +19,44 @@ permissions and limitations under the License. */
 namespace paddle {
 namespace framework {
 
-class OpRegistry;
-
-using VarIndexMap = std::unordered_map<std::string, int>;
+typedef std::vector<int> Ints;
 
 enum class OpArgType { IN, OUT };
 
-static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
-  std::string key = type == OpArgType::IN ? "input_format" : "output_format";
-  return op->attrs_.count(key)
-             ? &boost::get<std::vector<int>>(op->attrs_.at(key))
-             : nullptr;
+const Ints* AttrFormat(const AttributeMap& attrs, const std::string& key) {
+  return (attrs.count(key) > 0) ? &boost::get<Ints>(attrs.at(key)) : nullptr;
 }
 
-static const std::vector<int>* GetOpFormat(const OperatorBase* op,
-                                           const OpArgType& type) {
-  std::string key = type == OpArgType::IN ? "input_format" : "output_format";
-  return op->attrs_.count(key)
-             ? &boost::get<std::vector<int>>(op->attrs_.at(key))
-             : nullptr;
+Ints* AttrFormat(AttributeMap& attrs, const std::string& key) {
+  return (attrs.count(key) > 0) ? &boost::get<Ints>(attrs.at(key)) : nullptr;
 }
 
-static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
-                       const OpArgType& src_type, const OpArgType& dst_type,
+static void TransOpArg(const OperatorBase* src_op,
+                       std::vector<std::string>& grad_inputs,
+                       std::vector<std::string>& grad_outputs,
+                       AttributeMap& grad_attrs,
+                       std::unordered_map<std::string, int>& grad_idxs,
+                       const std::string& src_type, const std::string& dst_type,
                        int& idx, bool is_grad) {
   const std::vector<std::string>& src_inout =
-      src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
-  const std::vector<int>* src_format = GetOpFormat(src_op, src_type);
+      (src_type == "input_format") ? src_op->inputs_ : src_op->outputs_;
+
+  const std::vector<int>* src_format = AttrFormat(src_op->Attrs(), src_type);
 
   std::vector<std::string>& dst_inout =
-      dst_type == OpArgType::IN ? dst_op->inputs_ : dst_op->outputs_;
-  std::vector<int>* dst_format = GetOpFormat(dst_op, dst_type);
+      (dst_type == "input_format") ? grad_inputs : grad_outputs;
+
+  std::vector<int>* dst_format = AttrFormat(grad_attrs, dst_type);
+
   const OpProto& proto = OpRegistry::protos().at(src_op->type_);
+
   const auto& src_arg_list =
-      src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
+      (src_type == "input_format") ? proto.inputs() : proto.outputs();
 
   for (const auto& arg : src_arg_list) {
     std::string src_name = arg.name();
     std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
-    (*dst_op->in_out_idxs_)[dst_name] = idx++;
+    grad_idxs[dst_name] = idx++;
     int src_arg_idx = src_op->in_out_idxs_->at(src_name);
     int src_begin =
         src_format == nullptr ? src_arg_idx : src_format->at(src_arg_idx);
@@ -76,26 +75,42 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
 }
 
 OperatorBase* BuildGradOp(const OperatorBase* op) {
-  std::string grad_op_type = OpRegistry::grad_ops().at(op->type_);
-  OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
-  grad_op->type_ = grad_op_type;
-  grad_op->attrs_ = op->attrs_;
-  grad_op->attrs_.erase("input_format");
-  grad_op->attrs_.erase("output_format");
-  if (GetOpFormat(op, OpArgType::IN) != nullptr) {
-    grad_op->attrs_["output_format"] = std::vector<int>({0});
+  const std::string& grad_op_type = OpRegistry::grad_ops().at(op->Type());
+
+  AttributeMap grad_attrs(op->Attrs());
+  grad_attrs.erase("input_format");
+  grad_attrs.erase("output_format");
+  if (op->Attrs().count("input_format") > 0) {
+    grad_attrs["output_format"] = std::vector<int>({0});
   }
-  if (GetOpFormat(op, OpArgType::IN) != nullptr ||
-      GetOpFormat(op, OpArgType::OUT) != nullptr) {
-    grad_op->attrs_["input_format"] = std::vector<int>({0});
+  if (op->Attrs().count("input_format") > 0 ||
+      op->Attrs().count("output_format") > 0) {
+    grad_attrs["input_format"] = std::vector<int>({0});
   }
-  grad_op->in_out_idxs_.reset(new VarIndexMap());
+
+  std::vector<std::string> grad_inputs, grad_outputs;
+
+  using VarIndexMap = std::unordered_map<std::string, int>;
+  VarIndexMap* grad_idxs = new VarIndexMap;
   int in_idx = 0;
   int out_idx = 0;
-  TransOpArg(op, grad_op, OpArgType::IN, OpArgType::IN, in_idx, false);   // I
-  TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, false);  // G
-  TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, true);   // OG
-  TransOpArg(op, grad_op, OpArgType::IN, OpArgType::OUT, out_idx, true);  // IG
+  TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
+             "input_format", "input_format", in_idx, false);  // I
+  TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
+             "output_format", "input_format", in_idx, false);  // G
+  TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
+             "output_format", "input_format", in_idx, true);  // OG
+  TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
+             "input_format", "output_format", out_idx, true);  // IG
+
+  OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
+
+  grad_op->type_ = grad_op_type;
+  grad_op->inputs_ = grad_inputs;
+  grad_op->outputs_ = grad_outputs;
+  grad_op->attrs_ = grad_attrs;
+  grad_op->in_out_idxs_.reset(grad_idxs);
+
   return grad_op;
 }
 

paddle/framework/grad_op_builder_test.cc

@@ -10,6 +10,8 @@ namespace framework {
 
 class NOP : public OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(NOP, OperatorBase)
+
   void InferShape(const Scope &scope) const override {}
   void Run(const Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {}

paddle/framework/op_registry.h

@@ -69,18 +69,18 @@ class OpProtoAndCheckerMaker {
 
   VariableBuilder AddInput(const std::string& name,
                            const std::string& comment) {
-    auto input = proto_->mutable_inputs()->Add();
-    *input->mutable_name() = name;
-    *input->mutable_comment() = comment;
+    VarProto* input = proto_->add_inputs();
+    input->set_name(name);
+    input->set_comment(comment);
     return VariableBuilder{input, [=] { this->SetHasMultipleInput(); },
                            nullptr};
   }
 
   VariableBuilder AddOutput(const std::string& name,
                             const std::string& comment) {
-    auto output = proto_->mutable_outputs()->Add();
-    *output->mutable_name() = name;
-    *output->mutable_comment() = comment;
+    VarProto* output = proto_->add_outputs();
+    output->set_name(name);
+    output->set_comment(comment);
     return VariableBuilder{output, [=] { this->SetHasMultipleOutput(); },
                            [=] { this->SetHasTemporaryOutput(); }};
   }
@@ -89,17 +89,15 @@ class OpProtoAndCheckerMaker {
   TypedAttrChecker<T>& AddAttr(const std::string& name,
                                const std::string& comment,
                                bool generated = false) {
-    auto attr = proto_->mutable_attrs()->Add();
-    *attr->mutable_name() = name;
-    *attr->mutable_comment() = comment;
+    AttrProto* attr = proto_->add_attrs();
+    attr->set_name(name);
+    attr->set_comment(comment);
     attr->set_generated(generated);
     attr->set_type(AttrTypeID<T>());
     return op_checker_->AddAttrChecker<T>(name);
   }
 
-  void AddComment(const std::string& comment) {
-    *(proto_->mutable_comment()) = comment;
-  }
+  void AddComment(const std::string& comment) { proto_->set_comment(comment); }
 
  private:
   void SetHasMultiple(const std::string& in_out, bool* flag) {
@@ -187,7 +185,7 @@ class OpRegistry {
     OpProto& op_proto = protos()[op_type];
     auto maker = ProtoMakerType(&op_proto, &op_checker);
     maker.Validate();
-    *op_proto.mutable_type() = op_type;
+    op_proto.set_type(op_type);
     PADDLE_ENFORCE(
         op_proto.IsInitialized(),
         "Fail to initialize %s's OpProto, because %s is not initialized",
@@ -307,22 +305,45 @@ class OpRegistry {
   }
 };
 
