Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_dilation

.gitignore

@@ -28,3 +28,4 @@ cmake_install.cmake
 paddle/.timestamp
 python/paddlepaddle.egg-info/
 paddle/pybind/pybind.h
+python/paddle/v2/framework/tests/tmp/*

paddle/framework/CMakeLists.txt

@@ -15,7 +15,7 @@ nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
 
 cc_test(variable_test SRCS variable_test.cc)
 
-cc_library(scope SRCS scope.cc)
+cc_library(scope SRCS scope.cc DEPS glog)
 cc_test(scope_test SRCS scope_test.cc DEPS scope)
 
 

paddle/framework/op_desc.h

@@ -107,6 +107,8 @@ class OpDescBind {
 
   void InferVarType(BlockDescBind *block) const;
 
+  void MarkAsTarget() { desc_.set_is_target(true); }
+
   void Flush();
 
  private:

paddle/framework/program_desc.cc

@@ -49,6 +49,13 @@ ProgramDescBind::ProgramDescBind(const ProgramDescBind &o) {
   }
 }
 
+ProgramDescBind::ProgramDescBind(const ProgramDesc &desc) {
+  desc_ = desc;
+  for (auto &block_desc : *desc_.mutable_blocks()) {
+    blocks_.emplace_back(new BlockDescBind(this, &block_desc));
+  }
+}
+
 ProgramDescBind::ProgramDescBind(const std::string &binary_str) {
   PADDLE_ENFORCE(desc_.ParseFromString(binary_str),
                  "Fail to parse program_desc from binary string.");

paddle/framework/program_desc.h

@@ -29,6 +29,8 @@ class ProgramDescBind {
  public:
   ProgramDescBind();
 
+  explicit ProgramDescBind(const ProgramDesc &desc);
+
   ProgramDescBind(const ProgramDescBind &o);
 
   explicit ProgramDescBind(const std::string &binary_str);

paddle/framework/prune.cc

@@ -46,7 +46,7 @@ bool IsTarget(const OpDesc& op_desc) {
   return false;
 }
 
-void prune_impl(const ProgramDesc& input, ProgramDesc& output, int block_id) {
+void prune_impl(const ProgramDesc& input, ProgramDesc* output, int block_id) {
   // TODO(tonyyang-svail):
   //    - will change to use multiple blocks for RNN op and Cond Op
 
@@ -91,8 +91,8 @@ void prune_impl(const ProgramDesc& input, ProgramDesc& output, int block_id) {
   // we reverse the should_run vector
   std::reverse(should_run.begin(), should_run.end());
 
-  output = input;
-  auto* op_field = output.mutable_blocks(block_id)->mutable_ops();
+  *output = input;
+  auto* op_field = output->mutable_blocks(block_id)->mutable_ops();
   op_field->Clear();
   for (size_t i = 0; i < should_run.size(); ++i) {
     if (should_run[i]) {
@@ -101,7 +101,8 @@ void prune_impl(const ProgramDesc& input, ProgramDesc& output, int block_id) {
   }
 }
 
-void Prune(const ProgramDesc& input, ProgramDesc& output) {
+// TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies
+void Prune(const ProgramDesc& input, ProgramDesc* output) {
   prune_impl(input, output, 0);
 }
 

paddle/framework/prune.h

@@ -20,7 +20,7 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-void Prune(const ProgramDesc& input, ProgramDesc& output);
+void Prune(const ProgramDesc& input, ProgramDesc* output);
 
 }  // namespace framework
 }  // namespace paddle

paddle/framework/prune_test.cc

@@ -59,11 +59,11 @@ TEST(Prune, one_operator) {
   f::ProgramDesc *pdesc = program.Proto();
   f::ProgramDesc pruned;
 
-  Prune(*pdesc, pruned);
+  Prune(*pdesc, &pruned);
   PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 0);
 
   pdesc->mutable_blocks(0)->mutable_ops(0)->set_is_target(true);
-  Prune(*pdesc, pruned);
+  Prune(*pdesc, &pruned);
   PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 1);
 }
 
@@ -81,7 +81,7 @@ TEST(Prune, forward) {
   for (int i = 0; i < pdesc->blocks(0).ops_size(); ++i) {
     f::ProgramDesc pruned;
     pdesc->mutable_blocks(0)->mutable_ops(i)->set_is_target(true);
-    Prune(*pdesc, pruned);
+    Prune(*pdesc, &pruned);
     PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), i + 1);
   }
 }
@@ -100,7 +100,7 @@ TEST(Prune, multi_input_op) {
   pdesc->mutable_blocks(0)->mutable_ops(3)->set_is_target(true);
 
   f::ProgramDesc pruned;
-  Prune(*pdesc, pruned);
+  Prune(*pdesc, &pruned);
   PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 4);
 }
 
@@ -116,7 +116,7 @@ TEST(Prune, multi_output_op) {
   pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
 
   f::ProgramDesc pruned;
-  Prune(*pdesc, pruned);
+  Prune(*pdesc, &pruned);
   PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 2);
 }
 
@@ -133,6 +133,6 @@ TEST(Prune, multi_target) {
   pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
 
   f::ProgramDesc pruned;
-  Prune(*pdesc, pruned);
+  Prune(*pdesc, &pruned);
   PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 3);
 }

paddle/framework/scope.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <memory>  // for unique_ptr
 #include <mutex>   // for call_once
+#include "glog/logging.h"
 #include "paddle/string/printf.h"
 
 namespace paddle {
@@ -23,7 +24,10 @@ namespace framework {
 
 Scope::~Scope() {
   DropKids();
-  for (auto& kv : vars_) delete kv.second;
+  for (auto& kv : vars_) {
+    VLOG(3) << "Destroy variable " << kv.first;
+    delete kv.second;
+  }
 }
 
 Scope& Scope::NewScope() const {
@@ -38,6 +42,7 @@ Variable* Scope::Var(const std::string& name) {
   }
   Variable* v = new Variable();
   vars_[name] = v;
+  VLOG(3) << "Create variable " << name << " on scope";
   v->name_ = &(vars_.find(name)->first);
   return v;
 }

paddle/memory/CMakeLists.txt

 add_subdirectory(detail)
 
-cc_library(memory SRCS memory.cc)
+cc_library(memory SRCS memory.cc DEPS place)
 cc_library(memcpy SRCS memcpy.cc)
 
 cc_library(paddle_memory

paddle/memory/detail/meta_cache.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/memory/detail/meta_cache.h"
+#include "glog/logging.h"
 #include "paddle/memory/detail/memory_block.h"
 #include "paddle/platform/assert.h"
 
