Merge branch 'develop' into core_inference_multi_thread

doc/fluid/design/concurrent/select_op.md

+# select_op Design
+
+## Introduction
+
+In golang, the [**select**](https://golang.org/ref/spec#Select_statements) 
+statement lets a goroutine wait on multiple communication operations at the 
+same time. The **select** blocks until one of its cases can run, then 
+executes the case.  If multiple cases are ready to run, then one case is 
+choosen at random to be executed.
+
+With the introduction of CSP for Paddle, we mimic this behavior by 
+creating a ***select_op***.
+
+## How to use it
+
+The **select_op** is available as a c++ operator.  However most users
+will prefer to use the much simplier Python API.
+
+- **fluid.Select()**: Creates a select operator and adds it to the current
+block within the main program.  Also creates a sub block and adds it to the 
+main program.  This sub block is used to hold all variables and operators 
+used by the case statements.
+ 
+Within the select block, users can add cases by 
+calling **select.case** or **select.default** method.
+
+- **fluid.Select.case(channel_action, channel, result_variable)**: Represents
+a fluid channel send/recv case.  This method creates a SelectCase block
+guard and adds it to the Select block.  The arguments into this method tells
+the select which channel operation to listen to.
+
+- **fluid.Select.default()**: Represents the fluid default case.  This default
+case is executed if none of the channel send/recv cases are available to
+execute.
+
+**Example:**
+```
+ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+            
+x = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
+y = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=1)
+ 
+while_cond = fill_constant(shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True)
+while_op = While(cond=while_cond)    
+ 
+with while_op.block():
+    with fluid.Select() as select:
+        with select.case(fluid.channel_send, channel, x):
+            # Send x, then perform Fibonacci calculation on x and y
+            x_tmp = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
+            assign(input=x, output=x_tmp)
+            assign(input=y, output=x)
+            assign(elementwise_add(x=x_tmp, y=y), output=y)
+        with select.case(fluid.channel_recv, quit_channel, result2):
+            # Exit out of While loop
+            while_false = fill_constant(shape=[1], dtype=core.VarDesc.VarType.BOOL, value=False)
+            helper = layer_helper.LayerHelper('assign')
+            helper.append_op(
+                type='assign',
+                inputs={'X': [while_false]},
+                outputs={'Out': [while_cond]})
+```
+
+## How it Works
+
+### Program Description
+
+```
+blocks {
+  idx: 0
+  ...
+  // Create "case_to_execute" variable
+  ops {
+    outputs {
+      parameter: "Out"
+      arguments: "fill_constant_110.tmp_0"
+    }
+    type: "fill_constant"
+    attrs {
+      name: "force_cpu"
+      type: BOOLEAN
+      b: false
+    }
+    attrs {
+      name: "value"
+      type: FLOAT
+      f: -1.0
+    }
+    attrs {
+      name: "shape"
+      type: INTS
+      ints: 1
+    }
+    attrs {
+      name: "dtype"
+      type: INT
+      i: 2
+    }
+  }
+  // Create "select" operator.
+  // inputs: 
+  //   X: All input variables used by operators within the select block
+  //   case_to_execute: Variable filled in by select_op when it determines
+  //     which case to execute.
+  //  
+  // outputs:
+  //   Out: All output variables referenced by operators within select block. 
+  // 
+  // attrs:
+  //   sub_block: The block id containing the select "cases"
+  //   cases:  Serialized list of all cases in the select op. 
+  //     Each case is serialized as: '<index>,<type>,<channel>,<value>'
+  //     where type is 0 for default, 1 for send, and 2 for receive.
+  //     No channel and values are needed for default cases.
+  ops {
+    inputs {
+      parameter: "X"
+      arguments: "fill_constant_103.tmp_0"
+      arguments: "fill_constant_104.tmp_0"
+    }
+    inputs {
+      parameter: "case_to_execute"
+      arguments: "fill_constant_110.tmp_0"
+    }
+    outputs {
+      parameter: "Out"
+      arguments: "fill_constant_110.tmp_0"
+    }    
+    type: "select"
+    attrs {
+      name: "sub_block"
+      type: BLOCK
+      block_idx: 1
+    }
+    attrs {
+      name: "cases"
+      type: STRINGS
+      strings: "0,1,channel_101,fill_constant_109.tmp_0"
+      strings: "1,2,channel_102,fill_constant_108.tmp_0"
+    }
+  }
+  ...
+}
+```
+
+The python select API will add the **select_op** to the current block.  In addition, it will
+iterate through all it's case statements and add any input variables required by case statements
+into **X**.  It will also create a temp variable called **case_to_execute**.  This variable is
+filled in by the select_op after it has completed processing the case statements.
+
+If there are no available cases to execute (ie: all cases are blocked on channel operations, and
+there is no default statement), then the select_op will block the current thread.  The thread will 
+unblock once there is a channel operation affecting one of the case statements, at which point, the
+**select_op** will set the **case_to_execute** variable to the index of the case to execute.
+
+Finally the select_op will call executor.run on the **sub_block**.
+
+```
+blocks {
+  idx: 1
+  parent_idx: 0
+  ...
+  // Fill a tensor with the case index (ie: 0,1,2,3,ect.)
+  ops {
+    outputs {
+      parameter: "Out"
+      arguments: "fill_constant_111.tmp_0"
+    }
+    type: "fill_constant"
+    attrs {
+      name: "force_cpu"
+      type: BOOLEAN
+      b: false
+    }
+    attrs {
+      name: "value"
+      type: FLOAT
+      f: 0.0
+    }
+    attrs {
+      name: "shape"
+      type: INTS
+      ints: 1
+    }
+    attrs {
+      name: "dtype"
+      type: INT
+      i: 2
+    }
+  }
+  // Create an "equal" operator to compare the case index with the "case_to_execute"
+  // tensor (which was filled in by the select op).
+  ops {
+    inputs {
+      parameter: "X"
+      arguments: "fill_constant_111.tmp_0"  // case 0
+    }
+    inputs {
+      parameter: "Y"
+      arguments: "fill_constant_110.tmp_0"  // case_to_execute
+    }
+    outputs {
+      parameter: "Out"
+      arguments: "equal_0.tmp_0"
+    }
+    type: "equal"
+    attrs {
+      name: "axis"
+      type: INT
+      i: -1
+    }
+  }
+  // Use the output of the "equal" operator as a condition for the "conditional_block".
+  // If the condition evaluates to true, then execute the "sub_block" (which represents
+  // the select case's body)
+  ops {
+    inputs {
+      parameter: "Params"
+    }
+    inputs {
+      parameter: "X"
+      arguments: "equal_0.tmp_0"
+    }
+    outputs {
+      parameter: "Out"
+    }
+    outputs {
+      parameter: "Scope"
+      arguments: "_generated_var_0"
+    }
+    type: "conditional_block"
+    attrs {
+      name: "is_scalar_condition"
+      type: BOOLEAN
+      b: true
+    }
+    attrs {
+      name: "sub_block"
+      type: BLOCK
+      block_idx: 4
+    }
+  }
+  ...
+  // Repeat the above operators for each case statements inside the select body
+}
+
+```
+
+Cases are represented by a **conditional_block operator**, whose's condition is set as the output of 
+equal(**case_to_execute**, **case_index**).  Since each case index is unique in this sub-block, 
+only one case will be executed.
+
+### select_op flow
+
+<p align="center">
+<img src="./images/select_op_workflow.png"/><br/>
+</p>
+
+The select algorithm is inspired by golang's select routine.  Please refer to 
+http://www.tapirgames.com/blog/golang-concurrent-select-implementation for more information.
+
+## Backward Pass
+
+TODO