+class Registrar {
+ public:
+  // In our design, various kinds of classes, e.g., operators and kernels, have
+  // their corresponding registry and registrar. The action of registration is
+  // in the constructor of a global registrar variable, which, however, are not
+  // used in the code that calls package framework, and would be removed from
+  // the generated binary file by the linker. To avoid such removal, we add
+  // Touch to all registrar classes and make USE_OP macros to call this
+  // method. So, as long as the callee code calls USE_OP, the global
+  // registrar variable won't be removed by the linker.
+  void Touch() {}
+};
+
 template <typename OpType, typename ProtoMakerType>
-class OpRegisterHelper {
+class OpRegistrar : public Registrar {
  public:
-  explicit OpRegisterHelper(const char* op_type) {
+  explicit OpRegistrar(const char* op_type) {
     OpRegistry::RegisterOp<OpType, ProtoMakerType>(op_type);
   }
 };
 
 template <typename GradOpType>
-class GradOpRegisterHelper {
+class GradOpRegistrar : public Registrar {
  public:
-  GradOpRegisterHelper(const char* op_type, const char* grad_op_type) {
+  GradOpRegistrar(const char* op_type, const char* grad_op_type) {
     OpRegistry::RegisterGradOp<GradOpType>(op_type, grad_op_type);
   }
 };
 
+template <typename PlaceType, typename KernelType>
+class OpKernelRegistrar : public Registrar {
+ public:
+  explicit OpKernelRegistrar(const char* op_type) {
+    OperatorWithKernel::OpKernelKey key;
+    key.place_ = PlaceType();
+    OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KernelType);
+  }
+};
+
 /**
  * check if MACRO is used in GLOBAL NAMESPACE.
  */
@@ -333,97 +354,121 @@ class GradOpRegisterHelper {
                 msg)
 
 /**
- * Macro to Register Operator.
+ * Macro to register Operator.
  */
-#define REGISTER_OP(__op_type, __op_class, __op_maker_class)                 \
-  STATIC_ASSERT_GLOBAL_NAMESPACE(__reg_op__##__op_type,                      \
-                                 "REGISTER_OP must be in global namespace"); \
-  static ::paddle::framework::OpRegisterHelper<__op_class, __op_maker_class> \
-      __op_register_##__op_type##__(#__op_type);                             \
-  int __op_register_##__op_type##_handle__() { return 0; }
+#define REGISTER_OP(op_type, op_class, op_maker_class)                        \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                             \
+      __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
+  static ::paddle::framework::OpRegistrar<op_class, op_maker_class>           \
+      __op_registrar_##op_type##__(#op_type);                                 \
+  int TouchOpRegistrar_##op_type() {                                          \
+    __op_registrar_##op_type##__.Touch();                                     \
+    return 0;                                                                 \
+  }
 
 /**
- * Macro to Register Gradient Operator.
+ * Macro to register Gradient Operator.
  */
-#define REGISTER_GRADIENT_OP(__op_type, __grad_op_type, __grad_op_class)       \
+#define REGISTER_GRADIENT_OP(op_type, grad_op_type, grad_op_class)           \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                            \
-      __reg_gradient_op__##__op_type##__grad_op_type,                          \
-      "REGISTER_GRADIENT_OP must be in global namespace");                     \
-  static ::paddle::framework::GradOpRegisterHelper<__grad_op_class>            \
-      __op_gradient_register_##__op_type##__grad_op_type##__(#__op_type,       \
-                                                             #__grad_op_type); \
-  int __op_gradient_register_##__op_type##__grad_op_type##_handle__() {        \
+      __reg_gradient_op__##op_type##_##grad_op_type,                         \
+      "REGISTER_GRADIENT_OP must be called in global namespace");            \
+  static ::paddle::framework::GradOpRegistrar<grad_op_class>                 \
+      __op_gradient_registrar_##op_type##_##grad_op_type##__(#op_type,       \
+                                                             #grad_op_type); \
+  int TouchOpGradientRegistrar_##op_type() {                                 \
+    __op_gradient_registrar_##op_type##_##grad_op_type##__.Touch();          \
     return 0;                                                                \
   }
 
 /**
- * Macro to Forbid user register Gradient Operator.
+ * Macro to register OperatorKernel.
  */
-#define NO_GRADIENT(__op_type)                          \
+#define REGISTER_OP_KERNEL(op_type, DEVICE_TYPE, place_class, ...)        \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                         \
-      __reg_gradient_op__##__op_type##__op_type##_grad, \
-      "NO_GRADIENT must be in global namespace")
+      __reg_op_kernel_##op_type##_##DEVICE_TYPE##__,                      \
+      "REGISTER_OP_KERNEL must be called in global namespace");           \
+  static ::paddle::framework::OpKernelRegistrar<place_class, __VA_ARGS__> \
+      __op_kernel_registrar_##op_type##_##DEVICE_TYPE##__(#op_type);      \
+  int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE() {                \
+    __op_kernel_registrar_##op_type##_##DEVICE_TYPE##__.Touch();          \
+    return 0;                                                             \
+  }
 
 /**
- * Macro to Register OperatorKernel.
+ * Macro to Forbid user register Gradient Operator.
  */
-#define REGISTER_OP_KERNEL(type, DEVICE_TYPE, PlaceType, ...)             \
+#define NO_GRADIENT(op_type)                           \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                      \
-      __reg_op_kernel_##type##_##DEVICE_TYPE##__,                         \
-      "REGISTER_OP_KERNEL must be in global namespace");                  \
-  struct __op_kernel_register__##type##__##DEVICE_TYPE##__ {              \
-    __op_kernel_register__##type##__##DEVICE_TYPE##__() {                 \
-      ::paddle::framework::OperatorWithKernel::OpKernelKey key;           \
-      key.place_ = PlaceType();                                           \
-      ::paddle::framework::OperatorWithKernel::AllOpKernels()[#type][key] \
-          .reset(new __VA_ARGS__());                                      \
-    }                                                                     \
-  };                                                                      \
-  static __op_kernel_register__##type##__##DEVICE_TYPE##__                \
-      __reg_kernel_##type##__##DEVICE_TYPE##__;                           \
-  int __op_kernel_register_##type##_handle_##DEVICE_TYPE##__() { return 0; }
-
-// (type, KernelType)
-#define REGISTER_OP_GPU_KERNEL(type, ...) \
-  REGISTER_OP_KERNEL(type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
-
-// (type, KernelType)
-#define REGISTER_OP_CPU_KERNEL(type, ...) \
-  REGISTER_OP_KERNEL(type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
+      __reg_gradient_op__##op_type##_##op_type##_grad, \
+      "NO_GRADIENT must be called in global namespace")
+
+#define REGISTER_OP_GPU_KERNEL(op_type, ...) \
+  REGISTER_OP_KERNEL(op_type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
+
+#define REGISTER_OP_CPU_KERNEL(op_type, ...) \
+  REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
 
 /**
  * Macro to mark what Operator and Kernel we will use and tell the compiler to
  * link them into target.
  */
-#define USE_OP_WITHOUT_KERNEL(op_type)                      \
+#define USE_OP_ITSELF(op_type)                                    \
+  STATIC_ASSERT_GLOBAL_NAMESPACE(                                 \
+      __use_op_itself_##op_type,                                  \
+      "USE_OP_ITSELF must be called in global namespace");        \
+  extern int TouchOpRegistrar_##op_type();                        \
+  static int use_op_itself_##op_type##_ __attribute__((unused)) = \
+      TouchOpRegistrar_##op_type()
+
+// TODO(fengjiayi): Most ops' gradient op have not been compeleted. So we use
+// `NO_GRAD` to disable micro USE_OP_GRADIENT(op_type). Otherwise the code can't
+// be compiled. `NO_GRAD` should be removed after all gradient ops are
+// compeleted.
+#define NO_GRAD
+#ifndef NO_GRAD
+#define USE_OP_GRADIENT(op_type)                                    \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                   \
-      __use_op_without_kernel_##op_type,                    \
-      "USE_OP_WITHOUT_KERNEL must be in global namespace"); \
-  extern int __op_register_##op_type##_handle__();          \
-  static int __use_op_ptr_##op_type##_without_kernel__      \
-      __attribute__((unused)) = __op_register_##op_type##_handle__()
+      __use_op_gradient_##op_type,                                  \
+      "USE_OP_GRADIENT must be called in global namespace");        \
+  extern int TouchOpGradientRegistrar_##op_type();                  \
+  static int use_op_gradient_##op_type##_ __attribute__((unused)) = \
+      TouchOpGradientRegistrar_##op_type()
+#else
+#define USE_OP_GRADIENT(op_type)
+#endif
 
-#define USE_OP_KERNEL(op_type, DEVICE_TYPE)                               \
+#define USE_OP_DEVICE_KERNEL(op_type, DEVICE_TYPE)               \
   STATIC_ASSERT_GLOBAL_NAMESPACE(                                \
       __use_op_kernel_##op_type##_##DEVICE_TYPE##__,             \
-      "USE_OP_KERNEL must be in global namespace");                       \
-  extern int __op_kernel_register_##op_type##_handle_##DEVICE_TYPE##__(); \
-  static int __use_op_ptr_##op_type##_##DEVICE_TYPE##_kernel__            \
+      "USE_OP_DEVICE_KERNEL must be in global namespace");       \
+  extern int TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE(); \
+  static int use_op_kernel_##op_type##_##DEVICE_TYPE##_          \
       __attribute__((unused)) =                                  \
-          __op_kernel_register_##op_type##_handle_##DEVICE_TYPE##__()
+          TouchOpKernelRegistrar_##op_type##_##DEVICE_TYPE()
 
-// use Operator with only cpu kernel.
-#define USE_OP_CPU(op_type)       \
-  USE_OP_WITHOUT_KERNEL(op_type); \
-  USE_OP_KERNEL(op_type, CPU)
+// TODO(fengjiayi): The following macros seems ugly, do we have better method?
 