@@ -28,7 +29,9 @@ Metadata MetadataCache::load(const MemoryBlock* block) {
     PADDLE_ASSERT(existing_metadata->second.check_guards());
     return existing_metadata->second;
   } else {
-    PADDLE_ASSERT(reinterpret_cast<const Metadata*>(block)->check_guards());
+    auto* meta = reinterpret_cast<const Metadata*>(block);
+    VLOG(3) << "Load MetaData type=" << meta->type;
+    PADDLE_ASSERT(meta->check_guards());
     return *reinterpret_cast<const Metadata*>(block);
   }
 }

paddle/memory/memory.cc

@@ -39,11 +39,15 @@ BuddyAllocator* GetCPUBuddyAllocator() {
 
 template <>
 void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
-  return GetCPUBuddyAllocator()->Alloc(size);
+  VLOG(3) << "Allocate " << size << " bytes on " << platform::Place(place);
+  void* p = GetCPUBuddyAllocator()->Alloc(size);
+  VLOG(3) << "  pointer=" << p;
+  return p;
 }
 
 template <>
 void Free<platform::CPUPlace>(platform::CPUPlace place, void* p) {
+  VLOG(3) << "Free pointer=" << p << " on " << platform::Place(place);
   GetCPUBuddyAllocator()->Free(p);
 }
 

paddle/operators/batch_norm_op.cu

@@ -117,9 +117,6 @@ class BatchNormKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
     math::SetConstant<platform::GPUPlace, T> functor;
     functor(ctx.device_context(), saved_mean, 0);
     functor(ctx.device_context(), saved_variance, 0);
-    // FIXME(qiao) should not set zero self
-    functor(ctx.device_context(), mean_out, 0);
-    functor(ctx.device_context(), variance_out, 0);
 
     auto handle = ctx.cuda_device_context().cudnn_handle();
 
@@ -211,8 +208,15 @@ class BatchNormGradKernel<platform::GPUPlace, T>
     mode_ = CUDNN_BATCHNORM_SPATIAL;
 #endif
 
-    std::vector<int> dims = {N, C, H, W, D};
-    std::vector<int> strides = {H * W * C * D, 1, W * D * C, D * C, C};
+    std::vector<int> dims;
+    std::vector<int> strides;
+    if (tensor_format == TensorFormat::NCHW) {
+      dims = {N, C, H, W, D};
+      strides = {C * H * W * D, H * W * D, W * D, D, 1};
+    } else {
+      dims = {N, C, H, W, D};
+      strides = {H * W * C * D, 1, W * D * C, D * C, C};
+    }
     CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
         data_desc_, CudnnDataType<T>::type,
         x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));

paddle/operators/sequence_pool_op.h

@@ -144,11 +144,11 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
           Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
               in_t_map(in_t.data<T>(), h, w);
           int row_id;
-          Eigen::array<int, 2> extents = {1, 1};
+          Eigen::array<int, 2> extents{{1, 1}};
           for (int col_id = 0; col_id < w; col_id++) {
             in_t_map.col(col_id).maxCoeff(&row_id);
-            Eigen::array<int, 2> in_offsets = {row_id, col_id};
-            Eigen::array<int, 2> out_offsets = {0, col_id};
+            Eigen::array<int, 2> in_offsets{{row_id, col_id}};
+            Eigen::array<int, 2> out_offsets{{0, col_id}};
             in_g_e.slice(in_offsets, extents).device(place) =
                 out_g_e.slice(out_offsets, extents);
           }

paddle/operators/sign_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/sign_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SignOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SignOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SignOp should not be null.");
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+template <typename AttrType>
+class SignOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SignOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "(Tensor) Input tensor of sign operator.");
+    AddOutput("Out", "(Tensor) Output tensor of sign operator.");
+    AddComment(R"DOC(Sign operator
+
+The equation is: Out = X.sign()
+)DOC");
+  }
+};
+
+class SignGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *grad_op = new framework::OpDescBind();
+    grad_op->SetType("scale");
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttr("scale", 0.0f);
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(sign, ops::SignOp, ops::SignOpMaker<float>,
+                  ops::SignGradMaker);
+REGISTER_OP_CPU_KERNEL(sign,
+                       ops::SignKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sign_op.cu

+/* 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/sign_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+    sign, paddle::operators::SignKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sign_op.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. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+template <typename Place, typename T>
+class SignKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* out = context.Output<framework::Tensor>("Out");
+    auto* in = context.Input<framework::Tensor>("X");
+    out->mutable_data<T>(in->place());
+
+    auto eigen_out = framework::EigenVector<T>::Flatten(*out);
+    auto eigen_in = framework::EigenVector<T>::Flatten(*in);
+    auto& place = context.GetEigenDevice<Place>();
+    eigen_out.device(place) = eigen_in.sign();
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/pybind/CMakeLists.txt

 if(WITH_PYTHON)
   cc_library(paddle_pybind SHARED
     SRCS pybind.cc exception.cc protobuf.cc
-    DEPS pybind python backward proto_desc tensor_array paddle_memory executor
+    DEPS pybind python backward proto_desc tensor_array paddle_memory executor prune
     ${GLOB_OP_LIB})
 endif(WITH_PYTHON)
 

paddle/pybind/protobuf.cc

@@ -141,6 +141,13 @@ void BindProgramDesc(py::module &m) {
                  desc->SerializeToString(&res),
                  "Serialize ProgramDesc Error. This could be a bug of Paddle.");
              return res;
+           })
+      .def("parse_from_string",
+           [](ProgramDescBind &program_desc, const std::string &data) {
+             ProgramDesc *desc = program_desc.Proto();
+             PADDLE_ENFORCE(desc->ParseFromString(data),
+                            "Fail to parse ProgramDesc from string. This could "
+                            "be a bug of Paddle.");
            });
 }
 

paddle/pybind/pybind.cc

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/framework/feed_fetch_method.h"
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/prune.h"
 #include "paddle/framework/selected_rows.h"
 #include "paddle/framework/tensor_array.h"
 #include "paddle/operators/cond_op.h"
@@ -237,6 +238,16 @@ All parameter, weight, gradient are variables in Paddle.
     }
     return ret_values;
   });
+  m.def("prune", [](const ProgramDescBind &origin,
+                    const std::vector<std::array<size_t, 2>> &targets) {
+    ProgramDescBind prog_with_targets(origin);
+    for (const auto &t : targets) {
+      prog_with_targets.Block(t[0])->Op(t[1])->MarkAsTarget();
+    }
+    ProgramDesc pruned_desc;
+    Prune(*prog_with_targets.Proto(), &pruned_desc);
+    return new ProgramDescBind(pruned_desc);
+  });
   m.def_submodule(
        "var_names",
        "The module will return special predefined variable name in Paddle")

python/paddle/v2/framework/framework.py

@@ -251,6 +251,8 @@ class Operator(object):
                 self.desc.set_output(out_proto.name, out_argu_names)
 
         if attrs is not None:
+            if not isinstance(attrs, dict):
+                raise TypeError("'attrs' should be a dict.")
             for attr in proto.attrs:
                 attr_name = attr.name
                 if (not attr_name in attrs) or (attrs[attr_name] is None):
@@ -291,6 +293,14 @@ class Operator(object):
     def output_names(self):
         return self.desc.output_names()
 