doc/v2/getstarted/index_en.rst

 GET STARTED
 ============
 
+If you want to quickly know how to use PaddlePaddle, please refer to the following guide:
+
 ..  toctree::
   :maxdepth: 1
 
   quickstart_en.rst
+  
+  
+While using PaddlePaddle to build applications, please understand some basic concepts.
+
+Here is an example of linear regression. It introduces workflow of PaddlePaddle, including data format, model configuration and training, etc.
+  
+..  toctree::
+  :maxdepth: 1
+  
   concepts/use_concepts_en.rst

doc/v2/howto/cluster/index_en.rst

@@ -2,6 +2,9 @@ Distributed Training
 ====================
 
 The effectiveness of the deep learning model is often directly related to the scale of the data: it can generally achieve better results after increasing the size of the dataset on the same model. However, it can not fit in one single computer when the amount of data increases to a certain extent. At this point, using multiple computers for distributed training is a natural solution. In distributed training, the training data is divided into multiple copies (sharding), and multiple machines participating in the training read their own data for training and collaboratively update the parameters of the overall model.
+
+Distributed training generally has framwork as shown below:
+
 .. image:: src/ps_en.png
    :width: 500
 

paddle/fluid/framework/executor.cc

@@ -14,12 +14,8 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/executor.h"
 
-#include <set>
-
-#include "gflags/gflags.h"
 #include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
-#include "paddle/fluid/framework/feed_fetch_type.h"
 #include "paddle/fluid/framework/lod_rank_table.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
@@ -40,14 +36,13 @@ namespace {
 int kProgramId = -1;
 }  // namespace
 
-struct ExecutorPrepareContext {
-  ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id)
-      : prog_(prog), block_id_(block_id) {}
+ExecutorPrepareContext::ExecutorPrepareContext(
+    const framework::ProgramDesc& prog, size_t block_id)
+    : prog_(prog), block_id_(block_id) {}
 
-  const framework::ProgramDesc& prog_;
-  size_t block_id_;
-  std::vector<std::unique_ptr<OperatorBase>> ops_;
-};
+ExecutorPrepareContext::~ExecutorPrepareContext() {
+  VLOG(5) << "destroy ExecutorPrepareContext";
+}
 
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
@@ -101,9 +96,8 @@ static void CheckTensorNANOrInf(const std::string& name,
 void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
                    bool create_local_scope, bool create_vars) {
   platform::RecordBlock b(block_id);
-  auto* ctx = Prepare(pdesc, block_id);
-  RunPreparedContext(ctx, scope, create_local_scope, create_vars);
-  delete ctx;
+  auto ctx = Prepare(pdesc, block_id);
+  RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars);
 }
 
 // Check whether the block already has feed operators and feed_holder.
@@ -274,15 +268,15 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
   }
 }
 
-ExecutorPrepareContext* Executor::Prepare(const ProgramDesc& program,
-                                          int block_id) {
+std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
+    const ProgramDesc& program, int block_id) {
   auto* ctx = new ExecutorPrepareContext(program, block_id);
   PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), program.Size());
   auto& block = program.Block(block_id);
   for (auto& op_desc : block.AllOps()) {
     ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
   }
-  return ctx;
+  return std::unique_ptr<ExecutorPrepareContext>(ctx);
 }
 
 void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,

paddle/fluid/framework/executor.h

@@ -22,7 +22,16 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
-struct ExecutorPrepareContext;
+
+struct ExecutorPrepareContext {
+  ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id);
+  ~ExecutorPrepareContext();
+
+  const framework::ProgramDesc& prog_;
+  size_t block_id_;
+  std::vector<std::unique_ptr<OperatorBase>> ops_;
+};
+
 class Executor {
  public:
   // TODO(dzhwinter) : Do not rely on this function, it will be removed
@@ -47,8 +56,8 @@ class Executor {
            const std::string& feed_holder_name = "feed",
            const std::string& fetch_holder_name = "fetch");
 