 #ifdef PADDLE_ONLY_CPU
-#define USE_OP(op_type) USE_OP_CPU(op_type)
+#define USE_OP_KERNEL(op_type) USE_OP_DEVICE_KERNEL(op_type, CPU)
 #else
-#define USE_OP(op_type) \
-  USE_OP_CPU(op_type);  \
-  USE_OP_KERNEL(op_type, GPU)
+#define USE_OP_KERNEL(op_type)        \
+  USE_OP_DEVICE_KERNEL(op_type, CPU); \
+  USE_OP_DEVICE_KERNEL(op_type, GPU)
 #endif
 
+#define USE_NO_GRAD_OP(op_type) \
+  USE_OP_ITSELF(op_type);       \
+  USE_OP_KERNEL(op_type)
+
+#define USE_CPU_OP(op_type)           \
+  USE_OP_ITSELF(op_type);             \
+  USE_OP_DEVICE_KERNEL(op_type, CPU); \
+  USE_OP_GRADIENT(op_type)
+
+#define USE_OP(op_type)    \
+  USE_NO_GRAD_OP(op_type); \
+  USE_OP_GRADIENT(op_type)
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_registry_test.cc

@@ -7,6 +7,8 @@ namespace paddle {
 namespace framework {
 class CosineOp : public OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(CosineOp, OperatorBase)
+
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}
   void InferShape(const Scope& scope) const override {}
@@ -27,6 +29,8 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 
 class MyTestOp : public OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(MyTestOp, OperatorBase)
+
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {}

paddle/framework/operator.h

@@ -63,6 +63,17 @@ class ExecutionContext;
  */
 class OperatorBase {
  public:
+  OperatorBase() {}  // TODO(yi): This constructor is to be removed.
+  OperatorBase(const std::string& type, const std::vector<std::string>& inputs,
+               const std::vector<std::string>& outputs,
+               const AttributeMap& attrs,
+               std::unordered_map<std::string, int>* in_out_idxs)
+      : type_(type),
+        inputs_(inputs),
+        outputs_(outputs),
+        attrs_(attrs),
+        in_out_idxs_(in_out_idxs) {}
+
   virtual ~OperatorBase() {}
 
   template <typename T>
@@ -95,16 +106,24 @@ class OperatorBase {
 
   //! Get a input with argument's name described in `op_proto`
   const std::string& Input(const std::string& name) const;
-
   //! Get a input which has multiple variables.
   //! TODO add a vector_view to prevent memory copy.
   std::vector<std::string> Inputs(const std::string& name) const;
+
   //! Get a output with argument's name described in `op_proto`
   const std::string& Output(const std::string& name) const;
   //! Get an output which has multiple variables.
   //! TODO add a vector_view to prevent memory copy.
   std::vector<std::string> Outputs(const std::string& name) const;
 
+  const std::string Type() const { return type_; }
+  const std::vector<std::string> Inputs() const { return inputs_; }
+  const std::vector<std::string> Outputs() const { return outputs_; }
+  const AttributeMap& Attrs() const { return attrs_; }
+  const std::unordered_map<std::string, int>* InOutIdx() const {
+    return in_out_idxs_.get();
+  }
+
  public:
   std::string type_;
   // NOTE: in case of OpGrad, inputs_ contains:
@@ -281,6 +300,14 @@ class OpKernel {
 
 class OperatorWithKernel : public OperatorBase {
  public:
+  OperatorWithKernel() {}  // TODO(yi): This constructor is to be removed.
+  OperatorWithKernel(const std::string& type,
+                     const std::vector<std::string>& inputs,
+                     const std::vector<std::string>& outputs,
+                     const AttributeMap& attrs,
+                     std::unordered_map<std::string, int>* in_out_idxs)
+      : OperatorBase(type, inputs, outputs, attrs, in_out_idxs) {}
+
   struct OpKernelKey {
     platform::Place place_;
 
@@ -330,5 +357,15 @@ class OperatorWithKernel : public OperatorBase {
   virtual void InferShape(const InferShapeContext& ctx) const = 0;
 };
 
+#define DEFINE_OPERATOR_CTOR(Class, ParentClass)                         \
+ public:                                                                 \
+  Class() { /* TODO(yi): This constructor is to be removed. */           \
+  }                                                                      \
+  Class(const std::string& type, const std::vector<std::string>& inputs, \
+        const std::vector<std::string>& outputs,                         \
+        const ::paddle::framework::AttributeMap& attrs,                  \
+        std::unordered_map<std::string, int>* in_out_idxs)               \
+      : ParentClass(type, inputs, outputs, attrs, in_out_idxs) {}
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/operator_test.cc

@@ -23,6 +23,8 @@ static int op_run_num = 0;
 
 class OpWithoutKernelTest : public OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(OpWithoutKernelTest, OperatorBase)
+
   void Init() override { x = 1; }
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope,
@@ -97,6 +99,8 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
 static int cpu_kernel_run_num = 0;
 
 class OpWithKernelTest : public OperatorWithKernel {
+ public:
+  DEFINE_OPERATOR_CTOR(OpWithKernelTest, OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext& ctx) const override {}
 };
@@ -116,6 +120,8 @@ class CPUKernelTest : public OpKernel {
 // multiple inputs test
 class OperatorMultiInputsTest : public OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(OperatorMultiInputsTest, OperatorBase)
+
   void Init() override { x = 1; }
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope,

paddle/framework/pybind.cc

@@ -30,16 +30,15 @@ limitations under the License. */
 namespace py = pybind11;
 
 USE_OP(add_two);
-USE_OP_CPU(onehot_cross_entropy);
-USE_OP_WITHOUT_KERNEL(fc);
-USE_OP(sgd);
+USE_CPU_OP(onehot_cross_entropy);
+USE_NO_GRAD_OP(sgd);
 USE_OP(mul);
 USE_OP(mean);
 USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP(fill_zeros_like);
-USE_OP_WITHOUT_KERNEL(recurrent_op);
+USE_OP_ITSELF(recurrent_op);
 USE_OP(gaussian_random);
 USE_OP(uniform_random);
 

paddle/function/CMakeLists.txt

@@ -38,10 +38,11 @@ if(WITH_GPU)
     add_simple_unittest(RowConvOpTest)
     add_simple_unittest(BlockExpandOpTest)
     add_simple_unittest(CropOpTest)
+    add_simple_unittest(DepthwiseConvOpTest)
 endif()
 
-add_simple_unittest(ConvOpTest)
 add_simple_unittest(Im2ColTest)
+add_simple_unittest(GemmConvOpTest)
 endif()
 
 add_style_check_target(paddle_function ${h_files})