+    @property
+    def idx(self):
+        for i, op in enumerate(self.block.ops):
+            if op == self:
+                return i
+        raise ValueError(
+            "Can't find op itself in it's block. It could be a bug of Paddle.")
+
     def has_attr(self, name):
         return self.desc.has_attr(name)
 
@@ -440,10 +450,31 @@ class Program(object):
         p.sync_with_cpp()
         return p
 
+    def prune(self, targets):
+        if not isinstance(targets, list):
+            targets = [targets]
+        targets_idx = []
+        for t in targets:
+            if not isinstance(t, Operator):
+                if isinstance(t, Variable):
+                    t = t.op
+                else:
+                    raise ValueError(
+                        "All targets of prune() can only be Variable or Operator."
+                    )
+
+            targets_idx.append([t.block.idx, t.idx])
+        res = Program()
+        res.desc = core.prune(self.desc, targets_idx)
+        res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())]
+        res.sync_with_cpp()
+        return res
+
     @staticmethod
     def parse_from_string(binary_str):
         p = Program()
         p.desc = core.ProgramDesc(binary_str)
+        p.blocks = [Block(p, i) for i in xrange(p.desc.num_blocks())]
         p.sync_with_cpp()
         return p
 

python/paddle/v2/framework/io.py

 import os
+import cPickle as pickle
 
 from paddle.v2.framework.framework import Program, Parameter, g_program, \
     Variable
 
 __all__ = [
     'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
-    'load_persistables'
+    'load_persistables', "save_inference_model", "load_inference_model"
 ]
 
 
@@ -124,6 +125,7 @@ def load_vars(executor, dirname, program=None, vars=None, predicate=None):
                 inputs={},
                 outputs={"Out": [new_var]},
                 attrs={'file_path': os.path.join(dirname, new_var.name)})
+
         executor.run(load_prog)
 
 
@@ -141,3 +143,88 @@ def load_persistables(executor, dirname, program=None):
     """
     load_vars(
         executor, dirname=dirname, program=program, predicate=is_persistable)
+
+
+def save_inference_model(dirname,
+                         feeded_var_names,
+                         target_vars,
+                         executor,
+                         program=None):
+    """
+    Build a model especially for inference, 
+    and save it to directory by the executor.
+
+    :param dirname: directory path
+    :param feeded_var_names: Names of variables that need to be feeded data during inference
+    :param target_vars: Variables from which we can get inference results.
+    :param executor: executor that save inference model
+    :param program: original program, which will be pruned to build the inference model. 
+    Default g_program.
+
+    :return: None
+    """
+    if program is None:
+        program = g_program
+    if not isinstance(target_vars, list):
+        target_vars = [target_vars]
+
+    if not os.path.isdir(dirname):
+        os.makedirs(dirname)
+
+    pruned_program = program.prune(target_vars)
+    fetch_var_names = [v.name for v in target_vars]
+
+    model_file_name = dirname + "/__model__"
+    with open(model_file_name, "w") as f:
+        pickle.dump({
+            "program_desc_str": pruned_program.desc.serialize_to_string(),
+            "feed_var_names": feeded_var_names,
+            "fetch_var_names": fetch_var_names
+        }, f, -1)
+
+    save_params(executor, dirname, program)
+
+
+def load_persistables_if_exist(executor, dirname, program=None):
+    filenames = next(os.walk(dirname))[2]
+    filenames = set(filenames)
+
+    def _is_presistable_and_exist_(var):
+        if not is_persistable(var):
+            return False
+        else:
+            return var.name in filenames
+
+    load_vars(
+        executor,
+        dirname,
+        program=program,
+        vars=None,
+        predicate=_is_presistable_and_exist_)
+
+
+def load_inference_model(dirname, executor):
+    """
+    Load inference model from a directory
+
+    :param dirname: directory path
+    :param executor: executor that load inference model
+
+    :return: [program, feed_var_names, fetch_var_names]
+             program: program especially for inference.
+             feeded_var_names: Names of variables that need to feed data
+             fetch_vars: Variables from which we can get inference results.
+    """
+    if not os.path.isdir(dirname):
+        raise ValueError("There is no directory named '%s'", dirname)
+
+    model_file_name = dirname + "/__model__"
+    model = pickle.load(open(model_file_name, "r"))
+    program_desc_str = model["program_desc_str"]
+    feed_var_names = model["feed_var_names"]
+    fetch_var_names = model["fetch_var_names"]
+    program = Program.parse_from_string(program_desc_str)
+    load_persistables_if_exist(executor, dirname, program)
+    fetch_vars = [program.global_block().var(name) for name in fetch_var_names]
+
+    return [program, feed_var_names, fetch_vars]

python/paddle/v2/framework/layer_helper.py

@@ -131,12 +131,14 @@ class LayerHelper(object):
         return dtype
 
     def create_parameter(self, attr, shape, dtype, suffix='w'):
-        if attr['name'] is None:
-            attr['name'] = unique_name(".".join([self.name, suffix]))
+        # Deepcopy the attr so that parameters can be shared in program
+        attr_copy = copy.deepcopy(attr)
+        if attr_copy['name'] is None:
+            attr_copy['name'] = unique_name(".".join([self.name, suffix]))
         self.init_program.global_block().create_parameter(
-            dtype=dtype, shape=shape, **attr)
+            dtype=dtype, shape=shape, **attr_copy)
         return self.program.global_block().create_parameter(
-            name=attr['name'], dtype=dtype, shape=shape)
+            name=attr_copy['name'], dtype=dtype, shape=shape)
 
     def create_tmp_variable(self, dtype):
         return self.program.current_block().create_var(

python/paddle/v2/framework/optimizer.py

@@ -18,7 +18,8 @@ class Optimizer(object):
     but need to use one of it's implementation.
     """
 
-    def __init__(self):
+    def __init__(self, global_step=None):
+        self._global_step = global_step
         # Dictionary of accumulators. Some optimizer subclasses need to
         # allocate and manage extra variables associated with the parameters
         # to train. These variables are called accumulators.
@@ -109,6 +110,26 @@ class Optimizer(object):
                             format(name, param.name))
         return self._accumulators[name][param.name]
 
+    def _increment_global_step(self, block):
+        """Increment the global step by 1 after every iteration
+
+        Args:
+            block: the block in which the loss variable is present
+
+        Returns:
+            list with global_step increment op as its only element
+        """
+        assert isinstance(block, framework.Block)
+        assert self._global_step is not None
+        # create the increment op
+        increment_op = block.append_op(
+            type="increment",
+            inputs={"X": self._global_step},
+            outputs={"Out": self._global_step},
+            attrs={"step": 1.0})
+
+        return increment_op
+
     def create_optimization_pass(self, parameters_and_grads, loss):
         """Add optimization operators to update gradients to variables.
 