-  static ExecutorPrepareContext* Prepare(const ProgramDesc& program,
-                                         int block_id);
+  static std::unique_ptr<ExecutorPrepareContext> Prepare(
+      const ProgramDesc& program, int block_id);
 
   void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                           bool create_local_scope = true,

paddle/fluid/operators/elementwise_add_op.cu

@@ -14,19 +14,20 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 #include "paddle/fluid/operators/elementwise_add_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 
 REGISTER_OP_CUDA_KERNEL(
-    elementwise_add,
-    ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, int64_t>);
+    elementwise_add, ops::ElementwiseAddKernel<plat::CUDADeviceContext, float>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, double>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_add_grad,
-    ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext,
-                                  int64_t>);
+    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, float>,
+    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, double>,
+    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>);

paddle/fluid/operators/listen_and_serv_op.cc

@@ -24,6 +24,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/proto_desc.h"
+#include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/operators/detail/grpc_server.h"
 #include "paddle/fluid/operators/detail/sendrecvop_utils.h"
 #include "paddle/fluid/operators/detail/simple_block_queue.h"
@@ -89,6 +90,10 @@ class ListenAndServOp : public framework::OperatorBase {
 
     auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
     auto *program = block->Program();
+    int num_blocks = program->Size();
+    PADDLE_ENFORCE_GE(num_blocks, 2,
+                      "server program should have at least 2 blocks");
+
     framework::Executor executor(dev_place);
 
     // TODO(typhoonzero): change this to a while_op for every cluster-batch.
@@ -132,12 +137,36 @@ class ListenAndServOp : public framework::OperatorBase {
         rpc_service_->ShutDown();
         break;
       }
-      try {
-        executor.Run(*program, &recv_scope, block->ID(), /*global_block*/
-                     false /*create_local_scope*/, false /*create_vars*/);
-      } catch (std::exception &e) {
-        LOG(ERROR) << "run sub program error " << e.what();
+
+      // put optimize blocks in the thread pool to start run, the last block
+      // should be global ops.
+      // NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads
+      // and this will still work.
+      std::vector<std::future<void>> fs;
+      // block0 contains only listen_and_serv op, start run from block1.
+      for (int blkid = 1; blkid < num_blocks - 1; ++blkid) {
+        fs.push_back(framework::Async([&executor, &program, &recv_scope,
+                                       blkid]() {
+          int run_block = blkid;  // thread local
+          try {
+            executor.Run(*program, &recv_scope, run_block,
+                         false /*create_local_scope*/, false /*create_vars*/);
+          } catch (std::exception &e) {
+            LOG(ERROR) << "run sub program error " << e.what();
+          }
+        }));
+      }
+      for (int i = 0; i < num_blocks - 2; ++i) fs[i].wait();
+      // Run global block at final step, or block1 if there are only 2 blocks
+      if (num_blocks >= 2) {
+        try {
+          executor.Run(*program, &recv_scope, num_blocks - 1,
+                       false /*create_local_scope*/, false /*create_vars*/);
+        } catch (std::exception &e) {
+          LOG(ERROR) << "run sub program error " << e.what();
+        }
       }
+
       // Reset the received sparse variables, the sum operator would not
       // sum the input sparse variables which rows is empty at the next
       // mini-batch.

paddle/fluid/platform/float16.h

@@ -600,7 +600,7 @@ HOSTDEVICE inline bool operator>=(const float16& a, const float16& b) {
 
 // Arithmetic operators for float16 on ARMv8.2-A CPU
 #elif defined(PADDLE_WITH_NATIVE_FP16)
-HOST inline float16 operator+(const float16& a, const float16& b) {
+inline float16 operator+(const float16& a, const float16& b) {
   float16 res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -616,7 +616,7 @@ HOST inline float16 operator+(const float16& a, const float16& b) {
   return res;
 }
 
-HOST inline float16 operator-(const float16& a, const float16& b) {
+inline float16 operator-(const float16& a, const float16& b) {
   float16 res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -632,7 +632,7 @@ HOST inline float16 operator-(const float16& a, const float16& b) {
   return res;
 }
 
-HOST inline float16 operator*(const float16& a, const float16& b) {
+inline float16 operator*(const float16& a, const float16& b) {
   float16 res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -648,7 +648,7 @@ HOST inline float16 operator*(const float16& a, const float16& b) {
   return res;
 }
 
-HOST inline float16 operator/(const float16& a, const float16& b) {
+inline float16 operator/(const float16& a, const float16& b) {
   float16 res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -664,7 +664,7 @@ HOST inline float16 operator/(const float16& a, const float16& b) {
   return res;
 }
 
-HOST inline float16 operator-(const float16& a) {
+inline float16 operator-(const float16& a) {
   float16 res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -679,27 +679,27 @@ HOST inline float16 operator-(const float16& a) {
   return res;
 }
 
-HOST inline float16& operator+=(float16& a, const float16& b) {
+inline float16& operator+=(float16& a, const float16& b) {
   a = a + b;
   return a;
 }
 
-HOST inline float16& operator-=(float16& a, const float16& b) {
+inline float16& operator-=(float16& a, const float16& b) {
   a = a - b;
   return a;
 }
 