paddle/function/ConvOpTest.cpp

-/* 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 <gtest/gtest.h>
-#include <memory>
-#include "Function.h"
-#include "FunctionTest.h"
-
-namespace paddle {
-
-enum TestType {
-  kForwardTest = 0,
-  kBackwardInputTest = 1,
-  kBackwardFilterTest = 2,
-};
-
-template <DeviceType DType1, DeviceType DType2>
-class ConvolutionTest {
-public:
-  ConvolutionTest(const std::string& conv1,
-                  const std::string& conv2,
-                  TestType type,
-                  bool useGroups = true,
-                  std::string algo = "auto") {
-    for (size_t batchSize : {1, 32}) {
-      for (size_t inputSize : {7, 14, 54}) {
-        for (size_t filterSize : {1, 3, 5}) {
-          for (size_t inputChannels : {3, 64}) {
-            for (size_t outputChannels : {3, 64}) {
-              if (inputChannels > outputChannels) break;
-              size_t groups;
-              if (!useGroups) {
-                groups = 1;
-              } else {
-                if (outputChannels % inputChannels != 0) continue;
-                groups = inputChannels;
-              }
-
-              for (size_t stride : {1, 2}) {
-                for (size_t padding : {0, 1}) {
-                  if (padding >= filterSize) break;
-                  size_t outputSize =
-                      (inputSize - filterSize + 2 * padding + stride) / stride;
-                  VLOG(3) << " batchSize=" << batchSize
-                          << " inputChannels=" << inputChannels
-                          << " inputHeight=" << inputSize
-                          << " inputWidth=" << inputSize
-                          << " outputChannels=" << outputChannels
-                          << " filterHeight=" << filterSize
-                          << " filterWidth=" << filterSize
-                          << " outputHeight=" << outputSize
-                          << " outputWidth=" << outputSize
-                          << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DType1, DType2> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", groups)
-                          .set("algo", algo));
-
-                  TensorShape input{
-                      batchSize, inputChannels, inputSize, inputSize};
-
-                  TensorShape filter;
-                  if (groups > 1)
-                    filter = TensorShape({groups,
-                                          outputChannels / groups,
-                                          inputChannels / groups,
-                                          filterSize,
-                                          filterSize});
-                  else
-                    filter = TensorShape({outputChannels,
-                                          inputChannels,
-                                          filterSize,
-                                          filterSize});
-                  TensorShape output{
-                      batchSize, outputChannels, outputSize, outputSize};
-
-                  if (type == kForwardTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.run();
-                  } else if (type == kBackwardInputTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
-                    test.run();
-                  } else if (type == kBackwardFilterTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
-                                    ADD_TO);
-                    test.run();
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-// Mainly used to test cases where the height and width (input, filter)
-// are not equal.
-template <DeviceType DType1, DeviceType DType2>
-class ConvolutionTest2 {
-public:
-  ConvolutionTest2(const std::string& conv1,
-                   const std::string& conv2,
-                   TestType type,
-                   bool useGroups = true,
-                   std::string algo = "auto") {
-    for (size_t batchSize : {16}) {
-      for (size_t inputHeight : {7, 31}) {
-        for (size_t inputWidth : {10, 54}) {
-          for (size_t filterHeight : {1, 5}) {
-            for (size_t filterWidth : {3, 7}) {
-              for (size_t inputChannels : {7}) {
-                for (size_t outputChannels : {7}) {
-                  size_t groups;
-                  if (!useGroups) {
-                    groups = 1;
-                  } else {
-                    if (outputChannels % inputChannels != 0) continue;
-                    groups = inputChannels;
-                  }
-
-                  size_t stride = 1;
-                  size_t padding = 0;
-                  size_t outputHeight =
-                      (inputHeight - filterHeight + 2 * padding + stride) /
-                      stride;
-                  size_t outputWidth =
-                      (inputWidth - filterWidth + 2 * padding + stride) /
-                      stride;
-                  VLOG(3) << " batchSize=" << batchSize
-                          << " inputChannels=" << inputChannels
-                          << " inputHeight=" << inputHeight
-                          << " inputWidth=" << inputWidth
-                          << " outputChannels=" << outputChannels
-                          << " filterHeight=" << filterHeight
-                          << " filterWidth=" << filterWidth
-                          << " outputHeight=" << outputHeight
-                          << " outputWidth=" << outputWidth
-                          << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DType1, DType2> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", groups)
-                          .set("algo", algo));
-
-                  TensorShape input{
-                      batchSize, inputChannels, inputHeight, inputWidth};
-
-                  TensorShape filter;
-                  if (groups > 1)
-                    filter = TensorShape({groups,
-                                          outputChannels / groups,
-                                          inputChannels / groups,
-                                          filterHeight,
-                                          filterWidth});
-                  else
-                    filter = TensorShape({outputChannels,
-                                          inputChannels,
-                                          filterHeight,
-                                          filterWidth});
-                  TensorShape output{
-                      batchSize, outputChannels, outputHeight, outputWidth};
-
-                  if (type == kForwardTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.run();
-                  } else if (type == kBackwardInputTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
-                    test.run();
-                  } else if (type == kBackwardFilterTest) {
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
-                    test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
-                    test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
-                                    ADD_TO);
-                    test.run();
-                  }
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-// ======Start Convolution TEST======
-
-TEST(Forward, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
-      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
-      "NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
-}
-
-#ifndef PADDLE_ONLY_CPU
-TEST(Forward, GEMM2) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
-}
-
-TEST(BackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      kBackwardInputTest,
-      false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "GemmConvGradInput-GPU",
-      kBackwardInputTest,
-      false);
-}
-
-TEST(BackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      kBackwardFilterTest,
-      false);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "GemmConvGradFilter-GPU",
-      kBackwardFilterTest,
-      false);
-}
-#endif
-// ======End Convolution TEST======
-
-// ======Start DepthwiseConvolution TEST======
-
-// TODO(zhaolong) The depthwise convolution cpu test will be added when the cpu
-// version of depthwiseConv is implemented.
-
-#ifndef PADDLE_ONLY_CPU
-
-TEST(DepthwiseConvForward, GEMM2) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
-}
-
-TEST(DepthwiseConvBackwardInput, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradInput-CPU",
-      "DepthwiseConvGradInput-GPU",
-      kBackwardInputTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradInput-CPU",
-      "DepthwiseConvGradInput-GPU",
-      kBackwardInputTest);
-}
-
-TEST(DepthwiseConvBackwardFilter, GEMM) {
-  ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
-      "GemmConvGradFilter-CPU",
-      "DepthwiseConvGradFilter-GPU",
-      kBackwardFilterTest);
-  ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
-      "GemmConvGradFilter-CPU",
-      "DepthwiseConvGradFilter-GPU",
-      kBackwardFilterTest);
-}
-
-#endif
-// ======End DepthwiseConvolution TEST======
-
-}  // namespace paddle