@@ -152,6 +173,8 @@ class Optimizer(object):
         if finish_ops is not None:
             return_ops += finish_ops
 
+        if self._global_step is not None:
+            return_ops.append(self._increment_global_step(loss.block))
         return return_ops
 
     def minimize(self, loss, parameter_list=None, no_grad_set=None):
@@ -172,9 +195,9 @@ class SGDOptimizer(Optimizer):
     """ Simple SGD optimizer without any state.
     """
 
-    def __init__(self, learning_rate):
+    def __init__(self, learning_rate, global_step=None):
         assert learning_rate is not None
-        super(SGDOptimizer, self).__init__()
+        super(SGDOptimizer, self).__init__(global_step)
         self.type = "sgd"
         self._learning_rate = learning_rate
 
@@ -215,10 +238,14 @@ class MomentumOptimizer(Optimizer):
     """
     _velocity_acc_str = "velocity"
 
-    def __init__(self, learning_rate, momentum, use_nesterov=False):
+    def __init__(self,
+                 learning_rate,
+                 momentum,
+                 use_nesterov=False,
+                 global_step=None):
         assert learning_rate is not None
         assert momentum is not None
-        super(MomentumOptimizer, self).__init__()
+        super(MomentumOptimizer, self).__init__(global_step)
         self.type = "momentum"
         self._learning_rate = learning_rate
         self._momentum = momentum
@@ -275,10 +302,10 @@ class AdagradOptimizer(Optimizer):
     """
     _moment_acc_str = "moment"
 
-    def __init__(self, learning_rate, epsilon=1.0e-6):
+    def __init__(self, learning_rate, epsilon=1.0e-6, global_step=None):
         assert learning_rate is not None
         assert epsilon is not None
-        super(AdagradOptimizer, self).__init__()
+        super(AdagradOptimizer, self).__init__(global_step)
         self.type = "adagrad"
         self._learning_rate = learning_rate
         self._epsilon = epsilon
@@ -337,12 +364,13 @@ class AdamOptimizer(Optimizer):
                  learning_rate=0.001,
                  beta1=0.9,
                  beta2=0.999,
-                 epsilon=1e-8):
+                 epsilon=1e-8,
+                 global_step=None):
         assert learning_rate is not None
         assert beta1 is not None
         assert beta2 is not None
         assert epsilon is not None
-        super(AdamOptimizer, self).__init__()
+        super(AdamOptimizer, self).__init__(global_step)
         self.type = "adam"
         self._learning_rate = learning_rate
         self._beta1 = beta1
@@ -458,7 +486,8 @@ class AdamaxOptimizer(Optimizer):
                  learning_rate=0.001,
                  beta1=0.9,
                  beta2=0.999,
-                 epsilon=1e-8):
+                 epsilon=1e-8,
+                 global_step=None):
         assert learning_rate is not None
         assert beta1 is not None
         assert beta2 is not None

python/paddle/v2/framework/regularizer.py

 import paddle.v2.framework.framework as framework
 
-__all__ = ['append_regularization_ops', 'L2DecayRegularizer']
+__all__ = [
+    'append_regularization_ops', 'L2DecayRegularizer', 'L1DecayRegularizer'
+]
 
 
 def append_regularization_ops(parameters_and_grads):
@@ -97,3 +99,43 @@ class L2DecayRegularizer(WeightDecayRegularizer):
             attrs={"scale": self._regularization_coeff})
 
         return decay
+
+
+class L1DecayRegularizer(WeightDecayRegularizer):
+    """Implements the L1 Weight Decay Regularization
+    """
+
+    def __init__(self, regularization_coeff=0.0):
+        assert regularization_coeff is not None
+        super(L1DecayRegularizer, self).__init__()
+        self._regularization_coeff = regularization_coeff
+
+    def __call__(self, param, block):
+        """Add L1 weight decay ops to network
+
+        Adds L1 weight decay ops.
+        L1WeightDecay = reg_coeff * sign(parameter)
+
+        Args:
+            param: parameter variable for which regularization is applied
+            block: block in which variable is to be created
+
+        Returns:
+            new variable for weight decay
+        """
+        assert isinstance(param, framework.Parameter)
+        assert isinstance(block, framework.Block)
+        decay = block.create_var(
+            dtype="float32", shape=param.shape, lod_level=param.lod_level)
+        # Append sign op
+        block.append_op(
+            type='sign', inputs={"X": param}, outputs={"Out": decay})
+
+        # Append scale op to the output of sign op
+        block.append_op(
+            type='scale',
+            inputs={"X": decay},
+            outputs={"Out": decay},
+            attrs={"scale": self._regularization_coeff})
+
+        return decay

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

@@ -21,8 +21,26 @@ def get_backward_op(scope, op, no_grad_set):
 
 
 def _reference_training(x, scale, offset, epsilon, data_format):
-    if data_format != "NHWC":
-        raise ValueError("data_format must be NHWC, got %s." % data_format)
+    if data_format == "NCHW":
+        n, c, h, w = x.shape
+        x_square = x * x
+        x_square_sum = np.sum(x_square, (0, 2, 3))
+        x_sum = np.sum(x, axis=(0, 2, 3))
+        element_count = np.size(x) / int(np.shape(x)[1])
+        mean = x_sum / element_count
+        var = x_square_sum / element_count - mean * mean
+        mean_tile = np.reshape(mean, (1, c, 1, 1))
+        mean_tile = np.tile(mean_tile, (n, 1, h, w))
+        var_tile = np.reshape(var, (1, c, 1, 1))
+        var_tile = np.tile(var_tile, (n, 1, h, w))
+        normalized = (x - mean_tile) / np.sqrt(var_tile + epsilon)
+        scale_tile = np.reshape(scale, (1, c, 1, 1))
+        scale_tile = np.tile(scale_tile, (n, 1, h, w))
+        offset_tile = np.reshape(offset, (1, c, 1, 1))
+        offset_tile = np.reshape(offset_tile, (1, c, 1, 1))
+        y = normalized * scale_tile + offset_tile
+        return y, mean, var
+    elif data_format == "NHWC":
         x_square = x * x
         x_square_sum = np.sum(x_square, (0, 1, 2))
         x_sum = np.sum(x, axis=(0, 1, 2))
@@ -31,6 +49,8 @@ def _reference_training(x, scale, offset, epsilon, data_format):
         var = x_square_sum / element_count - mean * mean
         normalized = (x - mean) / np.sqrt(var + epsilon)
         return (normalized * scale + offset), mean, var
+    else:
+        raise ValueError("Unknown data order.")
 