-HOST inline float16& operator*=(float16& a, const float16& b) {
+inline float16& operator*=(float16& a, const float16& b) {
   a = a * b;
   return a;
 }
 
-HOST inline float16& operator/=(float16& a, const float16& b) {
+inline float16& operator/=(float16& a, const float16& b) {
   a = a / b;
   return a;
 }
 
-HOST inline bool operator==(const float16& a, const float16& b) {
+inline bool operator==(const float16& a, const float16& b) {
   uint16_t res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -715,11 +715,9 @@ HOST inline bool operator==(const float16& a, const float16& b) {
   return (res & 0xffff) != 0;
 }
 
-HOST inline bool operator!=(const float16& a, const float16& b) {
-  return !(a == b);
-}
+inline bool operator!=(const float16& a, const float16& b) { return !(a == b); }
 
-HOST inline bool operator<(const float16& a, const float16& b) {
+inline bool operator<(const float16& a, const float16& b) {
   uint16_t res;
   asm volatile(
       "ld1 {v1.h}[0], [%[a_ptr]]\n"
@@ -735,7 +733,7 @@ HOST inline bool operator<(const float16& a, const float16& b) {
   return (res & 0xffff) != 0;
 }
 
-HOST inline bool operator<=(const float16& a, const float16& b) {
+inline bool operator<=(const float16& a, const float16& b) {
   uint16_t res;
   asm volatile(
       "ld1 {v1.h}[0], [%[a_ptr]]\n"
@@ -751,7 +749,7 @@ HOST inline bool operator<=(const float16& a, const float16& b) {
   return (res & 0xffff) != 0;
 }
 
-HOST inline bool operator>(const float16& a, const float16& b) {
+inline bool operator>(const float16& a, const float16& b) {
   uint16_t res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -767,7 +765,7 @@ HOST inline bool operator>(const float16& a, const float16& b) {
   return (res & 0xffff) != 0;
 }
 
-HOST inline bool operator>=(const float16& a, const float16& b) {
+inline bool operator>=(const float16& a, const float16& b) {
   uint16_t res;
   asm volatile(
       "ld1 {v0.h}[0], [%[a_ptr]]\n"
@@ -785,69 +783,69 @@ HOST inline bool operator>=(const float16& a, const float16& b) {
 
 // Arithmetic operators for float16, software emulated on other CPU
 #else
-HOST inline float16 operator+(const float16& a, const float16& b) {
+inline float16 operator+(const float16& a, const float16& b) {
   return float16(float(a) + float(b));
 }
 
-HOST inline float16 operator-(const float16& a, const float16& b) {
+inline float16 operator-(const float16& a, const float16& b) {
   return float16(float(a) - float(b));
 }
 
-HOST inline float16 operator*(const float16& a, const float16& b) {
+inline float16 operator*(const float16& a, const float16& b) {
   return float16(float(a) * float(b));
 }
 
-HOST inline float16 operator/(const float16& a, const float16& b) {
+inline float16 operator/(const float16& a, const float16& b) {
   return float16(float(a) / float(b));
 }
 
-HOST inline float16 operator-(const float16& a) {
+inline float16 operator-(const float16& a) {
   float16 res;
   res.x = a.x ^ 0x8000;
   return res;
 }
 
-HOST inline float16& operator+=(float16& a, const float16& b) {
+inline float16& operator+=(float16& a, const float16& b) {
   a = float16(float(a) + float(b));
   return a;
 }
 
-HOST inline float16& operator-=(float16& a, const float16& b) {
+inline float16& operator-=(float16& a, const float16& b) {
   a = float16(float(a) - float(b));
   return a;
 }
 
-HOST inline float16& operator*=(float16& a, const float16& b) {
+inline float16& operator*=(float16& a, const float16& b) {
   a = float16(float(a) * float(b));
   return a;
 }
 
-HOST inline float16& operator/=(float16& a, const float16& b) {
+inline float16& operator/=(float16& a, const float16& b) {
   a = float16(float(a) / float(b));
   return a;
 }
 
-HOST inline bool operator==(const float16& a, const float16& b) {
+inline bool operator==(const float16& a, const float16& b) {
   return float(a) == float(b);
 }
 
-HOST inline bool operator!=(const float16& a, const float16& b) {
+inline bool operator!=(const float16& a, const float16& b) {
   return float(a) != float(b);
 }
 
-HOST inline bool operator<(const float16& a, const float16& b) {
+inline bool operator<(const float16& a, const float16& b) {
   return float(a) < float(b);
 }
 
-HOST inline bool operator<=(const float16& a, const float16& b) {
+inline bool operator<=(const float16& a, const float16& b) {
   return float(a) <= float(b);
 }
 
-HOST inline bool operator>(const float16& a, const float16& b) {
+inline bool operator>(const float16& a, const float16& b) {
   return float(a) > float(b);
 }
 