paddle/function/ConvOpTest.h

+/* 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 "FunctionTest.h"
+
+namespace paddle {
+
+template <DeviceType DType1, DeviceType DType2>
+void forward(Compare2Function<DType1, DType2>& test,
+             const TensorShape& input,
+             const TensorShape& filter,
+             const TensorShape& output) {
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
+  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
+  test.run();
+}
+
+template <DeviceType DType1, DeviceType DType2>
+void backward_input(Compare2Function<DType1, DType2>& test,
+                    const TensorShape& input,
+                    const TensorShape& filter,
+                    const TensorShape& output) {
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
+  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
+  test.run();
+}
+
+template <DeviceType DType1, DeviceType DType2>
+void backward_filter(Compare2Function<DType1, DType2>& test,
+                     const TensorShape& input,
+                     const TensorShape& filter,
+                     const TensorShape& output) {
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
+  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
+  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter), ADD_TO);
+  test.run();
+}
+
+template <DeviceType DType1, DeviceType DType2>
+using Function = void (*)(Compare2Function<DType1, DType2>& test,
+                          const TensorShape& input,
+                          const TensorShape& filter,
+                          const TensorShape& output);
+
+/**
+ * \brief A basic convolution function test interface.
+ *
+ * \param conv1         type name of convolution function 1.
+ * \param conv2         type name of convolution function 2.
+ * \param function      test function, can be one of the forward, backward_input
+ *                      backward_filter function.
+ * Example:
+ * 1. Compare GemmConv's CPU and GPU implementation:
+ *   Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+ *      "GemmConv-CPU", "GemmConv-GPU", forward);
+ */
+template <DeviceType DType1, DeviceType DType2>
+void Convolution(const std::string& conv1,
+                 const std::string& conv2,
+                 Function<DType1, DType2> function) {
+  for (size_t batchSize : {1, 5}) {
+    for (size_t inputSize : {7, 14, 31}) {
+      for (size_t filterSize : {1, 3, 5}) {
+        for (size_t inputChannels : {3, 16}) {
+          for (size_t outputChannels : {3, 16}) {
+            if (outputChannels < inputChannels) continue;
+            for (size_t stride : {1, 2}) {
+              for (size_t padding : {0, 1}) {
+                if (padding >= filterSize) break;
+
+                // NNPACK only supports stride = 1 if batchSize > 1
+                if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
+                    batchSize > 1 && stride > 1)
+                  break;
+
+                size_t outputSize =
+                    (inputSize - filterSize + 2 * padding + stride) / stride;
+                VLOG(3) << " batchSize=" << batchSize
+                        << " inputChannels=" << inputChannels
+                        << " inputHeight=" << inputSize
+                        << " inputWidth=" << inputSize
+                        << " outputChannels=" << outputChannels
+                        << " filterHeight=" << filterSize
+                        << " filterWidth=" << filterSize
+                        << " outputHeight=" << outputSize
+                        << " outputWidth=" << outputSize << " stride=" << stride
+                        << " padding=" << padding;
+
+                std::vector<size_t> paddings = {padding, padding};
+                std::vector<size_t> strides = {stride, stride};
+                Compare2Function<DType1, DType2> test(
+                    conv1,
+                    conv2,
+                    FuncConfig()
+                        .set("paddings", paddings)
+                        .set("strides", strides)
+                        .set("groups", (size_t)1)
+                        .set("algo", (std::string) "auto"));
+
+                TensorShape input{
+                    batchSize, inputChannels, inputSize, inputSize};
+                TensorShape filter{
+                    outputChannels, inputChannels, filterSize, filterSize};
+                TensorShape output{
+                    batchSize, outputChannels, outputSize, outputSize};
+
+                function(test, input, filter, output);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+/**
+ * \brief A convolution function test interface for
+ *        image height is not equal image width.
+ */
+template <DeviceType DType1, DeviceType DType2>
+void Convolution2(const std::string& conv1,
+                  const std::string& conv2,
+                  Function<DType1, DType2> function) {
+  for (size_t batchSize : {4}) {
+    for (size_t inputHeight : {7, 31}) {
+      for (size_t inputWidth : {10, 54}) {
+        for (size_t filterHeight : {1, 5}) {
+          for (size_t filterWidth : {3, 7}) {
+            for (size_t inputChannels : {7}) {
+              for (size_t outputChannels : {7}) {
+                size_t stride = 1;
+                size_t padding = 0;
+                size_t outputHeight =
+                    (inputHeight - filterHeight + 2 * padding + stride) /
+                    stride;
+                size_t outputWidth =
+                    (inputWidth - filterWidth + 2 * padding + stride) / stride;
+                VLOG(3) << " batchSize=" << batchSize
+                        << " inputChannels=" << inputChannels
+                        << " inputHeight=" << inputHeight
+                        << " inputWidth=" << inputWidth
+                        << " outputChannels=" << outputChannels
+                        << " filterHeight=" << filterHeight
+                        << " filterWidth=" << filterWidth
+                        << " outputHeight=" << outputHeight
+                        << " outputWidth=" << outputWidth
+                        << " stride=" << stride << " padding=" << padding;
+
+                std::vector<size_t> paddings = {padding, padding};
+                std::vector<size_t> strides = {stride, stride};
+                Compare2Function<DType1, DType2> test(
+                    conv1,
+                    conv2,
+                    FuncConfig()
+                        .set("paddings", paddings)
+                        .set("strides", strides)
+                        .set("groups", (size_t)1)
+                        .set("algo", (std::string) "auto"));
+
+                TensorShape input{
+                    batchSize, inputChannels, inputHeight, inputWidth};
+                TensorShape filter{
+                    outputChannels, inputChannels, filterHeight, filterWidth};
+                TensorShape output{
+                    batchSize, outputChannels, outputHeight, outputWidth};
+
+                function(test, input, filter, output);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+/**
+ * \brief A convolution function test interface for depthwise convolution.
+ */
+template <DeviceType DType1, DeviceType DType2>
+void DepthwiseConvolution(const std::string& conv1,
+                          const std::string& conv2,
+                          Function<DType1, DType2> function) {
+  for (size_t batchSize : {1, 32}) {
+    for (size_t inputSize : {7, 14, 54}) {
+      for (size_t filterSize : {3, 4}) {
+        for (size_t inputChannels : {32}) {
+          for (size_t outputChannels : {32, 64}) {
+            for (size_t stride : {1, 2}) {
+              for (size_t padding : {0, 1}) {
+                // NNPACK only supports stride = 1 if batchSize > 1,
+                // and there has some bug when batchSize > 1 and groups != 1
+                if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
+                    batchSize > 1)
+                  break;
+
+                size_t outputSize =
+                    (inputSize - filterSize + 2 * padding + stride) / stride;
+                VLOG(3) << " batchSize=" << batchSize
+                        << " inputChannels=" << inputChannels
+                        << " inputHeight=" << inputSize
+                        << " inputWidth=" << inputSize
+                        << " outputChannels=" << outputChannels
+                        << " filterHeight=" << filterSize
+                        << " filterWidth=" << filterSize
+                        << " outputHeight=" << outputSize
+                        << " outputWidth=" << outputSize << " stride=" << stride
+                        << " padding=" << padding;
+
+                std::vector<size_t> paddings = {padding, padding};
+                std::vector<size_t> strides = {stride, stride};
+                size_t groups = inputChannels;
+                Compare2Function<DType1, DType2> test(
+                    conv1,
+                    conv2,
+                    FuncConfig()
+                        .set("paddings", paddings)
+                        .set("strides", strides)
+                        .set("groups", groups)
+                        .set("algo", (std::string) "auto"));
+
+                TensorShape input{
+                    batchSize, inputChannels, inputSize, inputSize};
+                TensorShape filter{groups,
+                                   outputChannels / groups,
+                                   inputChannels / groups,
+                                   filterSize,
+                                   filterSize};
+                TensorShape output{
+                    batchSize, outputChannels, outputSize, outputSize};
+
+                function(test, input, filter, output);
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+}  // namespace paddle

paddle/function/DepthwiseConvOpTest.cpp

+/* 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 <gtest/gtest.h>
+#include "ConvOpTest.h"
+
+namespace paddle {
+
+#ifndef PADDLE_ONLY_CPU
+TEST(DepthwiseConv, Forward) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConv-CPU", "DepthwiseConv-GPU", forward);
+}
+
+TEST(DepthwiseConv, BackwardInput) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradInput-CPU", "DepthwiseConvGradInput-GPU", backward_input);
+}
+
+TEST(DepthwiseConv, BackwardFilter) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradFilter-CPU", "DepthwiseConvGradFilter-GPU", backward_filter);
+}
+#endif
+
+}  // namespace paddle

paddle/function/GemmConvOpTest.cpp

+/* 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 <gtest/gtest.h>
+#include "ConvOpTest.h"
+
+namespace paddle {
+
+TEST(GemmConv, NaiveConv) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "NaiveConv-CPU", "GemmConv-CPU", forward);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "NaiveConv-CPU", "GemmConv-CPU", forward);
+}
+
+#ifndef PADDLE_ONLY_CPU
+TEST(GemmConv, Forward) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConv-CPU", "GemmConv-GPU", forward);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConv-CPU", "GemmConv-GPU", forward);
+}
+
+TEST(GemmConv, BackwardInput) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradInput-CPU", "GemmConvGradInput-GPU", backward_input);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradInput-CPU", "GemmConvGradInput-GPU", backward_input);
+}
+
+TEST(GemmConv, BackwardFilter) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", backward_filter);
+  Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
+      "GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", backward_filter);
+}
+#endif
+
+}  // namespace paddle

paddle/function/nnpack/NNPACKConvOp.cpp

@@ -196,22 +196,23 @@ public:
         CHECK_EQ(status, nnp_status_success);
       }
     } else {
-      for (size_t g = 0; g < groups_; g++) {
       // only supports stride = 1
       CHECK_EQ(strideH(), 1);
       CHECK_EQ(strideW(), 1);
-        nnp_status status =
-            nnp_convolution_output(algorithm_,
+
+      // TODO(hedaoyuan): There has some bug when batchSize > 1 and groups_ > 1.
+      CHECK_EQ(groups_, static_cast<size_t>(1));
+      nnp_status status = nnp_convolution_output(algorithm_,
                                                  batchSize,
-                                   inputChannels / groups_,
-                                   outputChannels / groups_,
+                                                 inputChannels,
+                                                 outputChannels,
                                                  inputSize,
                                                  padding,
                                                  kernelSize,
-                                   inputData + inputOffset * g,
-                                   filterData + filterOffset * g,
+                                                 inputData,
+                                                 filterData,
                                                  nullptr, /* bias */
-                                   outputData + outputOffset * g,
+                                                 outputData,
                                                  bufferPtr,
                                                  sizePtr,
                                                  nnp_activation_identity,
@@ -221,7 +222,6 @@ public:
       CHECK_EQ(status, nnp_status_success);
     }
   }
-  }
 
   static void create_nnpack_threadpool() {
     if (FLAGS_nnpack_num_threads && threadpool_ == nullptr) {

paddle/function/nnpack/NNPACKConvOpTest.cpp

@@ -13,87 +13,18 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include "paddle/function/Function.h"
-#include "paddle/function/FunctionTest.h"
-
-DEFINE_string(algo,
-              "auto",
-              "The algorithm (auto, ft8x8, ft16x16, wt8x8, "
-              "implicit-gemm, or direct) for computing convolution of NNPACK.");
+#include "paddle/function/ConvOpTest.h"
 
