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

doc/design/prune.md

+# Prune
+
+## Motivation
+
+We want to support running inference, training and checkpointing in one `ProgramDesc`. We implement 
+`void Prune(const ProgramDesc* input, ProgramDesc* output)` function, which takes a `ProgramDesc`
+and generate a pruned `ProgramDesc`.
+
+## Challenge
+
+Pruning need to support both variables and operators being evaluation targets. Consider the following
+different situations.
+
+```python
+# Case 1: run foward pass.
+cost_np = session.run(target=cost)
+# Case 2: run backward passing.
+opts_np, _ = session.run(target=[cost, opt])
+# Case 3: run checkpointing
+_ = session.run(target=checkpoint)
+```
+
+## Solution
+
+To support evaluation of operators, we add `is_target` field in the `OpDesc`.
+
+```c++
+message OpDesc {
+  required string type = 3;
+  repeated Var inputs = 1;
+  repeated Var outputs = 2;
+  repeated Attr attrs = 4;
+  optional bool is_target = 5 [ default = false ];
+};
+```
+
+To support evaluation of variables, we add [fetch_op](https://github.com/PaddlePaddle/Paddle/pull/4599).
+For each variable in the `target`, we insert a `fetch_op` into the `ProgramDesc` with `variable` being
+`fetch_op`'s input. Then we also set `fetch_op` is a target.
+
+### Algorithm
+
+If an operator needs to be run, it must fall into one of the following cases:
+
+1. It is the target.
+2. It is depended by some other ops, meaning its output is some other op's input.
+
+The first case can be checked by `op_desc.is_traget()` . The second case can be implement as
+
+```c++
+bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {
+  for (auto& var : op_desc.outputs()) {
+    for (auto& argu : var.arguments()) {
+      if (dependent_vars.count(argu) != 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+```
+
+Then the whole algorithm can be implemented as the following [code](https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc).

doc/design/refactorization.md

@@ -177,9 +177,6 @@ REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class)
 REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
 ```
 
-### USE Macros
-Make sure the registration process is executed and linked.
-
 ---
 # Registration Process
 1. Write an Op class and its gradient Op class, if required.
@@ -188,8 +185,6 @@ Make sure the registration process is executed and linked.
 	1. Call maker class to complete `proto` and `checker`
 	2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap`
 
-4. Invoke the `USE` macro in which the Op is used to make sure that it is linked.
-
 ---
 # Backward Module (1/2)
 ### Create Backward Operator

doc/howto/usage/cluster/src/word2vec/api_train_v2.py

+import gzip
+import math
+
+import paddle.v2 as paddle
+
+embsize = 32
+hiddensize = 256
+N = 5
+
+
+def wordemb(inlayer):
+    wordemb = paddle.layer.embedding(
+        input=inlayer,
+        size=embsize,
+        param_attr=paddle.attr.Param(
+            name="_proj",
+            initial_std=0.001,
+            learning_rate=1,
+            l2_rate=0,
+            sparse_update=True))
+    return wordemb
+
+
+def main():
+    # for local training
+    cluster_train = False
+
+    if not cluster_train:
+        paddle.init(use_gpu=False, trainer_count=1)
+    else:
+        paddle.init(
+            use_gpu=False,
+            trainer_count=2,
+            port=7164,
+            ports_num=1,
+            ports_num_for_sparse=1,
+            num_gradient_servers=1)
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+    firstword = paddle.layer.data(
+        name="firstw", type=paddle.data_type.integer_value(dict_size))
+    secondword = paddle.layer.data(
+        name="secondw", type=paddle.data_type.integer_value(dict_size))
+    thirdword = paddle.layer.data(
+        name="thirdw", type=paddle.data_type.integer_value(dict_size))
+    fourthword = paddle.layer.data(
+        name="fourthw", type=paddle.data_type.integer_value(dict_size))
+    nextword = paddle.layer.data(
+        name="fifthw", type=paddle.data_type.integer_value(dict_size))
+
+    Efirst = wordemb(firstword)
+    Esecond = wordemb(secondword)
+    Ethird = wordemb(thirdword)
+    Efourth = wordemb(fourthword)
+
+    contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
+    hidden1 = paddle.layer.fc(input=contextemb,
+                              size=hiddensize,
+                              act=paddle.activation.Sigmoid(),
+                              layer_attr=paddle.attr.Extra(drop_rate=0.5),
+                              bias_attr=paddle.attr.Param(learning_rate=2),
+                              param_attr=paddle.attr.Param(
+                                  initial_std=1. / math.sqrt(embsize * 8),
+                                  learning_rate=1))
+    predictword = paddle.layer.fc(input=hidden1,
+                                  size=dict_size,
+                                  bias_attr=paddle.attr.Param(learning_rate=2),
+                                  act=paddle.activation.Softmax())
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                with gzip.open("batch-" + str(event.batch_id) + ".tar.gz",
+                               'w') as f:
+                    trainer.save_parameter_to_tar(f)
+                result = trainer.test(
+                    paddle.batch(
+                        paddle.dataset.imikolov.test(word_dict, N), 32))
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
+    cost = paddle.layer.classification_cost(input=predictword, label=nextword)
+
+    parameters = paddle.parameters.create(cost)
+    adagrad = paddle.optimizer.AdaGrad(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+    trainer = paddle.trainer.SGD(cost,
+                                 parameters,
+                                 adagrad,
+                                 is_local=not cluster_train)
+    trainer.train(
+        paddle.batch(paddle.dataset.imikolov.train(word_dict, N), 32),
+        num_passes=30,
+        event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    main()

doc/howto/usage/cluster/src/word2vec/api_train_v2_cluster.py

+import math
+import os
+import paddle.v2 as paddle
+import pickle
+
+embsize = 32
+hiddensize = 256
+N = 5
+cluster_train_file = "./train_data_dir/train/train.txt"
+cluster_test_file = "./test_data_dir/test/test.txt"
+node_id = os.getenv("OMPI_COMM_WORLD_RANK")
+if not node_id:
+    raise EnvironmentError("must provied OMPI_COMM_WORLD_RANK")
+
+
+def wordemb(inlayer):
+    wordemb = paddle.layer.embedding(
+        input=inlayer,
+        size=embsize,
+        param_attr=paddle.attr.Param(
+            name="_proj",
+            initial_std=0.001,
+            learning_rate=1,
+            l2_rate=0,
+            sparse_update=True))
+    return wordemb
+
+
+def cluster_reader_cluster(filename, node_id):
+    def cluster_reader():
+        with open("-".join([filename, "%05d" % int(node_id)]), "r") as f:
+            for l in f:
+                csv_data = [int(cell) for cell in l.split(",")]
+                yield tuple(csv_data)
+
+    return cluster_reader
+
+
+def main():
+    # get arguments from env
+
+    # for local training
+    TRUTH = ["true", "True", "TRUE", "1", "yes", "Yes", "YES"]
+    cluster_train = os.getenv('PADDLE_CLUSTER_TRAIN', "False") in TRUTH
+    use_gpu = os.getenv('PADDLE_INIT_USE_GPU', "False")
+
+    if not cluster_train:
+        paddle.init(
+            use_gpu=use_gpu,
+            trainer_count=int(os.getenv("PADDLE_INIT_TRAINER_COUNT", "1")))
+    else:
+        paddle.init(
+            use_gpu=use_gpu,
+            trainer_count=int(os.getenv("PADDLE_INIT_TRAINER_COUNT", "1")),
+            port=int(os.getenv("PADDLE_INIT_PORT", "7164")),
+            ports_num=int(os.getenv("PADDLE_INIT_PORTS_NUM", "1")),
+            ports_num_for_sparse=int(
+                os.getenv("PADDLE_INIT_PORTS_NUM_FOR_SPARSE", "1")),
+            num_gradient_servers=int(
+                os.getenv("PADDLE_INIT_NUM_GRADIENT_SERVERS", "1")),
+            trainer_id=int(os.getenv("PADDLE_INIT_TRAINER_ID", "0")),
+            pservers=os.getenv("PADDLE_INIT_PSERVERS", "127.0.0.1"))
+    fn = open("thirdparty/wuyi_train_thdpty/word_dict.pickle", "r")
+    word_dict = pickle.load(fn)
+    fn.close()
+    dict_size = len(word_dict)
+    firstword = paddle.layer.data(
+        name="firstw", type=paddle.data_type.integer_value(dict_size))
+    secondword = paddle.layer.data(
+        name="secondw", type=paddle.data_type.integer_value(dict_size))
+    thirdword = paddle.layer.data(
+        name="thirdw", type=paddle.data_type.integer_value(dict_size))
+    fourthword = paddle.layer.data(
+        name="fourthw", type=paddle.data_type.integer_value(dict_size))
+    nextword = paddle.layer.data(
+        name="fifthw", type=paddle.data_type.integer_value(dict_size))
+
+    Efirst = wordemb(firstword)
+    Esecond = wordemb(secondword)
+    Ethird = wordemb(thirdword)
+    Efourth = wordemb(fourthword)
+
+    contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
+    hidden1 = paddle.layer.fc(input=contextemb,
+                              size=hiddensize,
+                              act=paddle.activation.Sigmoid(),
+                              layer_attr=paddle.attr.Extra(drop_rate=0.5),
+                              bias_attr=paddle.attr.Param(learning_rate=2),
+                              param_attr=paddle.attr.Param(
+                                  initial_std=1. / math.sqrt(embsize * 8),
+                                  learning_rate=1))
+    predictword = paddle.layer.fc(input=hidden1,
+                                  size=dict_size,
+                                  bias_attr=paddle.attr.Param(learning_rate=2),
+                                  act=paddle.activation.Softmax())
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                result = trainer.test(
+                    paddle.batch(
+                        cluster_reader_cluster(cluster_test_file, node_id), 32))
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
+    cost = paddle.layer.classification_cost(input=predictword, label=nextword)
+    parameters = paddle.parameters.create(cost)
+    adagrad = paddle.optimizer.AdaGrad(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+    trainer = paddle.trainer.SGD(cost,
+                                 parameters,
+                                 adagrad,
+                                 is_local=not cluster_train)
+    trainer.train(
+        paddle.batch(cluster_reader_cluster(cluster_train_file, node_id), 32),
+        num_passes=30,
+        event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    main()

doc/howto/usage/cluster/src/word2vec/prepare.py

+import paddle.v2 as paddle
+import tarfile
+import os
+import pickle
+
+SPLIT_COUNT = 3
+N = 5
+
+
+def file_len(fd):
+    for i, l in enumerate(fd):
+        pass
+    return i + 1
+
+
+def split_from_reader_by_line(filename, reader, split_count):
+    fn = open(filename, "w")
+    for batch_id, batch_data in enumerate(reader()):
+        batch_data_str = [str(d) for d in batch_data]
+        fn.write(",".join(batch_data_str))
+        fn.write("\n")
+    fn.close()
+
+    fn = open(filename, "r")
+    total_line_count = file_len(fn)
+    fn.close()
+    per_file_lines = total_line_count / split_count + 1
+    cmd = "split -d -a 5 -l %d %s %s-" % (per_file_lines, filename, filename)