-HOST inline bool operator>=(const float16& a, const float16& b) {
+inline bool operator>=(const float16& a, const float16& b) {
   return float(a) >= float(b);
 }
 #endif

python/paddle/fluid/distribute_transpiler.py

@@ -307,15 +307,57 @@ class DistributeTranspiler:
         # Iterate through the ops, and if an op and the optimize ops
         # which located on current pserver are in one set, then 
         # append it into the sub program.
-        for _, op in enumerate(self.optimize_ops):
-            for _, opt_op in enumerate(opt_op_on_pserver):
-                if ufind.is_connected(op, opt_op):
-                    if self._is_opt_op(op):
-                        self._append_pserver_ops(optimize_block, op, endpoint,
-                                                 default_main_program())
-                    else:
-                        self._append_pserver_non_opt_ops(optimize_block, op)
-                    break
+
+        # We try to put optimization program run parallelly, assume
+        # optimization program always looks like:
+        #
+        # prevop -> prevop -> opt op -> following op -> following op; ->
+        # prevop -> prevop -> opt op -> following op -> following op; ->
+        # global op -> global op
+        #
+        # we put operators that can run parallelly to many program blocks.
+        # in above example, we seperate ops by the ";". Global ops must run
+        # after all the optimize ops finished.
+
+        global_ops = []
+        # HACK: optimization global ops only used to scale beta1 and beta2
+        # replace it with dependency engine.
+        for op in self.optimize_ops:
+            if op.type == "scale":
+                for in_name in op.input_arg_names:
+                    if in_name.startswith("beta1_pow_acc") or\
+                        in_name.startswith("beta2_pow_acc"):
+                        global_ops.append(op)
+
+        def __append_optimize_op__(op, block):
+            if self._is_opt_op(op):
+                self._append_pserver_ops(block, op, endpoint,
+                                         default_main_program())
+            else:
+                self._append_pserver_non_opt_ops(block, op)
+
+        # append op to the current block
+        per_opt_block = optimize_block
+        for _, opt_op in enumerate(opt_op_on_pserver):
+            for _, op in enumerate(self.optimize_ops):
+                # optimizer is connected to itself
+                if ufind.is_connected(op, opt_op) and \
+                    op not in global_ops:
+                    __append_optimize_op__(op, per_opt_block)
+            per_opt_block = pserver_program.create_block(0)
+
+        # append global ops
+        for glb_op in global_ops:
+            __append_optimize_op__(glb_op, per_opt_block)
+
+        # NOT USED: single block version:
+        #
+        # for _, op in enumerate(self.optimize_ops):
+        #     for _, opt_op in enumerate(opt_op_on_pserver):
+        #         if ufind.is_connected(op, opt_op):
+        #             __append_optimize_op__(glb_op, optimize_block)
+        #             break
+
         # step5 append the listen_and_serv op
         pserver_program.global_block().append_op(
             type="listen_and_serv",
@@ -660,10 +702,22 @@ class DistributeTranspiler:
         # If one op's input is another op's output or
         # one op's output is another op's input, we say
         # the two operator is connected.
-        op1_input_names = op1.desc.input_arg_names()
+        def _append_inname_remove_beta(varname_list):
+            op_input_names = []
+            for in_name in varname_list:
+                # HACK: remove beta1 and beta2 to avoid let all
+                # ops connected.
+                if in_name.startswith("beta2_pow_acc") or \
+                    in_name.startswith("beta1_pow_acc"):
+                    continue
+                else:
+                    op_input_names.append(in_name)
+            return op_input_names
+
+        op1_input_names = _append_inname_remove_beta(op1.desc.input_arg_names())
         op1_output_names = op1.desc.output_arg_names()
 
-        op2_input_names = op2.desc.input_arg_names()
+        op2_input_names = _append_inname_remove_beta(op2.desc.input_arg_names())
         op2_output_names = op2.desc.output_arg_names()
 
         if set(op1_output_names) & set(op2_input_names) or \

python/paddle/fluid/executor.py

@@ -235,6 +235,77 @@ class Executor(object):
             tensor.set_lod(lod)
             return tensor
 
+    def _get_program_cache(self, program_cache_key):
+        return self.program_caches.get(program_cache_key, None)
+
+    def _add_program_cache(self, program_cache_key, program):
+        self.program_caches[program_cache_key] = program
+
+    def _add_feed_fetch_ops(self, program, feed, fetch_list, feed_var_name,
+                            fetch_var_name):
+        tmp_program = program.clone()
+
+        global_block = tmp_program.global_block()
+
+        if feed_var_name in global_block.vars:
+            feed_var = global_block.var(feed_var_name)
+        else:
+            feed_var = global_block.create_var(
+                name=feed_var_name,
+                type=core.VarDesc.VarType.FEED_MINIBATCH,
+                persistable=True)
+
+        if fetch_var_name in global_block.vars:
+            fetch_var = global_block.var(fetch_var_name)
+        else:
+            fetch_var = global_block.create_var(
+                name=fetch_var_name,
+                type=core.VarDesc.VarType.FETCH_LIST,
+                persistable=True)
+
+        # prepend feed operators
+        if not has_feed_operators(global_block, feed, feed_var_name):
+            for i, name in enumerate(feed):
+                out = global_block.var(name)
+                global_block.prepend_op(
+                    type='feed',
+                    inputs={'X': [feed_var]},
+                    outputs={'Out': [out]},
+                    attrs={'col': i})
+
+        # append fetch_operators
+        if not has_fetch_operators(global_block, fetch_list, fetch_var_name):
+            for i, var in enumerate(fetch_list):
+                assert isinstance(var, Variable) or isinstance(var, str), (
+                    "Wrong type for fetch_list[%s]: %s" % (i, type(var)))
+                global_block.append_op(
+                    type='fetch',
+                    inputs={'X': [var]},
+                    outputs={'Out': [fetch_var]},
+                    attrs={'col': i})
+
+        return tmp_program
+
+    def _feed_data(self, program, feed, feed_var_name, scope):
+        # feed var to framework
+        for op in program.global_block().ops:
+            if op.desc.type() == 'feed':
+                feed_target_name = op.desc.output('Out')[0]
+                cur_feed = feed[feed_target_name]
+                if not isinstance(cur_feed, core.LoDTensor):
+                    cur_feed = self.aslodtensor(cur_feed)
+                idx = op.desc.attr('col')
+                core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
+            else:
+                break
+
+    def _fetch_data(self, fetch_list, fetch_var_name, scope):
+        outs = [
+            core.get_fetch_variable(scope, fetch_var_name, i)
+            for i in xrange(len(fetch_list))
+        ]
+        return outs
+
     def run(self,
             program=None,
             feed=None,
@@ -268,7 +339,6 @@ class Executor(object):
             raise TypeError("feed should be a map")
         if fetch_list is None:
             fetch_list = []
-
         if program is None:
             program = default_main_program()
 