 
 def _reference_grad(x, grad_y, scale, mean, var, epsilon, data_format):
@@ -43,8 +63,13 @@ def _reference_grad(x, grad_y, scale, mean, var, epsilon, data_format):
     # grad_x =
     #   1/N * scale * rsqrt(var + epsilon) * (N * grad_y - sum(grad_y) -
     #   (x - mean) * sum(grad_y * (x - mean)) / (var + epsilon))
-    if data_format != "NHWC":
-        raise ValueError("data_format must be NHWC, got %s." % data_format)
+
+    # transfer from (N, C, H, W) to (N, H, W, C) to simplify computation
+    if data_format == "NCHW":
+        x = np.transpose(x, (0, 2, 3, 1))
+        grad_y = np.transpose(grad_y, (0, 2, 3, 1))
+
+        # raise ValueError("data_format must be NHWC, got %s." % data_format)
     grad_x = scale * (grad_y - np.mean(
         grad_y, axis=(0, 1, 2)) - (x - mean) * np.mean(
             grad_y * (x - mean), axis=(0, 1, 2)) /
@@ -52,6 +77,12 @@ def _reference_grad(x, grad_y, scale, mean, var, epsilon, data_format):
     grad_scale = np.sum(grad_y * (x - mean) / np.sqrt(var + epsilon),
                         axis=(0, 1, 2))
     grad_offset = np.sum(grad_y, axis=(0, 1, 2))
+
+    # transfer back to N, C, H, W
+    if data_format == "NCHW":
+        grad_x = np.transpose(grad_x, (0, 3, 1, 2))
+        x = np.transpose(x, (0, 3, 1, 2))
+        grad_y = np.transpose(grad_y, (0, 3, 1, 2))
     return grad_x, grad_scale, grad_offset
 
 
@@ -65,61 +96,135 @@ def create_or_get_tensor(scope, var_name, var, place):
     return tensor
 
 
-def set_output_grad(scope, outputs, place):
-    def __set_tensor__(name):
+def set_output_grad(scope, outputs, place, feed_dict=None):
+    def __set_tensor__(name, data=None):
         out_tensor = scope.find_var(name).get_tensor()
         grad_tensor = scope.var(grad_var_name(name)).get_tensor()
         out_dtype = out_tensor.dtype()
+        if data is None:
             if out_dtype == core.DataType.FP64:
                 data = np.ones(out_tensor.shape(), dtype=np.float64)
             elif out_dtype == core.DataType.FP32:
                 data = np.ones(out_tensor.shape(), dtype=np.float32)
             else:
                 raise ValueError("Not supported data type " + str(out_dtype))
-
         grad_tensor.set(data, place)
 
     for output in outputs:
-        __set_tensor__(output)
+        data = None
+        if output in feed_dict:
+            data = feed_dict[output]
+        __set_tensor__(output, data)
 
 
 class TestBatchNormOp(OpTest):
     def __assert_close(self, tensor, np_array, msg, atol=1e-4):
         self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
 
+    def test_python(self):
+        data_format = "NHWC"
+        epsilon = 0.00001
+        momentum = 0.9
+
+        # N, H, W, C: 2, 3, 4, 2
+        n, h, w, c = 2, 3, 4, 2
+        x_shape = [n, h, w, c]
+        scale_shape = [c]
+
+        x_val = np.random.random_sample(x_shape).astype(np.float32)
+        scale_val = np.random.random_sample(scale_shape).astype(np.float32)
+        bias_val = np.random.random_sample(scale_shape).astype(np.float32)
+
+        mean = np.zeros(scale_shape).astype(np.float32)
+        variance = np.ones(scale_shape).astype(np.float32)
+
+        # run forward
+        y_out, saved_mean, var_ref = _reference_training(
+            x_val, scale_val, bias_val, epsilon, "NHWC")
+
+        #
+        mean_out = saved_mean * (1. - momentum) + momentum * mean
+        variance_out = var_ref * (1. - momentum) + momentum * variance
+        saved_variance = 1. / np.sqrt(var_ref + epsilon)
+
+        # running N, C, H, W case
+        # should produce the same results
+        x_shape2 = [n, c, h, w]
+        x_val2 = np.transpose(x_val, (0, 3, 1, 2))
+        y_out2, saved_mean2, var_ref2 = _reference_training(
+            x_val2, scale_val, bias_val, epsilon, "NCHW")
+
+        self.__assert_close(saved_mean, saved_mean2, "batch mean")
+        self.__assert_close(var_ref, var_ref2, "batch variance")
+
+        # transfer (N, C, H, W) back to (N, H, W, C)
+        y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
+        self.__assert_close(y_out, y_out2_trans, "batch variance")
+        print 'python: NHWC, NCHW, forward checking passed'
+
+        # test backward now
+        # NHWC
+        self.y_grad = np.random.random_sample(x_shape).astype(np.float32)
+        y_grad = self.y_grad
+        # y_grad = np.ones(x_shape).astype(np.float32)
+        x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_grad(
+            x_val, y_grad, scale_val, saved_mean, var_ref, epsilon, "NHWC")
+
+        # NCHW
+        y_grad2 = np.transpose(y_grad, (0, 3, 1, 2))
+        # y_grad2 = np.ones(x_shape2).astype(np.float32)
+        x_grad_ref2, scale_grad_ref2, bias_grad_ref2 = _reference_grad(
+            x_val2, y_grad2, scale_val, saved_mean2, var_ref2, epsilon, "NCHW")
+
+        self.__assert_close(scale_grad_ref, scale_grad_ref2, "scale gradient")
+        self.__assert_close(bias_grad_ref, bias_grad_ref2, "bias gradient")
+
+        x_grad_transpose = np.transpose(x_grad_ref2, (0, 2, 3, 1))
+        self.__assert_close(x_grad_ref, x_grad_transpose, "x gradient")
+        print 'python: NHWC, NCHW, backward checking passed'
+
     def test_forward_backward(self):
+        def test_with_place(place, tensor_format):
             # attr
-        data_format = "NHWC"
             epsilon = 0.00001
             momentum = 0.9
 
-        channel_num = 2
-        x_shape = [2, 3, 4, channel_num]
-        scale_shape = [channel_num]
+            # N, H, W, C: 12, 3, 4, 2
+            n, h, w, c = 2, 3, 4, 2
+
+            if data_format == "NHWC":
+                x_shape = [n, h, w, c]
+            elif data_format == "NCHW":
+                x_shape = [n, c, h, w]
+            else:
+                raise ValueError("Unknown data type.")
+            scale_shape = [c]
 
-        # input
             x_val = np.random.random_sample(x_shape).astype(np.float32)
             scale_val = np.random.random_sample(scale_shape).astype(np.float32)
             bias_val = np.random.random_sample(scale_shape).astype(np.float32)
 
             mean = np.zeros(scale_shape).astype(np.float32)
-        variance = np.zeros(scale_shape).astype(np.float32)
+            variance = np.ones(scale_shape).astype(np.float32)
 