+    os.system(cmd)
+
+
+word_dict = paddle.dataset.imikolov.build_dict()
+with open("word_dict.pickle", "w") as dict_f:
+    pickle.dump(word_dict, dict_f)
+
+split_from_reader_by_line("train.txt",
+                          paddle.dataset.imikolov.train(word_dict, N),
+                          SPLIT_COUNT)
+split_from_reader_by_line("test.txt",
+                          paddle.dataset.imikolov.test(word_dict, N),
+                          SPLIT_COUNT)

paddle/framework/CMakeLists.txt

@@ -20,13 +20,14 @@ proto_library(framework_proto SRCS framework.proto)
 
 cc_library(attribute SRCS attribute.cc DEPS framework_proto)
 cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim op_info)
+cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc)
 cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
 cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
-cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc)
+cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc glog)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
-cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator)
+cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog)
 cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
 
 py_proto_compile(framework_py_proto SRCS framework.proto)
@@ -42,7 +43,10 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
 cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
 
-cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
+cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward glog)
+
+cc_library(prune SRCS prune.cc DEPS framework_proto)
+cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
 
 cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
 cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)

paddle/framework/block_desc.cc

@@ -107,6 +107,19 @@ BlockDesc *BlockDescBind::Proto() {
   Flush();
   return desc_;
 }
+BlockDescBind::BlockDescBind(const BlockDescBind &other, BlockDesc *desc,
+                             ProgramDescBind *prog)
+    : prog_(prog), desc_(desc) {
+  need_update_ = true;
+  for (auto &op : other.ops_) {
+    ops_.emplace_back(new OpDescBind(*op));
+  }
+
+  for (auto &it : other.vars_) {
+    auto *var = new VarDescBind(*it.second);
+    vars_[it.first].reset(var);
+  }
+}
 
 void BlockDescBind::ClearPBOps() {
   auto ops = this->desc_->mutable_ops();

paddle/framework/block_desc.h

@@ -16,8 +16,10 @@ limitations under the License. */
 
 #include <deque>
 #include <memory>
+#include <set>
 #include <unordered_map>
 #include <vector>
+
 #include "paddle/framework/op_desc.h"
 #include "paddle/framework/var_desc.h"
 #include "paddle/platform/macros.h"
@@ -36,6 +38,9 @@ class BlockDescBind {
   BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
       : prog_(prog), desc_(desc), need_update_(false) {}
 
+  BlockDescBind(const BlockDescBind &other, BlockDesc *desc,
+                ProgramDescBind *prog);
+
   ~BlockDescBind() {
     this->ClearPBVars();
     this->ClearPBOps();
@@ -51,6 +56,14 @@ class BlockDescBind {
 
   bool HasVar(const std::string &var_name) const;
 
+  std::set<std::string> LocalVarNames() const {
+    std::set<std::string> var_names;
+    for (auto &var : vars_) {
+      var_names.insert(var.first);
+    }
+    return var_names;
+  }
+
   std::vector<VarDescBind *> AllVars() const;
 
   BlockDescBind *ParentBlock() const;

paddle/framework/executor.cc

@@ -68,9 +68,13 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
 
   for (auto& var : block.vars()) {
     if (var.persistable()) {
-      scope->Var(var.name());
+      auto* ptr = scope->Var(var.name());
+      VLOG(3) << "Create Variable " << var.name()
+              << " global, which pointer is " << ptr;
     } else {
-      local_scope.Var(var.name());
+      auto* ptr = local_scope.Var(var.name());
+      VLOG(3) << "Create Variable " << var.name()
+              << " locally, which pointer is " << ptr;
     }
   }
 

paddle/framework/feed_fetch_method.h

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include "glog/logging.h"
+#include "paddle/framework/feed_fetch_type.h"
 #include "paddle/framework/scope.h"
 #include "paddle/framework/variable.h"
 
@@ -24,6 +26,7 @@ void SetFeedVariable(const LoDTensor& input, const std::string& var_name,
                      size_t index) {
   // If var_name Variable is not found in GlobalScope, a new variable will
   // be created.
+  VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
   Variable* g_feed_value = GetGlobalScope().Var(var_name);
   auto& feed_inputs =
       *(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
@@ -40,10 +43,15 @@ LoDTensor& GetFetchVariable(const std::string& var_name, size_t index) {
   // Since we want to fetch LodTensor from a variable, the variable must
   // be created alreadly.
   Variable* g_fetch_value = GetGlobalScope().FindVar(var_name);
-  auto& fetch_outputs =
-      *(g_fetch_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
+  PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
+                 "Only %s can be invoked by GetFetchVariable",
+                 typeid(FeedFetchList).name());
+  auto& fetch_outputs = *g_fetch_value->GetMutable<FeedFetchList>();
+  auto& tensor = fetch_outputs[index];
+  VLOG(3) << "Fetch " << var_name << " with index " << index
+          << " shape= " << tensor.dims();
   PADDLE_ENFORCE_LT(index, fetch_outputs.size());
-  return fetch_outputs[index];
+  return tensor;
 }
 
 }  // namespace framework

paddle/framework/framework.proto

@@ -55,6 +55,7 @@ message OpDesc {
   repeated Var inputs = 1;
   repeated Var outputs = 2;
   repeated Attr attrs = 4;
+  optional bool is_target = 5 [ default = false ];
 };
 
 // OpProto describes a C++ framework::OperatorBase derived class.
@@ -111,6 +112,8 @@ message VarDesc {
   enum VarType {
     LOD_TENSOR = 1;
     SELECTED_ROWS = 2;
+    FEED_MINIBATCH = 3;
+    FETCH_LIST = 4;
   }
   required string name = 1;
   required VarType type = 2;

paddle/framework/lod_tensor.h

@@ -74,12 +74,12 @@ class LoDTensor : public Tensor {
   LoD lod() const { return lod_; }
 
   /*
-   * Get a element from LoD.
+   * Get the start offset and end offset of an  element from LoD.
    */
-  size_t lod_element(size_t level, size_t elem) const {
+  std::pair<size_t, size_t> lod_element(size_t level, size_t elem) const {
     PADDLE_ENFORCE_LT(level, NumLevels());
     PADDLE_ENFORCE_LT(elem, NumElements(level));
-    return (lod_)[level][elem];
+    return std::make_pair((lod_)[level][elem], (lod_)[level][elem + 1]);
   }
 
   /*

paddle/framework/lod_tensor_test.cu

@@ -36,8 +36,8 @@ TEST(LoDTensor, LoDInGPU) {
   lod_tensor.mutable_data<float>(place);
 
   lod_tensor.set_lod(src_lod);
-  CHECK_EQ(lod_tensor.lod_element(0, 2), 4UL);
-  CHECK_EQ(lod_tensor.lod_element(0, 4), 8UL);
+  CHECK_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
+  CHECK_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
 
   auto lod = lod_tensor.lod();
 

paddle/framework/op_registry.h

@@ -20,6 +20,8 @@ limitations under the License. */
 #include <typeinfo>
 #include <unordered_map>
 #include <unordered_set>
+
+#include "glog/logging.h"  // For VLOG()
 #include "paddle/framework/attribute.h"
 #include "paddle/framework/details/op_registry.h"
 #include "paddle/framework/framework.pb.h"

paddle/framework/operator.h

@@ -20,12 +20,13 @@ limitations under the License. */
 #include <unordered_map>
 #include <vector>
 
-#include "op_info.h"
+#include "glog/logging.h"  // For VLOG
 #include "paddle/framework/attribute.h"
 #include "paddle/framework/block_desc.h"
 #include "paddle/framework/data_type.h"
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/op_info.h"
 #include "paddle/framework/scope.h"
 #include "paddle/framework/shape_inference.h"
 #include "paddle/framework/tensor.h"
@@ -573,6 +574,7 @@ class OperatorWithKernel : public OperatorBase {
 
   void Run(const Scope& scope,
            const platform::DeviceContext& dev_ctx) const final {
+    VLOG(3) << "Running operator " << this->Type();
     RuntimeInferShapeContext infer_shape_ctx(*this, scope);
     this->InferShape(&infer_shape_ctx);
 

paddle/framework/program_desc.cc

@@ -39,5 +39,14 @@ ProgramDescBind::ProgramDescBind() {
   block->set_parent_idx(-1);
   blocks_.emplace_back(new BlockDescBind(this, block));
 }
+
+ProgramDescBind::ProgramDescBind(const ProgramDescBind &o) {
+  prog_ = o.prog_;
+
+  for (int i = 0; i < prog_.blocks_size(); ++i) {
+    auto *block = prog_.mutable_blocks(i);
+    blocks_.emplace_back(new BlockDescBind(*o.blocks_[i], block, this));
+  }
+}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/program_desc.h

@@ -28,6 +28,8 @@ class ProgramDescBind {
  public:
   ProgramDescBind();
 
+  ProgramDescBind(const ProgramDescBind &o);
+
   BlockDescBind *AppendBlock(const BlockDescBind &parent);
 
   BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); }
@@ -40,8 +42,6 @@ class ProgramDescBind {
   ProgramDesc prog_;
 
   std::vector<std::unique_ptr<BlockDescBind>> blocks_;
-
-  DISABLE_COPY_AND_ASSIGN(ProgramDescBind);
 };
 }  // namespace framework
 }  // namespace paddle

paddle/framework/program_desc_test.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/framework/program_desc.h"
+#include "gtest/gtest.h"
+#include "paddle/framework/block_desc.h"
+
+namespace paddle {
+namespace framework {
+TEST(ProgramDesc, copy_ctor) {
+  ProgramDescBind program;
+  auto* global_block = program.Block(0);
+  auto* x = global_block->Var("X");
+  x->SetType(VarDesc_VarType_LOD_TENSOR);
+  x->SetLoDLevel(0);
+  x->SetDataType(FP32);
+  x->SetShape({1000, 784});
+
+  auto* y = global_block->Var("Y");
+  y->SetType(VarDesc_VarType_LOD_TENSOR);
+  y->SetLoDLevel(0);
+  y->SetDataType(FP32);
+  y->SetShape({784, 100});
+
+  auto* op = global_block->AppendOp();
+  op->SetType("mul");
+  op->SetInput("X", {x->Name()});
+  op->SetInput("Y", {y->Name()});
+
+  auto* out = global_block->Var("Out");
+  out->SetType(VarDesc_VarType_LOD_TENSOR);
+  op->SetOutput("Y", {out->Name()});
+
+  ProgramDescBind program_copy(program);
+
+  auto* global_block_copy = program_copy.Block(0);
+  ASSERT_NE(global_block, global_block_copy);
+
+  auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
+    ASSERT_TRUE(global_block_copy->HasVar(name));
+    auto* copy = global_block_copy->Var(name);
+    ASSERT_NE(copy, var_before);
+    ASSERT_EQ(copy->Name(), var_before->Name());
+    ASSERT_EQ(copy->GetType(), var_before->GetType());
+    ASSERT_EQ(copy->Shape(), var_before->Shape());
+    ASSERT_EQ(copy->Proto()->SerializeAsString(),
+              var_before->Proto()->SerializeAsString());
+  };
+
+  ASSERT_EQ(global_block->LocalVarNames(), global_block_copy->LocalVarNames());
+  ASSERT_EQ(3, global_block_copy->LocalVarNames().size());
+  assert_same_var("X", x);
+  assert_same_var("Y", y);
+  assert_same_var("Out", out);
+
+  for (size_t i = 0; i < global_block->OpSize(); ++i) {
+    auto op_origin = global_block->Op(i);
+    auto op_copy = global_block->Op(i);
+
+    ASSERT_EQ(op_origin->Type(), op_copy->Type());
+    ASSERT_EQ(op_origin->Inputs(), op_copy->Inputs());
+    ASSERT_EQ(op_origin->Outputs(), op_copy->Outputs());
+
+    ASSERT_EQ(op_copy->Proto()->SerializeAsString(),
+              op_origin->Proto()->SerializeAsString());
+  }
+
+  // Not check block's protostr are same it because the order of vars could be
+  // different and it is correct.