@@ -278,79 +348,30 @@ class Executor(object):
         if scope is None:
             scope = global_scope()
 
-        program_cache = None
-        program_cache_key = get_program_cache_key(feed, fetch_list)
-
+        cache_key = get_program_cache_key(feed, fetch_list)
         if use_program_cache:
-            # find program cache by cache_key
-            program_cache = self.program_caches.get(program_cache_key, None)
-            # TODO(qiao): Should check program_cache and program are exactly the same.
+            cached_program = self._get_program_cache(cache_key)
+            if cached_program is None:
+                cached_program = self._add_feed_fetch_ops(
+                    program=program,
+                    feed=feed,
+                    fetch_list=fetch_list,
+                    feed_var_name=feed_var_name,
+                    fetch_var_name=fetch_var_name)
+                self._add_program_cache(cache_key, cached_program)
+            program = cached_program
         else:
-            self.program_caches.pop(program_cache_key, None)
-
-        if program_cache is None:
-            program_cache = program.clone()
-
-            if use_program_cache:
-                self.program_caches[program_cache_key] = program_cache
-
-            global_block = program_cache.global_block()
-
-            if feed_var_name in global_block.vars:
-                feed_var = global_block.var(feed_var_name)
-            else:
-                feed_var = global_block.create_var(
-                    name=feed_var_name,
-                    type=core.VarDesc.VarType.FEED_MINIBATCH,
-                    persistable=True)
-
-            if fetch_var_name in global_block.vars:
-                fetch_var = global_block.var(fetch_var_name)
-            else:
-                fetch_var = global_block.create_var(
-                    name=fetch_var_name,
-                    type=core.VarDesc.VarType.FETCH_LIST,
-                    persistable=True)
-
-            # prepend feed operators
-            if not has_feed_operators(global_block, feed, feed_var_name):
-                for i, name in enumerate(feed):
-                    out = global_block.var(name)
-                    global_block.prepend_op(
-                        type='feed',
-                        inputs={'X': [feed_var]},
-                        outputs={'Out': [out]},
-                        attrs={'col': i})
-
-            # append fetch_operators
-            if not has_fetch_operators(global_block, fetch_list,
-                                       fetch_var_name):
-                for i, var in enumerate(fetch_list):
-                    assert isinstance(var, Variable) or isinstance(var, str), (
-                        "Wrong type for fetch_list[%s]: %s" % (i, type(var)))
-                    global_block.append_op(
-                        type='fetch',
-                        inputs={'X': [var]},
-                        outputs={'Out': [fetch_var]},
-                        attrs={'col': i})
-
-        # feed var to framework
-        for op in program_cache.global_block().ops:
-            if op.desc.type() == 'feed':
-                feed_target_name = op.desc.output('Out')[0]
-                cur_feed = feed[feed_target_name]
-                if not isinstance(cur_feed, core.LoDTensor):
-                    cur_feed = self.aslodtensor(cur_feed)
-                idx = op.desc.attr('col')
-                core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
-            else:
-                break
-
-        self.executor.run(program_cache.desc, scope, 0, True, True)
-        outs = [
-            core.get_fetch_variable(scope, fetch_var_name, i)
-            for i in xrange(len(fetch_list))
-        ]
+            self.program_caches.pop(cache_key, None)
+            program = self._add_feed_fetch_ops(
+                program=program,
+                feed=feed,
+                fetch_list=fetch_list,
+                feed_var_name=feed_var_name,
+                fetch_var_name=fetch_var_name)
+
+        self._feed_data(program, feed, feed_var_name, scope)
+        self.executor.run(program.desc, scope, 0, True, True)
+        outs = self._fetch_data(fetch_list, fetch_var_name, scope)
         if return_numpy:
             outs = as_numpy(outs)
         return outs

python/paddle/fluid/layers/nn.py

@@ -1117,12 +1117,14 @@ def conv2d(input,
            filter_size,
            stride=1,
            padding=0,
+           dilation=1,
            groups=None,
            param_attr=None,
            bias_attr=None,
            use_cudnn=True,
            use_mkldnn=False,
-           act=None):
+           act=None,
+           name=None):
     """
     **Convlution2D Layer**
 
@@ -1183,6 +1185,9 @@ def conv2d(input,
        padding(int|tuple): The padding size. If padding is a tuple, it must
            contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: padding = 0.
+       dilation(int|tuple): The dilation size. If dilation is a tuple, it must
+           contain two integers, (dilation_H, dilation_W). Otherwise, the
+           dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups(int): The groups number of the Conv2d Layer. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
@@ -1193,6 +1198,8 @@ def conv2d(input,
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
        act(str): Activation type. Default: None
+       name(str|None): A name for this layer(optional). If set None, the layer
+           will be named automatically.
 