             # run forward
             y_out, saved_mean, var_ref = _reference_training(
                 x_val, scale_val, bias_val, epsilon, data_format)
 
-        # run backward
-        mean_out = saved_mean * (1 - momentum)
-        variance_out = var_ref * (1 - momentum)
-        saved_variance = 1 / np.sqrt(var_ref + epsilon)
+            # update moving mean and variance
+            mean_out = saved_mean * (1. - momentum) + momentum * mean
+            variance_out = var_ref * (1. - momentum) + momentum * variance
+            saved_variance = 1. / np.sqrt(var_ref + epsilon)
 
             #  for gradient test
-        y_grad = np.ones(x_shape).astype(np.float32)
+            # y_grad = np.ones(x_shape).astype(np.float32)
+            y_grad = np.zeros(x_shape).astype(np.float32)
+            y_grad[0, 0, 0, 0] = 1.
+            # y_grad = np.random.random_sample(x_shape).astype(np.float32)
             x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_grad(
-            x_val, y_grad, scale_val, saved_mean, var_ref, epsilon, data_format)
+                x_val, y_grad, scale_val, saved_mean, var_ref, epsilon,
+                data_format)
 
-        def test_with_place(place):
             scope = core.Scope()
 
             # create input
@@ -157,7 +262,7 @@ class TestBatchNormOp(OpTest):
                 SavedVariance="saved_variance",
                 # attrs
                 is_test=False,
-                tensor_format=data_format,
+                tensor_format=tensor_format,
                 momentum=momentum,
                 epsilon=epsilon)
 
@@ -170,20 +275,21 @@ class TestBatchNormOp(OpTest):
             self.__assert_close(saved_variance_tensor, saved_variance,
                                 "saved_variance")
             self.__assert_close(mean_out_tensor, mean_out, "mean_out")
-            # FIXME(qiao) figure out why with cuDNN variance_out have a higher error rate
             if isinstance(place, core.GPUPlace):
                 atol = 5e-2
             else:
                 atol = 1e-4
             self.__assert_close(variance_out_tensor, variance_out,
                                 "variance_out", atol)
+            print "op test forward passed: ", str(place), tensor_format
 
             # run backward
             batch_norm_op_grad = get_backward_op(scope, batch_norm_op, set())
             set_output_grad(
                 scope,
                 ["y_out", "mean", "variance", "saved_mean", "saved_variance"],
-                place)
+                place,
+                feed_dict={"y_out": y_grad})
             batch_norm_op_grad.run(scope, ctx)
 
             x_grad_tensor = create_or_get_tensor(scope,
@@ -200,12 +306,14 @@ class TestBatchNormOp(OpTest):
             self.__assert_close(x_grad_tensor, x_grad_ref, "x_grad")
             self.__assert_close(scale_grad_tensor, scale_grad_ref, "scale_grad")
             self.__assert_close(bias_grad_tensor, bias_grad_ref, "bias_grad")
+            print "op test backward passed: ", str(place), tensor_format
 
         places = [core.CPUPlace()]
         if core.is_compile_gpu() and core.op_support_gpu("batch_norm"):
             places.append(core.GPUPlace(0))
         for place in places:
-            test_with_place(place)
+            for data_format in ["NCHW", "NHWC"]:
+                test_with_place(place, data_format)
 
 
 if __name__ == '__main__':