+}
+}  // namespace framework
+}  // namespace paddle

paddle/framework/prune.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/framework/prune.h"
+
+#include <algorithm>
+#include <set>
+#include <string>
+#include <vector>
+
+#include <glog/logging.h>
+
+namespace paddle {
+namespace framework {
+
+const std::string kFeedOpType = "feed";
+const std::string kFetchOpType = "fetch";
+
+bool HasDependentVar(const OpDesc& op_desc,
+                     const std::set<std::string>& dependent_vars) {
+  for (auto& var : op_desc.outputs()) {
+    for (auto& argu : var.arguments()) {
+      if (dependent_vars.count(argu) != 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+bool IsTarget(const OpDesc& op_desc) {
+  if (op_desc.has_is_target()) {
+    return op_desc.is_target();
+  }
+  return false;
+}
+
+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
+
+  auto& block = input.blocks(block_id);
+  auto& ops = block.ops();
+
+  bool expect_feed = true;
+  for (auto& op_desc : ops) {
+    PADDLE_ENFORCE(op_desc.type() != kFeedOpType || expect_feed,
+                   "All FeedOps are at the beginning of the ProgramDesc");
+    expect_feed = (op_desc.type() == kFeedOpType);
+  }
+
+  bool expect_fetch = true;
+  for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
+    auto& op_desc = *op_iter;
+    PADDLE_ENFORCE(op_desc.type() != kFetchOpType || expect_fetch,
+                   "All FetchOps must at the end of the ProgramDesc");
+    expect_fetch = (op_desc.type() == kFetchOpType);
+  }
+
+  std::set<std::string> dependent_vars;
+  std::vector<bool> should_run;
+  for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
+    auto& op_desc = *op_iter;
+
+    if (IsTarget(op_desc) || HasDependentVar(op_desc, dependent_vars)) {
+      // insert its input to the dependency graph
+      for (auto& var : op_desc.inputs()) {
+        for (auto& argu : var.arguments()) {
+          dependent_vars.insert(argu);
+        }
+      }
+
+      should_run.push_back(true);
+    } else {
+      should_run.push_back(false);
+    }
+  }
+
+  // since we are traversing the ProgramDesc in reverse order
+  // 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();
+  op_field->Clear();
+  for (size_t i = 0; i < should_run.size(); ++i) {
+    if (should_run[i]) {
+      *op_field->Add() = input.blocks(block_id).ops(i);
+    }
+  }
+}
+
+void Prune(const ProgramDesc& input, ProgramDesc& output) {
+  prune_impl(input, output, 0);
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/prune.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/framework.pb.h"
+#include "paddle/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+
+void Prune(const ProgramDesc& input, ProgramDesc& output);
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/prune_test.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/framework/prune.h"
+
+#include "paddle/framework/attribute.h"
+#include "paddle/framework/operator.h"
+#include "paddle/operators/net_op.h"
+
+#include "paddle/framework/block_desc.h"
+#include "paddle/framework/op_desc.h"
+#include "paddle/framework/program_desc.h"
+
+#include <gtest/gtest.h>
+
+namespace f = paddle::framework;
+namespace ops = paddle::operators;
+
+void AddOp(const std::string &type, const f::VariableNameMap &inputs,
+           const f::VariableNameMap &outputs, f::AttributeMap attrs,
+           paddle::framework::BlockDescBind *block) {
+  // insert output
+  for (auto kv : outputs) {
+    for (auto v : kv.second) {
+      auto var = block->Var(v);
+      var->SetDataType(paddle::framework::DataType::FP32);
+    }
+  }
+
+  // insert op
+  auto op = block->AppendOp();
+  op->SetType(type);
+  for (auto &kv : inputs) {
+    op->SetInput(kv.first, kv.second);
+  }
+  for (auto &kv : outputs) {
+    op->SetOutput(kv.first, kv.second);
+  }
+  op->SetAttrMap(attrs);
+}
+
+TEST(Prune, one_operator) {
+  f::ProgramDescBind program;
+  f::BlockDescBind *block = program.Block(0);
+
+  AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
+
+  f::ProgramDesc *pdesc = program.Proto();
+  f::ProgramDesc 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);
+  PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 1);
+}
+
+TEST(Prune, forward) {
+  f::ProgramDescBind program;
+  f::BlockDescBind *block = program.Block(0);
+
+  AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
+  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"c"}}}, {}, block);
+  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"d"}}}, {}, block);
+  AddOp("one_one", {{"input", {"d"}}}, {{"output", {"e"}}}, {}, block);
+
+  f::ProgramDesc *pdesc = program.Proto();
+
+  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);
+    PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), i + 1);
+  }
+}
+
+TEST(Prune, multi_input_op) {
+  f::ProgramDescBind program;
+  f::BlockDescBind *block = program.Block(0);
+
+  AddOp("one_one", {{"input", {"a0"}}}, {{"output", {"b0"}}}, {}, block);
+  AddOp("one_one", {{"input", {"a1"}}}, {{"output", {"b1"}}}, {}, block);
+  AddOp("one_one", {{"input", {"a2"}}}, {{"output", {"b2"}}}, {}, block);
+  AddOp("three_one", {{"input", {"b0", "b1", "b2"}}}, {{"output", {"c"}}}, {},
+        block);
+
+  f::ProgramDesc *pdesc = program.Proto();
+  pdesc->mutable_blocks(0)->mutable_ops(3)->set_is_target(true);
+
+  f::ProgramDesc pruned;
+  Prune(*pdesc, pruned);
+  PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 4);
+}
+
+TEST(Prune, multi_output_op) {
+  f::ProgramDescBind program;
+  f::BlockDescBind *block = program.Block(0);
+
+  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
+  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
+  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, {}, block);
+
+  f::ProgramDesc *pdesc = program.Proto();
+  pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
+
+  f::ProgramDesc pruned;
+  Prune(*pdesc, pruned);
+  PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 2);
+}
+
+TEST(Prune, multi_target) {
+  f::ProgramDescBind program;
+  f::BlockDescBind *block = program.Block(0);
+
+  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
+  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
+  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, {}, block);
+
+  f::ProgramDesc *pdesc = program.Proto();
+  pdesc->mutable_blocks(0)->mutable_ops(1)->set_is_target(true);
+  pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
+
+  f::ProgramDesc pruned;
+  Prune(*pdesc, pruned);
+  PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 3);
+}

paddle/framework/var_desc.h

@@ -79,6 +79,10 @@ class VarDescBind {
 
   void SetType(VarDesc::VarType type) { desc_.set_type(type); }
 
+  bool Persistable() const { return desc_.persistable(); }
+
+  void SetPersistable(bool persistable) { desc_.set_persistable(persistable); }
+
  private:
   const TensorDesc &tensor_desc() const;
   TensorDesc *mutable_tensor_desc();

paddle/framework/variable.h

@@ -25,7 +25,10 @@ class Variable {
  public:
   template <typename T>
   const T& Get() const {
-    PADDLE_ENFORCE(IsType<T>(), "Variable must be type %s", typeid(T).name());
+    PADDLE_ENFORCE(holder_ != nullptr, "Variable must hold some thing");
+    PADDLE_ENFORCE(IsType<T>(),
+                   "Variable must be type %s, the holding type is %s",
+                   typeid(T).name(), holder_->Type().name());
     return *static_cast<const T*>(holder_->Ptr());
   }
 

paddle/operators/adam_op.cc

@@ -43,10 +43,6 @@ class AdamOp : public framework::OperatorWithKernel {
                    "Output(Moment1Out) of AdamOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Moment2Out"),
                    "Output(Moment2Out) of AdamOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"),
-                   "Output(Beta1PowOut) of AdamOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Beta2PowOut"),
-                   "Output(Beta2PowOut) of AdamOp should not be null.");
 
     auto lr_dims = ctx->GetInputDim("LearningRate");
     PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
@@ -72,8 +68,6 @@ class AdamOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("ParamOut", param_dims);
     ctx->SetOutputDim("Moment1Out", param_dims);
     ctx->SetOutputDim("Moment2Out", param_dims);
-    ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims);
-    ctx->SetOutputDim("Beta2PowOut", beta2_pow_dims);
   }
 };
 
@@ -92,8 +86,6 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("ParamOut", "(Tensor) Output parameter");
     AddOutput("Moment1Out", "(Tensor) Output first moment");
     AddOutput("Moment2Out", "(Tensor) Output second moment");
-    AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator");
-    AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator");
 
     AddAttr<float>("beta1",
                    "(float, default 0.9) "
@@ -121,10 +113,8 @@ Adam updates:
 
 moment1_out = beta1 * moment1 + (1 − beta1) * grad
 moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad
-beta1_pow_out = beta1_pow * beta1
-beta2_pow_out = beta2_pow * beta2
 learning_rate_t = learning_rate_t *
-                  sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
+                  sqrt(1 - beta2_pow) / (1 - beta1_pow)
 param_out = param - learning_rate_t * moment1/ (sqrt(moment2) + epsilon)
 
 References:

paddle/operators/adam_op.h

@@ -26,14 +26,10 @@ class AdamOpKernel : public framework::OpKernel<T> {
     auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
     auto moment1_out_tensor = ctx.Output<framework::Tensor>("Moment1Out");
     auto moment2_out_tensor = ctx.Output<framework::Tensor>("Moment2Out");
-    auto beta1_pow_out_tensor = ctx.Output<framework::Tensor>("Beta1PowOut");
-    auto beta2_pow_out_tensor = ctx.Output<framework::Tensor>("Beta2PowOut");
 
     param_out_tensor->mutable_data<T>(ctx.GetPlace());
     moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
     moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
-    beta1_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
-    beta2_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
 
     float beta1 = ctx.Attr<float>("beta1");
     float beta2 = ctx.Attr<float>("beta2");
@@ -56,18 +52,13 @@ class AdamOpKernel : public framework::OpKernel<T> {
     auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
     auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
     auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
-    auto beta1_pow_out =
-        framework::EigenVector<T>::Flatten(*beta1_pow_out_tensor);
-    auto beta2_pow_out =
-        framework::EigenVector<T>::Flatten(*beta2_pow_out_tensor);
     auto place = ctx.GetEigenDevice<Place>();
 
     moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad;
     moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square();
-    beta1_pow_out.device(place) = beta1_pow * beta1;
-    beta2_pow_out.device(place) = beta2_pow * beta2;
+
     // All of these are tensors of 1 element
-    auto lr_t = lr * (1 - beta2_pow_out).sqrt() / (1 - beta1_pow_out);
+    auto lr_t = lr * (1 - beta2_pow).sqrt() / (1 - beta1_pow);
     // Eigen does not support automatic broadcast
     // Get dimensions of moment vector to broadcast lr_t
     Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());

paddle/operators/adamax_op.cc

@@ -41,8 +41,6 @@ class AdamaxOp : public framework::OperatorWithKernel {
                    "Output(MomentOut) of AdamaxOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("InfNormOut"),
                    "Output(InfNormOut) of AdamaxOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"),
-                   "Output(Beta1PowOut) of AdamaxOp should not be null.");
 
     auto lr_dims = ctx->GetInputDim("LearningRate");
     PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
@@ -64,7 +62,6 @@ class AdamaxOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("ParamOut", param_dims);
     ctx->SetOutputDim("MomentOut", param_dims);
     ctx->SetOutputDim("InfNormOut", param_dims);
-    ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims);
   }
 };
 
@@ -86,7 +83,6 @@ class AdamaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("InfNormOut",
               "(Tensor) "
               "Output exponentially weighted infinity norm");
-    AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator");
 
     AddAttr<float>("beta1",
                    "(float, default 0.9) "
@@ -113,8 +109,7 @@ Adamax updates:
 
 moment_out = beta1 * moment + (1 - beta1) * grad
 inf_norm_out = max(beta2 * inf_norm + epsilon, abs(grad))
-beta1_pow_out = beta1_pow * beta1
-learning_rate_t = learning_rate/(1 - beta1_pow_out)
+learning_rate_t = learning_rate/(1 - beta1_pow)
 param_out = param - learning_rate_t * moment_out/inf_norm_out
 
 The original paper does not have an epsilon attribute.