     Returns:
         Variable: The tensor variable storing the convolution and \
@@ -1233,6 +1240,7 @@ def conv2d(input,
     filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
     stride = utils.convert_to_list(stride, 2, 'stride')
     padding = utils.convert_to_list(padding, 2, 'padding')
+    dilation = utils.convert_to_list(dilation, 2, 'dilation')
 
     if not isinstance(use_cudnn, bool):
         raise ValueError("use_cudnn should be True or False")
@@ -1262,6 +1270,7 @@ def conv2d(input,
         attrs={
             'strides': stride,
             'paddings': padding,
+            'dilations': dilation,
             'groups': groups,
             'use_cudnn': use_cudnn,
             'use_mkldnn': use_mkldnn
@@ -1670,7 +1679,9 @@ def conv2d_transpose(input,
                      stride=1,
                      dilation=1,
                      param_attr=None,
+                     bias_attr=None,
                      use_cudnn=True,
+                     act=None,
                      name=None):
     """
     **Convlution2D transpose layer**
@@ -1739,8 +1750,10 @@ def conv2d_transpose(input,
            dilation_H = dilation_W = dilation. Default: dilation = 1.
        param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
                               Default: None
+       bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
+       act(str): Activation type. Default: None
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
 
@@ -1793,12 +1806,12 @@ def conv2d_transpose(input,
     img_filter = helper.create_parameter(
         dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
 
-    out = helper.create_tmp_variable(dtype=input.dtype)
+    pre_bias = helper.create_tmp_variable(dtype=input.dtype)
     helper.append_op(
         type='conv2d_transpose',
         inputs={'Input': [input],
                 'Filter': [img_filter]},
-        outputs={'Output': out},
+        outputs={'Output': pre_bias},
         attrs={
             'strides': stride,
             'paddings': padding,
@@ -1806,6 +1819,8 @@ def conv2d_transpose(input,
             'use_cudnn': use_cudnn
         })
 
+    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
+    out = helper.append_activation(pre_act)
     return out
 
 

python/paddle/fluid/optimizer.py

@@ -664,6 +664,123 @@ class AdadeltaOptimizer(Optimizer):
         return adadelta_op
 
 
+class RMSPropOptimizer(Optimizer):
+    """
+    Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning
+    rate method. The original slides proposed RMSProp: Slide 29 of
+    http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf .
+
+    The original equation is as follows:
+
+    ..  math::
+
+        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 \\\\
+
+        w & = w - \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w)
+
+    The first equation calculates moving average of the squared gradient for
+    each weight. Then dividing the gradient by :math: `sqrt{v(w,t)}`.
+
+    In some cases, adding a momentum term :math: `\\beta` is beneficial.
+    In our implementation, Nesterov momentum is used:
+
+    ..  math::
+
+        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 \\\\
+
+        v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{v(w,t) +
+            \\epsilon}} \\nabla Q_{i}(w)
+
+        w & = w - v(w, t)
+
+    where, :math: `\\rho` is a hyperparameter and typical values are 0.9, 0.95
+    and so on. :math: `beta` is the momentum term. :math: `\\epsilon` is a
+    smoothing term to avoid division by zero, usually set somewhere in range
+    from 1e-4 to 1e-8.
+
+
+    Args:
+        learning_rate(float): global leraning rate.
+        rho(float): rho is :math: `\\rho` in equation, set 0.95 by default.
+        epsilon(float): :math: `\\epsilon` in equation is smoothing term to
+            avoid division by zero, set 1e-6 by default.
+        momentum(float): :math: `\\beta` in equation is the momentum term,
+            set 0.0 by default.
+
+    Raises:
+        ValueError: If learning_rate, rho, epsilon, momentum are None.
+
+    Examples:
+          .. code-block:: python
+
+              optimizer = fluid.optimizer.RMSProp(0.0001)
+              _, params_grads = optimizer.minimize(cost)
+    """
+
+    _momentum_acc_str = "momentum"
+    _mean_square_acc_str = "mean_square"
+
+    def __init__(self,
+                 learning_rate,
+                 rho=0.95,
+                 epsilon=1.0e-6,
+                 momentum=0.0,
+                 **kwargs):
+        super(RMSPropOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
+        if learning_rate is None:
+            raise ValueError("learning_rate is not set.")
+        if rho is None:
+            raise ValueError("rho is not set.")
+        if epsilon is None:
+            raise ValueError("epsilon is not set.")
+        if momentum is None:
+            raise ValueError("momentum is not set.")
+
+        self.type = "rmsprop"
+        self._rho = rho
+        self._epsilon = epsilon
+        self._momentum = momentum
+
+    def _create_accumulators(self, block, parameters):
+        if not isinstance(block, framework.Block):
+            raise TypeError("block is not instance of framework.Block.")
+
+        for p in parameters:
+            self._add_accumulator(self._momentum_acc_str, p)
+            self._add_accumulator(self._mean_square_acc_str, p)
+
+    def _append_optimize_op(self, block, param_and_grad):
+        if not isinstance(block, framework.Block):
+            raise TypeError("block is not instance of framework.Block.")
+
+        momentum_acc = self._get_accumulator(self._momentum_acc_str,
+                                             param_and_grad[0])
+        mean_square_acc = self._get_accumulator(self._mean_square_acc_str,
+                                                param_and_grad[0])
+        rmsprop_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "Moment": momentum_acc,
+                "MeanSquare": mean_square_acc,
+                "LearningRate": self._create_param_lr(param_and_grad),
+            },
+            outputs={
+                "ParamOut": param_and_grad[0],
+                "MomentOut": momentum_acc,
+                "MeanSquareOut": mean_square_acc
+            },
+            attrs={
+                "epsilon": self._epsilon,
+                "decay": self._rho,
+                "momentum": self._momentum
+            })
+
+        return rmsprop_op
+
+
 # We short the class name, since users will use the optimizer with the package
 # name. The sample code:
 #
@@ -679,3 +796,4 @@ Adam = AdamOptimizer
 Adamax = AdamaxOptimizer
 DecayedAdagrad = DecayedAdagradOptimizer
 Adadelta = AdadeltaOptimizer
+RMSProp = RMSPropOptimizer

python/paddle/fluid/tests/unittests/test_elementwise_add_op.py

@@ -13,158 +13,243 @@
 # limitations under the License.
 import unittest
 import numpy as np
+import paddle.fluid.core as core
 from op_test import OpTest
 