 namespace paddle {
 
-#define IS_NNPACK_SUPPORT(algo, filterSize, stride)        \
-  if (algo == "direct" && filterSize != 1) continue;       \
-  if (algo == "direct" && batchSize != 1) continue;        \
-  if (algo == "wt8x8" && filterSize != 3) continue;        \
-  if (algo == "implicit-gemm" && batchSize != 1) continue; \
-  if (algo != "auto" && algo != "implicit-gemm" && stride > 1) continue;
-
-class ConvolutionTest {
-public:
-  ConvolutionTest(const std::string& conv1,
-                  const std::string& conv2,
-                  std::string algo = "auto") {
-    for (size_t batchSize : {1, 32}) {
-      for (size_t inputSize : {7, 14, 54}) {
-        for (size_t filterSize : {1, 3, 5}) {
-          for (size_t inputChannels : {3, 64}) {
-            for (size_t outputChannels : {3, 64, 128}) {
-              if (inputChannels < outputChannels) break;
-              for (size_t stride : {1, 2}) {
-                // if batchSize > 1 NNPACKConv only supports stride = 1
-                if (batchSize > 1 && stride > 1) break;
-                for (size_t padding : {0, 1}) {
-                  if (padding >= filterSize) break;
-                  size_t outputSize =
-                      (inputSize - filterSize + 2 * padding + stride) / stride;
-                  IS_NNPACK_SUPPORT(algo, filterSize, stride);
-                  LOG(INFO) << " batchSize=" << batchSize
-                            << " inputChannels=" << inputChannels
-                            << " inputHeight=" << inputSize
-                            << " inputWidth=" << inputSize
-                            << " outputChannels=" << outputChannels
-                            << " filterHeight=" << filterSize
-                            << " filterWidth=" << filterSize
-                            << " outputHeight=" << outputSize
-                            << " outputWidth=" << outputSize
-                            << " stride=" << stride << " padding=" << padding;
-
-                  std::vector<size_t> paddings = {padding, padding};
-                  std::vector<size_t> strides = {stride, stride};
-                  Compare2Function<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
-                      conv1,
-                      conv2,
-                      FuncConfig()
-                          .set("paddings", paddings)
-                          .set("strides", strides)
-                          .set("groups", (size_t)1)
-                          .set("algo", algo));
-
-                  TensorShape shape0{
-                      batchSize, inputChannels, inputSize, inputSize};
-                  TensorShape shape1{
-                      outputChannels, inputChannels, filterSize, filterSize};
-                  TensorShape shape2{
-                      batchSize, outputChannels, outputSize, outputSize};
-                  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape0));
-                  test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape1));
-                  test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape2));
-                  test.run();
-                }
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-};
+TEST(NNPACK, Forward) {
+  Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "GemmConv-CPU", "NNPACKConv-CPU", forward);
+}
 
-TEST(Convolution, NNPACK) {
-  // NNPACK only supports stride = 1
-  ConvolutionTest test("GemmConv-CPU", "NNPACKConv-CPU", FLAGS_algo);
+TEST(NNPACK, Depthwise) {
+  DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
+      "GemmConv-CPU", "NNPACKConv-CPU", forward);
 }
 
 }  // namespace paddle

paddle/gserver/activations/ActivationFunction.cpp

@@ -112,7 +112,6 @@ BEGIN_DEFINE_ACTIVATION(softmax)
 private:
 MatrixPtr sftMaxSum_;
 MatrixPtr sftMaxDot_;
-MatrixPtr one_;
 
 public:
 Error __must_check forward(Argument& act) {
@@ -138,14 +137,6 @@ Error __must_check backward(Argument& act) {
                            1,
                            /* trans */ false,
                            useGpu(act.deviceId));
-    if (!one_ || one_->getWidth() != outputG->getWidth()) {
-      Matrix::resizeOrCreate(one_,
-                             1,
-                             outputG->getWidth(),
-                             /* trans */ false,
-                             useGpu(act.deviceId));
-      one_->one();
-    }
 
     sftMaxDot_->dotMul(*outputG, *outputV);
     sftMaxSum_->colMerge(*sftMaxDot_);

paddle/operators/CMakeLists.txt

@@ -61,9 +61,6 @@ op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
 
 op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 
-op_library(fc_op
-    SRCS fc_op.cc
-    DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
     DEPS op_desc tensor op_registry operator net_op)
 cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)

paddle/operators/add_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class AddOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(AddOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
@@ -47,6 +48,7 @@ The equation is: Out = X + Y
 };
 
 class AddOpGrad : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(AddOpGrad, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {}
 };

paddle/operators/cross_entropy_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(OnehotCrossEntropyOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 2,
@@ -38,6 +39,8 @@ class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
 };
 
 class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(OnehotCrossEntropyGradientOp,
+                       framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));

paddle/operators/fc_op.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/operators/net_op.h"
-
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-using OpRegistry = framework::OpRegistry;
-
-class FullyConnectedOp : public NetOp {
- public:
-  void Init() override {
-    AddOp(OpRegistry::CreateOp("mul",
-                               {
-                                   Input("X"), Input("W"),
-                               },
-                               {Output("before_act")}, {}));
-    auto b = Input("b");
-    if (b != framework::kEmptyVarName) {
-      AddOp(OpRegistry::CreateOp("rowwise_add",
-                                 {Output("before_act"), Input("b")},
-                                 {Output("before_act")}, {}));
-    }
-
-    auto activation = GetAttr<std::string>("activation");
-    AddOp(OpRegistry::CreateOp(activation, {Output("before_act")},
-                               {Output("Y")}, {}));
-    CompleteAddOp(false);
-  }
-};
-
-class FullyConnectedOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  FullyConnectedOpMaker(framework::OpProto *proto,
-                        framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "the input of fc operator");
-    AddInput("W", "the weight of fc operator");
-    AddInput("b", "the bias of fc operator");
-
-    AddOutput("Y", "the output of fc operator");
-    AddOutput("before_act", "the before activation output of fc operator")
-        .SetTemporary();
-    AddAttr<std::string>("activation", "The activation key for fc layer")
-        .SetDefault("sigmoid")
-        .InEnum({"sigmoid", "softmax"});
-
-    //! TODO(yuyang18): Complete comment;
-    AddComment("FullyConnected Operator");
-  }
-};
-}  // namespace operators
-}  // namespace paddle
-
-USE_OP(mul);
-USE_OP(rowwise_add);
-USE_OP(sigmoid);
-USE_OP(softmax);
-
-namespace ops = paddle::operators;
-REGISTER_OP(fc, ops::FullyConnectedOp, ops::FullyConnectedOpMaker);

paddle/operators/fill_zeros_like_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class FillZerosLikeOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(FillZerosLikeOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,

paddle/operators/gaussian_random_op.cc

@@ -43,6 +43,7 @@ class GaussianRandomKernel : public framework::OpKernel {
 };
 
 class GaussianRandomOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(GaussianRandomOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext& context) const override {
     auto* tensor = context.Output<framework::Tensor>(0);

paddle/operators/mean_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class MeanOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(MeanOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 1, "Input size of AddOp must be one");
@@ -39,6 +40,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
 };
 
 class MeanGradOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(MeanGradOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     ctx.Output<Tensor>(framework::GradVarName("X"))

paddle/operators/mul_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class MulOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(MulOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
@@ -53,6 +54,7 @@ The equation is: Out = X * Y
 };
 
 class MulOpGrad : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(MulOpGrad, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {}
   std::string DebugString() const override {

paddle/operators/net_op.h

@@ -35,6 +35,8 @@ namespace operators {
  */
 class NetOp : public framework::OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(NetOp, framework::OperatorBase)
+
   /**
    * Infer all the operators' input and output variables' shapes, will be called
    * before every mini-batch

paddle/operators/net_op_test.cc

@@ -12,6 +12,8 @@ static int run_cnt = 0;
 
 class TestOp : public framework::OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(TestOp, framework::OperatorBase)
+
   void InferShape(const Scope& scope) const override { ++infer_shape_cnt; }
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
@@ -21,6 +23,8 @@ class TestOp : public framework::OperatorBase {
 
 class EmptyOp : public framework::OperatorBase {
  public:
+  DEFINE_OPERATOR_CTOR(EmptyOp, framework::OperatorBase)
+
   void InferShape(const Scope& scope) const override {}
   void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {}
 };

paddle/operators/recurrent_op.h

@@ -100,6 +100,7 @@ class RecurrentGradientAlgorithm {
 };
 
 class RecurrentOp final : public framework::OperatorBase {
+  DEFINE_OPERATOR_CTOR(RecurrentOp, framework::OperatorBase)
  public:
   void Init() override;
 