paddle/operators/adamax_op.h

@@ -26,12 +26,10 @@ class AdamaxOpKernel : public framework::OpKernel<T> {
     auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
     auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
     auto inf_norm_out_tensor = ctx.Output<framework::Tensor>("InfNormOut");
-    auto beta1_pow_out_tensor = ctx.Output<framework::Tensor>("Beta1PowOut");
 
     param_out_tensor->mutable_data<T>(ctx.GetPlace());
     moment_out_tensor->mutable_data<T>(ctx.GetPlace());
     inf_norm_out_tensor->mutable_data<T>(ctx.GetPlace());
-    beta1_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
 
     float beta1 = ctx.Attr<float>("beta1");
     float beta2 = ctx.Attr<float>("beta2");
@@ -53,15 +51,12 @@ class AdamaxOpKernel : public framework::OpKernel<T> {
     auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
     auto inf_norm_out =
         framework::EigenVector<T>::Flatten(*inf_norm_out_tensor);
-    auto beta1_pow_out =
-        framework::EigenVector<T>::Flatten(*beta1_pow_out_tensor);
     auto place = ctx.GetEigenDevice<Place>();
 
     moment_out.device(place) = beta1 * moment + (1 - beta1) * grad;
     inf_norm_out.device(place) =
         grad.abs().cwiseMax((beta2 * inf_norm) + epsilon);
-    beta1_pow_out.device(place) = beta1_pow * beta1;
-    auto lr_t = lr / (1 - beta1_pow_out);
+    auto lr_t = lr / (1 - beta1_pow);
     Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
     param_out.device(place) =
         param - lr_t.broadcast(m_dsize) * (moment_out / inf_norm_out);

paddle/operators/feed_op.cc

@@ -26,8 +26,9 @@ class FeedOp : public framework::OperatorBase {
       : OperatorBase(type, inputs, outputs, attrs) {}
   void Run(const framework::Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {
-    auto feed_var_name = Input("Input");
+    auto feed_var_name = Input("X");
     auto *feed_var = scope.FindVar(feed_var_name);
+
     PADDLE_ENFORCE(feed_var != nullptr,
                    "Cannot find feed_var in scope, feed_var_name is %s",
                    feed_var_name);
@@ -40,6 +41,9 @@ class FeedOp : public framework::OperatorBase {
 
     auto col = Attr<int>("col");
 
+    VLOG(3) << "Feed Var " << feed_var_name << "'s " << col << " column to var"
+            << out_name;
+
     auto &feed_list = feed_var->Get<framework::FeedFetchList>();
     auto &feed_item = feed_list.at(static_cast<size_t>(col));
     auto *out_item = out_var->GetMutable<framework::FeedFetchType>();
@@ -48,10 +52,21 @@ class FeedOp : public framework::OperatorBase {
   }
 };
 
+class FeedOpInfoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FeedOpInfoMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input of feed op");
+    AddOutput("Out", "The output of feed op");
+    AddComment("feed op, it should not be configured by users directly");
+    AddAttr<int>("col", "column of feed");
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
-// We do not need to register OpInfoMaker,
-// since feed operator will not be used by end users directly
 REGISTER_OPERATOR(feed, paddle::operators::FeedOp,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  paddle::operators::FeedOpInfoMaker);

paddle/operators/fetch_op.cc

@@ -27,7 +27,7 @@ class FetchOp : public framework::OperatorBase {
 
   void Run(const framework::Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {
-    auto fetch_var_name = Input("Input");
+    auto fetch_var_name = Input("X");
     auto *fetch_var = scope.FindVar(fetch_var_name);
     PADDLE_ENFORCE(fetch_var != nullptr,
                    "Cannot find fetch variable in scope, fetch_var_name is %s",
@@ -52,13 +52,25 @@ class FetchOp : public framework::OperatorBase {
     // FIXME(yuyang18): Should we assume the fetch operator always generate
     // CPU outputs?
     dst_item.CopyFromTensor(src_item, platform::CPUPlace(), dev_ctx);
+
+    VLOG(3) << "Fetch variable " << fetch_var_name << " to " << out_name;
   }
 };
 
+class FetchOpInfoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FetchOpInfoMaker(framework::OpProto *proto,
+                   framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input of fetch op");
+    AddOutput("Out", "The output of fetch op");
+    AddComment("fetch op, it should not be configured by users directly");
+    AddAttr<int>("col", "column of fetch");
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
-// We do not need to register OpInfoMaker,
-// since fetch operator will not be used by end users directly
 REGISTER_OPERATOR(fetch, paddle::operators::FetchOp,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  paddle::operators::FetchOpInfoMaker);

paddle/operators/sgd_op.cc

@@ -21,7 +21,7 @@ class SGDOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Param"),
                    "Input(Param) of SGDOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Grad"),
@@ -35,15 +35,15 @@ class SGDOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
                       "Learning rate should have 1 element");
     auto param_dim = ctx->GetInputDim("Param");
-    PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Grad"),
-                      "Two input of SGD Op's dimension must be same.");
+    // TODO(qijun): check dimensions of Param and Grad at complie
+    // and run time.
     ctx->SetOutputDim("ParamOut", param_dim);
   }
 };
 
 class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+  SGDOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("Param", "Input parameter");
     AddInput("LearningRate", "Learning rate of SGD");
@@ -58,6 +58,38 @@ param_out = param - learning_rate * grad;
 )DOC");
   }
 };
+
+template <typename T>
+struct SparseSGDFunctor<platform::CPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input,
+                  const framework::Tensor& learning_rate,
+                  framework::Tensor* output) {
+    auto in_height = input.height();
+    auto out_dims = output->dims();
+    PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
+
+    auto& in_value = input.value();
+    auto& in_rows = input.rows();
+
+    int64_t in_row_numel = in_value.numel() / in_rows.size();
+    PADDLE_ENFORCE_EQ(in_row_numel, output->numel() / in_height);
+
+    auto* in_data = in_value.data<T>();
+    auto* out_data = output->data<T>();
+    auto* lr = learning_rate.data<T>();
+
+    for (size_t i = 0; i < in_rows.size(); i++) {
+      for (int64_t j = 0; j < in_row_numel; j++) {
+        out_data[in_rows[i] * in_row_numel + j] -=
+            lr[0] * in_data[i * in_row_numel + j];
+      }
+    }
+  }
+};
+
+template struct SparseSGDFunctor<platform::CPUPlace, float>;
+
 }  // namespace operators
 }  // namespace paddle
 

paddle/operators/sgd_op.cu

@@ -14,6 +14,66 @@
 
 #define EIGEN_USE_GPU
 #include "paddle/operators/sgd_op.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+
+namespace {
+template <typename T>
+__global__ void SparseSGDFunctorKernel(const T* selected_rows,
+                                       const int64_t* rows,
+                                       const T* learning_rate, T* tensor_out,
+                                       int64_t row_numel, int block_size) {
+  const int ty = blockIdx.y;
+  int tid = threadIdx.x;
+
+  selected_rows += ty * row_numel;
+  tensor_out += rows[ty] * row_numel;
+
+  for (int index = tid; index < row_numel; index += block_size) {
+    // Since index in rows of SelectedRows can be duplicate, we have to use
+    // Atomic Operation to avoid concurrent write error.
+    paddle::platform::CudaAtomicAdd(
+        tensor_out + index, -1.0 * learning_rate[0] * selected_rows[index]);
+  }
+}
+}  // namespace
+
+template <typename T>
+struct SparseSGDFunctor<platform::GPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input,
+                  const framework::Tensor& learning_rate,
+                  framework::Tensor* output) {
+    auto in_height = input.height();
+    auto out_dims = output->dims();
+    PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
+
+    auto& in_value = input.value();
+    auto& in_rows = input.rows();
+
+    int64_t in_row_numel = in_value.numel() / in_rows.size();
+    PADDLE_ENFORCE_EQ(in_row_numel, output->numel() / in_height);
+
+    auto* in_data = in_value.data<T>();
+    auto* out_data = output->data<T>();
+
+    int block_size = 256;
+    dim3 threads(block_size, 1);
+    dim3 grid(1, in_rows.size());
+    SparseSGDFunctorKernel<
+        T><<<grid, threads, 0,
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(in_data, in_rows.data(), learning_rate.data<T>(),
+                              out_data, in_row_numel, block_size);
+  }
+};
+
+template struct SparseSGDFunctor<platform::GPUPlace, float>;
+
+}  // namespace operators
+}  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(sgd,

paddle/operators/sgd_op.h

@@ -15,31 +15,53 @@ limitations under the License. */
 #pragma once
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/selected_rows.h"
 
 namespace paddle {
 namespace operators {
 
+template <typename Place, typename T>
+struct SparseSGDFunctor {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& input,
+                  const framework::Tensor& learning_rate,
+                  framework::Tensor* output);
+};
+
 template <typename Place, typename T>
 class SGDOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto param = ctx.Input<framework::Tensor>("Param");
-    auto grad = ctx.Input<framework::Tensor>("Grad");
-    auto param_out = ctx.Output<framework::Tensor>("ParamOut");
-    auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
+    auto* param = ctx.Input<framework::Tensor>("Param");
+    auto* param_out = ctx.Output<framework::Tensor>("ParamOut");
+    auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
 
-    param_out->mutable_data<T>(ctx.GetPlace());
+    auto* grad_var = ctx.InputVar("Grad");
+    // Actually, all tensors are LoDTensor except SelectedRows.