 
-class TestElementwiseOp(OpTest):
+class TestElementwiseAddOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_add"
+        self.dtype = np.float32
+        self.axis = -1
+        self.init_dtype()
+        self.init_input_output()
+        self.init_axis()
+
         self.inputs = {
-            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
-            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
         }
-        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['Y'])}
+        self.attrs = {'axis': self.axis}
+        self.outputs = {'Out': self.out}
 
     def test_check_output(self):
         self.check_output()
 
     def test_check_grad_normal(self):
+        if self.dtype == np.float16:
+            return
         self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
 
     def test_check_grad_ingore_x(self):
+        if self.dtype == np.float16:
+            return
         self.check_grad(
             ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
 
     def test_check_grad_ingore_y(self):
+        if self.dtype == np.float16:
+            return
         self.check_grad(
             ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
 
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.out = np.add(self.x, self.y)
 
-class TestElementwiseAddOp_scalar(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(1).astype(np.float32)
-        }
-        self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
+    def init_dtype(self):
+        pass
 
+    def init_axis(self):
+        pass
 
-class TestElementwiseAddOp_scalar2(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(1, 1).astype(np.float32)
-        }
-        self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
 
+class TestFP16ElementwiseAddOp(TestElementwiseAddOp):
+    def init_dtype(self):
+        self.dtype = np.float16
 
-class TestElementwiseAddOp_Vector(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.random((32, )).astype("float32"),
-            'Y': np.random.random((32, )).astype("float32")
-        }
-        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['Y'])}
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=1e-3)
 
 
-class TestElementwiseAddOp_broadcast_0(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(2).astype(np.float32)
-        }
+class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y
 
-        self.attrs = {'axis': 0}
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(2, 1, 1)
-        }
 
+class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y
 
-class TestElementwiseAddOp_broadcast_1(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(3).astype(np.float32)
-        }
 
-        self.attrs = {'axis': 1}
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 1)
-        }
+class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1, 1).astype(self.dtype)
+        self.out = self.x + self.y
 
 
-class TestElementwiseAddOp_broadcast_2(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(4).astype(np.float32)
-        }
+class TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(1, 1).astype(self.dtype)
+        self.out = self.x + self.y
 
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 1, 4)
-        }
 
+class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.random((32, )).astype(self.dtype)
+        self.y = np.random.random((32, )).astype(self.dtype)
+        self.out = np.add(self.x, self.y)
 
-class TestElementwiseAddOp_broadcast_3(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
-            'Y': np.random.rand(3, 4).astype(np.float32)
-        }
 
-        self.attrs = {'axis': 1}
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 4, 1)
-        }
+class TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.random((32, )).astype(self.dtype)
+        self.y = np.random.random((32, )).astype(self.dtype)
+        self.out = np.add(self.x, self.y)
 
 
-class TestElementwiseAddOp_broadcast_4(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
-            'Y': np.random.rand(2, 1).astype(np.float32)
-        }
+class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(2).astype(self.dtype)
+        self.out = self.x + self.y.reshape(2, 1, 1)
 
-        self.attrs = {'axis': 0}
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(2, 1, 1, 1)
-        }
+    def init_axis(self):
+        self.axis = 0
 
 
-class TestElementwiseAddOp_rowwise_add_0(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(3, 4).astype(np.float32)
-        }
+class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(2).astype(self.dtype)
+        self.out = self.x + self.y.reshape(2, 1, 1)
 
-        self.attrs = {'axis': 1}
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 4)
-        }
+    def init_axis(self):
+        self.axis = 0
 
 
-class TestElementwiseAddOp_rowwise_add_1(TestElementwiseOp):
-    def setUp(self):
-        self.op_type = "elementwise_add"
-        self.inputs = {
-            'X': np.random.rand(2, 1).astype(np.float32),
-            'Y': np.random.rand(1).astype(np.float32)
-        }
+class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(3).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 3, 1)
 
-        self.attrs = {'axis': 1}
-        self.outputs = {
-            'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 1)
-        }
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_broadcast_1(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(3).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 3, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(4).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1, 4)
+
+
+class TestFP16ElementwiseAddOp_broadcast_2(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(4).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1, 4)
+
+
+class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
+        self.y = np.random.rand(3, 4).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 3, 4, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_broadcast_3(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
+        self.y = np.random.rand(3, 4).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 3, 4, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
+        self.y = np.random.rand(2, 1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(2, 1, 1, 1)
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestFP16ElementwiseAddOp_broadcast_4(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
+        self.y = np.random.rand(2, 1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(2, 1, 1, 1)
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(3, 4).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 3, 4)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(3, 4).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 3, 4)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 1).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1)
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 1).astype(self.dtype)
+        self.y = np.random.rand(1).astype(self.dtype)
+        self.out = self.x + self.y.reshape(1, 1)
+
+    def init_axis(self):
+        self.axis = 1
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_executor_and_mul.py

@@ -16,7 +16,6 @@ import unittest
 
 import numpy
 import paddle.fluid.core as core
-
 from paddle.fluid.executor import Executor
 from paddle.fluid.layers import mul, data