paddle/operators/recurrent_op_test.cc

@@ -395,4 +395,4 @@ TEST(RecurrentOp, LinkMemories) {
 
 USE_OP(add_two);
 USE_OP(mul);
-USE_OP_WITHOUT_KERNEL(recurrent_op);
+USE_OP_ITSELF(recurrent_op);

paddle/operators/rowwise_add_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class RowWiseAddOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(RowWiseAddOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.InputSize() == 2UL,

paddle/operators/sgd_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class SGDOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(SGDOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 2, "Input size of SGDOp must be two");

paddle/operators/sigmoid_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class SigmoidOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(SigmoidOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
@@ -38,6 +39,7 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
 };
 
 class SigmoidOpGrad : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(SigmoidOpGrad, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());

paddle/operators/softmax_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 class SoftmaxOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(SoftmaxOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
@@ -42,6 +43,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
 };
 
 class SoftmaxOpGrad : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(SoftmaxOpGrad, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL,

paddle/operators/uniform_random_op.cc

@@ -46,6 +46,7 @@ class CPUUniformRandomKernel : public framework::OpKernel {
 };
 
 class UniformRandomOp : public framework::OperatorWithKernel {
+  DEFINE_OPERATOR_CTOR(UniformRandomOp, framework::OperatorWithKernel)
  protected:
   void InferShape(const framework::InferShapeContext& ctx) const override {
     PADDLE_ENFORCE(GetAttr<float>("min") < GetAttr<float>("max"),

paddle/trainer/NewRemoteParameterUpdater.cpp

@@ -66,28 +66,92 @@ void NewRemoteParameterUpdater::init(
   // from parameter server
   if (paddle_begin_init_params(parameterClient_)) {
     LOG(INFO) << "paddle_begin_init_params start";
+    // NOTE: convert V1 OptimizatioinConfig proto to V2 OptimizerConfig.
+    // This makes golang pserver compatible with handy V1 demos.
+    // TODO(wuyi): Refine or remove these ugly converting lines
+    OptimizerConfig optimizerConfigV2;
+    if (trainerConfig_.learning_method() == "momentum") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
+    } else if (trainerConfig_.learning_method() == "adagrad") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::Adagrad);
+      optimizerConfigV2.mutable_adagrad()->set_epsilon(
+          trainerConfig_.ada_epsilon());
+    } else if (trainerConfig_.learning_method() == "adadelta") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::Adagrad);
+      optimizerConfigV2.mutable_adadelta()->set_epsilon(
+          trainerConfig_.ada_epsilon());
+      optimizerConfigV2.mutable_adadelta()->set_rho(trainerConfig_.ada_rou());
+    } else if (trainerConfig_.learning_method() == "adam") {
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::Adam);
+      optimizerConfigV2.mutable_adam()->set_beta_1(trainerConfig_.adam_beta1());
+      optimizerConfigV2.mutable_adam()->set_beta_2(trainerConfig_.adam_beta2());
+      optimizerConfigV2.mutable_adam()->set_epsilon(
+          trainerConfig_.adam_epsilon());
+    } else {
+      LOG(ERROR) << "got unsupported v1 optimizer config: "
+                 << trainerConfig_.learning_method();
+      optimizerConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
+    }
+
+    if (trainerConfig_.learning_rate_schedule() == "constant") {
+      optimizerConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
+      optimizerConfigV2.mutable_const_lr()->set_learning_rate(
+          trainerConfig_.learning_rate());
+    } else if (trainerConfig_.learning_rate_schedule() == "linear") {
+      optimizerConfigV2.set_lr_policy(paddle::OptimizerConfig::Linear);
+      optimizerConfigV2.mutable_linear_lr()->set_learning_rate(
+          trainerConfig_.learning_rate());
+      optimizerConfigV2.mutable_linear_lr()->set_lr_decay_a(
+          trainerConfig_.learning_rate_decay_a());
+      optimizerConfigV2.mutable_linear_lr()->set_lr_decay_b(
+          trainerConfig_.learning_rate_decay_b());
+    } else {
+      LOG(ERROR) << "got unsupported v1 learning_rate_schedule config: "
+                 << trainerConfig_.learning_rate_schedule() << ", set to const";
+      optimizerConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
+    }
+
+    // overwrite optimizerConfigV2 for per-parameter(layer) configs
     for (int i = 0; i < parameterSize(); ++i) {
       auto paramConfig = parameters_[i]->getConfig();
-      LOG(INFO) << "old param config: " << paramConfig.DebugString();
-      // FIXME(typhoonzero): convert old paramConfig to optimizerConfig
-      OptimizerConfig optimizeConfigV2;
-      auto sgdConfigV2 = optimizeConfigV2.mutable_sgd();
-      sgdConfigV2->set_momentum(paramConfig.momentum());
-      sgdConfigV2->set_decay(paramConfig.decay_rate());
-      optimizeConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
-      auto constlr = optimizeConfigV2.mutable_const_lr();
+      if (paramConfig.has_momentum() &&
+          trainerConfig_.learning_method() == "momentum") {
+        optimizerConfigV2.mutable_sgd()->set_momentum(paramConfig.momentum());
+      }
       if (paramConfig.has_learning_rate()) {
-        constlr->set_learning_rate(paramConfig.learning_rate());
-      } else {
-        constlr->set_learning_rate(trainerConfig_.learning_rate());
+        switch (optimizerConfigV2.lr_policy()) {
+          case 0:
+            optimizerConfigV2.mutable_const_lr()->set_learning_rate(
+                paramConfig.learning_rate());
+            break;
+          case 1:
+            optimizerConfigV2.mutable_linear_lr()->set_learning_rate(
+                paramConfig.learning_rate());
+            break;
+        }
+      }
+      if (paramConfig.has_decay_rate()) {
+        switch (optimizerConfigV2.optimizer()) {
+          case 1:  // SGD
+            optimizerConfigV2.mutable_sgd()->set_decay(
+                paramConfig.decay_rate());
+            break;
+          case 2:  // Adadelta
+            optimizerConfigV2.mutable_adadelta()->set_decay(
+                paramConfig.decay_rate());
+            break;
+          case 3:  // Adagrad
+            optimizerConfigV2.mutable_adagrad()->set_decay(
+                paramConfig.decay_rate());
+            break;
+          case 4:  // Adam
+            optimizerConfigV2.mutable_adam()->set_decay(
+                paramConfig.decay_rate());
+            break;
         }
-      if (trainerConfig_.algorithm() == "sgd") {
-        optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
-        // FIXME: config all algorithms
-      } else {
-        optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
       }
-      std::string bytes = optimizeConfigV2.SerializeAsString();
+      // send param and config to pserver
+      std::string bytes = optimizerConfigV2.SerializeAsString();
       const char *array = bytes.data();
       int size = (int)bytes.size();
       paddle_init_param(

python/paddle/v2/framework/tests/CMakeLists.txt

 py_test(test_net SRCS test_net.py)
 
-py_test(test_fc_op SRCS test_fc_op.py)
 py_test(test_scope SRCS test_scope.py)
 
 py_test(test_tensor SRCS test_tensor.py)

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

@@ -73,21 +73,35 @@ def get_numeric_gradient(op,
     def product(dim):
         return reduce(lambda a, b: a * b, dim, 1)
 
+    # get the input tensor that we want to get it's numeric gradient.
     tensor_to_check = local_scope.find_var(input_to_check).get_tensor()
     tensor_size = product(tensor_to_check.get_dims())
+    # prepare a numpy array to store the gradient.
     gradient_flat = numpy.zeros(shape=(tensor_size, ), dtype='float32')
+
+    # we only compute gradient of one element each time.
+    # we use a for loop to compute the gradient of every element.
     for i in xrange(tensor_size):
+        # get one input element throw it's index i.
         origin = tensor_to_check.get_float_element(i)
+
+        # add delta to it, run op and then get the sum of the result tensor.
         x_pos = origin + delta
         tensor_to_check.set_float_element(i, x_pos)
         y_pos = get_output()
 
+        # plus delta to this element, run op and get the sum of the result tensor.
         x_neg = origin - delta
         tensor_to_check.set_float_element(i, x_neg)
         y_neg = get_output()
 
-        tensor_to_check.set_float_element(i, origin)  # restore old value
+        # restore old value
+        tensor_to_check.set_float_element(i, origin)
+
+        # compute the gradient of this element and store it into a numpy array.
         gradient_flat[i] = (y_pos - y_neg) / delta / 2
+
+    # reshape the gradient result to the shape of the source tensor.
     return gradient_flat.reshape(tensor_to_check.get_dims())
 
 