+    if (grad_var->IsType<framework::LoDTensor>()) {
+      param_out->mutable_data<T>(ctx.GetPlace());
+      auto* grad = ctx.Input<framework::Tensor>("Grad");
 
-    auto p = framework::EigenVector<T>::Flatten(*param);
-    auto g = framework::EigenVector<T>::Flatten(*grad);
-    auto o = framework::EigenVector<T>::Flatten(*param_out);
-    auto lr = framework::EigenVector<T>::Flatten(*learning_rate);
-    auto place = ctx.GetEigenDevice<Place>();
+      auto p = framework::EigenVector<T>::Flatten(*param);
+      auto g = framework::EigenVector<T>::Flatten(*grad);
+      auto o = framework::EigenVector<T>::Flatten(*param_out);
+      auto lr = framework::EigenVector<T>::Flatten(*learning_rate);
+      auto place = ctx.GetEigenDevice<Place>();
 
-    Eigen::DSizes<int, 1> grad_dsize(grad->numel());
-    o.device(place) = p - lr.broadcast(grad_dsize) * g;
+      Eigen::DSizes<int, 1> grad_dsize(grad->numel());
+      o.device(place) = p - lr.broadcast(grad_dsize) * g;
+    } else if (grad_var->IsType<framework::SelectedRows>()) {
+      // TODO(qijun): In Sparse SGD operator, in-place update is enforced.
+      // This manual optimization brings difficulty to track data dependency.
+      // It's better to find a more elegant solution.
+      PADDLE_ENFORCE_EQ(param, param_out);
+      auto* grad = ctx.Input<framework::SelectedRows>("Grad");
+      SparseSGDFunctor<Place, T> functor;
+      functor(ctx.device_context(), *grad, *learning_rate, param_out);
+    } else {
+      PADDLE_THROW("Unsupported Variable Type of Grad");
+    }
   }
 };
-
 }  // namespace operators
 }  // namespace paddle

paddle/parameter/FirstOrderOptimizer.h

@@ -265,6 +265,10 @@ public:
     addParameterType(PARAMETER_SECOND_MOMENTUM);
   }
 
+  virtual void startBatch(int64_t numSamplesProcessed) {
+    learningRate_ = calcLearningRate(numSamplesProcessed, pass_);
+  }
+
   virtual void finishBatch() { ++step_; }
 
   virtual void update(const VectorPtr vecs[],

paddle/pybind/protobuf.cc

@@ -101,6 +101,10 @@ using namespace paddle::framework;  // NOLINT
 void BindProgramDesc(py::module &m) {
   py::class_<ProgramDescBind>(m, "ProgramDesc", "")
       .def(py::init<>())
+      .def("__init__",
+           [](ProgramDescBind &self, const ProgramDescBind &other) {
+             new (&self) ProgramDescBind(other);
+           })
       .def("append_block", &ProgramDescBind::AppendBlock,
            py::return_value_policy::reference)
       .def("append_backward",
@@ -202,20 +206,25 @@ void BindVarDsec(py::module &m) {
       .def("set_lod_level", &VarDescBind::SetLoDLevel)
       .def("type", &VarDescBind::GetType)
       .def("set_type", &VarDescBind::SetType)
-      .def("serialize_to_string", [](VarDescBind &var_desc) -> py::bytes {
-        const VarDesc *desc = var_desc.Proto();
-        PADDLE_ENFORCE(desc->IsInitialized(),
-                       "VarDesc has not been initialized.");
-        std::string res;
-        PADDLE_ENFORCE(
-            desc->SerializeToString(&res),
-            "Serialize VarDesc Error. This could be a bug of Paddle.");
-        return res;
-      });
+      .def("serialize_to_string",
+           [](VarDescBind &var_desc) -> py::bytes {
+             const VarDesc *desc = var_desc.Proto();
+             PADDLE_ENFORCE(desc->IsInitialized(),
+                            "VarDesc has not been initialized.");
+             std::string res;
+             PADDLE_ENFORCE(
+                 desc->SerializeToString(&res),
+                 "Serialize VarDesc Error. This could be a bug of Paddle.");
+             return res;
+           })
+      .def("persistable", &VarDescBind::Persistable)
+      .def("set_persistable", &VarDescBind::SetPersistable);
 
   py::enum_<VarDesc::VarType>(var_desc, "VarType", "")
       .value("LOD_TENSOR", VarDesc::LOD_TENSOR)
-      .value("SELECTED_ROWS", VarDesc::SELECTED_ROWS);
+      .value("SELECTED_ROWS", VarDesc::SELECTED_ROWS)
+      .value("FEED_MINIBATCH", VarDesc::FEED_MINIBATCH)
+      .value("FETCH_LIST", VarDesc::FETCH_LIST);
 }
 
 void BindOpDesc(py::module &m) {

paddle/pybind/pybind.cc

@@ -111,6 +111,7 @@ PYBIND11_PLUGIN(core) {
              new (&instance) LoDTensor(new_lod);
 #endif
           })
+      .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
       .def("set_lod",
            [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
 #ifndef PADDLE_WITH_CUDA
@@ -154,7 +155,15 @@ PYBIND11_PLUGIN(core) {
            py::return_value_policy::reference)
       .def("set_height", &SelectedRows::set_height)
       .def("height", &SelectedRows::height)
-      .def("set_rows", &SelectedRows::set_rows)
+      .def("set_rows",
+           [](SelectedRows &self, std::vector<int64_t> rows) {
+#ifndef PADDLE_WITH_CUDA
+             self.set_rows(rows);
+#else
+        Vector<int64_t> new_rows(rows);
+        self.set_rows(new_rows);
+#endif
+           })
       .def("rows", [](SelectedRows &self) {
 #ifndef PADDLE_WITH_CUDA
         return self.rows();
@@ -187,6 +196,11 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<LoDTensor>();
            },
            py::return_value_policy::reference)
+      .def("get_selected_rows",
+           [](Variable &self) -> SelectedRows * {
+             return self.GetMutable<SelectedRows>();
+           },
+           py::return_value_policy::reference)
       .def("get_net",
            [](Variable &self) -> operators::NetOp * {
              return self.GetMutable<operators::NetOp>();
@@ -203,7 +217,8 @@ All parameter, weight, gradient are variables in Paddle.
       .def(py::init<>())
       .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
            py::return_value_policy::reference)
-      .def("drop_kids", &Scope::DropKids);
+      .def("drop_kids", &Scope::DropKids)
+      .def_static("global_scope", &GetGlobalScope);
 
   //! @note: Be careful! PyBind will return std::string as an unicode, not
   //! Python str. If you want a str object, you should cast them in Python.
@@ -251,6 +266,17 @@ All parameter, weight, gradient are variables in Paddle.
       .def(py::init<>())
       .def("__str__", string::to_string<const platform::CPUPlace &>);
 
+  py::class_<platform::Place>(m, "Place")
+      .def(py::init<>())
+      .def("set_place",
+           [](platform::Place &self, const platform::CPUPlace &cpu_place) {
+             self = cpu_place;
+           })
+      .def("set_place",
+           [](platform::Place &self, const platform::GPUPlace &gpu_place) {
+             self = gpu_place;
+           });
+
   py::class_<OperatorBase>(m, "Operator")
       .def_static("create",
                   [](py::bytes protobin) {
@@ -424,14 +450,15 @@ All parameter, weight, gradient are variables in Paddle.
   py::class_<framework::Executor>(m, "Executor")
       .def(py::init<std::vector<platform::Place> &>())
       .def("run",
-           [](Executor &self, const ProgramDesc &program_desc, int block_id) {
+           [](Executor &self, ProgramDescBind *program_bind, int block_id) {
              framework::Scope &global_scope = GetGlobalScope();
-             self.Run(program_desc, &global_scope, block_id);
+             self.Run(*program_bind->Proto(), &global_scope, block_id);
            });
 
   m.def("unique_integer", UniqueIntegerGenerator);
 
   m.def("is_compile_gpu", IsCompileGPU);
+  //! FIXME: it is no need to `set_xxx_float/double/int`
   m.def("set_feed_variable_float", framework::SetFeedVariable<float>);
   m.def("set_feed_variable_double", framework::SetFeedVariable<double>);
   m.def("set_feed_variable_int", framework::SetFeedVariable<int>);

paddle/scripts/cluster_train_v2/fabric/conf.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.
+
+HOSTS = [
+    "root@10.1.9.7",
+    "root@10.1.18.7",
+    "root@10.1.32.9",
+]
+'''
+workspace configuration
+'''
+#root dir for workspace, can be set as any director with real user account
+ROOT_DIR = "/root"
+'''
+network configuration
+'''
+#pserver nics
+PADDLE_NIC = "eth0"
+#pserver port
+PADDLE_PORT = 7164
+#pserver ports num
+PADDLE_PORTS_NUM = 1
+#pserver sparse ports num
+PADDLE_PORTS_NUM_FOR_SPARSE = 1
+#trainer whether use gpu
+PADDLE_USE_GPU = "False"
+#environments setting for all processes in cluster job
+LD_LIBRARY_PATH = "/usr/local/cuda/lib64:/usr/lib64"

paddle/scripts/cluster_train_v2/fabric/docker_cluster/Dockerfile

+FROM docker.paddlepaddlehub.com/paddle:0.10.0rc2
+RUN apt-get update && apt-get install -y openssh-server
+RUN mkdir /var/run/sshd
+
+RUN echo 'root:root' |chpasswd
+
+RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
+RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
+
+EXPOSE 22
+CMD ["/usr/sbin/sshd", "-D"]

paddle/scripts/cluster_train_v2/fabric/docker_cluster/ssh_servers.yaml

+apiVersion: extensions/v1beta1
+kind: Deployment
+metadata:
+  name: ssh-servers
+spec:
+  replicas: 3
+  template:
+    metadata:
+      labels:
+        app: ssh-servers
+    spec:
+      containers:
+      - name: ssh-servers
+        image: docker.paddlepaddlehub.com/paddlessh
+        resources:
+          limits:
+            cpu: 500m
+            memory: 1Gi
+          requests:
+            cpu: 500m
+            memory: 1Gi
+        ports:
+        - containerPort: 22

paddle/scripts/cluster_train_v2/fabric/run.sh

+#!/bin/bash
+
+python paddle.py \
+  --job_dispatch_package="/root/wuyi/fabric_submit/workspace" \
+  --dot_period=10 \
+  --ports_num_for_sparse=1 \
+  --log_period=50 \
+  --num_passes=5 \
+  --trainer_count=2 \
+  --saving_period=1 \
+  --local=0 \
+  --config=./trainer_config.py \
+  --save_dir=./output \
+  --use_gpu=0

paddle/scripts/cluster_train_v2/openmpi/docker_cluster/Dockerfile

+# Build this image:  docker build -t mpi .