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

+import paddle.v2 as paddle
+import paddle.v2.framework.layers as layers
+import paddle.v2.framework.core as core
+import paddle.v2.framework.optimizer as optimizer
+
+from paddle.v2.framework.framework import Program, g_program
+from paddle.v2.framework.io import save_inference_model, load_inference_model
+import paddle.v2.framework.executor as executor
+import unittest
+import numpy as np
+
+
+class TestBook(unittest.TestCase):
+    def test_fit_line_inference_model(self):
+        MODEL_DIR = "./tmp/inference_model"
+
+        init_program = Program()
+        program = Program()
+        x = layers.data(
+            name='x',
+            shape=[2],
+            data_type='float32',
+            program=program,
+            init_program=init_program)
+        y = layers.data(
+            name='y',
+            shape=[1],
+            data_type='float32',
+            program=program,
+            init_program=init_program)
+
+        y_predict = layers.fc(input=x,
+                              size=1,
+                              act=None,
+                              program=program,
+                              init_program=init_program)
+
+        cost = layers.square_error_cost(
+            input=y_predict,
+            label=y,
+            program=program,
+            init_program=init_program)
+        avg_cost = layers.mean(
+            x=cost, program=program, init_program=init_program)
+
+        sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
+        opts = sgd_optimizer.minimize(avg_cost)
+
+        place = core.CPUPlace()
+        exe = executor.Executor(place)
+
+        exe.run(init_program, feed={}, fetch_list=[])
+
+        for i in xrange(100):
+            x_data = np.array(
+                [[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
+            y_data = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
+
+            tensor_x = core.LoDTensor()
+            tensor_x.set(x_data, place)
+            tensor_y = core.LoDTensor()
+            tensor_y.set(y_data, place)
+            exe.run(program,
+                    feed={'x': tensor_x,
+                          'y': tensor_y},
+                    fetch_list=[avg_cost])
+
+        save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
+        outs = exe.run(program,
+                       feed={'x': tensor_x,
+                             'y': tensor_y},
+                       fetch_list=[avg_cost])
+        expected = np.array(outs[0])
+
+        reload(executor)  # reload to build a new scope
+        exe = executor.Executor(place)
+
+        [infer_prog, feed_var_names, fetch_vars] = load_inference_model(
+            MODEL_DIR, exe)
+
+        outs = exe.run(
+            infer_prog,
+            feed={feed_var_names[0]: tensor_x,
+                  feed_var_names[1]: tensor_y},
+            fetch_list=fetch_vars)
+        actual = np.array(outs[0])
+
+        self.assertEqual(feed_var_names, ["x", "y"])
+        self.assertEqual(len(fetch_vars), 1)
+        self.assertEqual(str(fetch_vars[0]), str(avg_cost))
+        self.assertEqual(expected, actual)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-import numpy
-import paddle.v2 as paddle
-exit(
-    0
-)  # FIXME(yuyang18): InferShape has been removed, this unittest should be changed until compile time is ready
-
-BATCH_SIZE = 100
-
-scope = core.Scope()
-place = core.CPUPlace()
-# if you want to test GPU training, you can use gpu place
-# place = core.GPUPlace(0)
-dev_ctx = core.DeviceContext.create(place)
-
-init_net = core.Net.create()
-forward_net = core.Net.create()
-backward_net = None
-optimize_net = core.Net.create()
-
-
-def atomic_id():
-    id = 0
-    while True:
-        yield id
-        id += 1
-
-
-uniq_id = atomic_id().next
-
-
-def data_layer(name, dims):
-    var = scope.var(name)
-    tensor = var.get_tensor()
-    tensor.set_dims(dims)  # 1 is batch size holder.
-    return name
-
-
-def feed_data(name, data):
-    assert isinstance(data, numpy.ndarray)
-    tensor = scope.find_var(name).get_tensor()
-    tensor.set_dims(data.shape)
-    if data.dtype == numpy.dtype("int32"):
-        tensor.alloc_int(place)
-    elif data.dtype == numpy.dtype("float32"):
-        tensor.alloc_float(place)
-    else:
-        raise ValueError("data type not supported")
-    tensor.set(data, place)
-
-
-def grad_var_name(var_name):
-    return var_name + "@GRAD"
-
-
-def sgd_optimizer(net, param_name, learning_rate=0.005):
-    grad_name = grad_var_name(param_name)
-    optimize_op = Operator(
-        "sgd",
-        param=param_name,
-        grad=grad_name,
-        param_out=param_name,
-        learning_rate=learning_rate)
-    net.append_op(optimize_op)
-
-
-# should use operator and add these to the init_network
-def init_param(net, param_name, dims):
-    scope.var(param_name)
-    op = Operator(
-        "uniform_random", Out=param_name, dims=dims, min=-0.5, max=0.5, seed=10)
-    op.infer_shape(scope)
-    net.append_op(op)
-
-
-# fc_layer
-def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
-    """
-    The fully connected layer.
-
-    :param input: The name of input variable.
-    :type input: str
-    :param size: The size of fully connected layer.
-    :param act: The name of activation.
-    :param param: The attribute of learnable parameter which can be used to
-                  modify initialization mean and std of the parameter.
-    :param bias: The attribute of bias. If set False, this layer does not have
-                 a bias.
-    :param name: The name of this layer. If it is not set explictly, a name
-                 will be generated automatically.
-    :return: The name of the output variable.
-    """
-
-    if name is None:
-        name = "fc_%d" % uniq_id()
-    if not isinstance(name, str):
-        raise ValueError("The name of a layer should be a string.")
-
-    input_dims = scope.find_var(input).get_tensor().get_dims()
-
-    w_name = param or name + ".w"
-    init_param(net=init_net, param_name=w_name, dims=[input_dims[1], size])
-    sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01)
-
-    pre_activation = name + ".mul.out"
-    scope.var(pre_activation)
-    mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation)
-    net.append_op(mul_op)
-
-    # create bias variable if needed
-    if bias:
-        bias_name = name + ".b"
-        init_param(net=init_net, param_name=bias_name, dims=[size])
-        sgd_optimizer(
-            net=optimize_net, param_name=bias_name, learning_rate=0.001)
-        bias_out = name + ".rowwise_add.out"
-        scope.var(bias_out)
-        rowwise_append_op = Operator(
-            "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out)
-        net.append_op(rowwise_append_op)
-        pre_activation = bias_out
-
-    activation_op = Operator(act, X=pre_activation, Y=name)
-    net.append_op(activation_op)
-    scope.var(name)
-    net.infer_shape(scope)
-    return name
-
-
-def cross_entropy_layer(net, input, label):
-    cost_name = "cross_entropy_%d" % uniq_id()
-    cross_entropy_op = Operator(
-        "cross_entropy", X=input, Label=label, Y=cost_name)
-    net.append_op(cross_entropy_op)
-    scope.var(cost_name)
-    net.infer_shape(scope)
-    return cost_name
-
-
-def create_backward_net(forward_net):
-    net = core.Operator.backward(forward_net, set())
-    for input in net.inputs()["all"]:
-        var = scope.var(input)
-        var.get_tensor()
-    for output in net.outputs()["all"]:
-        var = scope.var(output)
-        var.get_tensor()
-    return net
-
-
-def debug_print_op(op):
-    print("===============" + op.type() + "==============")
-    print("***inputs:***")
-    for input in op.inputs()["all"]:
-        print input, scope.find_var(input).get_tensor().get_dims()
-    print("\n***outputs:***")
-    for output in op.outputs()["all"]:
-        print output, scope.find_var(output).get_tensor().get_dims()
-    print("")
-    print("")
-
-
-def set_cost(cost):
-    cost_shape = numpy.array(scope.find_var(cost).get_tensor()).shape
-    cost_grad = \
-        scope.find_var(grad_var_name(cost)).get_tensor()
-    cost_grad.set_dims(cost_shape)
-    cost_grad.alloc_float(place)
-    cost_grad.set(numpy.ones(cost_shape).astype("float32"), place)
-
-
-def get_cost_mean(cost):
-    cost_data = numpy.array(scope.find_var(cost).get_tensor())
-    return cost_data.sum() / len(cost_data)
-
-
-def error_rate(predict, label):
-    predict_var = numpy.array(scope.find_var(predict).get_tensor()).argmax(
-        axis=1)
-    label = numpy.array(scope.find_var(label).get_tensor())
-    error_num = numpy.sum(predict_var != label)
-    return error_num / float(len(label))
-
-
-images = data_layer(name="pixel", dims=[BATCH_SIZE, 784])
-labels = data_layer(name="label", dims=[BATCH_SIZE, 1])
-fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
-fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
-predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax")
-cost = cross_entropy_layer(net=forward_net, input=predict, label=labels)
-
-init_net.complete_add_op(True)
-forward_net.complete_add_op(True)
-backward_net = create_backward_net(forward_net)
-optimize_net.complete_add_op(True)
-
-print(init_net)
-print(forward_net)
-print(backward_net)
-print(optimize_net)
-
-debug_print_op(forward_net)
-debug_print_op(backward_net)
-debug_print_op(optimize_net)
-
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.mnist.train(), buf_size=8192),
-    batch_size=BATCH_SIZE)
-
-
-def test(cost_name):
-    test_reader = paddle.batch(
-        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
-    cost = []
-    error = []
-    for data in test_reader():
-        image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
-        label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
-        label_data = numpy.expand_dims(label_data, axis=1)
-        feed_data(images, image_data)
-        feed_data(labels, label_data)
-
-        forward_net.infer_shape(scope)
-        forward_net.run(scope, dev_ctx)
-        cost.append(get_cost_mean(cost_name))
-        error.append(error_rate(predict, "label"))
-    print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str(
-        sum(error) / float(len(error))))
-
-
-PASS_NUM = 1
-
-init_net.run(scope, dev_ctx)
-for pass_id in range(PASS_NUM):
-    batch_id = 0
-
-    for data in train_reader():
-        image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
-        label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
-        label_data = numpy.expand_dims(label_data, axis=1)
-        feed_data(images, image_data)
-        feed_data(labels, label_data)
-
-        forward_net.infer_shape(scope)
-        forward_net.run(scope, dev_ctx)
-        set_cost(cost)
-        backward_net.infer_shape(scope)
-        backward_net.run(scope, dev_ctx)
-
-        optimize_net.run(scope, dev_ctx)
-        if batch_id % 100 == 0:
-            print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]")
-            test(cost)
-
-        batch_id = batch_id + 1