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

-import paddle.v2.framework.core as core
-import unittest
-import numpy
-from paddle.v2.framework.op import Operator
-
-
-class TestFc(unittest.TestCase):
-    def test_fc(self):
-        scope = core.Scope()
-        place = core.CPUPlace()
-        x = scope.new_var("X")
-
-        x_tensor = x.get_tensor()
-        x_tensor.set_dims([1000, 784])
-        x_tensor.alloc_float(place)
-
-        w = scope.new_var("W")
-        w_tensor = w.get_tensor()
-        w_tensor.set_dims([784, 100])
-        w_tensor.alloc_float(place)
-
-        w_tensor.set(numpy.random.random((784, 100)).astype("float32"), place)
-
-        # Set a real numpy array here.
-        # x_tensor.set(numpy.array([]))
-
-        op = Operator("fc", X="X", Y="Y", W="W")
-
-        for out in op.outputs():
-            if scope.find_var(out) is None:
-                scope.new_var(out).get_tensor()
-
-        tensor = scope.find_var("Y").get_tensor()
-        op.infer_shape(scope)
-        self.assertEqual([1000, 100], tensor.shape())
-
-        ctx = core.DeviceContext.create(place)
-
-        op.run(scope, ctx)
-
-        # After complete all ops, check Y is expect or not.
-
-
-if __name__ == '__main__':
-    unittest.main()

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

@@ -3,6 +3,15 @@ from paddle.v2.framework.op import Operator
 import unittest
 
 
+def fc(X, W, Y):
+    ret_v = core.Net.create()
+
+    ret_v.add_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
+    ret_v.add_op(Operator("sigmoid", X="pre_activation", Y=Y))
+    ret_v.complete_add_op(True)
+    return ret_v
+
+
 class TestNet(unittest.TestCase):
     def test_net_all(self):
         net = core.Net.create()
@@ -10,18 +19,18 @@ class TestNet(unittest.TestCase):
         net.add_op(op1)
 
         net2 = core.Net.create()
-        net2.add_op(Operator("fc", X="X", W="w", Y="fc.out"))
+        net2.add_op(fc(X="X", W="w", Y="fc.out"))
         net2.complete_add_op(True)
         net.add_op(net2)
         net.complete_add_op(True)
 
         expected = '''
-Op(plain_net), inputs:(@EMPTY@, X, Y, w), outputs:(@TEMP@fc@0, Out, fc.out).
+Op(plain_net), inputs:(W, X, Y), outputs:(Out, fc.out, pre_activation).
     Op(add_two), inputs:(X, Y), outputs:(Out).
-    Op(plain_net), inputs:(@EMPTY@, X, w), outputs:(@TEMP@fc@0, fc.out).
-        Op(fc), inputs:(X, w, @EMPTY@), outputs:(fc.out, @TEMP@fc@0).
-            Op(mul), inputs:(X, w), outputs:(@TEMP@fc@0).
-            Op(sigmoid), inputs:(@TEMP@fc@0), outputs:(fc.out).
+    Op(plain_net), inputs:(W, X), outputs:(fc.out, pre_activation).
+        Op(plain_net), inputs:(W, X), outputs:(fc.out, pre_activation).
+            Op(mul), inputs:(X, W), outputs:(pre_activation).
+            Op(sigmoid), inputs:(pre_activation), outputs:(fc.out).
 '''
         self.assertEqual(expected, "\n" + str(net))
 

python/paddle/v2/optimizer.py

-import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
-import paddle.trainer_config_helpers.optimizers as v1_optimizers
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
 """
 Optimizers(update equation) for SGD method.
 
-TODO(zhihong) : create new optimizer with proto config, add new optimizer here
-
 TODO(yuyang18): Complete comments.
 """
 
+import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
+import paddle.trainer_config_helpers.optimizers as v1_optimizers
+from paddle.proto.OptimizerConfig_pb2 import OptimizerConfig
+
 __all__ = [
     'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
     'RMSProp', 'ModelAverage', 'L2Regularization'
@@ -70,7 +83,8 @@ class Optimizer(object):
                         gradient_machine.prefetch(in_args)
                         parameter_updater.getParametersRemote()
 
-        :param pserver_spec: pserver location, eg: localhost:3000
+        :param pserver_spec: pserver location, eg: localhost:3000, if use etcd,
+        pserver_spec should be the etcd endpoints, eg: http://localhost:2379
         :return: parameter_updater
         """
         if is_local:

python/paddle/v2/parameters.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
+
 import numpy as np
 from paddle.proto.ParameterConfig_pb2 import ParameterConfig
 import paddle.trainer.config_parser as cp
@@ -113,16 +127,7 @@ class Parameters(object):
         """
         return iter(self.__param_conf__)
 
-    def __getitem__(self, key):
-        """
-        Get parameter by parameter name. It uses Python dict syntax.
-
-        :note: It will always copy the parameter from C++ side.
-        :param key: Parameter name
-        :type key: basestring
-        :return: parameter value
-        :rtype: np.ndarray
-        """
+    def __getter_inner(self, key, param_type):
         import py_paddle.swig_paddle as api
         shape = self.get_shape(key)
 
@@ -138,7 +143,7 @@ class Parameters(object):
                     each_gradient_machine, key)
                 # for simplify implementation now, we always copy from C++
                 assert isinstance(param, api.Parameter)
-                val = param.getBuf(api.PARAMETER_VALUE)
+                val = param.getBuf(param_type)
                 assert isinstance(val, api.Vector)
                 val = val.copyToNumpyArray()
                 return val
@@ -146,6 +151,19 @@ class Parameters(object):
 
             raise RuntimeError("Unexpected branch")
 
+    def __getitem__(self, key):
+        """
+        Get parameter by parameter name. It uses Python dict syntax.
+
+        :note: It will always copy the parameter from C++ side.
+        :param key: Parameter name
+        :type key: basestring
+        :return: parameter value
+        :rtype: np.ndarray
+        """
+        import py_paddle.swig_paddle as api
+        return self.__getter_inner(key, api.PARAMETER_VALUE)
+
     def get_shape(self, key):
         """
         get shape of the parameter.
@@ -202,6 +220,19 @@ class Parameters(object):
         """
         return self.__getitem__(key=parameter_name)
 
+    def get_grad(self, key):
+        """
+        Get grandient by parameter name.
+
+        :note: It will always copy the parameter from C++ side.
+        :param key: parameter name
+        :type key: basestring
+        :return: The grandient matrix.
+        :rtype: np.ndarray
+        """
+        import py_paddle.swig_paddle as api
+        return self.__getter_inner(key, api.PARAMETER_GRADIENT)
+
     def set(self, parameter_name, value):
         """
         Set parameter by parameter name & matrix.
@@ -250,7 +281,13 @@ class Parameters(object):
         size = reduce(lambda a, b: a * b, param.shape)
         f.write(struct.pack("IIQ", 0, 4, size))
         param = param.astype(np.float32)
-        f.write(param.tostring())
+        s = param.tostring()
+        wrote_size = 0
+        buf = buffer(s, wrote_size, 65535)
+        while buf:  # f.write crashes with big data blog.
+            f.write(buf)
+            wrote_size += 65535
+            buf = buffer(s, wrote_size, 65535)
 
     def deserialize(self, name, f):
         """

python/paddle/v2/trainer.py

@@ -161,14 +161,14 @@ class SGD(object):
                     self.__parameter_updater__.update(each_param)
                 cost_sum = out_args.sum()
                 cost = cost_sum / len(data_batch)
-                self.__parameter_updater__.finishBatch(cost)
-                batch_evaluator.finish()
                 event_handler(
                     v2_event.EndIteration(
                         pass_id=pass_id,
                         batch_id=batch_id,
                         cost=cost,
                         evaluator=batch_evaluator))
+                self.__parameter_updater__.finishBatch(cost)
+                batch_evaluator.finish()
 
             self.__parameter_updater__.finishPass()
             pass_evaluator.finish()

python/requirements.txt

+requests==2.9.2
+numpy>=1.12
+protobuf==3.1
+recordio
+matplotlib
+rarfile
+scipy>=0.19.0
+Pillow
+nltk>=3.2.2

python/setup.py.in

 from setuptools import setup, Distribution
-
 class BinaryDistribution(Distribution):
     def has_ext_modules(foo):
         return True
@@ -18,15 +17,8 @@ packages=['paddle',
           'paddle.v2.framework.proto',
           'py_paddle']
 
-setup_requires=["requests",
-                "numpy>=1.12",
-                "protobuf==3.1",
-                "recordio",
-                "matplotlib",
-                "rarfile",
-                "scipy>=0.19.0",
-                "Pillow",
-                "nltk>=3.2.2"]
+with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f:
+    setup_requires = f.read().splitlines()
 
 if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']:
     setup_requires+=["opencv-python"]