+#
+
+FROM paddledev/paddle:0.10.0rc3
+
+ENV DEBIAN_FRONTEND noninteractive
+
+RUN apt-get update -y && \
+    apt-get upgrade -y && \
+    apt-get install -y openssh-server zip unzip vim sudo \
+gcc gfortran openmpi-checkpoint binutils wget curl git openmpi-bin openmpi-common libopenmpi-dev && \
+pip install mpi4py numpy virtualenv scipy matplotlib lxml sqlalchemy suds ipython obspy && \
+mkdir /var/run/sshd && \
+echo 'root:tutorial' | chpasswd && \
+sed -i 's/PermitRootLogin without-password/PermitRootLogin yes/' /etc/ssh/sshd_config && \
+# SSH login fix. Otherwise user is kicked off after login
+sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd && \
+echo "export VISIBLE=now" >> /etc/profile && \
+adduser --disabled-password --gecos "" tutorial && \
+echo "tutorial ALL=(ALL) NOPASSWD:ALL" >> /etc/sudoers && \
+mkdir /home/tutorial/.ssh/
+
+ENV HOME /home/tutorial
+ENV NOTVISIBLE "in users profile"
+
+# ------------------------------------------------------------
+# Set-Up SSH with our Github deploy key
+# ------------------------------------------------------------
+
+ADD ssh/config /home/tutorial/.ssh/config
+ADD ssh/id_rsa.mpi /home/tutorial/.ssh/id_rsa
+ADD ssh/id_rsa.mpi.pub /home/tutorial/.ssh/id_rsa.pub
+ADD ssh/id_rsa.mpi.pub /home/tutorial/.ssh/authorized_keys
+
+#---------------------------------------------------------------
+#LD_LIBRARY_PATH
+#---------------------------------------------------------------
+
+RUN export LD_LIBRARY_PATH=/usr/lib/openmpi/lib/
+
+WORKDIR /home/tutorial
+EXPOSE 22
+CMD ["/usr/sbin/sshd", "-D"]

paddle/scripts/cluster_train_v2/openmpi/docker_cluster/head.yaml

+apiVersion: extensions/v1beta1
+kind: Deployment
+metadata:
+  name: mpi-header
+  labels:
+    app: mpi-header
+spec:
+  replicas: 1
+  template:
+    metadata:
+      labels:
+        app: mpi-header
+    spec:
+      containers:
+      - image: typhoon1986/paddle-openmpi
+        name : mpi-header
+        resources:
+          limits:
+            cpu: 500m
+            memory: 2Gi
+          requests:
+            cpu: 500m
+            memory: 2Gi
+        ports:
+        - containerPort: 22

paddle/scripts/cluster_train_v2/openmpi/docker_cluster/mpi-nodes.yaml

+apiVersion: extensions/v1beta1
+kind: Deployment
+metadata:
+  name: mpi-nodes
+  labels:
+    app: mpi-nodes
+spec:
+  replicas: 3
+  template:
+    metadata:
+      labels:
+        app: mpi-nodes
+    spec:
+      containers:
+      - image: typhoon1986/paddle-openmpi
+        name : mpi-nodes
+        resources:
+          limits:
+            cpu: 500m
+            memory: 2Gi
+          requests:
+            cpu: 500m
+            memory: 2Gi
+        ports:
+        - containerPort: 22
+        imagePullPolicy: Always

paddle/scripts/cluster_train_v2/openmpi/docker_cluster/ssh/config

+StrictHostKeyChecking no

paddle/scripts/cluster_train_v2/openmpi/docker_cluster/ssh/id_rsa.mpi

+-----BEGIN RSA PRIVATE KEY-----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+-----END RSA PRIVATE KEY-----

paddle/scripts/cluster_train_v2/openmpi/docker_cluster/ssh/id_rsa.mpi.pub

+ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQDs9YtmaB0nnTx0PXlPr7GoYO8v0SHOj1KCyc3rEn6qlS+86LjVrFbQCpHgyc9OD+66+T7Hi1Sm6585bjDbZRMaOx2+YhVZr26Mu1xGI3dg9j5JT5g7kV+oqDuv+EGf78YxxKh9/BKzu9WCaEEGen+YmHY7Vw4AVkR73z/juZuDf+QcECTfo05CS1pTeXjwHfwSEFOeNZ9wQabUdxNE8XFdXlaKSjpmEoUS4dxNUqeDhE61SoSaAfIjRoHtMDGDidrR6Mi2xhYQ9YZ4M7Dqaz/gzDKXE2ixbMcU9b2njOAsIs2v5vpsMN9gqNdl+UC5NC1CEuYlTAP265gsCKOlITZ9 oweidner@peahi

paddle/scripts/cluster_train_v2/openmpi/start_mpi_train.sh

+#!/bin/bash
+# General trainning configurations
+
+NICS=eth0
+PADDLE_INIT_PORT=7164
+PADDLE_INIT_PORTS_NUM=1
+PADDLE_INIT_PORTS_NUM_FOR_SPARSE=1
+PADDLE_INIT_PSERVERS=$(cat machines | sed -e ':a' -e 'N' -e '$!ba' -e 's/\n/,/g')
+PADDLE_INIT_USE_GPU=False
+
+PADDLE_INIT_NUM_GRADIENT_SERVERS=${OMPI_COMM_WORLD_SIZE}
+PADDLE_INIT_TRAINER_ID=${OMPI_COMM_WORLD_RANK}
+PADDLE_CLUSTER_TRAIN=True
+
+env
+
+# start pserver
+stdbuf -oL nohup paddle pserver --port=$PADDLE_INIT_PORT --ports_num=$PADDLE_INIT_PORTS_NUM \
+  --ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE --nics=$NICS \
+  --comment=paddle_cluster_pserver \
+  --num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS &> logs/pserver.log &
+
+# start trainer
+# NOTE: train.py will use the above environment variables as configuration
+python train.py &> logs/train.log
+
+# kill background pservers when train finishes
+ps -ef | grep pserver | awk '{print $2}' | xargs kill

python/paddle/v2/framework/executor.py

+import paddle.v2.framework.core as core
+from paddle.v2.framework.framework import Block, Program
+
+
+class Executor(object):
+    def __init__(self, places):
+        if not isinstance(places, list) and not isinstance(places, tuple):
+            places = [places]
+
+        act_places = []
+        for each in places:
+            p = core.Place()
+            p.set_place(each)
+            act_places.append(p)
+
+        self.executor = core.Executor(act_places)
+
+    def run(self,
+            program,
+            feed,
+            fetch_list,
+            feed_var_name='feed',
+            fetch_var_name='fetch'):
+        if not isinstance(program, Program):
+            raise TypeError()
+
+        program = program.clone()
+        global_block = program.global_block()
+        feed_var = global_block.create_var(
+            name=feed_var_name,
+            type=core.VarDesc.VarType.FEED_MINIBATCH,
+            persistable=True)
+
+        for i, name in enumerate(feed):
+            out = global_block.var(name)
+            global_block.prepend_op(
+                'feed',
+                inputs={'X': [feed_var]},
+                outputs={'Out': [out]},
+                attrs={'col': i})
+            # FIXME
+            core.set_feed_variable_float(feed[name], feed_var.name, i)
+
+        fetch_var = global_block.create_var(
+            name=fetch_var_name,
+            type=core.VarDesc.VarType.FETCH_LIST,
+            persistable=True)
+        for i, var in enumerate(fetch_list):
+            global_block.append_op(
+                type='fetch',
+                inputs={'X': [var]},
+                outputs={'Out': [fetch_var]},
+                attrs={'col': i})
+
+        self.executor.run(program.desc, 0)
+        return [
+            core.get_fetch_variable(fetch_var_name, i)
+            for i in xrange(len(fetch_list))
+        ]

python/paddle/v2/framework/framework.py

@@ -15,6 +15,7 @@ class Variable(object):
                  shape=None,
                  dtype=None,
                  lod_level=None,
+                 persistable=False,
                  **kwargs):
         self.block = block
 
@@ -70,6 +71,17 @@ class Variable(object):
                                      "lod_level is {2}. They are not "
                                      "matched".format(self.name, self.lod_level,
                                                       lod_level))
+        if persistable is not None:
+            if is_new_var:
+                self.desc.set_persistable(persistable)
+            else:
+                if persistable != self.persistable:
+                    raise ValueError(
+                        "Variable {0} has been created before."
+                        "The previous persistable is {1}; the new "
+                        "persistable is {2}. They are not matched".format(
+                            self.name, self.persistable, persistable))
+
         self.block.vars[name] = self
         self.op = None
 
@@ -80,6 +92,10 @@ class Variable(object):
 
     __repr__ = __str__
 
+    @property
+    def persistable(self):
+        return self.desc.persistable()
+
     @property
     def name(self):
         return self.desc.name()
@@ -240,7 +256,8 @@ class Operator(object):
                     self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
 
         self.desc.check_attrs()
-        self.desc.infer_shape(self.block.desc)
+        if type not in {'feed', 'fetch'}:
+            self.desc.infer_shape(self.block.desc)
 
     def __str__(self):
         protostr = self.desc.serialize_to_string()
@@ -307,9 +324,12 @@ class Block(object):
         return self.desc.id
 
     def var(self, name):
-        if name not in self.vars:
+        if not isinstance(name, basestring):
+            raise TypeError()
+        v = self.vars.get(name, None)
+        if v is None:
             raise ValueError("var %s not in this block" % name)
-        return self.vars[name]
+        return v
 
     def all_parameters(self):
         return {v for k, v in self.vars.iteritems() if isinstance(v, Parameter)}
@@ -348,18 +368,22 @@ class Block(object):
         for op_idx in range(0, self.desc.op_size()):
             ops_in_cpp.append(self.desc.op(op_idx))
 
-        first_op_in_python = self.ops[0].desc
-        last_op_in_python = self.ops[len(self.ops) - 1].desc
-        start_index = None
-        end_index = None
-        for index in range(len(ops_in_cpp)):
-            if first_op_in_python == ops_in_cpp[index]:
-                start_index = index
-            if last_op_in_python == ops_in_cpp[index]:
-                end_index = index
-        assert start_index is not None
-        assert end_index is not None
-        assert start_index <= end_index
+        if len(self.ops) != 0:
+            first_op_in_python = self.ops[0].desc
+            last_op_in_python = self.ops[len(self.ops) - 1].desc
+            start_index = None
+            end_index = None
+            for index in range(len(ops_in_cpp)):
+                if first_op_in_python == ops_in_cpp[index]:
+                    start_index = index
+                if last_op_in_python == ops_in_cpp[index]:
+                    end_index = index
+            assert start_index is not None
+            assert end_index is not None
+            assert start_index <= end_index
+        else:
+            start_index = 0
+            end_index = -1
 
         # sync ops append to the head of cpp_ops
         for index in range((start_index - 1 - 1), -1, -1):
@@ -379,14 +403,6 @@ class Block(object):
 
 
 class Program(object):
-    @classmethod
-    def instance(cls):
-        # From https://stackoverflow.com/questions/8212053
-        # Making Program as a Singleton class.