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

 import unittest
-from paddle.v2.framework.framework import Variable, g_program
+from paddle.v2.framework.framework import Variable, Program, g_program
 import paddle.v2.framework.core as core
 
 
@@ -21,7 +21,8 @@ class TestOperator(unittest.TestCase):
                              "Operator \"no_such_op\" has not been registered.")
 
     def test_op_desc_creation(self):
-        block = g_program.current_block()
+        program = Program()
+        block = program.current_block()
         mul_x = block.create_var(
             dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
         mul_y = block.create_var(
@@ -50,10 +51,12 @@ class TestOperator(unittest.TestCase):
         self.assertEqual(mul_op.has_attr("y_num_col_dims"), True)
         self.assertEqual(mul_op.attr_type("y_num_col_dims"), core.AttrType.INT)
         self.assertEqual(mul_op.attr("y_num_col_dims"), 1)
+        self.assertEqual(mul_op.idx, 0)
         self.assertEqual(mul_out.op, mul_op)
 
     def test_mult_input(self):
-        block = g_program.current_block()
+        program = Program()
+        block = program.current_block()
         sum_x1 = block.create_var(
             dtype="int", shape=[3, 4], lod_level=0, name="sum.x1")
         sum_x2 = block.create_var(
@@ -71,6 +74,7 @@ class TestOperator(unittest.TestCase):
         self.assertEqual(sum_op.input("X"), ["sum.x1", "sum.x2", "sum.x3"])
         self.assertEqual(sum_op.output_names, ["Out"])
         self.assertEqual(sum_op.output("Out"), ["sum.out"])
+        self.assertEqual(sum_op.idx, 0)
         self.assertEqual(sum_out.op, sum_op)
 
 

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

@@ -27,6 +27,32 @@ class TestOptimizer(unittest.TestCase):
         sgd_op = opts[0]
         self.assertEqual(sgd_op.type, "sgd")
 
+    def test_sgd_optimizer_with_global_step(self):
+        program = framework.Program()
+        block = program.global_block()
+        mul_x = block.create_parameter(
+            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        block.append_op(
+            type="mul",
+            inputs={"X": mul_x,
+                    "Y": mul_y},
+            outputs={"Out": mul_out},
+            attrs={"x_num_col_dims": 1})
+        global_step = block.create_var(
+            dtype="float32", shape=[1], lod_level=0, name="step")
+        sgd_optimizer = optimizer.SGDOptimizer(
+            learning_rate=0.01, global_step=global_step)
+        opts = sgd_optimizer.minimize(mul_out)
+        self.assertEqual(len(opts), 2)
+        sgd_op = opts[0]
+        self.assertEqual(sgd_op.type, "sgd")
+        increment_op = opts[1]
+        self.assertEqual(increment_op.type, "increment")
+
 
 class TestMomentumOptimizer(unittest.TestCase):
     class MockMomentum(optimizer.MomentumOptimizer):

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

@@ -99,6 +99,8 @@ class TestProgram(unittest.TestCase):
             outputs={"Out": add_out},
             attrs={"x_num_col_dims": 1})
 
+        self.assertEqual(mul_op.idx, 0)
+        self.assertEqual(add_op.idx, 1)
         param_to_grad = prog.append_backward(add_out, set())
 
         def grad_name(name):

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

@@ -5,9 +5,11 @@ import paddle.v2.framework.optimizer as optimizer
 
 from paddle.v2.framework.framework import Program, g_program
 from paddle.v2.framework.executor import Executor
+from paddle.v2.framework.regularizer import L2DecayRegularizer
 
 import numpy as np
 
+BATCH_SIZE = 128
 init_program = Program()
 program = Program()
 image = layers.data(
@@ -17,22 +19,35 @@ image = layers.data(
     program=program,
     init_program=init_program)
 
+param_attr = {
+    'name': None,
+    'init_attr': {
+        'type': 'uniform_random',
+        'min': -1.0,
+        'max': 1.0
+    },
+    'regularization': L2DecayRegularizer(0.0005 * BATCH_SIZE)
+}
+
 hidden1 = layers.fc(input=image,
                     size=128,
                     act='relu',
                     program=program,
-                    init_program=init_program)
+                    init_program=init_program,
+                    param_attr=param_attr)
 hidden2 = layers.fc(input=hidden1,
                     size=64,
                     act='relu',
                     program=program,
-                    init_program=init_program)
+                    init_program=init_program,
+                    param_attr=param_attr)
 
 predict = layers.fc(input=hidden2,
                     size=10,
                     act='softmax',
                     program=program,
-                    init_program=init_program)
+                    init_program=init_program,
+                    param_attr=param_attr)
 
 label = layers.data(
     name='y',
@@ -48,8 +63,6 @@ avg_cost = layers.mean(x=cost, program=program, init_program=init_program)
 sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
 opts = sgd_optimizer.minimize(avg_cost)
 
-BATCH_SIZE = 128
-
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.mnist.train(), buf_size=8192),

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

@@ -39,5 +39,39 @@ class TestL2DecayRegularizer(unittest.TestCase):
         self.assertEqual(block.ops[-2].type, 'scale')
 
 
+class TestL1DecayRegularizer(unittest.TestCase):
+    def test_l2decay_regularizer(self):
+        program = framework.Program()
+        block = program.global_block()
+        mul_x = block.create_parameter(
+            dtype="float32",
+            shape=[5, 10],
+            lod_level=0,
+            name="mul.x",
+            regularizer=regularizer.L1DecayRegularizer(0.5))
+        self.assertTrue(mul_x.regularizer is not None)
+        self.assertTrue(
+            isinstance(mul_x.regularizer, regularizer.L1DecayRegularizer))
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        block.append_op(
+            type="mul",
+            inputs={"X": mul_x,
+                    "Y": mul_y},
+            outputs={"Out": mul_out},
+            attrs={"x_num_col_dims": 1})
+        params_grads = append_backward_ops(mul_out)
+        self.assertEqual(len(params_grads), 1)
+        count_ops = len(block.ops)
+        params_grads = optimizer.append_regularization_ops(params_grads)
+        self.assertEqual(len(params_grads), 1)
+        self.assertEqual(len(block.ops), count_ops + 3)
+        self.assertEqual(block.ops[-1].type, 'elementwise_add')
+        self.assertEqual(block.ops[-2].type, 'scale')
+        self.assertEqual(block.ops[-3].type, 'sign')
+
+
 if __name__ == '__main__':
     unittest.main()

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestSignOp(OpTest):
+    def setUp(self):
+        self.op_type = "sign"
+        self.inputs = {
+            'X': np.random.uniform(-10, 10, (10, 10)).astype("float32")
+        }
+        self.outputs = {'Out': np.sign(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+if __name__ == "__main__":
+    unittest.main()