-        if not hasattr(cls, '_instance'):
-            cls._instance = cls()
-        return cls._instance
-
     def __init__(self):
         self.desc = core.ProgramDesc()
         self.blocks = [Block(self, 0)]
@@ -397,7 +413,15 @@ class Program(object):
         proto = framework_pb2.ProgramDesc.FromString(str(protostr))
         return proto.__str__()
 
-    __repr__ = __str__
+    def clone(self):
+        p = Program()
+        p.desc = core.ProgramDesc(self.desc)
+        p.blocks = [Block(p, i) for i in xrange(self.desc.num_blocks())]
+        p.sync_with_cpp()
+        return p
+
+    def __repr__(self):
+        return str(self)
 
     def global_block(self):
         return self.blocks[0]
@@ -408,11 +432,13 @@ class Program(object):
     def current_block(self):
         return self.blocks[self.current_block_idx]
 
-    def append_backward(self, target, no_grad_set):
+    def append_backward(self, target, no_grad_set=None):
         """
         return map(param_name -> (grad_name, block_index, op_index))
         """
         assert isinstance(target, Variable)
+        if no_grad_set is None:
+            no_grad_set = set()
         param_to_grad_info = self.desc.append_backward(target.desc, no_grad_set)
         self.sync_with_cpp()
         return param_to_grad_info
@@ -445,7 +471,9 @@ class Parameter(Variable):
             if each < 0:
                 raise ValueError("Parameter shape should not be related with "
                                  "batch-size")
-        Variable.__init__(self, block, shape=shape, dtype=dtype, **kwargs)
+
+        Variable.__init__(
+            self, block, persistable=True, shape=shape, dtype=dtype, **kwargs)
         self.trainable = kwargs.get('trainable', True)
         self.init_attr = kwargs.get('initialize_attr', {
             'type': 'uniform_random',
@@ -470,4 +498,4 @@ class Parameter(Variable):
 
 
 # program is a global instance.
-g_program = Program.instance()
+g_program = Program()

python/paddle/v2/framework/layers.py

@@ -3,7 +3,7 @@ import paddle.v2.framework.core as core
 from paddle.v2.framework.framework import OpProtoHolder, Variable
 import re
 
-__all__ = ['fc', 'data', 'cross_entropy', 'conv2d']
+__all__ = ['fc', 'data', 'cross_entropy', 'conv2d', 'pool2d']
 
 
 def fc(input,
@@ -35,7 +35,10 @@ def fc(input,
                 "Y": w,
             },
             outputs={"Out": tmp},
-            attrs={'x_num_col_dims': num_flatten_dims})
+            attrs={
+                'x_num_col_dims': num_flatten_dims,
+                'y_num_col_dims': len(input_shape) - num_flatten_dims
+            })
         mul_results.append(tmp)
 
     # sum
@@ -55,9 +58,11 @@ def data(name,
          shape,
          data_type='float32',
          type=core.VarDesc.VarType.LOD_TENSOR,
+         append_batch_size=True,
          program=None):
     helper = LayerHelper('data', **locals())
-    shape = [-1] + shape  # append batch size as -1
+    if append_batch_size:
+        shape = [-1] + shape  # append batch size as -1
     return helper.create_global_variable(
         name=name, shape=shape, dtype=data_type, type=type)
 
@@ -112,7 +117,7 @@ def _create_op_func_(op_type):
 
 
 _create_op_func_('mean')
-_create_op_func_('pool2d')
+_create_op_func_('mul')
 
 
 def cross_entropy(input, label, **kwargs):
@@ -167,6 +172,13 @@ def conv2d(input,
             raise ValueError("num_channels must be divisible by groups.")
         num_filter_channels = num_channels / groups
 
+    if isinstance(filter_size, int):
+        filter_size = [filter_size, filter_size]
+    if isinstance(stride, int):
+        stride = [stride, stride]
+    if isinstance(padding, int):
+        padding = [padding, padding]
+
     input_shape = input.shape
     filter_shape = [num_filters, num_filter_channels] + filter_size
     filter = helper.create_parameter(
@@ -187,3 +199,40 @@ def conv2d(input,
     pre_act = helper.append_bias_op(pre_bias)
 
     return helper.append_activation(pre_act)
+
+
+def pool2d(input,
+           pool_size,
+           pool_type,
+           pool_stride=[1, 1],
+           pool_padding=[0, 0],
+           global_pooling=False,
+           program=None):
+    if pool_type not in ["max", "avg"]:
+        raise ValueError(
+            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
+            str(pool_type))
+    if isinstance(pool_size, int):
+        pool_size = [pool_size, pool_size]
+    if isinstance(pool_stride, int):
+        pool_stride = [pool_stride, pool_stride]
+    if isinstance(pool_padding, int):
+        pool_padding = [pool_padding, pool_padding]
+
+    helper = LayerHelper('conv2d', **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type="pool2d",
+        inputs={"X": input},
+        outputs={"Out": pool_out},
+        attrs={
+            "pooling_type": pool_type,
+            "ksize": pool_size,
+            "global_pooling": global_pooling,
+            "strides": pool_stride,
+            "paddings": pool_padding
+        })
+
+    return pool_out

python/paddle/v2/framework/nets.py

+import paddle.v2.framework.layers as layers
+
+
+def simple_img_conv_pool(input,
+                         filter_size,
+                         num_filters,
+                         pool_size,
+                         pool_stride,
+                         act,
+                         program=None):
+    conv_out = layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        act=act,
+        program=program)
+
+    pool_out = layers.pool2d(
+        input=conv_out,
+        pool_size=pool_size,
+        pool_type='max',
+        pool_stride=pool_stride,
+        program=program)
+    return pool_out

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

@@ -33,14 +33,12 @@ class TestAdamOp1(OpTest):
 
         self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
 
-        param_out, moment1_out, moment2_out, beta1_pow_out, \
-            beta2_pow_out = adam_step(self.inputs, self.attrs)
+        param_out, moment1_out, \
+            moment2_out = adam_step(self.inputs, self.attrs)
 
         self.outputs = {
             'Moment1Out': moment1_out,
             'Moment2Out': moment2_out,
-            'Beta1PowOut': beta1_pow_out,
-            'Beta2PowOut': beta2_pow_out,
             'ParamOut': param_out
         }
 
@@ -78,14 +76,12 @@ class TestAdamOp2(OpTest):
 
         attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
 
-        param_out, moment1_out, moment2_out, beta1_pow_out, \
-            beta2_pow_out = adam_step(self.inputs, attributes)
+        param_out, moment1_out, \
+            moment2_out = adam_step(self.inputs, attributes)
 
         self.outputs = {
             'Moment1Out': moment1_out,
             'Moment2Out': moment2_out,
-            'Beta1PowOut': beta1_pow_out,
-            'Beta2PowOut': beta2_pow_out,
             'ParamOut': param_out
         }
 
@@ -127,14 +123,12 @@ class TestAdamOpMultipleSteps(OpTest):
 
     def test_check_output(self):
         for _ in range(self.num_steps):
-            param_out, moment1_out, moment2_out, beta1_pow_out, \
-                beta2_pow_out = adam_step(self.inputs, self.attrs)
+            param_out, moment1_out, \
+                moment2_out = adam_step(self.inputs, self.attrs)
 
             self.outputs = {
                 'Moment1Out': moment1_out,
                 'Moment2Out': moment2_out,
-                'Beta1PowOut': beta1_pow_out,
-                'Beta2PowOut': beta2_pow_out,
                 'ParamOut': param_out
             }
 
@@ -145,8 +139,10 @@ class TestAdamOpMultipleSteps(OpTest):
             self.inputs['Param'] = param_out
             self.inputs['Moment1'] = moment1_out
             self.inputs['Moment2'] = moment2_out
-            self.inputs['Beta1Pow'] = beta1_pow_out
-            self.inputs['Beta2Pow'] = beta2_pow_out
+
+            # Update powers of Beta1 and Beta2 for next time step
+            self.inputs['Beta1Pow'] *= self.attrs['beta1']
+            self.inputs['Beta2Pow'] *= self.attrs['beta1']
 
             # Randomize gradient for next step
             self.inputs['Grad'] = np.random.uniform(
@@ -175,11 +171,9 @@ def adam_step(inputs, attributes):
 
     moment1_out = beta1 * moment1 + (1 - beta1) * grad
     moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
-    beta1_pow_out = beta1_pow * beta1
-    beta2_pow_out = beta2_pow * beta2
-    lr_t = lr * np.sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
+    lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
     param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
-    return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out
+    return param_out, moment1_out, moment2_out
 
 
 if __name__ == "__main__":

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

@@ -31,14 +31,13 @@ class TestAdamaxOp1(OpTest):
 
         self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon}
 
-        param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step(
-            self.inputs, self.attrs)
+        param_out, moment_out, inf_norm_out = adamax_step(self.inputs,
+                                                          self.attrs)
 
         self.outputs = {
             'ParamOut': param_out,
             'MomentOut': moment_out,
-            'InfNormOut': inf_norm_out,
-            'Beta1PowOut': beta1_pow_out
+            'InfNormOut': inf_norm_out
         }
 
     def test_check_output(self):
@@ -73,14 +72,12 @@ class TestAdamaxOp2(OpTest):
         }
 
         attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon}
-        param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step(
-            self.inputs, attrs)
+        param_out, moment_out, inf_norm_out = adamax_step(self.inputs, attrs)
 
         self.outputs = {
             'ParamOut': param_out,
             'MomentOut': moment_out,
-            'InfNormOut': inf_norm_out,
-            'Beta1PowOut': beta1_pow_out
+            'InfNormOut': inf_norm_out
         }
 
     def test_check_output(self):
@@ -117,19 +114,15 @@ class TestAdamaxOpMultipleSteps(OpTest):
 
         self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon}
 
-        param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step(
-            self.inputs, self.attrs)
-
     def test_check_output(self):
         for _ in range(self.num_steps):
-            param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step(
-                self.inputs, self.attrs)
+            param_out, moment_out, inf_norm_out = adamax_step(self.inputs,
+                                                              self.attrs)
 
             self.outputs = {
                 'ParamOut': param_out,
                 'MomentOut': moment_out,
-                'InfNormOut': inf_norm_out,
-                'Beta1PowOut': beta1_pow_out
+                'InfNormOut': inf_norm_out
             }
 
             # Verify output for this step
@@ -139,7 +132,9 @@ class TestAdamaxOpMultipleSteps(OpTest):
             self.inputs['Param'] = param_out
             self.inputs['Moment'] = moment_out
             self.inputs['InfNorm'] = inf_norm_out
-            self.inputs['Beta1Pow'] = beta1_pow_out
+
+            # Update Beta1 Power accumulator for next step
+            self.inputs['Beta1Pow'] *= self.attrs['beta1']
 
             # Randomize gradient for next step
             self.inputs['Grad'] = np.random.uniform(
@@ -167,11 +162,10 @@ def adamax_step(inputs, attributes):
 
     moment_out = beta1 * moment + (1 - beta1) * grad
     inf_norm_out = np.maximum(beta2 * inf_norm + epsilon, np.abs(grad))
-    beta1_pow_out = beta1_pow * beta1
-    lr_t = (lr / (1 - beta1_pow_out))
+    lr_t = (lr / (1 - beta1_pow))
     param_out = param - lr_t * np.divide(moment_out, inf_norm_out)
 
-    return param_out, moment_out, inf_norm_out, beta1_pow_out
+    return param_out, moment_out, inf_norm_out
 
 
 if __name__ == "__main__":

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

+import unittest
+from paddle.v2.framework.layers import mul, data
+import paddle.v2.framework.core as core
+from paddle.v2.framework.executor import Executor
+from paddle.v2.framework.framework import g_program
+import numpy
+
+
+class TestExecutor(unittest.TestCase):
+    def test_mul(self):
+        a = data(name='a', shape=[784], data_type='float32')
+        b = data(
+            name='b',
+            shape=[784, 100],
+            data_type='float32',
+            append_batch_size=False)
+        out = mul(x=a, y=b)
+        place = core.CPUPlace()
+        a_np = numpy.random.random((100, 784)).astype('float32')
+        tensor_a = core.LoDTensor()
+        tensor_a.set(a_np, place)
+        b_np = numpy.random.random((784, 100)).astype('float32')
+        tensor_b = core.LoDTensor()
+        tensor_b.set(b_np, place)
+        exe = Executor(place)
+        outs = exe.run(g_program,
+                       feed={'a': tensor_a,
+                             'b': tensor_b},
+                       fetch_list=[out])
+        out = numpy.array(outs[0])
+        self.assertEqual((100, 100), out.shape)
+        self.assertTrue(numpy.allclose(out, numpy.dot(a_np, b_np)))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

 import paddle.v2.framework.layers as layers
+import paddle.v2.framework.nets as nets
 from paddle.v2.framework.framework import Program, g_program
 import paddle.v2.framework.core as core
 import unittest
@@ -18,7 +19,7 @@ class TestBook(unittest.TestCase):
 
         avg_cost = layers.mean(x=cost, program=program)
         self.assertIsNotNone(avg_cost)
-        program.append_backward(avg_cost, set())
+        program.append_backward(avg_cost)
         print str(program)
 
     def test_recognize_digits_mlp(self):
@@ -38,24 +39,52 @@ class TestBook(unittest.TestCase):
         cost = layers.cross_entropy(input=predict, label=label, program=program)
         avg_cost = layers.mean(x=cost, program=program)
         self.assertIsNotNone(avg_cost)
-        # print str(program)
+        print str(program)
 
     def test_simple_conv2d(self):
-        pd = core.ProgramDesc.__create_program_desc__()
-        program = Program(desc=pd)
-        images = data_layer(
+        program = Program()
+        images = layers.data(
             name='pixel', shape=[3, 48, 48], data_type='int32', program=program)
-        conv2d_layer(
+        layers.conv2d(
             input=images, num_filters=3, filter_size=[4, 4], program=program)
 
-        # print str(program)
+        print str(program)
 
-    def test_simple_conv2d(self):
+    def test_recognize_digits_conv(self):
         program = Program()
+
         images = layers.data(
-            name='pixel', shape=[3, 48, 48], data_type='int32', program=program)
-        layers.conv2d(
-            input=images, num_filters=3, filter_size=[4, 4], program=program)
+            name='pixel',
+            shape=[1, 28, 28],
+            data_type='float32',
+            program=program)
+        label = layers.data(
+            name='label', shape=[1], data_type='int32', program=program)
+        conv_pool_1 = nets.simple_img_conv_pool(
+            input=images,
+            filter_size=5,
+            num_filters=2,
+            pool_size=2,
+            pool_stride=2,
+            act="relu",
+            program=program)
+        conv_pool_2 = nets.simple_img_conv_pool(
+            input=conv_pool_1,
+            filter_size=5,
+            num_filters=4,
+            pool_size=2,
+            pool_stride=2,
+            act="relu",
+            program=program)
+
+        predict = layers.fc(input=conv_pool_2,
+                            size=10,
+                            act="softmax",
+                            program=program)
+        cost = layers.cross_entropy(input=predict, label=label, program=program)
+        avg_cost = layers.mean(x=cost, program=program)
+
+        program.append_backward(avg_cost)
 
         print str(program)
 

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

@@ -34,8 +34,26 @@ class TestProgram(unittest.TestCase):
         self.assertEqual(1, b.idx)
         self.assertEqual(0, b.parent_idx)
 
+    def test_program_clone(self):
+        prog = Program()
+
+        x = prog.global_block().create_var(
+            name='X', shape=[1000, 784], dtype='float32')
+
+        y = prog.global_block().create_var(
+            name='Y', shape=[784, 100], dtype='float32')
+        out = prog.global_block().create_var(name='Out', dtype='float32')
+        prog.global_block().append_op(
+            type="mul", inputs={'X': [x],
+                                'Y': [y]}, outputs={'Out': [out]})
+
+        # FIXME(yuyang18): We manual compare the output string, since the order
+        # of variable could be changed.
+        print prog
+        print prog.clone()
+
     def test_append_backward(self):
-        prog = Program.instance()
+        prog = Program()
         block = prog.global_block()
 
         mul_x = block.create_var(

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

@@ -8,29 +8,30 @@ class TestSelectedRows(unittest.TestCase):
         place = core.CPUPlace()
         height = 10
         rows = [0, 4, 7]
-        row_numel = 10
-        selcted_rows = core.SelectedRows(rows, row_numel)
-        np_array = np.ones((len(rows), height)).astype("float32")
+        row_numel = 12
+        selected_rows = core.SelectedRows(rows, height)
+        np_array = np.ones((len(rows), row_numel)).astype("float32")
         np_array[0, 0] = 2.0
         np_array[2, 8] = 4.0
-        tensor = selcted_rows.get_tensor()
+        tensor = selected_rows.get_tensor()
         tensor.set(np_array, place)
 
         # compare rows
-        self.assertEqual(0, selcted_rows.rows()[0])
-        self.assertEqual(4, selcted_rows.rows()[1])
-        self.assertEqual(7, selcted_rows.rows()[2])
+        self.assertEqual(0, selected_rows.rows()[0])
+        self.assertEqual(4, selected_rows.rows()[1])
+        self.assertEqual(7, selected_rows.rows()[2])
 
         # compare height
-        self.assertEqual(10, selcted_rows.height())
+        self.assertEqual(10, selected_rows.height())
 
         # compare tensor
         self.assertAlmostEqual(2.0,
-                               selcted_rows.get_tensor().get_float_element(0))
+                               selected_rows.get_tensor().get_float_element(0))
         self.assertAlmostEqual(1.0,
-                               selcted_rows.get_tensor().get_float_element(1))
+                               selected_rows.get_tensor().get_float_element(1))
         self.assertAlmostEqual(
-            4.0, selcted_rows.get_tensor().get_float_element(2 * row_numel + 8))
+            4.0,
+            selected_rows.get_tensor().get_float_element(2 * row_numel + 8))
 
 
 if __name__ == "__main__":

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

 import unittest
 import numpy as np
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
 from op_test import OpTest
 
 
@@ -17,5 +19,70 @@ class TestSGDOp(OpTest):
         self.check_output()
 
 
+class TestSparseSGDOp(unittest.TestCase):
+    def check_with_place(self, place):
+        scope = core.Scope()
+
+        # create and initialize Grad Variable   
+        height = 10
+        rows = [0, 4, 7]
+        row_numel = 12
+
+        grad_selected_rows = scope.var('Grad').get_selected_rows()
+        grad_selected_rows.set_height(height)
+        grad_selected_rows.set_rows(rows)
+        np_array = np.ones((len(rows), row_numel)).astype("float32")
+        np_array[0, 0] = 2.0
+        np_array[2, 8] = 4.0
+
+        grad_tensor = grad_selected_rows.get_tensor()
+        grad_tensor.set(np_array, place)
+
+        # create and initialize Param Variable
+        param = scope.var('Param').get_tensor()
+        param_array = np.full((height, row_numel), 5.0).astype("float32")
+        param.set(param_array, place)
+
+        # create and initialize LeraningRate Variable
+        lr = scope.var('LearningRate').get_tensor()
+        lr_array = np.full((1), 2.0).astype("float32")
+        lr.set(lr_array, place)
+
+        # create and run sgd operator
+        sgd_op = Operator(
+            "sgd",
+            Param='Param',
+            Grad='Grad',
+            ParamOut='Param',
+            LearningRate='LearningRate')
+        ctx = core.DeviceContext.create(place)
+        sgd_op.run(scope, ctx)
+
+        # get and compare result
+        result_array = np.array(param)
+
+        # rows[0] = 0, 5.0 - 2.0 * 2.0
+        self.assertAlmostEqual(1.0, result_array[rows[0], 0])
+        # rows[0] = 0, 5.0 - 2.0 * 1.0
+        self.assertAlmostEqual(3.0, result_array[rows[0], 2])
+        # 5.0 - 2.0 * 0.0
+        self.assertAlmostEqual(5.0, result_array[1, 0])
+        # rows[1] = 4, 5.0 - 2.0 * 1.0
+        self.assertAlmostEqual(3.0, result_array[rows[1], 10])
+        # 5.0 - 2.0 * 0.0
+        self.assertAlmostEqual(5.0, result_array[5, 8])
+        # rows[2] = 7, 5.0 - 2.0 * 1.0
+        self.assertAlmostEqual(3.0, result_array[rows[2], 1])
+        # rows[2] = 7, 5.0 - 2.0 * 4.0
+        self.assertAlmostEqual(-3.0, result_array[rows[2], 8])
+
+    def test_sparse_sgd(self):
+        places = [core.CPUPlace()]
+        if core.is_compile_gpu():
+            places.append(core.GPUPlace(0))
+        for place in places:
+            self.check_with_place(place)
+
+
 if __name__ == "__main__":
     unittest.main()